From f0746cd0aa508c310caa1809d8318d5aa8c82530 Mon Sep 17 00:00:00 2001 From: biplob <110578485+bks1984@users.noreply.github.com> Date: Sat, 8 Nov 2025 23:24:03 +0530 Subject: [PATCH 1/4] Created using Colab --- medical_assistant_project.ipynb | 7006 +++++++++++++++++++++++++++++++ 1 file changed, 7006 insertions(+) create mode 100644 medical_assistant_project.ipynb diff --git a/medical_assistant_project.ipynb b/medical_assistant_project.ipynb new file mode 100644 index 0000000..c0a2612 --- /dev/null +++ b/medical_assistant_project.ipynb @@ -0,0 +1,7006 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3CNz35ia6Bz3" + }, + "source": [ + "## Problem Statement" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CkRbhMJH6Bz3" + }, + "source": [ + "### Business Context" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3PBm5xaj6Bz3" + }, + "source": [ + "The healthcare industry is rapidly evolving, with professionals facing increasing challenges in managing vast volumes of medical data while delivering accurate and timely diagnoses. The need for quick access to comprehensive, reliable, and up-to-date medical knowledge is critical for improving patient outcomes and ensuring informed decision-making in a fast-paced environment.\n", + "\n", + "Healthcare professionals often encounter information overload, struggling to sift through extensive research and data to create accurate diagnoses and treatment plans. This challenge is amplified by the need for efficiency, particularly in emergencies, where time-sensitive decisions are vital. Furthermore, access to trusted, current medical information from renowned manuals and research papers is essential for maintaining high standards of care.\n", + "\n", + "To address these challenges, healthcare centers can focus on integrating systems that streamline access to medical knowledge, provide tools to support quick decision-making, and enhance efficiency. Leveraging centralized knowledge platforms and ensuring healthcare providers have continuous access to reliable resources can significantly improve patient care and operational effectiveness." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1xDPsqvO6Bz5" + }, + "source": [ + "**Common Questions to Answer**\n", + "\n", + "1. **Critical Care Protocols:** \"What is the protocol for managing sepsis in a critical care unit?\"\n", + "\n", + "2. **General Surgery:** \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\"\n", + "\n", + "3. **Dermatology:** \"What are the effective treatments or solutions for addressing sudden patchy hair loss, commonly seen as localized bald spots on the scalp, and what could be the possible causes behind it?\"\n", + "\n", + "4. **Neurology:** \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CARPKFwm6Bz4" + }, + "source": [ + "### Objective" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dOElOEXq6Bz4" + }, + "source": [ + "As an AI specialist, your task is to develop a RAG-based AI solution using renowned medical manuals to address healthcare challenges. The objective is to **understand** issues like information overload, **apply** AI techniques to streamline decision-making, **analyze** its impact on diagnostics and patient outcomes, **evaluate** its potential to standardize care practices, and **create** a functional prototype demonstrating its feasibility and effectiveness." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "by9EvAnkSpZf" + }, + "source": [ + "### Data Description" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jw5LievCSru2" + }, + "source": [ + "The **Merck Manuals** are medical references published by the American pharmaceutical company Merck & Co., that cover a wide range of medical topics, including disorders, tests, diagnoses, and drugs. The manuals have been published since 1899, when Merck & Co. was still a subsidiary of the German company Merck.\n", + "\n", + "The manual is provided as a PDF with over 4,000 pages divided into 23 sections." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lnwETBOE6Bz5" + }, + "source": [ + "## Installing and Importing Necessary Libraries and Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q4GgLhZhUM4V", + "outputId": "81845f22-556a-4a3d-fd43-64c05a16b1de" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/449.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.3/449.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m449.8/449.8 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "langchain-chroma 1.0.0 requires chromadb<2.0.0,>=1.0.20, but you have chromadb 1.0.15 which is incompatible.\n", + "langchain-chroma 1.0.0 requires langchain-core<2.0.0,>=1.0.0, but you have langchain-core 0.3.79 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# Install required libraries\n", + "!pip install -q langchain_community==0.3.27 \\\n", + " langchain==0.3.27 \\\n", + " chromadb==1.0.15 \\\n", + " pymupdf==1.26.3 \\\n", + " tiktoken==0.9.0 \\\n", + " datasets==4.0.0 \\\n", + " evaluate==0.4.5 \\\n", + " langchain_openai==0.3.30" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mDp-EYZH-69E" + }, + "source": [ + "**Note**:\n", + "- After running the above cell, kindly restart the runtime (for Google Colab) or notebook kernel (for Jupyter Notebook), and run all cells sequentially from the next cell.\n", + "- On executing the above line of code, you might see a warning regarding package dependencies. This error message can be ignored as the above code ensures that all necessary libraries and their dependencies are maintained to successfully execute the code in ***this notebook***." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "RTY9GN4oWK3g" + }, + "outputs": [], + "source": [ + "# Import core libraries\n", + "import os # Interact with the operating system (e.g., set environment variables)\n", + "import json # Read/write JSON data\n", + "import requests # Make HTTP requests (e.g., API calls); ignore type checker\n", + "\n", + "# Import libraries for working with PDFs and OpenAI\n", + "from langchain.document_loaders import PyMuPDFLoader # Load and extract text from PDF files\n", + "# from langchain_community.document_loaders import PyPDFLoader # Load and extract text from PDF files\n", + "from openai import OpenAI # Access OpenAI's models and services\n", + "\n", + "# Import libraries for processing dataframes and text\n", + "import tiktoken # Tokenizer used for counting and splitting text for models\n", + "import pandas as pd # Load, manipulate, and analyze tabular data\n", + "\n", + "# Import LangChain components for data loading, chunking, embedding, and vector DBs\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter # Break text into overlapping chunks for processing\n", + "from langchain.embeddings.openai import OpenAIEmbeddings # Create vector embeddings using OpenAI's models # type: ignore\n", + "from langchain.vectorstores import Chroma # Store and search vector embeddings using Chroma DB # type: ignore\n", + "\n", + "from datasets import Dataset # Used to structure the input (questions, answers, contexts etc.) in tabular format\n", + "from langchain_openai import ChatOpenAI # This is needed since LLM is used in metric computation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TtZWqj0wFTS1" + }, + "source": [ + "## Question Answering using LLM" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MfNKCvuzWSI-" + }, + "source": [ + "### OpenAI API Calling and Downloading and Loading the model\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "eMi5GjvNBrlO" + }, + "outputs": [], + "source": [ + "# Load the JSON file and extract values\n", + "file_name = \"config.json\" # Name of the configuration file\n", + "with open(file_name, 'r') as file: # Open the config file in read mode\n", + " config = json.load(file) # Load the JSON content as a dictionary\n", + " OPENAI_API_KEY = config.get(\"OPENAI_API_KEY\") # Extract the API key from the config\n", + " OPENAI_API_BASE = config.get(\"OPENAI_API_BASE\") # Extract the OpenAI base URL from the config\n", + "\n", + "# Store API credentials in environment variables\n", + "os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY # Set API key as environment variable\n", + "os.environ[\"OPENAI_BASE_URL\"] = OPENAI_API_BASE # Set API base URL as environment variable\n", + "\n", + "# Initialize OpenAI client\n", + "client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE) # Create an instance of the OpenAI client" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "Q9vjnCz0WSJC" + }, + "outputs": [], + "source": [ + "# Define a function to get a response\n", + "def ask_llm(user_prompt, max_tokens=512, temperature=0, top_p=0.95): # Complete the code to set default paramenters\n", + " # Create a chat completion using the OpenAI client\n", + " completion = client.chat.completions.create(\n", + " model=\"gpt-4o-mini\", # Complete the code by specifying the model to be used.\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": user_prompt} # User prompt is the input/query to respond to\n", + " ],\n", + " max_tokens=max_tokens, # Max number of tokens to generate in the response\n", + " temperature=temperature, # Controls randomness in output\n", + " top_p=top_p # Controls diversity via nucleus sampling\n", + " )\n", + " return completion.choices[0].message.content # Return the text content from the model's reply" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K8YgK91SFjVY" + }, + "source": [ + "### Question 1: What is the protocol for managing sepsis in a critical care unit?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "u2Q_QZ4OFjVa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "3f53c399-5a2e-4a32-d696-14c6634039b6" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Managing sepsis in a critical care unit involves a systematic approach that includes early recognition, prompt intervention, and ongoing monitoring. The following is a general protocol based on current guidelines, such as those from the Surviving Sepsis Campaign:\\n\\n### 1. **Early Recognition**\\n - **Identify Symptoms**: Look for signs of infection (fever, chills, tachycardia, tachypnea) and organ dysfunction (altered mental status, hypotension, oliguria).\\n - **Use Screening Tools**: Utilize tools like the qSOFA (quick Sequential Organ Failure Assessment) or SIRS (Systemic Inflammatory Response Syndrome) criteria to identify patients at risk.\\n\\n### 2. **Initial Assessment**\\n - **Obtain Vital Signs**: Monitor blood pressure, heart rate, respiratory rate, and temperature.\\n - **Assess Organ Function**: Evaluate renal function (urine output, creatinine), liver function (bilirubin, liver enzymes), and coagulation status (platelets, INR).\\n\\n### 3. **Immediate Interventions**\\n - **Fluid Resuscitation**: Administer intravenous (IV) fluids (crystalloids) promptly, typically 30 mL/kg within the first 3 hours.\\n - **Antibiotic Therapy**: Start broad-spectrum IV antibiotics within 1 hour of recognition of sepsis. Adjust based on culture results and sensitivity.\\n - **Source Control**: Identify and control the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n### 4. **Monitoring and Support**\\n - **Hemodynamic Monitoring**: Use invasive monitoring (e.g., arterial line, central venous pressure) if necessary to guide fluid resuscitation and vasopressor therapy.\\n - **Vasopressors**: If hypotension persists despite adequate fluid resuscitation, initiate vasopressors (e.g., norepinephrine) to maintain mean arterial pressure (MAP) ≥ 65 mmHg.\\n - **Oxygenation and Ventilation**: Provide supplemental oxygen and consider mechanical ventilation if respiratory failure occurs.\\n\\n### 5. **Ongoing Management**\\n - **Reassess Fluid Status**: Continuously evaluate the patient's response to fluids and adjust as necessary.\\n - **Monitor Laboratory Values**: Regularly check lactate levels, complete blood counts, and organ function tests to assess the patient's status.\\n - **Nutritional Support**: Initiate enteral nutrition as soon as feasible, typically within 24\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "question_1 = \"What is the protocol for managing sepsis in a critical care unit?\"\n", + "base_prompt_response_1=ask_llm(question_1)\n", + "base_prompt_response_1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J6yxICeVFjVc" + }, + "source": [ + "### Question 2: What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "WO1OTE9CFjVd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "eeee39fb-3d62-4a5d-e036-08c58622ec45" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Common symptoms of appendicitis include:\\n\\n1. **Abdominal Pain**: Typically starts around the navel and then moves to the lower right abdomen.\\n2. **Loss of Appetite**: A sudden decrease in appetite is common.\\n3. **Nausea and Vomiting**: Often follows the onset of abdominal pain.\\n4. **Fever**: A low-grade fever may develop.\\n5. **Constipation or Diarrhea**: Changes in bowel habits can occur.\\n6. **Abdominal Swelling**: In some cases, the abdomen may become swollen.\\n\\nAppendicitis cannot be effectively treated with medication alone. The standard treatment is surgical removal of the appendix, known as an **appendectomy**. This can be performed using two main techniques:\\n\\n1. **Open Appendectomy**: A larger incision is made in the lower right abdomen to remove the appendix.\\n2. **Laparoscopic Appendectomy**: This is a minimally invasive procedure where several small incisions are made, and the appendix is removed with the aid of a camera and special instruments.\\n\\nLaparoscopic appendectomy is often preferred due to its benefits, including less postoperative pain, shorter recovery time, and minimal scarring. However, the choice of procedure may depend on the patient's specific situation and the surgeon's expertise.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "question_2 = \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\" #Complete the code to define the question #2\n", + "base_prompt_response_2=ask_llm(question_2) #Complete the code to pass the user input\n", + "base_prompt_response_2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oflaoOGiFjVd" + }, + "source": [ + "### Question 3: What are the effective treatments or solutions for addressing sudden patchy hair loss, commonly seen as localized bald spots on the scalp, and what could be the possible causes behind it?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "JFm5Tq7RFjVe", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "fd5f4519-f515-4c51-d2ba-bbffc330456b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Sudden patchy hair loss, often referred to as alopecia areata, can manifest as localized bald spots on the scalp or other areas of the body. Here are some effective treatments and solutions, as well as potential causes behind this condition:\\n\\n### Possible Causes:\\n1. **Autoimmune Disorders**: The immune system mistakenly attacks hair follicles, leading to hair loss.\\n2. **Genetics**: A family history of alopecia or other autoimmune conditions can increase the risk.\\n3. **Stress**: Physical or emotional stress can trigger hair loss in some individuals.\\n4. **Hormonal Changes**: Changes in hormones, such as those occurring during pregnancy or menopause, can contribute.\\n5. **Nutritional Deficiencies**: Lack of essential nutrients, such as iron, zinc, or vitamins, can affect hair health.\\n6. **Infections**: Fungal infections like tinea capitis can cause patchy hair loss.\\n7. **Other Medical Conditions**: Conditions like thyroid disease or vitiligo can also lead to hair loss.\\n\\n### Effective Treatments:\\n1. **Topical Corticosteroids**: These are often the first line of treatment for alopecia areata. They help reduce inflammation and suppress the immune response.\\n \\n2. **Minoxidil (Rogaine)**: This over-the-counter topical treatment can stimulate hair growth and is sometimes used in conjunction with other therapies.\\n\\n3. **Intralesional Corticosteroid Injections**: For more severe cases, corticosteroids can be injected directly into the bald patches to promote hair regrowth.\\n\\n4. **Immunotherapy**: This involves applying a chemical solution (like diphencyprone) to the scalp to provoke an allergic reaction, which may help stimulate hair growth.\\n\\n5. **Oral Medications**: In some cases, oral corticosteroids or other immunosuppressive drugs may be prescribed for extensive hair loss.\\n\\n6. **Light Therapy (Phototherapy)**: This treatment uses ultraviolet light to stimulate hair follicles and promote regrowth.\\n\\n7. **Nutritional Support**: Ensuring a balanced diet rich in vitamins and minerals can support overall hair health. Supplements may be recommended if deficiencies are identified.\\n\\n8. **Stress Management**: Techniques such as mindfulness, yoga, or therapy can help manage stress, which may contribute to hair loss.\\n\\n9. **Hairpieces or Wigs**: For those who experience significant hair loss, cosmetic solutions like wigs or hairpieces can provide a temporary solution while exploring other treatments.\\n\\n### Consultation with'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "question_3 = \"What are the effective treatments or solutions for addressing sudden patchy hair loss, commonly seen as localized bald spots on the scalp, and what could be the possible causes behind it?\" #Complete the code to define the question #3\n", + "base_prompt_response_3=ask_llm(question_3) #Complete the code to pass the user input\n", + "base_prompt_response_3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WUUqY4FbFjVe" + }, + "source": [ + "### Question 4: What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "DGmG9hYzFjVf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "916a1a9a-95cb-45f3-d6b9-e39f173c25a8" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"The treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), can vary widely depending on the severity of the injury, the specific areas of the brain affected, and the resulting impairments. Here are some common approaches to treatment:\\n\\n1. **Emergency Care**: \\n - Immediate medical attention is crucial. This may involve stabilizing the patient, monitoring vital signs, and performing imaging studies (like CT or MRI scans) to assess the extent of the injury.\\n\\n2. **Surgical Interventions**: \\n - In some cases, surgery may be necessary to relieve pressure on the brain, remove blood clots (hematomas), or repair skull fractures.\\n\\n3. **Medication**: \\n - Medications may be prescribed to manage symptoms such as pain, seizures, or inflammation. Corticosteroids may be used to reduce swelling in the brain.\\n\\n4. **Rehabilitation**: \\n - **Physical Therapy**: To improve mobility and strength.\\n - **Occupational Therapy**: To help with daily living skills and regain independence.\\n - **Speech and Language Therapy**: To address communication difficulties and swallowing issues.\\n - **Neuropsychological Therapy**: To help with cognitive rehabilitation, including memory, attention, and problem-solving skills.\\n\\n5. **Psychological Support**: \\n - Counseling or therapy may be beneficial for coping with emotional and psychological challenges following a brain injury, such as depression, anxiety, or changes in personality.\\n\\n6. **Lifestyle Modifications**: \\n - Patients may need to make adjustments to their daily routines, including rest, nutrition, and avoiding activities that could lead to further injury.\\n\\n7. **Supportive Care**: \\n - Family support and education about the injury and its effects can be crucial for recovery. Support groups may also be helpful.\\n\\n8. **Long-term Management**: \\n - Ongoing follow-up with healthcare providers to monitor recovery and manage any long-term effects or complications.\\n\\n9. **Assistive Devices**: \\n - Depending on the nature of the impairment, assistive devices or technology may be recommended to aid in communication, mobility, or daily activities.\\n\\n10. **Alternative Therapies**: \\n - Some individuals may explore complementary therapies such as acupuncture, yoga, or meditation, although these should be discussed with a healthcare provider.\\n\\nIt's important for treatment plans to be individualized, taking into account the specific needs and circumstances of the person affected. A multidisciplinary team approach is often the most\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "question_4 = \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\" #Complete the code to define the question #4\n", + "base_prompt_response_4=ask_llm(question_4) #Complete the code to pass the user input\n", + "base_prompt_response_4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AxbCD0S56VSj" + }, + "source": [ + "### Storing the generated outputs from the base prompt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "MKc_HzFI6eRb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "outputId": "61d2f247-0aff-4547-e253-1b75bb3b1b12" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " questions \\\n", + "0 What is the protocol for managing sepsis in a ... \n", + "1 What are the common symptoms for appendicitis,... \n", + "2 What are the effective treatments or solutions... \n", + "3 What treatments are recommended for a person w... \n", + "\n", + " base_prompt_responses \n", + "0 Managing sepsis in a critical care unit involv... \n", + "1 Common symptoms of appendicitis include:\\n\\n1.... \n", + "2 Sudden patchy hair loss, often referred to as ... \n", + "3 The treatment for a person who has sustained a... " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
questionsbase_prompt_responses
0What is the protocol for managing sepsis in a ...Managing sepsis in a critical care unit involv...
1What are the common symptoms for appendicitis,...Common symptoms of appendicitis include:\\n\\n1....
2What are the effective treatments or solutions...Sudden patchy hair loss, often referred to as ...
3What treatments are recommended for a person w...The treatment for a person who has sustained a...
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "result_df", + "summary": "{\n \"name\": \"result_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"questions\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\",\n \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\",\n \"What is the protocol for managing sepsis in a critical care unit?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"base_prompt_responses\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Common symptoms of appendicitis include:\\n\\n1. **Abdominal Pain**: Typically starts around the navel and then moves to the lower right abdomen.\\n2. **Loss of Appetite**: A sudden decrease in appetite is common.\\n3. **Nausea and Vomiting**: Often follows the onset of abdominal pain.\\n4. **Fever**: A low-grade fever may develop.\\n5. **Constipation or Diarrhea**: Changes in bowel habits can occur.\\n6. **Abdominal Swelling**: In some cases, the abdomen may become swollen.\\n\\nAppendicitis cannot be effectively treated with medication alone. The standard treatment is surgical removal of the appendix, known as an **appendectomy**. This can be performed using two main techniques:\\n\\n1. **Open Appendectomy**: A larger incision is made in the lower right abdomen to remove the appendix.\\n2. **Laparoscopic Appendectomy**: This is a minimally invasive procedure where several small incisions are made, and the appendix is removed with the aid of a camera and special instruments.\\n\\nLaparoscopic appendectomy is often preferred due to its benefits, including less postoperative pain, shorter recovery time, and minimal scarring. However, the choice of procedure may depend on the patient's specific situation and the surgeon's expertise.\",\n \"The treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), can vary widely depending on the severity of the injury, the specific areas of the brain affected, and the resulting impairments. Here are some common approaches to treatment:\\n\\n1. **Emergency Care**: \\n - Immediate medical attention is crucial. This may involve stabilizing the patient, monitoring vital signs, and performing imaging studies (like CT or MRI scans) to assess the extent of the injury.\\n\\n2. **Surgical Interventions**: \\n - In some cases, surgery may be necessary to relieve pressure on the brain, remove blood clots (hematomas), or repair skull fractures.\\n\\n3. **Medication**: \\n - Medications may be prescribed to manage symptoms such as pain, seizures, or inflammation. Corticosteroids may be used to reduce swelling in the brain.\\n\\n4. **Rehabilitation**: \\n - **Physical Therapy**: To improve mobility and strength.\\n - **Occupational Therapy**: To help with daily living skills and regain independence.\\n - **Speech and Language Therapy**: To address communication difficulties and swallowing issues.\\n - **Neuropsychological Therapy**: To help with cognitive rehabilitation, including memory, attention, and problem-solving skills.\\n\\n5. **Psychological Support**: \\n - Counseling or therapy may be beneficial for coping with emotional and psychological challenges following a brain injury, such as depression, anxiety, or changes in personality.\\n\\n6. **Lifestyle Modifications**: \\n - Patients may need to make adjustments to their daily routines, including rest, nutrition, and avoiding activities that could lead to further injury.\\n\\n7. **Supportive Care**: \\n - Family support and education about the injury and its effects can be crucial for recovery. Support groups may also be helpful.\\n\\n8. **Long-term Management**: \\n - Ongoing follow-up with healthcare providers to monitor recovery and manage any long-term effects or complications.\\n\\n9. **Assistive Devices**: \\n - Depending on the nature of the impairment, assistive devices or technology may be recommended to aid in communication, mobility, or daily activities.\\n\\n10. **Alternative Therapies**: \\n - Some individuals may explore complementary therapies such as acupuncture, yoga, or meditation, although these should be discussed with a healthcare provider.\\n\\nIt's important for treatment plans to be individualized, taking into account the specific needs and circumstances of the person affected. A multidisciplinary team approach is often the most\",\n \"Managing sepsis in a critical care unit involves a systematic approach that includes early recognition, prompt intervention, and ongoing monitoring. The following is a general protocol based on current guidelines, such as those from the Surviving Sepsis Campaign:\\n\\n### 1. **Early Recognition**\\n - **Identify Symptoms**: Look for signs of infection (fever, chills, tachycardia, tachypnea) and organ dysfunction (altered mental status, hypotension, oliguria).\\n - **Use Screening Tools**: Utilize tools like the qSOFA (quick Sequential Organ Failure Assessment) or SIRS (Systemic Inflammatory Response Syndrome) criteria to identify patients at risk.\\n\\n### 2. **Initial Assessment**\\n - **Obtain Vital Signs**: Monitor blood pressure, heart rate, respiratory rate, and temperature.\\n - **Assess Organ Function**: Evaluate renal function (urine output, creatinine), liver function (bilirubin, liver enzymes), and coagulation status (platelets, INR).\\n\\n### 3. **Immediate Interventions**\\n - **Fluid Resuscitation**: Administer intravenous (IV) fluids (crystalloids) promptly, typically 30 mL/kg within the first 3 hours.\\n - **Antibiotic Therapy**: Start broad-spectrum IV antibiotics within 1 hour of recognition of sepsis. Adjust based on culture results and sensitivity.\\n - **Source Control**: Identify and control the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n### 4. **Monitoring and Support**\\n - **Hemodynamic Monitoring**: Use invasive monitoring (e.g., arterial line, central venous pressure) if necessary to guide fluid resuscitation and vasopressor therapy.\\n - **Vasopressors**: If hypotension persists despite adequate fluid resuscitation, initiate vasopressors (e.g., norepinephrine) to maintain mean arterial pressure (MAP) \\u2265 65 mmHg.\\n - **Oxygenation and Ventilation**: Provide supplemental oxygen and consider mechanical ventilation if respiratory failure occurs.\\n\\n### 5. **Ongoing Management**\\n - **Reassess Fluid Status**: Continuously evaluate the patient's response to fluids and adjust as necessary.\\n - **Monitor Laboratory Values**: Regularly check lactate levels, complete blood counts, and organ function tests to assess the patient's status.\\n - **Nutritional Support**: Initiate enteral nutrition as soon as feasible, typically within 24\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "# Create the DataFrame\n", + "result_df = pd.DataFrame({\n", + " \"questions\": [question_1, question_2, question_3, question_4],\n", + " \"base_prompt_responses\": [base_prompt_response_1, base_prompt_response_2, base_prompt_response_3, base_prompt_response_4]})\n", + "\n", + "# Display the DataFrame\n", + "result_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KQcOiXwSybZy" + }, + "source": [ + "**Observations:**\n", + "\n", + "1.The base LLM responses are clinically reasonable across critical care, surgery,dermatology, and neurology but lack citations, making them unsuitable for medical decision‑making without RAG.\n", + "\n", + "2.Generated answers are concise and structured but omit protocol depth such as dosage, contraindications, and clinical caveats typically found in medical manuals.\n", + "\n", + "3.The outputs demonstrate general medical knowledge rather than manual‑grounded evidence, confirming the need for retrieval from trusted clinical guidelines." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g5myZ5dOOefc" + }, + "source": [ + "## Question Answering using LLM with Prompt Engineering" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dHbFv8hO7Rjv" + }, + "source": [ + "In the next step, we will use prompt engineering to check the effect of a more detailed and well-engineered prompt on the output of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "id": "VMZqTudYBCWv" + }, + "outputs": [], + "source": [ + "system_prompt = \"\"\"\n", + "You are a helpful medical research assistant. Provide concise and accurate answers based on medical knowledge.\n", + "\"\"\" #system prompt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X69OhTHAX9xO" + }, + "source": [ + "### Defining the function to Generate a Response From the LLM" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "id": "x5Wi_VwNkipi" + }, + "outputs": [], + "source": [ + "# Define a function to get a response from the OpenAI chat model\n", + "def response(system_prompt, user_prompt, max_tokens=512, temperature=0, top_p=0.95): # set default paramenters\n", + " # Create a chat completion using the OpenAI client\n", + " completion = client.chat.completions.create(\n", + " model=\"gpt-4o-mini\", # specifying the model to be used.\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt}, # System prompt sets the assistant's behavior\n", + " {\"role\": \"user\", \"content\": user_prompt} # User prompt is the input/query to respond to\n", + " ],\n", + " max_tokens=max_tokens, # Max number of tokens to generate in the response\n", + " temperature=temperature, # Controls randomness in output (0 = deterministic)\n", + " top_p=top_p # Controls diversity via nucleus sampling\n", + " )\n", + " return completion.choices[0].message.content # Return the text content from the model's reply" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Jg3r_LWOeff" + }, + "source": [ + "### Question 1: What is the protocol for managing sepsis in a critical care unit?" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "id": "O5zh3HQoOeff", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "6f1b0eef-dfaa-4bb0-e590-83b53e3bab22" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'The management of sepsis in a critical care unit typically follows the Surviving Sepsis Campaign guidelines. Here’s a concise protocol:\\n\\n1. **Early Recognition**: Identify sepsis using clinical criteria (e.g., suspected infection plus organ dysfunction).\\n\\n2. **Immediate Resuscitation**:\\n - **Fluid Resuscitation**: Administer intravenous fluids (30 mL/kg of crystalloids within the first 3 hours).\\n - **Vasopressors**: If hypotension persists after fluid resuscitation, initiate norepinephrine to maintain mean arterial pressure (MAP) ≥ 65 mmHg.\\n\\n3. **Antibiotic Therapy**:\\n - Administer broad-spectrum antibiotics within 1 hour of sepsis recognition. Adjust based on culture results and local antibiograms.\\n\\n4. **Source Control**:\\n - Identify and manage the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n5. **Monitoring**:\\n - Continuously monitor vital signs, urine output, and laboratory parameters (e.g., lactate levels, complete blood count, renal function).\\n\\n6. **Supportive Care**:\\n - Provide supportive care, including oxygen therapy, mechanical ventilation if needed, and renal replacement therapy for acute kidney injury.\\n\\n7. **Reassessment**:\\n - Reassess hemodynamic status and organ function frequently, adjusting treatment as necessary.\\n\\n8. **Consideration of Corticosteroids**:\\n - In cases of septic shock, consider low-dose corticosteroids (e.g., hydrocortisone) if there is no response to fluid resuscitation and vasopressors.\\n\\n9. **Glucose Control**:\\n - Maintain blood glucose levels between 140-180 mg/dL.\\n\\n10. **Communication and Team Approach**:\\n - Ensure effective communication among the healthcare team and involve specialists as needed.\\n\\nThis protocol should be tailored to individual patient needs and institutional protocols. Regular training and updates on sepsis management are essential for critical care staff.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 124 + } + ], + "source": [ + "response_with_prompt_eng_1=response(system_prompt,question_1)\n", + "response_with_prompt_eng_1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iYpyw4HjOeff" + }, + "source": [ + "### Question 2: What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "id": "WPPpDM6cOeff", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 70 + }, + "outputId": "e152969a-5fc6-4f54-cffc-108a9fccf818" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Common symptoms of appendicitis include:\\n\\n1. Abdominal pain, often starting near the belly button and then moving to the lower right abdomen.\\n2. Loss of appetite.\\n3. Nausea and vomiting.\\n4. Fever.\\n5. Constipation or diarrhea.\\n6. Abdominal swelling.\\n\\nAppendicitis cannot be effectively treated with medication alone; it typically requires surgical intervention. The standard surgical procedure for treating appendicitis is an appendectomy, which involves the removal of the inflamed appendix. This can be performed as an open surgery or laparoscopically, depending on the case and the surgeon's preference.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 125 + } + ], + "source": [ + "response_with_prompt_eng_2=response(system_prompt,question_2)\n", + "response_with_prompt_eng_2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dRp92JQZOeff" + }, + "source": [ + "### Question 3: What are the effective treatments or solutions for addressing sudden patchy hair loss, commonly seen as localized bald spots on the scalp, and what could be the possible causes behind it?" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "id": "sC6rrtblOefg", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "c3eb7703-3724-414e-c717-e4753b0fc0f7" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Sudden patchy hair loss, often referred to as alopecia areata, can manifest as localized bald spots on the scalp. Here are effective treatments and potential causes:\\n\\n### Treatments:\\n1. **Corticosteroids**: Topical or intralesional corticosteroids can reduce inflammation and promote hair regrowth.\\n2. **Minoxidil (Rogaine)**: Over-the-counter topical solution that may stimulate hair growth.\\n3. **Immunotherapy**: Treatments like diphencyprone (DPCP) can provoke an allergic reaction to stimulate hair regrowth.\\n4. **Anthralin**: A topical medication that can help in some cases by irritating the skin.\\n5. **JAK Inhibitors**: Oral medications like tofacitinib and ruxolitinib have shown promise in clinical trials for alopecia areata.\\n6. **Light Therapy**: Phototherapy can be beneficial for some patients.\\n7. **Supportive Care**: Counseling and support groups can help manage the psychological impact of hair loss.\\n\\n### Possible Causes:\\n1. **Autoimmune Response**: The immune system mistakenly attacks hair follicles.\\n2. **Genetics**: Family history of alopecia or other autoimmune diseases may increase risk.\\n3. **Stress**: Physical or emotional stress can trigger hair loss.\\n4. **Hormonal Changes**: Changes in hormones, such as those during pregnancy or menopause, can contribute.\\n5. **Nutritional Deficiencies**: Lack of certain nutrients (e.g., iron, vitamin D) may play a role.\\n6. **Infections**: Fungal infections like tinea capitis can cause patchy hair loss.\\n\\nIf experiencing sudden hair loss, it is advisable to consult a healthcare professional or dermatologist for an accurate diagnosis and tailored treatment plan.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 126 + } + ], + "source": [ + "response_with_prompt_eng_3=response(system_prompt,question_3)\n", + "response_with_prompt_eng_3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AA45zwyUOefg" + }, + "source": [ + "### Question 4: What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "id": "Ue8Lk8uXOefg", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "a963bbff-e927-40df-cf52-fe174ba83fd8" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), typically involves a multidisciplinary approach and may include the following:\\n\\n1. **Emergency Care**: Immediate medical attention may involve stabilizing the patient, ensuring adequate oxygenation, and managing intracranial pressure.\\n\\n2. **Surgery**: In some cases, surgical intervention may be necessary to remove hematomas, repair skull fractures, or relieve pressure on the brain.\\n\\n3. **Medications**: \\n - **Analgesics** for pain management.\\n - **Anticonvulsants** to prevent seizures.\\n - **Diuretics** to reduce swelling.\\n - **Corticosteroids** may be used to decrease inflammation.\\n\\n4. **Rehabilitation**: \\n - **Physical therapy** to improve mobility and strength.\\n - **Occupational therapy** to assist with daily living activities.\\n - **Speech therapy** for communication and swallowing difficulties.\\n - **Neuropsychological therapy** to address cognitive and emotional challenges.\\n\\n5. **Supportive Care**: This may include counseling, support groups, and education for patients and families about the injury and recovery process.\\n\\n6. **Long-term Management**: Ongoing assessment and management of cognitive, emotional, and physical impairments may be necessary, including regular follow-ups with healthcare providers.\\n\\nThe specific treatment plan will depend on the severity of the injury, the areas of the brain affected, and the individual needs of the patient.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 127 + } + ], + "source": [ + "response_with_prompt_eng_4=response(system_prompt,question_4)\n", + "response_with_prompt_eng_4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QgSCW-EIDBlA" + }, + "source": [ + "### Storing the generated outputs from the structured prompts" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "id": "N1hn6-lxDKy-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "outputId": "ce4b9ed8-46da-405f-afbf-5583f64f58ed" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " questions \\\n", + "0 What is the protocol for managing sepsis in a ... \n", + "1 What are the common symptoms for appendicitis,... \n", + "2 What are the effective treatments or solutions... \n", + "3 What treatments are recommended for a person w... \n", + "\n", + " base_prompt_responses \\\n", + "0 Managing sepsis in a critical care unit involv... \n", + "1 Common symptoms of appendicitis include:\\n\\n1.... \n", + "2 Sudden patchy hair loss, often referred to as ... \n", + "3 The treatment for a person who has sustained a... \n", + "\n", + " responses_with_prompt_eng \\\n", + "0 The management of sepsis in a critical care un... \n", + "1 Common symptoms of appendicitis include:\\n\\n1.... \n", + "2 Sudden patchy hair loss, often referred to as ... \n", + "3 Treatment for a person who has sustained a phy... \n", + "\n", + " responses_with_RAG \n", + "0 Answer:\\nThe protocol for managing sepsis in a... \n", + "1 Answer:\\nThe common symptoms of appendicitis i... \n", + "2 Answer:\\nThe effective treatment for sudden pa... \n", + "3 Answer:\\nInitial treatment for a person who ha... " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
questionsbase_prompt_responsesresponses_with_prompt_engresponses_with_RAG
0What is the protocol for managing sepsis in a ...Managing sepsis in a critical care unit involv...The management of sepsis in a critical care un...Answer:\\nThe protocol for managing sepsis in a...
1What are the common symptoms for appendicitis,...Common symptoms of appendicitis include:\\n\\n1....Common symptoms of appendicitis include:\\n\\n1....Answer:\\nThe common symptoms of appendicitis i...
2What are the effective treatments or solutions...Sudden patchy hair loss, often referred to as ...Sudden patchy hair loss, often referred to as ...Answer:\\nThe effective treatment for sudden pa...
3What treatments are recommended for a person w...The treatment for a person who has sustained a...Treatment for a person who has sustained a phy...Answer:\\nInitial treatment for a person who ha...
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "result_df", + "summary": "{\n \"name\": \"result_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"questions\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\",\n \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\",\n \"What is the protocol for managing sepsis in a critical care unit?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"base_prompt_responses\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Common symptoms of appendicitis include:\\n\\n1. **Abdominal Pain**: Typically starts around the navel and then moves to the lower right abdomen.\\n2. **Loss of Appetite**: A sudden decrease in appetite is common.\\n3. **Nausea and Vomiting**: Often follows the onset of abdominal pain.\\n4. **Fever**: A low-grade fever may develop.\\n5. **Constipation or Diarrhea**: Changes in bowel habits can occur.\\n6. **Abdominal Swelling**: In some cases, the abdomen may become swollen.\\n\\nAppendicitis cannot be effectively treated with medication alone. The standard treatment is surgical removal of the appendix, known as an **appendectomy**. This can be performed using two main techniques:\\n\\n1. **Open Appendectomy**: A larger incision is made in the lower right abdomen to remove the appendix.\\n2. **Laparoscopic Appendectomy**: This is a minimally invasive procedure where several small incisions are made, and the appendix is removed with the aid of a camera and special instruments.\\n\\nLaparoscopic appendectomy is often preferred due to its benefits, including less postoperative pain, shorter recovery time, and minimal scarring. However, the choice of procedure may depend on the patient's specific situation and the surgeon's expertise.\",\n \"The treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), can vary widely depending on the severity of the injury, the specific areas of the brain affected, and the resulting impairments. Here are some common approaches to treatment:\\n\\n1. **Emergency Care**: \\n - Immediate medical attention is crucial. This may involve stabilizing the patient, monitoring vital signs, and performing imaging studies (like CT or MRI scans) to assess the extent of the injury.\\n\\n2. **Surgical Interventions**: \\n - In some cases, surgery may be necessary to relieve pressure on the brain, remove blood clots (hematomas), or repair skull fractures.\\n\\n3. **Medication**: \\n - Medications may be prescribed to manage symptoms such as pain, seizures, or inflammation. Corticosteroids may be used to reduce swelling in the brain.\\n\\n4. **Rehabilitation**: \\n - **Physical Therapy**: To improve mobility and strength.\\n - **Occupational Therapy**: To help with daily living skills and regain independence.\\n - **Speech and Language Therapy**: To address communication difficulties and swallowing issues.\\n - **Neuropsychological Therapy**: To help with cognitive rehabilitation, including memory, attention, and problem-solving skills.\\n\\n5. **Psychological Support**: \\n - Counseling or therapy may be beneficial for coping with emotional and psychological challenges following a brain injury, such as depression, anxiety, or changes in personality.\\n\\n6. **Lifestyle Modifications**: \\n - Patients may need to make adjustments to their daily routines, including rest, nutrition, and avoiding activities that could lead to further injury.\\n\\n7. **Supportive Care**: \\n - Family support and education about the injury and its effects can be crucial for recovery. Support groups may also be helpful.\\n\\n8. **Long-term Management**: \\n - Ongoing follow-up with healthcare providers to monitor recovery and manage any long-term effects or complications.\\n\\n9. **Assistive Devices**: \\n - Depending on the nature of the impairment, assistive devices or technology may be recommended to aid in communication, mobility, or daily activities.\\n\\n10. **Alternative Therapies**: \\n - Some individuals may explore complementary therapies such as acupuncture, yoga, or meditation, although these should be discussed with a healthcare provider.\\n\\nIt's important for treatment plans to be individualized, taking into account the specific needs and circumstances of the person affected. A multidisciplinary team approach is often the most\",\n \"Managing sepsis in a critical care unit involves a systematic approach that includes early recognition, prompt intervention, and ongoing monitoring. The following is a general protocol based on current guidelines, such as those from the Surviving Sepsis Campaign:\\n\\n### 1. **Early Recognition**\\n - **Identify Symptoms**: Look for signs of infection (fever, chills, tachycardia, tachypnea) and organ dysfunction (altered mental status, hypotension, oliguria).\\n - **Use Screening Tools**: Utilize tools like the qSOFA (quick Sequential Organ Failure Assessment) or SIRS (Systemic Inflammatory Response Syndrome) criteria to identify patients at risk.\\n\\n### 2. **Initial Assessment**\\n - **Obtain Vital Signs**: Monitor blood pressure, heart rate, respiratory rate, and temperature.\\n - **Assess Organ Function**: Evaluate renal function (urine output, creatinine), liver function (bilirubin, liver enzymes), and coagulation status (platelets, INR).\\n\\n### 3. **Immediate Interventions**\\n - **Fluid Resuscitation**: Administer intravenous (IV) fluids (crystalloids) promptly, typically 30 mL/kg within the first 3 hours.\\n - **Antibiotic Therapy**: Start broad-spectrum IV antibiotics within 1 hour of recognition of sepsis. Adjust based on culture results and sensitivity.\\n - **Source Control**: Identify and control the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n### 4. **Monitoring and Support**\\n - **Hemodynamic Monitoring**: Use invasive monitoring (e.g., arterial line, central venous pressure) if necessary to guide fluid resuscitation and vasopressor therapy.\\n - **Vasopressors**: If hypotension persists despite adequate fluid resuscitation, initiate vasopressors (e.g., norepinephrine) to maintain mean arterial pressure (MAP) \\u2265 65 mmHg.\\n - **Oxygenation and Ventilation**: Provide supplemental oxygen and consider mechanical ventilation if respiratory failure occurs.\\n\\n### 5. **Ongoing Management**\\n - **Reassess Fluid Status**: Continuously evaluate the patient's response to fluids and adjust as necessary.\\n - **Monitor Laboratory Values**: Regularly check lactate levels, complete blood counts, and organ function tests to assess the patient's status.\\n - **Nutritional Support**: Initiate enteral nutrition as soon as feasible, typically within 24\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"responses_with_prompt_eng\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Common symptoms of appendicitis include:\\n\\n1. Abdominal pain, often starting near the belly button and then moving to the lower right abdomen.\\n2. Loss of appetite.\\n3. Nausea and vomiting.\\n4. Fever.\\n5. Constipation or diarrhea.\\n6. Abdominal swelling.\\n\\nAppendicitis cannot be effectively treated with medication alone; it typically requires surgical intervention. The standard surgical procedure for treating appendicitis is an appendectomy, which involves the removal of the inflamed appendix. This can be performed as an open surgery or laparoscopically, depending on the case and the surgeon's preference.\",\n \"Treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), typically involves a multidisciplinary approach and may include the following:\\n\\n1. **Emergency Care**: Immediate medical attention may involve stabilizing the patient, ensuring adequate oxygenation, and managing intracranial pressure.\\n\\n2. **Surgery**: In some cases, surgical intervention may be necessary to remove hematomas, repair skull fractures, or relieve pressure on the brain.\\n\\n3. **Medications**: \\n - **Analgesics** for pain management.\\n - **Anticonvulsants** to prevent seizures.\\n - **Diuretics** to reduce swelling.\\n - **Corticosteroids** may be used to decrease inflammation.\\n\\n4. **Rehabilitation**: \\n - **Physical therapy** to improve mobility and strength.\\n - **Occupational therapy** to assist with daily living activities.\\n - **Speech therapy** for communication and swallowing difficulties.\\n - **Neuropsychological therapy** to address cognitive and emotional challenges.\\n\\n5. **Supportive Care**: This may include counseling, support groups, and education for patients and families about the injury and recovery process.\\n\\n6. **Long-term Management**: Ongoing assessment and management of cognitive, emotional, and physical impairments may be necessary, including regular follow-ups with healthcare providers.\\n\\nThe specific treatment plan will depend on the severity of the injury, the areas of the brain affected, and the individual needs of the patient.\",\n \"The management of sepsis in a critical care unit typically follows the Surviving Sepsis Campaign guidelines. Here\\u2019s a concise protocol:\\n\\n1. **Early Recognition**: Identify sepsis using clinical criteria (e.g., suspected infection plus organ dysfunction).\\n\\n2. **Immediate Resuscitation**:\\n - **Fluid Resuscitation**: Administer intravenous fluids (30 mL/kg of crystalloids within the first 3 hours).\\n - **Vasopressors**: If hypotension persists after fluid resuscitation, initiate norepinephrine to maintain mean arterial pressure (MAP) \\u2265 65 mmHg.\\n\\n3. **Antibiotic Therapy**:\\n - Administer broad-spectrum antibiotics within 1 hour of sepsis recognition. Adjust based on culture results and local antibiograms.\\n\\n4. **Source Control**:\\n - Identify and manage the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n5. **Monitoring**:\\n - Continuously monitor vital signs, urine output, and laboratory parameters (e.g., lactate levels, complete blood count, renal function).\\n\\n6. **Supportive Care**:\\n - Provide supportive care, including oxygen therapy, mechanical ventilation if needed, and renal replacement therapy for acute kidney injury.\\n\\n7. **Reassessment**:\\n - Reassess hemodynamic status and organ function frequently, adjusting treatment as necessary.\\n\\n8. **Consideration of Corticosteroids**:\\n - In cases of septic shock, consider low-dose corticosteroids (e.g., hydrocortisone) if there is no response to fluid resuscitation and vasopressors.\\n\\n9. **Glucose Control**:\\n - Maintain blood glucose levels between 140-180 mg/dL.\\n\\n10. **Communication and Team Approach**:\\n - Ensure effective communication among the healthcare team and involve specialists as needed.\\n\\nThis protocol should be tailored to individual patient needs and institutional protocols. Regular training and updates on sepsis management are essential for critical care staff.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"responses_with_RAG\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Answer:\\nThe common symptoms of appendicitis include epigastric or periumbilical pain followed by nausea, vomiting, and anorexia, with pain shifting to the right lower quadrant. Classic signs include right lower quadrant tenderness at McBurney's point, Rovsing sign, psoas sign, and obturator sign. Appendicitis cannot be cured via medicine; the treatment is surgical removal, specifically an open or laparoscopic appendectomy.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 11. Acute Abdomen & Surgical Gastroenterology, pages 163.\",\n \"Answer:\\nInitial treatment for a person who has sustained a physical injury to brain tissue includes ensuring a reliable airway and maintaining adequate ventilation, oxygenation, and blood pressure. Surgery may be needed for severe injuries to monitor and treat intracranial pressure, decompress the brain, or remove hematomas. Subsequently, many patients require rehabilitation, which should be planned early and may involve a team approach including physical, occupational, and speech therapy, as well as cognitive therapy for those with severe cognitive dysfunction.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 324. Traumatic Brain Injury, and Chapter 350. Rehabilitation.\",\n \"Answer:\\nThe protocol for managing sepsis in a critical care unit includes the following steps: \\n1. Obtain specimens of blood, body fluids, and wound sites for Gram stain and culture before starting parenteral antibiotics.\\n2. Initiate very prompt empiric antibiotic therapy immediately after suspecting sepsis, which may include gentamicin or tobramycin plus a 3rd-generation cephalosporin (e.g., cefotaxime or ceftriaxone), or ceftazidime plus a fluoroquinolone if Pseudomonas is suspected. Vancomycin should be added if resistant staphylococci or enterococci are suspected, and if there is an abdominal source, include a drug effective against anaerobes (e.g., metronidazole).\\n3. Change the antibiotic regimen based on culture and sensitivity results when available, continuing antibiotics for at least 5 days after shock resolves and evidence of infection subsides.\\n4. Drain abscesses and surgically excise necrotic tissues as necessary.\\n5. Monitor and manage blood glucose levels with a continuous IV insulin infusion to maintain glucose between 80 to 110 mg/dL.\\n6. Provide supportive care, including adequate nutrition and prevention of infections and complications.\\n\\nSource:\\nCritical Care Medicine, Chapter 222. Approach to the Critically Ill Patient.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 128 + } + ], + "source": [ + "# creating a dataframe\n", + "result_df['responses_with_prompt_eng'] = [response_with_prompt_eng_1, response_with_prompt_eng_2, response_with_prompt_eng_3, response_with_prompt_eng_4]\n", + "\n", + "# Display the DataFrame\n", + "result_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kStw6hGgyhzW" + }, + "source": [ + "**Observations**:\n", + "\n", + "1.The base LLM responses are clinically reasonable across critical care, surgery,dermatology, and neurology but lack citations, making them unsuitable for medical decision‑making without RAG.\n", + "\n", + "2.Generated answers are concise and structured but omit protocol depth such as dosage, contraindications, and clinical caveats typically found in medical manuals.\n", + "\n", + "3.The outputs demonstrate general medical knowledge rather than manual‑grounded evidence, confirming the need for retrieval from trusted clinical guidelines.\n", + "\n", + "4.The system consistently returns answers for all four use‑case questions, validating the basic inference pipeline before adding context‑grounding.\n", + "\n", + "5.The notebook setup correctly loads configs and models, but embedding/RAG steps are not yet grounded in medical source documents.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t_O1PGdNO2M9" + }, + "source": [ + "## Data Preparation for RAG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uTpWESc53dL9" + }, + "source": [ + "### Loading the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "ksv9hSCR4BM_" + }, + "outputs": [], + "source": [ + "manual_pdf_path = \"/content/medical_diagnosis_manual.pdf\" #Complete the code to define the file name" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "jhf34I1eYNtR" + }, + "outputs": [], + "source": [ + "pdf_loader = PyMuPDFLoader(manual_pdf_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "YChLS31TxC3-" + }, + "outputs": [], + "source": [ + "manual = pdf_loader.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffj0ca3eZT4u" + }, + "source": [ + "### Data Overview" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f9weTDzMxRRS" + }, + "source": [ + "#### Checking the first 5 pages" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "JSOv3q2pxX4z", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dec9cda4-d282-4e27-d978-e94c152b4028" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Page Number : 1\n", + "biplobsinha25@gmail.com\n", + "9X5AUD3EIR\n", + "ant for personal use by biplobsinha25@g\n", + "shing the contents in part or full is liable\n", + "Page Number : 2\n", + "biplobsinha25@gmail.com\n", + "9X5AUD3EIR\n", + "This file is meant for personal use by biplobsinha25@gmail.com only.\n", + "Sharing or publishing the contents in part or full is liable for legal action.\n", + "Page Number : 3\n", + "Table of Contents\n", + "1\n", + "Front ................................................................................................................................................................................................................\n", + "1\n", + "Cover .......................................................................................................................................................................................................\n", + "2\n", + "Front Matter ...........................................................................................................................................................................................\n", + "53\n", + "1 - Nutritional Disorders ...............................................................................................................................................................\n", + "53\n", + "Chapter 1. Nutrition: General Considerations .....................................................................................................................\n", + "59\n", + "Chapter 2. Undernutrition .............................................................................................................................................................\n", + "69\n", + "Chapter 3. Nutritional Support ...................................................................................................................................................\n", + "76\n", + "Chapter 4. Vitamin Deficiency, Dependency & Toxicity ..................................................................................................\n", + "99\n", + "Chapter 5. Mineral Deficiency & Toxicity ..............................................................................................................................\n", + "108\n", + "Chapter 6. Obesity & the Metabolic Syndrome ...............................................................................................................\n", + "120\n", + "2 - Gastrointestinal Disorders ..............................................................................................................................................\n", + "120\n", + "Chapter 7. Approach to the Patient With Upper GI Complaints ...............................................................................\n", + "132\n", + "Chapter 8. Approach to the Patient With Lower GI Complaints ...............................................................................\n", + "143\n", + "Chapter 9. Diagnostic & Therapeutic GI Procedures ....................................................................................................\n", + "150\n", + "Chapter 10. GI Bleeding ............................................................................................................................................................\n", + "158\n", + "Chapter 11. Acute Abdomen & Surgical Gastroenterology .........................................................................................\n", + "172\n", + "Chapter 12. Esophageal & Swallowing Disorders ..........................................................................................................\n", + "183\n", + "Chapter 13. Gastritis & Peptic Ulcer Disease ..................................................................................................................\n", + "196\n", + "Chapter 14. Bezoars & Foreign Bodies ..............................................................................................................................\n", + "199\n", + "Chapter 15. Pancreatitis ............................................................................................................................................................\n", + "206\n", + "Chapter 16. Gastroenteritis ......................................................................................................................................................\n", + "213\n", + "Chapter 17. Malabsorption Syndromes ..............................................................................................................................\n", + "225\n", + "Chapter 18. Irritable Bowel Syndrome ................................................................................................................................\n", + "229\n", + "Chapter 19. Inflammatory Bowel Disease .........................................................................................................................\n", + "241\n", + "Chapter 20. Diverticular Disease ...........................................................................................................................................\n", + "246\n", + "Chapter 21. Anorectal Disorders ............................................................................................................................................\n", + "254\n", + "Chapter 22. Tumors of the GI Tract ......................................................................................................................................\n", + "275\n", + "3 - Hepatic & Biliary Disorders ............................................................................................................................................\n", + "275\n", + "Chapter 23. Approach to the Patient With Liver Disease ...........................................................................................\n", + "294\n", + "Chapter 24. Testing for Hepatic & Biliary Disorders ......................................................................................................\n", + "305\n", + "Chapter 25. Drugs & the Liver ................................................................................................................................................\n", + "308\n", + "Chapter 26. Alcoholic Liver Disease ....................................................................................................................................\n", + "314\n", + "Chapter 27. Fibrosis & Cirrhosis ............................................................................................................................................\n", + "322\n", + "Chapter 28. Hepatitis ..................................................................................................................................................................\n", + "333\n", + "Chapter 29. Vascular Disorders of the Liver .....................................................................................................................\n", + "341\n", + "Chapter 30. Liver Masses & Granulomas ..........................................................................................................................\n", + "348\n", + "Chapter 31. Gallbladder & Bile Duct Disorders ...............................................................................................................\n", + "362\n", + "4 - Musculoskeletal & Connective Tissue Disorders .........................................................................................\n", + "362\n", + "Chapter 32. Approach to the Patient With Joint Disease ............................................................................................\n", + "373\n", + "Chapter 33. Autoimmune Rheumatic Disorders ..............................................................................................................\n", + "391\n", + "Chapter 34. Vasculitis .................................................................................................................................................................\n", + "416\n", + "Chapter 35. Joint Disorders .....................................................................................................................................................\n", + "435\n", + "Chapter 36. Crystal-Induced Arthritides ..............................................................................................................................\n", + "443\n", + "Chapter 37. Osteoporosis .........................................................................................................................................................\n", + "448\n", + "Chapter 38. Paget's Disease of Bone ..................................................................................................................................\n", + "451\n", + "Chapter 39. Osteonecrosis .......................................................................................................................................................\n", + "455\n", + "Chapter 40. Infections of Joints & Bones ...........................................................................................................................\n", + "463\n", + "Chapter 41. Bursa, Muscle & Tendon Disorders .............................................................................................................\n", + "470\n", + "Chapter 42. Neck & Back Pain ...............................................................................................................................................\n", + "481\n", + "Chapter 43. Hand Disorders ....................................................................................................................................................\n", + "biplobsinha25@gmail.com\n", + "9X5AUD3EIR\n", + "This file is meant for personal use by biplobsinha25@gmail.com only.\n", + "Sharing or publishing the contents in part or full is liable for legal action.\n", + "Page Number : 4\n", + "491\n", + "Chapter 44. Foot & Ankle Disorders .....................................................................................................................................\n", + "502\n", + "Chapter 45. Tumors of Bones & Joints ...............................................................................................................................\n", + "510\n", + "5 - Ear, Nose, Throat & Dental Disorders ..................................................................................................................\n", + "510\n", + "Chapter 46. Approach to the Patient With Ear Problems ...........................................................................................\n", + "523\n", + "Chapter 47. Hearing Loss .........................................................................................................................................................\n", + "535\n", + "Chapter 48. Inner Ear Disorders ............................................................................................................................................\n", + "542\n", + "Chapter 49. Middle Ear & Tympanic Membrane Disorders ........................................................................................\n", + "550\n", + "Chapter 50. External Ear Disorders .....................................................................................................................................\n", + "554\n", + "Chapter 51. Approach to the Patient With Nasal & Pharyngeal Symptoms .......................................................\n", + "567\n", + "Chapter 52. Oral & Pharyngeal Disorders .........................................................................................................................\n", + "578\n", + "Chapter 53. Nose & Paranasal Sinus Disorders .............................................................................................................\n", + "584\n", + "Chapter 54. Laryngeal Disorders ...........................................................................................................................................\n", + "590\n", + "Chapter 55. Tumors of the Head & Neck ...........................................................................................................................\n", + "600\n", + "Chapter 56. Approach to Dental & Oral Symptoms .......................................................................................................\n", + "619\n", + "Chapter 57. Common Dental Disorders .............................................................................................................................\n", + "629\n", + "Chapter 58. Dental Emergencies ..........................................................................................................................................\n", + "635\n", + "Chapter 59. Temporomandibular Disorders ......................................................................................................................\n", + "641\n", + "6 - Eye Disorders ............................................................................................................................................................................\n", + "641\n", + "Chapter 60. Approach to the Ophthalmologic Patient ..................................................................................................\n", + "669\n", + "Chapter 61. Refractive Error ...................................................................................................................................................\n", + "674\n", + "Chapter 62. Eyelid & Lacrimal Disorders ...........................................................................................................................\n", + "680\n", + "Chapter 63. Conjunctival & Scleral Disorders .................................................................................................................\n", + "690\n", + "Chapter 64. Corneal Disorders ...............................................................................................................................................\n", + "703\n", + "Chapter 65. Glaucoma ...............................................................................................................................................................\n", + "710\n", + "Chapter 66. Cataract ...................................................................................................................................................................\n", + "713\n", + "Chapter 67. Uveitis ......................................................................................................................................................................\n", + "719\n", + "Chapter 68. Retinal Disorders .................................................................................................................................................\n", + "731\n", + "Chapter 69. Optic Nerve Disorders ......................................................................................................................................\n", + "737\n", + "Chapter 70. Orbital Diseases ..................................................................................................................................................\n", + "742\n", + "7 - Dermatologic Disorders ....................................................................................................................................................\n", + "742\n", + "Chapter 71. Approach to the Dermatologic Patient .......................................................................................................\n", + "755\n", + "Chapter 72. Principles of Topical Dermatologic Therapy ............................................................................................\n", + "760\n", + "Chapter 73. Acne & Related Disorders ...............................................................................................................................\n", + "766\n", + "Chapter 74. Bullous Diseases .................................................................................................................................................\n", + "771\n", + "Chapter 75. Cornification Disorders .....................................................................................................................................\n", + "775\n", + "Chapter 76. Dermatitis ...............................................................................................................................................................\n", + "786\n", + "Chapter 77. Reactions to Sunlight ........................................................................................................................................\n", + "791\n", + "Chapter 78. Psoriasis & Scaling Diseases ........................................................................................................................\n", + "799\n", + "Chapter 79. Hypersensitivity & Inflammatory Disorders .............................................................................................\n", + "808\n", + "Chapter 80. Sweating Disorders ............................................................................................................................................\n", + "811\n", + "Chapter 81. Bacterial Skin Infections ...................................................................................................................................\n", + "822\n", + "Chapter 82. Fungal Skin Infections ......................................................................................................................................\n", + "831\n", + "Chapter 83. Parasitic Skin Infections ...................................................................................................................................\n", + "836\n", + "Chapter 84. Viral Skin Diseases ............................................................................................................................................\n", + "841\n", + "Chapter 85. Pigmentation Disorders ....................................................................................................................................\n", + "846\n", + "Chapter 86. Hair Disorders .......................................................................................................................................................\n", + "855\n", + "Chapter 87. Nail Disorders .......................................................................................................................................................\n", + "861\n", + "Chapter 88. Pressure Ulcers ...................................................................................................................................................\n", + "867\n", + "Chapter 89. Benign Tumors .....................................................................................................................................................\n", + "874\n", + "Chapter 90. Cancers of the Skin ............................................................................................................................................\n", + "882\n", + "8 - Endocrine & Metabolic Disorders .............................................................................................................................\n", + "882\n", + "Chapter 91. Principles of Endocrinology ............................................................................................................................\n", + "887\n", + "Chapter 92. Pituitary Disorders ..............................................................................................................................................\n", + "901\n", + "Chapter 93. Thyroid Disorders ................................................................................................................................................\n", + "biplobsinha25@gmail.com\n", + "9X5AUD3EIR\n", + "This file is meant for personal use by biplobsinha25@gmail.com only.\n", + "Sharing or publishing the contents in part or full is liable for legal action.\n", + "Page Number : 5\n", + "921\n", + "Chapter 94. Adrenal Disorders ................................................................................................................................................\n", + "936\n", + "Chapter 95. Polyglandular Deficiency Syndromes ........................................................................................................\n", + "939\n", + "Chapter 96. Porphyrias ..............................................................................................................................................................\n", + "949\n", + "Chapter 97. Fluid & Electrolyte Metabolism .....................................................................................................................\n", + "987\n", + "Chapter 98. Acid-Base Regulation & Disorders ..............................................................................................................\n", + "1001\n", + "Chapter 99. Diabetes Mellitus & Disorders of Carbohydrate Metabolism ........................................................\n", + "1024\n", + "Chapter 100. Lipid Disorders ................................................................................................................................................\n", + "1034\n", + "Chapter 101. Amyloidosis ......................................................................................................................................................\n", + "1037\n", + "Chapter 102. Carcinoid Tumors ..........................................................................................................................................\n", + "1040\n", + "Chapter 103. Multiple Endocrine Neoplasia Syndromes .........................................................................................\n", + "1046\n", + "9 - Hematology & Oncology ...............................................................................................................................................\n", + "1046\n", + "Chapter 104. Approach to the Patient With Anemia ..................................................................................................\n", + "1050\n", + "Chapter 105. Anemias Caused by Deficient Erythropoiesis ...................................................................................\n", + "1061\n", + "Chapter 106. Anemias Caused by Hemolysis ...............................................................................................................\n", + "1078\n", + "Chapter 107. Neutropenia & Lymphocytopenia ...........................................................................................................\n", + "1086\n", + "Chapter 108. Thrombocytopenia & Platelet Dysfunction .........................................................................................\n", + "1097\n", + "Chapter 109. Hemostasis ......................................................................................................................................................\n", + "1104\n", + "Chapter 110. Thrombotic Disorders ...................................................................................................................................\n", + "1107\n", + "Chapter 111. Coagulation Disorders ..................................................................................................................................\n", + "1113\n", + "Chapter 112. Bleeding Due to Abnormal Blood Vessels ...........................................................................................\n", + "1116\n", + "Chapter 113. Spleen Disorders ............................................................................................................................................\n", + "1120\n", + "Chapter 114. Eosinophilic Disorders .................................................................................................................................\n", + "1126\n", + "Chapter 115. Histiocytic Syndromes .................................................................................................................................\n", + "1131\n", + "Chapter 116. Myeloproliferative Disorders .....................................................................................................................\n", + "1141\n", + "Chapter 117. Leukemias .........................................................................................................................................................\n", + "1154\n", + "Chapter 118. Lymphomas ......................................................................................................................................................\n", + "1164\n", + "Chapter 119. Plasma Cell Disorders .................................................................................................................................\n", + "1172\n", + "Chapter 120. Iron Overload ...................................................................................................................................................\n", + "1177\n", + "Chapter 121. Transfusion Medicine ...................................................................................................................................\n", + "1186\n", + "Chapter 122. Overview of Cancer ......................................................................................................................................\n", + "1198\n", + "Chapter 123. Tumor Immunology .......................................................................................................................................\n", + "1204\n", + "Chapter 124. Principles of Cancer Therapy ...................................................................................................................\n", + "1215\n", + "10 - Immunology; Allergic Disorders ...........................................................................................................................\n", + "1215\n", + "Chapter 125. Biology of the Immune System ...............................................................................................................\n", + "1227\n", + "Chapter 126. Immunodeficiency Disorders ....................................................................................................................\n", + "1243\n", + "Chapter 127. Allergic & Other Hypersensitivity Disorders .......................................................................................\n", + "1263\n", + "Chapter 128. Transplantation ...............................................................................................................................................\n", + "1281\n", + "11 - Infectious Diseases ........................................................................................................................................................\n", + "1281\n", + "Chapter 129. Biology of Infectious Disease ...................................................................................................................\n", + "1300\n", + "Chapter 130. Laboratory Diagnosis of Infectious Disease ......................................................................................\n", + "1306\n", + "Chapter 131. Immunization ...................................................................................................................................................\n", + "1313\n", + "Chapter 132. Bacteria & Antibacterial Drugs .................................................................................................................\n", + "1353\n", + "Chapter 133. Gram-Positive Cocci ....................................................................................................................................\n", + "1366\n", + "Chapter 134. Gram-Positive Bacilli ...................................................................................................................................\n", + "1376\n", + "Chapter 135. Gram-Negative Bacilli .................................................................................................................................\n", + "1405\n", + "Chapter 136. Spirochetes ......................................................................................................................................................\n", + "1413\n", + "Chapter 137. Neisseriaceae .................................................................................................................................................\n", + "1419\n", + "Chapter 138. Chlamydia & Mycoplasmas ......................................................................................................................\n", + "1421\n", + "Chapter 139. Rickettsiae & Related Organisms ..........................................................................................................\n", + "1431\n", + "Chapter 140. Anaerobic Bacteria ........................................................................................................................................\n", + "1450\n", + "Chapter 141. Mycobacteria ...................................................................................................................................................\n", + "1470\n", + "Chapter 142. Fungi ...................................................................................................................................................................\n", + "1493\n", + "Chapter 143. Approach to Parasitic Infections .............................................................................................................\n", + "1496\n", + "Chapter 144. Nematodes (Roundworms) .......................................................................................................................\n", + "biplobsinha25@gmail.com\n", + "9X5AUD3EIR\n", + "This file is meant for personal use by biplobsinha25@gmail.com only.\n", + "Sharing or publishing the contents in part or full is liable for legal action.\n" + ] + } + ], + "source": [ + "for i in range(5):\n", + " print(f\"Page Number : {i+1}\",end=\"\\n\")\n", + " print(manual[i].page_content,end=\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LECMxTH-zB-R" + }, + "source": [ + "### Data Chunking" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oQfw-qErRoGr" + }, + "source": [ + "#### Chunk the PDF into Manageable Text Sections Using a Token-Based Splitter" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "uG0_pBmizGGt" + }, + "outputs": [], + "source": [ + "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n", + " encoding_name='cl100k_base',\n", + " chunk_size= 500, #Complete the code to define the chunk size\n", + " chunk_overlap= 50 #Complete the code to define the chunk overlap\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P7XqisNKR3DZ" + }, + "source": [ + "#### Split the Loaded PDF into Chunks for Further Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "w76ji7ECzLLQ" + }, + "outputs": [], + "source": [ + "document_chunks = pdf_loader.load_and_split(text_splitter)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gkkBb1GmSDTp" + }, + "source": [ + "#### Check the Number of Chunks Created" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "i6TQ-mmLzR9I", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5d1c5923-9386-4e73-f127-c423dd4e72f4" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "8875" + ] + }, + "metadata": {}, + "execution_count": 41 + } + ], + "source": [ + "len(document_chunks)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yKnhARSu0d8u" + }, + "source": [ + "### Generate Vector Embeddings for Text Chunks Using OpenAI" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true, + "id": "c6cZVZWQz15c", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d943ba50-86c9-470e-f500-1135c1cfec96" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Dimension of the embedding vector 1536\n" + ] + } + ], + "source": [ + "# Initialize the OpenAI Embeddings model with API credentials\n", + "embedding_model = OpenAIEmbeddings(\n", + " openai_api_key=OPENAI_API_KEY, # Your OpenAI API key for authentication\n", + " openai_api_base=OPENAI_API_BASE # The OpenAI API base URL endpoint\n", + ")\n", + "\n", + "# Generate embeddings (vector representations) for the first two document chunks\n", + "embedding_1 = embedding_model.embed_query(document_chunks[0].page_content) # Embedding for chunk 0\n", + "embedding_2 = embedding_model.embed_query(document_chunks[1].page_content) # Embedding for chunk 1\n", + "\n", + "# Check and print the dimension (length) of the embedding vector\n", + "print(\"Dimension of the embedding vector \", len(embedding_1)) # Typically 1536 or 2048 depending on model" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true, + "id": "W0qy6xOZ0UBe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "aebee7db-6b2c-485b-8a47-44cd83a1409a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "([-0.013844738714396954,\n", + " 0.015309492126107216,\n", + " 0.008478669449687004,\n", + " -0.020055856555700302,\n", + " -0.012936308979988098,\n", + " 0.016126373782753944,\n", + " -0.027478214353322983,\n", + " -0.03104150854051113,\n", + " -0.020407961681485176,\n", + " -0.02126709558069706,\n", + " 0.04222434014081955,\n", + " 0.019605163484811783,\n", + " -0.00669702235609293,\n", + " 0.019520658999681473,\n", + " 0.0016249610343948007,\n", + " 0.017971400171518326,\n", + " 0.024281106889247894,\n", + " 0.014830630272626877,\n", + " 0.012971519492566586,\n", + " 0.004387218505144119,\n", + " -0.01829533651471138,\n", + " 0.03095700405538082,\n", + " -0.02168961986899376,\n", + " 0.01354897115379572,\n", + " -0.022633260115981102,\n", + " -0.023943087086081505,\n", + " 0.028985220938920975,\n", + " -0.034759730100631714,\n", + " -0.024731801822781563,\n", + " -0.01809815689921379,\n", + " 0.01074622105807066,\n", + " -0.010196938179433346,\n", + " -0.011823659762740135,\n", + " -0.019576994702219963,\n", + " -0.008013891987502575,\n", + " 0.00029576756060123444,\n", + " 0.02042204514145851,\n", + " -0.004249898251146078,\n", + " 0.02191496640443802,\n", + " 0.01597144827246666,\n", + " 0.006816737819463015,\n", + " -0.0028714099898934364,\n", + " -0.009703992865979671,\n", + " -0.004538623616099358,\n", + " -0.009422308765351772,\n", + " 0.006070276722311974,\n", + " -0.011549018323421478,\n", + " -0.022464249283075333,\n", + " -0.0013133487664163113,\n", + " -0.013710938394069672,\n", + " 0.02854861132800579,\n", + " 0.013344750739634037,\n", + " -0.02409801259636879,\n", + " -0.004408345092087984,\n", + " -0.004207645542919636,\n", + " -0.01166873425245285,\n", + " 0.007767419330775738,\n", + " -0.008591343648731709,\n", + " -0.0004022790817543864,\n", + " 0.004010467324405909,\n", + " 0.0036900523118674755,\n", + " -0.003144290763884783,\n", + " -0.01650664582848549,\n", + " -0.01599961705505848,\n", + " 0.0034206926357001066,\n", + " -0.018562935292720795,\n", + " -0.014957387931644917,\n", + " -0.00935893040150404,\n", + " -0.01028144359588623,\n", + " 0.010767347179353237,\n", + " 0.029238734394311905,\n", + " 0.012225058861076832,\n", + " 0.009443435817956924,\n", + " -0.010140601545572281,\n", + " 0.0033502718433737755,\n", + " -0.007739251013845205,\n", + " -0.005837887991219759,\n", + " 0.014929219149053097,\n", + " 0.0041653928346931934,\n", + " -0.01040820125490427,\n", + " 0.022717764601111412,\n", + " -0.02030937187373638,\n", + " 0.005633667577058077,\n", + " -0.0023115642834454775,\n", + " 0.018534766510128975,\n", + " 0.0007451405981555581,\n", + " 0.0020228386856615543,\n", + " 0.011661692522466183,\n", + " -0.012985603883862495,\n", + " -0.011872954666614532,\n", + " 0.003774557262659073,\n", + " 0.014520778320729733,\n", + " 0.022069893777370453,\n", + " 0.02294311113655567,\n", + " -0.013457424007356167,\n", + " 0.028041580691933632,\n", + " -0.0128165939822793,\n", + " 0.03740755468606949,\n", + " 0.002249946119263768,\n", + " -0.009992717765271664,\n", + " 0.0020545281004160643,\n", + " 0.00784488208591938,\n", + " -0.00799980852752924,\n", + " -0.012288437224924564,\n", + " -0.03298512473702431,\n", + " -0.012950393371284008,\n", + " 0.010387075133621693,\n", + " -0.017098180949687958,\n", + " -0.0005426806164905429,\n", + " 0.02143610641360283,\n", + " -0.0042745452374219894,\n", + " 0.022436082363128662,\n", + " 0.017168601974844933,\n", + " -0.05797044187784195,\n", + " 0.01829533651471138,\n", + " 0.006785048637539148,\n", + " 0.018309419974684715,\n", + " -0.01340812910348177,\n", + " -0.028590863570570946,\n", + " -0.020238950848579407,\n", + " -0.003992862068116665,\n", + " 0.0064047761261463165,\n", + " -0.003897793823853135,\n", + " 0.009767371229827404,\n", + " 0.016239047050476074,\n", + " 0.017253106459975243,\n", + " -0.025675440207123756,\n", + " -0.030280964449048042,\n", + " -0.0017658027354627848,\n", + " -0.0043836976401507854,\n", + " 0.0050139641389250755,\n", + " 0.02656274288892746,\n", + " 0.007045605685561895,\n", + " 0.01602778397500515,\n", + " -0.03273160755634308,\n", + " 0.025013484060764313,\n", + " -0.017041845247149467,\n", + " -0.027379624545574188,\n", + " -0.033576659858226776,\n", + " -0.020745981484651566,\n", + " 0.017126349732279778,\n", + " 0.04084409028291702,\n", + " -0.012668710201978683,\n", + " -0.006753358989953995,\n", + " 0.004760449286550283,\n", + " 0.007851924747228622,\n", + " 0.01026735920459032,\n", + " 0.0158869419246912,\n", + " 0.015732016414403915,\n", + " -0.009443435817956924,\n", + " -0.018055904656648636,\n", + " 0.005218184553086758,\n", + " 0.02387266606092453,\n", + " 0.005334379151463509,\n", + " 0.004228771664202213,\n", + " 0.020084025338292122,\n", + " 0.007302641868591309,\n", + " 0.030703488737344742,\n", + " -0.008584300987422466,\n", + " -0.020605139434337616,\n", + " -0.015393996611237526,\n", + " 0.004795659799128771,\n", + " 0.0022728329058736563,\n", + " -0.0004207645542919636,\n", + " 0.027393709868192673,\n", + " 0.034337203949689865,\n", + " -0.004668902140110731,\n", + " -0.0046372124925255775,\n", + " -0.006042108405381441,\n", + " -0.006126613821834326,\n", + " 0.002014036290347576,\n", + " 0.020295288413763046,\n", + " -0.017746053636074066,\n", + " -0.016675656661391258,\n", + " -0.015309492126107216,\n", + " 0.019478406757116318,\n", + " 0.022731849923729897,\n", + " 0.002410153392702341,\n", + " -0.025266999378800392,\n", + " -0.015084145590662956,\n", + " -0.015929196029901505,\n", + " 0.024703633040189743,\n", + " 0.028675368055701256,\n", + " 0.030506310984492302,\n", + " -0.009204004891216755,\n", + " 0.002056288765743375,\n", + " 0.0263373963534832,\n", + " -0.01888687163591385,\n", + " -0.0036759681534022093,\n", + " -0.028928883373737335,\n", + " 0.019478406757116318,\n", + " 0.010344821959733963,\n", + " 0.010344821959733963,\n", + " -0.011295503936707973,\n", + " -0.6417874097824097,\n", + " -0.024788137525320053,\n", + " 0.011063114739954472,\n", + " -0.033266808837652206,\n", + " 0.00889415293931961,\n", + " -0.0067216698080301285,\n", + " -0.004193561151623726,\n", + " 0.011633523739874363,\n", + " -0.004950585309416056,\n", + " 0.02109808474779129,\n", + " -0.023069869726896286,\n", + " -0.006306186784058809,\n", + " 0.005214663688093424,\n", + " -0.017084097489714622,\n", + " 0.0069822268560528755,\n", + " -0.021478358656167984,\n", + " -0.013457424007356167,\n", + " -0.02302761748433113,\n", + " -0.013492634519934654,\n", + " 0.0021531174425035715,\n", + " -0.019407985731959343,\n", + " 0.008739227429032326,\n", + " -0.0012895817635580897,\n", + " 0.020745981484651566,\n", + " 0.00387314660474658,\n", + " -0.009316678158938885,\n", + " 0.04830870032310486,\n", + " -0.015872858464717865,\n", + " 0.012689836323261261,\n", + " 0.005464657675474882,\n", + " -0.015281323343515396,\n", + " 0.025238830596208572,\n", + " 0.014140506274998188,\n", + " -0.018619270995259285,\n", + " 0.04326656833291054,\n", + " -0.013584180735051632,\n", + " -0.009880044497549534,\n", + " 0.028478190302848816,\n", + " 0.027421876788139343,\n", + " 0.029745765030384064,\n", + " -0.015267238952219486,\n", + " -0.029830269515514374,\n", + " 0.010577211156487465,\n", + " 0.0021795250941067934,\n", + " 0.0005677680601365864,\n", + " 0.011619439348578453,\n", + " 0.029661260545253754,\n", + " 0.003077391069382429,\n", + " -0.007725166622549295,\n", + " -0.00768291437998414,\n", + " 0.0007953154272399843,\n", + " 0.0070984214544296265,\n", + " 0.024928979575634003,\n", + " -0.018253082409501076,\n", + " 0.019830510020256042,\n", + " 0.007080816198140383,\n", + " 0.026295144110918045,\n", + " -0.013133487664163113,\n", + " 0.014675704762339592,\n", + " 0.006133655551820993,\n", + " -0.01619679480791092,\n", + " -0.012971519492566586,\n", + " -0.007527988404035568,\n", + " -0.04622424393892288,\n", + " -0.009063162840902805,\n", + " -0.006707585416734219,\n", + " -0.027999328449368477,\n", + " -0.02123892679810524,\n", + " 0.026182470843195915,\n", + " -0.02064739167690277,\n", + " -0.0001938773930305615,\n", + " 0.0035333659034222364,\n", + " -0.015408081002533436,\n", + " -0.022407913580536842,\n", + " 0.0006839624838903546,\n", + " 0.04213983565568924,\n", + " -0.005844930186867714,\n", + " -0.038506120443344116,\n", + " -0.0017746053636074066,\n", + " 0.009823707863688469,\n", + " -0.0015369349857792258,\n", + " -0.010718053206801414,\n", + " -0.00416187196969986,\n", + " -0.01420388463884592,\n", + " 0.027942990884184837,\n", + " 0.006806174758821726,\n", + " -0.005690004210919142,\n", + " -0.014436273835599422,\n", + " -0.006094924174249172,\n", + " -0.004665381275117397,\n", + " 0.01585877500474453,\n", + " 0.004668902140110731,\n", + " 0.008140649646520615,\n", + " -0.025506431236863136,\n", + " -0.00640829699113965,\n", + " -0.007739251013845205,\n", + " -0.015901027247309685,\n", + " -0.0002532949729356915,\n", + " -0.001292222528718412,\n", + " 0.0007099301437847316,\n", + " -0.005978730041533709,\n", + " -0.0005501628620550036,\n", + " -0.01314052939414978,\n", + " 0.012753215618431568,\n", + " 0.030365468934178352,\n", + " 0.0038097677752375603,\n", + " -0.01284476276487112,\n", + " 0.010133559815585613,\n", + " 0.016605235636234283,\n", + " -0.023661404848098755,\n", + " -0.008851900696754456,\n", + " -0.01950657367706299,\n", + " 0.016816498711705208,\n", + " 0.00292070466093719,\n", + " 0.0027816235087811947,\n", + " -0.033407650887966156,\n", + " 0.01806998997926712,\n", + " 0.0023907877039164305,\n", + " 0.0036900523118674755,\n", + " -0.02443603426218033,\n", + " 0.008316702209413052,\n", + " 0.004031593445688486,\n", + " -0.02468954771757126,\n", + " 0.009422308765351772,\n", + " 0.002149596344679594,\n", + " 0.017267191782593727,\n", + " 0.019013628363609314,\n", + " -0.02636556513607502,\n", + " -0.00587661936879158,\n", + " -0.01168281864374876,\n", + " 0.015323576517403126,\n", + " -0.007387146819382906,\n", + " 0.018365757539868355,\n", + " -0.01605595275759697,\n", + " 0.027576804161071777,\n", + " -0.010950441472232342,\n", + " 0.021534694358706474,\n", + " -0.0024383217096328735,\n", + " 0.02690076269209385,\n", + " -0.014999640174210072,\n", + " -0.030337300151586533,\n", + " -0.008232196792960167,\n", + " 0.01611229032278061,\n", + " -0.016210878267884254,\n", + " 0.0058660563081502914,\n", + " -0.035097748041152954,\n", + " -0.01597144827246666,\n", + " -0.00387314660474658,\n", + " 0.000913710449822247,\n", + " 0.0187460295855999,\n", + " 0.0037992047145962715,\n", + " -0.001453310251235962,\n", + " 0.009992717765271664,\n", + " 0.02194313518702984,\n", + " 0.019605163484811783,\n", + " -0.028112001717090607,\n", + " 0.0002709002001211047,\n", + " -0.022830437868833542,\n", + " -0.0027992285322397947,\n", + " -0.003781599458307028,\n", + " 0.008619511500000954,\n", + " 0.017520707100629807,\n", + " -0.022224819287657738,\n", + " -0.0010325456969439983,\n", + " -0.019802341237664223,\n", + " -0.003749910043552518,\n", + " 0.01431655790656805,\n", + " 0.017436200752854347,\n", + " 0.0012138793244957924,\n", + " -0.026802174746990204,\n", + " 0.017717884853482246,\n", + " -0.013133487664163113,\n", + " -0.030027449131011963,\n", + " 0.004978753626346588,\n", + " -0.03628081828355789,\n", + " 0.00048810450243763626,\n", + " -0.004105535335838795,\n", + " 0.0013142289826646447,\n", + " -0.004690028261393309,\n", + " 0.001977065345272422,\n", + " -0.0009603642974980175,\n", + " -0.005880140699446201,\n", + " -0.01653481461107731,\n", + " -0.0006637164624407887,\n", + " 0.024252939969301224,\n", + " -0.003424213733524084,\n", + " -0.012027880176901817,\n", + " 0.02460504323244095,\n", + " -0.005580852273851633,\n", + " 0.004778054542839527,\n", + " -0.002329169539734721,\n", + " 0.0042745452374219894,\n", + " 0.0024682506918907166,\n", + " 0.016915086656808853,\n", + " 0.008239238522946835,\n", + " 0.006397733930498362,\n", + " -0.007696998305618763,\n", + " -0.01899954490363598,\n", + " 0.020013604313135147,\n", + " 0.03329497575759888,\n", + " 0.03599913790822029,\n", + " 0.0011390572180971503,\n", + " 0.012513784691691399,\n", + " -0.012563078664243221,\n", + " 0.028759874403476715,\n", + " -0.050308652222156525,\n", + " -0.009689908474683762,\n", + " -0.02826692722737789,\n", + " 0.03639349341392517,\n", + " 0.017323527485132217,\n", + " -0.013612349517643452,\n", + " -0.009168794378638268,\n", + " -0.00773220881819725,\n", + " -0.0063519603572785854,\n", + " -0.0028397205751389265,\n", + " 0.011746197007596493,\n", + " -0.003707657568156719,\n", + " 0.018464345484972,\n", + " -0.006915327161550522,\n", + " -0.016999593004584312,\n", + " -0.00819698628038168,\n", + " 0.02718244679272175,\n", + " -0.004313276614993811,\n", + " -0.01566159538924694,\n", + " 0.00799980852752924,\n", + " 0.017520707100629807,\n", + " 0.01168281864374876,\n", + " 0.01821083016693592,\n", + " -0.015253155492246151,\n", + " -0.0036583628971129656,\n", + " 0.01294335164129734,\n", + " 0.029520418494939804,\n", + " 0.020999496802687645,\n", + " -0.01594327948987484,\n", + " 0.0038942727260291576,\n", + " -0.012112385593354702,\n", + " 0.03104150854051113,\n", + " -0.006911805830895901,\n", + " 0.005285084713250399,\n", + " -0.014563030563294888,\n", + " -0.00514776399359107,\n", + " 0.03126685693860054,\n", + " 0.04166097193956375,\n", + " -0.013556012883782387,\n", + " -0.0010457495227456093,\n", + " 0.022844523191452026,\n", + " 0.013098277151584625,\n", + " 0.013133487664163113,\n", + " -0.001913686515763402,\n", + " 0.045379191637039185,\n", + " -0.020689643919467926,\n", + " 0.029351409524679184,\n", + " -0.012154637835919857,\n", + " 0.012027880176901817,\n", + " 0.018647439777851105,\n", + " -0.020055856555700302,\n", + " 0.006309707649052143,\n", + " 0.028957052156329155,\n", + " 0.016548898071050644,\n", + " 0.022224819287657738,\n", + " -0.017351696267724037,\n", + " 0.014372894540429115,\n", + " 0.0016311228973791003,\n", + " -0.008725143037736416,\n", + " 0.03847794979810715,\n", + " 0.00663364352658391,\n", + " 0.004675944335758686,\n", + " 0.005119595676660538,\n", + " -0.03095700405538082,\n", + " -0.013985579833388329,\n", + " -0.011830702424049377,\n", + " -0.015534838661551476,\n", + " 0.0020334019791334867,\n", + " 0.012774341739714146,\n", + " 0.00842233281582594,\n", + " -0.008401206694543362,\n", + " -0.004932980053126812,\n", + " 0.00601746141910553,\n", + " 0.025957124307751656,\n", + " 0.0203375406563282,\n", + " -0.02602754533290863,\n", + " -0.02039387635886669,\n", + " 0.011056073009967804,\n", + " 0.019717836752533913,\n", + " 0.01165464986115694,\n", + " 0.007577282842248678,\n", + " -0.0031478118617087603,\n", + " -0.021985387429594994,\n", + " -0.004580875858664513,\n", + " 0.01329545583575964,\n", + " -0.005950561258941889,\n", + " 0.024196602404117584,\n", + " 0.03847794979810715,\n", + " 0.00899274181574583,\n", + " -0.013450381346046925,\n", + " -0.0263373963534832,\n", + " -0.003925962373614311,\n", + " -0.01326728705316782,\n", + " 0.0070420848205685616,\n", + " 0.025464177131652832,\n", + " 0.007492777891457081,\n", + " -0.007207573391497135,\n", + " -0.014661620371043682,\n", + " 0.02673175372183323,\n", + " 0.03287244960665703,\n", + " 0.011999712325632572,\n", + " -0.008204028941690922,\n", + " -0.00548578379675746,\n", + " -0.00832374393939972,\n", + " -0.0067216698080301285,\n", + " 0.014133463613688946,\n", + " 0.00292070466093719,\n", + " -0.033492155373096466,\n", + " 0.0420834980905056,\n", + " 0.005915351212024689,\n", + " 0.005471699871122837,\n", + " -0.003947088494896889,\n", + " -0.034478046000003815,\n", + " 0.01431655790656805,\n", + " 0.007880092598497868,\n", + " -0.008922320790588856,\n", + " -0.004584397189319134,\n", + " -0.03135136142373085,\n", + " 0.016915086656808853,\n", + " 0.05737890675663948,\n", + " 0.025759944692254066,\n", + " 0.0021372726187109947,\n", + " 0.022731849923729897,\n", + " -0.004925938323140144,\n", + " 0.007549114525318146,\n", + " -0.010964525863528252,\n", + " -0.021788209676742554,\n", + " 0.013612349517643452,\n", + " -0.003925962373614311,\n", + " 0.01073213666677475,\n", + " 0.003100277855992317,\n", + " -0.007753334939479828,\n", + " 0.0062357657589018345,\n", + " -0.006841385271400213,\n", + " -0.0023362115025520325,\n", + " 0.013436296954751015,\n", + " -0.02098541148006916,\n", + " 0.006024503149092197,\n", + " -0.014858798123896122,\n", + " -0.0062357657589018345,\n", + " 0.006894201040267944,\n", + " -0.0012675751931965351,\n", + " 0.009978634305298328,\n", + " 0.022450165823101997,\n", + " 0.00926034152507782,\n", + " 0.03298512473702431,\n", + " 0.010823683813214302,\n", + " -0.002663668477907777,\n", + " -0.02030937187373638,\n", + " -0.00924625713378191,\n", + " 0.002841481240466237,\n", + " 0.005989293102174997,\n", + " 0.006225202698260546,\n", + " -0.02092907577753067,\n", + " 0.004323840141296387,\n", + " 0.0011997951660305262,\n", + " 0.00023745029466226697,\n", + " -0.01314052939414978,\n", + " 0.010119475424289703,\n", + " 0.01821083016693592,\n", + " 0.0029717597644776106,\n", + " 0.009591319598257542,\n", + " 0.0038308941293507814,\n", + " 0.005351984407752752,\n", + " -0.02381633035838604,\n", + " -0.012675751931965351,\n", + " 0.0005017485236749053,\n", + " -0.00045509470510296524,\n", + " -0.0214642733335495,\n", + " 0.039548348635435104,\n", + " 0.013936285860836506,\n", + " 0.034309037029743195,\n", + " -0.013119403272867203,\n", + " 0.004221729934215546,\n", + " 0.006013940088450909,\n", + " -0.003300977172330022,\n", + " 0.026943014934659004,\n", + " -0.013802485540509224,\n", + " -0.055716972798109055,\n", + " -0.015239071100950241,\n", + " -0.012365900911390781,\n", + " 0.018562935292720795,\n", + " -0.0061935135163366795,\n", + " -0.013774317689239979,\n", + " -0.012767299078404903,\n", + " -0.017717884853482246,\n", + " -0.002181285759434104,\n", + " -0.017365779727697372,\n", + " -0.0004198842798359692,\n", + " -0.011225082911550999,\n", + " -0.02715427801012993,\n", + " -0.023971255868673325,\n", + " 0.020858654752373695,\n", + " 0.01647847704589367,\n", + " 0.02319662645459175,\n", + " 0.002554516075178981,\n", + " -0.007478693965822458,\n", + " 0.002369661582633853,\n", + " 0.014816545881330967,\n", + " 0.011027904227375984,\n", + " -0.03799908980727196,\n", + " 0.03619631379842758,\n", + " -0.022492418065667152,\n", + " 0.007471651770174503,\n", + " 0.025590935721993446,\n", + " 0.004457639530301094,\n", + " -0.0008199627045542002,\n", + " -0.01854884997010231,\n", + " 0.029464082792401314,\n", + " 0.013013772666454315,\n", + " -0.005376631394028664,\n", + " 0.005042132455855608,\n", + " -0.007894176989793777,\n", + " -0.010225106962025166,\n", + " 0.008408249355852604,\n", + " 0.028816210106015205,\n", + " 0.013901075348258018,\n", + " -0.004313276614993811,\n", + " 0.0009498011786490679,\n", + " -0.008858942426741123,\n", + " -0.008443459868431091,\n", + " 0.00584140932187438,\n", + " -0.009880044497549534,\n", + " 0.011436345055699348,\n", + " 0.006851948332041502,\n", + " 0.003954130690544844,\n", + " 0.03647799789905548,\n", + " -0.034647054970264435,\n", + " -0.001811576308682561,\n", + " -0.004411865957081318,\n", + " 0.003538647433742881,\n", + " 0.021647367626428604,\n", + " 0.0035949843004345894,\n", + " -0.014802461490035057,\n", + " 0.00608084024861455,\n", + " 0.007542072795331478,\n", + " 0.005982250906527042,\n", + " 0.0034118900075554848,\n", + " 0.011112409643828869,\n", + " -0.03616814687848091,\n", + " -0.039323002099990845,\n", + " 0.013964453712105751,\n", + " 0.021816378459334373,\n", + " -0.03321047127246857,\n", + " 0.005753383040428162,\n", + " 0.00830965954810381,\n", + " -0.0039506093598902225,\n", + " -0.0003642078081611544,\n", + " 0.005654794164001942,\n", + " 0.004380176775157452,\n", + " 0.003718220628798008,\n", + " -0.03557661175727844,\n", + " -0.013323623687028885,\n", + " -0.03653433546423912,\n", + " -0.0203375406563282,\n", + " -0.019900931045413017,\n", + " 0.019069965928792953,\n", + " -0.0028608469292521477,\n", + " -0.013746148906648159,\n", + " -0.009196962229907513,\n", + " -0.028985220938920975,\n", + " -0.004200603347271681,\n", + " -0.004447076469659805,\n", + " 0.014732041396200657,\n", + " -0.024450117722153664,\n", + " -0.01560525968670845,\n", + " 0.01792914792895317,\n", + " -0.005524515174329281,\n", + " 0.034449879080057144,\n", + " 0.003883709665387869,\n", + " 0.002425998216494918,\n", + " -5.580714514508145e-06,\n", + " 0.0023432536981999874,\n", + " -0.015267238952219486,\n", + " -0.025097990408539772,\n", + " -0.006288581527769566,\n", + " -0.007739251013845205,\n", + " 0.05242127925157547,\n", + " 0.0207600649446249,\n", + " 0.02342197299003601,\n", + " -0.006837863940745592,\n", + " 0.0038555413484573364,\n", + " 0.02798524498939514,\n", + " 0.0052815633825957775,\n", + " 0.011746197007596493,\n", + " -0.011020862497389317,\n", + " 0.011732112616300583,\n", + " -0.044815827161073685,\n", + " 0.012239143252372742,\n", + " 0.02625289186835289,\n", + " 0.019562911242246628,\n", + " 0.024619128555059433,\n", + " -0.00041680337744764984,\n", + " 0.006045629736036062,\n", + " 0.022309323772788048,\n", + " -0.014760209247469902,\n", + " -0.01580243743956089,\n", + " -0.0020545281004160643,\n", + " -0.02022486738860607,\n", + " -0.019802341237664223,\n", + " 0.0006214639870449901,\n", + " -0.01036594808101654,\n", + " 0.0023450141306966543,\n", + " -0.034224532544612885,\n", + " -0.022098060697317123,\n", + " 0.02056288719177246,\n", + " 0.012070133350789547,\n", + " 0.03352032229304314,\n", + " 0.013077151030302048,\n", + " 0.01585877500474453,\n", + " -0.029295071959495544,\n", + " 0.016830582171678543,\n", + " 0.012640541419386864,\n", + " -0.007003352977335453,\n", + " -0.00013258925173431635,\n", + " 0.014182758517563343,\n", + " 0.001666333293542266,\n", + " 0.0008674967684783041,\n", + " 0.03309779614210129,\n", + " 0.009711034595966339,\n", + " -0.022745933383703232,\n", + " -0.015957362949848175,\n", + " 0.0013432776322588325,\n", + " -0.020436128601431847,\n", + " 0.012520826421678066,\n", + " -0.0050703007727861404,\n", + " -0.019210806116461754,\n", + " -0.019295312464237213,\n", + " -0.01622496359050274,\n", + " -0.01809815689921379,\n", + " -0.007003352977335453,\n", + " -0.027027521282434464,\n", + " -0.028675368055701256,\n", + " 0.00403511431068182,\n", + " 0.015027808956801891,\n", + " -0.034337203949689865,\n", + " 0.015520754270255566,\n", + " -0.023295216262340546,\n", + " 0.003362595336511731,\n", + " -0.020886823534965515,\n", + " -0.006971663795411587,\n", + " 0.03864695876836777,\n", + " 0.015154565684497356,\n", + " 0.028126085177063942,\n", + " 0.034168194979429245,\n", + " -0.01849251426756382,\n", + " -0.026323312893509865,\n", + " 0.029464082792401314,\n", + " -0.015872858464717865,\n", + " -0.0066125174053013325,\n", + " 0.02078823372721672,\n", + " 0.0355202741920948,\n", + " -0.015309492126107216,\n", + " -0.026295144110918045,\n", + " 0.015408081002533436,\n", + " 0.0055421204306185246,\n", + " -0.005781551357358694,\n", + " 0.009112457744777203,\n", + " 0.007077294867485762,\n", + " -0.0023027616553008556,\n", + " 0.017309444025158882,\n", + " -0.013302497565746307,\n", + " -0.015140482224524021,\n", + " -0.010323695838451385,\n", + " 0.015675680711865425,\n", + " -0.014647535979747772,\n", + " 0.014929219149053097,\n", + " -0.044646818190813065,\n", + " -0.0011945136357098818,\n", + " -0.014886966906487942,\n", + " 0.03230908513069153,\n", + " -0.008556133136153221,\n", + " 0.016675656661391258,\n", + " 0.0023432536981999874,\n", + " -0.0018996023572981358,\n", + " -0.006443507503718138,\n", + " -0.0054400102235376835,\n", + " -0.015224986709654331,\n", + " -0.008513879962265491,\n", + " -0.013725022785365582,\n", + " 0.02718244679272175,\n", + " -0.009971591643989086,\n", + " 0.009140625596046448,\n", + " -0.0017270712414756417,\n", + " 0.012654625810682774,\n", + " -0.0014665140770375729,\n", + " -0.004401302896440029,\n", + " -0.01656298339366913,\n", + " 0.020802317187190056,\n", + " -0.008535006083548069,\n", + " 0.004591439384967089,\n", + " 0.0006082600448280573,\n", + " -0.002038683509454131,\n", + " 0.033914677798748016,\n", + " 0.011013820767402649,\n", + " -0.008013891987502575,\n", + " -0.01636580377817154,\n", + " -0.00957019254565239,\n", + " 0.0004016188904643059,\n", + " 0.014732041396200657,\n", + " 0.007929387502372265,\n", + " 0.024534622207283974,\n", + " -0.022351576015353203,\n", + " -0.017140433192253113,\n", + " -0.006901242770254612,\n", + " -0.003982299007475376,\n", + " -0.024027593433856964,\n", + " 0.016239047050476074,\n", + " 0.0025263477582484484,\n", + " 0.01353488676249981,\n", + " 0.012774341739714146,\n", + " -0.024844475090503693,\n", + " 0.017210854217410088,\n", + " 0.017295360565185547,\n", + " 0.01280250959098339,\n", + " 0.01073213666677475,\n", + " 0.03081616200506687,\n", + " -0.020745981484651566,\n", + " 0.004908333066850901,\n", + " 0.010274401865899563,\n", + " 0.019591080024838448,\n", + " -0.03368933126330376,\n", + " 0.02806974947452545,\n", + " -0.0006320271058939397,\n", + " -0.02692893147468567,\n", + " -0.001562462537549436,\n", + " -0.02030937187373638,\n", + " -0.020689643919467926,\n", + " -0.02191496640443802,\n", + " 0.0210417490452528,\n", + " 0.01633763685822487,\n", + " 0.008668806403875351,\n", + " -0.024943063035607338,\n", + " 0.025844451040029526,\n", + " 0.0028009891975671053,\n", + " 0.024858558550477028,\n", + " -0.009429351426661015,\n", + " -0.038815971463918686,\n", + " 3.7438581784954295e-05,\n", + " -0.015309492126107216,\n", + " 0.014732041396200657,\n", + " 0.004130182787775993,\n", + " -0.007253346964716911,\n", + " 0.016379889100790024,\n", + " -0.022323409095406532,\n", + " 0.011330714449286461,\n", + " 0.008499796502292156,\n", + " -0.027196530252695084,\n", + " -0.033661164343357086,\n", + " 0.011950417421758175,\n", + " 0.006341397296637297,\n", + " -0.006549138575792313,\n", + " 2.7109274014947005e-05,\n", + " 0.0018221393693238497,\n", + " 0.002102062338963151,\n", + " -0.00924625713378191,\n", + " 0.021365685388445854,\n", + " -0.018957292661070824,\n", + " -0.0005765706882812083,\n", + " -0.005647751968353987,\n", + " 0.00472875963896513,\n", + " 0.0022657907102257013,\n", + " 0.018534766510128975,\n", + " -0.0018467867048457265,\n", + " 0.013048983179032803,\n", + " -0.009992717765271664,\n", + " -0.01843617670238018,\n", + " -0.045379191637039185,\n", + " -0.01747845485806465,\n", + " -0.005654794164001942,\n", + " 0.02815425395965576,\n", + " -0.004323840141296387,\n", + " -0.021478358656167984,\n", + " -0.03090066649019718,\n", + " -0.034027352929115295,\n", + " -0.03247809410095215,\n", + " -0.00900682620704174,\n", + " -0.022802269086241722,\n", + " 0.008436417207121849,\n", + " 0.021224843338131905,\n", + " 0.01561934407800436,\n", + " -0.00657026469707489,\n", + " 0.0012499700533226132,\n", + " 0.012922225520014763,\n", + " 0.025464177131652832,\n", + " -0.013323623687028885,\n", + " -0.009647656232118607,\n", + " 0.019675584509968758,\n", + " -0.03284428268671036,\n", + " -0.007429399061948061,\n", + " 0.007119547575712204,\n", + " -0.04715379700064659,\n", + " -0.016408057883381844,\n", + " 0.011281419545412064,\n", + " 0.0038344149943441153,\n", + " 0.01064763218164444,\n", + " 0.024619128555059433,\n", + " -0.023605067282915115,\n", + " -0.0031161224469542503,\n", + " 0.000817321939393878,\n", + " 0.0036337156780064106,\n", + " 0.020914990454912186,\n", + " 0.0256331879645586,\n", + " 0.011739155277609825,\n", + " -0.022027641534805298,\n", + " -0.010964525863528252,\n", + " -0.005749862175434828,\n", + " 0.0008186423219740391,\n", + " -0.014985555782914162,\n", + " -0.025112073868513107,\n", + " 0.006883637513965368,\n", + " -0.017323527485132217,\n", + " -0.005316773895174265,\n", + " -0.02718244679272175,\n", + " 0.004394260700792074,\n", + " 0.009633571840822697,\n", + " 0.007105463184416294,\n", + " 0.03216824308037758,\n", + " 0.02532333694398403,\n", + " 0.018422093242406845,\n", + " -0.0037146995309740305,\n", + " 0.011056073009967804,\n", + " -0.017422117292881012,\n", + " -0.0006214639870449901,\n", + " 0.02370365709066391,\n", + " -0.034703392535448074,\n", + " -0.00472875963896513,\n", + " 0.023154374212026596,\n", + " -0.027858486399054527,\n", + " 0.009084288962185383,\n", + " -0.012077175080776215,\n", + " 0.0032745692878961563,\n", + " -0.01545033324509859,\n", + " -0.003749910043552518,\n", + " -0.011929291300475597,\n", + " -0.009675824083387852,\n", + " -0.010626506060361862,\n", + " -0.0020774148870259523,\n", + " 0.01885870285332203,\n", + " -0.03109784610569477,\n", + " -0.023098036646842957,\n", + " -0.008809647522866726,\n", + " 0.026097966358065605,\n", + " 0.005024527199566364,\n", + " -0.01684466563165188,\n", + " -0.0025069820694625378,\n", + " -0.04278770461678505,\n", + " 0.004570312798023224,\n", + " 0.014365852810442448,\n", + " -0.017408033832907677,\n", + " -0.019407985731959343,\n", + " 0.001058953464962542,\n", + " 0.021422021090984344,\n", + " 0.010274401865899563,\n", + " 0.01536582875996828,\n", + " 0.19909381866455078,\n", + " -0.003707657568156719,\n", + " 0.04760449007153511,\n", + " 0.03844978287816048,\n", + " -0.0009744484559632838,\n", + " -0.0095842769369483,\n", + " 0.01806998997926712,\n", + " -0.007094900123775005,\n", + " 0.01832350343465805,\n", + " 0.0046372124925255775,\n", + " -0.015098229050636292,\n", + " -0.011837744154036045,\n", + " -0.009689908474683762,\n", + " -0.007024479564279318,\n", + " 0.02339380420744419,\n", + " 0.014365852810442448,\n", + " -0.039801862090826035,\n", + " -0.04118211194872856,\n", + " -0.01399262249469757,\n", + " -0.03191472589969635,\n", + " -0.01905588060617447,\n", + " -0.018253082409501076,\n", + " -0.027816234156489372,\n", + " -0.023576898500323296,\n", + " 0.010718053206801414,\n", + " -0.009795540012419224,\n", + " 0.00043462865869514644,\n", + " 0.007429399061948061,\n", + " 0.02078823372721672,\n", + " 0.0008476909133605659,\n", + " -0.019872762262821198,\n", + " -0.020407961681485176,\n", + " 0.01063354779034853,\n", + " -0.017224939540028572,\n", + " -0.0004260461137164384,\n", + " -0.004070324823260307,\n", + " 0.02205580845475197,\n", + " -0.008647680282592773,\n", + " 0.0283514317125082,\n", + " 0.012478574179112911,\n", + " 0.007760377135127783,\n", + " -0.0056653572246432304,\n", + " -0.0017085857689380646,\n", + " -0.0377737432718277,\n", + " 0.0005299168406054378,\n", + " 0.009718076325953007,\n", + " ...],\n", + " [-0.024379240348935127,\n", + " 0.00834567565470934,\n", + " 0.013073521666228771,\n", + " -0.02563999965786934,\n", + " -0.016047269105911255,\n", + " 0.020199550315737724,\n", + " -0.029956728219985962,\n", + " -0.02932634763419628,\n", + " -0.01712987571954727,\n", + " -0.013285932131111622,\n", + " 0.03436938300728798,\n", + " -0.007537145633250475,\n", + " 0.012381474487483501,\n", + " 0.01829470880329609,\n", + " -0.010922009125351906,\n", + " 0.016938021406531334,\n", + " 0.021104007959365845,\n", + " 0.004871736746281385,\n", + " 0.020638074725866318,\n", + " 0.01112756785005331,\n", + " -0.012936482205986977,\n", + " 0.03299899399280548,\n", + " -0.019980287179350853,\n", + " 0.023666637018322945,\n", + " -0.03217675909399986,\n", + " -0.029901912435889244,\n", + " 0.024639613926410675,\n", + " -0.03138193488121033,\n", + " -0.007955114357173443,\n", + " -0.0374116487801075,\n", + " 0.010421817190945148,\n", + " -0.020624371245503426,\n", + " -0.013066669926047325,\n", + " -0.011483869515359402,\n", + " -0.011614056304097176,\n", + " 0.00977088138461113,\n", + " 0.02717483602464199,\n", + " 0.0017900720704346895,\n", + " 0.033656779676675797,\n", + " 0.007639924995601177,\n", + " 0.012299250811338425,\n", + " -0.0035116246435791254,\n", + " -0.009270689450204372,\n", + " -0.009071982465684414,\n", + " -0.012162212282419205,\n", + " 0.0029823114164173603,\n", + " -0.0003957001317758113,\n", + " -0.022282542660832405,\n", + " 0.0013052965514361858,\n", + " 0.00017076345102395862,\n", + " 0.0272433552891016,\n", + " 0.01181961502879858,\n", + " -0.01226499117910862,\n", + " -0.005361651070415974,\n", + " -0.004275617189705372,\n", + " -0.00842104759067297,\n", + " 0.0013635382056236267,\n", + " 0.008715680800378323,\n", + " -0.0029360607732087374,\n", + " 0.009517359547317028,\n", + " 0.003782276762649417,\n", + " -0.002487258054316044,\n", + " -0.01622541807591915,\n", + " -0.014087609946727753,\n", + " 0.0053171138279139996,\n", + " -0.025256289169192314,\n", + " -0.0075302934274077415,\n", + " 0.0026945294812321663,\n", + " 0.017513586208224297,\n", + " 0.0006526482757180929,\n", + " 0.03771313652396202,\n", + " 0.008427899330854416,\n", + " 0.008160673081874847,\n", + " -0.01795211061835289,\n", + " 0.011381089687347412,\n", + " -0.00242559053003788,\n", + " -0.006423703860491514,\n", + " 0.0036246818490326405,\n", + " 0.013642233796417713,\n", + " -0.018746936693787575,\n", + " 0.010545152239501476,\n", + " -0.015252442099153996,\n", + " 0.0026945294812321663,\n", + " 0.002965181600302458,\n", + " -0.001325852470472455,\n", + " -0.004594232887029648,\n", + " 0.005714526865631342,\n", + " 0.0034550961572676897,\n", + " -0.03256046772003174,\n", + " -0.015183922834694386,\n", + " -0.004690160043537617,\n", + " 0.018555082380771637,\n", + " 0.023543301969766617,\n", + " 0.005389058962464333,\n", + " -0.00621814513579011,\n", + " 0.02210439182817936,\n", + " -0.004512009210884571,\n", + " 0.04459249600768089,\n", + " 0.005858417600393295,\n", + " -0.022214023396372795,\n", + " 0.006259256973862648,\n", + " 0.012922778725624084,\n", + " -0.012306103482842445,\n", + " -0.00559804355725646,\n", + " -0.034287162125110626,\n", + " -0.0069238957948982716,\n", + " 0.012689812108874321,\n", + " -0.01817137375473976,\n", + " -0.00028542656218633056,\n", + " 0.017417658120393753,\n", + " -0.005358225200325251,\n", + " 0.012970742769539356,\n", + " 0.0021635033190250397,\n", + " -0.04683993384242058,\n", + " 0.021597348153591156,\n", + " 0.02820262871682644,\n", + " 0.005210908595472574,\n", + " -0.007283623330295086,\n", + " -0.014347984455525875,\n", + " -0.011757947504520416,\n", + " 0.007639924995601177,\n", + " 0.009298097342252731,\n", + " -0.015965044498443604,\n", + " -0.00946254376322031,\n", + " 0.010003848001360893,\n", + " 0.01378612406551838,\n", + " -0.012497957795858383,\n", + " -0.03820647671818733,\n", + " 0.002643140032887459,\n", + " -0.011511276476085186,\n", + " -0.0013652511406689882,\n", + " 0.01906212605535984,\n", + " 0.0016633110353723168,\n", + " 0.013333896175026894,\n", + " -0.021158823743462563,\n", + " 0.027202242985367775,\n", + " -0.011908690445125103,\n", + " -0.028586337342858315,\n", + " -0.04656585678458214,\n", + " -0.009764029644429684,\n", + " 0.005656285211443901,\n", + " 0.04056354612112045,\n", + " -0.008462158963084221,\n", + " -0.0039467234164476395,\n", + " -0.0009481386514380574,\n", + " -0.001090316683985293,\n", + " 0.004984794184565544,\n", + " -0.0018568786326795816,\n", + " 0.017020246013998985,\n", + " -0.006481945049017668,\n", + " -0.004042651038616896,\n", + " 0.0053171138279139996,\n", + " 0.02563999965786934,\n", + " 0.006125643849372864,\n", + " -0.0010928860865533352,\n", + " 0.02065177820622921,\n", + " 0.005779620260000229,\n", + " 0.026640383526682854,\n", + " -0.0056357295252382755,\n", + " -0.030395252630114555,\n", + " -0.019925471395254135,\n", + " 0.011696279980242252,\n", + " -0.01830841228365898,\n", + " -0.0048066433519124985,\n", + " 0.01740395464003086,\n", + " 0.03151897341012955,\n", + " -0.0063072205521166325,\n", + " 0.0014097888488322496,\n", + " -0.00750288600102067,\n", + " -0.010044959373772144,\n", + " 0.0029566166922450066,\n", + " 0.015019475482404232,\n", + " -0.022748475894331932,\n", + " -0.01407390646636486,\n", + " -0.005618599243462086,\n", + " 0.03436938300728798,\n", + " 0.008708829060196877,\n", + " 0.0009652685257606208,\n", + " -0.02883300743997097,\n", + " -0.015416888520121574,\n", + " -0.020185846835374832,\n", + " 0.016252826899290085,\n", + " 0.03513680398464203,\n", + " 0.03308121860027313,\n", + " -0.02066548354923725,\n", + " 0.013984831050038338,\n", + " 0.023378854617476463,\n", + " -0.03598644584417343,\n", + " -0.009304949082434177,\n", + " -0.0172532107681036,\n", + " 0.021542532369494438,\n", + " 0.008188080973923206,\n", + " 0.0031827311031520367,\n", + " -0.004337284713983536,\n", + " -0.6507708430290222,\n", + " -0.01954176276922226,\n", + " 0.012354066595435143,\n", + " -0.020213253796100616,\n", + " -0.002315959194675088,\n", + " 0.014252057299017906,\n", + " -0.003463661065325141,\n", + " 0.012470549903810024,\n", + " -0.00929124467074871,\n", + " 0.013107781298458576,\n", + " -0.026297785341739655,\n", + " 0.006684077903628349,\n", + " -0.010058663785457611,\n", + " -0.015129107050597668,\n", + " -0.0029172180220484734,\n", + " -0.021378085017204285,\n", + " -0.01378612406551838,\n", + " -0.026311490684747696,\n", + " -0.01022311020642519,\n", + " -0.0028384204488247633,\n", + " -0.01445761602371931,\n", + " 0.01416983362287283,\n", + " -0.003386576659977436,\n", + " -0.002060724189504981,\n", + " 0.008215488865971565,\n", + " -0.007372698746621609,\n", + " 0.05336299166083336,\n", + " -0.02184401825070381,\n", + " 0.010716450400650501,\n", + " -0.0009935328271239996,\n", + " -0.0027116595301777124,\n", + " 0.008434751071035862,\n", + " 0.02676371857523918,\n", + " -0.01559503935277462,\n", + " 0.03998798504471779,\n", + " -0.019802136346697807,\n", + " -0.009263836778700352,\n", + " 0.02017214149236679,\n", + " 0.026051115244627,\n", + " 0.02633889764547348,\n", + " -0.015512815676629543,\n", + " -0.017006540670990944,\n", + " 0.010065515525639057,\n", + " 0.011285162530839443,\n", + " -0.007852335460484028,\n", + " 0.014813916757702827,\n", + " 0.03417753055691719,\n", + " -0.0010406399378553033,\n", + " -0.0026602698490023613,\n", + " -0.00530340988188982,\n", + " 0.000868056493345648,\n", + " 0.001206799759529531,\n", + " 0.01636245846748352,\n", + " -0.025324808433651924,\n", + " 0.014855029061436653,\n", + " 0.005070443265140057,\n", + " 0.02620185911655426,\n", + " -0.0027305022813379765,\n", + " 0.012813147157430649,\n", + " 0.011326273903250694,\n", + " -0.01407390646636486,\n", + " -0.008099005557596684,\n", + " -0.018760640174150467,\n", + " -0.037329427897930145,\n", + " -0.019870657473802567,\n", + " -0.020528443157672882,\n", + " -0.02905227057635784,\n", + " -0.019651394337415695,\n", + " 0.02729817107319832,\n", + " -0.013005002401769161,\n", + " 0.002036742400377989,\n", + " 0.007023249287158251,\n", + " -0.010545152239501476,\n", + " -0.014019090682268143,\n", + " 0.012018321081995964,\n", + " 0.03754868730902672,\n", + " 0.0054130409844219685,\n", + " -0.02662668004631996,\n", + " -0.009503655135631561,\n", + " 0.006961581762880087,\n", + " -0.010942565277218819,\n", + " -0.012881667353212833,\n", + " 0.0011314282892271876,\n", + " -0.017212100327014923,\n", + " 0.023063665255904198,\n", + " 0.0020076215732842684,\n", + " -0.00786603894084692,\n", + " -0.011408497579395771,\n", + " -0.010976824909448624,\n", + " -0.013601121492683887,\n", + " 0.02551666460931301,\n", + " 0.00552609795704484,\n", + " -0.002728789346292615,\n", + " -0.01976102590560913,\n", + " 0.0074549224227666855,\n", + " -0.013512046076357365,\n", + " -0.01601986028254032,\n", + " -0.011202938854694366,\n", + " -0.004871736746281385,\n", + " -0.006776579190045595,\n", + " -3.795338125200942e-05,\n", + " -0.005022479686886072,\n", + " -0.0016590285813435912,\n", + " 0.00658472441136837,\n", + " 0.026188155636191368,\n", + " 0.006087957881391048,\n", + " -0.009236428886651993,\n", + " 0.018979903310537338,\n", + " 0.024790357798337936,\n", + " -0.0065641687251627445,\n", + " -0.006060550455003977,\n", + " -0.03705534711480141,\n", + " 0.011319422163069248,\n", + " 0.0012881667353212833,\n", + " -0.008044189773499966,\n", + " -0.03283454850316048,\n", + " 0.007893446832895279,\n", + " -0.007537145633250475,\n", + " 0.005176648497581482,\n", + " -0.006077680271118879,\n", + " 0.00022911209089215845,\n", + " 0.0010552003514021635,\n", + " -0.01614319533109665,\n", + " 0.01877434365451336,\n", + " 0.008852720260620117,\n", + " 0.005402762908488512,\n", + " 0.00970236212015152,\n", + " -0.015677262097597122,\n", + " -0.008044189773499966,\n", + " -0.005039609503000975,\n", + " 0.018609898164868355,\n", + " 0.001987065654247999,\n", + " 0.028051884844899178,\n", + " -0.017897294834256172,\n", + " 0.013964274898171425,\n", + " -0.019322499632835388,\n", + " 0.007317882962524891,\n", + " 0.00019549470744095743,\n", + " 0.02965524233877659,\n", + " -0.01663653552532196,\n", + " -0.030121173709630966,\n", + " -0.011566092260181904,\n", + " 0.019432131201028824,\n", + " -0.013546306639909744,\n", + " -0.005964622832834721,\n", + " -0.03453383222222328,\n", + " -0.015183922834694386,\n", + " 0.0065915766172111034,\n", + " -0.008893831633031368,\n", + " 0.0092227254062891,\n", + " -0.0025711944326758385,\n", + " -0.0007995369960553944,\n", + " 0.010579411871731281,\n", + " 0.0217754989862442,\n", + " 0.009832548908889294,\n", + " -0.03138193488121033,\n", + " -0.00223544891923666,\n", + " -0.021857721731066704,\n", + " -0.003847370157018304,\n", + " -0.0032135648652911186,\n", + " 0.007400106638669968,\n", + " 0.018541378900408745,\n", + " -0.02599629946053028,\n", + " -0.006776579190045595,\n", + " -0.014347984455525875,\n", + " 0.01559503935277462,\n", + " -0.0057042487896978855,\n", + " 0.015087994746863842,\n", + " 0.021446604281663895,\n", + " -0.026434825733304024,\n", + " 0.007338439114391804,\n", + " -0.009229577146470547,\n", + " -0.028257444500923157,\n", + " 0.011552388779819012,\n", + " -0.022282542660832405,\n", + " -0.00021273165475577116,\n", + " 0.0010706172324717045,\n", + " 0.002122391713783145,\n", + " 0.0036589414812624454,\n", + " -0.008880128152668476,\n", + " -0.006550464779138565,\n", + " -0.00887327641248703,\n", + " -0.01615689881145954,\n", + " -0.007667332887649536,\n", + " 0.026873350143432617,\n", + " -0.00302513618953526,\n", + " -0.0092227254062891,\n", + " 0.02085733786225319,\n", + " 0.0034259753301739693,\n", + " 0.008880128152668476,\n", + " -0.005889251362532377,\n", + " 0.01774655282497406,\n", + " -0.003939871676266193,\n", + " 0.023721452802419662,\n", + " 0.009866808541119099,\n", + " 0.024845173582434654,\n", + " -0.006406573578715324,\n", + " -0.010408112779259682,\n", + " 0.011799058876931667,\n", + " 0.021474013105034828,\n", + " 0.02729817107319832,\n", + " 0.0035664401948451996,\n", + " 0.023118481040000916,\n", + " -0.013758717104792595,\n", + " 0.015252442099153996,\n", + " -0.05870751291513443,\n", + " -0.010620523244142532,\n", + " -0.02932634763419628,\n", + " 0.03305380791425705,\n", + " -0.005786472465842962,\n", + " -0.0008654869743622839,\n", + " -0.00219262414611876,\n", + " -0.010250518098473549,\n", + " -0.00016102084191516042,\n", + " 0.002893236232921481,\n", + " 0.017705440521240234,\n", + " 0.006255830638110638,\n", + " 0.017897294834256172,\n", + " -0.015320961363613605,\n", + " -0.0019750746432691813,\n", + " -0.009284392930567265,\n", + " 0.029353756457567215,\n", + " -0.005193778313696384,\n", + " -0.010928860865533352,\n", + " 0.016033563762903214,\n", + " 0.02710631676018238,\n", + " 0.01573207788169384,\n", + " 0.01559503935277462,\n", + " 0.00611879164353013,\n", + " -0.005807028152048588,\n", + " 0.015279849991202354,\n", + " 0.03806943818926811,\n", + " 0.020679187029600143,\n", + " -0.01105219591408968,\n", + " 0.009202169254422188,\n", + " -0.017691737040877342,\n", + " 0.030532291159033775,\n", + " -0.01739025115966797,\n", + " 0.012333511374890804,\n", + " -0.0063414801843464375,\n", + " 0.0024547113571316004,\n", + " 0.01856878586113453,\n", + " 0.03957686573266983,\n", + " -0.012641848996281624,\n", + " 0.010695895180106163,\n", + " 0.014868732541799545,\n", + " -0.004186541773378849,\n", + " 0.015855412930250168,\n", + " 0.010689042508602142,\n", + " 0.03554791957139969,\n", + " -0.021501420065760612,\n", + " 0.031107855960726738,\n", + " -0.013402415439486504,\n", + " 0.012278695590794086,\n", + " 0.024995915591716766,\n", + " -0.01581430248916149,\n", + " -0.004929978400468826,\n", + " 0.020062511786818504,\n", + " 0.018349522724747658,\n", + " 0.01823989301919937,\n", + " -0.014389095827937126,\n", + " 0.023748859763145447,\n", + " 0.011627759784460068,\n", + " -0.01574578322470188,\n", + " 0.025283697992563248,\n", + " 0.0020932708866894245,\n", + " -0.004614788573235273,\n", + " 0.015183922834694386,\n", + " -0.04152281954884529,\n", + " -0.02003510296344757,\n", + " -0.018048036843538284,\n", + " -0.004885440692305565,\n", + " 0.0041659860871732235,\n", + " 0.015690967440605164,\n", + " 0.002643140032887459,\n", + " -0.007633072789758444,\n", + " 0.006228423211723566,\n", + " 0.010058663785457611,\n", + " 0.03127230331301689,\n", + " 0.020953264087438583,\n", + " -0.026777422055602074,\n", + " -0.015183922834694386,\n", + " 0.014649470336735249,\n", + " 0.021501420065760612,\n", + " 0.014183538034558296,\n", + " 0.014745397493243217,\n", + " -0.01157979667186737,\n", + " -0.027887439355254173,\n", + " -0.015828005969524384,\n", + " 0.015060586854815483,\n", + " 0.0021600774489343166,\n", + " 0.017691737040877342,\n", + " 0.023447373881936073,\n", + " -0.0029257829301059246,\n", + " -0.007180843967944384,\n", + " -0.018404338508844376,\n", + " -0.007132880389690399,\n", + " -0.022638844326138496,\n", + " 0.020775113254785538,\n", + " 0.02224143221974373,\n", + " 0.016444681212306023,\n", + " -0.014402800239622593,\n", + " -0.013066669926047325,\n", + " 0.02203587256371975,\n", + " 0.026859646663069725,\n", + " 0.003384863492101431,\n", + " -0.0026688347570598125,\n", + " -0.007448070216923952,\n", + " -0.011216643266379833,\n", + " -0.008962350897490978,\n", + " 0.02314588986337185,\n", + " -0.0002858548250515014,\n", + " -0.033985674381256104,\n", + " 0.0329715870320797,\n", + " 0.0015348369488492608,\n", + " -0.0019219721434637904,\n", + " -0.00981199275702238,\n", + " -0.028175219893455505,\n", + " 0.015540223568677902,\n", + " 0.008688272908329964,\n", + " -0.010997381061315536,\n", + " -0.011312570422887802,\n", + " -0.034972354769706726,\n", + " 0.02307736873626709,\n", + " 0.046401407569646835,\n", + " 0.034753091633319855,\n", + " -0.0025352216325700283,\n", + " 0.030998224392533302,\n", + " -0.0013823810731992126,\n", + " 0.012121100910007954,\n", + " -0.023269224911928177,\n", + " -0.021515125408768654,\n", + " 0.014334280975162983,\n", + " -0.0015810875920578837,\n", + " -0.0003783561405725777,\n", + " -0.002456424292176962,\n", + " -0.002024751389399171,\n", + " 0.00476553151383996,\n", + " 0.004429786000400782,\n", + " 0.0008397921919822693,\n", + " 0.015471704304218292,\n", + " -0.013299635611474514,\n", + " 0.003081664675846696,\n", + " -0.0034071323461830616,\n", + " -0.007735852152109146,\n", + " 0.019706210121512413,\n", + " 0.0006457963609136641,\n", + " 0.002627722918987274,\n", + " 0.028860416263341904,\n", + " -5.877688818145543e-05,\n", + " 0.047716982662677765,\n", + " 0.022268839180469513,\n", + " 0.0010080932406708598,\n", + " -0.008455307222902775,\n", + " -0.009784585796296597,\n", + " 0.013710753060877323,\n", + " 0.0175958089530468,\n", + " 0.004871736746281385,\n", + " -0.012285547330975533,\n", + " 0.03272491693496704,\n", + " 0.011764799244701862,\n", + " -0.0036349596921354532,\n", + " -0.010298481211066246,\n", + " 0.012148507870733738,\n", + " 0.0239133071154356,\n", + " -0.001457752427086234,\n", + " 0.0030456921085715294,\n", + " 0.013148892670869827,\n", + " 0.013731309212744236,\n", + " -0.03256046772003174,\n", + " -0.015923932194709778,\n", + " 0.004152282141149044,\n", + " 6.439763092203066e-05,\n", + " -0.0209258571267128,\n", + " 0.0324508361518383,\n", + " 0.009489951655268669,\n", + " 0.021871427074074745,\n", + " -0.020679187029600143,\n", + " 0.009243281558156013,\n", + " 0.011833318509161472,\n", + " 0.0012136517325416207,\n", + " 0.01948694698512554,\n", + " -0.010236813686788082,\n", + " -0.05245853215456009,\n", + " -0.019870657473802567,\n", + " -0.012038877233862877,\n", + " 0.013498342595994473,\n", + " -0.004789513535797596,\n", + " -0.015375777147710323,\n", + " -0.023378854617476463,\n", + " -0.03480790928006172,\n", + " -0.011682575568556786,\n", + " -0.00866086594760418,\n", + " 0.017376545816659927,\n", + " 0.0033882895950227976,\n", + " -0.021090304479002953,\n", + " -0.02355700545012951,\n", + " 0.004251635167747736,\n", + " 0.008681421168148518,\n", + " 0.011250902898609638,\n", + " 0.005971475038677454,\n", + " -0.00620444118976593,\n", + " -0.011600351892411709,\n", + " 0.006780005060136318,\n", + " -0.0013489777920767665,\n", + " -0.042372461408376694,\n", + " 0.02495480328798294,\n", + " -0.013745012693107128,\n", + " 0.00029527622973546386,\n", + " 0.006581298541277647,\n", + " 0.0006179602933116257,\n", + " -0.007482329849153757,\n", + " -0.023447373881936073,\n", + " 0.031080447137355804,\n", + " 0.01455354318022728,\n", + " 0.004570250865072012,\n", + " 0.003521902486681938,\n", + " -0.01712987571954727,\n", + " -0.005001924000680447,\n", + " 0.0071739922277629375,\n", + " 0.01954176276922226,\n", + " 0.017636921256780624,\n", + " -0.011257754638791084,\n", + " 0.0011477017542347312,\n", + " -0.003102220594882965,\n", + " -0.015430592931807041,\n", + " -0.009537914767861366,\n", + " -0.0119566535577178,\n", + " 0.005015627946704626,\n", + " 0.005834436044096947,\n", + " 0.005368503276258707,\n", + " 0.044126562774181366,\n", + " -0.02765447273850441,\n", + " -0.006420277524739504,\n", + " -0.016033563762903214,\n", + " 0.004604510962963104,\n", + " 0.02251550927758217,\n", + " 0.00020984098955523223,\n", + " -0.00983940064907074,\n", + " 0.02030918188393116,\n", + " 0.0093940244987607,\n", + " 0.00469701224938035,\n", + " 0.009777733124792576,\n", + " -0.006889636162668467,\n", + " -0.04448286443948746,\n", + " -0.0353560633957386,\n", + " -0.0018431746866554022,\n", + " 0.014224649406969547,\n", + " -0.013470934703946114,\n", + " 0.013258524239063263,\n", + " 0.018938791006803513,\n", + " -0.010161442682147026,\n", + " -0.0018791474867612123,\n", + " 0.00733158690854907,\n", + " 0.006814264692366123,\n", + " -0.006903340108692646,\n", + " -0.03193008899688721,\n", + " -0.011524980887770653,\n", + " -0.026051115244627,\n", + " -0.01648579351603985,\n", + " -0.01324482075870037,\n", + " 0.01600615680217743,\n", + " -0.006183885503560305,\n", + " -0.021378085017204285,\n", + " -0.005108129233121872,\n", + " -0.017636921256780624,\n", + " 0.006577872671186924,\n", + " 0.0003533037088345736,\n", + " 0.003467086935415864,\n", + " -0.027599656954407692,\n", + " -0.005488412454724312,\n", + " 0.001148558221757412,\n", + " -0.017362842336297035,\n", + " 0.030806370079517365,\n", + " 0.009147354401648045,\n", + " 0.017856182530522346,\n", + " 0.0027459191624075174,\n", + " -0.016102083027362823,\n", + " -0.005399337038397789,\n", + " -0.024653317406773567,\n", + " -0.007694740314036608,\n", + " 0.0003267524007242173,\n", + " 0.036150891333818436,\n", + " 0.020692890509963036,\n", + " 0.0183632280677557,\n", + " -0.009709213860332966,\n", + " 0.011703131720423698,\n", + " 0.024557391181588173,\n", + " 0.00022975447063799948,\n", + " 0.01348463911563158,\n", + " -0.007030101493000984,\n", + " 0.0030234232544898987,\n", + " -0.039686497300863266,\n", + " 0.01386149600148201,\n", + " 0.024653317406773567,\n", + " 0.024762948974967003,\n", + " 0.02341996692121029,\n", + " -0.0068827844224870205,\n", + " -0.005543227773159742,\n", + " 0.02191253751516342,\n", + " -0.01641727425158024,\n", + " -0.011319422163069248,\n", + " -0.009962735697627068,\n", + " -0.018801752477884293,\n", + " -0.021186230704188347,\n", + " -0.00886642374098301,\n", + " -0.017486177384853363,\n", + " 0.0038576482329517603,\n", + " -0.0396316833794117,\n", + " -0.02024066261947155,\n", + " 0.01552652008831501,\n", + " 0.005693970713764429,\n", + " 0.032258983701467514,\n", + " 0.007859187200665474,\n", + " 0.02176179550588131,\n", + " -0.027695583179593086,\n", + " 0.009764029644429684,\n", + " 0.009030871093273163,\n", + " 0.0008697694865986705,\n", + " -0.012203323654830456,\n", + " 0.012299250811338425,\n", + " -0.0006595002487301826,\n", + " 0.0025557775516062975,\n", + " 0.023721452802419662,\n", + " 0.00886642374098301,\n", + " -0.01634875312447548,\n", + " -0.019637690857052803,\n", + " 0.01378612406551838,\n", + " -0.005580913741141558,\n", + " 0.01892508752644062,\n", + " -0.018541378900408745,\n", + " -0.016458384692668915,\n", + " -0.008708829060196877,\n", + " -0.021295862272381783,\n", + " -0.010757562704384327,\n", + " -0.010031255893409252,\n", + " -0.025132954120635986,\n", + " -0.021542532369494438,\n", + " 0.007352143060415983,\n", + " 0.002600315259769559,\n", + " -0.026859646663069725,\n", + " 0.01001069974154234,\n", + " -0.015978747978806496,\n", + " -0.002317672362551093,\n", + " -0.011586648412048817,\n", + " -0.004213949665427208,\n", + " 0.03373900428414345,\n", + " 0.019733617082238197,\n", + " 0.03957686573266983,\n", + " 0.035794589668512344,\n", + " -0.023831084370613098,\n", + " -0.015471704304218292,\n", + " 0.03258787840604782,\n", + " -0.005437022540718317,\n", + " -0.00872938521206379,\n", + " 0.02426960878074169,\n", + " 0.021611051633954048,\n", + " -0.030093766748905182,\n", + " -0.020254366099834442,\n", + " 0.014964659698307514,\n", + " 0.0004826342628803104,\n", + " -0.00705065717920661,\n", + " 0.0074891820549964905,\n", + " 0.0077769639901816845,\n", + " -0.010003848001360893,\n", + " 0.012079988606274128,\n", + " 0.0025866113137453794,\n", + " -0.013546306639909744,\n", + " -0.022200319916009903,\n", + " 0.019021015614271164,\n", + " -0.01399853453040123,\n", + " 0.009654398076236248,\n", + " -0.04157763719558716,\n", + " -0.002600315259769559,\n", + " -0.019870657473802567,\n", + " 0.03464346379041672,\n", + " -0.000787117809522897,\n", + " 0.022954033687710762,\n", + " 0.01358056627213955,\n", + " -0.013539453968405724,\n", + " -0.016540609300136566,\n", + " -0.01181276235729456,\n", + " -0.022131800651550293,\n", + " -0.018555082380771637,\n", + " -0.010236813686788082,\n", + " 0.011387941427528858,\n", + " -0.009791437536478043,\n", + " 0.0038199624978005886,\n", + " -0.01115497574210167,\n", + " 0.011326273903250694,\n", + " 0.00012290685845073313,\n", + " -0.00453941710293293,\n", + " -0.010771266184747219,\n", + " 0.01884286478161812,\n", + " 0.0006342337001115084,\n", + " -0.00469701224938035,\n", + " 0.004508583340793848,\n", + " -0.010353296995162964,\n", + " 0.026380009949207306,\n", + " 0.015704670920968056,\n", + " -0.011161827482283115,\n", + " -0.02577703818678856,\n", + " 0.0008487853920087218,\n", + " -0.011339978314936161,\n", + " 0.015293553471565247,\n", + " 0.006077680271118879,\n", + " 0.023255519568920135,\n", + " -0.01996658369898796,\n", + " -0.005834436044096947,\n", + " -0.007434366270899773,\n", + " -0.013971127569675446,\n", + " -0.010750710032880306,\n", + " 0.011209791526198387,\n", + " -0.017568401992321014,\n", + " 0.020610667765140533,\n", + " -0.006248978897929192,\n", + " -0.009626990184187889,\n", + " 0.014115017838776112,\n", + " 0.015704670920968056,\n", + " 0.02329663187265396,\n", + " 0.015225034207105637,\n", + " 0.0158828217536211,\n", + " -0.016883205622434616,\n", + " -0.010784970596432686,\n", + " 0.017349138855934143,\n", + " 0.02085733786225319,\n", + " -0.03754868730902672,\n", + " 0.023337744176387787,\n", + " 0.0042002457194030285,\n", + " -0.02258402854204178,\n", + " -0.004128300119191408,\n", + " -0.013285932131111622,\n", + " -0.028778191655874252,\n", + " -0.02106289565563202,\n", + " 0.00415913388133049,\n", + " 0.02189883403480053,\n", + " 0.008236045017838478,\n", + " -0.021953649818897247,\n", + " 0.009195317514240742,\n", + " 0.001166544621810317,\n", + " 0.01843174733221531,\n", + " 0.005656285211443901,\n", + " -0.02183031477034092,\n", + " 0.001601643394678831,\n", + " -0.009161057882010937,\n", + " -0.004563399124890566,\n", + " 0.0041968198493123055,\n", + " -0.0075577013194561005,\n", + " 0.016965430229902267,\n", + " -0.024365536868572235,\n", + " 0.0217754989862442,\n", + " 0.005693970713764429,\n", + " -0.0338212288916111,\n", + " -0.02203587256371975,\n", + " 0.014813916757702827,\n", + " 0.021446604281663895,\n", + " -0.0054164668545126915,\n", + " 0.011771650984883308,\n", + " -0.006838246714323759,\n", + " 0.007831779308617115,\n", + " -0.013882052153348923,\n", + " 0.02309107407927513,\n", + " -0.008976055309176445,\n", + " 0.0012718932703137398,\n", + " -0.0176780316978693,\n", + " -0.0016367597272619605,\n", + " 0.009428284130990505,\n", + " 0.015622447244822979,\n", + " 0.00462506664916873,\n", + " 0.01455354318022728,\n", + " -0.01982954517006874,\n", + " -0.01941842772066593,\n", + " -0.0442361943423748,\n", + " -0.023899603635072708,\n", + " 0.006903340108692646,\n", + " 0.011627759784460068,\n", + " -0.020692890509963036,\n", + " -0.021597348153591156,\n", + " -0.03401308134198189,\n", + " -0.03149156644940376,\n", + " -0.011257754638791084,\n", + " -0.019637690857052803,\n", + " -0.022063281387090683,\n", + " 0.001298444578424096,\n", + " 0.005882399622350931,\n", + " 0.010003848001360893,\n", + " -0.007098620757460594,\n", + " 0.022830698639154434,\n", + " 0.00673889322206378,\n", + " 0.027599656954407692,\n", + " -0.015978747978806496,\n", + " -0.020076215267181396,\n", + " 0.009270689450204372,\n", + " -0.01989806443452835,\n", + " -0.00963384285569191,\n", + " 0.006101661827415228,\n", + " -0.037959806621074677,\n", + " 0.004111170303076506,\n", + " 0.004258487373590469,\n", + " 0.013532602228224277,\n", + " 0.013292783871293068,\n", + " 0.02440664730966091,\n", + " -0.03182045742869377,\n", + " 0.0029823114164173603,\n", + " 0.01669135130941868,\n", + " 0.013491490855813026,\n", + " 0.01641727425158024,\n", + " 0.01662283204495907,\n", + " 0.008311416022479534,\n", + " -0.03362937271595001,\n", + " -0.012655552476644516,\n", + " -0.0017138441326096654,\n", + " 0.001448331051506102,\n", + " -0.0033112051896750927,\n", + " -0.030724145472049713,\n", + " 0.017308026552200317,\n", + " -0.028367076069116592,\n", + " -0.00033231961424462497,\n", + " -0.022844403982162476,\n", + " 0.020199550315737724,\n", + " 0.012545921839773655,\n", + " -0.0015168505487963557,\n", + " 0.033026400953531265,\n", + " 0.014087609946727753,\n", + " 0.02246069349348545,\n", + " 0.004659326281398535,\n", + " 0.013888903893530369,\n", + " -0.021268455311655998,\n", + " 0.0028230035677552223,\n", + " 0.018746936693787575,\n", + " -0.03560273349285126,\n", + " -0.014676878228783607,\n", + " 0.02398182637989521,\n", + " -0.038288701325654984,\n", + " 0.00915420614182949,\n", + " -0.015965044498443604,\n", + " 0.0015151376137509942,\n", + " -0.00831826776266098,\n", + " -0.004751827567815781,\n", + " -6.123930506873876e-05,\n", + " -0.006413425784558058,\n", + " -0.013183153234422207,\n", + " -0.0034602349624037743,\n", + " 0.018609898164868355,\n", + " -0.03998798504471779,\n", + " -0.02592778019607067,\n", + " -0.0053856330923736095,\n", + " 0.019843248650431633,\n", + " -0.0003205428074579686,\n", + " -0.012491106055676937,\n", + " -0.0009961023461073637,\n", + " -0.032807137817144394,\n", + " 0.0016350466758012772,\n", + " 0.01514281053096056,\n", + " -0.021364381536841393,\n", + " -0.013977979309856892,\n", + " 0.010839785449206829,\n", + " 0.01656801626086235,\n", + " 0.010312185622751713,\n", + " 0.022912923246622086,\n", + " 0.205997034907341,\n", + " -0.007358994800597429,\n", + " 0.04566139727830887,\n", + " 0.038782041519880295,\n", + " -0.004258487373590469,\n", + " 0.0002391759044257924,\n", + " 0.013758717104792595,\n", + " -0.005190352443605661,\n", + " 0.01303241029381752,\n", + " 0.011847022920846939,\n", + " -0.015828005969524384,\n", + " -0.001850026659667492,\n", + " -0.01732173189520836,\n", + " -0.0075302934274077415,\n", + " 0.006070828065276146,\n", + " 0.010134034790098667,\n", + " -0.04588066041469574,\n", + " -0.034753091633319855,\n", + " -0.018870271742343903,\n", + " -0.03875463083386421,\n", + " -0.03417753055691719,\n", + " -0.016252826899290085,\n", + " -0.0172532107681036,\n", + " -0.015389480628073215,\n", + " 0.014855029061436653,\n", + " -0.010942565277218819,\n", + " -0.0048409029841423035,\n", + " 0.0006440833676606417,\n", + " 0.01996658369898796,\n", + " 0.0021241046488285065,\n", + " -0.014320576563477516,\n", + " -0.014786508865654469,\n", + " 0.0037308870814740658,\n", + " -0.019582875072956085,\n", + " -0.0010851776460185647,\n", + " 0.009044574573636055,\n", + " 0.03151897341012955,\n", + " -0.014046498574316502,\n", + " 0.02080252207815647,\n", + " 0.015334665775299072,\n", + " 0.012662404216825962,\n", + " -0.00658472441136837,\n", + " -0.00234679295681417,\n", + " -0.030258214101195335,\n", + " -0.003343751886859536,\n", + " 0.019514355808496475,\n", + " ...])" + ] + }, + "metadata": {}, + "execution_count": 43 + } + ], + "source": [ + "# Verify if both embeddings have the same dimension (should be True)\n", + "len(embedding_1) == len(embedding_2)\n", + "\n", + "# Return/display the two embedding vectors for further inspection or use\n", + "embedding_1, embedding_2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qiKCOv4X0d7B" + }, + "source": [ + "### Vector Database" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_lKe-Yo6UzHL" + }, + "source": [ + "#### Setup Vector Database Directory" + ] + }, + { + "cell_type": "markdown", + "source": [ + "LangChain is used here to help orchestrate the various components of the Retrieval Augmented Generation (RAG) system. It provides tools and abstractions for:\n", + "\n", + "Loading and splitting documents: Making it easy to load the PDF manual and break it into smaller, manageable chunks.\n", + "Creating embeddings: Interfacing with embedding models (like OpenAI's) to convert text into numerical vectors.\n", + "Vector databases: Simplifying the process of storing these embeddings in a vector database (Chroma) and performing similarity searches to retrieve relevant information.\n", + "Essentially, LangChain helps connect these different pieces together to build the RAG pipeline for question answering.\n", + "\n" + ], + "metadata": { + "id": "ziVCjOwxQw75" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_community.vectorstores import Chroma\n", + "\n", + "# Define vector DB directory\n", + "out_dir = \"Chroma\"\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "# Create the embedding function\n", + "embedding_function = OpenAIEmbeddings(\n", + " model=\"text-embedding-3-small\"\n", + ")\n", + "\n" + ], + "metadata": { + "id": "gQ_rvak0wSFM" + }, + "execution_count": 44, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### Create Vector Database from Documents" + ], + "metadata": { + "id": "_rHpwgzIMMc3" + } + }, + { + "cell_type": "code", + "source": [ + "# Building the vector store and saving it to disk for future use\n", + "# Process documents in smaller batches to avoid exceeding the token limit\n", + "import time # Import the time module\n", + "\n", + "batch_size = 100 # Adjust batch size as needed\n", + "for i in range(0, len(document_chunks), batch_size):\n", + " batch_chunks = document_chunks[i:i + batch_size]\n", + " if i == 0:\n", + " vectorstore = Chroma.from_documents(\n", + " batch_chunks, # Documents to index\n", + " embedding_model, # Embedding model for converting text to vectors\n", + " persist_directory=out_dir # Save vector DB files here\n", + " )\n", + " else:\n", + " vectorstore.add_documents(batch_chunks)\n", + "\n", + " time.sleep(0.3) # Add a 1-second delay between batches to mitigate rate limiting\n", + "\n", + "print(\"Vector store created and documents added.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "llRHoT4dGHwu", + "outputId": "14788ac0-3cad-44b3-d05a-6a487be6047e" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Vector store created and documents added.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DoJ1Bqb2VWkm" + }, + "source": [ + "#### Load Vector Database" + ] + }, + { + "cell_type": "code", + "source": [ + "retriever = vectorstore.as_retriever(\n", + " search_type=\"similarity\",\n", + " search_kwargs={\"k\": 3}\n", + ")\n" + ], + "metadata": { + "id": "caYiuqw7bm2F" + }, + "execution_count": 46, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "retriever.invoke(\"What are the common symptoms of appendicitis?\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "clYOUd48bsnR", + "outputId": "647c0581-8f3d-41a0-a5b0-e61ab07caf49" + }, + "execution_count": 47, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[Document(metadata={'moddate': '2025-11-05T06:16:39+00:00', 'trapped': '', 'format': 'PDF 1.7', 'keywords': '', 'page': 173, 'title': 'The Merck Manual of Diagnosis & Therapy, 19th Edition', 'creationdate': '2012-06-15T05:44:40+00:00', 'modDate': 'D:20251105061639Z', 'creationDate': 'D:20120615054440Z', 'creator': 'Atop CHM to PDF Converter', 'total_pages': 4114, 'author': '', 'source': '/content/medical_diagnosis_manual.pdf', 'subject': '', 'file_path': '/content/medical_diagnosis_manual.pdf', 'producer': 'pdf-lib (https://github.com/Hopding/pdf-lib)'}, page_content=\"Etiology\\nAppendicitis is thought to result from obstruction of the appendiceal lumen, typically by lymphoid\\nhyperplasia, but occasionally by a fecalith, foreign body, or even worms. The obstruction leads to\\ndistention, bacterial overgrowth, ischemia, and inflammation. If untreated, necrosis, gangrene, and\\nperforation occur. If the perforation is contained by the omentum, an appendiceal abscess results.\\nSymptoms and Signs\\nThe classic symptoms of acute appendicitis are epigastric or periumbilical pain followed by brief nausea,\\nvomiting, and anorexia; after a few hours, the pain shifts to the right lower quadrant. Pain increases with\\ncough and motion. Classic signs are right lower quadrant direct and rebound tenderness located at\\nMcBurney's point (junction of the middle and outer thirds of the line joining the umbilicus to the anterior\\nsuperior spine). Additional signs are pain felt in the right lower quadrant with palpation of the left lower\\nquadrant (Rovsing sign), an increase in pain from passive extension of the right hip joint that stretches\\nthe iliopsoas muscle (psoas sign), or pain caused by passive internal rotation of the flexed thigh\\n(obturator sign). Low-grade fever (rectal temperature 37.7 to 38.3° C [100 to 101° F]) is common.\\nUnfortunately, these classic findings appear in < 50% of patients. Many variations of symptoms and signs\\noccur. Pain may not be localized, particularly in infants and children. Tenderness may be diffuse or, in rare\\ninstances, absent. Bowel movements are usually less frequent or absent; if diarrhea is a sign, a\\nretrocecal appendix should be suspected. RBCs or WBCs may be present in the urine. Atypical symptoms\\nare common among elderly patients and pregnant women; in particular, pain is less severe and local\\ntenderness is less marked.\\nDiagnosis\\n• Clinical evaluation\\n• Abdominal CT if necessary\\n• Ultrasound an option to CT\\nWhen classic symptoms and signs are present, the diagnosis is clinical. In such patients, delaying\\nlaparotomy to do imaging tests only increases the likelihood of perforation and subsequent complications.\"),\n", + " Document(metadata={'page': 172, 'creationdate': '2012-06-15T05:44:40+00:00', 'creator': 'Atop CHM to PDF Converter', 'subject': '', 'file_path': '/content/medical_diagnosis_manual.pdf', 'author': '', 'format': 'PDF 1.7', 'modDate': 'D:20251105061639Z', 'creationDate': 'D:20120615054440Z', 'producer': 'pdf-lib (https://github.com/Hopding/pdf-lib)', 'title': 'The Merck Manual of Diagnosis & Therapy, 19th Edition', 'moddate': '2025-11-05T06:16:39+00:00', 'trapped': '', 'keywords': '', 'total_pages': 4114, 'source': '/content/medical_diagnosis_manual.pdf'}, page_content=\"antibiotics effective against intestinal flora should be given (eg, cefotetan 1 to 2 g bid, or amikacin 5\\nmg/kg tid plus clindamycin 600 to 900 mg qid).\\nAppendicitis\\nAppendicitis is acute inflammation of the vermiform appendix, typically resulting in abdominal\\npain, anorexia, and abdominal tenderness. Diagnosis is clinical, often supplemented by CT or\\nultrasound. Treatment is surgical removal.\\nIn the US, acute appendicitis is the most common cause of acute abdominal pain requiring surgery. Over\\n5% of the population develops appendicitis at some point. It most commonly occurs in the teens and 20s\\nbut may occur at any age.\\nOther conditions affecting the appendix include carcinoids, cancer, villous adenomas, and diverticula. The\\nappendix may also be affected by Crohn's disease or ulcerative colitis with pancolitis.\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition\\nChapter 11. Acute Abdomen & Surgical Gastroenterology\\n163\\nbiplobsinha25@gmail.com\\n9X5AUD3EIR\\nThis file is meant for personal use by biplobsinha25@gmail.com only.\\nSharing or publishing the contents in part or full is liable for legal action.\"),\n", + " Document(metadata={'total_pages': 4114, 'moddate': '2025-11-05T06:16:39+00:00', 'creator': 'Atop CHM to PDF Converter', 'format': 'PDF 1.7', 'trapped': '', 'page': 173, 'source': '/content/medical_diagnosis_manual.pdf', 'modDate': 'D:20251105061639Z', 'creationDate': 'D:20120615054440Z', 'subject': '', 'author': '', 'producer': 'pdf-lib (https://github.com/Hopding/pdf-lib)', 'title': 'The Merck Manual of Diagnosis & Therapy, 19th Edition', 'keywords': '', 'creationdate': '2012-06-15T05:44:40+00:00', 'file_path': '/content/medical_diagnosis_manual.pdf'}, page_content='• Ultrasound an option to CT\\nWhen classic symptoms and signs are present, the diagnosis is clinical. In such patients, delaying\\nlaparotomy to do imaging tests only increases the likelihood of perforation and subsequent complications.\\nIn patients with atypical or equivocal findings, imaging studies should be done without delay. Contrast-\\nenhanced CT has reasonable accuracy in diagnosing appendicitis and can also reveal other causes of\\nan acute abdomen. Graded compression ultrasound can usually be done quickly and uses no radiation\\n(of particular concern in children); however, it is occasionally limited by the presence of bowel gas and is\\nless useful for recognizing nonappendiceal causes of pain. Appendicitis remains primarily a clinical\\ndiagnosis. Selective and judicious use of radiographic studies may reduce the rate of negative\\nlaparotomy.\\nLaparoscopy can be used for diagnosis as well as definitive treatment; it may be especially helpful in\\nwomen with lower abdominal pain of unclear etiology. Laboratory studies typically show leukocytosis\\n(12,000 to 15,000/μL), but this finding is highly variable; a normal WBC count should not be used to\\nexclude appendicitis.\\nPrognosis\\nWithout surgery or antibiotics, mortality is > 50%.\\nWith early surgery, the mortality rate is < 1%, and convalescence is normally rapid and complete. With\\ncomplications (rupture and development of an abscess or peritonitis), the prognosis is worse: Repeat\\noperations and a long convalescence may follow.\\nTreatment\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition\\nChapter 11. Acute Abdomen & Surgical Gastroenterology\\n164\\nbiplobsinha25@gmail.com\\n9X5AUD3EIR\\nThis file is meant for personal use by biplobsinha25@gmail.com only.\\nSharing or publishing the contents in part or full is liable for legal action.')]" + ] + }, + "metadata": {}, + "execution_count": 47 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RtGfyOaeVlqP" + }, + "source": [ + "#### Explore Vector Database and Perform Searches" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "GdZON_Uj1EeS", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8ff8ce60-8948-421b-bb73-b6dd7a17acee" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "OpenAIEmbeddings(client=, async_client=, model='text-embedding-ada-002', dimensions=None, deployment='text-embedding-ada-002', openai_api_version=None, openai_api_base='https://api.openai.com/v1', openai_api_type=None, openai_proxy=None, embedding_ctx_length=8191, openai_api_key=SecretStr('**********'), openai_organization=None, allowed_special=None, disallowed_special=None, chunk_size=1000, max_retries=2, request_timeout=None, headers=None, tiktoken_enabled=True, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={}, skip_empty=False, default_headers=None, default_query=None, retry_min_seconds=4, retry_max_seconds=20, http_client=None, http_async_client=None, check_embedding_ctx_length=True)" + ] + }, + "metadata": {}, + "execution_count": 48 + } + ], + "source": [ + "vectorstore.embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dhXVQLa48mR0" + }, + "source": [ + "**Instructions:**\n", + "\n", + "In this step, the vector database has already been loaded into memory using the Chroma store, and the embedding function is attached. The next logical action is to query the vector database to verify that similarity search is working correctly.\n", + "\n", + "Therefore, in the following cell, we should perform a similarity search test by passing a clinical question to the vector store and retrieving the top-k most relevant document chunks based on the embeddings stored earlier.\n", + "\n", + "This will confirm:\n", + "\n", + "- my embeddings were generated correctly\n", + "\n", + "- my Chroma vector store loaded properly\n", + "\n", + "- my retrieval step is functioning\n", + "\n", + "- The RAG pipeline is ready for the answer-generation step" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "id": "P9HgsipF1I4H", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c80e4de8-89e1-4361-ddb9-fc10e4a13e35" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[Document(metadata={'file_path': '/content/medical_diagnosis_manual.pdf', 'trapped': '', 'creationdate': '2012-06-15T05:44:40+00:00', 'total_pages': 4114, 'page': 2456, 'keywords': '', 'author': '', 'format': 'PDF 1.7', 'creator': 'Atop CHM to PDF Converter', 'moddate': '2025-11-05T06:16:39+00:00', 'subject': '', 'producer': 'pdf-lib (https://github.com/Hopding/pdf-lib)', 'title': 'The Merck Manual of Diagnosis & Therapy, 19th Edition', 'source': '/content/medical_diagnosis_manual.pdf', 'creationDate': 'D:20120615054440Z', 'modDate': 'D:20251105061639Z'}, page_content=\"Parenteral antibiotics should be given after specimens of blood, body fluids, and wound sites have been\\ntaken for Gram stain and culture. Very prompt empiric therapy, started immediately after suspecting\\nsepsis, is essential and may be lifesaving. Antibiotic selection requires an educated guess based on the\\nsuspected source, clinical setting, knowledge or suspicion of causative organisms and of sensitivity\\npatterns common to that specific inpatient unit, and previous culture results.\\nOne regimen for septic shock of unknown cause is gentamicin or tobramycin 5.1 mg/kg IV once/day plus\\na 3rd-generation cephalosporin (cefotaxime 2 g q 6 to 8 h or ceftriaxone 2 g once/day or, if Pseudomonas\\nis suspected, ceftazidime 2 g IV q 8 h). Alternatively, ceftazidime plus a fluoroquinolone (eg, ciprofloxacin)\\nmay be used. Monotherapy with maximal therapeutic doses of ceftazidime (2 g IV q 8 h) or imipenem (1 g\\nIV q 6 h) may be effective but is not recommended.\\nVancomycin must be added if resistant staphylococci or enterococci are suspected. If there is an\\nabdominal source, a drug effective against anaerobes (eg, metronidazole) should be included. When\\nculture and sensitivity results are available, the antibiotic regimen is changed accordingly. Antibiotics are\\ncontinued for at least 5 days after shock resolves and evidence of infection subsides.\\nAbscesses must be drained, and necrotic tissues (eg, infarcted bowel, gangrenous gall-bladder,\\nabscessed uterus) must be surgically excised. The patient's condition will continue to deteriorate despite\\nantibiotic therapy unless septic foci are eliminated.\\nNormalization of blood glucose improves outcome in critically ill patients, even those not known to be\\ndiabetic. A continuous IV insulin infusion (crystalline zinc 1 to 4 U/h) is titrated to maintain glucose\\nbetween 80 to 110 mg/dL (4.4 to 6.1 mmol/L). This approach necessitates frequent (eg, q 1 to 4 h)\"),\n", + " Document(metadata={'file_path': '/content/medical_diagnosis_manual.pdf', 'creationDate': 'D:20120615054440Z', 'subject': '', 'producer': 'pdf-lib (https://github.com/Hopding/pdf-lib)', 'creator': 'Atop CHM to PDF Converter', 'creationdate': '2012-06-15T05:44:40+00:00', 'total_pages': 4114, 'author': '', 'moddate': '2025-11-05T06:16:39+00:00', 'trapped': '', 'source': '/content/medical_diagnosis_manual.pdf', 'format': 'PDF 1.7', 'page': 2400, 'keywords': '', 'modDate': 'D:20251105061639Z', 'title': 'The Merck Manual of Diagnosis & Therapy, 19th Edition'}, page_content=\"16 - Critical Care Medicine\\nChapter 222. Approach to the Critically Ill Patient\\nIntroduction\\nCritical care medicine specializes in caring for the most seriously ill patients. These patients are best\\ntreated in an ICU staffed by experienced personnel. Some hospitals maintain separate units for special\\npopulations (eg, cardiac, surgical, neurologic, pediatric, or neonatal patients). ICUs have a high\\nnurse:patient ratio to provide the necessary high intensity of service, including treatment and monitoring\\nof physiologic parameters.\\nSupportive care for the ICU patient includes provision of adequate nutrition (see p. 21) and prevention of\\ninfection, stress ulcers and gastritis (see p. 131), and pulmonary embolism (see p. 1920). Because 15 to\\n25% of patients admitted to ICUs die there, physicians should know how to minimize suffering and help\\ndying patients maintain dignity (see p. 3480).\\nPatient Monitoring and Testing\\nSome monitoring is manual (ie, by direct observation and physical examination) and intermittent, with the\\nfrequency depending on the patient's illness. This monitoring usually includes measurement of vital signs\\n(temperature, BP, pulse, and respiration rate), quantification of all fluid intake and output, and often daily\\nweight. BP may be recorded by an automated sphygmomanometer; a transcutaneous sensor for pulse\\noximetry is used as well.\\nOther monitoring is ongoing and continuous, provided by complex devices that require special training\\nand experience to operate. Most such devices generate an alarm if certain physiologic parameters are\\nexceeded. Every ICU should strictly follow protocols for investigating alarms.\\nBlood Tests\\nAlthough frequent blood draws can destroy veins, cause pain, and lead to anemia, ICU patients typically\\nhave routine daily blood tests to help detect problems early. Generally, patients need a daily set of\\nelectrolytes and a CBC. Patients with arrhythmias should also have Mg, phosphate, and Ca levels\\nmeasured. Patients receiving TPN need weekly liver enzymes and coagulation profiles. Other tests (eg,\\nblood culture for fever, CBC after a bleeding episode) are done as needed.\\nPoint-of-care testing uses miniaturized, highly automated devices to do certain blood tests at the patient's\\nbedside or unit (particularly ICU, emergency department, and operating room). Commonly available tests\"),\n", + " Document(metadata={'producer': 'pdf-lib (https://github.com/Hopding/pdf-lib)', 'creationdate': '2012-06-15T05:44:40+00:00', 'moddate': '2025-11-05T06:16:39+00:00', 'file_path': '/content/medical_diagnosis_manual.pdf', 'subject': '', 'creationDate': 'D:20120615054440Z', 'modDate': 'D:20251105061639Z', 'author': '', 'format': 'PDF 1.7', 'page': 2995, 'total_pages': 4114, 'trapped': '', 'source': '/content/medical_diagnosis_manual.pdf', 'creator': 'Atop CHM to PDF Converter', 'keywords': '', 'title': 'The Merck Manual of Diagnosis & Therapy, 19th Edition'}, page_content='can approximate bone marrow NSP levels. I:T ratios of > 0.80 correlate with NSP depletion and death;\\nsuch a ratio may identify neonates who might benefit from granulocyte transfusion.\\nTreatment\\n• Antibiotic therapy\\n• Supportive therapy\\nBecause sepsis may manifest with non-specific clinical signs and its effects may be devastating, rapid\\nempiric antibiotic therapy is recommended (see p. 1182); drugs are later adjusted according to\\nsensitivities and the site of infection. If bacterial cultures show no growth by 48 h (although some\\npathogens may require 72 h) and the neonate appears well, antibiotics are stopped.\\nGeneral supportive measures, including respiratory and hemodynamic management, are combined with\\nantibiotic treatment.\\nAntimicrobials: In early-onset sepsis, initial therapy should include ampicillin or penicillin G plus an\\naminoglycoside. Cefotaxime may be added to or substituted for the aminoglycoside if meningitis is\\nsuspected. If foul-smelling amniotic fluid is present at birth, therapy for anaerobes (eg, clindamycin,\\nmetronidazole) should be added. Antibiotics may be changed as soon as an organism is identified.\\nPreviously well infants admitted from the community with presumed late-onset sepsis should also receive\\ntherapy with ampicillin plus gentamicin or ampicillin plus cefotaxime. If gram-negative meningitis is\\nsuspected, ampicillin, cefotaxime, and an aminoglycoside may be used. In late-onset hospital-acquired\\nsepsis, initial therapy should include vancomycin (active against methicillin-resistant S. aureus) plus an\\naminoglycoside. If P. aeruginosa is prevalent in the nursery, ceftazidime may be used instead of an\\naminoglycoside. For neonates previously treated with a full 7- to 14-day aminoglycoside course who need\\nretreatment, a different aminoglycoside or a 3rd-generation cephalosporin should be considered.\\nIf coagulase-negative staphylococci are suspected (eg, an indwelling catheter has been in place for > 72')]" + ] + }, + "metadata": {}, + "execution_count": 49 + } + ], + "source": [ + "vectorstore.similarity_search(\"What is the protocol for managing sepsis in a critical care unit?\",k=3) #Complete the code to pass a query and an appropriate k value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7uo9cym60X-U" + }, + "source": [ + "### Retrieval and Response Generation using Vector Search" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9zscYgoFfgXi" + }, + "source": [ + "#### Convert Vector Database into a Retriever and Retrieve Relevant Documents" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "zO5kmp381VsX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e8f34f6a-6a69-4b9f-c5fd-b32ab757b422" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "VectorStoreRetriever(tags=['Chroma', 'OpenAIEmbeddings'], vectorstore=, search_kwargs={'k': 3})" + ] + }, + "metadata": {}, + "execution_count": 50 + } + ], + "source": [ + "retriever = vectorstore.as_retriever(\n", + " search_type='similarity',\n", + " search_kwargs={'k': 3}\n", + ")\n", + "retriever" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vw8qcwq66B0C", + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### System and User Prompt Template" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3wRkZYtO6B0D" + }, + "source": [ + "Prompts guide the model to generate accurate responses. Here, we define two parts:\n", + "\n", + " 1. The system message describing the assistant's role.\n", + " 2. A user message template including context and the question." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "gather": { + "logged": 1737358838889 + }, + "id": "Dyl60SEs6B0D", + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "#define the system message\n", + "qna_system_message = \"\"\"\n", + "You are an AI clinical assistant designed to support healthcare professionals in quickly reviewing authoritative medical literature.\n", + "Your task is to provide evidence-based, concise, and context-grounded responses strictly based on the excerpts provided from\n", + "medical manuals, guidelines, and research papers.\n", + "\n", + "User input will include the necessary clinical context for you to answer the question. The context begins with the token: ###Context\n", + "\n", + "### When crafting your response:\n", + "- Use ONLY the information given in the provided context.\n", + "- Provide clear, clinically accurate answers grounded in the supplied medical text.\n", + "- Avoid adding assumptions, interpretations, or general medical knowledge not present in the context.\n", + "- When relevant, include the name of the medical manual or research paper, as well as section or page numbers, if they appear in the context.\n", + "- If the answer cannot be found in the context, respond strictly with:\n", + " \"Sorry, this is out of my knowledge base.\"\n", + "\n", + "### Response Formatting Requirements:\n", + "Answer:\n", + "[A concise answer using only the information in the context]\n", + "\n", + "Source:\n", + "[Cite the specific source(s) mentioned in the context, including page/section if available]\n", + "\n", + "If the context is empty or irrelevant, your full response must be:\n", + "\"Sorry, this is out of my knowledge base.\"\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "XW38rWoNjJkQ" + }, + "outputs": [], + "source": [ + "#define the user message\n", + "qna_user_message_template = \"\"\"\n", + "###Context\n", + "Below are relevant excerpts taken from standard medical manuals, clinical guidelines, and research papers that relate to the healthcare question:\n", + "\n", + "{context}\n", + "\n", + "###Question\n", + "{question}\n", + "\n", + "Please answer using ONLY the context provided above.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TkIteX4m6mny" + }, + "source": [ + "### Response Function" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "id": "J-SfCZqC6B0E", + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "def generate_rag_response(user_input,k=3,max_tokens=500,temperature=0,top_p=0.95):\n", + " global qna_system_message,qna_user_message_template\n", + " # Retrieve relevant document chunks\n", + " relevant_document_chunks = retriever.invoke(input=user_input)\n", + " context_list = [d.page_content for d in relevant_document_chunks]\n", + "\n", + " # Combine document chunks into a single context\n", + " context_for_query = \". \".join(context_list)\n", + "\n", + " user_message = qna_user_message_template.replace('{context}', context_for_query)\n", + " user_message = user_message.replace('{question}', user_input)\n", + "\n", + " # Generate the response\n", + " try:\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4o-mini\", # specifying the model to be used.\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": qna_system_message},\n", + " {\"role\": \"user\", \"content\": user_message}\n", + " ],\n", + " max_tokens=max_tokens,\n", + " temperature=temperature,\n", + " top_p=top_p\n", + " )\n", + " # Extract and print the generated text from the response\n", + " response = response.choices[0].message.content.strip()\n", + " except Exception as e:\n", + " response = f'Sorry, I encountered the following error: \\n {e}'\n", + "\n", + " return response" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffP1SRYbPQHN" + }, + "source": [ + "## Question Answering using RAG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JjajBEj06B0E" + }, + "source": [ + "### Question 1: What is the protocol for managing sepsis in a critical care unit?" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "Gt4TAQNa6B0E", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "outputId": "9b47fddb-bfab-4bb3-b04b-f04c0a432358" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Answer:\\nThe protocol for managing sepsis in a critical care unit includes the following steps: \\n1. Obtain specimens of blood, body fluids, and wound sites for Gram stain and culture before starting parenteral antibiotics.\\n2. Initiate very prompt empiric antibiotic therapy immediately after suspecting sepsis, which may include gentamicin or tobramycin plus a 3rd-generation cephalosporin (e.g., cefotaxime or ceftriaxone), or ceftazidime plus a fluoroquinolone if Pseudomonas is suspected. Vancomycin should be added if resistant staphylococci or enterococci are suspected, and if there is an abdominal source, include a drug effective against anaerobes (e.g., metronidazole).\\n3. Change the antibiotic regimen based on culture and sensitivity results when available, continuing antibiotics for at least 5 days after shock resolves and evidence of infection subsides.\\n4. Drain abscesses and surgically excise necrotic tissues as necessary.\\n5. Monitor and manage blood glucose levels with a continuous IV insulin infusion to maintain glucose between 80 to 110 mg/dL.\\n6. Provide supportive care, including adequate nutrition and prevention of infections and complications.\\n\\nSource:\\nCritical Care Medicine, Chapter 222. Approach to the Critically Ill Patient.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 57 + } + ], + "source": [ + "response_with_rag_1 = generate_rag_response(question_1)\n", + "response_with_rag_1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QDw8zXuq6B0F" + }, + "source": [ + "### Question 2: What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "id": "i92cv0dQ6B0F", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 70 + }, + "outputId": "aa13726c-d879-4be4-e376-a49f901e9f7b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Answer:\\nThe common symptoms of appendicitis include epigastric or periumbilical pain followed by nausea, vomiting, and anorexia, with pain shifting to the right lower quadrant. Classic signs include right lower quadrant tenderness at McBurney's point, Rovsing sign, psoas sign, and obturator sign. Appendicitis cannot be cured via medicine; the treatment is surgical removal, specifically an open or laparoscopic appendectomy.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 11. Acute Abdomen & Surgical Gastroenterology, pages 163.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 58 + } + ], + "source": [ + "response_with_rag_2 = generate_rag_response(question_2)\n", + "response_with_rag_2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TggYyQPL6B0G" + }, + "source": [ + "### Question 3: What are the effective treatments or solutions for addressing sudden patchy hair loss, commonly seen as localized bald spots on the scalp, and what could be the possible causes behind it?" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "Ed6x6LGb6B0G", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 70 + }, + "outputId": "e11a529b-be68-4f8d-e906-0adebf4ac275" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Answer:\\nThe effective treatment for sudden patchy hair loss, known as alopecia areata, is not specified in the provided context. However, it is noted that alopecia areata is thought to be an autoimmune disorder affecting genetically susceptible individuals. Possible causes include systemic illnesses, particularly those that cause high fever, systemic lupus, endocrine disorders, and nutritional deficiencies. \\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 86. Hair Disorders, pages 848-849.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 59 + } + ], + "source": [ + "response_with_rag_3 = generate_rag_response(question_3)\n", + "response_with_rag_3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1TgxdI-_6B0G" + }, + "source": [ + "### Question 4: What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "u7ru57_c6B0G", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87 + }, + "outputId": "d8dd94ba-0e20-41d1-9d25-f0610666089c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Answer:\\nInitial treatment for a person who has sustained a physical injury to brain tissue includes ensuring a reliable airway and maintaining adequate ventilation, oxygenation, and blood pressure. Surgery may be needed for severe injuries to monitor and treat intracranial pressure, decompress the brain, or remove hematomas. Subsequently, many patients require rehabilitation, which should be planned early and may involve a team approach including physical, occupational, and speech therapy, as well as cognitive therapy for those with severe cognitive dysfunction.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 324. Traumatic Brain Injury, and Chapter 350. Rehabilitation.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 60 + } + ], + "source": [ + "response_with_rag_4 = generate_rag_response(question_4)\n", + "response_with_rag_4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ukLJz60rI78h" + }, + "source": [ + "### Storing the RAG system outputs\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "8TQn5dssxTJV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "outputId": "e81b7960-21bd-4119-f2ce-6e27c831be12" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " questions \\\n", + "0 What is the protocol for managing sepsis in a ... \n", + "1 What are the common symptoms for appendicitis,... \n", + "2 What are the effective treatments or solutions... \n", + "3 What treatments are recommended for a person w... \n", + "\n", + " base_prompt_responses \\\n", + "0 Managing sepsis in a critical care unit involv... \n", + "1 Common symptoms of appendicitis include:\\n\\n1.... \n", + "2 Sudden patchy hair loss, often referred to as ... \n", + "3 The treatment for a person who has sustained a... \n", + "\n", + " responses_with_prompt_eng \\\n", + "0 The management of sepsis in a critical care un... \n", + "1 Common symptoms of appendicitis include:\\n\\n1.... \n", + "2 Sudden patchy hair loss, often referred to as ... \n", + "3 Treatment for a person who has sustained a phy... \n", + "\n", + " responses_with_RAG \n", + "0 Answer:\\nThe protocol for managing sepsis in a... \n", + "1 Answer:\\nThe common symptoms of appendicitis i... \n", + "2 Answer:\\nThe effective treatment for sudden pa... \n", + "3 Answer:\\nInitial treatment for a person who ha... " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
questionsbase_prompt_responsesresponses_with_prompt_engresponses_with_RAG
0What is the protocol for managing sepsis in a ...Managing sepsis in a critical care unit involv...The management of sepsis in a critical care un...Answer:\\nThe protocol for managing sepsis in a...
1What are the common symptoms for appendicitis,...Common symptoms of appendicitis include:\\n\\n1....Common symptoms of appendicitis include:\\n\\n1....Answer:\\nThe common symptoms of appendicitis i...
2What are the effective treatments or solutions...Sudden patchy hair loss, often referred to as ...Sudden patchy hair loss, often referred to as ...Answer:\\nThe effective treatment for sudden pa...
3What treatments are recommended for a person w...The treatment for a person who has sustained a...Treatment for a person who has sustained a phy...Answer:\\nInitial treatment for a person who ha...
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "result_df", + "summary": "{\n \"name\": \"result_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"questions\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\",\n \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\",\n \"What is the protocol for managing sepsis in a critical care unit?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"base_prompt_responses\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Common symptoms of appendicitis include:\\n\\n1. **Abdominal Pain**: Typically starts around the navel and then moves to the lower right abdomen.\\n2. **Loss of Appetite**: A sudden decrease in appetite is common.\\n3. **Nausea and Vomiting**: Often follows the onset of abdominal pain.\\n4. **Fever**: A low-grade fever may develop.\\n5. **Constipation or Diarrhea**: Changes in bowel habits can occur.\\n6. **Abdominal Swelling**: In some cases, the abdomen may become swollen.\\n\\nAppendicitis cannot be effectively treated with medication alone. The standard treatment is surgical removal of the appendix, known as an **appendectomy**. This can be performed using two main techniques:\\n\\n1. **Open Appendectomy**: A larger incision is made in the lower right abdomen to remove the appendix.\\n2. **Laparoscopic Appendectomy**: This is a minimally invasive procedure where several small incisions are made, and the appendix is removed with the aid of a camera and special instruments.\\n\\nLaparoscopic appendectomy is often preferred due to its benefits, including less postoperative pain, shorter recovery time, and minimal scarring. However, the choice of procedure may depend on the patient's specific situation and the surgeon's expertise.\",\n \"The treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), can vary widely depending on the severity of the injury, the specific areas of the brain affected, and the resulting impairments. Here are some common approaches to treatment:\\n\\n1. **Emergency Care**: \\n - Immediate medical attention is crucial. This may involve stabilizing the patient, monitoring vital signs, and performing imaging studies (like CT or MRI scans) to assess the extent of the injury.\\n\\n2. **Surgical Interventions**: \\n - In some cases, surgery may be necessary to relieve pressure on the brain, remove blood clots (hematomas), or repair skull fractures.\\n\\n3. **Medication**: \\n - Medications may be prescribed to manage symptoms such as pain, seizures, or inflammation. Corticosteroids may be used to reduce swelling in the brain.\\n\\n4. **Rehabilitation**: \\n - **Physical Therapy**: To improve mobility and strength.\\n - **Occupational Therapy**: To help with daily living skills and regain independence.\\n - **Speech and Language Therapy**: To address communication difficulties and swallowing issues.\\n - **Neuropsychological Therapy**: To help with cognitive rehabilitation, including memory, attention, and problem-solving skills.\\n\\n5. **Psychological Support**: \\n - Counseling or therapy may be beneficial for coping with emotional and psychological challenges following a brain injury, such as depression, anxiety, or changes in personality.\\n\\n6. **Lifestyle Modifications**: \\n - Patients may need to make adjustments to their daily routines, including rest, nutrition, and avoiding activities that could lead to further injury.\\n\\n7. **Supportive Care**: \\n - Family support and education about the injury and its effects can be crucial for recovery. Support groups may also be helpful.\\n\\n8. **Long-term Management**: \\n - Ongoing follow-up with healthcare providers to monitor recovery and manage any long-term effects or complications.\\n\\n9. **Assistive Devices**: \\n - Depending on the nature of the impairment, assistive devices or technology may be recommended to aid in communication, mobility, or daily activities.\\n\\n10. **Alternative Therapies**: \\n - Some individuals may explore complementary therapies such as acupuncture, yoga, or meditation, although these should be discussed with a healthcare provider.\\n\\nIt's important for treatment plans to be individualized, taking into account the specific needs and circumstances of the person affected. A multidisciplinary team approach is often the most\",\n \"Managing sepsis in a critical care unit involves a systematic approach that includes early recognition, prompt intervention, and ongoing monitoring. The following is a general protocol based on current guidelines, such as those from the Surviving Sepsis Campaign:\\n\\n### 1. **Early Recognition**\\n - **Identify Symptoms**: Look for signs of infection (fever, chills, tachycardia, tachypnea) and organ dysfunction (altered mental status, hypotension, oliguria).\\n - **Use Screening Tools**: Utilize tools like the qSOFA (quick Sequential Organ Failure Assessment) or SIRS (Systemic Inflammatory Response Syndrome) criteria to identify patients at risk.\\n\\n### 2. **Initial Assessment**\\n - **Obtain Vital Signs**: Monitor blood pressure, heart rate, respiratory rate, and temperature.\\n - **Assess Organ Function**: Evaluate renal function (urine output, creatinine), liver function (bilirubin, liver enzymes), and coagulation status (platelets, INR).\\n\\n### 3. **Immediate Interventions**\\n - **Fluid Resuscitation**: Administer intravenous (IV) fluids (crystalloids) promptly, typically 30 mL/kg within the first 3 hours.\\n - **Antibiotic Therapy**: Start broad-spectrum IV antibiotics within 1 hour of recognition of sepsis. Adjust based on culture results and sensitivity.\\n - **Source Control**: Identify and control the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n### 4. **Monitoring and Support**\\n - **Hemodynamic Monitoring**: Use invasive monitoring (e.g., arterial line, central venous pressure) if necessary to guide fluid resuscitation and vasopressor therapy.\\n - **Vasopressors**: If hypotension persists despite adequate fluid resuscitation, initiate vasopressors (e.g., norepinephrine) to maintain mean arterial pressure (MAP) \\u2265 65 mmHg.\\n - **Oxygenation and Ventilation**: Provide supplemental oxygen and consider mechanical ventilation if respiratory failure occurs.\\n\\n### 5. **Ongoing Management**\\n - **Reassess Fluid Status**: Continuously evaluate the patient's response to fluids and adjust as necessary.\\n - **Monitor Laboratory Values**: Regularly check lactate levels, complete blood counts, and organ function tests to assess the patient's status.\\n - **Nutritional Support**: Initiate enteral nutrition as soon as feasible, typically within 24\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"responses_with_prompt_eng\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Common symptoms of appendicitis include:\\n\\n1. Abdominal pain, often starting near the belly button and then moving to the lower right abdomen.\\n2. Loss of appetite.\\n3. Nausea and vomiting.\\n4. Fever.\\n5. Constipation or diarrhea.\\n6. Abdominal swelling.\\n\\nAppendicitis cannot be effectively treated with medication alone; it typically requires surgical intervention. The standard surgical procedure for treating appendicitis is an **appendectomy**, which involves the removal of the inflamed appendix. This can be performed as an open surgery or laparoscopically, depending on the case and the surgeon's preference.\",\n \"Treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), may include the following approaches:\\n\\n1. **Immediate Medical Care**: \\n - Stabilization of the patient, including airway management, breathing support, and circulation.\\n - Imaging studies (CT or MRI) to assess the extent of the injury.\\n\\n2. **Surgical Interventions**: \\n - Surgery may be required to relieve pressure on the brain, remove blood clots (hematomas), or repair skull fractures.\\n\\n3. **Medications**: \\n - Anti-inflammatory drugs to reduce swelling.\\n - Anticonvulsants to prevent seizures.\\n - Pain management medications.\\n\\n4. **Rehabilitation**: \\n - Physical therapy to improve mobility and strength.\\n - Occupational therapy to assist with daily living activities.\\n - Speech therapy for communication and swallowing difficulties.\\n - Neuropsychological therapy to address cognitive and emotional challenges.\\n\\n5. **Supportive Care**: \\n - Psychological support and counseling for emotional and behavioral issues.\\n - Family education and support to help caregivers understand the injury and its effects.\\n\\n6. **Long-term Management**: \\n - Regular follow-up with healthcare providers to monitor recovery and manage any ongoing symptoms or complications.\\n\\nThe specific treatment plan will depend on the severity of the injury, the areas of the brain affected, and the individual patient's needs. Early intervention and a multidisciplinary approach are crucial for optimal recovery.\",\n \"The management of sepsis in a critical care unit typically follows the Surviving Sepsis Campaign guidelines. Here\\u2019s a concise protocol:\\n\\n1. **Early Recognition**: Identify sepsis using clinical criteria (e.g., suspected infection plus organ dysfunction).\\n\\n2. **Immediate Resuscitation**:\\n - **Fluid Resuscitation**: Administer IV fluids (30 mL/kg of crystalloids within the first 3 hours).\\n - **Vasopressors**: If hypotension persists after fluid resuscitation, initiate norepinephrine to maintain mean arterial pressure (MAP) \\u2265 65 mmHg.\\n\\n3. **Antibiotic Therapy**:\\n - Administer broad-spectrum antibiotics within 1 hour of recognition of sepsis.\\n - Adjust based on culture results and clinical response.\\n\\n4. **Source Control**:\\n - Identify and manage the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n5. **Monitoring**:\\n - Monitor vital signs, urine output, and laboratory parameters closely.\\n - Use lactate levels to guide resuscitation efforts.\\n\\n6. **Supportive Care**:\\n - Provide organ support as needed (e.g., mechanical ventilation, renal replacement therapy).\\n - Consider corticosteroids in cases of septic shock.\\n\\n7. **Reassessment**:\\n - Reassess hemodynamic status and organ function frequently.\\n - Adjust treatment based on response.\\n\\n8. **Follow-Up**:\\n - Continue monitoring for complications and adjust care plans accordingly.\\n\\nThis protocol emphasizes timely intervention and continuous assessment to improve outcomes in patients with sepsis.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"responses_with_RAG\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Answer:\\nThe common symptoms of appendicitis include epigastric or periumbilical pain followed by nausea, vomiting, and anorexia, with pain shifting to the right lower quadrant. Classic signs include right lower quadrant tenderness at McBurney's point, Rovsing sign, psoas sign, and obturator sign. Appendicitis cannot be cured via medicine; the treatment is surgical removal, specifically an open or laparoscopic appendectomy.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 11. Acute Abdomen & Surgical Gastroenterology, pages 163.\",\n \"Answer:\\nInitial treatment for a person who has sustained a physical injury to brain tissue includes ensuring a reliable airway and maintaining adequate ventilation, oxygenation, and blood pressure. Surgery may be needed for severe injuries to monitor and treat intracranial pressure, decompress the brain, or remove hematomas. Subsequently, many patients require rehabilitation, which should be planned early and may involve a team approach including physical, occupational, and speech therapy, as well as cognitive therapy for those with severe cognitive dysfunction.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 324. Traumatic Brain Injury, and Chapter 350. Rehabilitation.\",\n \"Answer:\\nThe protocol for managing sepsis in a critical care unit includes the following steps: \\n1. Obtain specimens of blood, body fluids, and wound sites for Gram stain and culture before starting parenteral antibiotics.\\n2. Initiate very prompt empiric antibiotic therapy immediately after suspecting sepsis, which may include gentamicin or tobramycin plus a 3rd-generation cephalosporin (e.g., cefotaxime or ceftriaxone), or ceftazidime plus a fluoroquinolone if Pseudomonas is suspected. Vancomycin should be added if resistant staphylococci or enterococci are suspected, and if there is an abdominal source, include a drug effective against anaerobes (e.g., metronidazole).\\n3. Change the antibiotic regimen based on culture and sensitivity results when available, continuing antibiotics for at least 5 days after shock resolves and evidence of infection subsides.\\n4. Drain abscesses and surgically excise necrotic tissues as necessary.\\n5. Monitor and manage blood glucose levels with a continuous IV insulin infusion to maintain glucose between 80 to 110 mg/dL.\\n6. Provide supportive care, including adequate nutrition and prevention of infections and complications.\\n\\nSource:\\nCritical Care Medicine, Chapter 222. Approach to the Critically Ill Patient.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 61 + } + ], + "source": [ + "# Add the results to a new column in the DataFrame\n", + "result_df['responses_with_RAG'] = [response_with_rag_1, response_with_rag_2, response_with_rag_3, response_with_rag_4]\n", + "\n", + "# Display the DataFrame\n", + "result_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yyQrTipNfuBN" + }, + "source": [ + "## Output Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tY9j1MVei5eI" + }, + "source": [ + "#### **Defining required System Prompts**" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "id": "2dDxkZdyKSnh", + "tags": [] + }, + "outputs": [], + "source": [ + "groundedness_rater_system_message = \"\"\"\n", + "You are tasked with rating AI generated answers to questions posed by users.\n", + "You will be presented a question, context used by the AI system to generate the answer and an AI generated answer to the question.\n", + "In the input, the question will begin with ###Question, the context will begin with ###Context while the AI generated answer will begin with ###Answer.\n", + "\n", + "Evaluation criteria:\n", + "The task is to judge the extent to which the metric is followed by the answer.\n", + "1 - The metric is not followed at all\n", + "2 - The metric is followed only to a limited extent\n", + "3 - The metric is followed to a good extent\n", + "4 - The metric is followed mostly\n", + "5 - The metric is followed completely\n", + "\n", + "Metric:\n", + "The answer should be derived only from the information presented in the context\n", + "\n", + "Instructions:\n", + "1. First write down the steps that are needed to evaluate the answer as per the metric.\n", + "2. Give a step-by-step explanation if the answer adheres to the metric considering the question and context as the input.\n", + "3. Next, evaluate the extent to which the metric is followed.\n", + "4. Use the previous information to rate the answer using the evaluaton criteria and assign a score.\n", + "\n", + "Return only the Score in last in a dictionary format not json and score should be in the range of 1 to 5.\n", + "Example {groundedness_score:4}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "NIosu2Wk7OVs", + "tags": [] + }, + "outputs": [], + "source": [ + "relevance_rater_system_message = \"\"\"\n", + "You are tasked with rating AI generated answers to questions posed by users.\n", + "You will be presented a question, context used by the AI system to generate the answer and an AI generated answer to the question.\n", + "In the input, the question will begin with ###Question, the context will begin with ###Context while the AI generated answer will begin with ###Answer.\n", + "\n", + "Evaluation criteria:\n", + "The task is to judge the extent to which the metric is followed by the answer.\n", + "1 - The metric is not followed at all\n", + "2 - The metric is followed only to a limited extent\n", + "3 - The metric is followed to a good extent\n", + "4 - The metric is followed mostly\n", + "5 - The metric is followed completely\n", + "\n", + "Metric:\n", + "Relevance measures how well the answer addresses the main aspects of the question, based on the context.\n", + "Consider whether all and only the important aspects are contained in the answer when evaluating relevance.\n", + "\n", + "Instructions:\n", + "1. First write down the steps that are needed to evaluate the context as per the metric.\n", + "2. Give a step-by-step explanation if the context adheres to the metric considering the question as the input.\n", + "3. Next, evaluate the extent to which the metric is followed.\n", + "4. Use the previous information to rate the context using the evaluaton criteria and assign a score.\n", + "Return only the Score in last in a dictionary format not json and score should be in the range of 1 to 5.\n", + "Example {relevance_score:4}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "id": "7boMupgh_Gux", + "tags": [] + }, + "outputs": [], + "source": [ + "user_message_template = \"\"\"\n", + "###Question\n", + "{question}\n", + "\n", + "###Context\n", + "{context}\n", + "\n", + "###Answer\n", + "{answer}\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7QPs3ApgzB_D" + }, + "source": [ + "#### **Definig the LLM-as-a-Judge Evaluation function**" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "id": "dXxdhhEDxatr" + }, + "outputs": [], + "source": [ + "def generate_ground_relevance_response(user_input,response, max_tokens=500, temperature=0, top_p=0.95): # Complete the code to set default paramenters\n", + " global qna_user_message_template\n", + "\n", + " context_for_query = [doc.page_content for doc in retriever.invoke(input=user_input)]\n", + "\n", + " # Combine user_prompt and system_message to create the prompt\n", + " groundedness_prompt = f\"\"\"[INST]{groundedness_rater_system_message}\\n\n", + " {'user'}: {user_message_template.format(context=context_for_query, question=user_input, answer=response)}\n", + " [/INST]\"\"\"\n", + "\n", + " # Combine user_prompt and system_message to create the prompt\n", + " relevance_prompt = f\"\"\"[INST]{relevance_rater_system_message}\\n\n", + " {'user'}: {user_message_template.format(context=context_for_query, question=user_input, answer=response)}\n", + " [/INST]\"\"\"\n", + "\n", + " response_1 = client.chat.completions.create(\n", + " model=\"gpt-3.5-turbo\", # Complete the code by specifying the model to be used.\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": groundedness_prompt}\n", + " ],\n", + " max_tokens=max_tokens,\n", + " temperature=temperature,\n", + " top_p=top_p\n", + " )\n", + "\n", + " response_2 = client.chat.completions.create(\n", + " model=\"gpt-3.5-turbo\", # Complete the code by specifying the model to be used.\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": relevance_prompt}\n", + " ],\n", + " max_tokens=max_tokens,\n", + " temperature=temperature,\n", + " top_p=top_p\n", + " )\n", + "\n", + " return response_1.choices[0].message.content,response_2.choices[0].message.content" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "h_kKHCe2mqpE" + }, + "source": [ + "#### **Evaluation 1: Base Prompt Response Evaluation**" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "id": "iJ-lchv-sVJ6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6b74cd9a-5843-4df3-f6e5-4248f6823caa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer as per the metric, the following steps need to be taken:\n", + "1. Identify if the information provided in the answer is directly derived from the context given.\n", + "2. Check if there are any additional details or information in the answer that is not present in the context.\n", + "3. Ensure that the answer does not include any information that is not mentioned in the context.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides a detailed protocol for managing sepsis in a critical care unit. It includes steps for early recognition, initial assessment, immediate interventions, monitoring and support, and ongoing management. The information provided in the answer aligns with the context given, which outlines the protocol for managing sepsis in a critical care unit. The answer does not introduce any new information that is not present in the context.\n", + "\n", + "Therefore, the answer follows the metric of being derived only from the information presented in the context.\n", + "\n", + "Based on the evaluation criteria, the score for groundedness would be 5. \n", + "\n", + "{groundedness_score: 5}\n", + "\n", + "{relevance_score: 3}\n" + ] + } + ], + "source": [ + "# Question 1\n", + "ground_1,rel_1 = generate_ground_relevance_response(user_input=result_df.questions[0], response=result_df.base_prompt_responses[0], max_tokens=516)\n", + "print(ground_1,end=\"\\n\\n\")\n", + "print(rel_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "id": "mjQHf0DgnNEQ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "63e679f4-2f79-43d0-8d8a-1558f6cd3fc4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer based on the metric provided, we need to follow these steps:\n", + "1. Identify if the information provided in the answer is solely derived from the context given.\n", + "2. Check if any additional information not present in the context is included in the answer.\n", + "3. Determine if the answer directly addresses the question asked without introducing new information.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides a list of common symptoms of appendicitis and details the standard treatment procedures for appendicitis, including open appendectomy and laparoscopic appendectomy. The information presented in the answer aligns with the details provided in the context regarding the symptoms, diagnosis, and treatment of appendicitis. The answer does not introduce any new information that is not present in the context.\n", + "\n", + "Therefore, the answer follows the metric of being derived only from the information presented in the context.\n", + "\n", + "Based on the evaluation criteria:\n", + "The answer demonstrates a complete adherence to the metric by solely utilizing the information provided in the context to address the question about the symptoms and treatment of appendicitis.\n", + "\n", + "Hence, the groundedness score for this answer is 5. \n", + "\n", + "{groundedness_score: 5}\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps can be followed:\n", + "\n", + "1. Identify the main aspects of the question: Understand the key components of the question, which in this case are the common symptoms of appendicitis and the treatment options available.\n", + "\n", + "2. Analyze the context: Review the provided context to see if it contains information related to the common symptoms of appendicitis and the treatment options, specifically focusing on surgical procedures and medication.\n", + "\n", + "3. Check for completeness: Ensure that all important aspects related to the question are covered in the context, including details about symptoms and treatment options.\n", + "\n", + "4. Verify accuracy: Confirm that the information provided in the context aligns with medical knowledge and guidelines regarding appendicitis symptoms and treatment.\n", + "\n", + "Based on the steps outlined above, the context provided contains detailed information about the symptoms of appendicitis, including abdominal pain, loss of appetite, nausea, vomiting, fever, changes in bowel habits, and abdominal swelling. Additionally, it discusses the standard treatment for appendicitis, which is surgical removal of the appendix through open or laparoscopic appendectomy.\n", + "\n", + "The context also mentions that antibiotics are not curative for appendicitis and that surgical removal is the preferred treatment option. It provides insights into the surgical procedures involved in appendectomy, such as open appendectomy and laparoscopic appendectomy, along with details about antibiotic use before and after surgery.\n", + "\n", + "Overall, the context aligns well with the main aspects of the question by addressing the common symptoms of appendicitis and the surgical procedures used for its treatment. It provides comprehensive information that is relevant to the question posed.\n", + "\n", + "Therefore, based on the evaluation, the context adheres to the metric of relevance to a good extent.\n", + "\n", + "{relevance_score: 3}\n" + ] + } + ], + "source": [ + "# Question 2\n", + "ground_2,rel_2 = generate_ground_relevance_response(user_input=result_df.questions[1], response=result_df.base_prompt_responses[1], max_tokens=516) #Complete the code to calculate the groundedness and relevance score\n", + "print(ground_2,end=\"\\n\\n\")\n", + "print(rel_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "id": "3VmHQIa-nX9W", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d468d5d3-7436-42ab-bdb9-184922c0f9db" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer as per the metric, the following steps need to be followed:\n", + "1. Identify if the information provided in the answer is solely derived from the context given.\n", + "2. Check if the answer includes any additional information not present in the context.\n", + "3. Verify if the treatments and causes mentioned in the answer align with the information provided in the context regarding sudden patchy hair loss and alopecia areata.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides information on the possible causes and effective treatments for sudden patchy hair loss, specifically focusing on alopecia areata. It mentions autoimmune disorders, genetics, stress, hormonal changes, nutritional deficiencies, infections, and other medical conditions as possible causes. For effective treatments, it lists topical corticosteroids, minoxidil, intralesional corticosteroid injections, immunotherapy, oral medications, light therapy, nutritional support, stress management, and cosmetic solutions like wigs.\n", + "\n", + "The context also discusses alopecia areata as sudden patchy hair loss, autoimmune disorders affecting hair follicles, and the treatment options including topical corticosteroids, minoxidil, and other therapies. It also mentions possible causes such as autoimmune disorders, genetics, stress, hormonal changes, nutritional deficiencies, infections, and other medical conditions.\n", + "\n", + "The AI-generated answer aligns well with the information provided in the context regarding the causes and treatments for sudden patchy hair loss, staying grounded in the context.\n", + "\n", + "Therefore, the answer follows the metric of being derived only from the information presented in the context.\n", + "\n", + "Based on the evaluation criteria, the score for groundedness would be 5. \n", + "\n", + "{groundedness_score: 5}\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps need to be followed:\n", + "\n", + "1. Identify the main aspects of the question: The question asks about effective treatments or solutions for sudden patchy hair loss and the possible causes behind it.\n", + "2. Determine if the context provides information on the effective treatments or solutions for sudden patchy hair loss, including localized bald spots on the scalp.\n", + "3. Check if the context mentions the possible causes behind sudden patchy hair loss, such as autoimmune disorders, genetics, stress, hormonal changes, nutritional deficiencies, infections, and other medical conditions.\n", + "4. Evaluate whether all and only the important aspects related to treatments and causes of sudden patchy hair loss are addressed in the context.\n", + "\n", + "Explanation:\n", + "The context provided includes detailed information on the causes and treatments for sudden patchy hair loss, which aligns well with the main aspects of the question. It covers autoimmune disorders, genetics, stress, hormonal changes, nutritional deficiencies, infections, and other medical conditions as possible causes. Additionally, it lists effective treatments such as topical corticosteroids, minoxidil, corticosteroid injections, immunotherapy, oral medications, light therapy, nutritional support, stress management, and cosmetic solutions like wigs.\n", + "\n", + "Based on the evaluation, the context follows the metric of relevance by addressing all the important aspects related to the question about sudden patchy hair loss, its causes, and effective treatments.\n", + "\n", + "Therefore, the relevance score for this context is 5. \n", + "\n", + "{relevance_score: 5}\n" + ] + } + ], + "source": [ + "# Question 3\n", + "ground_3,rel_3 = generate_ground_relevance_response(user_input=result_df.questions[2], response=result_df.base_prompt_responses[2], max_tokens=516) #Complete the code to calculate the groundedness and relevance score\n", + "print(ground_3,end=\"\\n\\n\")\n", + "print(rel_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "id": "ebZtHibwnbe2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f672d1ff-f5b2-43d8-e173-ae9b40fef49e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer based on the metric provided, we need to ensure that the information presented in the context is the sole source for generating the answer. Here are the steps to evaluate the answer:\n", + "\n", + "1. Identify if the information provided in the answer is directly sourced from the context given.\n", + "2. Check if the details mentioned in the answer align with the information presented in the context.\n", + "3. Verify if there are no additional external sources or information used to formulate the answer.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides a comprehensive list of treatments recommended for a person who has sustained a physical injury to brain tissue, specifically a traumatic brain injury (TBI). The answer includes emergency care, surgical interventions, medication, rehabilitation, psychological support, lifestyle modifications, supportive care, long-term management, assistive devices, and alternative therapies. Each treatment option is explained in detail, covering various aspects of care and support.\n", + "\n", + "The answer directly reflects the information provided in the context, mentioning the need for immediate medical attention, surgical interventions to relieve pressure on the brain, rehabilitation through physical, occupational, speech, and neuropsychological therapy, psychological support, lifestyle modifications, family education, long-term management, assistive devices, and alternative therapies. The details align closely with the content presented in the context regarding the recommended treatments for brain injuries.\n", + "\n", + "Therefore, the answer follows the metric of being derived solely from the information presented in the context.\n", + "\n", + "Based on the evaluation, the score for groundedness is 5.\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps can be followed:\n", + "\n", + "1. Identify the main aspects of the question:\n", + " - The question asks about the recommended treatments for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function.\n", + "\n", + "2. Analyze the context provided:\n", + " - The context includes information about traumatic brain injury (TBI), rehabilitation, head injury, spinal cord injury, and the pathology of brain injuries.\n", + " - It discusses the importance of rehabilitation, early intervention, cognitive therapy, and family education in the treatment of brain injuries.\n", + " - Surgical interventions, medication, rehabilitation therapies, psychological support, lifestyle modifications, supportive care, long-term management, assistive devices, and alternative therapies are also mentioned.\n", + "\n", + "3. Compare the context with the main aspects of the question:\n", + " - The context covers a wide range of treatments and interventions for brain injuries, including emergency care, surgical interventions, medication, rehabilitation, psychological support, lifestyle modifications, supportive care, long-term management, assistive devices, and alternative therapies.\n", + " - It specifically addresses the need for rehabilitation, cognitive therapy, and family education, which are important aspects of treating brain injuries.\n", + "\n", + "Based on the evaluation of the context, it can be seen that the context aligns well with the main aspects of the question by providing detailed information about the recommended treatments for a person with a brain injury. Therefore, the relevance score for the context is 5.\n", + "\n", + "{\n", + "\"relevance_score\": 5\n", + "}\n" + ] + } + ], + "source": [ + "# Question 4\n", + "ground_4,rel_4 = generate_ground_relevance_response(user_input=result_df.questions[3], response=result_df.base_prompt_responses[3], max_tokens=516) #Complete the code to calculate the groundedness and relevance score\n", + "print(ground_4,end=\"\\n\\n\")\n", + "print(rel_4)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Evaluation 1: Base Prompt Response Evaluation**" + ], + "metadata": { + "id": "grgj2iGSRT8a" + } + }, + { + "cell_type": "code", + "source": [ + "# Create a DataFrame to store the base prompt evaluation results\n", + "base_prompt_evaluation_df = pd.DataFrame({\n", + " \"question\": [question_1, question_2, question_3,question_4],\n", + " \"base_prompt_response\": [result_df.base_prompt_responses[0], result_df.base_prompt_responses[1], result_df.base_prompt_responses[2],result_df.base_prompt_responses[3]],\n", + " \"groundedness_score\": [ground_1[-2], ground_2[-2], ground_3[-2],ground_4[-2]],\n", + " \"relevance_score\": [rel_1[-2], rel_2[-2], rel_3[-2],rel_4[-2]]\n", + "})\n", + "\n", + "base_prompt_evaluation_df['groundedness_score'] = pd.to_numeric(base_prompt_evaluation_df['groundedness_score'], errors='coerce')\n", + "base_prompt_evaluation_df['relevance_score'] = pd.to_numeric(base_prompt_evaluation_df['relevance_score'], errors='coerce')\n", + "\n", + "# Display the DataFrame\n", + "display(base_prompt_evaluation_df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 836 + }, + "id": "XHg3NGwrOluD", + "outputId": "cec27acc-dde5-49cb-8dd0-5cd325c5bc7f" + }, + "execution_count": 139, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " question \\\n", + "0 What is the protocol for managing sepsis in a ... \n", + "1 What are the common symptoms for appendicitis,... \n", + "2 What are the effective treatments or solutions... \n", + "3 What treatments are recommended for a person w... \n", + "\n", + " base_prompt_response groundedness_score \\\n", + "0 Managing sepsis in a critical care unit involv... 5 \n", + "1 Common symptoms of appendicitis include:\\n\\n1.... 5 \n", + "2 Sudden patchy hair loss, often referred to as ... 5 \n", + "3 The treatment for a person who has sustained a... 5 \n", + "\n", + " relevance_score \n", + "0 3.0 \n", + "1 3.0 \n", + "2 5.0 \n", + "3 NaN " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
questionbase_prompt_responsegroundedness_scorerelevance_score
0What is the protocol for managing sepsis in a ...Managing sepsis in a critical care unit involv...53.0
1What are the common symptoms for appendicitis,...Common symptoms of appendicitis include:\\n\\n1....53.0
2What are the effective treatments or solutions...Sudden patchy hair loss, often referred to as ...55.0
3What treatments are recommended for a person w...The treatment for a person who has sustained a...5NaN
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "base_prompt_evaluation_df", + "summary": "{\n \"name\": \"base_prompt_evaluation_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"question\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\",\n \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\",\n \"What is the protocol for managing sepsis in a critical care unit?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"base_prompt_response\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Common symptoms of appendicitis include:\\n\\n1. **Abdominal Pain**: Typically starts around the navel and then moves to the lower right abdomen.\\n2. **Loss of Appetite**: A sudden decrease in appetite is common.\\n3. **Nausea and Vomiting**: Often follows the onset of abdominal pain.\\n4. **Fever**: A low-grade fever may develop.\\n5. **Constipation or Diarrhea**: Changes in bowel habits can occur.\\n6. **Abdominal Swelling**: In some cases, the abdomen may become swollen.\\n\\nAppendicitis cannot be effectively treated with medication alone. The standard treatment is surgical removal of the appendix, known as an **appendectomy**. This can be performed using two main techniques:\\n\\n1. **Open Appendectomy**: A larger incision is made in the lower right abdomen to remove the appendix.\\n2. **Laparoscopic Appendectomy**: This is a minimally invasive procedure where several small incisions are made, and the appendix is removed with the aid of a camera and special instruments.\\n\\nLaparoscopic appendectomy is often preferred due to its benefits, including less postoperative pain, shorter recovery time, and minimal scarring. However, the choice of procedure may depend on the patient's specific situation and the surgeon's expertise.\",\n \"The treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), can vary widely depending on the severity of the injury, the specific areas of the brain affected, and the resulting impairments. Here are some common approaches to treatment:\\n\\n1. **Emergency Care**: \\n - Immediate medical attention is crucial. This may involve stabilizing the patient, monitoring vital signs, and performing imaging studies (like CT or MRI scans) to assess the extent of the injury.\\n\\n2. **Surgical Interventions**: \\n - In some cases, surgery may be necessary to relieve pressure on the brain, remove blood clots (hematomas), or repair skull fractures.\\n\\n3. **Medication**: \\n - Medications may be prescribed to manage symptoms such as pain, seizures, or inflammation. Corticosteroids may be used to reduce swelling in the brain.\\n\\n4. **Rehabilitation**: \\n - **Physical Therapy**: To improve mobility and strength.\\n - **Occupational Therapy**: To help with daily living skills and regain independence.\\n - **Speech and Language Therapy**: To address communication difficulties and swallowing issues.\\n - **Neuropsychological Therapy**: To help with cognitive rehabilitation, including memory, attention, and problem-solving skills.\\n\\n5. **Psychological Support**: \\n - Counseling or therapy may be beneficial for coping with emotional and psychological challenges following a brain injury, such as depression, anxiety, or changes in personality.\\n\\n6. **Lifestyle Modifications**: \\n - Patients may need to make adjustments to their daily routines, including rest, nutrition, and avoiding activities that could lead to further injury.\\n\\n7. **Supportive Care**: \\n - Family support and education about the injury and its effects can be crucial for recovery. Support groups may also be helpful.\\n\\n8. **Long-term Management**: \\n - Ongoing follow-up with healthcare providers to monitor recovery and manage any long-term effects or complications.\\n\\n9. **Assistive Devices**: \\n - Depending on the nature of the impairment, assistive devices or technology may be recommended to aid in communication, mobility, or daily activities.\\n\\n10. **Alternative Therapies**: \\n - Some individuals may explore complementary therapies such as acupuncture, yoga, or meditation, although these should be discussed with a healthcare provider.\\n\\nIt's important for treatment plans to be individualized, taking into account the specific needs and circumstances of the person affected. A multidisciplinary team approach is often the most\",\n \"Managing sepsis in a critical care unit involves a systematic approach that includes early recognition, prompt intervention, and ongoing monitoring. The following is a general protocol based on current guidelines, such as those from the Surviving Sepsis Campaign:\\n\\n### 1. **Early Recognition**\\n - **Identify Symptoms**: Look for signs of infection (fever, chills, tachycardia, tachypnea) and organ dysfunction (altered mental status, hypotension, oliguria).\\n - **Use Screening Tools**: Utilize tools like the qSOFA (quick Sequential Organ Failure Assessment) or SIRS (Systemic Inflammatory Response Syndrome) criteria to identify patients at risk.\\n\\n### 2. **Initial Assessment**\\n - **Obtain Vital Signs**: Monitor blood pressure, heart rate, respiratory rate, and temperature.\\n - **Assess Organ Function**: Evaluate renal function (urine output, creatinine), liver function (bilirubin, liver enzymes), and coagulation status (platelets, INR).\\n\\n### 3. **Immediate Interventions**\\n - **Fluid Resuscitation**: Administer intravenous (IV) fluids (crystalloids) promptly, typically 30 mL/kg within the first 3 hours.\\n - **Antibiotic Therapy**: Start broad-spectrum IV antibiotics within 1 hour of recognition of sepsis. Adjust based on culture results and sensitivity.\\n - **Source Control**: Identify and control the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n### 4. **Monitoring and Support**\\n - **Hemodynamic Monitoring**: Use invasive monitoring (e.g., arterial line, central venous pressure) if necessary to guide fluid resuscitation and vasopressor therapy.\\n - **Vasopressors**: If hypotension persists despite adequate fluid resuscitation, initiate vasopressors (e.g., norepinephrine) to maintain mean arterial pressure (MAP) \\u2265 65 mmHg.\\n - **Oxygenation and Ventilation**: Provide supplemental oxygen and consider mechanical ventilation if respiratory failure occurs.\\n\\n### 5. **Ongoing Management**\\n - **Reassess Fluid Status**: Continuously evaluate the patient's response to fluids and adjust as necessary.\\n - **Monitor Laboratory Values**: Regularly check lactate levels, complete blood counts, and organ function tests to assess the patient's status.\\n - **Nutritional Support**: Initiate enteral nutrition as soon as feasible, typically within 24\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"groundedness_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 5,\n \"max\": 5,\n \"num_unique_values\": 1,\n \"samples\": [\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"relevance_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.1547005383792515,\n \"min\": 3.0,\n \"max\": 5.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 5.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2J55TQZTnlYE" + }, + "source": [ + "#### **Evaluation 2: Prompt Engineering Response Evaluation**" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "id": "6yEkbFmHnxpe", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3cb0547a-3814-4936-9d42-8326d44f356c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer based on the metric provided, which states that the answer should be derived only from the information presented in the context, the following steps need to be followed:\n", + "\n", + "1. Identify the key points and information provided in the context regarding the protocol for managing sepsis in a critical care unit.\n", + "2. Compare the details mentioned in the AI-generated answer with the information presented in the context.\n", + "3. Determine if the answer provided by the AI system aligns with the specific details and guidelines outlined in the context.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides a detailed protocol for managing sepsis in a critical care unit, including steps for early recognition, resuscitation, antibiotic therapy, source control, monitoring, supportive care, reassessment, corticosteroid consideration, glucose control, and communication. These steps are not directly mentioned in the context provided.\n", + "\n", + "Therefore, the AI-generated answer does not strictly adhere to the metric of deriving the answer solely from the information presented in the context. The answer goes beyond the specific details provided in the context and introduces additional guidelines and recommendations that are not explicitly stated in the context.\n", + "\n", + "Based on the evaluation criteria:\n", + "The answer partially follows the metric, as it includes some relevant information but also introduces additional details not present in the context.\n", + "\n", + "Hence, the score for groundedness is 2.\n", + "\n", + "{relevance_score:3}\n" + ] + } + ], + "source": [ + "# Question 1\n", + "ground_1,rel_1 = generate_ground_relevance_response(user_input=result_df.questions[0], response=result_df.responses_with_prompt_eng[0], max_tokens=516)\n", + "print(ground_1,end=\"\\n\\n\")\n", + "print(rel_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "id": "BHZzDnLsnxpf", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2125425c-d1bf-4f80-99f2-821310bda46c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Steps to evaluate the answer:\n", + "1. Identify if the information provided in the answer is solely based on the context.\n", + "2. Check if the symptoms of appendicitis, treatment options, and surgical procedures mentioned in the answer are supported by the context.\n", + "3. Evaluate if any additional information not present in the context is included in the answer.\n", + "\n", + "Explanation:\n", + "The answer provided lists common symptoms of appendicitis and explains that appendicitis cannot be cured with medicine alone, requiring surgical intervention in the form of an appendectomy. It also mentions the two types of surgical procedures for appendicitis treatment - open surgery and laparoscopic surgery. All the information provided in the answer is directly derived from the context given.\n", + "\n", + "The symptoms mentioned in the answer align with the symptoms described in the context, such as abdominal pain, loss of appetite, nausea, vomiting, fever, constipation or diarrhea, and abdominal swelling. The answer also correctly states that appendicitis cannot be cured with medication alone and requires surgical removal of the inflamed appendix, which is supported by the context mentioning appendectomy as the treatment for acute appendicitis.\n", + "\n", + "Therefore, the answer follows the metric of being derived only from the information presented in the context.\n", + "\n", + "Score: {groundedness_score: 5}\n", + "\n", + "Steps to evaluate the context for relevance:\n", + "1. Identify the main aspects of the question, which include common symptoms of appendicitis and the treatment options (medicine or surgical procedure).\n", + "2. Check if the context provides information on the common symptoms of appendicitis and the treatment options mentioned in the question.\n", + "3. Evaluate whether the context covers all the important aspects related to appendicitis symptoms and treatment options.\n", + "4. Consider if the context aligns with the question and provides relevant information that directly addresses the query.\n", + "\n", + "Explanation:\n", + "The context provided includes detailed information about the etiology, symptoms, diagnosis, and treatment of appendicitis. It covers the common symptoms of appendicitis, such as abdominal pain, loss of appetite, nausea, vomiting, fever, constipation or diarrhea, and abdominal swelling. Additionally, it explains that appendicitis cannot be effectively treated with medication alone and typically requires surgical intervention in the form of an appendectomy. The context also describes the surgical procedure for treating appendicitis, including open or laparoscopic appendectomy, IV fluids, and antibiotics.\n", + "\n", + "Based on the evaluation criteria, the context aligns well with the question by providing comprehensive information on the common symptoms of appendicitis and the surgical procedure required for its treatment. It covers all the important aspects related to the question and directly addresses the query about symptoms and treatment options for appendicitis.\n", + "\n", + "Therefore, the relevance score for the context is 5. \n", + "\n", + "{relevance_score: 5}\n" + ] + } + ], + "source": [ + "# Question 2\n", + "ground_2,rel_2 = generate_ground_relevance_response(user_input=result_df.questions[1], response=result_df.responses_with_prompt_eng[1], max_tokens=516) #Complete the code to calculate the groundedness and relevance score\n", + "print(ground_2,end=\"\\n\\n\")\n", + "print(rel_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "id": "v4dCtQMCnxpg", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0e699b91-e40b-4d8e-e01a-31a2feb93c61" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer as per the metric, the following steps need to be followed:\n", + "1. Identify if the information provided in the answer is directly derived from the context.\n", + "2. Check if any additional information not present in the context is included in the answer.\n", + "3. Ensure that the treatments and possible causes mentioned in the answer are supported by the context provided.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides information on effective treatments and possible causes for sudden patchy hair loss, specifically focusing on alopecia areata. The context mentions various treatments and causes related to hair loss, including corticosteroids, minoxidil, immunotherapy, genetics, stress, hormonal changes, and nutritional deficiencies. The answer aligns with the context by including treatments like corticosteroids, minoxidil, immunotherapy, and JAK inhibitors, which are supported by the context. The possible causes mentioned in the answer, such as autoimmune response, genetics, stress, hormonal changes, nutritional deficiencies, and infections, are also supported by the context.\n", + "\n", + "Therefore, the answer follows the metric by deriving information solely from the context provided.\n", + "\n", + "Based on the evaluation, the score for groundedness is 5.\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps need to be followed:\n", + "\n", + "1. Identify the main aspects of the question: The main aspects of the question include effective treatments or solutions for sudden patchy hair loss (alopecia areata) and the possible causes behind it.\n", + "\n", + "2. Check if the context provides information on effective treatments: Look for details on treatments such as corticosteroids, minoxidil, immunotherapy, anthralin, JAK inhibitors, light therapy, and supportive care.\n", + "\n", + "3. Check if the context mentions possible causes: Look for information on autoimmune response, genetics, stress, hormonal changes, nutritional deficiencies, and infections as potential causes of sudden patchy hair loss.\n", + "\n", + "4. Evaluate if all and only the important aspects are contained in the context: Ensure that the context addresses both the effective treatments and possible causes related to sudden patchy hair loss.\n", + "\n", + "Based on the evaluation steps, the context provided aligns well with the metric of relevance. It covers the effective treatments and possible causes of sudden patchy hair loss as requested in the question. The context includes detailed information on various treatment options and potential causes, providing a comprehensive overview of the topic.\n", + "\n", + "Therefore, the relevance score for the context is 5. \n", + "\n", + "{relevance_score: 5}\n" + ] + } + ], + "source": [ + "# Question 3\n", + "ground_3,rel_3 = generate_ground_relevance_response(user_input=result_df.questions[2], response=result_df.responses_with_prompt_eng[2], max_tokens=516) #Complete the code to calculate the groundedness and relevance score\n", + "print(ground_3,end=\"\\n\\n\")\n", + "print(rel_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "id": "jWBc97uynxpg", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ff7fd2ce-2ede-4136-b8fb-53aa9ad6a711" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer as per the metric, the following steps need to be followed:\n", + "1. Identify if the information provided in the answer is directly derived from the context.\n", + "2. Check if the treatments and possible causes mentioned in the answer are supported by the information given in the context.\n", + "3. Ensure that no additional information beyond what is provided in the context is included in the answer.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides information on effective treatments and possible causes for sudden patchy hair loss, specifically focusing on alopecia areata. The context mentions various treatments for different types of hair loss, including corticosteroids, minoxidil, immunotherapy, anthralin, JAK inhibitors, and light therapy. It also discusses possible causes such as autoimmune response, genetics, stress, hormonal changes, nutritional deficiencies, and infections. The answer aligns with the information presented in the context and does not introduce any new information.\n", + "\n", + "Therefore, the answer follows the metric by deriving all the information solely from the context.\n", + "\n", + "Based on the evaluation, the score for groundedness is 5.\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps can be followed:\n", + "\n", + "1. Identify the main aspects of the question:\n", + " - Effective treatments for sudden patchy hair loss\n", + " - Possible causes behind sudden patchy hair loss\n", + "\n", + "2. Check if the context provides information related to the effective treatments and possible causes of sudden patchy hair loss.\n", + "\n", + "3. Ensure that the treatments and causes mentioned in the context align with the information required to address the question.\n", + "\n", + "Explanation:\n", + "The context provided includes detailed information about the different types of hair loss, including sudden patchy hair loss (alopecia areata). It covers various treatments and possible causes related to sudden patchy hair loss, such as corticosteroids, minoxidil, immunotherapy, genetics, stress, hormonal changes, and more. The context also emphasizes the importance of consulting healthcare professionals for accurate diagnosis and treatment plans.\n", + "\n", + "Based on the evaluation, the context aligns well with the main aspects of the question regarding effective treatments and possible causes of sudden patchy hair loss. It provides comprehensive information that directly addresses the user's query.\n", + "\n", + "Therefore, the relevance score for the context is 5. \n", + "\n", + "{relevance_score: 5}\n" + ] + } + ], + "source": [ + "# Question 4\n", + "ground_4,rel_4 = generate_ground_relevance_response(user_input=result_df.questions[2], response=result_df.responses_with_prompt_eng[2], max_tokens=516) #Complete the code to calculate the groundedness and relevance score\n", + "print(ground_4,end=\"\\n\\n\")\n", + "print(rel_4)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Create a DataFrame to store the prompt engineering evaluation results\n", + "prompt_engineering_evaluation_df = pd.DataFrame({\n", + " \"question\": [question_1, question_2, question_3,question_4],\n", + " \"prompt_engg_response\": [result_df.responses_with_prompt_eng[0], result_df.responses_with_prompt_eng[1], result_df.responses_with_prompt_eng[2],result_df.responses_with_prompt_eng[3]],\n", + " \"groundedness_score\": [ground_1[-2], ground_2[-2], ground_3[-2],ground_4[-2]],\n", + " \"relevance_score\": [rel_1[-2], rel_2[-2], rel_3[-2],rel_4[-2]]\n", + "})\n", + "\n", + "prompt_engineering_evaluation_df['groundedness_score'] = pd.to_numeric(prompt_engineering_evaluation_df['groundedness_score'], errors='coerce')\n", + "prompt_engineering_evaluation_df['relevance_score'] = pd.to_numeric(prompt_engineering_evaluation_df['relevance_score'], errors='coerce')\n", + "\n", + "# Display the DataFrame\n", + "display(prompt_engineering_evaluation_df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 637 + }, + "id": "YIF50xSFTDD5", + "outputId": "4e3c0984-df4f-44e4-c653-e60a1c086d70" + }, + "execution_count": 134, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " question \\\n", + "0 What is the protocol for managing sepsis in a ... \n", + "1 What are the common symptoms for appendicitis,... \n", + "2 What are the effective treatments or solutions... \n", + "3 What treatments are recommended for a person w... \n", + "\n", + " prompt_engg_response groundedness_score \\\n", + "0 The management of sepsis in a critical care un... 2 \n", + "1 Common symptoms of appendicitis include:\\n\\n1.... 5 \n", + "2 Sudden patchy hair loss, often referred to as ... 5 \n", + "3 Treatment for a person who has sustained a phy... 5 \n", + "\n", + " relevance_score \n", + "0 3 \n", + "1 5 \n", + "2 5 \n", + "3 5 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
questionprompt_engg_responsegroundedness_scorerelevance_score
0What is the protocol for managing sepsis in a ...The management of sepsis in a critical care un...23
1What are the common symptoms for appendicitis,...Common symptoms of appendicitis include:\\n\\n1....55
2What are the effective treatments or solutions...Sudden patchy hair loss, often referred to as ...55
3What treatments are recommended for a person w...Treatment for a person who has sustained a phy...55
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "prompt_engineering_evaluation_df", + "summary": "{\n \"name\": \"prompt_engineering_evaluation_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"question\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\",\n \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\",\n \"What is the protocol for managing sepsis in a critical care unit?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"prompt_engg_response\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Common symptoms of appendicitis include:\\n\\n1. Abdominal pain, often starting near the belly button and then moving to the lower right abdomen.\\n2. Loss of appetite.\\n3. Nausea and vomiting.\\n4. Fever.\\n5. Constipation or diarrhea.\\n6. Abdominal swelling.\\n\\nAppendicitis cannot be effectively treated with medication alone; it typically requires surgical intervention. The standard surgical procedure for treating appendicitis is an appendectomy, which involves the removal of the inflamed appendix. This can be performed as an open surgery or laparoscopically, depending on the case and the surgeon's preference.\",\n \"Treatment for a person who has sustained a physical injury to brain tissue, such as a traumatic brain injury (TBI), typically involves a multidisciplinary approach and may include the following:\\n\\n1. **Emergency Care**: Immediate medical attention may involve stabilizing the patient, ensuring adequate oxygenation, and managing intracranial pressure.\\n\\n2. **Surgery**: In some cases, surgical intervention may be necessary to remove hematomas, repair skull fractures, or relieve pressure on the brain.\\n\\n3. **Medications**: \\n - **Analgesics** for pain management.\\n - **Anticonvulsants** to prevent seizures.\\n - **Diuretics** to reduce swelling.\\n - **Corticosteroids** may be used to decrease inflammation.\\n\\n4. **Rehabilitation**: \\n - **Physical therapy** to improve mobility and strength.\\n - **Occupational therapy** to assist with daily living activities.\\n - **Speech therapy** for communication and swallowing difficulties.\\n - **Neuropsychological therapy** to address cognitive and emotional challenges.\\n\\n5. **Supportive Care**: This may include counseling, support groups, and education for patients and families about the injury and recovery process.\\n\\n6. **Long-term Management**: Ongoing assessment and management of cognitive, emotional, and physical impairments may be necessary, including regular follow-ups with healthcare providers.\\n\\nThe specific treatment plan will depend on the severity of the injury, the areas of the brain affected, and the individual needs of the patient.\",\n \"The management of sepsis in a critical care unit typically follows the Surviving Sepsis Campaign guidelines. Here\\u2019s a concise protocol:\\n\\n1. **Early Recognition**: Identify sepsis using clinical criteria (e.g., suspected infection plus organ dysfunction).\\n\\n2. **Immediate Resuscitation**:\\n - **Fluid Resuscitation**: Administer intravenous fluids (30 mL/kg of crystalloids within the first 3 hours).\\n - **Vasopressors**: If hypotension persists after fluid resuscitation, initiate norepinephrine to maintain mean arterial pressure (MAP) \\u2265 65 mmHg.\\n\\n3. **Antibiotic Therapy**:\\n - Administer broad-spectrum antibiotics within 1 hour of sepsis recognition. Adjust based on culture results and local antibiograms.\\n\\n4. **Source Control**:\\n - Identify and manage the source of infection (e.g., drainage of abscess, removal of infected devices).\\n\\n5. **Monitoring**:\\n - Continuously monitor vital signs, urine output, and laboratory parameters (e.g., lactate levels, complete blood count, renal function).\\n\\n6. **Supportive Care**:\\n - Provide supportive care, including oxygen therapy, mechanical ventilation if needed, and renal replacement therapy for acute kidney injury.\\n\\n7. **Reassessment**:\\n - Reassess hemodynamic status and organ function frequently, adjusting treatment as necessary.\\n\\n8. **Consideration of Corticosteroids**:\\n - In cases of septic shock, consider low-dose corticosteroids (e.g., hydrocortisone) if there is no response to fluid resuscitation and vasopressors.\\n\\n9. **Glucose Control**:\\n - Maintain blood glucose levels between 140-180 mg/dL.\\n\\n10. **Communication and Team Approach**:\\n - Ensure effective communication among the healthcare team and involve specialists as needed.\\n\\nThis protocol should be tailored to individual patient needs and institutional protocols. Regular training and updates on sepsis management are essential for critical care staff.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"groundedness_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 2,\n \"max\": 5,\n \"num_unique_values\": 2,\n \"samples\": [\n 5,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"relevance_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 3,\n \"max\": 5,\n \"num_unique_values\": 2,\n \"samples\": [\n 5,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y7wXIFmfn4tX" + }, + "source": [ + "#### **Evaluation 3: RAG Response Evaluation**" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "id": "lPGdtRCcoBIi", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6ffcdced-bc86-49d4-cf13-74bf236ed95a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer as per the metric, the following steps need to be followed:\n", + "1. Identify if the information provided in the answer is directly derived from the context.\n", + "2. Check if any additional information not present in the context is included in the answer.\n", + "3. Verify if the steps mentioned in the answer align with the protocol for managing sepsis in a critical care unit as described in the context.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides a detailed protocol for managing sepsis in a critical care unit based on the information presented in the context. It includes steps such as obtaining specimens for culture, initiating prompt empiric antibiotic therapy, changing antibiotic regimen based on culture and sensitivity results, draining abscesses, monitoring blood glucose levels, and providing supportive care. All the steps mentioned in the answer are directly derived from the context provided.\n", + "\n", + "Therefore, the answer follows the metric completely as it only uses the information presented in the context to formulate the protocol for managing sepsis in a critical care unit.\n", + "\n", + "Score: {groundedness_score: 5}\n", + "\n", + "{\n", + "relevance_score: 5\n", + "}\n" + ] + } + ], + "source": [ + "# Question 1\n", + "ground_1,rel_1 = generate_ground_relevance_response(user_input=result_df.questions[0], response=result_df.responses_with_RAG[0], max_tokens=516) #Complete the code to calculate the groundedness and relevance\n", + "print(ground_1,end=\"\\n\\n\")\n", + "print(rel_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "id": "ZrB06dkBoBIj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bf5873bb-b903-49fa-80c7-58dc07623b87" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Steps to evaluate the answer:\n", + "1. Identify if the information provided in the answer is directly from the context.\n", + "2. Check if the symptoms of appendicitis and the treatment mentioned in the answer are supported by the context.\n", + "3. Ensure that no additional information beyond what is provided in the context is included in the answer.\n", + "\n", + "Explanation:\n", + "The answer directly mentions the common symptoms of appendicitis as described in the context, such as epigastric or periumbilical pain, nausea, vomiting, and anorexia, with pain shifting to the right lower quadrant. It also includes classic signs like right lower quadrant tenderness at McBurney's point, Rovsing sign, psoas sign, and obturator sign. The answer correctly states that appendicitis cannot be cured via medicine and requires surgical removal, specifically an open or laparoscopic appendectomy. The source of the information is also mentioned.\n", + "\n", + "Therefore, the answer strictly adheres to the metric by deriving all the information solely from the context provided.\n", + "\n", + "Evaluation: \n", + "The answer follows the metric completely by providing information solely from the context without introducing any external information. Hence, the answer deserves a score of 5.\n", + "\n", + "{groundedness_score: 5}\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps can be followed:\n", + "\n", + "1. Identify the main aspects of the question: The question asks about the common symptoms of appendicitis and inquires about the possibility of curing it with medicine or the surgical procedure required for treatment.\n", + "\n", + "2. Check if the context provides information relevant to the question: Look for details in the context that discuss the symptoms of appendicitis, whether it can be cured with medicine, and the surgical procedure for treatment.\n", + "\n", + "3. Evaluate if all and only the important aspects are contained in the answer: Ensure that the answer addresses all the key points mentioned in the question and context, without including irrelevant information.\n", + "\n", + "Explanation:\n", + "The context provides detailed information about the etiology, symptoms, signs, diagnosis, and treatment of appendicitis. It mentions that appendicitis is acute inflammation of the vermiform appendix, resulting in abdominal pain, anorexia, and abdominal tenderness. It also states that the treatment for appendicitis is surgical removal through open or laparoscopic appendectomy. Additionally, it specifies the use of IV antibiotics before appendectomy and the continuation of antibiotics in case of perforation.\n", + "\n", + "The AI-generated answer accurately addresses the common symptoms of appendicitis, the inability to cure it with medicine, and the recommended surgical procedure for treatment, which aligns well with the main aspects of the question and context.\n", + "\n", + "Therefore, the relevance of the context to the question is high, as it covers all the important aspects related to appendicitis symptoms, treatment, and surgical procedures.\n", + "\n", + "Based on the evaluation, the relevance score for the context is 5. \n", + "\n", + "{relevance_score: 5}\n" + ] + } + ], + "source": [ + "# Question 2\n", + "ground_2,rel_2 = generate_ground_relevance_response(user_input=result_df.questions[1], response=result_df.responses_with_RAG[1], max_tokens=516) #Complete the code to calculate the groundedness and relevance\n", + "print(ground_2,end=\"\\n\\n\")\n", + "print(rel_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "id": "pCFMhRGUoBIj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e0ea5905-b980-4767-e512-66e23776f685" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer based on the metric provided, we need to follow these steps:\n", + "1. Identify if the information provided in the answer is solely derived from the context given.\n", + "2. Check if the answer directly addresses the question asked.\n", + "3. Determine if the answer includes any additional information not present in the context.\n", + "\n", + "Explanation:\n", + "The AI-generated answer correctly identifies that the effective treatment for sudden patchy hair loss, specifically alopecia areata, is not specified in the provided context. It mentions that alopecia areata is thought to be an autoimmune disorder affecting genetically susceptible individuals and lists possible causes such as systemic illnesses, high fever, systemic lupus, endocrine disorders, and nutritional deficiencies. This information is directly derived from the context provided, specifically from the section on alopecia areata and its possible causes.\n", + "\n", + "Therefore, the answer adheres to the metric by providing information solely based on the context and directly addressing the question asked.\n", + "\n", + "Based on the evaluation criteria:\n", + "The answer demonstrates a good extent of adherence to the metric, as it is derived entirely from the context provided and directly addresses the question asked.\n", + "\n", + "Hence, the score for groundedness is 3.\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps can be followed:\n", + "\n", + "1. Identify the main aspects of the question:\n", + "- Effective treatments or solutions for sudden patchy hair loss (alopecia areata)\n", + "- Possible causes behind sudden patchy hair loss\n", + "\n", + "2. Check if the context addresses these main aspects:\n", + "- Look for information on effective treatments for sudden patchy hair loss, such as alopecia areata.\n", + "- Check if the context mentions possible causes behind sudden patchy hair loss, including systemic illnesses, autoimmune diseases, and other factors.\n", + "\n", + "3. Evaluate if all and only the important aspects are contained in the answer:\n", + "- Ensure that the context provides relevant information on treatments and causes related to sudden patchy hair loss, specifically alopecia areata.\n", + "\n", + "Now, considering the question and the context provided, the AI-generated answer does address some important aspects related to the question. It mentions that alopecia areata is thought to be an autoimmune disorder affecting genetically susceptible individuals and lists possible causes such as systemic illnesses, lupus, endocrine disorders, and nutritional deficiencies. However, it does not specify the effective treatments for sudden patchy hair loss, which was a key aspect of the question.\n", + "\n", + "Based on the evaluation, the relevance of the context can be rated as follows:\n", + "- The context is followed only to a limited extent.\n", + "\n", + "Therefore, the relevance score for the context is 2. \n", + "\n", + "{\n", + "relevance_score: 2\n", + "}\n" + ] + } + ], + "source": [ + "# Question 3\n", + "ground_3,rel_3 = generate_ground_relevance_response(user_input=result_df.questions[2], response=result_df.responses_with_RAG[2], max_tokens=516) #Complete the code to calculate the groundedness and relevance\n", + "print(ground_3,end=\"\\n\\n\")\n", + "print(rel_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "id": "Tf-SrQOgoBIj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a504b8a5-4d11-4046-b313-239c040dc746" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To evaluate the answer based on the metric provided, we need to follow these steps:\n", + "1. Identify if the information provided in the answer is solely derived from the context given.\n", + "2. Check if the answer includes any additional information not present in the context.\n", + "3. Ensure that the answer does not introduce any new concepts or details that are not mentioned in the context.\n", + "4. Verify that the answer directly relates to the question asked about treatments recommended for a person with a physical injury to brain tissue.\n", + "\n", + "Explanation:\n", + "The AI-generated answer provides information on the initial treatment for a person with a physical injury to brain tissue, which includes ensuring a reliable airway, maintaining ventilation, oxygenation, and blood pressure. It also mentions the possibility of surgery for severe injuries and the need for subsequent rehabilitation involving a team approach. The answer directly relates to the context provided, which discusses rehabilitation and treatment for traumatic brain injury.\n", + "\n", + "Based on the evaluation criteria, the answer follows the metric of being derived only from the information presented in the context. It does not introduce any new concepts or details that are not mentioned in the context. Therefore, the answer adheres to the metric.\n", + "\n", + "Therefore, the groundedness score for this answer is 5.\n", + "\n", + "To evaluate the context based on the metric of relevance, the following steps can be followed:\n", + "\n", + "1. Identify the main aspects of the question:\n", + "- The question asks about the recommended treatments for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function.\n", + "\n", + "2. Analyze the context provided:\n", + "- The context includes information about traumatic brain injury (TBI), rehabilitation, and the importance of early intervention and rehabilitation therapy for patients with cognitive dysfunction.\n", + "- It also mentions the need for a team approach in rehabilitation, including physical, occupational, and speech therapy, as well as cognitive therapy for severe cognitive dysfunction.\n", + "- The context discusses the initial treatment for brain injuries, which involves ensuring a reliable airway, maintaining adequate ventilation, oxygenation, and blood pressure, and the possibility of surgery for severe cases.\n", + "\n", + "3. Evaluate the AI-generated answer:\n", + "- The answer provided includes information about the initial treatment for brain injuries, the possibility of surgery for severe cases, and the importance of rehabilitation for patients with cognitive dysfunction.\n", + "- It mentions the team approach in rehabilitation and the different types of therapy involved.\n", + "\n", + "Based on the evaluation, the AI-generated answer addresses the main aspects of the question by providing information about initial treatment, surgery, and rehabilitation for brain injuries. It includes relevant details from the context that are essential for understanding the recommended treatments for individuals with brain injuries.\n", + "\n", + "Therefore, the relevance score for this context is 5. \n", + "\n", + "{relevance_score: 5}\n" + ] + } + ], + "source": [ + "# Question 4\n", + "ground_4,rel_4 = generate_ground_relevance_response(user_input=result_df.questions[3], response=result_df.responses_with_RAG[3], max_tokens= 516) #Complete the code to calculate the groundedness and relevance\n", + "print(ground_4,end=\"\\n\\n\")\n", + "print(rel_4)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Create a DataFrame to store the Rag evaluation results\n", + "Rag_evaluation_df = pd.DataFrame({\n", + " \"question\": [question_1, question_2, question_3,question_4],\n", + " \"rag_response\": [result_df.responses_with_RAG[0], result_df.responses_with_RAG[1], result_df.responses_with_RAG[2],result_df.responses_with_RAG[3]],\n", + " \"groundedness_score\": [ground_1[-2], ground_2[-2], ground_3[-2],ground_4[-2]],\n", + " \"relevance_score\": [rel_1[-2], rel_2[-2], rel_3[-2],rel_4[-2]]\n", + "})\n", + "\n", + "Rag_evaluation_df['groundedness_score'] = pd.to_numeric(Rag_evaluation_df['groundedness_score'], errors='coerce')\n", + "Rag_evaluation_df['relevance_score'] = pd.to_numeric(Rag_evaluation_df['relevance_score'], errors='coerce')\n", + "\n", + "# Display the DataFrame\n", + "display(Rag_evaluation_df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 505 + }, + "id": "SQnXolywWm--", + "outputId": "520fd586-2969-4969-db78-9d229f7a9e56" + }, + "execution_count": 144, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " question \\\n", + "0 What is the protocol for managing sepsis in a ... \n", + "1 What are the common symptoms for appendicitis,... \n", + "2 What are the effective treatments or solutions... \n", + "3 What treatments are recommended for a person w... \n", + "\n", + " rag_response groundedness_score \\\n", + "0 Answer:\\nThe protocol for managing sepsis in a... 5 \n", + "1 Answer:\\nThe common symptoms of appendicitis i... 5 \n", + "2 Answer:\\nThe effective treatment for sudden pa... 3 \n", + "3 Answer:\\nInitial treatment for a person who ha... 5 \n", + "\n", + " relevance_score \n", + "0 NaN \n", + "1 5.0 \n", + "2 NaN \n", + "3 5.0 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
questionrag_responsegroundedness_scorerelevance_score
0What is the protocol for managing sepsis in a ...Answer:\\nThe protocol for managing sepsis in a...5NaN
1What are the common symptoms for appendicitis,...Answer:\\nThe common symptoms of appendicitis i...55.0
2What are the effective treatments or solutions...Answer:\\nThe effective treatment for sudden pa...3NaN
3What treatments are recommended for a person w...Answer:\\nInitial treatment for a person who ha...55.0
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "Rag_evaluation_df", + "summary": "{\n \"name\": \"Rag_evaluation_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"question\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"What are the common symptoms for appendicitis, and can it be cured via medicine? If not, what surgical procedure should be followed to treat it?\",\n \"What treatments are recommended for a person who has sustained a physical injury to brain tissue, resulting in temporary or permanent impairment of brain function?\",\n \"What is the protocol for managing sepsis in a critical care unit?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rag_response\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Answer:\\nThe common symptoms of appendicitis include epigastric or periumbilical pain followed by nausea, vomiting, and anorexia, with pain shifting to the right lower quadrant. Classic signs include right lower quadrant tenderness at McBurney's point, Rovsing sign, psoas sign, and obturator sign. Appendicitis cannot be cured via medicine; the treatment is surgical removal, specifically an open or laparoscopic appendectomy.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 11. Acute Abdomen & Surgical Gastroenterology, pages 163.\",\n \"Answer:\\nInitial treatment for a person who has sustained a physical injury to brain tissue includes ensuring a reliable airway and maintaining adequate ventilation, oxygenation, and blood pressure. Surgery may be needed for severe injuries to monitor and treat intracranial pressure, decompress the brain, or remove hematomas. Subsequently, many patients require rehabilitation, which should be planned early and may involve a team approach including physical, occupational, and speech therapy, as well as cognitive therapy for those with severe cognitive dysfunction.\\n\\nSource:\\nThe Merck Manual of Diagnosis & Therapy, 19th Edition, Chapter 324. Traumatic Brain Injury, and Chapter 350. Rehabilitation.\",\n \"Answer:\\nThe protocol for managing sepsis in a critical care unit includes the following steps: \\n1. Obtain specimens of blood, body fluids, and wound sites for Gram stain and culture before starting parenteral antibiotics.\\n2. Initiate very prompt empiric antibiotic therapy immediately after suspecting sepsis, which may include gentamicin or tobramycin plus a 3rd-generation cephalosporin (e.g., cefotaxime or ceftriaxone), or ceftazidime plus a fluoroquinolone if Pseudomonas is suspected. Vancomycin should be added if resistant staphylococci or enterococci are suspected, and if there is an abdominal source, include a drug effective against anaerobes (e.g., metronidazole).\\n3. Change the antibiotic regimen based on culture and sensitivity results when available, continuing antibiotics for at least 5 days after shock resolves and evidence of infection subsides.\\n4. Drain abscesses and surgically excise necrotic tissues as necessary.\\n5. Monitor and manage blood glucose levels with a continuous IV insulin infusion to maintain glucose between 80 to 110 mg/dL.\\n6. Provide supportive care, including adequate nutrition and prevention of infections and complications.\\n\\nSource:\\nCritical Care Medicine, Chapter 222. Approach to the Critically Ill Patient.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"groundedness_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 3,\n \"max\": 5,\n \"num_unique_values\": 2,\n \"samples\": [\n 3,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"relevance_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 5.0,\n \"max\": 5.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 5.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(\"Average scores for Base Prompt Evaluation:\")\n", + "print(base_prompt_evaluation_df[['groundedness_score', 'relevance_score']].mean(numeric_only=True))\n", + "print(\"Average scores for Prompt engg Evaluation:\")\n", + "print(prompt_engineering_evaluation_df[['groundedness_score', 'relevance_score']].mean(numeric_only=True))\n", + "print(\"\\nAverage scores for RAG Response Evaluation:\")\n", + "print(Rag_evaluation_df[['groundedness_score', 'relevance_score']].mean(numeric_only=True))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ud7OiP5gaZhj", + "outputId": "a4ee9adb-d831-4a45-d2b2-b84395e2c681" + }, + "execution_count": 146, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Average scores for Base Prompt Evaluation:\n", + "groundedness_score 5.000000\n", + "relevance_score 3.666667\n", + "dtype: float64\n", + "Average scores for Prompt engg Evaluation:\n", + "groundedness_score 4.25\n", + "relevance_score 4.50\n", + "dtype: float64\n", + "\n", + "Average scores for RAG Response Evaluation:\n", + "groundedness_score 4.5\n", + "relevance_score 5.0\n", + "dtype: float64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y7QICRU-njdj" + }, + "source": [ + "## Actionable Insights and Business Recommendations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ObyXYhOojIaY" + }, + "source": [ + "1. Implement Retrieval‑Augmented Generation using authoritative medical manuals (e.g., Merck Manual, Harrison’s) to ensure evidence‑based answers.\n", + "2. RAG is your best-performing method — with a perfect relevance score of 5.0, confirming that your retrieval pipeline is correctly matching medical queries to the right chunks.\n", + "3. Groundedness still needs improvement (4.5 vs 5.0) — meaning the answers are clinically correct but not always fully supported by retrieved evidence. This is now the main improvement area.\n", + "4. Base Prompt’s perfect groundedness is misleading — it’s “correct-looking” medical reasoning, not actually evidence-based. we should not rely on this for clinical use.\n", + "5. Chunking issues are visible because RAG groundedness is lower than Base Prompt.If RAG was fully optimized:\n", + "Groundedness should be higher than Base Prompt.\n", + "Instead:\n", + "✅ RAG groundedness = 4.5\n", + "✅ Base Prompt = 5.0\n", + "This confirms our chunk overlap or chunk size is suboptimal.\n", + "\n", + "5. Chunking strategy needs refinement — increase chunk size to 800–1000 tokens with 150–200 token overlap, however due to limitation of tokens couldn't carried out the higher chunking. But to boost evidence density, we need to use higher chunking strategy with proper overlapping.\n", + "\n", + "6. we need to add a cross-encoder reranker — to guarantee the most evidence-rich chunk is ranked #1, directly improving groundedness from 4.5 → 5.0.\n", + "7. Need to standardize a clinical answer template —\n", + "Assessment → Management → Red Flags → Evidence\n", + "This ensures the model maps retrieved facts into structured, grounded output.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "###Here are the business recommendations:\n", + "\n", + "1. Strengthen the RAG pipeline with hybrid retrieval, re-ranking, and strict evidence-only answer generation to ensure fully grounded medical outputs.\n", + "\n", + "2. Expand the evaluation dataset to a comprehensive 30–50 question medical benchmark covering multiple specialties for reliable performance validation.\n", + "\n", + "3. Introduce mandatory citation formatting and full traceability (chunk IDs, page numbers, document metadata) to increase clinical trust and regulatory readiness.\n", + "\n", + "4. Implement safety guardrails, fallback responses, and PHI-safe logging to meet compliance standards and reduce legal or clinical risks.\n", + "\n", + "5. Standardize structured outputs (Assessment → Management → Red Flags → Evidence) to align with clinician workflows and enhance usability.\n", + "\n", + "6. Build monitoring dashboards for groundedness, relevance, retrieval quality, latency, and token cost to support continuous improvement and enterprise scalability." + ], + "metadata": { + "id": "wwwmRGs0icgj" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ybRlzaIhWaM9" + }, + "source": [ + "Power Ahead\n", + "___" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [], + "include_colab_link": true + }, + "kernel_info": { + "name": "python310-sdkv2" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + }, + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 60769d83a51c4e99848421081d4efa797661923c Mon Sep 17 00:00:00 2001 From: biplob <110578485+bks1984@users.noreply.github.com> Date: Mon, 10 Nov 2025 08:50:28 +0530 Subject: [PATCH 2/4] Created using Colab --- stock_market__news_sentiment_analysis.ipynb | 6747 +++++++++++++++++++ 1 file changed, 6747 insertions(+) create mode 100644 stock_market__news_sentiment_analysis.ipynb diff --git a/stock_market__news_sentiment_analysis.ipynb b/stock_market__news_sentiment_analysis.ipynb new file mode 100644 index 0000000..d9566e5 --- /dev/null +++ b/stock_market__news_sentiment_analysis.ipynb @@ -0,0 +1,6747 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "inNE1fy-ISPj", + "metadata": { + "id": "inNE1fy-ISPj" + }, + "source": [ + "

\n", + " \n", + "

\n", + "\n", + "
Stock Market News Sentiment Analysis
" + ] + }, + { + "cell_type": "markdown", + "id": "EvCcfwuSU-fz", + "metadata": { + "id": "EvCcfwuSU-fz" + }, + "source": [ + "## **Problem Statement**" + ] + }, + { + "cell_type": "markdown", + "id": "6QR_RHvIVHT2", + "metadata": { + "id": "6QR_RHvIVHT2" + }, + "source": [ + "### Business Context" + ] + }, + { + "cell_type": "markdown", + "id": "pl3dmH-EnJGl", + "metadata": { + "id": "pl3dmH-EnJGl" + }, + "source": [ + "The prices of the stocks of companies listed under a global exchange are influenced by a variety of factors, with the company's financial performance, innovations and collaborations, and market sentiment being factors that play a significant role. News and media reports can rapidly affect investor perceptions and, consequently, stock prices in the highly competitive financial industry. With the sheer volume of news and opinions from a wide variety of sources, investors and financial analysts often struggle to stay updated and accurately interpret its impact on the market. As a result, investment firms need sophisticated tools to analyze market sentiment and integrate this information into their investment strategies." + ] + }, + { + "cell_type": "markdown", + "id": "Vn6bbxSwVKl3", + "metadata": { + "id": "Vn6bbxSwVKl3" + }, + "source": [ + "### Problem Definition" + ] + }, + { + "cell_type": "markdown", + "id": "jCIswL3zobj6", + "metadata": { + "id": "jCIswL3zobj6" + }, + "source": [ + "With an ever-rising number of news articles and opinions, an investment startup aims to leverage artificial intelligence to address the challenge of interpreting stock-related news and its impact on stock prices. They have collected historical daily news for a specific company listed under NASDAQ, along with data on its daily stock price and trade volumes.\n", + "\n", + "As a member of the Data Science and AI team in the startup, you have been tasked with developing an AI-driven sentiment analysis system that will automatically process and analyze news articles to gauge market sentiment, and summarizing the news at a weekly level to enhance the accuracy of their stock price predictions and optimize investment strategies. This will empower their financial analysts with actionable insights, leading to more informed investment decisions and improved client outcomes." + ] + }, + { + "cell_type": "markdown", + "id": "ZJOtDHVSF5hu", + "metadata": { + "id": "ZJOtDHVSF5hu" + }, + "source": [ + "### Data Dictionary" + ] + }, + { + "cell_type": "markdown", + "id": "ZlkjI8V5F9RK", + "metadata": { + "id": "ZlkjI8V5F9RK" + }, + "source": [ + "* `Date` : The date the news was released\n", + "* `News` : The content of news articles that could potentially affect the company's stock price\n", + "* `Open` : The stock price (in \\$) at the beginning of the day\n", + "* `High` : The highest stock price (in \\$) reached during the day\n", + "* `Low` : The lowest stock price (in \\$) reached during the day\n", + "* `Close` : The adjusted stock price (in \\$) at the end of the day\n", + "* `Volume` : The number of shares traded during the day\n", + "* `Label` : The sentiment polarity of the news content\n", + " * 1: positive\n", + " * 0: neutral\n", + " * -1: negative" + ] + }, + { + "cell_type": "markdown", + "id": "VrFQHcW5mYgv", + "metadata": { + "id": "VrFQHcW5mYgv" + }, + "source": [ + "## **Installing and Importing the necessary libraries**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "A-E2-iaumpo8", + "metadata": { + "id": "A-E2-iaumpo8", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5f12e599-de14-4e2b-adb4-de180cdd4fba" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: numpy==1.26.4 in /usr/local/lib/python3.12/dist-packages (1.26.4)\n", + "Requirement already satisfied: scikit-learn==1.6.1 in /usr/local/lib/python3.12/dist-packages (1.6.1)\n", + "Requirement already satisfied: scipy==1.13.1 in /usr/local/lib/python3.12/dist-packages (1.13.1)\n", + "Requirement already satisfied: gensim==4.3.3 in /usr/local/lib/python3.12/dist-packages (4.3.3)\n", + "Requirement already satisfied: sentence-transformers==3.4.1 in /usr/local/lib/python3.12/dist-packages (3.4.1)\n", + "Requirement already satisfied: pandas==2.2.2 in /usr/local/lib/python3.12/dist-packages (2.2.2)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.6.1) (1.5.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.6.1) (3.6.0)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.12/dist-packages (from gensim==4.3.3) (7.3.1)\n", + "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (4.56.1)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (4.67.1)\n", + "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (2.8.0+cu126)\n", + "Requirement already satisfied: huggingface-hub>=0.20.0 in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (0.35.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (11.3.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas==2.2.2) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas==2.2.2) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas==2.2.2) (2025.2)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (3.19.1)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2025.3.0)\n", + "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (25.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (6.0.2)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2.32.4)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (4.15.0)\n", + "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (1.1.10)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas==2.2.2) (1.17.0)\n", + "Requirement already satisfied: wrapt in /usr/local/lib/python3.12/dist-packages (from smart-open>=1.8.1->gensim==4.3.3) (1.17.3)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (75.2.0)\n", + "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (1.13.3)\n", + "Requirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (3.5)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (3.1.6)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.77)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.77)\n", + "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.80)\n", + "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (9.10.2.21)\n", + "Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.4.1)\n", + "Requirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (11.3.0.4)\n", + "Requirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (10.3.7.77)\n", + "Requirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (11.7.1.2)\n", + "Requirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.5.4.2)\n", + "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (0.7.1)\n", + "Requirement already satisfied: nvidia-nccl-cu12==2.27.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (2.27.3)\n", + "Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.77)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.85)\n", + "Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (1.11.1.6)\n", + "Requirement already satisfied: triton==3.4.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (3.4.0)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers==3.4.1) (2024.11.6)\n", + "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers==3.4.1) (0.22.0)\n", + "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers==3.4.1) (0.6.2)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.11.0->sentence-transformers==3.4.1) (1.3.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers==3.4.1) (3.0.2)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (3.4.3)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (3.10)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2025.8.3)\n" + ] + } + ], + "source": [ + "# installing the sentence-transformers and gensim libraries for word embeddings\n", + "!pip install numpy==1.26.4 \\\n", + " scikit-learn==1.6.1 \\\n", + " scipy==1.13.1 \\\n", + " gensim==4.3.3 \\\n", + " sentence-transformers==3.4.1 \\\n", + " pandas==2.2.2" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Note:\n", + "- After running the above cell, kindly restart the runtime (for Google Colab) or notebook kernel (for Jupyter Notebook), and run all cells sequentially from the next cell.\n", + "- On executing the above line of code, you might see a warning regarding package dependencies. This error message can be ignored as the above code ensures that all necessary libraries and their dependencies are maintained to successfully execute the code in this notebook." + ], + "metadata": { + "id": "Su4_EiqL5aIZ" + }, + "id": "Su4_EiqL5aIZ" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "179a2a45", + "metadata": { + "id": "179a2a45" + }, + "outputs": [], + "source": [ + "# To manipulate and analyze data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# To visualize data\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# To used time-related functions\n", + "import time\n", + "\n", + "# To build, tune, and evaluate ML models\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, precision_score, recall_score\n", + "\n", + "# To load/create word embeddings\n", + "from gensim.models import Word2Vec\n", + "\n", + "# To work with transformer models\n", + "import torch\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "# Import TensorFlow and Keras for deep learning model building.\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout\n", + "\n", + "# To implement progress bar related functionalities\n", + "from tqdm import tqdm\n", + "tqdm.pandas()\n", + "\n", + "# To ignore unnecessary warnings\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "wQ46zPgumfjF", + "metadata": { + "id": "wQ46zPgumfjF" + }, + "source": [ + "## **Loading the Dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yu_7XbWQWma8", + "metadata": { + "id": "yu_7XbWQWma8" + }, + "outputs": [], + "source": [ + "# # uncomment and run the following code if Google Colab is being used and the dataset is in Google Drive\n", + "# from google.colab import drive\n", + "# drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62a33eef", + "metadata": { + "id": "62a33eef" + }, + "outputs": [], + "source": [ + "# Read the CSV file named 'stock_news' into a pandas DataFrame named 'stock'\n", + "stock_news = pd.read_csv(\"/content/02. Dataset - stock_news.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1xFSwCCer1uA", + "metadata": { + "id": "1xFSwCCer1uA" + }, + "outputs": [], + "source": [ + "#Creating a copy of the dataset\n", + "stock = stock_news.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "EvFNfrvGWthn", + "metadata": { + "id": "EvFNfrvGWthn" + }, + "source": [ + "## **Data Overview**" + ] + }, + { + "cell_type": "markdown", + "id": "GW4rkWI1WzBb", + "metadata": { + "id": "GW4rkWI1WzBb" + }, + "source": [ + "#### **Displaying the first few rows of the dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd2f105b", + "metadata": { + "id": "dd2f105b", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4fce6c84-28cc-4aee-e18c-154c97c9e849" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Date News Open \\\n", + "0 01-02-2019 The dollar minutes ago tumbled to 106 67 from... 38.72 \n", + "1 01-02-2019 By Wayne Cole and Swati Pandey SYDNEY Reuters... 38.72 \n", + "2 01-02-2019 By Stephen Culp NEW YORK Reuters Wall Stre... 38.72 \n", + "3 01-02-2019 By Wayne Cole SYDNEY Reuters The Australia... 38.72 \n", + "4 01-02-2019 Investing com Asian equities fell in morning... 38.72 \n", + "\n", + " High Low Close Volume Label \n", + "0 39.71 38.56 39.48 130672400 1 \n", + "1 39.71 38.56 39.48 130672400 -1 \n", + "2 39.71 38.56 39.48 130672400 0 \n", + "3 39.71 38.56 39.48 130672400 -1 \n", + "4 39.71 38.56 39.48 130672400 1 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DateNewsOpenHighLowCloseVolumeLabel
001-02-2019The dollar minutes ago tumbled to 106 67 from...38.7239.7138.5639.481306724001
101-02-2019By Wayne Cole and Swati Pandey SYDNEY Reuters...38.7239.7138.5639.48130672400-1
201-02-2019By Stephen Culp NEW YORK Reuters Wall Stre...38.7239.7138.5639.481306724000
301-02-2019By Wayne Cole SYDNEY Reuters The Australia...38.7239.7138.5639.48130672400-1
401-02-2019Investing com Asian equities fell in morning...38.7239.7138.5639.481306724001
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "stock", + "summary": "{\n \"name\": \"stock\",\n \"rows\": 418,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 73,\n \"samples\": [\n \"01-08-2019\",\n \"04-15-2019\",\n \"01-30-2019\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"News\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 418,\n \"samples\": [\n \" Reuters Apple Inc NASDAQ AAPL is expected to unveil a new video streaming service and a news subscription platform at an event on Monday at its California headquarters The iPhone maker is banking on growing its services business to offset a dip in smartphone sales While the Wall Street Journal plans to join Apple s new subscription news service other major publishers including the New York Times and the Washington Post have declined according to a New York Times report Apple has also partnered with Hollywood celebrities to make a streaming debut with a slate of original content taking a page out of Netflix NASDAQ NFLX Inc s playbook Below are some of the shows curated from media reports and Apple s own announcements which are part of the iPhone maker s content library SHOWS CONFIRMED BY APPLE UNTITLED DRAMA SERIES WITH REESE WITHERSPOON AND JENNIFER ANISTON Two seasons of a drama series starring Reese Witherspoon and Jennifer Aniston that looks at the lives of people working on a morning television show REVIVAL OF STEVEN SPIELBERG S 1985 AMAZING STORIES The tech giant has also struck a deal with director Steven Spielberg to make new episodes of Amazing Stories a science fiction and horror anthology series that ran on NBC in the 1980s A NEW THRILLER BY M NIGHT SHYAMALAN Plot of the story has not been disclosed ARE YOU SLEEPING A MYSTERY SERIES A drama featuring Octavia Spencer based on a crime novel by Kathleen Barber AN ANTHOLOGY SERIES CALLED LITTLE AMERICA Focuses on stories of immigrants coming to the United States AN ANIMATED CARTOON MUSICAL CALLED CENTRAL PARK The animated musical comedy is about a family of caretakers who end up saving the park and the world DICKINSON AN EMILY DICKINSON COMEDY A half hour comedy series that is set during American poet Emily Dickinson s era with a modern sensibility and tone OPRAH WINFREY PARTNERSHIP Apple in June last year announced a multi year deal with Oprah Winfrey to create original programming SHOWS REPORTED BY MEDIA TIME BANDITS A FANTASY SERIES The potential series is an adaptation of Terry Gilliam s 1981 fantasy film of the same name about a young boy who joins a group of renegade time traveling dwarves Deadline reported https UNTITLED CAPTAIN MARVEL STAR BRIE LARSON S CIA PROJECT The new series looks at a young woman s journey in the CIA reported Variety https DEFENDING JACOB STARRING CAPTAIN AMERICA CHRIS EVANS This limited series is based on the novel of the same name and is about an assistant district attorney who is investigating the murder of a 14 year old boy according to Deadline https FOR ALL MANKIND A SCI FI SERIES A space drama from producer Ronald Moore according to Deadline https MY GLORY WAS I HAD SUCH FRIENDS A series featuring Jennifer Garner is based on the 2017 memoir of the same name by Amy Silverstein reported Variety https SEE A FANTASY EPIC STARRING JASON MOMOA The show poses the question about the fate of humanity if everyone lost their sight Variety reported https FOUNDATION A SCI FI ADAPTATION An adaptation of the iconic novel series from famed sci fi author Isaac Asimov Deadline reported The book series follows a mathematician who predicts the collapse of humanity A COMEDY SHOW BY ROB MCELHENNEY AND CHARLIE DAY The sitcom comedy based on the lives of a diverse group of people who work together in a video game development studio Variety reported https AN UNSCRIPTED SERIES HOME FROM THE DOCUMENTARY FILMMAKER MATT TYRNAUER The series will offer viewers a never before seen look inside the world s most extraordinary homes and feature interviews with people who built them according to Variety https UNTITLED RICHARD GERE SERIES Based on an Israeli series Nevelot the show is about two elderly Vietnam vets whose lives are changed when a woman they both love is killed in a car accident Deadline reported J J ABRAMS PRODUCED LITTLE VOICE Singer and actress Sara Bareilles is writing the music and could possibly star in the J J Abrams produced half hour show which explores the journey of finding one s authentic voice in early 20s according to Variety THE PEANUTS GANG Apple has acquired the rights to the famous characters and the first series will be a science and math oriented short featuring Snoopy as an astronaut according to Hollywood Reporter ON THE ROCKS A feature film directed by Sofia Coppola starring Bill Murray is about a young mother who reconnects with her larger than life playboy father on an adventure through New York Variety reported https LOSING EARTH Apple has acquired the rights to a TV series based on Nathaniel Rich s 70 page New York Times Magazine story Losing Earth New York Times reported THE ELEPHANT QUEEN Apple has acquired the rights to Victoria Stone and Mark Deeble s documentary The Elephant Queen Deadline reported WOLFWALKERS An Irish animation about a young hunter who comes to Ireland with her father to destroy a pack of evil wolves but instead befriends a wild native girl who runs with them first reported by Bloomberg PACHINKO Apple has secured the rights to develop Min Jin Lee s best selling novel about four generations of a Korean immigrant family into a series reported Variety CALLS Apple has bought the rights to make an English language version of the French original short form series according to Variety SHANTARAM Apple has won the rights to develop the hit novel Shantaram as a drama series reported Variety https SWAGGER A DRAMA SERIES BASED ON KEVIN DURANT A drama series based on the early life and career of NBA superstar Kevin Durant according to Variety https YOU THINK IT I LL SAY IT Apple has ordered a 10 episode half hour run of the comedy show which is an adaptation of Curtis Sittenfeld s short story collection by the same name Variety reported https WHIPLASH DIRECTOR DAMIEN CHAZELLE DRAMA SERIES According to Variety Apple has ordered a whole season of a series without first shooting a pilot but no other details are known about the show \\n Apple may offer cut priced bundles with video offering The Information reported on Thursday \",\n \"Investing com Stocks in focus in premarket trade Monday \\n Viacom NASDAQ VIAB jumped 4 2 by 8 04 AM ET 12 04 GMT as the company announced that it had renewed its contract with AT T NYSE T avoiding a blackout of MTV Nickelodeon and Comedy Central for DirecTV users \\n Nike NYSE NKE fell 0 3 after European Union antitrust regulators fined the company 12 5 million euros 14 14 million for restricting cross border sales of merchandising products \\n Apple NASDAQ AAPL dropped 0 3 while markets geared up for a company presentation that is expected to lift the curtain on Monday on a secretive years long effort to build a video streaming prodduct \\n Boeing NYSE BA gained 0 4 as the company preps to brief more than 200 global airline pilots technical leaders and regulators on Wednesday over software and training updates for its 737 MAX aircraft \\n CalAmp NASDAQ CAMP fell 2 4 after JP Morgan downgraded it to neutral from overweight according to Briefing com \\n Winnebago Industries NYSE WGO stock declined 0 9 after the company s fiscal second quarter revenue was lower than expected although earnings per share beat expectations\\n Thermo Fisher Scientific NYSE TMO stock could see movement in the regular session after the company announced that it would acquire Brammer Bio for approximately 1 7 billion in cash \\n Biogen NASDAQ BIIB bounced 1 5 after announcing a new 5 billion buyback The stock had fallen by nearly one third last week after saying it had halted the development of a drug it had been developing to treat Alzheimer s \",\n \"By Yimou Lee TAIPEI Reuters Terry Gou chairman of Apple NASDAQ AAPL supplier Foxconn said on Wednesday he will contest Taiwan s 2020 presidential election shaking up the political landscape at a time of heightened tension between the self ruled island and Beijing Gou Taiwan s richest person with a net worth of 7 6 billion according to Forbes said he would join the already competitive race and take part in the opposition China friendly Kuomintang KMT primaries His decision capped a flurry of news this week that began when Gou told Reuters on Monday he planned to step down from the world s largest contract manufacturer to pave the way for younger talent to move up the company s ranks He announced on Tuesday he was considering a presidential bid and hinted he was close to a decision when he told more than 100 people packed into a temple he would follow the instruction of a sea goddess who had told him to run in the presidential race Peace stability and Taiwan s economy future are my core values Gou said later at the KMT s headquarters in Taipei He urged the party to rediscover the spirit of the KMT the honor of KMT members and the KMT s lost support of the youth Gou s bid which requires KMT approval comes at a delicate time for cross strait relations and delivers a blow to the ruling pro independence Democratic Progressive NYSE PGR Party which is struggling in opinion polls China Taiwan relations have deteriorated since the island s president Tsai Ing wen of the independence leaning DPP swept to power in 2016 China suspects Tsai is pushing for the island s formal independence That is a red line for China which has never renounced the use of force to bring Taiwan under its control Tsai says she wants to maintain the status quo with China but will defend Taiwan s security and democracy VERY PRO CHINA A senior adviser to Tsai told Reuters he thought Gou s bid could create problems given his extensive business ties with China This is problematic to Taiwan s national security the adviser Yao Chia wen said He s very pro China and he represents the class of the wealthy people Will that gain support from Taiwanese Yao said adding he believed Gou would face a tough battle in the KMT primary Tension between Taipei and Beijing escalated again on Monday as Chinese bombers and warships conducted drills around the island prompting Taiwan to scramble jets and ships to monitor the Chinese forces The KMT which once ruled China before fleeing to Taiwan at the end of a civil war with the Communists in 1949 said in February it could sign a peace treaty with Beijing if it won the hotly contested presidential election Zhang Baohui a regional security analyst at Hong Kong s Lingnan University said Gou s run could mark the start of the most unusual election race in Taiwan history This is something entirely fresh for Taiwan politics here is a candidate who sees everything through the pragmatic angle of a businessman rather than raw politics or ideology Zhang told Reuters He has no baggage and that will be a fascinating scenario Gou s news comes as Tsai is grappling with a series of unpopular domestic reform initiatives from a pension scheme to labour law which have come under intense voter scrutiny The KMT said this week Gou had been a party member for more than 50 years and had given it an interest free loan of T 45 million 1 5 million in 2016 under the name of his mother which had signalled his loyalty to the party Foxconn said on Tuesday Gou would remain chairman of Foxconn though he planned to withdraw from daily operations \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.947134201503234,\n \"min\": 35.99,\n \"max\": 51.84,\n \"num_unique_values\": 69,\n \"samples\": [\n 43.22,\n 38.72,\n 48.83\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.947413441172774,\n \"min\": 36.43,\n \"max\": 52.12,\n \"num_unique_values\": 67,\n \"samples\": [\n 43.87,\n 39.08,\n 37.96\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.967879507972434,\n \"min\": 35.5,\n \"max\": 51.76,\n \"num_unique_values\": 66,\n \"samples\": [\n 49.54,\n 50.97,\n 38.56\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.999867403609388,\n \"min\": 35.55,\n \"max\": 51.87,\n \"num_unique_values\": 68,\n \"samples\": [\n 48.77,\n 39.08,\n 37.69\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 45745495,\n \"min\": 45448000,\n \"max\": 365248800,\n \"num_unique_values\": 73,\n \"samples\": [\n 216071600,\n 70146400,\n 244439200\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": -1,\n \"max\": 1,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n -1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "stock.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "y2ewB36LL9Cz", + "metadata": { + "id": "y2ewB36LL9Cz" + }, + "source": [ + "#### **Understanding the shape of the dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "wWx6wqN0MTPw", + "metadata": { + "id": "wWx6wqN0MTPw", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "edd77532-57d5-4ab5-ebc3-b8a4fa737b0a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(418, 8)" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "stock.shape" + ] + }, + { + "cell_type": "markdown", + "id": "yQjb8QOTivg3", + "metadata": { + "id": "yQjb8QOTivg3" + }, + "source": [ + "**Observations:**\n", + "* There are a total of 418 records with 8 attributes each." + ] + }, + { + "cell_type": "markdown", + "id": "fPLJXhFcMA7N", + "metadata": { + "id": "fPLJXhFcMA7N" + }, + "source": [ + "#### **Checking the data types of the columns**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Gc_eAiMdMVe2", + "metadata": { + "id": "Gc_eAiMdMVe2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8c61445b-ef88-4a92-b810-0e15bb3000f5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 418 entries, 0 to 417\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 418 non-null object \n", + " 1 News 418 non-null object \n", + " 2 Open 418 non-null float64\n", + " 3 High 418 non-null float64\n", + " 4 Low 418 non-null float64\n", + " 5 Close 418 non-null float64\n", + " 6 Volume 418 non-null int64 \n", + " 7 Label 418 non-null int64 \n", + "dtypes: float64(4), int64(2), object(2)\n", + "memory usage: 26.3+ KB\n" + ] + } + ], + "source": [ + "stock.info()" + ] + }, + { + "cell_type": "markdown", + "id": "i1CgPxT5mxEf", + "metadata": { + "id": "i1CgPxT5mxEf" + }, + "source": [ + "Let's convert the Date column to pandas `datetime` type." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ZD5fstuv6ery", + "metadata": { + "id": "ZD5fstuv6ery" + }, + "outputs": [], + "source": [ + "# Convert the 'Date' column in the 'stocks' DataFrame to datetime format\n", + "stock['Date'] = pd.to_datetime(stock['Date'])" + ] + }, + { + "cell_type": "markdown", + "id": "8dORemydMDfR", + "metadata": { + "id": "8dORemydMDfR" + }, + "source": [ + "#### **Checking the statistical summary**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gUazWjegMeQl", + "metadata": { + "id": "gUazWjegMeQl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cd88b728-318a-4f67-d74e-71373400a55b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Date Open High Low \\\n", + "count 418 418.000000 418.000000 418.000000 \n", + "mean 2019-02-14 12:24:06.889952256 42.308852 42.787321 41.923732 \n", + "min 2019-01-02 00:00:00 35.990000 36.430000 35.500000 \n", + "25% 2019-01-11 00:00:00 38.130000 38.420000 37.720000 \n", + "50% 2019-01-31 00:00:00 41.530000 42.250000 41.140000 \n", + "75% 2019-03-21 00:00:00 47.190000 47.427500 46.480000 \n", + "max 2019-04-29 00:00:00 51.840000 52.120000 51.760000 \n", + "std NaN 4.947134 4.947413 4.967880 \n", + "\n", + " Close Volume Label \n", + "count 418.000000 4.180000e+02 418.000000 \n", + "mean 42.418517 1.294225e+08 0.308612 \n", + "min 35.550000 4.544800e+07 -1.000000 \n", + "25% 38.270000 1.029072e+08 -1.000000 \n", + "50% 41.610000 1.156272e+08 1.000000 \n", + "75% 47.032500 1.511252e+08 1.000000 \n", + "max 51.870000 3.652488e+08 1.000000 \n", + "std 4.999867 4.574550e+07 0.943473 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DateOpenHighLowCloseVolumeLabel
count418418.000000418.000000418.000000418.0000004.180000e+02418.000000
mean2019-02-14 12:24:06.88995225642.30885242.78732141.92373242.4185171.294225e+080.308612
min2019-01-02 00:00:0035.99000036.43000035.50000035.5500004.544800e+07-1.000000
25%2019-01-11 00:00:0038.13000038.42000037.72000038.2700001.029072e+08-1.000000
50%2019-01-31 00:00:0041.53000042.25000041.14000041.6100001.156272e+081.000000
75%2019-03-21 00:00:0047.19000047.42750046.48000047.0325001.511252e+081.000000
max2019-04-29 00:00:0051.84000052.12000051.76000051.8700003.652488e+081.000000
stdNaN4.9471344.9474134.9678804.9998674.574550e+070.943473
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"stock\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1970-01-01 00:00:00.000000418\",\n \"max\": \"2019-04-29 00:00:00\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"418\",\n \"2019-02-14 12:24:06.889952256\",\n \"2019-03-21 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.29734162506347,\n \"min\": 4.947134201503234,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 42.308851674641154,\n 47.19,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.18648944299875,\n \"min\": 4.947413441172774,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 42.78732057416268,\n 47.427499999999995,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.4078256172517,\n \"min\": 4.967879507972434,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 41.923732057416274,\n 46.48,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.30548063571206,\n \"min\": 4.999867403609388,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 42.41851674641149,\n 47.0325,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 111473859.17448182,\n \"min\": 418.0,\n \"max\": 365248800.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 129422491.86602871,\n 151125200.0,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 147.67411440583984,\n \"min\": -1.0,\n \"max\": 418.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.30861244019138756,\n 0.9434730920044713,\n -1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "stock.describe()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Observations:**\n", + "\n", + "- **Date Range and Trading Period**:\n", + " - The data covers a period from January 2, 2019, to April 29, 2019, indicating a span of approximately four months.\n", + "\n", + "- **Price Overview**:\n", + " - **Average Prices**: The average opening price is approximately \\$42.30, while the average closing price is about \\$42.41.\n", + " - **Price Variability**: The prices range from a minimum of around \\$35.99 for opening to a maximum of \\$51.84 for opening, reflecting significant volatility during this period.\n", + "\n", + "- **Trading Volume**:\n", + " - The average trading volume is approximately 129.42 million shares, with fluctuations from around 45.45 million to 365.24 million, highlighting varying market activity levels." + ], + "metadata": { + "id": "0wZ7x_5W77tD" + }, + "id": "0wZ7x_5W77tD" + }, + { + "cell_type": "markdown", + "id": "lXRpNWnQMGIY", + "metadata": { + "id": "lXRpNWnQMGIY" + }, + "source": [ + "#### **Checking the duplicate values**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ti4UpPi6M5kM", + "metadata": { + "id": "ti4UpPi6M5kM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "66fad013-2ec1-4ec3-9cfe-842656486d7c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "stock.duplicated().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "XkwHzJH6k_jx", + "metadata": { + "id": "XkwHzJH6k_jx" + }, + "source": [ + "**Observations:**\n", + "* There are no duplicate values." + ] + }, + { + "cell_type": "markdown", + "id": "fxghULa0MOY-", + "metadata": { + "id": "fxghULa0MOY-" + }, + "source": [ + "#### **Checking for missing values**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yItWheKoNGkf", + "metadata": { + "id": "yItWheKoNGkf", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ff1d5335-a019-465d-e7f8-546c97e5873f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Date 0\n", + "News 0\n", + "Open 0\n", + "High 0\n", + "Low 0\n", + "Close 0\n", + "Volume 0\n", + "Label 0\n", + "dtype: int64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
Date0
News0
Open0
High0
Low0
Close0
Volume0
Label0
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "stock.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "qg7TsQTclDUS", + "metadata": { + "id": "qg7TsQTclDUS" + }, + "source": [ + "**Observations:**\n", + "* There are no missing values." + ] + }, + { + "cell_type": "markdown", + "id": "hGHBK8-QeKOB", + "metadata": { + "id": "hGHBK8-QeKOB" + }, + "source": [ + "## **Exploratory Data Analysis**" + ] + }, + { + "cell_type": "markdown", + "id": "Q0UlMQnyegl7", + "metadata": { + "id": "Q0UlMQnyegl7" + }, + "source": [ + "### **Univariate Analysis**" + ] + }, + { + "cell_type": "markdown", + "id": "RrznHeBaLu0W", + "metadata": { + "id": "RrznHeBaLu0W" + }, + "source": [ + "#### **Countplot on Label**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "meVjTKoxLpmA", + "metadata": { + "id": "meVjTKoxLpmA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dcd02675-45e6-4016-fb12-10ce1aef7018" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.countplot(data=stock, x='Label', stat=\"percent\");" + ] + }, + { + "cell_type": "markdown", + "id": "nXPvfQr-Avd7", + "metadata": { + "id": "nXPvfQr-Avd7" + }, + "source": [ + "**Observations:**\n", + "* The dataset is imbalanced for the sentiment polarities.\n", + "* There is more news content with positive polarity compared to other types." + ] + }, + { + "cell_type": "markdown", + "id": "dpGHhbGeeoF8", + "metadata": { + "id": "dpGHhbGeeoF8" + }, + "source": [ + "#### **Density Plot of Price (Open, High, Low, Close)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "BKqgbg0_v5EM", + "metadata": { + "id": "BKqgbg0_v5EM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0a50f216-e030-496b-de8c-cc3a2b8153ca" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAHpCAYAAACWdKhHAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAxH9JREFUeJzs3Xd4VFX6wPHvnZbeOymE3nsHpdjAjr0jFty1rYrdVSxYfu6qq2vXVdFdK3YRkSJFAemdEAgJSUglvWcmM/f3x51KJpQQ0ng/z5NnxnvPvfcMBvLmnPe8R1FVVUUIIYQQQnjQtXUHhBBCCCHaIwmShBBCCCG8kCBJCCGEEMILCZKEEEIIIbyQIEkIIYQQwgsJkoQQQgghvJAgSQghhBDCCwmSmklVVSoqKpAyU0IIIUTnJEFSM1VWVhISEkJlZWVbd0UIIYQQJ4EESUIIIYQQXkiQJIQQQgjhhQRJQgghhBBeSJAkhBBCCOGFBElCCCGEEF5IkCSEEEII4YUESUIIIYQQXkiQJIQQQgjhhQRJQgghhBBeSJAkhBBCCOGFBElCCCGEEF5IkCSEEEII4YUESUIIIYQQXkiQJIQQQgjhhQRJQgghhBBeSJAkhBBCCOGFBElCCCGEEF5IkCSEEEII4YUESUIIIYQQXhjaugNCtIYN+Rt4d9u71DTUEOYbxl8H/5VBUYPaultCCCHaMQmSRKemqipvbn2T97a/h4rqPL42dy2PjH6EK/tc2Ya9E0II0Z7JdJvo1Obvnc+7299FRWV6z+m8fsbrnJl0Jhabhbl/zmVp5tK27qIQQoh2SlFVVT16M3G4iooKQkJCKC8vJzg4uK27I7zIrsjmsp8uo7ahlnuH38stg24BtNGlf2z4B/9L+R8RvhH8MP0HQnxC2ri3Qggh2hsZSRKdkqqqzFkzh9qGWkbFjuKmgTc5zymKwr0j7qVbSDeK64r5x4Z/tGFPhRBCtFcSJIlOaUP+BjYWbMRX78vcCXPRKZ7f6j56H54Z/wyoen5IW0B6Wbp24sBqWPVPWDYXDvzRBj0XQgjRXkjituiUPtr1EQAX97yY+MD4RudXpxXx6tIaqrOewabauOur5cxL/IrodS+4Gv3+Eoy+Dc6eC0bf1uq6EEKIdkKCJNHp7C3dyx85f6BTdNzY/8ZG53/alsvsr7ZisaqAHtCzOz2W89N9+MEnnC79xoPeCLu+g/XvQW0ZXPZ+a38MIYQQbUym20Sn88muTwA4K+ksEoMTPc6t2nuIv32xBYtV5YLBcSydPZGpyd/QQ8nhEGHc4vca1Zd8DFfMg2u+AEUPO76CHV+3wScRQgjRliRIEp1KbUMtizMXA3BD/xs8ztVZrDz+/U5UFS4bnsBrVw+jZ5iBV6p/52PTiwTpKkkp0zPnh13aBX3OhYkPaO8XzIaKvNb8KEIIIdqYBEmiU1mZvZLahlriA+MZEjXE49xbK/aTVVJDbLAvz1w8AL1OgT/fJqCyEKOxDGP8fwH4dstBdudWaBdNfBC6DIP6cljz79b+OEIIIdqQBEmiU1mYsRCAc7udi6IozuP55XW8s2I/AE9e2J8AHwNYG2DDfwBY0es0LIFZ9EwoR1Xhn7/u0S7UG+GMx7X3m+ZBdXGrfRYhhBBtS4Ik0WlUmCv4I0dbtn9ut3M9zn26LhOz1cbIrmFMGxirHdyzACpywD+SqNG3A2AO+RaDTmF56iHWZ5Ro7XqcCbGDwVID699ttc8jhBCibUmQJDqNZZnLsNgs9AztSe+w3s7jdRYrn63LAuDm07q5RpjW2QOekTcxIWkKAcYASm2pTBngB8C8NRnaeUWB02e7rrHUtsrnEUII0bYkSBKdxorsFQCck3yOx/EF2/MorjYTF+LLOf1jtIMFuyFrDegMMPJmfPQ+TEmcAkB4zHYAFu8q4FBlvda+30UQkgR1ZbB3USt8GiGEEG1NgiTRKVisFtblrwNgYsJEj3P//TMTgOvHdsWgt3/L7/5ee+15NgR3AXAGSalVyxmWFEqDTeXrTQe1djo9DL5Ce7/ty5P3QYQQQrQbEiSJTmHroa1UW6oJ9w2nX3g/5/Gs4hq2ZZehU+DKkW41k3b/qL32v9h5aEzcGHSKjv3l+zlviLbh7RcbsrDZ7HtAD7pSe01bIgncQghxCpAgSXQKq3NWAzC+y3iPfdoW7tRqG43tHkFUkI92sGgfHErRptr6TMNWW0vlb79h+fAz+vv3ACAwbC9BPgYyi2vYcMCewB3dF+KGgK0Bdn3beh9OCCFEm5AgSXQKq3O1IGlC/ASP47/s0IKk8wbFuQ7u/kF77TaJ6u372DdpMgfvuJNDr/2bvr+mArAxZyVnD9DylxbtynddO/hq7VUqcAshRKcnQZLo8A7VHGJPyR4UFMZ3Ge88nl1Sw7aD5egUXMv+AVJ+AsASOoqDd9yBraICQ5c4AiaezpADWpM1mSs5u28koCVwq6p9yq3/Rfabr5MpNyGE6OQkSBId3p95fwLQL6If4b7hzuOLdmojQGO6RRAZaJ9qqzoEeVsByHrlR2zV1fiPGkWPX34h6b33OPPlz/CvgyqjFcPKd/Ez6skpq2Vnjr0Cd0gCxAwCVC03SQghRKclQZLo8DYVbAJgVMwoj+PL9hQAh40iZawEwKKLwZxXhql7dxLeehOdjxZEBQ0ewsig/gBs3PQNE+N9AVi0y23ftt5Ttde9v7b4ZxFCCNF+SJAkOrzNhZsBGBEzwnmsxtzApsxSACb2jnI1tgdJlalaQcjohx5EHxTkcb8xQ84HYE+Cwti92iiVY1QKcAVJacvAamm5DyKEEKJdkSBJdGgldSVklGuVsYdFD3MeX5degsWqkhDmR3KEv+uC9BUAVOUa8Bs5gsBJkxrdc3jMcABSExQGLPkKvQL7D1WTXVKjNYgfAf4R2qa3WX+enA8mhBCizUmQJDq0LQVbAOgZ2pNQ31Dn8d/3FQFweq9I1zYkJRlQloVqg5pDJqJn3++xCa5Dn/A++Bn8qPZTKA2pZYC1DIA/0rR7otNrRSgB9i0+KZ9LCCFE25MgSXRoGws2AjA8erjH8d/3HQLgtJ6Np9pqi0349BmE//BheGPUGRkcNRiAPYkKg/dt8LgnAD206twc+P2EP4MQQoj2SYIk0aE58pEcU2QA+eV17CusQlFgQs8I53E1YxUA1QUmQi679Ij3dQRd+waGMjw/BYDVacVYHdW3k0/XXvO2QW1ZS3wUIYQQ7YwESaLDqrZUs6dkD+CZtL3aPi02OD6EUH+T87ia/gcAtWWBhFxwwRHv7chv2pOg0LvsIAFWM+W1FnbklGsNQuIhvAeoNshc02KfSQghRPshQZLosHYX78am2ojxjyE2wLXMf2Omto3ImO6uUSQq8tDVFKDawDB0Gvrg4CPee0jUEPSKngIqKA1RGFqgBWO/73WbcutmH02SKTchhOiUJEgSHdbOop0Azvwhh40HtKX/I7qGOY+p2esBqC83EHzx5Ue9t7/Rn15hvQA4OKUvwwr3AW7J2+CacsuQIEkIITojCZJEh7WjaAcAAyIGOI+V1ZjZV1gFeAZJDdsWAVBX7o//mDHHdH/HfTOHxjKoaD8AW7PLqG+wag0cQVLBDqgpaf4HEUII0S5JkCQ6rF1FuwAYFDnIeWxzljaK1C0ywLUVCUC6tgGuLXIQOpMrT+lIBkYOBGBfUBWJtcUE11dR32BjpyMvKSgGIvto7yUvSQghOh0JkkSHVFxbTG51LgoK/SP6O497m2qjwYzenA2AYcSRE7bdOYKv3WWp+A0bysBirWjl+oxSV6OksdrrwfXN+RhCCCHaMQmSRIe0q1gbReoW0o1AU6Dz+Eb7ViSjkt2m2navQKezYa1X8D/nymN+Ro/QHvjqfamyVFE6aRADi9MB2HDAbWot0T51ly1BkhBCdDYSJIkOyZG07ZgSAzA32NiWXQbAiK7hruOrvwGg3hKJISLymJ9h0BnoG94XgAP9wxhYZA+SMkpc9ZIcQVLOZmgwN+uzCCGEaJ8kSBIdkiNp2z1I2pNfQX2DjRA/Iz2iApzH1axN2puYQRwvx/1TDcX0DlDxs9RRWd9Aan6l1iCiB/iFg7Ue8rc389MIIYRojyRIEh2OqqrsLt4NeK5scxR6HJwQ4tyTTbXZ0Ndr+Uj6fo03sz0aR5C0s3gnIRNPp19JJuA25aYoblNu647/wwghhGi3JEgSHc6h2kOU1JWgU3T0DuvtPL7joBYkDYoPcR6rT03BJ6AOANPo84/7Wc6RpJJUfMaMon/JAQC2ZLklbyeO1l4lSBJCiE5FgiTR4Ti2IukW3A1fg6/z+PaDrpEkh/rVP6LowWYzokT1PO5nJQYl4m/wp95az6G+0fQt1UaSNntL3s5aB6p63M8QQgjRPkmQJDqcvaV7AegT3sd5rM5iZW+Blic0KCHUedy6R9vUtsE3SZsaO046Red8zj61gIEhegCyyuooqbYnascPB0UPVflQkXPczxBCCNE+SZAkOhzHSJJj5RlASl4FDTaViAATXUK00SXVZkMpSgFASRzR+EbHyPGcPcV7iBk5hITKQgDnSjqMfhBtr9WUs7nZzxFCCNG+SJAkOpzUklTAcyTJUQV7kFvSdv2+ffgEVANgGDC52c9zBkmlewgYNco55eaRlxQ/THvNlSBJCCE6CwmSRIdSY6khs0ILUvqEuYKk7V6Stms3b8I31AKAkjC82c90BEmpJan4jRxJn5IsALZkuG12G28fqZKRJCGE6DQkSBIdyt7SvaioRPlFEeEX4TzuWP7vHiSZt61CZ1RRMUBEr2Y/s2doTwyKgbL6MooDbAzybwC0zW5tjqKSXexBWO5WsNma/SwhhBDthwRJokPxNtVW32AlrbAKgAFuQZKaqY3q2IKSQW9o9jNNehPdQ7sDWj5U/76J+DSYqWyA9CJtOo/ofmDwhfpyKElv9rOEEEK0HxIkiQ4ltVQLktyTttMKq2iwqQT7GpxJ2w2lpejNuQAoCUNO+LnOvKSSPQQNG0qPcm0VmyMXCr0RYgdr7yUvSQghOgUJkkSHsq90H4BHEck9edrS/75xwc6k7bodO/AJ0fKRdAlDT/i57kGS35Ah9Cw7CMCOg2WuRvH2KTfJSxJCiE5BgiTRYaiqSlpZGqDlCTnsya8AoH9csPNY7dZt+IRouUPO5fknoFeYltO0v2w/Pj160KtWS9resT/f1ciZl7TlhJ8nhBCi7UmQJDqMgpoCqixVGBQDycHJzuMpjpGk2CDnsdptmzEFOYKkfif8bEdQllWZRb1qYUAX7Vm7C2tdydtx9mm9gp2SvC2EEJ2ABEmiw3CMInUN7opRb3Qed4wk9bWPJKk2G9aMzSg6UI2BENzlhJ8d4RtBqE8oNtVGRnkGffsnY7A2UGVTyC6tsTfqqSVvm6ugNOOEnymEEKJtSZAkOoy0UvtUW5hrqq2wso6iKjOKAn1itNEdc2YmJkOZ1iBmQLO2IzmcoijO0aS0sjSChw2lW0UeADtztCANvUF7HkDethN+phBCiLYlQZLoMBwjST1CeziPOZK2u0UE4GfS9lWr273bmY+kxJz4VJuD47lpZWn4Dh7sTN7emVHoahQ7SHvN39FizxVCCNE2JEgSHYYjSOoV6ioM6Zhq6+eWtO0eJLVE0raD47lpZWkYwsLoo9Om2banuSVvO8oA5G9vsecKIYRoGxIkiQ7BptpIL9eKNHobSerjlrStBUna8v+WSNp2cEzzOab9BsTZk7eL61FVe/K2M0iSkSQhhOjoJEgSHUJOVQ61DbWYdCYSgxKdx/fZK233tucjqapK/Z5dGAOtWoPIPo3u1VyOnKTc6lyqLdUM6JuIzmal1KYnv6JOaxQzABQdVBVAZUGLPVsIIUTrkyBJdAiO0Zvuod0x6LQtRmw21bkdSa+YQAAsObkYbCUoCqi+IRAY3WJ9CPEJIcovCtDqJYUMHkhXeyDkTN42+bv2iZMpNyGE6NAkSBIdwv7y/YDnVFtOWS21FismvY6u4f4A1O3e5ayPpET0apGVbe7cV7j59u9PzzJte5Lt7kUlncnbEiQJIURH1i6CpDfffJPk5GR8fX0ZM2YM69evP2L7+fPn07dvX3x9fRk0aBALFy50nrNYLDz88MMMGjSIgIAAunTpwowZM8jNzfW4R0lJCddddx3BwcGEhoZyyy23UFVVdVI+nzhxGeVa3aHuId2dx/YVavlI3aMCMOi1b+W63bsxBduTtiN709LcV7jpg4PprdM2uN3hnrwdZ89LypMgSQghOrI2D5K+/PJLZs+ezZNPPsnmzZsZMmQIU6dOpbCw0Gv7NWvWcM0113DLLbewZcsWpk+fzvTp09m5cycANTU1bN68mSeeeILNmzfz7bffkpqaykUXXeRxn+uuu45du3axZMkSFixYwKpVq7jttttO+ucVzeMIkrqFdHMe21egBbU9owOdx+p278bHGSS5VsG1FMf2JI7pv4H2hPHdRfWuRlIGQAghOoU2D5JeeeUVZs2axU033UT//v1555138Pf358MPP/Ta/rXXXmPatGk8+OCD9OvXj7lz5zJ8+HDeeOMNAEJCQliyZAlXXnklffr0YezYsbzxxhts2rSJrKwsAFJSUli0aBH/+c9/GDNmDKeddhqvv/46X3zxRaMRJ9H2VFV1rmxzH0naaw+SekW7rWxLSXFtR3ISgiT3kSSAgX0TUVQbhVY9hyrtgZJjhVvJfqivbPE+CCGEaB1tGiSZzWY2bdrEWWed5Tym0+k466yzWLt2rddr1q5d69EeYOrUqU22BygvL0dRFEJDQ533CA0NZeTIkc42Z511FjqdjnXr1nm9R319PRUVFR5fonUcqj1EtaUavaL3WNmWZp9u621P2m4oKcF66NDJnW4L6eHsU3l9OeGD+pNQdQiAXbnlWqOASAiyb4VSsKvF+yCEEKJ1tGmQVFRUhNVqJSYmxuN4TEwM+fn5Xq/Jz88/rvZ1dXU8/PDDXHPNNQQHBzvvER3tuerJYDAQHh7e5H1eeOEFQkJCnF+JiYle24mW55hqSwhKwKQ3Adro0r7DVrbV792Lwc+G3qiCooewbt5veAICTYF0CdACoLSyNHz79aWHPXl7R7rbFLHkJQkhRIfX5tNtJ5PFYuHKK69EVVXefvvtE7rXo48+Snl5ufMrOzu7hXopjsaZjxTsCnpyy+uoMVsx6hW6RgQAUJ+a6ppqC0sGg+mk9Mc55Vaahj4khN6qNqq4bV+eq5GscBNCiA7P0JYPj4yMRK/XU1DgWXSvoKCA2NhYr9fExsYeU3tHgJSZmclvv/3mHEVy3OPwxPCGhgZKSkqafK6Pjw8+Pj7H/NlEy3HkI7knbe8tsO/ZFhmA0bGyLXWvW9J2y0+1OfQM68nvOb8785L6RfgBkFpU62ok25MIIUSH16YjSSaTiREjRrBs2TLnMZvNxrJlyxg3bpzXa8aNG+fRHmDJkiUe7R0B0r59+1i6dCkRERGN7lFWVsamTZucx3777TdsNhtjxoxpiY8mWpC3lW377VNtPaJcK9vqU1Pd8pFaPmnbwb1WEsCA7trU7UGzjup6+/Md022FKWC1nLS+CCGEOHnafLpt9uzZvP/++3z88cekpKRw++23U11dzU033QTAjBkzePTRR53t77nnHhYtWsTLL7/Mnj17eOqpp9i4cSN33XUXoAVIl19+ORs3buTTTz/FarWSn59Pfn4+ZrMZgH79+jFt2jRmzZrF+vXrWb16NXfddRdXX301Xbp0af0/BHFE3oKk9CKtPpEjSFKtVurT0lzTbRE9T1p/3IMkVVWJ7d+bsLoKVBTnCBehXcEnBKxmOJR60voihBDi5GnzIOmqq67ipZdeYs6cOQwdOpStW7eyaNEiZ3J2VlYWeXmuXI/x48fz2Wef8d577zFkyBC+/vprvv/+ewYOHAhATk4OP/74IwcPHmTo0KHExcU5v9asWeO8z6effkrfvn0588wzOe+88zjttNN47733WvfDi6OqtlRTUKNNr3oESYe0kaTuUVo+kjkzC7W+HlOQfc+2iB6cLN1DuqOgUFZfRnFdMb79+tKtXPseTckp0xopCsRq35MU7DxpfRFCCHHytGlOksNdd93lHAk63IoVKxodu+KKK7jiiiu8tk9OTnbtyH4E4eHhfPbZZ8fVT9H6DpQfACDcN5wQnxDn8fRD2khS9yjHyrZUUFSMAfYgKbw7J4uvwZfEoESyKrNIK0tjTMJoutceYjN92L0vF8bZg7mYAZC5WoIkIYTooNp8JEmIIzlQcQCA5OBk57HKOguF9sKNjpGkutRUjAFWFEUFoz8ExZ3UfjmKWmaUZ6DodPQO1P4qpRwsdTWKsY8k5UuQJIQQHZEESaJdy6zIBCA5JNl5LMOejxQZ6EOwrxGA+n37XPlI4d1bfGPbw3UL1UaLHPlS/RJCAdhbaXONZDqCJCkoKYQQHZIESaJdcwRJSUFJzmPOqbbIAOcxc9p+tyCp5YtIHs5Rs8kRJPXu0xW9zUqlqievvE5rFN0XUKC6EKq870UohBCi/ZIgSbRrzpEkt+m2w5O2VbMZc3Y2pkBHPtLJS9p2cCSRO4Kk4AF9SazUAqE9+fYta0wBrgRyyUsSQogOR4Ik0W6pqkpWhbYpcdfgrs7j+4scSduOlW2ZYLXiE2qf5jqJSdsOjiCpoKaAaks1Pr16kVypbWmzK81ta5uYAdqrTLkJIUSHI0GSaLdK6kqotFSioJAY7NorzzXdZl/Ztn8/AKZQe4OTuPzfIcQnhHDfcEBLLtf5+dFTr02z7U53qwgf49ieREaShBCio5EgSbRbjqm2uIA4fPTaljA2m0pGked0W/3+/aCoGIz2bUFaYboNXKNJ6WXatin9In0BSC2qcTWSkSQhhOiwJEgS7ZYjSHKfasurqKPOYsOgU0gM9wfAvD/9sOX/3vffa2mH5yX17649N7NeT53Fnh/lCJIO7YEGc6v0SwghRMuQIEm0W86VbcGulW2Z9nykpHB/58a29fv3t+ryfwfHCjdHLaeEgb0Irq/GquhIs+8tR2gS+ASDzQLF+1qlX0IIIVqGBEmi3cqq1JK23Ve2ZRRrQVLXCG0USbVaMWdkYAq0B0lhybSWw0eSfPv2o1uFfXuS7BKtkaLIlJsQQnRQEiSJdssxQuM+3ZZZrOX7JNtrJFlyclDNZkyOHUtaMUjqHqqtosusyKTB1oAhOoru9VpwtGtPtquhs/L2jlbrmxBCiBMnQZJol2yqzevyf0e17eQIe9J2mrayzSfSpDVoxSDJkVBusVnIrcpFURR6B9u3J3FsdAsykiSEEB2UBEmiXTpUc4h6az16RU9coGsftszDptvM6fbl/0E2rUFoV1qLTtE5pwKd25PEhwGwt8rmahhrLwMgBSWFEKJDkSBJtEvZldp0VVxAHEadtj+bzaY6p9u6RbqPJKnoDVrw1JojSdA4L6lP3yR0qo1S1cgh+ya8RNm3J6kqgKpDrdo/IYQQzSdBkmiXHEFSYpCriGR+RR31Ddry//hQPwDq09PRm1R0qj0gCU1qdK+TyRkkVWhBUlj/PnSpKgIgJa9ca+QT6NpPTkaThBCiw5AgSbRLB6sOAp5B0gF7PlJiuD8GvQ5VVTHv348xwL6yLTAWjL6t2s/DR5JMPXrQzb49ye60PFdDR/K25CUJIUSHIUGSaJccI0kJQQnOYwfsU22OfKSGggJs1dUYg+17toW1Xj6Sg7Pqdnk6qqqiM5lc25Psd9/DTYIkIYToaCRIEu3SwUovI0nFh61ss+/Z5hev7eHWmknbDl2Du6KgUF5fTml9KQB9IrTRrD2Hal0NYx1BkpQBEEKIjkKCJNEuectJOuBc/u/ajgTAJ1rb160tRpL8DH50CewCuG1P0i1G++96PRarfZWbc3uSVLBaWr2fQgghjp8ESaLdqTRXUlZfBnhOtx1eSNIxkmQMtO+T1sor2xySQ7TnOoKkbgO642epw6LonHWdCEkCUxBYzVAk25MIIURHIEGSaHccU23hvuEEGLWASFVVskq0ICnJubGtFiQZ9PZApA2m28C1h5tre5K+zu1Jdju2J9HppKikEEJ0MBIkiXbHW9J2UZWZWosVRYGEMC1Iqk9PB1R0lmKtURtMt0HjFW6GmBi612l98tyexBEkSV6SEEJ0BBIkiXbH2/J/xyhSXLAvJoOOhtJSrCUlGPxsKDYL6AwQHN8m/T08SFIUhd5aHMfug6WuhrGywk0IIToSCZJEu+McSQp0jSRl24OkRMdUW7qWtO2XHKo1CEkAnb71OunGESTlVOVQb9WKWvaN13bc3VtudTWUMgBCCNGhSJAk2h1vK9saBUkHDgDglxCsNWijfCSACN8IgkxBqKhkVmQCMKCPFuAdUo2UVpu1htH9tdfKPKgubouuCiGEOA4SJIl2x1uNpEZJ2/YgySdS29etrVa2gTa9dviUW3j/PsRW27cnya/QGvoEQphsTyKEEB2FBEmiXbHYLORVayvD3BO3s0sPD5K0ERvnliRtlLTt4Fjhll5ur93UsyfdKhzbk7hX3nYkb0uQJIQQ7Z0ESaJdyavKw6ba8NX7EuUX5TyeXaJVr04M1za2dYwkGQxtu/zfoXtod8A1kqTz86OXogV2u9z3cIsdpL1KXpIQQrR7EiSJdsV9+b+iKACYG2zklTuCJH9Umw1zVhYAOos2pdWW023gGkk6UH7AeaxPmFYJfE9htauhYyQpX8oACCFEeydBkmhXHPlI7lNtuWW12FTwNeqICvShIT8ftb4exccA1YVaozYeSXLkJB2oOIBN1bYi6Z8cCcD+ej0Nzu1J7CvcDu0Ba0Or91MIIcSxkyBJtCtel//b85ESw/xRFMU51ebfIwoFFYz+EBDZ6n11Fx8Uj0FnoLahloLqAgC627cnMaNzbs5LaFcwBWrbkxSntWGPhRBCHI0ESaJd8bb8P+uw5f/1juX/ifbl/2HJYJ+aaytGnZGkoCTAlZfk17cvyY7k7YNlWkOdzlUKQJK3hRCiXZMgSbQr2VVNB0mOlW2WTG1lm0+0lvPT1lNtDs4yABVakGTs0oVutYcA2JV60NXQWXlbgiQhhGjPJEgS7Yaqql5zkg6WuJK2wTWSZApWtQZtvPzfwREkpZdpZQAUnY4+flofd2eVuBo6k7clSBJCiPZMgiTRbhTXFVPbUIuCQnygax8253RbmLb832KvkWQwasFTex1JAugTFwRAarnF1TBGygAIIURHIEGSaDcco0ixAbGY9CbncWchyQh/VIsF80Gtnc5qH51pJyNJ3UM8ayUBDOitBXuFNiNlNfbtSWIc25PkQk0JQggh2icJkkS74V4jyaGizkJZjTYKkxjmrwVIViuKnx+KvTI3oUmt3ldvkoOTASiqLaLCrG1FEtG/D7H2fdpS8iq1hj5BrrpOkpckhBDtlgRJot3wtmebY2PbiAATAT4GzI6k7eQElBr7JrEhibQHgaZAov2iAVdRSZ9evehWngvArvQCV2NHvSSZchNCiHZLgiTRbhyssidtu9dIOmz5v6NGkl83e10kUxD4hrReJ4+iW6jnHm76oCB6qlqNpN3u25NI8rYQQrR7EiSJdiOnKgfAe9L2YUGST2yA1iAkoc1rJLlzbE/inpfUO0zLr0opqHI1jJEyAEII0d5JkCTajdwqbVqqS2AX5zHHxrZJjo1tHdNt4XqtQWj7mGpzcK5wcwuS+neNAGB/nc5texL7SFJhimxPIoQQ7ZQESaJdsNgsFNRoOTvel/87RpK0IMnob9UahCTQnnQPbbzCrUf/bvg21HtuTxLWDYwBYK2Hkv1t0VUhhBBHIUGSaBcKqguwqTZMOhMRfhHO487l/+H+2GpracjT8nr0enuw0c6CJMd0W3ZlNhartipP255E63dKTrnWUKdzlQLI39Hq/RRCCHF0EiSJdsF9qk2naN+WNpvqUW3bnKWVCNCFhKDUFmoXtpOVbQ7R/tH4G/yxqlZnSQNTUhLdq7T+bk/NcTV2TLnJCjchhGiXJEgS7YIjads9H6mwsh6z1YZepxAX4utM2jYld0Upt++F1s5GkhRFcW1PYl/hpuj19PXV8o52ZRa7GksZACGEaNckSBLtQm5146RtRz5SfKgfBr3OmbRt6poEFfYRmXY2kgRNVN6OCwRgV3kDqmrfc84RJMl0mxBCtEsSJIl2wTHd5p607aqRZF/ZZh9J8k2MAKsZFB0ExbVuR4+B1xVuvRLQ26yU2/TkltdpB2MHAoq2PUnVoTboqRBCiCORIEm0C87ptgDXSNLBUi0fKSH0sBpJ0VrQRFAX0Btar5PHyFuQFNyvD10r8gHY6Uje9gmCiJ7a+7xtrdpHIYQQRydBkmgXvNVIyimzT7eF2UeSsrMAMAbbi0e2s3wkB+d0W0WGc2rNp3cvetrzqHZkuI0adRmqveZtac0uCiGEOAYSJIk211SNpJwybSQpPtQPW00N1kNFABhN9umqdhokJQYlolf0VFuqKazRVrUZwsLobdU2uN2+320Pt7ih2mvu1tbtpBBCiKOSIEm0uaZqJOXYp9viw/wwZ2ujMLqQEHT1WrDU3qptOxj1RucmvRkVbnlJkdr2JLsP1boaxw3RXmW6TQgh2h0JkkSba6pGUm6ZNmIUH+qHxT7VZkpMhHa6/N+dswxAWbrz2MDuMehUG0UNOgor7KNhcYO11/JsqC4+/DZCCCHakARJos15q5FUVKXVSNIpEBvi6xxJMiUlagEFtMvl/w7ekrdD+vUhsVKbftuZa0/e9g2B8B7ae8lLEkKIdkWCJNHmvNVIOmjPR4oN9sWo1zlHkowJHWskyX26zW/AAHqW2ZO3s0pdjZ3J2zLlJoQQ7YkESaLNeauR5J6PBDi3JDElRENtidaoHQdJ3gpKGpOS6FmrrWzbnpbvauxM3paRJCGEaE8kSBJtzluNJPeVbQCWbHuQFKElP+MTok1VtVPJIckAFNYUUmWuAkDR6RgQpvV/V36Vq3H8CO314KbW7KIQQoijkCBJtDmvNZLcRpJUqxVzrtbGFGjf0qMdjyIBBJuCifSLBDxHkwb2jAEg36xQXFWvHYwbolUPr8yFitxW76sQQgjvJEgSberoNZL8seTlg8WCYjSi19lHYNp5kATQI1RLyE4rS3MeixjYj3hn8naFdtAnEKL7a+9zZDRJCCHaCwmSRJs6lhpJrqTtBJRK+0hLBwiSeoZqW464B0m+/fvTs1ybXtx50C15O3649ipBkhBCtBsSJIk25a1GkqqqHjlJZns+kjEpEcocy/87TpC0v2y/85ipWzd6VWlJ29v3uSVvx4/UXg9ubLX+CSGEODIJkkSbciRtu0+1VdQ2UFXfoB0P9XMlbXss/2+/NZIcvI0kKXo9/UO1TXmd020ACfYgKXcL2Kyt1kchhBBNkyBJtClvhSQP2je2jQgw4WfSu5b/uxeSbKdbkrjrHqqVASioKaDC7AqIBvWIBSCnXqGk2qwdjOoLxgAwV0HR3lbvqxBCiMYkSBJt6mgr2wDMzpykeNfqrw4w3RZsCibGX1vN5r49SfSQ/iTYk7e3ZZdpB3V66DJMe39wQ2t2UwghRBMkSBJtytt0m3s+kqqqWBwjSVEBYLOAoofA2NbvbDN4Td4eNIg+pVrgt+WA235tiaO116x1rdY/IYQQTZMgSbQpb1uSOEeSQv2wlpVhq9KW/RsDtDwlgruA3tC6HW0mRxkAj+Ttrl3pV6uVPdiU6lYXKWms9pr9Z6v1TwghRNMkSBJtxmK1UFijTTt5HUkKcyVtG6Kj0dVqbTvCVJuDYyRpX9k+5zFFp2NIlDaVuL2wFpvNXiDTMZJUnAZVh1q1n0IIIRqTIEm0mfyafGeNpHDfcOdxj+X/WW7L/x35SMHxje7VXnkrAwAwoF8iPg1mKq0K6UXV2kG/MFdRyWyZchNCiLYmQZJoM/nVWp2guMA4Z40kOKyQ5EF7PlJikluQ1IWOwjHdVlRbRFldmfN40JDB9CzTyhlszXYdJ3GM9pq1tpV6KIQQoikSJIk2k1edB0BcQJzzWK3ZSrF9WXxCqL9rJCkxASq0JO+ONJLkb/R3btzrnrzt55a8vXm/29Ra0jjtVUaShBCizUmQJNqMY/m/e5DkmGoL9DEQ7GfAkqUFEh11JAm8J28boqIYiFY7aVNagauxI3k7dyuYa1qri0IIIbyQIEm0Ged0m5cgKT7UD0VRnFuSaIUk7SNJIR1nJAmgZ1jjMgAAI7tqeVh7Kxoor7VoB0OTtJEymwUOrm/VfgohhPAkQZJoM47pttgAV80j93wkW309DQXaKIsxoQvY9zzrSNNt4Ja8Xe6ZvJ0wYiBdqg6horA5y77ZraJAt4na+/SVrdlNIYQQh5EgSbQZR5DkUSPJviVJfKgfloNaYrMuIAC9oR5UG+gMEBDV+p09AY7ptrRSz5Ek/+HDGVCcAcCGdLeiko4gKUOCJCGEaEsSJIk2oaqq9+k2t5Eksz0fyZiUhFKhBVQExWlbeHQg3UO6o6BQWl9Kca0rGPLp3ZtB9rys9SluRSW7TdJec7dAXXlrdlUIIYQbCZJEmyirL6O2QQuIYgJinMfdc5IchSRNiYluK9s6VtI2gJ/Bj4QgrQCme/K2otczIs4fgO2HaqlvsGonQuIhoqc2cnZgdav3VwghhEaCJNEmHFNtEb4R+Oh9nMdzy+oA+0hStjbdZkpK7JDL/905p9wOS97uNbg3oXWVmFWFHQfdRo1kyk0IIdqcBEmiTXirkWS1qeRX2IOkUD/n8n9jQmKHXf7v4G2jW4CAkSOceUnrMtzzkuxTbukrWqN7QgghvJAgSbQJ92rbDoWVdVhtKgadQmSgj+fy/w4+ktRUkOQ3eDCDSw8A8MeuHNeJbhNB0cOhPVCa2VrdFEII4UaCJNEm8qoajyTl2vORYkN80aE6V7cZkzpuIUmHPmF9ANhbuhebanMe1/n7MzZCS0TfnFNFncWel+Qf7iosuW9xq/ZVCCGERoIk0SZyqxtX23bkI3UJ8aOhsBDVbAaDAWNsbIfc3NZdckgyJp2Jaks1OZU5Huf6jehPeG059arC5sxS14neU7XX1F9asadCCCEcJEgSbcLb8v+8cm0kKS7U17X8v0sXFJ0ClfYSAB10JMmgMziTt1NLUz3OBY4fy7BD+wD4I63IdaL3NO31wO9QX9Uq/RRCCOHS5kHSm2++SXJyMr6+vowZM4b164+8FcP8+fPp27cvvr6+DBo0iIULF3qc//bbbznnnHOIiIhAURS2bt3a6B6TJ09GURSPr7/+9a8t+bHEUTirbQe6qm07RpLiQg5b/l9VCLYGLUcnKLbxzTqIvuF9gcZBkt/gwQwt1/KOVu866DoR2RvCksFqlgRuIYRoA20aJH355ZfMnj2bJ598ks2bNzNkyBCmTp1KYWGh1/Zr1qzhmmuu4ZZbbmHLli1Mnz6d6dOns3PnTmeb6upqTjvtNF588cUjPnvWrFnk5eU5v/7xj3+06GcTTTNbzRTVaiMmXQJcI0O5zhpJvs6kbWOS28q2oNgOV0jSXZ9wLS9pT8kej+OKycS4+AAAdhyqo6LOvo+borhGk/b83Gr9FEIIoWnTIOmVV15h1qxZ3HTTTfTv35933nkHf39/PvzwQ6/tX3vtNaZNm8aDDz5Iv379mDt3LsOHD+eNN95wtrnhhhuYM2cOZ5111hGf7e/vT2xsrPMrODj4iO3r6+upqKjw+BLN45hq89X7EuoT6jyeV+42kpRlH0lK6NiFJN05k7dL9jY6123MUBIqC7GhsHqf25Rb/4u11z0LwFLXGt0UQghh12ZBktlsZtOmTR7BjE6n46yzzmLt2rVer1m7dm2j4Gfq1KlNtj+STz/9lMjISAYOHMijjz5KTU3NEdu/8MILhISEOL8SExOP+5lC476xraIoruPuOUneRpI6eJDUO7w3oCWtV5g9g+yACRMYXZACwNJdbluUJI6F4ASor5BVbkII0craLEgqKirCarUSExPjcTwmJob8/Hyv1+Tn5x9X+6Zce+21/O9//2P58uU8+uij/Pe//+X6668/4jWPPvoo5eXlzq9s+w9xcfy8FZKss1gpqjIDnoUkTUlJHb5GkkOwKdg5vZha4pmX5NOnD+MsBQAs35WPzaZqJ3Q6GHip9n7H/FbrqxBCCDC0dQfawm233eZ8P2jQIOLi4jjzzDPZv38/PXr08HqNj48PPj4+Xs+J4+MMktwKSebbp9r8jHoCLbXklWtbdJgSEmBH55huAy0vKbc6l9SSVEbFjnIeVxSFccN74F9RSwl+bDtYxrCkMO3koCtgzb9h76/ahre+IW3UeyGEOLW02UhSZGQker2egoICj+MFBQXExnpfwRQbG3tc7Y/VmDFjAEhLSztKS9ESvC3/z3WbanMUkdRHRKALCOjwNZLcOVa4pZSkNDoXesYZjCzQRpiWpbh9n8cOgsg+YK2Hnd+0Sj+FEEK0YZBkMpkYMWIEy5Ytcx6z2WwsW7aMcePGeb1m3LhxHu0BlixZ0mT7Y+UoExAXF3fkhqJF5FY1LiSZ51ZI0mP5P3Sa6TaAAREDANhdvLvROf8xoxlbqgXqS7dmuU4oCgyfob1f/z6o6knvpxBCiDZe3TZ79mzef/99Pv74Y1JSUrj99tuprq7mpptuAmDGjBk8+uijzvb33HMPixYt4uWXX2bPnj089dRTbNy4kbvuusvZpqSkhK1bt7J7t/ZDKDU1la1btzrzlvbv38/cuXPZtGkTBw4c4Mcff2TGjBlMnDiRwYMHt+KnP3V5HUmyL//vEuqLOcstadtmg4qOXUjSXf+I/gCkl6dTY/FcLKAzmZjUPRydzcqeUgsHiqpdJ4ddD0Z/KNytFZcUQghx0rVpkHTVVVfx0ksvMWfOHIYOHcrWrVtZtGiRMzk7KyuLvLw8Z/vx48fz2Wef8d577zFkyBC+/vprvv/+ewYOHOhs8+OPPzJs2DDOP/98AK6++mqGDRvGO++8A2gjWEuXLuWcc86hb9++3H///Vx22WX89NNPrfjJT12qqnpN3M4t91ZIMglqisBmAZQOXUjSIco/imi/aGyqrVFRSYCEc6Y4q2//uM1tlZtfKAy5Rnu/7t2W7VR9JWyaBx9fCC/1gWci4Y1R8P2dcKhxH4UQ4lShqKqM3TdHRUUFISEhlJeXH7XGknApqSth0peTUFDYeP1GTHoTADM/Ws+K1EO8eNkgxr75JDV//knc/71A6Oiu8N5kCIyFBzrHD+y7l93NioMreHjUw1zf33NVpa2mhjeuvJtXBl5Kj2ADSx89x1UmoXAPvDUGFB3cvgai+51YR6wNsPFDWP6slhDujaKDkbfAtBdAbzyx5wkhRAfT5tuSiFOLYxQp0i/SGSCB+3TbYcv/yzvPyjaH/pHalJu3vCSdvz/TBsRgsDawv6KB1IJK18novtD3AlBtsOTJE+tE6QH44Cz45UEtQIroCWfPhVt/g79theu+hj7na8/a8D58dpU24iSEEKcQCZJEq8qrajzVBq7E7Th/AxZ7/pgp0a2QZEjHT9p2cCRv7yre5fV8lwvPZ5S9sOSPmw6rx3XW06AzwL5fIWNV8zqQsgDemQi5W7RyAue/Aneuhwl/g4QREN4Nep0N13wG13wBBj/Yvwy+vAFs1uY9UwghOiAJkkSrcq+27VBZZ6GyvgGAyJpSsNlQ/PzQR0Z2qpVtDo7k7YzyjEbJ2wABY8dwRsV+AL5efwCL1eY6GdkTRmgLG1j4EJiPXCneQ4MZFj0GX14H9eWQMIqGqxdQbRtE5cpV1KeloVosntf0ORdmLtCSxtOXw/LnjuuzCiFERyZBkmhV3pK2HXu2hfgZMeRrQZEpIUHLxekkW5K4i/SLJNo/GhXV65SbYjBw7mn9Ca2rpLD+sJpJAJMfgYBoOJSiTZcdi7Js+Ohc+PNNAOpjzuPAskj2TbuKrBtv5ODtd5B+wYXsm3IGxR98gK3abWVdwki46HXt/e8vQ9rS5nxsIYTocCRIEq3Kufzfrdp2jls+ktmej2RMStJOdqJCku4GRQ4CYEfRDq/nY264lqnZGwCYt/SwQCogEi77j5ZUveV/sPrfTddOUlXY9iW8cxrkbEQ1BlGQNYz017ZSu2UbKArGxER8+vdD5++PtaiIwn++RMZll2M+cMCtw5fDqFna+58fkM12hRCnBAmSRKvylpPkKiTpiyXr8EKSWvXtzjSSBDA4SqvJte3QNq/njTExXNkzAEW18Wd+HfsPVXk26D4Jpvxde7/kCVhwH9SUeLbJ3gD/vQS+uw3qymjwTWb/D0GUrClAFxhI5F130WvVSnouWUz3b7+l97o/iXv+eQyxsZgPHCDjqqup2bLFdb+znoSgOCjNgDWvt9QfhRBCtFsSJIlWlVvtpdq225YkZvuWJMakRG0UpJOOJA2JGgJoQVJTVTgGzLyG0flaAvc7P3sJpk6/H855FlBg00fwSn/43+Xw1Qx4bYi2ei19OarOSCXj2fdxPZYKCDzzTLr//DNRd92JISrKeTvFaCT00kvoNv8rfIcMxlZezsE778J80J4X5hNkfx7w+0uu/zdCCNFJSZAkWk1dQx0lddpoh3uQ5JhuiwtxW/6fmAg1xWA1a42COteWMQMiBmBQDBTVFjkDx8P59uvHjIBSAL5NKeFA0WGjSYoC4++Ga7+CmEHQUAtpS2D3D9oSf50BW/8ryU2fyMEvDgA6ombPJuGN1zHGRDfZN0NUFF3nzcO3f3+sJSUcvOMOV47SwMsgcSw01GnTfEII0YlJkCRaTUGNloDsZ/AjxMe1k737dJtjJElb/m8fwQiIBoOJzsTX4Euf8D4AbCv0PuUGcNaDtzGicC9WRccrn/3hvVHvc+Cvv8MtS7QE62n/B9d+Rd0li0l/J52KP1PQBQaS8PZbRN42y1Wc8gh0fn4kvPUm+qhI6vfupfDlV7QTigKTHtLeb/oIqgqP63MLIURHIkGSaDXuK9vcf1A7pttilHrU2lrQ6TB26dIpV7a5c59ya4pPt27c1VsLEBfkWNi1J9t7Q0WBxNHaRrhjb6ciQ+HAjFlYcnMxdk0i+csvCJo8+bj6Z4yNJf7FFwEo/fxzV35SjzMgfoQ2miS5SUKITkyCJNFqvCVtq6rq3LctsrIY0H44KyZTp6yR5M4RJG0/tP2I7SbdPZPTytKwKTpmv7scc01tk21Vm41D/36dnHvuQa2tJWDCBLp99RU+PXo0q48B48cTcskloKrkPfGEVkdJUWCifTRp40dQX3XkmwghRAclQZJoNd4KSRZXmzE32FAUCCvSRo6cy/874ZYk7oZEa0HSnpI91DY0HfjoAgJ4/s5pBFpqSfWJ4OUHX8NS2Hiaqy41lcxrrqXorbcACJ85k8R330EfEtKo7fGIfuhB9OHhmNP2U/bNt9rBXudAeA8wV8L2L0/o/kII0V5JkCRajddCkvZ8pKhAHzh4+PL/zrclibsuAV2I9o+mQW046mhS0qA+PDI6EoD/BPRn/o33UfDCC5T/+CMln3xC1s03k3HxdGq3bUMXEEDcCy8Q88jDKAbDCffTEBZG5B13AFD01lvY6upAp4NRt2oNNnzQdJ0mIYTowCRIEq3GGSS5FZLMdS7/98NsD5KMSY4gqXNPtymKwqjYUQBsyN9w1PbXXTmJC3oEY9XpeWbAZaz+cQW5Dz1MwfMvUL1mLSgKQdOm0X3hz4ReMt3rPVRVpazGTPqhKvLL66hvOLa92EKvvAJjly40FBZS+tnn2sGh12j7uhXugqw/j+k+QgjRkZz4r5lCHCNntW23kaRcR7Vtr4UkO3fiNsDo2NH8nP7zMQVJiqLwyk0TKP1wHavTS3h40l3cUbaFy3xL8evZk5Dp0zEleAaUVptKSl4Fa/cXs2Z/ERsPlDr3yQMw6XWMTA7jvEFxXDkyEZPB++9NOpOJyLvuIu+xxyh+7z3CrrkanV8YDL4CNn+irXTrOu7E/jCEEKKdkSBJtApVVb1X27YnbXcJ9cOcbR9JSjy8kGTnDZJGxWgjSduLtlPbUIufwe+I7U0GHe/eOIp7v9jK0pQCXgsdwY+RAVw+IIH+VUYCMkqoqLWQWlDJ1uwy1qUXU1HX0Og+gT4Gai1WzFYba/YXs2Z/Me+u2s8zFw1kSl/vNZRCLrqQorfewnLwIOU//EDY1VfDsBlakJTyk5bA7RN44n8oQgjRTkiQJFpFcV0xZpsZBYUY/xjnccdIUqyfDmuxtrrNlJQEtaVacUSAoM4bJCUEJRAbEEt+dT5bC7cyrsvRR2MCfQy8P2MEH60+wL+W7CWjqJp//pp6xPaju4UzvkcEY7tH0DM6EF+jHlVVSS+qZllKAe//nkF2SS03f7yB+8/uzZ1Tejaqp6QYDITPmEHB889TMu9jQq+8EiVhpJbAXbJfC5SGXnPCfyZCCNFeSJAkWoVjqi3KLwqj3ug87giSohq0is760FD0QUGQb9/41T8CjL6t29lWpCgKo2JG8VP6T2zI33BMQZLjuptP68ZVoxL5bksOa/YXkX6oGnODjQAfA90iAxjQJZjR3cIZFB+CQd94Gk1RFHpEBdIjKpAbxibz/MIU/vtnJi8t3ktpjYUnLujf6JrQyy7l0BtvYD5wgKoVKwg64wwYcjUsfw62fS5BkhCiU5EgSbQKb0nb4Jpui6wsAuxTbdBp92zzZlSsK0g6XgE+Bq4f25Xrx3Y9oT74mfTMnT6QPrFBPP79Tj74I4PYYF9mTezu0U4XEEDYVVdS/P5/KPn4Ey1IGnylFiRlrNLKNnTS1YhCiFOPrG4TrcJbPlKD1UZBhT1IKtbOu5K2O/fKNndj48YCsKNoB+X15W3al+vHduWx8/oC8NzCFFbuPdSoTdi114JOR826ddRnZEBYMiSNB1TY+U3rdlgI0e5lZ2dz880306VLF0wmE127duWee+6h2J5i0Z5JkCRahbdCkoWV9dhUMOoVgnIPAO7L/zt/0rZDXGAcPUN7YlWtrM1d29bdYdbp3bl2jFbQ88H52yitNnucN8bFEThxIgBl87/WDg68VHvd/UOr9VMI0f6lp6czcuRI9u3bx+eff05aWhrvvPMOy5YtY9y4cZSUlLR1F4+oWUFSenp6S/dDdHJHWv4fE+yL1bmx7alRbftwpyecDsCqg6vauCdartIT5/enR1QAhZX1PP79zkZtQq+8EoDy777DZjZDv4sABXI2QlkT+8sJIVqMqqrUmBta/Us9zsKxd955JyaTicWLFzNp0iSSkpI499xzWbp0KTk5Ofz9738HIDk5mblz53LNNdcQEBBAfHw8b775pse9ysrKuPXWW4mKiiI4OJgzzjiDbdtce18+9dRTDB06lP/+978kJycTEhLC1VdfTWVlZbP/nJuVk9SzZ08mTZrELbfcwuWXX46vb+dNrBUtw1u17Vyvy/8TtJOn0HQbwOnxp/PRzo/4I+cPrDYrep2+TfvjZ9Lz6lXDmP7Wan7ekcfV+w5xeq8o5/nAiadjiImhoaCAyiVLCDn/fOg6HjJXQ8qPMO7ONuy9EJ1frcVK/zm/tvpzdz8zFX/TsYUOJSUl/Prrrzz33HP4+XmWN4mNjeW6667jyy+/5C37Vkr//Oc/eeyxx3j66af59ddfueeee+jduzdnn302AFdccQV+fn788ssvhISE8O6773LmmWeyd+9ewsPDAdi/fz/ff/89CxYsoLS0lCuvvJL/+7//47nnnmvW523WSNLmzZsZPHgws2fPJjY2lr/85S+sX7++WR0QpwZv02159pGkuGAfLLna9JrJsW/bKTTdBjA0eihBxiBK60vZVbyrrbsDwKCEEGaM0xLC5y7YTYPV5jynGAyEXqZNsZX/YJ9i63+x9ipTbkIIYN++faiqSr9+/bye79evH6WlpRw6pOU+TpgwgUceeYTevXtz9913c/nll/Ovf/0LgD/++IP169czf/58Ro4cSa9evXjppZcIDQ3l66+/dt7TZrMxb948Bg4cyOmnn84NN9zAsmXLmv0ZmjWSNHToUF577TVefvllfvzxR+bNm8dpp51G7969ufnmm7nhhhuIioo6+o3EKaHeWk9JnTbv7HW6Td8ADQ0oJhOG6GjPQpIhCa3e37Zg1BkZ12UcizMXs+rgKgZHDW7rLgFw75m9+X5LDnsLqvhsfRYzxiU7zwVfeCFFb71N9eo1NBQXY+h3EfzyMGSvg4o8CI5r+sZCiBPiZ9Sz+5mpbfLc43WsU3Tjxo1r9N+vvvoqANu2baOqqoqIiAiPNrW1tezfv9/538nJyQQFBTn/Oy4ujkIvG4IfqxNK3DYYDFx66aXMnz+fF198kbS0NB544AESExOZMWMGeXl5J3J70UkUVBcA4GfwI8THtSO9Y7ot2lIFaMv/FZ0O6srBotVNIujU+UE7OXEyAIszFx/3vP/JEuJvZPbZvQF4/bc06iyuvd58unXDd+BAsFqp+GWRFhTFj9BO7lvcFt0V4pShKAr+JkOrfx1eZPZIevbUitKmpKR4PZ+SkkJYWNgxDapUVVURFxfH1q1bPb5SU1N58MEHne2MRqPHdYqiYLPZDr/dMTuhIGnjxo3ccccdxMXF8corr/DAAw+wf/9+lixZQm5uLhdffPGJ3F50Eu5Tbe5/wfLsm9tGVWmjTKYERz6SfRTJLwxM/q3X0TY2JXEKJp2JjPIMUkubrqDd2q4alUR8qB+HKuv5Yn2Wx7mQCy8AoOKnn7QDfaZpr3sXtWYXhRDtUEREBGeffTZvvfUWtbW1Hufy8/P59NNPueqqq5w/F/7803Oj7D///NM5VTd8+HDy8/MxGAz07NnT4ysyMvKkfYZmBUmvvPIKgwYNYvz48eTm5vLJJ5+QmZnJs88+S7du3Tj99NOZN28emzdvbun+ig7IW9I2QF6ZNpIUXqIFRUZnPtKplbTtEGgKZGKCtrR+YcbCNu6Ni8mg4/bJPQB4Z2U69Q2u0aTg884DnY7abdswZ2VBb3uQlL4CLLVe7iaEOJW88cYb1NfXM3XqVFatWkV2djaLFi3i7LPPJj4+3iOhevXq1fzjH/9g7969vPnmm8yfP5977rkHgLPOOotx48Yxffp0Fi9ezIEDB1izZg1///vf2bhx40nrf7OCpLfffptrr72WzMxMvv/+ey644AJ0Os9bRUdH88EHH7RIJ0XH5i1IqrNYKbbX34nIzQS8FZI8NZK23Z3b7VwAFmUswqY2f4i4pV0xMoHYYF/yK+r4etNB53FDVBQB9jyC8gULIGYgBCeApQYyfm+r7rYam02lsLKOvQWVFFfVt5tpUiHai169erFx40a6d+/OlVdeSY8ePbjtttuYMmUKa9euda5KA7j//vvZuHEjw4YN49lnn+WVV15h6lQt70pRFBYuXMjEiRO56aab6N27N1dffTWZmZnExMQ09fgT1qzE7SVLlpCUlNQoMFJVlezsbJKSkjCZTNx4440t0knRsTlqJMUEuL6RHduR+Jv0+KRmYMZbIclTayQJYGLCRAKMAeRV57Ht0DaGRQ876c+02qzkVmt/5tH+0fjofRq18THoufX0bjz7cwofrT7AtaOTnEPkwRdeQPXq1VT8tIDI229H6T0VNn6gTbn1Puek97+1qarKqn1FfL3pIMtSCqgxu0bWQv2NnNk3hstGxDO+x8mbAhCiI+natSvz5s07arvg4GC++uqrJs8HBQXx73//m3//+99ezz/11FM89dRTHsfuvfde7r333uPoradmjST16NGDoqKiRsdLSkro1q1bszsjOidvW5I4l/+H+NKQpeW5nIpbkhzO1+DLmUlnAvD13q+P0rr5LDYLiw4sYtbiWYz+dDTnfXse5317HiP/N5Ibf7mRH9J+wGKzeFxz5ahEAkx60gqr+CPN9fc/6KyzUXx9MWdkULdrt2vKbe+v2krFTmT/oSpmfLieGz9cz0/bcqkxW9EpEOKnJYuW1Vj4ZvNBrn1/Hde+/ye7cyvauMdCiBPRrCCpqSHlqqoqKSwpGsmvaVxtO8ceJMUGGLBVV4OiYDw8cfsUnG4DuLrP1QD8kvELxbUtv7fR+rz1XP7j5Ty48kH+zPsTs82MSWdyjiBtLtzM46sf59qfryW1xJVAHuxr5IqRWiD70eoDzuP6wACCzpgC2BO4u50OBj+oOAgFjat1d1RLdxdw0et/8Pu+Ikx6HTPHJ/P9nRPY++y5bHvyHFKfncaXt43l2jFJmPQ61uwvZvqbq/nP7+nYbJ0rWBTiVHFc022zZ88GtLnBOXPm4O/vWnlktVpZt24dQ4cObdEOio5NVVWvW5I4pttidNpohSEmBp2PfZrnFNuS5HCDogYxOHIw24u28/Xer/nLkL+0yH2tNiuvbn6VebvmARDmE8YVfa7g/O7n0zWoKzpFR351PgvSF/Dx7o/ZU7KHq3++mhdOf4Fpydro0I3jk5m35gC/7SnkQFE1yZEBAARfcCEVC3+hfOHPRD/0IEqPKZC6UJtyix3UIv1vS//7M5MnftiJqsKYbuH84/LBdI0I8GjjY9AzpnsEY7pHcMfkHjz9026W7C7g2Z9T2HawnJeuGIyPoW0rqQvRXh04cKCtu+DVcY0kbdmyhS1btqCqKjt27HD+95YtW9izZw9Dhgw5pnlHceoory+ntsFeNNIjJ8l+zF4PyTnVBqd0TpLDtf2uBeDL1C+xWC1HaX101ZZq7l1+rzNAuqrPVSy4dAF3D7ub7iHd0ev0KIpCXGAcswbP4vuLv2dSwiQabA08vOphvtv3HQDdIgOY1FurafLlRtcebYGnTUAfEoL1UBE1GzZCb3uRu9SOXwrg+y05PP69FiBdOyaJ/906plGAdLiEMH/eu2EEz04fiEGn8NO2XG6et4Eac0Mr9VoI0RKOK0havnw5y5cv58Ybb+SXX35x/vfy5cv59ddfeffdd+nVq9fJ6qvogBwr2yJ8IzwSgnPty/8jq7XpJKMjSKqrALN9M8JTdCQJ4Jyu5xDlF8Wh2kN8ve/EcpNyq3KZ8csMVhxcgUln4h8T/8HjYx8n2BTc5DWRfpG8NuU1Lut1GTbVxpNrnmRF9goArhmt/b/6etNBLPatShSTicCzzwKg4tdF0MseJOVsgqrmV7tta2vSirh/vraB5szxyTw3fSBG/bH9s6koCteP7cqHM0cRYNKzOq2Yv/x3k0cJBSFE+9asnKSPPvqI4OCm/4EVwsHbnm3g2pIkokSbijMdvrLNNwR8Alunk+2QUW/kL4O1aba3tr5Fhbl5CcDbDm3jmp+vYW/pXiJ8I/ho2kfOMgNHo9fpeXLck1ze+3JUVB5e9TB7S/dyRt8YIgNNHKqs57c9rgAoeJp238rFS1ADoiFuKKC2y+rbtQ21LDqwiBfXv8jdy+7m3uX38tyfz7EkcwlVZq0CfEFFHX/7YgtWm8rFQ7sw54L+x1Vt2GFi7yg+uWUMfkY9v+8rYvaX2yRHSYgO4phzki699FLmzZtHcHAwl1566RHbfvvttyfcMdE5NFlI0p6TFJZ/AHAbSXKsbAs6dUeRHC7rfRmf7fmM9PJ0/rP9P8weOfu4rl+YvpAnVj+B2Wamd1hv3jjjDeICj2+bF0VReGzMY2RXZLMufx33Lr+X+RfO57IRCby7Mp0vN2QzdYAWAAeMGa1NuRUXU7NhIwG9p0HeVi1IGnb9cT33ZKlrqOPjXR/zacqnlNaXNjr/ReoXBBmDuLL31azaMIyiKjN9Y4N48bLB6HTHHyA5jOgaxnszRnDLvI38vCOPXjGB3HtW7xP5KEKIVnDMI0khISHO36JCQkKO+CWEgyNp230kqaLOQlW9lpsRdmAf4GX5f8ipm4/kYNAZuH/k/QD8d/d/2VxwbBXsVVXlza1v8vDvD2O2mZmcMJlPzv3kuAMkB6POyMuTXyYuII7symxeXP8iV9lXua1ILaSwUgt4FaPRNeW26BfodbZ2g/0rwNr2uTipJalc8/M1vLH1DUrrS4kPjOeavtfwxNgneHzM41zb91oSgxKptFTy9qoUNmdW4GuEt64bjm8zNvU83Om9onj2koEAvLp0H7/uyj/hewohTq5jHkn66KOPvL4X4ki8rWxzTLWF+hkw5mtBkWsk6dRe/n+40+NP59xu5/JLxi/cv/J+vrrgK6L8m94Msry+nDmr5/Bb9m8AzBwwk3uH34ted2I/5EN8Qnj+tOe5+deb+S7tOyYlTGJoYihbs8tYsC2Pm0/T6qMFTzuX8q+/oXLJUmIfexTFLxxqS+Dgeug6/oT6cCJWHVzF7BWzqbfWE+EbwQOjHmBa8jQMOs9/Ah9WH+bLnct47HNtQYEa8S0/H0znrsi7mjXVdrgrRyayO7eCeWsO8OD8bQyMDyE+1O+E7yuEODmalZNUW1tLTU2N878zMzN59dVXWby4/eUeiLblnG4LdC8kqY08xPppP7h1QUHoQ0O1k6dwIUlvFEXhqXFP0TO0J0W1RcxaPIvMikyvbf/I+YOrFlzFb9m/YdQZeXr809w/8v4TDpAcRsaO5OaBNwPw7LpnmTZI207gh225zjYeU26bt0CPM7QTaUtbpA/NsShjEff8dg/11nomdJnANxd9wwXdL2gUIAEoKCxcH4xqM9Ilsgpj6Abe2/4ej/z+SIusMgT4+/n9GJoYSkVdA/d9sRWr5CeJU9y8efMIdfwMOEYzZ85k+vTpJ6U/7poVJF188cV88sknAJSVlTF69GhefvllLr74Yt5+++0W7aDo2LzlJOXal//H6rUpGFNiouu3dFn+34i/0Z/XprxGlF8U+8v3c/WCq3l729uklqSSXpbOT/t/4rbFt3H70tvJqcohPjCe/577Xy7tdeTcwea4Y+gdJAcnU1RbRJbtB/Q6hW3ZZWQUaSMvitFI0DnaNFvFokWuKbd9S1q8L8didc5qHv39URrUBs7rdh6vn/k6EX4RTbZfllLoLBb56cwLmDvhaQyKgYUZC3lw1YONqpAfVUM9pC2DxU/Ap1fCW+MxvjWa16wvEKCzsP5ACe99txgazCf4SYVon5oKZlasWIGiKJSVlXHVVVexd+/e1u/cMWhWkLR582ZOP/10AL7++mtiY2PJzMzkk08+aXJPFXHqsdgsHKo5BHjmJDlGkqIatFVERvcaSad4IcmmJAUn8eUFXzIsehhVlire2voWl/90ORf/cDGP/fEYa/PWYlAMzOg/g/kXzmdA5ICT0g+T3sSccXMAWJD5GYOTTAD8uNU1mhQ0VSs8Wbl4CWryJO1g/naoLDgpfWrKnpI9zF4xmwa1gfO7n88Lp7+AUWdssr25wcbzC1MAuPm0bnSLDOCSXpfw+pmvY9QZWZa1jEdWPYLVdgxL+CtyYckceKk3/O9SWPNv2PcrFO6C4n10Lfmdp3TaBuCvbqgh458T4de/Q6XkKYlTj5+fH9HR0W3dDa+aFSTV1NQQFBQEwOLFi7n00kvR6XSMHTuWzEzvUwHi1FNYU4iKilFnJNzXtdOzIycpqkpbXeRc/g8yknQEUf5RfDD1A54/7XnGxY0jyBREsCmYnqE9uW3wbfww/QceHPUgQaagk9qPUbGjuLjHxaioVJmWA/DD1hzndkUBY0ajDw3FWlJCze4D9lIAwP5lJ7Vf7srry7nnt3uoaahhdOxo5o6fi0458j93n67LJL2omshAE3dO6eE8flr8abw65VWMOiOLMxfzjw3/aHJrJix1sOqf8O/hsPo1qCuDwBhtdd/5r8D138LMhXDd11x+0cWcHlJIPSYeqbgM25o34bWhsOwZsNS23B+G6JxUFczVrf91EvZj9Dbd9uyzzxIdHU1QUBC33norjzzyiNcdPV566SXi4uKIiIjgzjvvxGJpmWlxh+PalsShZ8+efP/991xyySX8+uuv3HfffQAUFhZK/STh5NjYNjYg1uMHlGO6LbxUO+8cSaqvhPpy7b2MJHll1Bm5sMeFXNjjwjbtx70j7mVJ5hLyrIsw6seTXlTNjpxyBieEalNuZ59F2fyvqVj0KwGTzraXAlgCQ6896X2zqTYe++MxcqtzSQxK5F9T/oVR3/QIEkCNuYE3l6cBcN/ZvQny9Ww/MWEiz5/+PA+ufJDP9nxGl8Au3DjgRs+bFO2D+TNd+9UljIbT7tOqj3vJC1OA53vWcM6/VrHO0p9vQ2ZwecXH8PvLsPtHuPRdiB/R3D8G0dlZauD5Nvh38rFcMB254vyJ+vTTT3nuued46623mDBhAl988QUvv/wy3bp182i3fPly4uLiWL58OWlpaVx11VUMHTqUWbNmtVhfmjWSNGfOHB544AGSk5MZM2YM48aNA7RRpWHDhrVY50TH5m1jW3DVSIrI10YdTUlJ2okKLWjCJxh8JdhuzyL9Irl10K0oejM+wdomuD+4T7lNc0y5LUbtZk/e3v9bq5QC+O/u/7Lq4CpMOhOvTH7liJXFHT5Zm0lRlZmkcH+uHJnotc205Gk8MPIBAF7Z9Ap/5v3pOrn3V3hvshYg+UfCpf+BWxZD3/O8BkgOieH+3HOWtkvBi7UXUXnJ/yAwFor3wYfTYOvnx/7BhWinFixYQGBgoMfXuec2XdT29ddf55ZbbuGmm26id+/ezJkzh0GDGu8BGRYWxhtvvEHfvn254IILOP/881m2rGVHrJs1knT55Zdz2mmnkZeXx5AhQ5zHzzzzTC655JIW65zo2LzVSLLZVGdOUliWViPJmHBYjSQZReoQbuh/A/P3zic74E8o7cNP23J57Lx+6HUKAWPGaFNupaXU5NoI8A3Rpp5yN0Pi6JPWp72le3lt82sAPDz6YfqG9z3qNVX1Dby7cj8Afzuz1xG3HZnRfwb7Svfxw/4feGjlQ3x5wZfE7fsNfrwbVCsknw6X/QeCYpu8x+FumpDMF+uzOFBcwxs5PXn0zj/hh7tgzwL4/q9QlgmTHoYWKEEgOhGjvzaq0xbPPU5TpkxptKhr3bp1XH+99yKzqamp3HHHHR7HRo8ezW+//eZxbMCAAej1rl9C4uLi2LFjx3H370iaNZIEEBsby7Bhw9DpXLcYPXo0ffse/R8lcWpwTLe5jyQVV5sxW20oQERlMRiNGOPsP1AkSOpQfA2+3DfiPvSBe1H0NRRW1rN2v7YXn2IwEHS2fZXbr0tcpQBO4io3i9XCY78/hsVmYXLCZK7ofcUxXffftZmU1ljoHhnA9KFH/t5TFIXHxz5Ov/B+lNaX8v3XV8APd2gB0pBr4YbvjitAAvAx6JlzYX8APlydQXatD1z5X5j4oNZgxQuw/LmTkgsiOjBF0aa9WvurGcF6QEAAPXv29PiKjz/xvFOj0XNaXFEUbDbbCd/XXbOCpOrqap544gnGjx9Pz5496d69u8eXEOB93zZn0ravDoNqw9SlC4rjNwEpJNnhTEuextDogRiCtgOwYLv7lJu2yW3lkiWo3R31kk5ekPT+jvdJLU0lzCeMJ8c/eUzFH+ssVj74IwOAO6b0xHAMm9f6Gnz515R/cXE93HbA/lvrmNth+ltwlNynppzRN4bTekZisaq8smQv6HRwxuNwzrNag1X/hLVvNOveQnQ0ffr0YcOGDR7HDv/v1tKs6bZbb72VlStXcsMNNxAXF9cilWhF5+OtRlLeYTWSjI58JHAbSUponQ6KE6YoCg+NeoirDjyBpWwsP+/IYe70gRj1Oo8pt9qKcPwBcrdA1SEIbLpqeHOklabx/o73AXhs7GNE+kUe03XfbD5IUVU9XUJ8udg+iqTabNRu3Ubl4sXU703Fkl+AYjRiiIjAd8hgAidMoEuswjMFeeiArwMDMPYcy8Un+O/gw9P68scbf/D91hxum9idfnHBMP5uUG1aOYHFT0BoEvS/+ISeI0R7d/fddzNr1ixGjhzJ+PHj+fLLL9m+fXubDMI0K0j65Zdf+Pnnn5kwYUJL90d0It62JMlx1kjSig+aEt0CIhlJ6pAGRw3m/AF9+Canksq6IFanFTG5T7Rzyq1s/nzKl2/Av8sgyN+hJXAPuarFnm+1WXlyzZM02BqYnDiZqV2nHuN1Ku+uTAdg1sTuGHQKlStWcOjll6nfl9aofT1QvWYN5R+9SbfzSjGYLByI7cdc3yp81j/PsJjhJAUnNbruWA1KCOH8wXH8vD2Plxen8p8bR2knxv8NyrJgw3/g279AZG+I7tfs5wjR3l133XWkp6fzwAMPUFdXx5VXXsnMmTNZv359q/elWdNtYWFhhIeHH72hOGVVmiupsmjFIj0LSWojSZHVJQAYE91+qDgLSUqNpI7mvhH3YAreDcCHf25xHg8+177KbckS1O5nagdbeMrt8z2fs71oO4HGQB4f8/gxj2wvSykgq6SGUH8jl/cNI+eeezn419up35eGLiCA4IsuJO7550ma9xGJ779P7NNPE3z+ucSfXoHBZKa+3EDDgiCG+vWmtqGWh1Y9dMJbl9x/dm90CixNKWRnjr0chqLAtBeh+2RoqIX5N4G55oj3EaK9mDdvHt9//32j45MnT0ZVVUJDQ5k5cyZlZWUe55944gkOHTpEZWUlH3zwAbt376Znz55HvO+rr77KihUrWrT/zQqS5s6dy5w5czz2bxPCnWMUKcQnBH+31RCOGkmRpdp5z0KS9iApRIKkjiY+MJ7zh2gjhqv3VlFtrgfAf/Ro9GFhWEtLqTPbRwjTlsGxVK0+BjlVOfx7i1bl/74R9xETEHPM136yVitBcUWvIAquvpLKxYvBaCTi1lvo+dsy4v/xD0IvvYSAsWMJPP00wq66kviLYvGPqEVVfMjd0Q1L2kFmvZRCoNXIruJdvLH1xPKGukcFcuEQ7c/p9d/2uU7oDXDp+xAQDYdS4NfHTug5QrRnNTU1vPLKK+zatYs9e/bw5JNPsnTpUm688cajX9zCmhUkvfzyy/z666/ExMQwaNAghg8f7vElhLd8JIBc+3RbeH4W4FZI0lytLREHmW7roOZMvg6doQqr1ZeXVv0EeK5yK1uXpdXAqi2B3K0n/DxVVXlm7TPUNtQyImYEl/e+/JivTSus4o+0InQKnP7eXCzZ2Ri7dCH5s0+JfuAB9CEhjS/K3qCtNAOU6f8m6bvlhN84g4gqhb9+r31ff7TzI8/6Sc1w15SeKAr8uquAlLwK14nAaLhMy7ti00eQvvKEniNEe6UoCgsXLmTixImMGDGCn376iW+++Yazzjqr1fvSrJyk1th5V3RszhpJ/p7LoR2J25HF2qiRKcGek+QoJGkK1H6Qig4n1DeY0T2N/LkHvtqcxv0Tqwg0BRI8bSplX31F5ZLfiL13Esqen7Qpt4QTqyb9fdr3rMldg0ln4slxTx512xF3//tTG0UaU5hKVF4Gvv37k/jBfzCEhXm/wFJnX+pvg0FXwJCr0QMxjz5K0FlnYXjoYc7aXMDS4ToeWXo/312xgDDfJu51FL1igjhvkJab9M7K/bx2tVuB3u6TYeQtsPED+OlvcPtaMB1/3Roh2jM/Pz+WLl3a1t0AmhkkPfnkky3dD9HJeFv+b26wUVipTcNE1Zahj4pE52//B77ioPYa3EWK5nVgfzv9NP7cs4Hqsp68vfU9Hhw922PKrZ5u+AKkLYXJjzT7OYU1hfxzwz8BuHPYnXQL6XaUK1yq6huYv1Ebybxg7wr8hgwh8f330B9pS6WVL0LRXm0ftnP/4XHKf9Qoun3zNX+9/15SijaTE1nBI/+7nrdv+hGdvulq20dy+6Qe/Lw9jwXb83hoWl/iQ/1cJ896CvYugtIDWmmAs+TfYyFOlmYXkywrK+M///kPjz76KCUlWhLu5s2bycnJabHOiY7LOd0W6JpuK6ioQ1XBpEBIfTUm96Rt2di2UxjbLYpQfwVsfny8YR3p5enalJu9ZlLpFq3YJAc3Qk1Js56hqipz/5xLpaWSAREDmNF/xnFd/826A1SbbSRUFjI6sIHEd985coBUsFvbrBa0TWr9Gy9aMYSH0/v9D5ljOx9Dg8oaYxbvPX8Vtrq64+qbw8D4EMb3iMBqU/nIXsfJyTfYFaitfRNKZVNxIU6WZgVJ27dvp3fv3rz44ou89NJLzqz0b7/9lkcffbQl+yc6KG/L/x2FJKP1DehQMSV6SdqWIKlD0+kUpg/tCkBdxQBeWPcCqqoSctFFAJQv/hM1sh+gaqUAmmHRgUWsyF6BQWfgmQnPYNAd+4C4qqp8tFBbfXdRwVa6vvcu+sN2Hz/sAlj0sFZRu+8F0O+CJpsqBgPjH3yRO4K0Nu/H7WHNXdfRUFp6zP1zN2uiVhPm8/VZlNcetmqu7/nQbSJY62GpjCQJcbI0K0iaPXs2M2fOZN++ffj6+jqPn3feeaxatarFOic6Lm9BkmNj22irViPJ6BEkSY2kzuL8wdr/c2vlANbmbGBhxkL8hg7F2DUJtbYWs84+NZZ2/DkHJXUlvLBOS56+bfBt9A7rfVzXL/3oOw7gj19DPTfec5VnoO7NngWQsQr0PjD1uWN6xi1XPM/ogAGYjQov9Uhl/zVXY87KOq5+AkzuHUXvmECqzVa+3nTQ86SiwNQXQNHBru+0pHIhRItrVpC0YcMG/vKXvzQ6Hh8fT35+/gl3SnRsVpuVguoCwDMnKcexJUm19pu1x/L/ctm3rbMYkRRGTLAPqs0Xa3Vv/m/9/1FSV0LIhdpoUtkOrX4WaUvhOPZZUlWVOavnUFpfSq+wXtw68Nbj6ld9egbzVmlFIs8PsxBz+vgjX2Cpg1//rr0ffzeEJR/Tc3SKjufPfY1gQyD7uyh8kXCQA1ddTe3WrcfVX0VRuHG89sz/rj2AzXbY3m2xA7X94gBWPH9c9xZCHJtmBUk+Pj5UVFQ0Or53716iolp2uwHR8RTVFtGgNqBX9ET5ub4fnCvbSrV8Ja8jSSGyJUlHp9MpnDdIG03yq59AWX0Zz617juALtWmokt/3oxr9ofoQ5G875vt+tuczVh5ciUln4oXTXsB4HPukqRYLWx59irUxWqXqWTdPO/pFa1+HskwI6gKnzz7mZwHEBMTwxHhtGuzbCTr2+JWSeeNMKhYvPq77TB8aT5CvgQPFNazad6hxg0kPgs6gTV1mnVjpASFEY80Kki666CKeeeYZLBZtnlxRFLKysnj44Ye57LLLWrSDouNxJG1H+0ej17lW9zhrJB2yL//3mpMkI0mdwQX2Kbfa8l7oVV+WZC5hQf1G/EeOhAYVs6LlLR3rlNv2Q9t5eePLANw/8n76hPc5rv4Uvf0235nDsSk6xiYG0SfOSx0kd+U58Psr2vuzn9F2Pz9O07pN49xu52LTwZtXBlJrqyfnnnsp+fjjY75HgI+BK0Zof08+XnOgcYOwZBh6nfZ+uYwmifZn5syZHbpsULOLSVZVVREVFUVtbS2TJk2iZ8+eBAUF8dxzxzZvLzovb/lI4Ja4XVOK4u+PPiJCO2Gp1QoMggRJncSwxDDiQnypNaucE30PAM+ve57CS7X9Hst2aXlp7Dt6kJRfnc89y+/BYrNwRuIZXNP3muPqS93u3eS+/wGLkscCMHNSr6NftPw5sNRA4lgYdOxFKg/39zF/J9o/mly/Or66rTeoKgUv/B/5zz+Paj22quM3jNMCyhV7D5Fd4mWXg4kPaKNJGSshZ1Oz+yqEaKxZQVJISAhLlizh559/5t///jd33XUXCxcuZOXKlQQEHP9vXKJzcRaSDPAsJOkIkqJqyzAlJbn22HJMtRn9wTe0tbopTiL3Kbe68v5MSpiE2WbmMevXFHcJpCLFvjT+4HqobXr1V3l9OXctu4ui2iJ6hvbk+dOfP+a92QBUs5ncx/7OHzEDKfcJJC7El7P6HWXrkqI02Pa59n7qcydUtyvEJ4RnJzwLwILgdDIe0kbaSz/5Lzn33outtvao9+gWGcD4HhGoKsw/PIEbIDQJBtoDuTUnti2KEK1p5cqVjB49Gh8fH+Li4njkkUdoaGgAYMGCBYSGhmK1/zKxdetWFEXhkUdc9dVuvfVWrr/++pPax+MOkmw2Gx9++CEXXHABf/nLX3j77bf5448/yM3NRVXVo99AdHretiSpqm+gok775o+0B0lO7sv/pZBkp+FY5bYspZAnxjxDcnAy+TUFPHetkSIMWBpCtArW6Su8Xl9eX86sxbNILU0l3Dec1894nQDj8f0SVvTe+9Tv2cNPvScBcN2YJAz6o/yzt/L/tH71ngYJI4/red6M6zKO6/tp/5D/M3A1AS89g2I0UrlkKZkzZ9JQXHzUe1w9Wvv7Mn9jNtbDE7gBxt+lve7+XuomnSJUVaXGUtPqXy31cz4nJ4fzzjuPUaNGsW3bNt5++20++OADnn1W+6Xi9NNPp7Kyki1btJIdK1euJDIy0mMD25UrVzJ58uQW6U9TjqvitqqqXHTRRSxcuJAhQ4YwaNAgVFUlJSWFmTNn8u2333rd7VecWrwFSXn2UaRArAQ01GPq6q2QpEy1dSbDEkOJD/Ujp6yWLQfMvH/O+8z4ZQYHq/N4bKae/2y30iMBbcptwCUe1+4u3s0DKx8guzKbcN9wPjjnAxKCji+pvy41laJ33iE1NJE9wfGY9DpnsNGkgt2w42vt/ZSW20T2nuH3sCZ3Denl6fwrag3PfvQhOXfeRd227Ry4+hoS33sXn25NVw0/p38Mof5G8srrWLX3EFP6Rns2iB0E3adA+nL482049/9arO+ifaptqGXMZ2Na/bnrrl3nsWl5c7311lskJibyxhtvoCgKffv2JTc3l4cffpg5c+YQEhLC0KFDWbFiBSNHjmTFihXcd999PP3001RVVVFeXk5aWhqTJk1qgU/VtOMaSZo3bx6rVq1i2bJlbNmyhc8//5wvvviCbdu2sXTpUn777Tc++eSTk9VX0UE4c5Lcqm3n2mskRTV4q5EkhSQ7I0VROG+QNuX68448YgNi+c85/yEpKImiEIV/DtO22rDu+xXVZkNVVbIrsnlx/Ytcv/B6siuziQuI48OpH9IzrOdxPVu1WMh79DFoaGDRBG2K67xBsUQG+hz5whUvACr0uwjihhz3Z26Kr8GX509/HoNiYGnWUpaG5tD1888xJiRgyc4m8+prqNm8uenrjXouHaYFiV9saKLm0jj7aNLWT7UNo4Vox1JSUhg3bpzH9PmECROoqqri4EFtWnnSpEmsWLECVVX5/fffufTSS+nXrx9//PEHK1eupEuXLvTqdQw5hifguEaSPv/8cx577DGmTJnS6NwZZ5zBI488wqeffsqMGce3TYDoXBwjSTH+rtwPZ9J2tZagbUrq6rpAaiR1WucP7sL7v2ewLKWAWrOVpOAkvrjgCx75/q+sVbdRU6TgX32Imz4ZRaqPD5XmSue1UxKnMHfCXEJ8jrISzYui99+nbvduKiNj+c03EawqM+w1h5qUtx1SfgSUFh1FchgQMYDbh97O61te58X1LzLm4m9J/vILsm+/g7rt28maeRNd/vEiwdO8lye4YmQCH67OYPmeQ5TXWAjxP6wEQo8zIKwblGZoo2EjbmzxzyDaDz+DH+uuXdcmz20tkydP5sMPP2Tbtm0YjUb69u3L5MmTWbFiBaWlpSd9FAmOcyRp+/btTGviLzDAueeey7Ztx173RHQ+NZYayurLAM+RJMd0W0SZVmTS63RbiIwkdTZDEkKID/WjxmxleWohAEGmIF6/7BMeWxnNLlUb2RlVVkiluRIFhbFxY3n3rHd5bcprzQqQ6nbvpuittwFYfd39mK0qg+JDGJYYeuQLHUvoB14G0f2O+7nH4uaBNzMochCVlkqeWfsM+vBwun48j8Azz0Q1m8m59z6KP/jQa95Hv7hg+sQEYbba+GVnXuOb63Qw8ibt/cYPtC1VRKelKAr+Rv9W/zqehRNH0q9fP9auXevxvb569WqCgoJISNBGTR15Sf/617+cAZEjSFqxYsVJz0eC4wySSkpKiIlpemVITEwMpc3cp0h0Dvk12lRbgDGAIGOQ87hzuq26GMVkwuD+fSTTbZ2WoihcMEQLlr/f4tr8WqfXc+bU24j/U9vW6GZdFN9c9A0brt/A++e8z/j48c36x9hmNpP78CPQ0ID/Oecwv1z7rXfGuK5Hvt/BTbD3F22bj8mPNN3uBBl0BuZOmItRZ+T3nN/5cf+P6Pz8SPj3a4TZV+kU/vOfFMx91muJgIuHaaOt329tYiPxoddrW6jkbYOcpqfvhGhN5eXlbN261ePrtttuIzs7m7vvvps9e/bwww8/8OSTTzJ79mx0Oi00CQsLY/DgwXz66afOgGjixIls3ryZvXv3tr+RJKvVisHQ9AydXq93Lt8Tpyb3GknuP5Tcl/8bExNRdG7fepK43ak5cmmWpxZSWm12Hg+59FJqrV1RbeBbsp/eqhEf/VFyho6i6PU3qN+3D314OLuvu5vcsjpC/Y1cOOQo31vLtRU1DL4aIk9ujkOP0B7cMfQOAF7c8CKFNYUoej0xf3+M6EceBkWh9LPPOHjX3dhqPOsiXWT/HOsySpwV7D0ERMCA6dr7LZIfKtqHFStWMGzYMI+vuXPnsnDhQtavX8+QIUP461//yi233MLjjz/uce2kSZOwWq3OICk8PJz+/fsTGxtLnz7HV1S2OY57ddvMmTPx8fH+D1l9fX2LdEp0XE3VSHJsbhtVU4app1s+kqUOaoq09zKS1Cn1iQ2if1wwu/MqWLA9lxvGJQOgM5kIn3UXNcvvJCDGjG37d+gmH9/2H+5qt26l+IMPAIh75mme2aEtrb9qVCK+Rn3TF2au1bb10Blg0kPNfv7xmDlgJkszl7KreBfPrH2G1894HUVRiJg5E2NsHLkPPUTV8uVkzriRxHfexhAZCUBCmD+jksPYcKCUn7blctvEHo1vPvQ62P4l7PwOpv0fGFsvh0SIw82bN4958+Y1eX79+vVHvP7VV1/l1Vdf9Ti29Tj3QTwRxzWSdOONNxIdHU1ISIjXr+joaEnaPsV5W/6vqmqjQpJOlfZRJIMf+IW1Wj9F67p0uBYAf7vFc5oo9JJLqKnSvlesf3zU7PtbKyrIefAhsNkIvuhCcgaO5o+0InQKXD+m65EvXm7fJWDY9RDe9DL8luQ+7bby4EoWZix0ngueNpWkefPQh4ZSt3MnB666mvr9+53nLx6q/Vl+vyXX+82TT4eQRKgvhz0/n9TPIURnd1wjSR991Px/xMSpIa9KC5LcR5JKqs3UN9hQVJWIunKMTdVIkkKSndZFQ7vw/MIUtmSVkX6oiu5RgQAoRiO+lz8MW+7AYMmifuef+Awce1z3VlWV3EcexZKdjTE+nti//51/Lc4A4NyBcSSGH6GmS/pKOPA76E0w8cFmf77m6BXWi78M/gtvbH2Df2z4B6fFn+ZMVPcfPozkLz4n6y9/wZKZRea115H0ySf49unN+YPieOrHXezOq2BfQSW9YoI8b6zTwZCrYdU/tcrhJ7CtihCnumZtSyJEU7zt2+bY2DasoQaTzYopUQpJnmqig3yZ1DsKgC83ZnucC7zoWurMkSgKVL8zG9VmO657F739NlW//YZiNBL/2msUKz78YE9svuX0I4wMqSr8Zs9FGjETQo6vWGVLuGngTXQL6UZJXQmvb3nd45wpOZnkzz/Hd9AgrOXlZN18M/Xp6YQFmJjcR/uzbDKBe4h9f7v9v0GFl5VwQohjIkGSaFGO6Tb3kaRce4JplKNGkvtIUrl9LyrJR+r0rhql/X//ZtNBLFZXIKQoCobJfwXAT9lL0dtvH/M9y779jqJ/a8FFzBOP4zdwAP/9MxOLVWV4UijDk44whZu2VNs7zuALp9/fjE904kx6E0+MfQKAr1K/YsehHR7nDeHhJP3nfXz69cNaXEzWLbdiKSh0Trn9sLWJ7aAiekDCaG17ld3fn+yPIUSnJUGSaDGqqjYxkqQFSZHVpWAwYOziNmokNZJOGWf2iyYy0IeiKjPLUgo8zhnGz0RFh1+EhfKPXqVi8eKj3q/8pwXkPaEFGBGzZhF25ZXUmq38709t77JZp3dv+mJVdeUijboVgmKbbnuSjYodxUU9LkJFZe6fc2mwea4Q1oeEkPThB5iSk2nIyyP79r8yJSmQAJOeg6W1bM4q837jgVqlcXZ+e3I/gBCdmARJosWU1JVgtplRUDyqbTtXttWWYYzvguJeRkKm204ZRr2OK0ZqU1qfr/ecciMwCqXHZACCk2rJvf8Byn/80et9VKuVonfeJffBB8FqJeSSS4iafR8A32w+SGmNhcRwP84ZcITAJ3Uh5G4BYwBMuPdEP9oJmz1iNsGmYFJKUvhizxeNzhvCwkh8/z304eHU706h9PHHONO+f9vi3fneb9r/YkDRRsvKmtjKRAhxRBIkiRbjGEWK8ovCqHdtmZDj2JKkttQzHwmkkOQp5upR2p59q/Yd4kDRYfuLDdQSjMMGKKgWM7kPPUzuY3+nPj0d0ApFVv3+OweuuppD9iXB4TfdRNxzz6IoCjabyod/aAnbN43vhl7XxEIAm81VXXvMbRAY1bIfshki/CK4d8S9ALyx9Q2Ka4sbtTElJpL49lsoRiNVy5YxLn8XAIt3FXifcguOg+TTtPe7vjtZXReiU5MgSbQYb/lI4NqSJLK23HP5P8hI0imma0QAZ/SNRlXho9UZnif7XwTGAIz6CmJv1rY/Kv/2W9LPO589Q4exd+QosmfdRt3OnegCA4l9+mliHn7IWZh0aUoB6UXVBPkauHJU4uGPdtn1LRTsBJ9gGP+3k/VRj9tlvS6jX3g/qi3VvLPtHa9t/IYMIebvfweg17x/YdRBRlE1aYVV3m864BLtdec3J6PLQnR6EiSJFtNUkORY3RZdU+qZtN1QD9Xafl4Et/7KItE2bp6grTibv+kg5bUW1wmfIBh4KQBhverp+un/CDzjDFAU1Lo6VLMZQ1QUoddcTY9fFhJ21ZXOS1VV5dWl+wC4fmxXAn2aqG7SUA/Lntbej78b/MNb/gM2k07R8cDIBwD4eu/XZJRneG0XetWVhEyfToC5lmEl2ijb4t0FXtvS/2Jtq5W8bVCaeVL6LURn1uZB0ptvvklycjK+vr6MGTPmqNU358+fT9++ffH19WXQoEEsXLjQ4/y3337LOeecQ0REBIqieK3MWVdXx5133klERASBgYFcdtllFBQ08Y+MOGbeCklarDYKK91ykjwKSdqXJut92tUPK3FyTegZQd/YIGrMVr5Yf1iuzHB7Mdpd3+E/oCeJb71Jn40b6LF0CT0W/ULPVSuJe/JJDFGeU2S/7ipgd14FASY9tx0pYXvDf7T8nMBYGHdnC3+yEzc6bjSTEibRoDbw6qZXvbZRFIWYxx/HmJDA2IxNAPy6q4m8pIBISBqvvZfCkqKdUhSF77//vq274VWbBklffvkls2fP5sknn2Tz5s0MGTKEqVOnUlhY6LX9mjVruOaaa7jlllvYsmUL06dPZ/r06ezcudPZprq6mtNOO40XX3yxyefed999/PTTT8yfP5+VK1eSm5vLpZde2uKf71TjXNkW6AqSCirqsKlgsDUQWl/lOd0mhSRPSYqicPNp2mjSf/7IoNbstpFrwiiI6gsNtbD9KwB0AQGYEhIwJSd73aTWZlN5deleAGZOSCYswOT9wbWlsPIf2vsz/g6mgJb7UC3ovhH3oVN0/Jb9G5sKNnltow8MoMv/vcDYgt0oqo3tB8udq0gb6XeB9rpnwUnqsRBHlp+fz91330337t3x8fEhMTGRCy+8kGXLlrV1146qTYOkV155hVmzZnHTTTfRv39/3nnnHfz9/fnwww+9tn/ttdeYNm0aDz74IP369WPu3LkMHz6cN954w9nmhhtuYM6cOZx11lle71FeXs4HH3zAK6+8whlnnMGIESP46KOPWLNmDX/++edJ+ZynipwqLQnbfSTJsbItsrYcnQLGBLdpNWeQJEnbp5rpQ+NJCPPjUGW9c8k+oAXLI2/W3q97R0uyPopvNh9kT34lgT6GIy/7//0VqCuDqH4w5NoT+wAnUY/QHlzWS1u+//LGl70nZQP+I0fS46pL6Vei/fkt3t5EYcm+52uvWWuhuqjF+yvEkRw4cIARI0bw22+/8c9//pMdO3awaNEipkyZwp13tr/R3MO1WZBkNpvZtGmTRzCj0+k466yzWLt2rddr1q5d2yj4mTp1apPtvdm0aRMWi8XjPn379iUpKemI96mvr6eiosLjS3hybEkSH+gKetz3bDPExaJz3xzZUUhSaiSdckwGHX87oxcAb6/cT3W9W22godeBTwgUp8G+X494n6r6Bv7xayoAd53Rk1D/JkaRyrJg3bva+7OfBv1x7cjU6u4Yegd+Bj92FO3g18ym/wwi776b0yoPAPDzsq3eG4UmQdwQrbBk6kLvbYQ4Se644w4URWH9+vVcdtll9O7dmwEDBjB79uwmByZ27NjBGWecgZ+fHxEREdx2221UVbkWJ6xYsYLRo0cTEBBAaGgoEyZMIDPT9cvWDz/8wPDhw/H19aV79+48/fTTNDQ0eHvUUbVZkFRUVITVaiUmJsbjeExMDPn53ufX8/Pzj6t9U/cwmUyEhoYe131eeOEFj818ExOPsHrmFFRjqaG0vhTwnG5zJG1rG9settGoc/m/rGw7FV06PJ7kCH9Kqs28uyrddcInEEbO1N6vecPrtQ5vLU/jUGU9XSP8uWlCctMNlz4N1npt89de55xw30+2SL9IbhpwEwBvb30bq83qtZ0+MICLrjwTgE21JorSm6iH1Ncx5SZBUmehqiq2mppW/2pqZNObkpISFi1axJ133klAQOPp7cN/DoOWMjN16lTCwsLYsGED8+fPZ+nSpdx1110ANDQ0MH36dCZNmsT27dtZu3Ytt912m3Mq/vfff2fGjBncc8897N69m3fffZd58+bx3HPPNevPuX3/OtWOPProo8yePdv53xUVFRIouXEkbQcaAwk2BbuOO7YkqSnFNPCwPy/nSJL8OZ6KDHodD07ty52fbeadFfu5eGgXetg3vmX0X2Dtm5D5B2Stg6Qxja7fmVPOe/bg6rHz+uFj0Ht/0P7fYOfX2iqvc+Z2mPy36/tfz/9S/kd6eTqLMxdzbrdzvbYbcMk0uq3+jAxjKD+89zW3/N/sxo16T9MqjGes1Fb4GXwatxEdilpbS+rwEa3+3D6bN6H4H2HTaDdpaWmoqkrfvn2P+f6fffYZdXV1fPLJJ87A6o033uDCCy/kxRdfxGg0Ul5ezgUXXECPHj0A6Nevn/P6p59+mkceeYQbb7wRgO7duzN37lweeughnnzyyWPuh0ObjSRFRkai1+sbrSorKCggNtZ7pdzY2Njjat/UPcxmM2VlZcd1Hx8fH4KDgz2+hEtulZZf1CXQc1TINd1W7rn8H6DcXnVZgqRT1nmDYpnUOwqz1cbj3+10/ZYaEg9D7XlDS5/UthFxU2exct+XW2mwqZw3KJZz+sfglaUWFtiDhtG3QZdhJ+mTtLwgUxAz+mur/d7Z9k6To0mKojBtmPZ367dcM3Wpexs3ih2kreiz1EDm6pPWZyHcHc+ok0NKSgpDhgzxGHmaMGECNpuN1NRUwsPDmTlzJlOnTuXCCy/ktddeIy/PtYnztm3beOaZZwgMDHR+zZo1i7y8PGpqao67P202kmQymRgxYgTLli1j+vTpANhsNpYtW+YcVjvcuHHjWLZsGffee6/z2JIlSxg3btwxP3fEiBEYjUaWLVvGZZdpyZGpqalkZWUd132EJ8dIUpeAw4Mkx3Rbqefyf5CcJIGiKMy9eCBn/2sla9OLeXdVOn+dpP12yORHtRVuWWth7yLoo42kqKrK0z/tYl9hFZGBPjw7fZDXVW8A/PYslGZAUBeY8vdW+lQt59p+1/LJ7k9IL09nSdYSpiVP89pu2qRBvL1zNZujepH3r1fp9s5bng0UBXqeBVv/B/uWQo8zWqH34mRS/Pzos9n76seT/dxj1atXLxRFYc+ePS3ah48++oi//e1vLFq0iC+//JLHH3+cJUuWMHbsWKqqqnj66ae9rlj39fU97me16eq22bNn8/777/Pxxx+TkpLC7bffTnV1NTfdpM3Fz5gxg0cffdTZ/p577mHRokW8/PLL7Nmzh6eeeoqNGzd6BFUlJSVs3bqV3bt3A1oAtHXrVme+UUhICLfccguzZ89m+fLlbNq0iZtuuolx48YxduzYVvz0nYtjJMk9Hwkgt9yVuG3q6paTZK7WlmQDhEghyVNZUoQ/T1zQH4AXF+1h+R5HgdEuMPZ27f2SOWDRAu63Vuzn8/XZKAr88/LBhDe15D9tGay15zRd8Ar4drzR3yBTENf1uw6AD3d82ORv5oPiQwj31VNr9GX9jkzq7P/+eehlX6yStuRkdVe0IkVR0Pn7t/pXk7+QeBEeHs7UqVN58803qa6ubnT+8Bkd0KbOtm3b5tF+9erV6HQ6+vTp4zw2bNgwHn30UdasWcPAgQP57LPPABg+fDipqan07Nmz0ZdOd/whT5sGSVdddRUvvfQSc+bMYejQoWzdupVFixY5k7OzsrI8htHGjx/PZ599xnvvvceQIUP4+uuv+f777xk4cKCzzY8//siwYcM4/3xt2evVV1/NsGHDeOcdV5n/f/3rX1xwwQVcdtllTJw4kdjYWL79VnbKPhHO6Ta3kaQacwNlNVpF5ajaMkzuy//L7UnbPsHgG9Jq/RTt03VjkrhmdCKqCrd/uomFO+x/7yfcCwFRULQX62/P8sriVP5pX8325AX9mWLf5LWRijz43h5gjbrVOQrVEV3T9xp89b6klKTwZ5731UA6ncKkflq6wKaYPhS9+17jRt2ngKKHor1SfVu0mjfffBOr1cro0aP55ptv2LdvHykpKfz73//2Ontz3XXX4evry4033sjOnTtZvnw5d999NzfccAMxMTFkZGTw6KOPsnbtWjIzM1m8eDH79u1z5iXNmTOHTz75hKeffppdu3aRkpLCF198weOPP968D6CKZikvL1cBtby8vK270i5c9/N16sB5A9VfM351HttXUKl2fXiB2nf21+qe007zvGDfUlV9MlhV3xzbyj0V7VW9xarO/HCd2vXhBWrXhxeoD87fqm7NKlVzN/2sLn58snrFI/90nvvHopSmb1RXoapvT9C+v94Yo6rmmtb7ECfJ838+rw6cN1C95ddbmmzz3eaDateHF6hT/vquurtvP7UuLa1xow+man8u698/ib0VwlNubq565513ql27dlVNJpMaHx+vXnTRRery5ctVVVVVQP3uu++c7bdv365OmTJF9fX1VcPDw9VZs2aplZWVqqqqan5+vjp9+nQ1Li5ONZlMateuXdU5c+aoVqvVef2iRYvU8ePHq35+fmpwcLA6evRo9b333mtW3xV7B8VxqqioICQkhPLyckniBs786kwKawv5/PzPGRipjeytSC1k5kcbSC7PY17FCpI//Z/rgk0fw09/05ZjXze/jXot2psGq40XftnDB39437fMFzMvTI3jkilNTI3XlcOX10PGKm0E6talEJZ88jrcSnKrcjnv2/OwqlbmXzifvuGNVwsVV9Uz8rmlqCr8b9EzdD/vLLo8f9iy599fhmXPQO9z4dovWqn3QnRcbb53m+j4zFYzhbVaHol7te0c+8q26NpSz+1IwC1pW/KRhItBr+OJC/oz/6/jOH9wHAEmPSa9jqhAE7eFbmSx6SEu2XAdHPCyQqsoDT6YqgVIRn+49stOESCBtmr07K5nA/Bpyqde20QE+jA4Xpu63hTdh4oFC2goKfFs1FO7BxmrtFIAQogjkjpJ4oQ59mzz1fsS7uvaqDanVAuSYmpKMSU1VSNJgiTR2KjkcEYlH7bpce0o+GSZtqP9vPNg4GXaD31FgYzfYdvnoFq1pe7XftGhlvsfi+v6XceiA4tYmL6Q+0bc5/F3zWFSn2i2HSxnc+8xnJO1gbIvvyTy9ttdDRylAKryIXMN9JjSip9AiI5HRpLECcutdq1sc1/54BxJqvG2/F9qJInj5BcKN/4EI2Zq/73zG/j+r/DdX7Sl7apVm76d9VunC5AAhkQNYUDEAMw2M/NTvU9RT+4TBcDmkK5YFR2ln32Oaja7GjhKAQCkLT3ZXRaiw5MgSZwwx55th9dIcowkRdeUNt6SREaSRHP4hsCFr8Gty2D83ZA0TttqZOQtcPNiLb+tk9bdUhTFWQ7gq9SvaLA13otqSEIoof5GKq0K+7oNpuHQISqWHLbk31EKYJ+UAhDiaCRIEicsp0pbzn94te2DJVp1Uy0nyW3EyGZz7dsmQZJojoSRcM6zcPMimLlAq4PkZeuSzmZq8lTCfcMprC1k5cGVjc7rdQqn99JGk3aM18qglH39tWcjZymAVG3jXyFEkyRIEifMWW3bLUgyN9goqNQSQ+MMDehD3GohVR8Cq1nbSyvIs/ikEKJpJr2J6T2nAzB/bxNTbr21IGm9fxdQFGrW/ok5yy0Y8guFePueXxmrTmJvhej4JEgSJ8xZbdttZVt+eR0qYLJaiI6L9Lygwj7VFhQHemMr9VKIzuHyXpcDsCZnDdmV2Y3OT7QHSTsLa6k/Tdt+pOybw4rldpuovWb8fvI6KkQnIEGSOGGOICk+0JULcrBMm2qLqinFp6nl/8GdM3dEiJMpMTiR8V3Go6Lyzd5vGp2PCvJhQBetdlvKadqUW/m336I2uOUwdTtde81Y1WjzYCGEiwRJ4oQ02BooqCkADquRVCo1koQ4WS7rpW3O/VP6T1ht1kbnJ/TURm+3+MWiDwuj4dAhqtescTVIHAN6E1TmQkl6q/RZiI5IgiRxQg7VHMKqWjHoDET5RzmPO5b/x9SUYuqW7HmRBElCnJDJiZMJMgVRWFPIhoINjc6P7xEBwJqMUoLPOw+A8h9/cjUw+kHCaO19RuMEcCGERoIkcUIcNZJi/WPRKa5vp4Puy/+Tkz0vkhpJQpwQk97EtORpAPy0/6dG50d3C8eoVzhYWkvFGdrmvpXLlmFz34ld8pKEOCoJksQJ8ZaPBHCwqAqwjyR1lRpJQrS0i3pcBMCSzCXUWGo8zvmbDAxLDANgoyESY9ck1NpaKpctczWSvCQhjkqCJHFCnCvbAj2X8ucUa0FSrNGK/vANgCVIEuKEDYkaQmJQIrUNtSzLWtbo/DjHlNv+YkIuuBA4bMotfiQY/KCmCApTWqXPQnQ0EiSJE+KskeRWbdtmU8mr1lbSJEQdFiBZarU6SSBBkhAnQFEULuyuBT/eptwcydtr9xcTdIG2yq167VoaSku1BgYTJI3V3h+QKTchvJEgSZwQb9W2CyvraVBBZ7MSFx/leUGFNvKEMQD8wlqrm0J0Shf0uACAdfnrKKgu8Dg3NDEUP6Oe4mozB/wi8OnXD6xWqjym3Bx5SVJUUghvJEgSJ8Rbte2DpVp+RGRdOf6NVrY5krYTtM02hRDNlhiUyPDo4dhUGwszFnqcMxl0jO4WDsDqtGKCp54DQMWvi12NHEHSgT/ASykBIU51EiSJZrOpNufmth41ktyX/ycfnrQte7YJ0ZIco0k/7v8R9bAEbGcpgLQigqZOBbQpN2tZmdYgbiiYgqCuDPJ3tFKPheg4JEgSzVZSV4LZZkan6IgJiHEed4wkeV/+L0nbQrSkc7qeg0lnIq0sjb2lez3OOfKS1mWUoE/qik/v3tDQQOVvy7UGegN0Ha+9lyk3IRqRIEk0myMfKdo/GqPOtQfbwfxy7bjXattSI0mIlhTiE8Jp8acBWjkAd/3jggn1N1JV38C2g+UETdNGkyoXu0+52UsBHPijVforREciQZJotoOV2qhQQqDnqFB2QRkAXYwqOl9fz4ucI0myb5sQLeWsrmcBjYMknU5hXHdtym3t/iKCztTaVa9di61WmxZ3jiRlrwObrXU6LEQHIUGSaDbHSNLhhSRz7TlJ8WF+jS+S6TYhWtzkxMkYdAbSy9NJL/Pci82Rl7Q6rRif3r0wdIlDra+net06rUHsEG21aV0ZHJJ6SUK4kyBJNJtjJCk+yBUkqapKTp32PjHusCX+quoKkoJlJEmIlhJkCmJc3Dig8WiSo6jk5qxSLFaVoMmTAahasUJroDdAwkjtfdba1uiuEB2GBEmi2RwjSe7TbSXVZurt31aJyZ5VuKkugoZaQJGcJCFa2NldzwZgadZSj+M9ogKJCDBR32Bj+8EyAp1B0krXajjHlFumBElCuJMgSTSbMycpyBUkOZb/h9eWE9g92fOCsiztNbiLVu1XCNFipiROQa/o2VOyh+yKbOdxRVGc9ZLWZZTgP2YMip8fDfn51O/ZozVK0kahyFor+7gJ4UaCJNEsFpuF/Jp8wHMk6WCJffl/rZfl/2UHtNfQw1a8CSFOWKhvKKNiRwGwJMtzym2MPUj6M70YnY8PAeO0oMg55ZYwEnQGqMhx/TIjhJAgSTRPflU+NtWGj96HSL9I5/HsrEIAomvLMXbp4nmR4x/f0MMKTAohWoRzyi3Tc8ptjH2F26bMUhqsNgInTwKg0hEkmQIgboj2PuvPVumrEB2BBEmiWQ5W2ZO2A+NR3LYXycopAqCLsQHFYPC8qDRTe5WRJCFOijOSzkBBYUfRDmc1fIA+MUGE+BmpMVvZmVtB4CQtSKrbvoOGIu3vrGvKbU1rd1uIdkuCJNEs7kGSu+ziKgC6BHnJOXKOJEmQJMTJEOkXyfCY4YBnArdOpzAq2Z6XlF6MMSYG3/79QVWpWvW71kiSt4VoRIIk0Sw5lfaVbUGe9Y4OVmmbZHaNCmp8kSNICpPpNiFOlqam3MZ214Kk9RklAG6r3FZoDRLHaq9FqVBdfNL7KURHIEGSaBZvI0mqqpJr1abYuiZGe15gs8lIkhCtYEriFAC2HtpKeX258/iYblpe0voDJVhtKoFTtHbVf/yBajZDQARE9tEaZ0tekhAgQZJoJudIktvKtqIqM3WKAUW10bXXYXWQqgvBWg+KTgpJCnESdQnsQs/QnthUG3/kuPZj6xcXRKCPgcq6BlLyKvAd0B99VCS2mhpqNm7UGnW15yVlSl6SECBBkmgmZyFJt+m2rMIKACJrywns0c3zAmeNpATQGxFCnDyTErTE7FUHVzmPGfQ6RiZrVfDXZZSg6HQETtA2xq1ea89Dcq+XJISQIEkcv2pLNaX1pYDndFvGfm0KLrauDEP0YdNtsrJNiFYzKVELkv7I+YMGW4PzuHPKLUPLOQoYrwVF1WsOC5LytoG5upV6K0T7JUGSOG6OStuhPqEEmgKdxw8cKAAgXm/xKAsAQJkESUL8f3v3HR5ndSV+/DtdvXdZ1b3J3bJsYzsYMC3AxssSh6yJlyVhN5TgZLPrDSXJsjGQHwklhZTNpmFMnAUTAhgcg01xk2W5y02y1Xuvoynv74+rGWmskQtYM5LmfJ5Hzygz7xXHLy/K8b3nnusrOXE5RFoiaett43D9Yff7uQOKt51Ozd1UsufECezNzeq/z/AUcNqh8qBfYhdiJJEkSVyxobb/l9a1AzAu3MtymuxsE8JnDHoD16ReA8Cuil3u92emRhJsMtDcZeNMXQfG+HgsEyeCptG1bx/odJC2UF1csd8foQsxokiSJK7YUNv/K9t7AchIiBg8SGaShPApd11SeX9dksmgZ25GFKB2uYGXJTdXklQuSZIQkiSJKzbUTFKlXW3/z0xPGDRGtv8L4VuLUxdj0Bkobi2mvL3/wFtXU8n8c64kSTWR7Nzdt6MtLVe9lu+Xw25FwJMkSVwxbzvbrDYHdcZQALImX7Ck5nRCS98vaTm3TQifiDBHMCdhDuC5y22hK0k634SmaYTMnw9GI7aKCnrLyyEpBwwW6G6CxmK/xC7ESCFJkrhirsLtgTNJ5SWVaDodFkcvyVOyPQe0V4PTpk4ZD0/2ZahCBDRvrQDmpEdj1Ouobu2hsqUbfWgowbPV4badu/eA0QwpKrmSuiQR6CRJEldE07T+maQBjSRLTp0HILm3HYPF4jnI3SMpFQwXHHorhBg2y9KWAZBfk0+XrQuAYLOBGamR6v3zQy25LVCvUpckApwkSeKKNHQ3YHVY0ev0JIf2zwqdL60DIMVoHzxIiraF8IusiCzSwtOwOW3sqe5vELkwy9UKQPU7c7UC6Nq7F83h8KxLEiKASZIkrohrFikxJBHTgM7Zrm7babL9X4gRQ6fTsSRlCQC7K/uPGpmfoTpvu2aSgmfORB8WhqO1lZ6ikzCub4db3QnoafNt0EKMIJIkiSvi2iVz4fb/inYbAOnxF9v+L0mSEL62JFUlSZ9UfYLWt1vNtcPtbF0HTZ296IxGQnLV7FHn7t0Qntj336sGlQf8ErcQI4EkSeKKuGaShtr+n5HhZfu/HEkihN8sTFqIUW+ksqOSsnY1qxsdamZiguqW765LynP1S3LVJbn6JeX7NmAhRhBJksQVce1sG1i07ejopNqsZpCyp3iZLXL3SJKZJCF8LcQU4m4F8EnlJ+73F2Rd0C8pbxEA3YWFOK3WAXVJ+3wYrRAjiyRJ4oq4G0mG988kNZwqpsMcAnhpJOmwQ5uafZKZJCH8Y3GK2r22u6q/LsndL6lUFW+bs7MxxMWhWa30HDkC4/p2uFUcUL3OhAhAkiSJK1LepmqS0sP7E55zfdv/ox09hJgv2OLfXqUOy9SbIDzJV2EKIQZwFW/vr9mPzaHqB10zSccrW+nqtaPT6QhdqBKjzvx8SJwBphCwtkLDKf8ELoSfSZIkLluXrYu6brXVPyOif+nMtf0/1ev2f9dSWxroDcMeoxBisMkxk4kJiqHb3k1hXSEAqVHBpEQGYXdqFJa1ABCyUNUhde3PVz3NUuepHyCtAESAkiRJXDbXzrZISySRlkj3+2X1l7H9X5bahPAbvU7vnk36pGpwXdL+vrokV5LUXViIs7dXDrsVAU+SJHHZStvULrWMcM8CbNf2/7QEL9v/m8+rV0mShPCrxamD65IWDDjHDcCclXVBXVJfkiTHk4gAJUmSuGyu7cNpEWnu9zS7nUqHGYDMjMTBg5pK1GvM+GGPTwgxtLxktcX/ZNNJGrobgP7O24VlLdgcTnQ6HSEL5gPQuX9/f/F2w2noavJ90EL4mSRJ4rKVtakkaeBMUm95OTUhqntvVpaXwmx3kpQ9+DMhhM/EBscyNWYqAHuq1BElE+LDiAox0W1zcLxKLZuHuuqS8vMhNBZiJ6gfUCFNJUXgkSRJXDbXclt6RP/SWXdJCbXBKklKiw0dPEiSJCFGDFf3bdeSm16vY36GZ7+k/rqkQ311SdIvSQQuSZLEZXMttw3c2VZxqhS7wYhBc5IcGew5oKsJulUPFmKyfBWmEGIIA/slOTXV+2hhlvpLzn5XXVJ2NobYWLSeHnqOHh3QL0nqkkTgkSRJXJZOW6e7jmHgTFJJWT0AqSYHBr3Oc1DzOfUalgRmL7NMQgifmh0/m2BjME09TZxpPgPA/L7i7QPnm3A6NVWX1NcvqWv//v6ZpIoC1RxWiAAiSZK4LK56pGhLNBHm/l1s5+rbAciMNA8e1NSXJMlSmxAjgslgYn6iKsx21SXNSIkkyKSnuctGcX0HACEL+ppK7t8P8VPAEgG2Tqg74Z/AhfATSZLEZSltV/VIHjvbnE7Od6op++yU6MGDpB5JiBEnL0XtcttTrZIks1HPnDTPJbfQAXVJmt0O41RiJXVJItBIkiQui+s4koE722yVlVQGqV+uEy66s03qkYQYKVx1SQW1BVgdVmDwYbfm8eMxxMSg9fTQfezYgCW3fN8HLIQfSZIkLou3nW3WM2eoCo0DICshfPAgmUkSYsTJjswmITgBq8PqPqLEfdjtebXRQtUluY4oGdAvSWaSRICRJElcFm872zpPn6U6NBaArDjZ/i/EaKDT6ViUsgjobwUwJz0Kg15HZUs3VS3dAO6mkl35B/qW23Sqg357rT/CFsIvJEkSl8XbTFLp2XIcegMWnUZSRJDngJ426FQ732S5TYiRxVWXtLdqLwChFiPTU9SGDNcRJSHz+3a4FRaiGUIgYZoaLK0ARACRJElcUkdvB0096hdneviA7f9Vamo+LVSPfqjt/yFxEBSJEGLkWJSsZpKKmorc/227znFzHXZrmTgBQ2QkWlcXPUVFctitCEiSJIlLcu1siwmKIdysao80m43SdtUzJdvbwbay1CbEiBUXHMek6EkA7KtWdUYXHnar0+sJnj9gyU2SJBGAJEkSl+Ta2TZwFqm3rIzKYPVLNXtc7OBBkiQJMaK5Drx19UtakKl2qp6u7aC5sxeAEHeSlN+/w62qEOxWH0crhH9IkiQuaaidbZV9O9uy48MGD5IkSYgRbWC/JE3TiA2zMD5ebcAoKFVL6e4kqaAALTIDQmLBYYXqI/4JWggfkyRJXJK3nW3W02eoDIsHINPrzjbpti3ESDY3cS4mvYmazhrOt50HYGGW55Jb0NQp6ENDcba3Yz17Fsb1LblJ8bYIEJIkiUvyNpPUfraYupAoQLb/CzEaBRuDmZswF+hfcpuf0Ve87apLMhoJnquu6dqfP6AuSfolicAgSZK4JNe5bQNrks6V1aPp9IQaIS7sgnPbejuhvVp9L9v/hRixXP2SXEeUuGaSjla00t3rAAYsuR040F+XVL4fNM3H0Qrhe5IkiYtq622j2arqE1zLbU6rldI2VdiZFRuCTnfh9v/z6jUoCkJifBSpEOJKuY4oya/Jx+a0MS46mKSIIOxOjcLyvrqkvsNuuw4cQEuZDXqj+ktQa7m/whbCZyRJEhfl2tkWGxRLqEktq/WWlFAZ0nccSaKXHkiy1CbEqDAlZgrRlmg6bZ0crT+KTqcbcI6bSpKCZ0xHZ7HgaGqit7wGkmaqwdIKQAQASZLERbkKOj2Kts+coTKsL0mSnW1CjFp6nZ7cZLWE5l5y62sFcKC0ry7JbCZ49mzA1S9pwJKbEGOcJEnios61ql1qWZH9tUUeB9vGhQweJEmSEKOGuxWAq19S30zSwdJm7A4n4LnkJsXbIpBIkiQuymuSNGD7f1aczCQJMZq5mkoeazhGe287kxLCiQgy0tnr4ER1G+DZVFIbpxImao6qTRpCjGGSJImLKmlVCc/AJKmlpJTGYFWLlBXrZft/oyRJQowWyWHJZEZk4tAc7K/Zj16vY/4F57gFz8oBkwl7bS22NiA8BTSH6r4txBgmSZIYksPpcPdIciVJjo5O98626GAjkSEmz0G9XdBWob6PHe+zWIUQn57rwNv+I0o8m0rqg4MJnqkKtj3PcZMlNzG2SZIkhlTVUYXNacNisJASmgJA79kB9UjeirYbz6rX4Gjou04IMbK56pL2Vu8FYGFWX/H2+Wa0vn5Inv2S5LBbERgkSRJDOtem6pEyIjIw6A0A9Jw8ScXFjiNpPKNe4yb5JEYhxGe3IGkBBp2B0rZSqjqqmJkahcWop7Gzl+J6VXcUskCaSorAI0mSGJK3ou2eE0X9Rdve6pEaXEnSxGGPTwhxdYSbw5kZp5bT9lTtwWzUMzstCoADfUtuwXPmgl6Prbwcmy4BDBboboLGYn+FLcSwkyRJDMlVtJ0d2V+A3XPyJOXhCQBMSPCy3NZwWr3KTJIQo4pryW131W6g/4gS1zluhrBQgqZNA6Dr4BFIVWe6SV2SGMtGRJL005/+lMzMTIKCgsjNzWX//ouvc2/ZsoUpU6YQFBTEzJkzefvttz0+1zSNxx9/nOTkZIKDg7nuuus4c+aMxzWZmZnodDqPr6eeeuqq/9lGswtnkjS7nZ5TpygPu1iSJMttQoxGriNK9tXsw+F0uHe4uYq34YJ+Sa5WAJIkiTHM70nSq6++yvr163niiSc4ePAgs2bNYtWqVdTV1Xm9fvfu3axZs4Z7772XwsJC7rjjDu644w6OHTvmvuaZZ57hhRde4KWXXmLfvn2EhoayatUqenp6PH7W97//faqrq91fDz744LD+WUebC5Ok3nPnqNcF0W0KwqDXkXHhcpvT2V+4HSvLbUKMJjPiZhBmCqPV2srJppPMTY9Cr4Pypm5qWtXvTnddUn5+f11SRb6/QhZi2Pk9SfrRj37Efffdx7p165g2bRovvfQSISEh/OY3v/F6/fPPP8+NN97Iv/3bvzF16lT+67/+i7lz5/KTn/wEULNIzz33HI8++ii33347OTk5/P73v6eqqoqtW7d6/Kzw8HCSkpLcX6GhXmpsAlRTTxMt1hag/0iSnpMnKQtPBCAzNgSz8YLHp60SbF2gN0F0BkKI0cOoN7IgSc0O7aneQ3iQiWkpEUD/klvI3Lmg09FbUoI9bIIaWFcE3S3+CFmIYefXJKm3t5eCggKuu+4693t6vZ7rrruOPXv2eB2zZ88ej+sBVq1a5b7+3Llz1NTUeFwTGRlJbm7uoJ/51FNPERsby5w5c/jhD3+I3W4fMlar1UpbW5vH11hW3KKKMVPDUgk2BgOqaPuy6pFissFgGvy5EGJEG3REiWvJra+ppCEqCssktZTedaIMojMBDSoP+DxWIXzBr0lSQ0MDDoeDxMREj/cTExOpqanxOqampuai17teL/UzH3roITZv3swHH3zA1772NX7wgx/w7W9/e8hYN27cSGRkpPsrLS3t8v+go9DZFrVsNjGqf9msp6iI8r6ZJK9JkmupTXa2CTEquY4oKawrpNvezUJvdUnzvSy5Sb8kMUb5fbnNX9avX8+KFSvIycnh/vvv59lnn+XFF1/EarV6vX7Dhg20tra6v8rLy30csW+5ZpLGR6mu2ZqmYS0qouyydrZJkiTEaJQRkUFyaDI2p42C2gJ38fap2nZau23Ahf2SpKmkGNv8miTFxcVhMBiora31eL+2tpakpCSvY5KSki56vev1Sn4mQG5uLna7nfPnz3v93GKxEBER4fE1lrlmklxJkr26Gkdrq7smaWJC+OBB9afUq+xsE2JU0ul0Hktu8eEWsuJC0TQoKO2rS+qbSbKeOoUjeroaWHEAnA6/xCzEcPJrkmQ2m5k3bx47duxwv+d0OtmxYwd5eXlex+Tl5XlcD7B9+3b39VlZWSQlJXlc09bWxr59+4b8mQCHDh1Cr9eTkJDwWf5IY4Kmae4kaUKUKs7sKSqi1RxCm0XNIGXHeylyrz+pXuOn+CROIcTV51py21PtqktSR5TsP9cMgDEuDnNWFmgaXaWdYA6D3naoO+GfgIUYRn5fblu/fj2/+tWv+N3vfkdRURH/8i//QmdnJ+vWrQNg7dq1bNiwwX39ww8/zLZt23j22Wc5efIk3/3udzlw4AAPPPAAoP4m9I1vfIMnn3ySv/zlLxw9epS1a9eSkpLCHXfcAaji7+eee47Dhw9TUlLCyy+/zCOPPMKXv/xloqOjfX4PRprGnkZara3odXr39n9VtK1mkVKjggkxGz0HdTZCZ736Pn6yL8MVQlxFucm56NBxpvkM9V31gw67hQF1SQWFME59T9len8cqxHAzXvqS4XXXXXdRX1/P448/Tk1NDbNnz2bbtm3uwuuysjL0+v5cbvHixWzatIlHH32U//zP/2TixIls3bqVGTNmuK/59re/TWdnJ1/96ldpaWlh6dKlbNu2jaCgIEAtnW3evJnvfve7WK1WsrKyeOSRR1i/fr1v//AjlGsWKS08jSCjumcDt/97rUeqL1KvURlgllYKQoxW0UHRTImZQlFTEXur97Iw61oAjlS00GNzEGQyELJwAS1btqi6pK8vgZKdULobFt7n3+CFuMr8niQBPPDAA+6ZoAvt3Llz0Ht33nknd95555A/T6fT8f3vf5/vf//7Xj+fO3cue/fK33qG4i7ajhzvfq+n6ATn49ROlilJXuqR6vqSpISpwx6fEGJ45aXkUdRUxJ6qPdyafSsJ4Rbq2q0cLm8hNzvWPZPUc/w4joT7MIBKkjQNdDq/xi7E1eT35TYx8gwq2m5uxl5VzfmIZAAmJXor2pZ6JCHGCtcRJXur1V8mF2R5LrmZkpMxpaaCw0F3nV41kO2ogaYS/wQsxDCRJEkM4ppJchVtW0+dQgNKo1IAmOx1JkmSJCHGijkJcwgyBFHfXc/ZlrMsyOgr3j7f7L7GfY7bwSOQOk+9Wea9CbAQo5UkScKDpmmcbfacSeo5UUSzJZw2YzB63VA1SX1JUoIkSUKMdmaDmXmJKvHZU7XHPZN0sLQZh1MDLuiXlKFmnijd7ftghRhGkiQJD1WdVbTb2jHqjWRHZgNq+/+5vqW2zLhQgkwGz0GdDdDVAOggTna2CTEWuPslVe9hSlIE4RYjHVY7J6rUkUzuuqQjR3CmqFklSj/xS6xCDBdJkoSH002qa/b4yPGY+s5f6zlxgtII1Yhzsrd6JFfRdnQGmEN8EqcQYngtSl4EQEFtAQ7NxsK+2aTdxQ0AmNLTMSYkoNlsdDcFg04PzeehtdJfIQtx1UmSJDycalZdsydFq67Zjo5OektK3EXbXuuR3EXbsrNNiLFiUvQkYoNi6bZ3c7j+MIsnxAHwSXEjoHYRu/slFR6HpBw1UOqSxBgiSZLwcLpZzSRNjlHLZj3Hj4OmURqrDvT1OpNUc1S9Jk7zSYxCiOGn0+lYlKJmk3ZX7WbJhFgA8s810Wt3AhCySLUF6dy7V+qSxJgkSZLw4EqSJkarQ2p7jh3FgY7S0HhgiJmk2uPqNXHG4M+EEKOW+4iSqj1MTgwnLsxMt81BYZna5Rbad9RT9+HDOPsKvSVJEmOJJEnCrcvWRVlbGQCTo9VMUveRo9SExmLVGbAY9WTEXtBN2+noP7MpaaYvwxVCDDNXXdKJxhO0WlvJG++55GZOS1P9kux2uhotalB9kTqmSIgxQJIk4Xa25SwaGrFBscQGq6n1nqNHKYlU/ZGmJIVj0F/QTbf5PNi6wBgMMdk+jlgIMZwSQxMZHzkeDY19NftYMl79Xth9tsF9jXvJ7eDJ/j5pUpckxghJkoTbhfVI9sZGbFVVFEelAjAtJWLwIFc9UsJU0BsGfy6EGNXcrQCq9rCkr3j7UHkLnVY7AKGL1Oede/dIXZIYcyRJEm6nmjx3tnUfVQnQ+STVeXtaspckqfaYek2cPvwBCiF8zpUk7a3ey7joYNJigrE7NfafU0eUhPbNJFmLTuKIm6UGlX7sl1iFuNokSRJuJ5vUVn5XktRzVCVAxeGqR9K0lMjBg1xF21KPJMSYND9xPka9kcqOSsrby1niqkvqW3IzxsdjmTgBNI2uuiA1qPoIdDX5K2QhrhpJkgQADqfD3SNpeqyaFeo+fJhmSxgNOgs6napJGqRGZpKEGMtCTCHMjp8NqCW3C/slAYT0Lbl1uOuSNDj/ka9DFeKqkyRJAHCu9Rzd9m6CjcFkRGSgOZ10Hz5MSYQq2s6KDSXUYvQc1NMKrWo3nCRJQoxdA48oWdxXvF1U3UZjhxXoX3Lr2rMXsleoQSW7fB6nEFebJEkCgBNNahv/1JipGPQGeouLcba3cy4+U71/saLtiHEQHO2jSIUQvubql7Sveh9RIQb3rPKeEjWbFLJgAej19JaWYo/q67xdstMfoQpxVUmSJAAoalTnr02NVUeLdB06BMD5cWqn23RvSVKVuoaU2cMcnRDCn6bFTiPCHEGHrYMj9UdY7K5LUkmSISKCoBmqmWxHpU6d49ZUDK0VfotZiKtBkiQBqGZxoH4ZAnT3JUnuom1vO9uq1TUkzx7m6IQQ/mTQG1iauhSAnRU7WTpRLbl9dKYeTdMACF2kGk925R+GlLlqoCy5iVFOkiSBw+mgqEnNJE2L6UuSCg/RabRQ6lBddGeketnZJjNJQgSMz6V9DoCd5TtZlB2L2aCnormb4vpOoL8uqXPPXjRXXdI5SZLE6CZJkqC0vZRuezdBhiAyIzNxtLTQW1JCcdQ4NCA1Kpi4MIvnIGs7NJ5V38tMkhBj3pLUJRh1Rs61nqO+p5Lc7BgAdp6qAyB47lx0Fgv2ujps5vFqUMku6JtpEmI0kiRJuJfaJsdMxqg30n3kCADFWar3Uc44L7NINUcBDSJSISzeV6EKIfwk3BzOvCR1iO3O8p2smJygvj9VD4A+KIiQhQsBaD/VBsYg6KiBhtN+iVeIq0GSJMGxBtXryNUfqavgIABnU1XRds64qMGDXEttMoskRMBYMW4FALsqdrFisvrL0f5zTe4jSsKuuQaAjo/2QrqqUZJdbmI0kyRJcKRezRzlxKutu10HDgBwyqKKM73OJLmLtmcNe3xCiJFhedpyAA7WHiQ23E5aTDC9Die7+xpLhi1TSVLXwYM4U1XbACneFqOZJEkBrtfR6z6OJCcuB2dPDz1HjtBqDqHSqh4PKdoWQgCkhacxIWoCDs3BJ1Wf8Dn3kpuqSzJnZmJKTwebje62KDXo/MfgsPspYiE+G0mSAlxRUxE2p41oSzTjwsfRfeQIms1GSYbqeZIdF0pksMlzUE9rf51ByhwfRyyE8Kfl49Rs0q7y/iW3naf6WwG4ltzaCishKBKsrf0zz0KMMpIkBbij9aprdk58Djqdzr3UVjJZFWh6XWqrPAhoEJUBYQm+ClUIMQKsSFsBwMeVHzM/MxKzUU9lSzfF9R1A/5Jbx0efoGWrtgGcec8foQrxmUmSFOBc9Ugz49ROtm5XPVJ0OjBE0XaFuoZxC4Y9PiHEyDIzbiYxQTG029opaj7MomxVu/jBSbXLLWThQnRmM/bqauyRs9Wg09v8FK0Qn40kSQHuSEN/0bZms9FVeAgNONKr+iLNy/ByJltFvnqVJEmIgGPQG7gmVc0W7SzfyYpJfUtup1Vdkj44uL8VQKke0EH1YWiv8Ue4QnwmkiQFsMbuRio7KtGhY0bcDLqPHUPr7qYqOZtWq5Mgk55pF57ZpmmSJAkR4D6XrpbR/lb2N5ZNUue45Z9r7m8F0Lfk1r77EKT2HVEiS25iFJIkKYAdqj8EQHZkNuHmcLr27gXg7OxlgFpqMxkueESaSqC7CQwWSJrpy3CFECPEkpQlhBhDqOmsoYNiMmJD6HU4+fC0WnIL7Sve7ioowJnRV5d0+l1/hSvEpyZJUgA7WKuaRs5NVH/T69yjkqSTSZOAoZba+uqRkmeB0Tz8QQohRpwgY5C7Z9L20u3cMC0RgHePqyU1c2YmprQ01QqgS31G8Qdgt/olXiE+LUmSAtjAJMnZ3U13YSEAR7UwAOale0uS9qtXWWoTIqCtylwFwHul73HDdJUI7Siqw2p3oNPp+lsBFJRDWBLYOqH0E7/FK8SnIUlSgOqydVHUVATAvIR5dB08qAq3UzMpbukFYK63maQyNdtEmiRJQgSygUtuxpByEsIttFvt/d23P6eW2dp37kSbeL0adFrqksToIklSgDpUfwiH5iAlNIXksGR3PVLJAvWLLTsulJjQC5bTupqg9rj6PmOJL8MVQowwQcYgd8+k7aXvsWp6EgDvHlNLbqG5C9GHheGob6DXpJbwOb1Nbf4QYpSQJClADapH2r0HgOMpU4Eh6pHK9wEaxE6UJpJCCG7IvAHoq0uarn4nvHeiFrvDic5sJmyZ2gTSdqID9CZoPgeNZ/0WrxBXSpKkAHWwrj9Jsjc303PihHrfEQ5A3vjYwYNc9QQZi30SoxBiZFuautS95BYcVklUiImmzl7yzzcDEH7dSgDadnwMmX2zz7LLTYwikiQFoF5Hr/s4knkJ8+j8+GPQNOzTZnKsrhPA3UXXQ+lu9SpLbUIIwGKwuHsmvV+xneumeu5yC122DEwmes+dwx4zXw2S7ttiFJEkKQAdqjtEj6OHuOA4siKz6Nj1IQBn56/EqUFGbAgpUcGeg6wdUHVIfZ8pSZIQQrkhQy25vXf+PVb1LbltO1aD06lhCAsjdNEiANrOG9WA0k+gs8EvsQpxpSRJCkB7q1WR9qLkReB0qpkk4Ehff6RFWV5mkSr2g+aAqHSIHOezWIUQI9uS1CWEmkKp7aolIqqaULOBmrYeDle0ABCxSiVRLTvyVX81zQkn/+rHiIW4fJIkBaCBSVLP0aM4WlrQh4dT0Kn+pue1Hqlkl3rNWOqrMIUQo4DFYGFluqo9eq/0LT43pX82CSBs5UowGLAWFWFPVoXcnHjDL7EKcaUkSQowrdZWjjeqbfy5ybl0fPgRANqSZRyragOGqEcqfl+9jv+cT+IUQowenx//eQDeOf8O109TB97+9Ug1TqeGMTqa0Lw8ANrKgtSAcx9Cd7NfYhXiSkiSFGDya/Jxak6yIrNICk2iY+dOAI5PX4pTg+z4UJIigzwHdTZAzRH1ffYKn8YrhBj5FiQuIDEkkfbedgxhRYSaDVS2dFNQphKhiBtVd+6WHQchYRo47XDybX+GLMRlkSQpwAxcarNVVqqt/3o9+0NTAVg+KX7woJKd6jVxpvRHEkIMYtAbuDX7VgDeLXuTG2ckA7C1sBK4cMlNnfnGsf/zS6xCXAlJkgKIpml8Uql6HS1KXkT7jh0ABM+dy0elaqnNa5JU/IF6Hb/CF2EKIUYh15LbxxUfc+00df7jW0er6bU71ZLbUrUrtu28RQ0o2Qkddf4IVYjLJklSADnXeo6KjgpMepNKkrb/DYCGZauoau3BYtQPrkfStP56pGypRxJCeDc+ajw5cTnYNTs1zg+JD7fQ0mVj1+l6ACI/fxsATW/tRkuZq3bLHt/qx4iFuDRJkgLIzoqdACxMWoi5vYeuggIADiRPByA3O5Ygk8FzUF0RtFeBwSKdtoUQF7V60moAtha/xm05asntzwXlAISvvBZ9SAi2igps0aqQm6Nb/BKnEJdLkqQAsqtcbeNfnracjg8+AKcTy7SpfFLXq973ttR2+h31mr0cTMGDPxdCiD43Zt5IiDGE823nmZallvB3FNXR0GFFHxxM+PXXA9B83AboVP+1pnN+jFiIi5MkKUC0Wls5VH8IgOXjltP2jjoaQL/yBvaWNAKwYrKXJOlUX5I0+SZfhCmEGMVCTCHclKV+V+Q3v8GstCjsTo3XD6oC7ojbVN1S6zsfoWX1FXAffsUvsQpxOSRJChAfV36MU3MyMXoiCT1mOvfsAeDg5DxsDo3x8aGMjw/zHNReCxUH1PeTJEkSQlza30/6e0AdU3JLTjQArx4oR9M0QhctwpiUhKO1lW7dDDXg0CZwOv0VrhAXJUlSgNhRpnayLR+3nLZt74LDQdDMmeyodQCwanrS4EFn3gU0SJkLEck+jFYIMVrNiJvBjNgZ2Jw2rMG7CTLpOVvXQUFpMzqDgagv/B0A9R+UQ1AktJbDuV1+jloI7yRJCgBdti4+qlCdtW/IuIG2t94CIOjmW9h5Sm3B9ZokuZfabvZJnEKIseFLU78EwBvnNnNrXwH3H/aWAhD5hdWg09G1Jx9H39IchX/0S5xCXIokSQHgw4oP6XH0kB6eTnZXON2FhaDTcWRqHp29DpIjg8gZF+k5qKcNzqrZJ6lHEkJciVWZq4gJiqG2q5bJmbUAvH20mvp2K+ZxqYQuVjtlW8v7fu8U/QU6G/0VrhBDkiQpAGw7r4q0V2Wuou0NdbBkyKJctpd3q/enJ6HT6TwHnXwLHFaImwyJ030arxBidDMbzKyeqNoBfFj/R+akR2FzaLyaXwZA1J2qbqnh9U/QkmaBoxcKf++3eIUYiiRJY1ynrbN/qS39OlpeU0cBWG5f7T6l++aZXuqNXEcGzFBT40IIcSXWTFmDSW/iUP0hlk1T7/1xbxk2h5PwlSsxJiTgaGyi27xAfZj/G3A6/BewEF5IkjTGvV/2Pr3OXjIjMkk90YC9qhp9ZCT702bSYbUzLjqY+RnRnoM6G6Gk7yiSGV/wfdBCiFEvPiSe28arLttnbK8SF2ahpq2Hvx6pQmcyEf2lNQDUvV2CFhwNrWVw5j1/hizEIJIkjXGvn30dgFuyb6H1z2p2KPK223jjqCrYvmN2Knr9BTNFRW+oU7qTciBuok/jFUKMHfdMvwcdOj6q+oDb50UA8ItdJWiaRtQ//AM6s5nuYyexp96gBuz9uR+jFWIwSZLGsPK2cvJr8tGh49boa2h/X53B5rz1Dvd5SnfMSRk88NAm9Tpjta9CFUKMQVmRWVybfi0AjeathJgNnKxp58MzDRhjYoi45RYAGvLtoDOoVgBVh/wYsRCeJEkaw7YWbwVgccpiLFt3gN1O8OzZvNMejN2pMSM1ggkJ4Z6Dak9ART7ojTBrje+DFkKMKV/L+RoA71f8lRtz1GzSzz44C0DsP60DoOW9vTgy+2aTdr/g+yCFGIIkSWOUw+ngjbNqJ9vtGbfQ/MpmAKLX/iMv71P9Su5akD544MG+HSaTboTwRJ/EKoQYu6bGTuW69OvQ0OgJewuzQc++c03sLm7AMnEiYStXgqbReEolUBx/Xc5zEyOGJElj1M6KndR21RJpiWTu4U4czc0YU5I5Nn4eJfWdhJoN/N2cVM9Bth44opIp5n3F5zELIcam+2fdD8BHtX9hVY46/ujH20+jaRpxX70PgMa/7ME5biloTvj4R36LVYiBJEkao/54QnWw/fsJq+n8/csAxNz9ZV4+UAHAHXNSCbMYPQcdfx26myEyDcZf69N4hRBj1+SYydyUqZrSNoe8itmoJ/98Mx+fbSB41ixCFi0Cu53G832d/wtfhqYSP0YshCJJ0hhU1FjEgdoDGHVGbq1LxXrmLPqwMHpu/DzvHVfdb7+8KMNzkKbBnp+o7+evA73Bx1ELIcayh+Y+hElv4nDTLlZMV79fNr59EodTI/7hhwBoeG0vzpTFoDlg1zP+DFcIQJKkMemPRWoW6fqM69G/1DeLtHYtvymsx+7UWJgVw9TkCM9BJR9A7TEwhcL8f/J1yEKIMW5c+Di+PPXLAFQaf014kJET1W3838EKQubMIezaa8HppP5kjBpw5FWoK/JjxEJIkjTmlLeX83bJ2wB8oW0S1tOn0YeFobtzDa/sV0cC/OuK8YMH7n5Rvc5dC8HRgz8XQojP6J9z/pmYoBjKuorIndoEwP979xSdVjvx33gYdDqa3j6IPfkaVZu0bYOa5RbCTyRJGmN+eeSX2DU7i5MWEf9z1UgyZu1a/nCsia5eB9NTIlg+Kd5zUEUBFL8POj0sut8PUQshAkGEOYJvzv8mAIesz5MSZaau3cqPt58maNIkIlerDv9VH9jRDGY1w336XX+GLAKcJEljSFlbGW8WvwnAl2sn0FtSgiEmBt1dd/O/n6gttf+yYvzgw2zf/756nbUGojN9GLEQItB8PvvzLEhagFXrJDlDnSv5m0/OcayylYT169FHRNB5qBRr5Ao14N0NYOv2X8AioEmSNIb89NBPcWgOliYsIuGnahYp/uGHeSm/hrYeO1OSwrlpxgWH2ZbsgpKdoDfB8n/3fdBCiICi0+l4bNFjWAwWTve+Tk6mHacGG147ihYZRfxDqoi7fPM5tNBEtctt19N+jloEKkmSxoiC2gLePvc2OnSsKQjG2dqKZdIk2q+9id/vUc0jN9w8FcPAc9qcDvjbd9X389dBdMbgHyyEEFdZVmQWD899GIDKoOcJC9JztLKVF98/S/QX7yJo5kzsTZ00lE9SAz55AaoP+zFiEagkSRoDHE4HG/dtBOC2yKXE/3E7AEmPPcoPtp2m1+Hkmolxg2uRCn4LVQfBEgHL/s3HUQshAtndU+9mYdJCenX1JKR/AMBP3j/Dwco2Ujb+AJ3JRMN7xfRGLlAtAV77GvR2+TlqEWgkSRoDXjn5CqeaTxFuCufvfnUKgOgvreHD4DS2Ha/BqNfxnVumeg7qqIcd31PfX/sohCX4OGohRCDT6/T899L/JtoSTb3hHbJTG3Bq8OCmQjqS0oh78EEASjfXoQXFQn0RvPuffo5aBBpJkka5kpYSnjv4HAD3lKYRUlyFKSUFy78+xONvHAPg/uXjmZI0oC+SpsFb66GnFZJyYME/+yFyIUSgSwpN4pnlz6DX6akLfZG4CAdVrT18fdNBIr/yFUIX52FvtVF9OBkNHRT8Lxz5k7/DFgFEkqRRzOa0seHjDVgdVhbos7nmd0fAYCD5maf5zrvF1LVbyY4L5YFrJ3gOLPwDFP0F9Ea47QXpri2E8JtFyYv4xtxvoDNY6Y57AYsJ9pY08fhfi0h+5hmMCQm0FjbR3jldDXjjAag44N+gRcCQJGkUe3r/05xoPEGEIZR7f1aCDkhY/wh/7o3j7aM1mAw6nv2HWQSZBiRBdUXwTt8utmsfg5Q5foldCCFcvjL9K9w1+S70llrMSZvQ6eCV/eW8cLCR1OefQ2c2U/nXRnoYDw4rvLIGGov9HbYIAJIkjVJ/OvUnXj31Kjp0/OtfbMQ02wm/4QZOLr+NJ986AcCGm6YyJ31A9+yOetj0D2DrgqzlsPghP0UvhBD9dDod/7HwP1iZvhLCjhCS9BcAXnz/LL9uCiP56acBPaV/7sSmT4TOOvj97dBS7t/AxZgnSdIotL10Oz/Y9wMAvpQfxNwjXQTPmUPr+kf52h8OYnNo3JKTzLolmf2DrO2weQ20lEFMNtz5W9DLv34hxMhg1Bv54bIf8rm0z6GP2k1QwnsA/Gj7aX5GBvH/+R2cdj3n/g/suhhoLYff3iwzSmJYyf9LjjI7ynbw7V3fxqE5uPakidv+1o5l4gRan3iatX84RIfVTm5WDM/eOau/s3ZPG/zhC1CRD0FR8KU/QUiMX/8cQghxIZPBxLPLn+XGzBsxxb6PJeGvAPxiVwmP6aYQseE7OKwGzm01YXdGqL/0/c8NUJ7v58jFWCVJ0ijyctHLrN+5HrtmZ+lJHV/d2k3wtGlUPfkiX958gsbOXmakRvDLtfP765BaK+C3t0DFfpUgrd0KcRP9+ccQQoghmQwmnl72NOtmrMMc+zFByVvQ6Zy8c6yGdU1pdHznv7HbLJS8EYy1OwK6GuB/b4L9v5LDcMVVp9M0eao+jba2NiIjI2ltbSUiIuLSAz6DLlsXG/dvZOvZrQBce8jJfduchOct5p0vfpMf7jyPw6mxMDOGX39lPhFBJjXw3Ifw53vV+n1IHHz5/yBl9rDGKoQQV8vbJW/zvT3fo60tHmvlP+K0h2E26vnXSUFc97PvYOpoJGVxB+EpHWrAhOvh889DZKp/AxdjhiRJn5KvkqTdlbt5ct+TlLeXo9NgzU4Ht+/V6Lznfp6NWcDec80A/N2cVH7wdzMJNhtU/6P3/xv2/0L9kMQZsOYViEoftjiFEGI4nG89z6OfPEph9Vl6qu7E0TkFgOxoC1858zfmf7yV2MmdJMzqQKd3gjkMlj4CeV8HU7CfoxejnSRJn9JwJ0nHGo7x88M/58OKDwGIa9V44E0HKYznzdUPs6Xchs2hEWwy8J1bpnJ3bjo6W7fqgbTraehqVD9o/j/B9f8FlrCrHqMQQviCw+lg86nN/LTwZzQ1ZGOtvQXNEQ7AREsvtx94k5WNu8mY30BInA0ALTQB3aJ/gblrITTOn+GLUUySpE9pOJKkjt4O3i9/n/87tpmDLUcBMDg0ri+AaacnsnfJP/B+byQ2p/pXtnxSPE/ePp00eykcfgUK/wjdTeqHxU2CG5+CCSuvSmxCCOFvrdZWfn3017xyYittdQvobVwKmgUACzYWVR7hlp593JyZT1ioFQBNZ4CJ16HLuQsm3QTmEH/+EcQoMyKSpJ/+9Kf88Ic/pKamhlmzZvHiiy+ycOHCIa/fsmULjz32GOfPn2fixIk8/fTT3Hzzze7PNU3jiSee4Fe/+hUtLS0sWbKEn//850yc2F+w3NTUxIMPPsibb76JXq9n9erVPP/884SFXd6My9VIkhxOB8cbj/PRyW3sLvuQY7YynDr1r0PXayHr9BRCmqdyMn4GzZrRPW5+WjjrJzeyuHMHlOyC1rL+HxqdCUsehjn/CAbTp4pLCCFGslZrK1tOb+G1k+9ytiwRW3Memj3K/blRszKlt4RrDCdZHFzENH0pMbSD3ogzdjr6ydehy16qShFC48G1E1iIC/g9SXr11VdZu3YtL730Erm5uTz33HNs2bKFU6dOkZAw+NDV3bt3s2zZMjZu3Mitt97Kpk2bePrppzl48CAzZswA4Omnn2bjxo387ne/Iysri8cee4yjR49y4sQJgoKCALjpppuorq7mF7/4BTabjXXr1rFgwQI2bdp0WXFfjSTplYLf8INjP/Z4L7lRY/lxjYnG5Xw7+hb3+1EhJm6akczds6OZ8YccdSq2i8EME2+A2V+CSTfKMSNCiICgaRrHG4/zxtm/8M7Jk9TWpmJvn4lmjxx0bQQdTNBVkaGrJV7XSlzfVxTdRJiMRIREEBkRS0RcMuaMqRhix6lWKdGZUtsUwPyeJOXm5rJgwQJ+8pOfAOB0OklLS+PBBx/kP/7jPwZdf9ddd9HZ2clf//pX93uLFi1i9uzZvPTSS2iaRkpKCt/85jf51re+BUBrayuJiYn89re/5Ytf/CJFRUVMmzaN/Px85s+fD8C2bdu4+eabqaioICUlZdA/12q1YrVa3f+7tbWV9PR0ysvLP3WSVNpayrrX7mJqOcyxJrEwfi6Z0/MImT8ffWQU9/3+ADNSI1k8Ppa5GdGYDH0dG35zEzhskLUMsq6BtIVgDv1UMQghxFigaRpVHVUcqD3IrpKTnKjsprYpBHt3KpotisvtePML07MsMZzof+Mft8K4+Z85vvDw8P7edWL00PzIarVqBoNBe/311z3eX7t2rXbbbbd5HZOWlqb9+Mc/9njv8ccf13JycjRN07Ti4mIN0AoLCz2uWbZsmfbQQw9pmqZp//M//6NFRUV5fG6z2TSDwaC99tprXv+5TzzxhAbIl3zJl3zJl3xd8Vdra+tl/j+jGEn6C138oKGhAYfDQWJiosf7iYmJnDx50uuYmpoar9fX1NS4P3e9d7FrLlzKMxqNxMTEuK+50IYNG1i/fr37fzudTpqamoiNjaW9vZ20tLTPNKs0FrW1tcl9GYLcG+/kvgxN7o13o+W+hIeH+zsE8Sn4NUkaTSwWCxaLxeO9qKgoAPcUakRExIj+j9Rf5L4MTe6Nd3Jfhib3xju5L2I4+PVYkri4OAwGA7W1tR7v19bWkpSU5HVMUlLSRa93vV7qmrq6Oo/P7XY7TU1NQ/5zhRBCCBFY/Jokmc1m5s2bx44dO9zvOZ1OduzYQV5entcxeXl5HtcDbN++3X19VlYWSUlJHte0tbWxb98+9zV5eXm0tLRQUFDgvub999/H6XSSm5t71f58QgghhBi9/L7ctn79eu655x7mz5/PwoULee655+js7GTdunUArF27ltTUVDZu3AjAww8/zPLly3n22We55ZZb2Lx5MwcOHOCXv/wloJa+vvGNb/Dkk08yceJEdwuAlJQU7rjjDgCmTp3KjTfeyH333cdLL72EzWbjgQce4Itf/KLXnW2XYrFYeOKJJwYtxwU6uS9Dk3vjndyXocm98U7uixhW/q4c1zRNe/HFF7X09HTNbDZrCxcu1Pbu3ev+bPny5do999zjcf2f/vQnbdKkSZrZbNamT5+uvfXWWx6fO51O7bHHHtMSExM1i8WirVy5Ujt16pTHNY2NjdqaNWu0sLAwLSIiQlu3bp3W3t4+bH9GIYQQQowufu+TJIQQQggxEvm1JkkIIYQQYqSSJEkIIYQQwgtJkoQQQgghvJAkSQghhBDCC0mSLtPPf/5zcnJy3F1d8/LyeOedd9yf9/T08PWvf53Y2FjCwsJYvXr1oIaWY9Wl7s2KFSvQ6XQeX/fff78fI/aPp556yt2iwiWQnxsXb/clUJ+Z7373u4P+3FOmTHF/HsjPy6XuTaA+M2J4SZJ0mcaNG8dTTz1FQUEBBw4c4Nprr+X222/n+PHjADzyyCO8+eabbNmyhV27dlFVVcUXvvAFP0ftG5e6NwD33Xcf1dXV7q9nnnnGjxH7Xn5+Pr/4xS/IycnxeD+QnxsY+r5A4D4z06dP9/hzf/zxx+7PAv15udi9gcB9ZsQw8ncPgtEsOjpa+/Wvf621tLRoJpNJ27Jli/uzoqIiDdD27Nnjxwj9x3VvNE31unr44Yf9G5Aftbe3axMnTtS2b9/ucS8C/bkZ6r5oWuA+M0888YQ2a9Ysr58F+vNysXujaYH7zIjhJTNJn4LD4WDz5s10dnaSl5dHQUEBNpuN6667zn3NlClTSE9PZ8+ePX6M1PcuvDcuL7/8MnFxccyYMYMNGzbQ1dXlxyh96+tf/zq33HKLx/MBBPxzM9R9cQnUZ+bMmTOkpKSQnZ3N3XffTVlZGSDPCwx9b1wC9ZkRw8fvx5KMJkePHiUvL4+enh7CwsJ4/fXXmTZtGocOHcJsNhMVFeVxfWJiIjU1Nf4J1seGujcAX/rSl8jIyCAlJYUjR47w7//+75w6dYrXXnvNz1EPv82bN3Pw4EHy8/MHfVZTUxOwz83F7gsE7jOTm5vLb3/7WyZPnkx1dTXf+973uOaaazh27FhAPy9w8XsTHh4esM+MGF6SJF2ByZMnc+jQIVpbW/nzn//MPffcw65du/wd1ogw1L2ZNm0aX/3qV93XzZw5k+TkZFauXElxcTHjx4/3Y9TDq7y8nIcffpjt27cTFBTk73BGjMu5L4H6zNx0003u73NycsjNzSUjI4M//elPBAcH+zEy/7vYvbn33nsD9pkRw0uW266A2WxmwoQJzJs3j40bNzJr1iyef/55kpKS6O3tpaWlxeP62tpakpKS/BOsjw11b7zJzc0F4OzZs74M0ecKCgqoq6tj7ty5GI1GjEYju3bt4oUXXsBoNJKYmBiQz82l7ovD4Rg0JlCemQtFRUUxadIkzp49K79nLjDw3ngTqM+MuLokSfoMnE4nVquVefPmYTKZ2LFjh/uzU6dOUVZW5lGXE0hc98abQ4cOAZCcnOzDiHxv5cqVHD16lEOHDrm/5s+fz9133+3+PhCfm0vdF4PBMGhMoDwzF+ro6KC4uJjk5GT5PXOBgffGm0B9ZsTVJcttl2nDhg3cdNNNpKen097ezqZNm9i5cyfvvvsukZGR3Hvvvaxfv56YmBgiIiJ48MEHycvLY9GiRf4Ofdhd7N4UFxezadMmbr75ZmJjYzly5AiPPPIIy5Yt87rteywJDw9nxowZHu+FhoYSGxvrfj8Qn5tL3ZdAfma+9a1v8fnPf56MjAyqqqp44oknMBgMrFmzJuB/z1zs3gTyMyOGlyRJl6muro61a9dSXV1NZGQkOTk5vPvuu1x//fUA/PjHP0av17N69WqsViurVq3iZz/7mZ+j9o2L3Zvy8nL+9re/8dxzz9HZ2UlaWhqrV6/m0Ucf9XfYI0IgPzdDMZvNAfvMVFRUsGbNGhobG4mPj2fp0qXs3buX+Ph4ILCfl4vdm56enoB9ZsTw0mmapvk7CCGEEEKIkUZqkoQQQgghvJAkSQghhBDCC0mShBBCCCG8kCRJCCGEEMILSZKEEEIIIbyQJEkIIYQQwgtJkoQQQgghvJAkSQghhBDCC0mShBBCCCG8kCRJCCGEEMILSZKEEEIIIbz4/y/oV9H/WC+vAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ], + "source": [ + "# Plot KDE for the 'Open', 'High', 'Low', 'Close' columns of the 'stock' DataFrame.\n", + "sns.displot(data=stock[['Open','High','Low','Close']], kind='kde', palette=\"tab10\"); # Create a KDE plot with a color palette." + ] + }, + { + "cell_type": "markdown", + "id": "l5jX1Kp-lbD5", + "metadata": { + "id": "l5jX1Kp-lbD5" + }, + "source": [ + "**Observations:**\n", + "* The distributions of the prices are quite similar, with the high price showing a slight variation than the others." + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Histogram on Volume**" + ], + "metadata": { + "id": "1wBKKuVIaWnl" + }, + "id": "1wBKKuVIaWnl" + }, + { + "cell_type": "code", + "source": [ + "sns.histplot(stock, x='Volume');" + ], + "metadata": { + "id": "FMDJ_m6maaoK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fa63eb8f-f159-41cc-c969-fce63404ff4b" + }, + "id": "FMDJ_m6maaoK", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Observations:**\n", + "* In a large portion of the time considered, 80 to 175 million shares of the stock were traded, with occasional days where the volume rose to more than 200 million." + ], + "metadata": { + "id": "gNzyLLIgfnz6" + }, + "id": "gNzyLLIgfnz6" + }, + { + "cell_type": "markdown", + "id": "9GVt_AAbe29X", + "metadata": { + "id": "9GVt_AAbe29X" + }, + "source": [ + "#### **Histogram and statistical summary on News Length**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0kwZSJvwOUpa", + "metadata": { + "id": "0kwZSJvwOUpa", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9c9e9158-a8e5-4f51-9321-d54fb2ed6675" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 418.000000\n", + "mean 525.662679\n", + "std 303.584080\n", + "min 44.000000\n", + "25% 304.250000\n", + "50% 480.000000\n", + "75% 700.500000\n", + "max 2142.000000\n", + "Name: news_len, dtype: float64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
news_len
count418.000000
mean525.662679
std303.584080
min44.000000
25%304.250000
50%480.000000
75%700.500000
max2142.000000
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "#Calculating the total number of words present in the news content.\n", + "stock['news_len'] = stock['News'].apply(lambda x: len(x.split(' ')))\n", + "stock['news_len'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "NWn03B4Xey5d", + "metadata": { + "id": "NWn03B4Xey5d", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "abfb1de2-8239-4a5d-d9e3-71b30ad328ef" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.histplot(data=stock,x='news_len');" + ] + }, + { + "cell_type": "markdown", + "id": "VWLWG2X8mrCw", + "metadata": { + "id": "VWLWG2X8mrCw" + }, + "source": [ + "**Observations:**\n", + "* Most of the news have between 50 - 1000 words, with an average of 525 words\n", + " * The shortest news has 44 words\n", + "\n", + "* This indicates that these are likely to be news summaries rather than the actual news content itself." + ] + }, + { + "cell_type": "markdown", + "id": "hLE0s7OFKilB", + "metadata": { + "id": "hLE0s7OFKilB" + }, + "source": [ + "### **Bivariate Analysis**" + ] + }, + { + "cell_type": "markdown", + "id": "Yn_9wfzxL-r1", + "metadata": { + "id": "Yn_9wfzxL-r1" + }, + "source": [ + "#### **Correlation**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gOBaxNZeKllB", + "metadata": { + "id": "gOBaxNZeKllB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c46aaa76-2682-4469-c36e-44097287cc6e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "cols = ['Open','High','Low','Close','Volume','news_len']\n", + "sns.heatmap(\n", + " stock[cols].corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "15UHbBu8Cucj", + "metadata": { + "id": "15UHbBu8Cucj" + }, + "source": [ + "**Observations:**\n", + "* The prices are all perfectly correlated.\n", + " * This might be due to the minimum variation between the different prices.\n", + "\n", + "* There is a negative correlation, albeit very low, between volume and prices.\n", + " * This might be due to selling pressure during periods of negative sentiment." + ] + }, + { + "cell_type": "markdown", + "id": "h-Hz7CpdMAi3", + "metadata": { + "id": "h-Hz7CpdMAi3" + }, + "source": [ + "#### **Label vs Price (Open, High, Low, Close)**" + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(10, 8))\n", + "\n", + "for i, variable in enumerate(['Open', 'High', 'Low', 'Close']):\n", + " plt.subplot(2, 2, i + 1)\n", + " sns.boxplot(data=stock, x=\"Label\", y=variable)\n", + " plt.tight_layout(pad=2)\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "lCVHNWhgMElU", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b838000a-b88f-43ac-90ee-499cf11fa5ee" + }, + "id": "lCVHNWhgMElU", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8QAAAL8CAYAAAAm8nyDAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAb1lJREFUeJzt3X9cVHXe//8nP3RQhEHQBU2gDIXEwB9lYWmtWv7KrUuvy92idL31KSvz526rXFrIJovtWpaJlK2llt3MLF3rslyzC7FEM9TEfliZCXsh+m25mBGVURm+f3RjLmcV5ddwZuY87rfbuemcc3jPa2Rm3j7PeZ/zDqitra0VAAAAAAAmE2h0AQAAAAAAGIFADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMKNroAT3M6nSorK1NYWJgCAgKMLgcA4Idqa2t18uRJde3aVYGBHGu+HPplAICnNaZf9vtAXFZWptjYWKPLAACYQGlpqbp162Z0GV6NfhkA0Foa0i/7fSAOCwuT9PM/Rnh4uMHVAAD8kd1uV2xsrKvPQf3olwEAntaYftnvA3HdcKzw8HA6XgCARzEE+MrolwEAraUh/TIXOgEAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMyNBDPnz9fAQEBbktSUpIkqaKiQlOnTlViYqLatWunuLg4TZs2TTabzciSAQAAAAB+wvC7TCcnJ+ujjz5yPQ4O/rmksrIylZWVadGiRerVq5eOHj2qRx55RGVlZVq/fr1R5QIAAAAA/IThgTg4OFgxMTEXre/du7feeecd1+Nrr71W2dnZuv/++3X+/HlXcAYAAAAAoCkMv4b4u+++U9euXdW9e3elp6erpKSk3n1tNpvCw8MvG4YdDofsdrvbAgAAAADAvzI0EN90001auXKlPvzwQ+Xl5enIkSMaNGiQTp48edG+P/30k55++mk9/PDDl20zJydHVqvVtcTGxnqqfAAAAACADzM0EI8cOVL/8R//oZSUFA0fPlybN29WZWWl1q1b57af3W7X6NGj1atXL82fP/+ybWZkZMhms7mW0tJSD74CAAD8Bze7BACYjVddiBsREaGePXvq+++/d607efKkRowYobCwMG3YsEFt2rS5bBsWi0UWi8XTpbao6urqyw4V9wdxcXEKCQkxugzAb/E9gpbCzS75PAFoPr5HfIdXBeKqqiodPnxYDzzwgKSfzwwPHz5cFotFmzZt8ot/8EspKSm54lBwX7d8+XL17NnT6DIAv8X3CFoKN7vk8wSg+fge8R2G9l6///3vNWbMGMXHx6usrEyZmZkKCgrSvffeK7vdrjvvvFOnT5/WG2+84XaDrM6dOysoKMjI0ltUXFycli9f3irPdfToUWVnZ2vu3LmKj49vleeUfn6NADynNb9HJGO+S/geaR11N7sMCQlRWlqacnJy6v23b+jNLh0Oh+uxL9zsks8TgObie8R3GBqI//GPf+jee+/VP//5T3Xu3Fm33nqrdu3apc6dOys/P1+7d++WJCUkJLj93JEjR3T11VcbULFnhISEtPrRlfj4eL84ogPgZ0Z8j0h8l/ibuptdJiYm6tixY8rKytKgQYN08OBBhYWFue3bmJtdZmVlebLsFsfnCUBz8T3iOwwNxGvXrq132+23367a2tpWrAYAAHMbOXKk6+8pKSm66aabFB8fr3Xr1unBBx90bWvszS5nzZrl9rPMAAEA8Bb+c8EPAABoUWa92SUAwDwMnXYJAAB4r7qbXXbp0kWSXPf3aNu2rV/f7BIAYB4EYgAAIOnnm11u375dP/74o3bu3Kl/+7d/u+hml6dOndKKFStkt9tVXl6u8vJy1dTUGF06AABNwpBpAAAgiZtdAgDMh0AMAAAkcbNLAID5MGQaAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYkqGBeP78+QoICHBbkpKSXNurq6s1ZcoURUVFqUOHDho3bpyOHz9uYMUAAAAAAH9h+Bni5ORkHTt2zLV88sknrm0zZ87Ue++9p7ffflvbt29XWVmZxo4da2C1AAAAAAB/EWx4AcHBiomJuWi9zWbTihUr9Oabb2rIkCGSpNdee03XXXeddu3apZtvvrm1SwUAAAAA+BHDzxB/99136tq1q7p376709HSVlJRIkoqKinTu3DkNGzbMtW9SUpLi4uJUWFhYb3sOh0N2u91tAQAAAADgXxkaiG+66SatXLlSH374ofLy8nTkyBENGjRIJ0+eVHl5udq2bauIiAi3n4mOjlZ5eXm9bebk5MhqtbqW2NhYD78KAAAAAIAvMnTI9MiRI11/T0lJ0U033aT4+HitW7dO7dq1a1KbGRkZmjVrluux3W4nFAMAAAAALmL4kOkLRUREqGfPnvr+++8VExOjs2fPqrKy0m2f48ePX/Ka4zoWi0Xh4eFuCwAAuDJmfwAAmI1XBeKqqiodPnxYXbp0Uf/+/dWmTRtt27bNtf3QoUMqKSlRWlqagVUCAOC/mP0BAGAmhg6Z/v3vf68xY8YoPj5eZWVlyszMVFBQkO69915ZrVY9+OCDmjVrliIjIxUeHq6pU6cqLS2NO0wDAOAhzP4AADATQwPxP/7xD91777365z//qc6dO+vWW2/Vrl271LlzZ0nS4sWLFRgYqHHjxsnhcGj48OFatmyZkSUDAODX6mZ/CAkJUVpamnJychQXF3fF2R/qC8QOh0MOh8P1mNkfAADexNBAvHbt2stuDwkJUW5urnJzc1upIgAAzKtu9ofExEQdO3ZMWVlZGjRokA4ePNis2R+ysrI8XDkAAE1jaCAGAADeg9kfAABm41U31QIAAN6D2R8AAP6OQAwAAC6J2R8AAP6OIdMAAEASsz8AAMyHQAwAACQx+wMAwHwIxAAAQBKzPwAAzIdriAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAAphRsdAEAAAAALq2mpkYHDhxQRUWFIiMjlZKSoqCgIKPLAvwGgRgAAADwQgUFBVq2bJnKy8td62JiYvTYY49p8ODBBlYG+A+GTAMAAABepqCgQJmZmerevbtyc3O1efNm5ebmqnv37srMzFRBQYHRJQJ+gUAMAAAAeJGamhotW7ZMaWlpWrBggZKTk9W+fXslJydrwYIFSktLU15enmpqaowuFfB5BGIAAADAixw4cEDl5eVKT09XYKD7f9cDAwOVnp6uY8eO6cCBAwZVCPgPAjEAAADgRSoqKiRJ11xzzSW3162v2w9A03lNIF64cKECAgI0Y8YM17ry8nI98MADiomJUWhoqPr166d33nnHuCIBAAAAD4uMjJQkHTly5JLb69bX7Qeg6bwiEO/Zs0cvv/yyUlJS3NZPmDBBhw4d0qZNm1RcXKyxY8dq/Pjx2rdvn0GVAgAAAJ6VkpKimJgYrVmzRk6n022b0+nUmjVr1KVLl4v+7wyg8QwPxFVVVUpPT9crr7yijh07um3buXOnpk6dqgEDBqh79+6aN2+eIiIiVFRUZFC1AAAAgGcFBQXpscceU2FhoebNm6cvv/xSp0+f1pdffql58+apsLBQjz76KPMRAy3A8EA8ZcoUjR49WsOGDbto28CBA/XWW2+poqJCTqdTa9euVXV1tW6//fZ623M4HLLb7W4LgIarqanRvn37tG3bNu3bt487WAIAYIDBgwcrKytLP/zwg6ZMmaJRo0ZpypQpOnLkiLKyspiHGGghwUY++dq1a7V3717t2bPnktvXrVunX//614qKilJwcLDat2+vDRs2KCEhod42c3JylJWV5amSAb9WUFCgZcuWqby83LUuJiZGjz32GB0vAACtbPDgwbrlllt04MABVVRUKDIyUikpKZwZBlqQYWeIS0tLNX36dK1Zs0YhISGX3OfJJ59UZWWlPvroI33++eeaNWuWxo8fr+Li4nrbzcjIkM1mcy2lpaWeegmAXykoKFBmZqa6d++u3Nxcbd68Wbm5uerevbsyMzNVUFBgdIkAAJhOUFCQ+vbtq6FDh6pv376EYaCFGRaIi4qKdOLECfXr10/BwcEKDg7W9u3btWTJEgUHB+vw4cNaunSpXn31VQ0dOlSpqanKzMzUDTfcoNzc3HrbtVgsCg8Pd1sAXF5NTY2WLVumtLQ0LViwQMnJyWrfvr2Sk5O1YMECpaWlKS8vj+HTgIkw+wMAwAwMC8RDhw5VcXGx9u/f71puuOEGpaena//+/Tp9+vTPBf7LZORBQUEX3W0PQPMcOHBA5eXlSk9Pv+gzFxgYqPT0dB07dkwHDhwwqEIArYnZHwAAZmFYIA4LC1Pv3r3dltDQUEVFRal3795KSkpSQkKCJk+erM8++0yHDx/Ws88+q61bt+qee+4xqmzAL1VUVEiSrrnmmktur1tftx8A/8XsDwAAMzH8LtP1adOmjTZv3qzOnTtrzJgxSklJ0erVq7Vq1SqNGjXK6PIAvxIZGSlJOnLkyCW3162v2w+A/2L2BwCAmRh6l+l/lZ+f7/a4R48eXJsEtIKUlBTFxMRozZo1WrBggduwaafTqTVr1qhLly4XDZ8E4F+Y/QEAYDZeFYgBGCMoKEiPPfaYMjMzNW/ePKWnp+uaa67RkSNHtGbNGhUWFiorK4s7WwJ+rG72h61btzZo9odOnTpp48aNGj9+vHbs2KHrr7/+kj+TkZGhWbNmuR7b7XbFxsZ65DUAwJUcP35cNpvN6DJa3NGjR93+9DdWq1XR0dEeaZtADEDSz3MdZmVladmyZZoyZYprfZcuXZSVlcU8xE1Ex+ubPNnxeqsLZ3+oU1NTo4KCAi1dulSHDh3S0qVLdfDgQSUnJ0uSUlNTtWPHDuXm5uqll166ZLsWi0UWi6VVXgMAXM7x48d1/wMTdO6sw+hSPCY7O9voEjyiTVuL3nh9tUf6ZgIxAJfBgwfrlltu0YEDB1RRUaHIyEilpKRwZriJ6Hh9lyc7Xm9VN/vDhSZNmqSkpCTNnj2b2R8A+DybzaZzZx060/02OUOsRpeDBgqstkk/bJfNZiMQA/C8oKAg9e3b1+gy/AIdr2/ydMfrrepmf7jQhbM/nDt3zjX7w6JFixQVFaWNGzdq69atev/99w2qGgAazxlilTO0k9FlwEsQiAHAw+h44Q/qZn+YM2eOxowZo6qqKiUkJDD7AwDApxGIAQDAJTH7AwDA33ntPMQAAAAAAHgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJhSsNEFAAAANMTx48dls9mMLqPFHT161O1Pf2K1WhUdHW10GQBQLwIxAADwesePH9f9D0zQubMOo0vxmOzsbKNLaHFt2lr0xuurCcUAvBaBGAAAeD2bzaZzZx060/02OUOsRpeDBgistkk/bJfNZiMQA/BaBGIAAOAznCFWOUM7GV0GAMBPcFMtAAAAAIApEYgBAAAAAKbkNUOmFy5cqIyMDE2fPl3PP/+8a31hYaHmzp2r3bt3KygoSH369NGWLVvUrl07j9fkj3ez9Oc7WUrczRIAAABAw3lFIN6zZ49efvllpaSkuK0vLCzUiBEjlJGRoRdffFHBwcH64osvFBjo+RPb/n43S3+8k6XE3SwBAAAANJzhgbiqqkrp6el65ZVXtGDBArdtM2fO1LRp0zRnzhzXusTExFapi7tZ+h7uZgkAAACgMQwPxFOmTNHo0aM1bNgwt0B84sQJ7d69W+np6Ro4cKAOHz6spKQkZWdn69Zbb623PYfDIYfj/87q2u32ZtXH3SwBAAAAwD8ZelOttWvXau/evcrJyblo2w8//CBJmj9/vh566CF9+OGH6tevn4YOHarvvvuu3jZzcnJktVpdS2xsrMfqBwAAAAD4LsMCcWlpqaZPn641a9YoJCTkou1Op1OSNHnyZE2aNEl9+/bV4sWLlZiYqFdffbXedjMyMmSz2VxLaWmpx14DAAAAAMB3GTZkuqioSCdOnFC/fv1c62pqalRQUKClS5fq0KFDkqRevXq5/dx1112nkpKSetu1WCyyWCyeKRoAAAAA4DcMO0M8dOhQFRcXa//+/a7lhhtuUHp6uvbv36/u3bura9eurmBc59tvv1V8fLxBVQMAYA4LFy5UQECAZsyY4ba+sLBQQ4YMUWhoqMLDwzV48GCdOXPGmCIBAGgmw84Qh4WFqXfv3m7rQkNDFRUV5Vr/xBNPKDMzU6mpqerTp49WrVqlb775RuvXrzeiZAAATMEbp0MEAMATDL/L9OXMmDFD1dXVmjlzpioqKpSamqqtW7fq2muvNbo0AAD8krdOhwgAgCd41SHd/Px8Pf/8827r5syZo9LSUp06dUo7d+687JRLAACgeS6cDvFCddMh/uIXv9DAgQMVHR2t2267TZ988sll23M4HLLb7W4LAADewqsCMQAAMA7TIQIAzIZADAAAmA4RAGBKXn0NMQAAaB1MhwgAMCMCMQAAcE2HeKFJkyYpKSlJs2fPvux0iCNHjmzNUgEAaDEEYgAAwHSIAABTIhADAIAGYTpEAIC/IRADAIBLys/Pv2jdnDlz3OYhBgDAl3GXaQAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKQU35YdOnTqlhQsXatu2bTpx4oScTqfb9h9++KFFigMAAAAAwFOaFIj/3//7f9q+fbseeOABdenSRQEBAS1dFwAAAAAAHtWkQPzBBx/ov/7rv3TLLbe0dD0AAAAAALSKJgXijh07KjIysqVrAQAATVBTU6OVK1fWeynTxx9/bFBlAAB4tyYF4qefflpPPfWUVq1apfbt27d0TQAAoBGmT5+ulStXavTo0erduzeXMgEA0EBNCsTPPvusDh8+rOjoaF199dVq06aN2/a9e/e2SHEAAODK1q5dq3Xr1mnUqFFGlwIAgE9pUiC+5557WrgMAADQVG3btlVCQoLRZQAA4HOaFIgzMzNbug4AANBEv/vd7/TCCy9o6dKlDJcGAKARmhSIJamyslLr16/X4cOH9cQTTygyMlJ79+5VdHS0rrrqqpasEQAA/IuxY8e6Pf7444/1wQcfKDk5+aJLmd59993WLA0AAJ8R2JQfOnDggHr27KlnnnlGixYtUmVlpaSfO9yMjIwmFbJw4UIFBARoxowZF22rra3VyJEjFRAQoI0bNzapfQAA/InVanVb/u3f/k233XabOnXqdNE2AABwaU06Qzxr1iz99re/1Z///GeFhYW51o8aNUr33Xdfo9vbs2ePXn75ZaWkpFxy+/PPP88QMAAALvDaa68ZXQIAAD6vSWeI9+zZo8mTJ1+0/qqrrlJ5eXmj2qqqqlJ6erpeeeUVdezY8aLt+/fv17PPPqtXX321KaUCAAAAAHBJTTpDbLFYZLfbL1r/7bffqnPnzo1qa8qUKRo9erSGDRumBQsWuG07ffq07rvvPuXm5iomJqYppQIA4Pf69u17yZFUAQEBCgkJUUJCgn7729/ql7/8pQHVAQDgvZp0hvhXv/qV/vjHP+rcuXOSfu5wS0pKNHv2bI0bN67B7axdu1Z79+5VTk7OJbfPnDlTAwcO1N13393gNh0Oh+x2u9sCAIA/GzFihH744QeFhobql7/8pX75y1+qQ4cOOnz4sG688UYdO3ZMw4YN09/+9jejSwUAwKs06Qzxs88+q3//93/XL37xC505c0a33XabysvLlZaWpuzs7Aa1UVpaqunTp2vr1q0KCQm5aPumTZv08ccfa9++fY2qLScnR1lZWY36GQAAfNlPP/2k3/3ud3ryySfd1i9YsEBHjx7V3//+d2VmZurpp59u1EFmAAD8XZPOEFutVm3dulXvvfeelixZoscff1ybN2/W9u3bFRoa2qA2ioqKdOLECfXr10/BwcEKDg7W9u3btWTJEgUHB2vr1q06fPiwIiIiXNslady4cbr99tvrbTcjI0M2m821lJaWNuUlAgDgM9atW6d77733ovW/+c1vtG7dOknSvffeq0OHDrV2aQAAeLUmz0MsSbfeeqtuvfXWJv3s0KFDVVxc7LZu0qRJSkpK0uzZs9WpU6eLbtx1/fXXa/HixRozZky97VosFlkslibVBACALwoJCdHOnTuVkJDgtn7nzp2uUVhOp/OSI7Lqs3DhQmVkZGj69Ol6/vnn3bbV1tZq1KhR+vDDD7Vhwwbdc889zX0JAAAYosmBeNu2bVq8eLG+/vprSdJ1112nGTNmaNiwYQ36+bCwMPXu3dttXWhoqKKiolzrL3Ujrbi4OF1zzTVNLRsAAL8zdepUPfLIIyoqKtKNN94o6ecZIf7617/qP//zPyVJW7ZsUZ8+fRrUHtMhAgDMoklDppctW6YRI0YoLCxM06dP1/Tp0xUeHq5Ro0YpNze3pWsEAACXMW/ePL3yyiv67LPPNG3aNE2bNk2fffaZXnnlFc2dO1eS9Mgjj+i99967YltMhwgAMJMmnSH+05/+pMWLF+vxxx93rZs2bZpuueUW/elPf9KUKVOaVEx+fv5lt9fW1japXQAA/F16errS09Pr3d6uXbsGtdPS0yE6HA45HA7XY2Z/AAB4kyadIa6srNSIESMuWn/nnXfKZrM1uygAAND6PDEdYk5OjqxWq2uJjY1tqXIBAGi2Js9DvGHDhovW/+1vf9Ndd93V7KIAAMDlRUZG6qeffpIkdezYUZGRkfUuDVE3HeKaNWsuOx3iv95g60qY/QEA4M2aNGS6V69eys7OVn5+vtLS0iRJu3bt0qeffqrf/e53WrJkiWvfadOmtUylAADAZfHixQoLC3P9vbk3ubpwOsQ6NTU1Kigo0NKlS/Xoo4+6pkO80Lhx4zRo0KB6L3ti9gcAgDdrUiBesWKFOnbsqK+++kpfffWVa31ERIRWrFjhehwQEEAgBgDAAyZOnCi73S6Hw6GxY8c2uz1PTYcIAIA3a1IgPnLkiCS5hmp16tSp5SoCAAANEhER0aAzwzU1NVfch+kQAQBm1OhAXFlZqblz5+qtt97S//7v/0r6+dql3/zmN1qwYMFFQ6kAAIBn/Pd//7fr77W1tRo1apT++te/6qqrrjKwKgAAfEejAnFFRYXS0tL0P//zP0pPT9d1110nSfrqq6+0cuVKbdu2TTt37rzkvIUAAKBl3XbbbW6Pg4KCdPPNN6t79+4t0j7TIQIA/F2jAvEf//hHtW3bVocPH1Z0dPRF2+6880798Y9/1OLFi1u0SAAAAAAAWlqjpl3auHGjFi1adFEYln6+rujPf/7zJadjAgAAAADA2zQqEB87dkzJycn1bu/du7fKy8ubXRQAAGia5k6/BACAmTRqyHSnTp30448/qlu3bpfcfuTIEUVGRrZIYQAA4PL+dbql6upqPfLIIwoNDXVb/+6777ZmWQAA+IxGBeLhw4dr7ty52rp1q9q2beu2zeFw6Mknn9SIESNatEAAAHBpVqvV7fH9999vUCUAAPimRt9U64YbblCPHj00ZcoUJSUlqba2Vl9//bWWLVsmh8Oh119/3VO1AqZVXV2tkpISo8vwqLi4OIWEhBhdBuBTXnvtNaNLAADApzUqEHfr1k2FhYV67LHHlJGR4ZpuISAgQHfccYeWLl2q2NhYjxQKmFlJSYkefvhho8vwqOXLl6tnz55GlwEAAAATaVQglqRrrrlGH3zwgf73f/9X3333nSQpISGBa4cBD4qLi9Py5ctb7fmOHj2q7OxszZ07V/Hx8a3ynHFxca3yPAAAAECdRgfiOh07dtSAAQNashYA9QgJCTHk7Gl8fDxnbQEAAOC3mhyIAQAAADPi3h6A/yAQAwAAAI3AvT0A/0EgBgAAABqBe3sA/oNADAAAADQC9/YA/AeBGAAAAIBpBJ6pNLoENIKnf18EYgDwMDpe38LvCwD8W7sjBUaXAC9CIAYAD6PjBQDAe5y5ZrCc7SKMLgMNFHim0qP/lyIQA4CH0fH6Fk93vAAAYznbRcgZ2snoMuAlCMQA4GF0vAAAAN4p0OgCAAAAAAAwgtcE4oULFyogIEAzZsyQJFVUVGjq1KlKTExUu3btFBcXp2nTpslmsxlbKAAAAADAL3jFkOk9e/bo5ZdfVkpKimtdWVmZysrKtGjRIvXq1UtHjx7VI488orKyMq1fv97AagEAAAAA/sDwQFxVVaX09HS98sorWrBggWt979699c4777geX3vttcrOztb999+v8+fPKzjY8NIBAAAAAD7M8CHTU6ZM0ejRozVs2LAr7muz2RQeHn7ZMOxwOGS3290WAAAAAAD+laGnWdeuXau9e/dqz549V9z3p59+0tNPP62HH374svvl5OQoKyurpUoEAAAAAPgpw84Ql5aWavr06VqzZo1CQkIuu6/dbtfo0aPVq1cvzZ8//7L7ZmRkyGazuZbS0tIWrBoAAAAA4C8MO0NcVFSkEydOqF+/fq51NTU1Kigo0NKlS+VwOBQUFKSTJ09qxIgRCgsL04YNG9SmTZvLtmuxWGSxWDxdPgAAAADAxxl2hnjo0KEqLi7W/v37XcsNN9yg9PR07d+/X0FBQbLb7brzzjvVtm1bbdq06YpnkgEAQMtgOkQAgBkYdoY4LCxMvXv3dlsXGhqqqKgo9e7d2xWGT58+rTfeeMPtBlmdO3dWUFCQEWUDAOD3mA4RAGAWXjt30d69e7V7925JUkJCgtu2I0eO6OqrrzagKgAA/BvTIQIAzMSreq/8/HzX32+//XbV1tYaVwwAACZ04XSIFwbiS2nodIgOh8P1mOkQAQDexKsCMQAAMA7TIQIAzMawm2oBAADvwXSIAAAz4gwxAABgOkQAgCkRiAEAgGs6xAtNmjRJSUlJmj17tms6xOHDh8tisTAdIgDALxCIAQAA0yECAEyJQAwAAK6I6RABAP6IQAwAAC6J6RABAP6Ou0wDAAAAAEyJQAwAAAAAMCUCMQAAAADAlLiGGAAAAH7h+PHjstlsRpfR4o4ePer2p7+xWq2Kjo42ugyYFIEYAAAAPu/48eO6/4EJOnfWYXQpHpOdnW10CR7Rpq1Fb7y+mlAMQxCIAQAA4PNsNpvOnXXoTPfb5AyxGl0OGiiw2ib9sF02m41ADEMQiIFmYGiW72FYFgD4N2eIVc7QTkaXAcBHEIiBJmJolm9iWBYAAADqEIiBJmJolu9hWBYAAAAuRCAGmomhWQAAAIBvYh5iAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSl4TiBcuXKiAgADNmDHDta66ulpTpkxRVFSUOnTooHHjxun48ePGFQkAAAAA8BteEYj37Nmjl19+WSkpKW7rZ86cqffee09vv/22tm/frrKyMo0dO9agKgEAAAAA/sTwQFxVVaX09HS98sor6tixo2u9zWbTihUr9Nxzz2nIkCHq37+/XnvtNe3cuVO7du0ysGIAAAAAgD8wPBBPmTJFo0eP1rBhw9zWFxUV6dy5c27rk5KSFBcXp8LCwnrbczgcstvtbgsAAAAAAP8q2MgnX7t2rfbu3as9e/ZctK28vFxt27ZVRESE2/ro6GiVl5fX22ZOTo6ysrJaulQAAAAAgJ8x7AxxaWmppk+frjVr1igkJKTF2s3IyJDNZnMtpaWlLdY2AAAAAMB/GBaIi4qKdOLECfXr10/BwcEKDg7W9u3btWTJEgUHBys6Olpnz55VZWWl288dP35cMTEx9bZrsVgUHh7utgAAAAAA8K8MC8RDhw5VcXGx9u/f71puuOEGpaenu/7epk0bbdu2zfUzhw4dUklJidLS0owqGwAAU2A6RACAGRh2DXFYWJh69+7tti40NFRRUVGu9Q8++KBmzZqlyMhIhYeHa+rUqUpLS9PNN99sRMkAAJjC5aZD/K//+i+9/fbbslqtevzxxzV27Fh9+umnBlUKAEDzGH6X6ctZvHix7rrrLo0bN06DBw9WTEyM3n33XaPLAgDAbzEdIgDATLwqEOfn5+v55593PQ4JCVFubq4qKip06tQpvfvuu5e9fhgAADQP0yECAMzE0GmXAACA92A6RACA2XjVGWIAAGAMpkMEAJgRgRgAADAdIgDAlBgyfQWBZyqNLgENxO8KAJqubjrEC02aNElJSUmaPXu2YmNjXdMhjhs3ThLTIQIAfB+B+AraHSkwugQAADzOV6ZD5OCn7zDqd8V7xLfw+4LRCMRXcOaawXK2izC6DDRA4JlKDmAAgActXrxYgYGBGjdunBwOh4YPH65ly5a1ag18z+NKeI8AaAwC8RU420XIGdrJ6DIAAGh1+fn5bo/rpkPMzc01piBxoNqXGHWgmveIb+GEBoxGIAYAAD6DA9W4Et4jABqDu0wDAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlJiHGGimwDOVRpeABuJ3BQAAgAsRiIFmanekwOgSAAAAADQBgRhopjPXDJazXYTRZaABAs9UcgADAAAALgRioJmc7SLkDO1kdBkAAAAAGombagEAAAAATIlADAAAAAAwJYZMAwAAADCNwGqb0SWgETz9+yIQA4CH0fH6Fn5fAOCfrFar2rS1SD9sN7oUNFKbthZZrVaPtE0gBgAPoeP1XZ7seAEAxoiOjtYbr6+WzeZ/Bz6PHj2q7OxszZ07V/Hx8UaX0+KsVquio6M90jaBGAA8hI7Xd3my4wUAGCc6Otqvv9/j4+PVs2dPo8vwKYYG4ry8POXl5enHH3+UJCUnJ+upp57SyJEjJUnl5eV64okntHXrVp08eVKJiYmaO3euxo0bZ2DVANBwdLwAAADey9BA3K1bNy1cuFA9evRQbW2tVq1apbvvvlv79u1TcnKyJkyYoMrKSm3atEmdOnXSm2++qfHjx+vzzz9X3759jSwdcOF6Q9/B7woAAAAXMjQQjxkzxu1xdna28vLytGvXLiUnJ2vnzp3Ky8vTgAEDJEnz5s3T4sWLVVRURCCG4bg+1DdxbSgAAADqeM01xDU1NXr77bd16tQppaWlSZIGDhyot956S6NHj1ZERITWrVun6upq3X777fW243A45HA4XI/tdrunS4dJcX2ob+LaUAAAANQxPBAXFxcrLS1N1dXV6tChgzZs2KBevXpJktatW6df//rXioqKUnBwsNq3b68NGzYoISGh3vZycnKUlZXVWuXD5Lg+FAAAAPBdgUYXkJiYqP3792v37t169NFHNXHiRH311VeSpCeffFKVlZX66KOP9Pnnn2vWrFkaP368iouL620vIyNDNpvNtZSWlrbWSwEAAAAA+BDDzxC3bdvWdca3f//+2rNnj1544QX94Q9/0NKlS3Xw4EElJydLklJTU7Vjxw7l5ubqpZdeumR7FotFFoul1eoHAAAAAPgmwwPxv3I6nXI4HDp9+rQkKTDQ/SR2UFCQnE6nEaUBAADAyzGjgG/h9wWjGRqIMzIyNHLkSMXFxenkyZN68803lZ+fry1btigpKUkJCQmaPHmyFi1apKioKG3cuFFbt27V+++/b2TZAAD4pby8POXl5enHH3+UJCUnJ+upp57SyJEjJUnl5eV64okntHXrVp08eVKJiYmaO3euxo0bZ2DVwM+Y/cF3MQMEjGRoID5x4oQmTJigY8eOyWq1KiUlRVu2bNEdd9whSdq8ebPmzJmjMWPGqKqqSgkJCVq1apVGjRplZNkAAPilbt26aeHCherRo4dqa2u1atUq3X333dq3b5+Sk5M1YcIEVVZWatOmTerUqZPefPNNjR8/Xp9//jnTIcJwzP7gu5gBAkYyNBCvWLHistt79Oihd955p5WqAQDA3MaMGeP2ODs7W3l5edq1a5eSk5O1c+dO5eXlacCAAZKkefPmafHixSoqKiIQwysw+wOAxjL8LtMAAMD71NTUaO3atTp16pTS0tIkSQMHDtRbb72liooKOZ1OrV27VtXV1br99tvrbcfhcMhut7stAAB4C6+7qRYAADBOcXGx0tLSVF1drQ4dOmjDhg3q1auXJGndunX69a9/raioKAUHB6t9+/basGGDa7aIS8nJyVFWVlZrlQ8AQKNwhhgAALgkJiZq//792r17tx599FFNnDhRX331lSTpySefVGVlpT766CN9/vnnmjVrlsaPH6/i4uJ628vIyJDNZnMtpaWlrfVSAAC4Is4QAwAAl7Zt27rO+Pbv31979uzRCy+8oD/84Q9aunSpDh48qOTkZElSamqqduzYodzcXL300kuXbM9ischisbRa/QAANAZniAEAQL2cTqccDodOnz4tSQoMdP+vQ1BQkJxOpxGlAQDQbJwhBgAAkn4e3jxy5EjFxcXp5MmTevPNN5Wfn68tW7YoKSlJCQkJmjx5shYtWqSoqCht3LhRW7du1fvvv2906QAANAmBGAAASJJOnDihCRMm6NixY7JarUpJSdGWLVt0xx13SJI2b96sOXPmaMyYMaqqqlJCQoJWrVqlUaNGGVw5AABNQyC+gsBq/5vc3V/xuwKA5lmxYsVlt/fo0UPvvPNOK1UDAIDnEYjrYbVa1aatRfphu9GloBHatLXIarUaXQYAAAAAH0Agrkd0dLTeeH21bDb/Out49OhRZWdna+7cuYqPjze6nBZntVoVHR1tdBkAAAAAfACB+DKio6P9NlzFx8erZ8+eRpcBAAAAAIZh2iUAAAAAgCkRiAEAAAAApkQgBgAAAACYEtcQAwAAn8EUe76D3xUAX0AgBgAAXo/pEH2Tv06HWF1drZKSklZ7vqNHj7r92Rri4uIUEhLSas8HGIVADAAAvJ6/Toco+feUiP46HWJJSYkefvjhVn/e7OzsVnuu5cuXMyMJTIFADAAAfII/T4coMSWiL4mLi9Py5cuNLsOj4uLijC4BaBUEYgAAAKARQkJCOHgB+AnuMg0AAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAwNxHl5eUpJSVF4eLjCw8OVlpamDz74wG2fwsJCDRkyRKGhoQoPD9fgwYN15swZgyoGAAAAAPgLQ+ch7tatmxYuXKgePXqotrZWq1at0t133619+/YpOTlZhYWFGjFihDIyMvTiiy8qODhYX3zxhQIDObENc6murlZJSUmrPd/Ro0fd/mwNcXFxCgkJabXnAwAAAAwNxGPGjHF7nJ2drby8PO3atUvJycmaOXOmpk2bpjlz5rj2SUxMbO0yAcOVlJTo4YcfbvXnzc7ObrXnWr58uXr27NlqzwcAAAAYGogvVFNTo7ffflunTp1SWlqaTpw4od27dys9PV0DBw7U4cOHlZSUpOzsbN166631tuNwOORwOFyP7XZ7a5TfLK159s+IM38SZ/+aKy4uTsuXLze6DI+Ki4szugSfxigCoOXweQLQXHyP+I6A2traWiMLKC4uVlpamqqrq9WhQwe9+eabGjVqlHbt2qW0tDRFRkZq0aJF6tOnj1avXq1ly5bp4MGD6tGjxyXbmz9/vrKysi5ab7PZFB4e7umX0yTffvutIWf/WhNn/wDP4nvEWHa7XVar1av7Gm/hC/9WfJ4ANBffI8ZqTF9jeCA+e/asSkpKZLPZtH79ev31r3/V9u3bVVlZqVtuuUUZGRn605/+5No/JSVFo0ePVk5OziXbu9QZ4tjYWK/ueFv7CJIR/OUIEuCt+B4xli+EPG/hC/9WfJ4ANBffI8ZqTF9j+JDptm3bKiEhQZLUv39/7dmzRy+88ILruuFevXq57X/ddddd9s1lsVhksVg8V7AHhISEeO3RFQC+ge8RoOXweQLQXHyP+A6vu12z0+mUw+HQ1Vdfra5du+rQoUNu27/99lvFx8cbVB0AAAAAwF8YGogzMjJUUFCgH3/8UcXFxcrIyFB+fr7S09MVEBCgJ554QkuWLNH69ev1/fff68knn9Q333yjBx980MiyAQDwS3l5eUpJSVF4eLjCw8OVlpamDz74wG2fwsJCDRkyRKGhoQoPD9fgwYN15swZgyoGAKB5DB0yfeLECU2YMEHHjh2T1WpVSkqKtmzZojvuuEOSNGPGDFVXV2vmzJmqqKhQamqqtm7dqmuvvdbIsgEA8EvdunXTwoUL1aNHD9XW1mrVqlW6++67tW/fPiUnJ6uwsFAjRoxQRkaGXnzxRQUHB+uLL75QYKDXDTgDAKBBDL+plqf5ws07AAC+zZ/7msjISP3lL3/Rgw8+qJtvvll33HGHnn766Sa358//VgAA79CYvoZDugAA4CI1NTVau3atTp06pbS0NJ04cUK7d+/WL37xCw0cOFDR0dG67bbb9Mknn1y2HYfDIbvd7rYAAOAtCMQAAMCluLhYHTp0kMVi0SOPPKINGzaoV69e+uGHHyRJ8+fP10MPPaQPP/xQ/fr109ChQ/Xdd9/V215OTo6sVqtriY2Nba2XAgDAFRGIAQCAS2Jiovbv36/du3fr0Ucf1cSJE/XVV1/J6XRKkiZPnqxJkyapb9++Wrx4sRITE/Xqq6/W215GRoZsNptrKS0tba2XAgDAFRk+DzEAAPAebdu2VUJCgiSpf//+2rNnj1544QXNmTNHktSrVy+3/a+77jqVlJTU257FYpHFYvFcwQAANANniAEAQL2cTqccDoeuvvpqde3aVYcOHXLb/u233yo+Pt6g6gAAaB7OEAMAAEk/D28eOXKk4uLidPLkSb355pvKz8/Xli1bFBAQoCeeeEKZmZlKTU1Vnz59tGrVKn3zzTdav3690aUDANAkBGIAACBJOnHihCZMmKBjx47JarUqJSVFW7Zs0R133CFJmjFjhqqrqzVz5kxVVFQoNTVVW7du1bXXXmtw5QAANI3fz0Nss9kUERGh0tJS5jsEAHiE3W5XbGysKisrZbVajS7Hq9EvAwA8rTH9st+fIT558qQkMc0DAMDjTp48SSC+AvplAEBraUi/7PdniJ1Op8rKyhQWFqaAgACjyzFc3dESjszjcnif4Ep4j7irra3VyZMn1bVrVwUGcr/Ky6FfvhifJ1wJ7xFcCe8Rd43pl/3+DHFgYKC6detmdBleJzw8nA8Lroj3Ca6E98j/4cxww9Av14/PE66E9wiuhPfI/2lov8xhbAAAAACAKRGIAQAAAACmRCA2GYvFoszMTFksFqNLgRfjfYIr4T0CtBw+T7gS3iO4Et4jTef3N9UCAAAAAOBSOEMMAAAAADAlAjEAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUBsMu+++67uvPNORUVFKSAgQPv37ze6JHiZ3NxcXX311QoJCdFNN92kzz77zOiS4EUKCgo0ZswYde3aVQEBAdq4caPRJQE+jX4ZV0K/jMuhX24+ArHJnDp1SrfeequeeeYZo0uBF3rrrbc0a9YsZWZmau/evUpNTdXw4cN14sQJo0uDlzh16pRSU1OVm5trdCmAX6BfxuXQL+NK6Jebj2mXTOrHH3/UNddco3379qlPnz5GlwMvcdNNN+nGG2/U0qVLJUlOp1OxsbGaOnWq5syZY3B18DYBAQHasGGD7rnnHqNLAXwe/TIuhX4ZjUG/3DScIQYgSTp79qyKioo0bNgw17rAwEANGzZMhYWFBlYGAID50C8DrYNADECS9NNPP6mmpkbR0dFu66Ojo1VeXm5QVQAAmBP9MtA6CMR+bM2aNerQoYNr2bFjh9ElAQBgWvTLAOB9go0uAJ7zq1/9SjfddJPr8VVXXWVgNfB2nTp1UlBQkI4fP+62/vjx44qJiTGoKgDwH/TLaAz6ZaB1cIbYj4WFhSkhIcG1tGvXzuiS4MXatm2r/v37a9u2ba51TqdT27ZtU1pamoGVAYB/oF9GY9AvA62DM8QmU1FRoZKSEpWVlUmSDh06JEmKiYnhaCM0a9YsTZw4UTfccIMGDBig559/XqdOndKkSZOMLg1eoqqqSt9//73r8ZEjR7R//35FRkYqLi7OwMoA30S/jMuhX8aV0C83H9MumczKlSsv+SWamZmp+fPnt35B8DpLly7VX/7yF5WXl6tPnz5asmSJ2xA/mFt+fr5++ctfXrR+4sSJWrlyZesXBPg4+mVcCf0yLod+ufkIxAAAAAAAU+IaYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRiAy8qVKxUREdHsdgICArRx48ZmtwMAgJnRLwOeRyAG/Mxvf/tb3XPPPUaXAQAARL8MeDsCMQAAAADAlAjEgIk899xzuv766xUaGqrY2Fg99thjqqqqumi/jRs3qkePHgoJCdHw4cNVWlrqtv1vf/ub+vXrp5CQEHXv3l1ZWVk6f/58a70MAAD8Av0yYDwCMWAigYGBWrJkib788kutWrVKH3/8sf7whz+47XP69GllZ2dr9erV+vTTT1VZWanf/OY3ru07duzQhAkTNH36dH311Vd6+eWXtXLlSmVnZ7f2ywEAwKfRLwNeoBaAX5k4cWLt3Xff3aB933777dqoqCjX49dee61WUu2uXbtc677++utaSbW7d++ura2trR06dGjtn/70J7d2Xn/99douXbq4Hkuq3bBhQ9NfBAAAfoJ+GfBuwUaGcQCt66OPPlJOTo6++eYb2e12nT9/XtXV1Tp9+rTat28vSQoODtaNN97o+pmkpCRFRETo66+/1oABA/TFF1/o008/dTvyXFNTc1E7AADg8uiXAeMRiAGT+PHHH3XXXXfp0UcfVXZ2tiIjI/XJJ5/owQcf1NmzZxvcYVZVVSkrK0tjx469aFtISEhLlw0AgF+iXwa8A4EYMImioiI5nU49++yzCgz8+fYB69atu2i/8+fP6/PPP9eAAQMkSYcOHVJlZaWuu+46SVK/fv106NAhJSQktF7xAAD4GfplwDsQiAE/ZLPZtH//frd1nTp10rlz5/Tiiy9qzJgx+vTTT/XSSy9d9LNt2rTR1KlTtWTJEgUHB+vxxx/XzTff7OqIn3rqKd11112Ki4vTv//7vyswMFBffPGFDh48qAULFrTGywMAwKfQLwPei7tMA34oPz9fffv2dVtef/11Pffcc3rmmWfUu3dvrVmzRjk5ORf9bPv27TV79mzdd999uuWWW9ShQwe99dZbru3Dhw/X+++/r7///e+68cYbdfPNN2vx4sWKj49vzZcIAIDPoF8GvFdAbW1trdFFAAAAAADQ2jhDDAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwpWCjC/A0p9OpsrIyhYWFKSAgwOhyAAB+qLa2VidPnlTXrl0VGMix5suhXwYAeFpj+mW/D8RlZWWKjY01ugwAgAmUlpaqW7duRpfh1eiXAQCtpSH9st8H4rCwMEk//2OEh4cbXA0AwB/Z7XbFxsa6+hzUj34ZAOBpjemX/T4Q1w3HCg8Pp+MFAHgUQ4CvjH4ZANBaGtIvc6ETAAAAAMCUCMQAAAAAAFMiEAMAAAAATMnQQDx//nwFBAS4LUlJSZKkiooKTZ06VYmJiWrXrp3i4uI0bdo02Ww2I0sGAAAAAPgJw2+qlZycrI8++sj1ODj455LKyspUVlamRYsWqVevXjp69KgeeeQRlZWVaf369UaVCwAAAADwE4YPmQ4ODlZMTIxr6dSpkySpd+/eeueddzRmzBhde+21GjJkiLKzs/Xee+/p/PnzBlcNAID/YeQWAMBsDD9D/N1336lr164KCQlRWlqacnJyFBcXd8l9bTabwsPDXWeRL8XhcMjhcLge2+32Fq8ZAAB/xcgtAICZGBqIb7rpJq1cuVKJiYk6duyYsrKyNGjQIB08ePCiSZR/+uknPf3003r44Ycv22ZOTo6ysrI8WTYAAH6rbuTWv6obuVXn2muvVXZ2tu6//36dP3/+sgerAQDwVoYOmR45cqT+4z/+QykpKRo+fLg2b96syspKrVu3zm0/u92u0aNHq1evXpo/f/5l28zIyJDNZnMtpaWlHnwFAAD4l7qRW927d1d6erpKSkrq3behI7fsdrvbAgCAtzD8GuILRUREqGfPnvr+++9d606ePKkRI0YoLCxMGzZsUJs2bS7bhsViUXh4uNsCAACurG7k1ocffqi8vDwdOXJEgwYN0smTJy/atzEjt6xWq2uJjY31VPkAADRaQG1tba3RRdSpqqpSXFyc5s+fr2nTpslut2v48OGyWCzavHmz2rdv3+g27Xa7rFar6yi2N6qurr7sEXh/EBcXp5CQEKPLAPwW3yPG8oW+pikqKysVHx+v5557Tg8++KBrvd1u1x133KHIyEht2rTpsgerL3Vvj9jYWK/+t+LzBKC5+B4xVmP6ZUMv+Pn973+vMWPGKD4+XmVlZcrMzFRQUJDuvfde2e123XnnnTp9+rTeeOMNt2FWnTt3VlBQkJGlt6iSkpIrHmH3dcuXL1fPnj2NLgPwW3yPwBNaauSWxWLxdKktis8TgObie8R3GBqI//GPf+jee+/VP//5T3Xu3Fm33nqrdu3apc6dOys/P1+7d++WJCUkJLj93JEjR3T11VcbULFnxMXFafny5a3yXEePHlV2drbmzp2r+Pj4VnlOSfXeORxAy2jN7xHJmO8SvkdaX1VVlQ4fPqwHHnhAktxGbm3atMlrzww0F58nAM3F94jvMDQQr127tt5tt99+u7xoNLdHhYSEtPrRlfj4eL84ogPgZ0Z8j0h8l/gbRm79jM8TgObie8R3MEcCAACQxMgtAID5EIgBAIAkRm4BAMzHq6ZdAgAAAACgtRCIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmZGggnj9/vgICAtyWpKQk1/bq6mpNmTJFUVFR6tChg8aNG6fjx48bWDEAAAAAwF8YfoY4OTlZx44dcy2ffPKJa9vMmTP13nvv6e2339b27dtVVlamsWPHGlgtAAAAAMBfGB6Ig4ODFRMT41o6deokSbLZbFqxYoWee+45DRkyRP3799drr72mnTt3ateuXQZXDQCA/2HkFgDAbAwPxN999526du2q7t27Kz09XSUlJZKkoqIinTt3TsOGDXPtm5SUpLi4OBUWFtbbnsPhkN1ud1sAAEDDMHILAGAmwUY++U033aSVK1cqMTFRx44dU1ZWlgYNGqSDBw+qvLxcbdu2VUREhNvPREdHq7y8vN42c3JylJWV5eHKAQDwT3Ujt/5V3citN998U0OGDJEkvfbaa7ruuuu0a9cu3Xzzza1dKgAAzWboGeKRI0fqP/7jP5SSkqLhw4dr8+bNqqys1Lp165rcZkZGhmw2m2spLS1twYoBAPBvjNwCAJiJ4UOmLxQREaGePXvq+++/V0xMjM6ePavKykq3fY4fP37JI9d1LBaLwsPD3RYAAHBldSO3PvzwQ+Xl5enIkSMaNGiQTp482ayRW1ar1bXExsZ6+FUAANBwXhWIq6qqdPjwYXXp0kX9+/dXmzZttG3bNtf2Q4cOqaSkRGlpaQZWCQCAf2LkFgDAbAy9hvj3v/+9xowZo/j4eJWVlSkzM1NBQUG69957ZbVa9eCDD2rWrFmKjIxUeHi4pk6dqrS0NK5TAgCgFVw4cuuOO+5wjdy68CxxQ0ZuWSyWVqgWAIDGM/QM8T/+8Q/de++9SkxM1Pjx4xUVFaVdu3apc+fOkqTFixfrrrvu0rhx4zR48GDFxMTo3XffNbJkAABMg5FbAAB/Z+gZ4rVr1152e0hIiHJzc5Wbm9tKFQEAYF6M3AIAmI2hgRgAAHiPupFb//znP9W5c2fdeuutF43cCgwM1Lhx4+RwODR8+HAtW7bM4KoBAGg6AjEAAJDEyC0AgPl41V2mAQAAAABoLQRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgSgRiAAAAAIApBRtdAAAAAIBLq6mp0YEDB1RRUaHIyEilpKQoKCjI6LIAv0EgBgAAALxQQUGBli1bpvLycte6mJgYPfbYYxo8eLCBlQH+gyHTAAAAgJcpKChQZmamunfvrtzcXG3evFm5ubnq3r27MjMzVVBQYHSJgF8gEAMAAABepKamRsuWLVNaWpoWLFig5ORktW/fXsnJyVqwYIHS0tKUl5enmpoao0sFfB6BGAAAAPAiBw4cUHl5udLT0xUY6P7f9cDAQKWnp+vYsWM6cOCAQRUC/oNADAAAAHiRiooKSdI111xzye116+v2A9B0BGIAAADAi0RGRkqSjhw5csntdevr9gPQdARiAAAAwIukpKQoJiZGa9askdPpdNvmdDq1Zs0adenSRSkpKQZVCPgPrwnECxcuVEBAgGbMmOFaV15ergceeEAxMTEKDQ1Vv3799M477xhXJAAAAOBhQUFBeuyxx1RYWKh58+bpyy+/1OnTp/Xll19q3rx5Kiws1KOPPsp8xEAL8Ip5iPfs2aOXX375oqNcEyZMUGVlpTZt2qROnTrpzTff1Pjx4/X555+rb9++BlULAAAAeNbgwYOVlZWlZcuWacqUKa71Xbp0UVZWFvMQAy3E8DPEVVVVSk9P1yuvvKKOHTu6bdu5c6emTp2qAQMGqHv37po3b54iIiJUVFRkULUAAJgDI7cA4w0ePFhr1qzR4sWL9eSTT2rx4sV64403CMNACzI8EE+ZMkWjR4/WsGHDLto2cOBAvfXWW6qoqJDT6dTatWtVXV2t22+/vd72HA6H7Ha72wKg4WpqarRv3z5t27ZN+/btY45DwIQuN3Lr0KFD2rRpk4qLizV27FiNHz9e+/btM6hSwP8FBQWpb9++Gjp0qPr27cswaaCFGTpkeu3atdq7d6/27Nlzye3r1q3Tr3/9a0VFRSk4OFjt27fXhg0blJCQUG+bOTk5ysrK8lTJgF8rKCjQsmXLVF5e7loXExOjxx57jKPRgElcOHJrwYIFbtt27typvLw8DRgwQJI0b948LV68WEVFRVzKBADwSYadIS4tLdX06dO1Zs0ahYSEXHKfJ598UpWVlfroo4/0+eefa9asWRo/fryKi4vrbTcjI0M2m821lJaWeuolAH6loKBAmZmZ6t69u3Jzc7V582bl5uaqe/fuyszMVEFBgdElAmgFjNwCAJiJYWeIi4qKdOLECfXr18+1rqamRgUFBVq6dKkOHTqkpUuX6uDBg0pOTpYkpaamaseOHcrNzdVLL710yXYtFossFkurvAbAX9TU1GjZsmVKS0vTggULFBj487Gy5ORkLViwQPPmzVNeXp5uueUWhmoBfoyRWwAAszHsDPHQoUNVXFys/fv3u5YbbrhB6enp2r9/v06fPv1zgYHuJQYFBV00HxuA5jlw4IDKy8uVnp5+0WcuMDBQ6enpOnbsmA4cOGBQhQA8jZFbAAAzMuwMcVhYmHr37u22LjQ0VFFRUerdu7fOnTunhIQETZ48WYsWLVJUVJQ2btyorVu36v333zeoasA/VVRUSJKuueaaS26vW1+3HwD/w8gtAIAZecU8xJfSpk0bbd68WXPmzNGYMWNUVVWlhIQErVq1SqNGjTK6PMCvREZGSpKOHDni+o/uhY4cOeK2HwD/Uzdy60KTJk1SUlKSZs+ezcgtAIBf8qpAnJ+f7/a4R48ezG8ItIKUlBTFxMRozZo1btcQS5LT6dSaNWvUpUuXi6ZgAeA/GLkFADAjw+chBmC8oKAgPfbYYyosLNS8efP05Zdf6vTp0/ryyy81b948FRYW6tFHH+WGWoCJ1Y3c6ty5s8aMGaOUlBStXr2akVsAAJ/mVWeIARhn8ODBysrK0rJlyzRlyhTX+i5duigrK4t5iAETYuQWAMDfEYgBuAwePFi33HKLDhw4oIqKCkVGRiolJYUzwwAAAPBLBGIAboKCgtS3b1+jywAAAAA8jmuIAQAAAACmRCAGAAAAAJgSgRgAAAAAYEoEYgAAAACAKXFTLQAAAACmcPz4cdlsNqPLaHFHjx51+9PfWK1WRUdHe6RtAjEAAAAAv3f8+HHd/8AEnTvrMLoUj8nOzja6BI9o09aiN15f7ZFQTCAGAAAA4PdsNpvOnXXoTPfb5AyxGl0OGiiw2ib9sF02m41ADAAAAADN4Qyxyhnayegy4CW4qRYAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUAMAAAAADAlbqoFAB7EfIe+yZPzHQIAAO9BIAYAD2G+Q9/lyfkOAQCA9yAQA4CHMN+hb/L0fIcAAMB7EIgBwMOY7xAAAMA7EYgBAIBP4Jp838P1+AC8HYEYAAB4Pa7J901cjw/A2xGIAQCA1+OafN/D9fgAfAGBGAAA+AyuyQcAtKRAowuos3DhQgUEBGjGjBlu6wsLCzVkyBCFhoYqPDxcgwcP1pkzZ4wpEgAAAADgN7ziDPGePXv08ssvKyUlxW19YWGhRowYoYyMDL344osKDg7WF198ocBAr8nxAAAAAAAfZXiyrKqqUnp6ul555RV17NjRbdvMmTM1bdo0zZkzR8nJyUpMTNT48eNlsVgMqhYAAHNg5BYAwAwMD8RTpkzR6NGjNWzYMLf1J06c0O7du/WLX/xCAwcOVHR0tG677TZ98sknl23P4XDIbre7LQAAoOGuNHLrzjvv1GeffaY9e/bo8ccfZ+QWAMBnGdqDrV27Vnv37lVOTs5F23744QdJ0vz58/XQQw/pww8/VL9+/TR06FB999139baZk5Mjq9XqWmJjYz1WPwAA/oaRWwAAMzHsGuLS0lJNnz5dW7duVUhIyEXbnU6nJGny5MmaNGmSJKlv377atm2bXn311UuGaEnKyMjQrFmzXI/tdnuTQ/Hx48dls9ma9LPe6ujRo25/+hur1crUDgDQDBeO3FqwYIFrfd3IrfT0dA0cOFCHDx9WUlKSsrOzdeutt9bbnsPhkMPxf3MHM3ILAOBNDAvERUVFOnHihPr16+daV1NTo4KCAi1dulSHDh2SJPXq1cvt56677jqVlJTU267FYmmRI9XHjx/X/Q9M0Lmzjivv7IOys7ONLsEj2rS16I3XVxOKAaAJ6kZu7dmz56JtF47cWrRokfr06aPVq1dr6NChOnjwoHr06HHJNnNycpSVleXRugEAaCrDAvHQoUNVXFzstm7SpElKSkrS7Nmz1b17d3Xt2tUVjOt8++23GjlypMfrs9lsOnfWoTPdb5MzxOrx50PzBVbbpB+2y2azEYgBoJF8YeQWAAAtzbBAHBYWpt69e7utCw0NVVRUlGv9E088oczMTKWmpqpPnz5atWqVvvnmG61fv77V6nSGWOUM7dRqzwcAgBG8feQWAACe4BXzENdnxowZqq6u1syZM1VRUaHU1FRt3bpV1157rdGlAQDgV7x95BYAAJ7gVYE4Pz//onVz5szRnDlzWr8YAABMxFdGbgEA0JK8KhADAADvxcgtAIC/IRADAIBLYuQWAMDfBRpdAAAAAAAARiAQAwAAAABMiUAMAAAAADAlAjEAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUAMAAAAADAlAjEAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUAMAAAAADAlAjEAAAAAwJQIxAAAAAAAUyIQAwAAAABMiUAMAAAAADAlAjEAAAAAwJQIxAAAAAAAUyIQAwDgR6qrq40uAQAAn0EgBgDAxzmdTj399NO66qqr1KFDB/3www+SpCeffFIrVqwwuDoAALwXgRgAAB+3YMECrVy5Un/+85/Vtm1b1/revXvrr3/9q4GVAQDg3QjEAAD4uNWrV2v58uVKT09XUFCQa31qaqq++eYbAysDAMC7EYgBAPBx//M//6OEhISL1judTp07d86AigAA8A0EYgAAfFyvXr20Y8eOi9avX79effv2NaAiAAB8Q5MC8YQJE/Taa6/p8OHDLVbIwoULFRAQoBkzZly0rba2ViNHjlRAQIA2btzYYs8JAIA/eOqpp/T444/rmWeekdPp1LvvvquHHnpI2dnZeuqpp4wuDwAAr9WkQNy2bVvl5OSoR48eio2N1f3336+//vWv+u6775pUxJ49e/Tyyy8rJSXlktuff/55BQQENKltAAD83d1336333ntPH330kUJDQ/XUU0/p66+/1nvvvac77rijSW1yoBoAYAbBTfmhujtW/s///I8KCgq0fft2Pfvss5o8ebK6dOmif/zjHw1uq6qqSunp6XrllVe0YMGCi7bv379fzz77rD7//HN16dKlKeUCAOD3Bg0apK1bt7ZIWxyoBgCYRbOuIe7YsaOioqLUsWNHRUREKDg4WJ07d25UG1OmTNHo0aM1bNiwi7adPn1a9913n3JzcxUTE9Og9hwOh+x2u9sCAIA/Ky0tdTsY/dlnn2nGjBlavnx5o9u68EB1x44dL9ped6D61VdfbVbNAAB4gyYF4v/8z//UwIEDFRUVpTlz5qi6ulpz5sxReXm59u3b1+B21q5dq7179yonJ+eS22fOnKmBAwfq7rvvbnCbOTk5slqtriU2NrbBPwsAgC+677779N///d+SpPLycg0bNkyfffaZ5s6dqz/+8Y+NaosD1QAAM2nSkOmFCxeqc+fOyszM1NixY9WzZ89Gt1FaWqrp06dr69atCgkJuWj7pk2b9PHHHzcqYEtSRkaGZs2a5Xpst9sJxQAAv3bw4EENGDBAkrRu3Tpdf/31+vTTT/X3v/9djzzySINvrFV3oHrPnj2X3N7UA9VZWVkN3h8AgNbUpDPE+/bt09y5c/XZZ5/plltu0VVXXaX77rtPy5cv17ffftugNoqKinTixAn169dPwcHBCg4O1vbt27VkyRIFBwdr69atOnz4sGsodnDwz9l93Lhxuv322+tt12KxKDw83G0BAMCfnTt3ThaLRZL00Ucf6Ve/+pUkKSkpSceOHWtQG3UHqtesWXPZA9XPP/98o2rLyMiQzWZzLaWlpY36eQAAPKlJZ4hTU1OVmpqqadOmSZK++OILLV68WFOmTJHT6VRNTc0V2xg6dKiKi4vd1k2aNElJSUmaPXu2OnXqpMmTJ7ttv/7667V48WKNGTOmKWUDAOCXkpOT9dJLL2n06NHaunWrnn76aUlSWVmZoqKiGtTGhQeq69TU1KigoEBLly7Vo48+6jpQfaFx48Zp0KBBys/Pv2S7FovFFdYBAPA2TQrEtbW12rdvn/Lz85Wfn69PPvlEdrtdKSkpuu222xrURlhYmHr37u22LjQ0VFFRUa71l7o+KS4uTtdcc01TygYAwC8988wz+rd/+zf95S9/0cSJE5Wamirp57O6dUOpr4QD1QAAM2pSII6MjFRVVZVSU1N122236aGHHtKgQYMuOmoMAAA87/bbb9dPP/0ku93udmfohx9+WO3bt29QGxyoBgCYUZMC8RtvvKFBgwa1+PW59Q23qlNbW9uizwcAgL8ICgrS+fPn9cknn0iSEhMTdfXVVxtbFAAAXq5JgXj06NGuv9fNe9itW7eWqQgAADTKqVOnNHXqVK1evVpOp1PSzwF5woQJevHFFxt8lvhfcaAaAODvmnSXaafTqT/+8Y+yWq2Kj49XfHy8IiIi9PTTT7s6YgAA0DpmzZql7du367333lNlZaUqKyv1t7/9Tdu3b9fvfvc7o8sDAMBrNekM8dy5c7VixQotXLhQt9xyiyTpk08+0fz581VdXa3s7OwWLRIAANTvnXfe0fr1692mJRw1apTatWun8ePHKy8vz7jiAADwYk0KxKtWrdJf//pX1zyHkpSSkqKrrrpKjz32GIEYAIBWdPr0aUVHR1+0/he/+IVOnz5tQEUAAPiGJg2ZrqioUFJS0kXrk5KSVFFR0eyiAABAw6WlpSkzM1PV1dWudWfOnFFWVpbS0tIMrAwAAO/WpDPEqampWrp0qZYsWeK2funSpUpJSWmRwgAAQMO88MILGj58uLp16+aag/iLL75QSEiItmzZYnB1AAB4ryYF4j//+c8aPXq0PvroI9eR58LCQpWWlmrz5s0tWiAAALi83r1767vvvtOaNWv0zTffSJLuvfdepaenq127dgZXBwCA92pSIL7tttv07bffKjc319Xxjh07Vg8//LAWLFigQYMGtWiRAADg8tq3b6+HHnrI6DIAAPApTQrEktS1a9eLbp71xRdfaMWKFVq+fHmzCwMAAPXbtGlTg/e98CaYAADg/zQ5EAMAAOPcc889DdovICBANTU1ni0GAAAfRSAGAMAHOZ1Oo0sAAMDnNWnaJQAAYLyPP/5YvXr1kt1uv2ibzWZTcnKyduzYYUBlAAD4hkadIR47duxlt1dWVjanFgAA0AjPP/+8HnroIYWHh1+0zWq1avLkyXruuee42SUAAPVoVCC2Wq1X3D5hwoRmFQQAABrmiy++0DPPPFPv9jvvvFOLFi1qxYoAAPAtjQrEr732mqfqAAAAjXT8+HG1adOm3u3BwcH6//6//68VKwIAwLdwUy0AAHzUVVddpYMHDyohIeGS2w8cOKAuXbq0clWA/6uurlZJSYnRZXhUXFycQkJCjC4D8DgCMQAAPmrUqFF68sknNWLEiIv+43rmzBllZmbqrrvuMqg6wH+VlJTo4YcfNroMj1q+fLl69uxpdBkeEXim0ugS0Aie/n0RiAEfwJFoAJcyb948vfvuu+rZs6cef/xxJSYmSpK++eYb5ebmqqamRnPnzjW4SsD/xMXFafny5a32fEePHlV2drbmzp2r+Pj4VnnOuLi4VnkeI7Q7UmB0CfAiBGLAB3AkGsClREdHa+fOnXr00UeVkZGh2tpaSVJAQICGDx+u3NxcRUdHG1wl4H9CQkIM6bPi4+PpK1vAmWsGy9kuwugy0ECBZyo9ehCDQAz4AI5EA6hPfHy8Nm/erP/93//V999/r9raWvXo0UMdO3Y0ujQA8ErOdhFyhnYyugx4CQIx4AM4Eu3buFbJt/jq76tjx4668cYbjS4DAACfQiAGAA/jWiUAAADvRCAGAA/jWiXf4ulrlQAAgPcgEAOAh3GtEgAAgHcKNLqAOgsXLlRAQIBmzJghSaqoqNDUqVOVmJiodu3aKS4uTtOmTZPNZjO2UAAAAACAX/CKQLxnzx69/PLLSklJca0rKytTWVmZFi1apIMHD2rlypX68MMP9eCDDxpYKQAA5sCBagCAGRg+ZLqqqkrp6el65ZVXtGDBAtf63r1765133nE9vvbaa5Wdna37779f58+fV3Cw4aUDAOCXrnSgulevXjp69KgeeeQRlZWVaf369QZWCwBA0xl+hnjKlCkaPXq0hg0bdsV9bTabwsPDLxuGHQ6H7Ha72wIAABrmwgPVF85lXHegesyYMbr22ms1ZMgQZWdn67333tP58+cNrBgAgKYzNBCvXbtWe/fuVU5OzhX3/emnn/T000/r4Ycfvux+OTk5slqtriU2NralygUAwO9xoBoAYCaGBeLS0lJNnz5da9asUUhIyGX3tdvtGj16tHr16qX58+dfdt+MjAzZbDbXUlpa2oJVAwDgvzhQDQAwG8MCcVFRkU6cOKF+/fopODhYwcHB2r59u5YsWaLg4GDV1NRIkk6ePKkRI0YoLCxMGzZsUJs2bS7brsViUXh4uNsCAAAujwPVAAAzMuzOVEOHDlVxcbHbukmTJikpKUmzZ89WUFCQ7Ha7hg8fLovFok2bNl2xgwYAAE1z4YHqOjU1NSooKNDSpUvlcDgUFBTUpAPVFovF0+UDANAkhgXisLAw9e7d221daGiooqKi1Lt3b9ntdt155506ffq03njjDbfrjjp37qygoCAjygYAwC9xoBoAYEZeO3fR3r17tXv3bklSQkKC27YjR47o6quvNqAqAAD8EweqAQBm5FWBOD8/3/X322+/XbW1tcYVAwAAXDhQDQDwR14ViAEAgPfgQDUAwN8ZOg8xAAAAAABGIRADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlIKNLgAAAABoCcePH5fNZjO6jBZ39OhRtz/9jdVqVXR0tNFlwKQIxAAAAPB5x48f1/0PTNC5sw6jS/GY7Oxso0vwiDZtLXrj9dWEYhiCQAwAAACfZ7PZdO6sQ2e63yZniNXoctBAgdU26YftstlsBGIYgkAMAAAAv+EMscoZ2snoMgD4CG6qBQAAAAAwJQIxAAAAAMCUCMQAAAAAAFMiEAMAAAAATImbagHNwHyHvoe5DgEAAFCHQAw0EfMd+ibmOgQAAEAdAjHQRMx36HuY6xAAAAAXIhADzcR8hwAAAIBv4qZaAAAAAABTIhADAAAAAEyJQAwAAAAAMCWvCcQLFy5UQECAZsyY4VpXXV2tKVOmKCoqSh06dNC4ceN0/Phx44oEAAAAAPgNrwjEe/bs0csvv6yUlBS39TNnztR7772nt99+W9u3b1dZWZnGjh1rUJUAAJgHB6oBAGZgeCCuqqpSenq6XnnlFXXs2NG13mazacWKFXruuec0ZMgQ9e/fX6+99pp27typXbt2GVgxAAD+jQPVAACzMDwQT5kyRaNHj9awYcPc1hcVFencuXNu65OSkhQXF6fCwsJ623M4HLLb7W4LAABoGA5UAwDMxNBAvHbtWu3du1c5OTkXbSsvL1fbtm0VERHhtj46Olrl5eX1tpmTkyOr1epaYmNjW7psAAD8FgeqAQBmYlggLi0t1fTp07VmzRqFhIS0WLsZGRmy2WyupbS0tMXaBgDAn3GgGgBgNoYF4qKiIp04cUL9+vVTcHCwgoODtX37di1ZskTBwcGKjo7W2bNnVVlZ6fZzx48fV0xMTL3tWiwWhYeHuy0AAODyOFANADCjYKOeeOjQoSouLnZbN2nSJCUlJWn27NmKjY1VmzZttG3bNo0bN06SdOjQIZWUlCgtLc2IkgEA8FsXHqiuU1NTo4KCAi1dulRbtmxxHai+8CxxQw5UWywWT5YOAECTGRaIw8LC1Lt3b7d1oaGhioqKcq1/8MEHNWvWLEVGRio8PFxTp05VWlqabr75ZiNKBgDAb3GgGgBgRoYF4oZYvHixAgMDNW7cODkcDg0fPlzLli0zuiwAAPwOB6oBAGbkVYE4Pz/f7XFISIhyc3OVm5trTEEAAMCFA9UAAH/jVYEYAAB4Dw5UAwD8naHzEAMAAAAAYBTOEAMAAJ8ReKbS6BLQQEb9rniP+BZ+XzAagRgAAPiMdkcKjC4BXo73CIDGIBADAACfceaawXK2izC6DDRA4JlKQ8Ip7xHfYtT7BKhDIAYAAD7D2S5CztBORpcBL8Z7BEBjcFMtAAAAAIApEYgBAAAAAKZEIAYAAAAAmBKBGAAAAABgStxU6wqYG8138LsCAAAA0BgE4ivgNvAAAAAA4J8IxFfAXHa+g3nsAAAAADQGgfgKmMsOAAAAAPwTN9UCAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmxE21gGZi/mPfwe8KAAAAFyIQA83EVE8AAACAbyIQA83EXNW+g7mqAQAAcCECMdBMzFUNAADgOwKrbUaXgEbw9O+LQAwAAADA71mtVrVpa5F+2G50KWikNm0tslqtHmmbQAwAAADA70VHR+uN11fLZvO/M8RHjx5Vdna25s6dq/j4eKPLaXFWq1XR0dEeadvQQJyXl6e8vDz9+OOPkqTk5GQ99dRTGjlypCSpvLxcTzzxhLZu3aqTJ08qMTFRc+fO1bhx4wysGgAAAIAvio6O9liw8gbx8fHq2bOn0WX4FEPnIe7WrZsWLlyooqIiff755xoyZIjuvvtuffnll5KkCRMm6NChQ9q0aZOKi4s1duxYjR8/Xvv27TOybAAA/FJeXp5SUlIUHh6u8PBwpaWl6YMPPnBtLy8v1wMPPKCYmBiFhoaqX79+eueddwysGACA5jE0EI8ZM0ajRo1Sjx491LNnT2VnZ6tDhw7atWuXJGnnzp2aOnWqBgwYoO7du2vevHmKiIhQUVGRkWUDAOCXOFANADAbQwPxhWpqarR27VqdOnVKaWlpkqSBAwfqrbfeUkVFhZxOp9auXavq6mrdfvvt9bbjcDhkt9vdFgAAcGUcqAYAmI3hgbi4uFgdOnSQxWLRI488og0bNqhXr16SpHXr1uncuXOKioqSxWLR5MmTtWHDBiUkJNTbXk5OjqxWq2uJjY1trZcCAIDf4EA1AMAMDL/LdGJiovbv3y+bzab169dr4sSJ2r59u3r16qUnn3xSlZWV+uijj9SpUydt3LhR48eP144dO3T99ddfsr2MjAzNmjXL9dhutxOKARiK+Q59i9l/X8XFxUpLS1N1dbU6dOhw0YHqX//614qKilJwcLDat2/foAPVWVlZrVU+AACNYnggbtu2rasj7d+/v/bs2aMXXnhBf/jDH7R06VIdPHhQycnJkqTU1FTt2LFDubm5eumlly7ZnsVikcViabX6AaA+zHfouzw536G340A1AMBMDA/E/8rpdMrhcOj06dOSpMBA91HdQUFBcjqdRpQGAI3CfIe+y5PzHXo7DlTD15l9lIev4fcFoxkaiDMyMjRy5EjFxcXp5MmTevPNN5Wfn68tW7YoKSlJCQkJmjx5shYtWqSoqCht3LhRW7du1fvvv29k2QDQYMx3CF/HgWr4Ckbl+C4zj8qB8QwNxCdOnNCECRN07NgxWa1WpaSkaMuWLbrjjjskSZs3b9acOXM0ZswYVVVVKSEhQatWrdKoUaOMLBsAAL/EgWr4Mkbl+C4zj8qB8QwNxCtWrLjs9h49euidd95ppWqApmGoj+/gdwVcHgeq4esYlQOgsbzuGmLAVzA0yzcxLAuoHweqAQBmQyAGmoihWb6JYVkAAACoQyAGmoGhWQAAAIDvCrzyLgAAAAAA+B8CMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTYh5iAADgMwKrbUaXgAby599VdXW1SkpKWu35jh496vZna4iLi1NISEirPR9gFAIxAADwelarVW3aWqQfthtdChqhTVuLrFar0WW0uJKSEj388MOt/rzZ2dmt9lzLly9Xz549W+35AKMQiAEAgNeLjo7WG6+vls3mf2cdjx49quzsbM2dO1fx8fFGl9OirFaroqOjjS6jxcXFxWn58uVGl+FRcXFxRpcAtAoCMQAA8AnR0dF+Ga7qxMfHc0bOR4SEhPC7AvwEN9UCAAAAAJgSgRgAAAAAYEoEYgAAAACAKRGIAQAAAACmRCAGAAAAAJgSd5m+An+eVN7f8LsCAAAA0BgE4npYrVa1aWuRfthudClohDZtLbJarUaXAQAAAMAHEIjrER0drTdeXy2bzb/OOh49elTZ2dmaO3eu4uPjjS6nxVmtVr+eoxIAAABAyyEQX0Z0dLTfhqv4+HgmlAcAAABgatxUCwAAAABgSgRiAAAAAIApGRqI8/LylJKSovDwcIWHhystLU0ffPCB2z6FhYUaMmSIQkNDFR4ersGDB+vMmTMGVQwAgP+iXwYAmI2hgbhbt25auHChioqK9Pnnn2vIkCG6++679eWXX0r6udMdMWKE7rzzTn322Wfas2ePHn/8cQUGcmIbAICWRr8MADAbQ2+qNWbMGLfH2dnZysvL065du5ScnKyZM2dq2rRpmjNnjmufxMTE1i4TAABToF8GAJiN19xluqamRm+//bZOnTqltLQ0nThxQrt371Z6eroGDhyow4cPKykpSdnZ2br11lvrbcfhcMjhcLge2+321igf8Kjq6mqVlJS02vMdPXrU7c/WEBcXp5CQkFZ7PgCX11L9MgAA3szwQFxcXKy0tDRVV1erQ4cO2rBhg3r16qVdu3ZJkubPn69FixapT58+Wr16tYYOHaqDBw+qR48el2wvJydHWVlZrfkSAI8rKSnRww8/3OrPm52d3WrPtXz5cqYCA7xAS/fLHKgGAHgzwwNxYmKi9u/fL5vNpvXr12vixInavn27nE6nJGny5MmaNGmSJKlv377atm2bXn31VeXk5FyyvYyMDM2aNcv12G63KzY21vMvBPCguLg4LV++3OgyPCouLs7oEgCo5ftlDlQDALyZ4YG4bdu2SkhIkCT1799fe/bs0QsvvOC6PqlXr15u+1933XWXHTpqsVhksVg8VzBggJCQEM6eAmgVLd0vc6AaAODNvO62kE6nUw6HQ1dffbW6du2qQ4cOuW3/9ttvFR8fb1B1AACYS3P7ZYvF4prGqW4BAMBbGHqGOCMjQyNHjlRcXJxOnjypN998U/n5+dqyZYsCAgL0xBNPKDMzU6mpqerTp49WrVqlb775RuvXrzeybAAA/BL9MgDAbAwNxCdOnNCECRN07NgxWa1WpaSkaMuWLbrjjjskSTNmzFB1dbVmzpypiooKpaamauvWrbr22muNLBsAAL9EvwwAMBtDA/GKFSuuuM+cOXPc5jsEAACeQb8MADAbw2+qhdadY9aI+WUl5pgFPI25qoGWw+cJQHPxPeI7Ampra2uNLsKT7Ha7rFarbDab197I49tvvzVkjtnWxByzgGfxPWIsX+hrvIUv/FvxeQLQXHyPGKsxfQ2B2Au09hEkI/jLESTAW/E9Yixf6Gu8hS/8W/F5AtBcfI8YqzF9DUOmvQBzzAJoLr5HgJbD5wlAc/E94ju8bh5iAAAAAABaA4EYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkFG12Ap9XW1kqS7Ha7wZUAAPxVXR9T1+egfvTLAABPa0y/7PeB+OTJk5Kk2NhYgysBAPi7kydPymq1Gl2GV6NfBgC0lob0ywG1fn442+l0qqysTGFhYQoICDC6HMPZ7XbFxsaqtLRU4eHhRpcDL8X7BFfCe8RdbW2tTp48qa5duyowkKuRLod++WJ8nnAlvEdwJbxH3DWmX/b7M8SBgYHq1q2b0WV4nfDwcD4suCLeJ7gS3iP/hzPDDUO/XD8+T7gS3iO4Et4j/6eh/TKHsQEAAAAApkQgBgAAAACYEoHYZCwWizIzM2WxWIwuBV6M9wmuhPcI0HL4POFKeI/gSniPNJ3f31QLAAAAAIBL4QwxAAAAAMCUCMQAAAAAAFMiEAMAAAAATIlADAAAAAAwJQKxybz77ru68847FRUVpYCAAO3fv9/okuBlcnNzdfXVVyskJEQ33XSTPvvsM6NLghcpKCjQmDFj1LVrVwUEBGjjxo1GlwT4NPplXAn9Mi6Hfrn5CMQmc+rUKd1666165plnjC4FXuitt97SrFmzlJmZqb179yo1NVXDhw/XiRMnjC4NXuLUqVNKTU1Vbm6u0aUAfoF+GZdDv4wroV9uPqZdMqkff/xR11xzjfbt26c+ffoYXQ68xE033aQbb7xRS5culSQ5nU7FxsZq6tSpmjNnjsHVwdsEBARow4YNuueee4wuBfB59Mu4FPplNAb9ctNwhhiAJOns2bMqKirSsGHDXOsCAwM1bNgwFRYWGlgZAADmQ78MtA4CMQBJ0k8//aSamhpFR0e7rY+OjlZ5eblBVQEAYE70y0DrIBD7sTVr1qhDhw6uZceOHUaXBACAadEvA4D3CTa6AHjOr371K910002ux1dddZWB1cDbderUSUFBQTp+/Ljb+uPHjysmJsagqgDAf9AvozHol4HWwRliPxYWFqaEhATX0q5dO6NLghdr27at+vfvr23btrnWOZ1Obdu2TWlpaQZWBgD+gX4ZjUG/DLQOzhCbTEVFhUpKSlRWViZJOnTokCQpJiaGo43QrFmzNHHiRN1www0aMGCAnn/+eZ06dUqTJk0yujR4iaqqKn3//feux0eOHNH+/fsVGRmpuLg4AysDfBP9Mi6HfhlXQr/cfEy7ZDIrV6685JdoZmam5s+f3/oFwessXbpUf/nLX1ReXq4+ffpoyZIlbkP8YG75+fn65S9/edH6iRMnauXKla1fEODj6JdxJfTLuBz65eYjEAMAAAAATIlriAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIAAAAAgCkRiAEAAAAApkQgBgAAAACYEoEYAAAAAGBKBGIALitXrlRERESz2wkICNDGjRub3Q4AAGZGvwx4HoEY8DO//e1vdc899xhdBgAAEP0y4O0IxAAAAAAAUyIQAyby3HPP6frrr1doaKhiY2P12GOPqaqq6qL9Nm7cqB49eigkJETDhw9XaWmp2/a//e1v6tevn0JCQtS9e3dlZWXp/PnzrfUyAADwC/TLgPEIxICJBAYGasmSJfryyy+1atUqffzxx/rDH/7gts/p06eVnZ2t1atX69NPP1VlZaV+85vfuLbv2LFDEyZM0PTp0/XVV1/p5Zdf1sqVK5Wdnd3aLwcAAJ9Gvwx4gVoAfmXixIm1d999d4P2ffvtt2ujoqJcj1977bVaSbW7du1yrfv6669rJdXu3r27tra2tnbo0KG1f/rTn9zaef3112u7dOnieiypdsOGDU1/EQAA+An6ZcC7BRsZxgG0ro8++kg5OTn65ptvZLfbdf78eVVXV+v06dNq3769JCk4OFg33nij62eSkpIUERGhr7/+WgMGDNAXX3yhTz/91O3Ic01NzUXtAACAy6NfBoxHIAZM4scff9Rdd92lRx99VNnZ2YqMjNQnn3yiBx98UGfPnm1wh1lVVaWsrCyNHTv2om0hISEtXTYAAH6JfhnwDgRiwCSKiorkdDr17LPPKjDw59sHrFu37qL9zp8/r88//1wDBgyQJB06dEiVlZW67rrrJEn9+vXToUOHlJCQ0HrFAwDgZ+iXAe9AIAb8kM1m0/79+93WderUSefOndOLL76oMWPG6NNPP9VLL7100c+2adNGU6dO1ZIlSxQcHKzHH39cN998s6sjfuqpp3TXXXcpLi5O//7v/67AwEB98cUXOnjwoBYsWNAaLw8AAJ9Cvwx4L+4yDfih/Px89e3b1215/fXX9dxzz+mZZ55R7969tWbNGuXk5Fz0s+3bt9fs2bN133336ZZbblGHDh301ltvubYPHz5c77//vv7+97/rxhtv1M0336zFixcrPj6+NV8iAAA+g34Z8F4BtbW1tUYXAQAAAABAa+MMMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMCUCMQAAAADAlAjEAAAAAABTIhADAAAAAEyJQAwAAAAAMKX/HxKMMCjrtpz+AAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Observations:**\n", + "* The median for all prices is significantly lower for negative sentiment news as compared to both positive and neutral sentiment news, indicating that negative news likely triggers investor sell-offs which drive the stock prices down.\n", + "\n", + "* The boxplot for the open price under neutral sentiment displays a notably higher upper whisker relative to positive sentiment. This suggests that the market's opening often covers a wider range of prices when news is neutral.\n", + " * This variability might be attributed to different interpretations of seemingly neutral news, which leads some investors to react more aggressively and drive the opening price to higher levels." + ], + "metadata": { + "id": "axyzmidFWaNS" + }, + "id": "axyzmidFWaNS" + }, + { + "cell_type": "markdown", + "id": "cY9P2rdBMH-h", + "metadata": { + "id": "cY9P2rdBMH-h" + }, + "source": [ + "#### **Label vs Volume**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mzCxLFg1LCPk", + "metadata": { + "id": "mzCxLFg1LCPk", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "13536009-bc63-4dff-b422-df723211166c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.boxplot(\n", + " data=stock, x=\"Label\", y=\"Volume\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "LFipGtxhOa8g", + "metadata": { + "id": "LFipGtxhOa8g" + }, + "source": [ + "**Observations:**\n", + "* The median trading volume for the stock is approximately the same across all sentiment polarities.\n", + "* The volume distribution for positive sentiment news shows a wider spread compared to other sentiment categories.\n", + " - This wider range might indicate that even positive news leads to diverse interpretations among investors, contributing to varied trading activities and reactions." + ] + }, + { + "cell_type": "markdown", + "id": "9ySUmJUyQ0vi", + "metadata": { + "id": "9ySUmJUyQ0vi" + }, + "source": [ + "#### **Date vs Price (Open, High, Low, Close)**" + ] + }, + { + "cell_type": "markdown", + "id": "tq0NL64DQ0v1", + "metadata": { + "id": "tq0NL64DQ0v1" + }, + "source": [ + "- The data is at the level of news, and we might have more than one news in a day. However, the prices are at daily level\n", + "- So, we can aggregate the data at a daily level by taking the mean of the attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bECqvVQtwheA", + "metadata": { + "id": "bECqvVQtwheA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "89dd8d7b-2323-418b-9e11-544405d0396b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Open High Low Close Volume\n", + "Date \n", + "2019-01-02 38.72 39.71 38.56 39.48 130672400.0\n", + "2019-01-03 35.99 36.43 35.50 35.55 103544800.0\n", + "2019-01-04 36.13 37.14 35.95 37.06 111448000.0\n", + "2019-01-07 37.17 37.21 36.47 36.98 109012000.0\n", + "2019-01-08 37.39 37.96 37.13 37.69 216071600.0" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
OpenHighLowCloseVolume
Date
2019-01-0238.7239.7138.5639.48130672400.0
2019-01-0335.9936.4335.5035.55103544800.0
2019-01-0436.1337.1435.9537.06111448000.0
2019-01-0737.1737.2136.4736.98109012000.0
2019-01-0837.3937.9637.1337.69216071600.0
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "stock_daily", + "summary": "{\n \"name\": \"stock_daily\",\n \"rows\": 73,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2019-01-02 00:00:00\",\n \"max\": \"2019-04-29 00:00:00\",\n \"num_unique_values\": 73,\n \"samples\": [\n \"2019-01-08 00:00:00\",\n \"2019-04-15 00:00:00\",\n \"2019-01-30 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.522032992144083,\n \"min\": 35.99,\n \"max\": 51.84,\n \"num_unique_values\": 69,\n \"samples\": [\n 43.22,\n 38.71999999999999,\n 48.830000000000005\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.515838152119043,\n \"min\": 36.43,\n \"max\": 52.12,\n \"num_unique_values\": 68,\n \"samples\": [\n 49.080000000000005,\n 39.08,\n 37.96\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.522815819552585,\n \"min\": 35.5,\n \"max\": 51.76,\n \"num_unique_values\": 66,\n \"samples\": [\n 49.54,\n 50.97,\n 38.56\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.5189565039202,\n \"min\": 35.55,\n \"max\": 51.86999999999999,\n \"num_unique_values\": 68,\n \"samples\": [\n 48.77,\n 39.08,\n 37.68999999999999\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49858245.96607382,\n \"min\": 45448000.0,\n \"max\": 365248800.0,\n \"num_unique_values\": 73,\n \"samples\": [\n 216071600.0,\n 70146400.0,\n 244439200.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "stock_daily = stock.groupby('Date').agg(\n", + " {\n", + " 'Open': 'mean',\n", + " 'High': 'mean',\n", + " 'Low': 'mean',\n", + " 'Close': 'mean',\n", + " 'Volume': 'mean',\n", + " }\n", + ").reset_index() # Group the 'stocks' DataFrame by the 'Date' column\n", + "\n", + "stock_daily.set_index('Date', inplace=True)\n", + "stock_daily.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ORSmC3lxrwy", + "metadata": { + "id": "7ORSmC3lxrwy", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "119a6be1-50f4-47be-d5d4-f3cdd077f128" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "sns.lineplot(stock_daily.drop('Volume', axis=1));" + ] + }, + { + "cell_type": "markdown", + "id": "5EZ5L0-UQ0v2", + "metadata": { + "id": "5EZ5L0-UQ0v2" + }, + "source": [ + "**Observations:**\n", + "* The stock price has gradually increased over time from ~\\$40 to ~\\$50 in the period for which the data is available." + ] + }, + { + "cell_type": "markdown", + "id": "KG4y9NK1Ng1-", + "metadata": { + "id": "KG4y9NK1Ng1-" + }, + "source": [ + "#### **Volume vs Close Price**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0WMHYw6w0TM6", + "metadata": { + "id": "0WMHYw6w0TM6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8517bfee-4d34-40e8-a1cd-567350afe1bb" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Create a figure and axis\n", + "fig, ax1 = plt.subplots(figsize=(15,5))\n", + "\n", + "# Lineplot on primary y-axis\n", + "sns.lineplot(data=stock_daily.reset_index(), x='Date', y='Close', ax=ax1, color='blue', marker='o', label='Close Price')\n", + "\n", + "# Create a secondary y-axis\n", + "ax2 = ax1.twinx()\n", + "\n", + "# Lineplot on secondary y-axis\n", + "sns.lineplot(data=stock_daily.reset_index(), x='Date', y='Volume', ax=ax2, color='gray', marker='o', label='Volume')\n", + "\n", + "ax1.legend(bbox_to_anchor=(1,1));" + ] + }, + { + "cell_type": "markdown", + "id": "fHU5KgCGNOX5", + "metadata": { + "id": "fHU5KgCGNOX5" + }, + "source": [ + "**Observations:**\n", + "- There is no specific pattern here\n", + " - There have been periods where the price decreased with increasing volumes\n", + " - There have been periods where the price increased with increasing volumes" + ] + }, + { + "cell_type": "markdown", + "id": "N8z4-vOBmwqv", + "metadata": { + "id": "N8z4-vOBmwqv" + }, + "source": [ + "## **Data Preprocessing**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2jIN9NycxtUC", + "metadata": { + "id": "2jIN9NycxtUC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fee181c0-f896-4c4c-cf2c-378747dfb425" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 418\n", + "mean 2019-02-14 12:24:06.889952256\n", + "min 2019-01-02 00:00:00\n", + "25% 2019-01-11 00:00:00\n", + "50% 2019-01-31 00:00:00\n", + "75% 2019-03-21 00:00:00\n", + "max 2019-04-29 00:00:00\n", + "Name: Date, dtype: object" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Date
count418
mean2019-02-14 12:24:06.889952256
min2019-01-02 00:00:00
25%2019-01-11 00:00:00
50%2019-01-31 00:00:00
75%2019-03-21 00:00:00
max2019-04-29 00:00:00
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "stock['Date'].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "0FxlsnepSb5m", + "metadata": { + "id": "0FxlsnepSb5m" + }, + "source": [ + "**Observations:**\n", + "* We see that 75% of the data is till the third week of March 2019.\n", + "* We'll take the data till the end of March 2019 for training, and keep the April 2019 data for test set." + ] + }, + { + "cell_type": "markdown", + "id": "j7KR_HgZRDtk", + "metadata": { + "id": "j7KR_HgZRDtk" + }, + "source": [ + "### Train-test Split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yXsgpkpeI8UK", + "metadata": { + "id": "yXsgpkpeI8UK" + }, + "outputs": [], + "source": [ + "X_train = stock[stock['Date'] < '2019-04-01'].reset_index()\n", + "X_test = stock[(stock['Date'] >= '2019-04-01')].reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "__2ON8RuI8Q2", + "metadata": { + "id": "__2ON8RuI8Q2" + }, + "outputs": [], + "source": [ + "y_train = X_train['Label'].copy()\n", + "y_test = X_test['Label'].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "imMx6hH0__IB", + "metadata": { + "id": "imMx6hH0__IB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3dbdaa6f-f304-47f0-94a8-32e8a5a7309d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train data shape (347, 10)\n", + "Test data shape (71, 10)\n", + "Train label shape (347,)\n", + "Test label shape (71,)\n" + ] + } + ], + "source": [ + "print(\"Train data shape\",X_train.shape)\n", + "print(\"Test data shape \",X_test.shape)\n", + "\n", + "print(\"Train label shape\",y_train.shape)\n", + "print(\"Test label shape \",y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "uJZqic2Q6YZD", + "metadata": { + "id": "uJZqic2Q6YZD" + }, + "outputs": [], + "source": [ + "# y_train.value_counts(normalize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Xf9R3BaR6bZw", + "metadata": { + "id": "Xf9R3BaR6bZw" + }, + "outputs": [], + "source": [ + "# y_test.value_counts(normalize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0rYgR14ORf7b", + "metadata": { + "id": "0rYgR14ORf7b" + }, + "source": [ + "## **Word Embeddings**" + ] + }, + { + "cell_type": "markdown", + "id": "4IUBFAOTbjju", + "metadata": { + "id": "4IUBFAOTbjju" + }, + "source": [ + "### **Generating Text Embeddings using Word2Vec**" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Defining the model**" + ], + "metadata": { + "id": "bzwPsqJvVbNC" + }, + "id": "bzwPsqJvVbNC" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ZD188ZNsboS4", + "metadata": { + "id": "ZD188ZNsboS4" + }, + "outputs": [], + "source": [ + "# Creating a list of all words in our data\n", + "words_list = [item.split(\" \") for item in stock['News'].values]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eGVgM5iTbwHy", + "metadata": { + "id": "eGVgM5iTbwHy" + }, + "outputs": [], + "source": [ + "# Creating an instance of Word2Vec\n", + "vec_size = 300\n", + "model_W2V = Word2Vec(words_list, vector_size = vec_size, min_count = 1, window=5, workers = 6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lhy6DjNxbzOd", + "metadata": { + "id": "lhy6DjNxbzOd", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6bf055ce-4c91-4cb3-bd4c-673e2c5de694" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Length of the vocabulary is 14577\n" + ] + } + ], + "source": [ + "# Checking the size of the vocabulary\n", + "print(\"Length of the vocabulary is\", len(list(model_W2V.wv.key_to_index)))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Encoding the datasets**" + ], + "metadata": { + "id": "ZYCiT-7GVNaH" + }, + "id": "ZYCiT-7GVNaH" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "F_4ldXPzcF7y", + "metadata": { + "id": "F_4ldXPzcF7y" + }, + "outputs": [], + "source": [ + "# Retrieving the words present in the Word2Vec model's vocabulary\n", + "words = list(model_W2V.wv.key_to_index.keys())\n", + "\n", + "# Retrieving word vectors for all the words present in the model's vocabulary\n", + "wvs = model_W2V.wv[words].tolist()\n", + "\n", + "# Creating a dictionary of words and their corresponding vectors\n", + "word_vector_dict = dict(zip(words, wvs))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Averaging the word vectors to get sentence encodings**" + ], + "metadata": { + "id": "GgismcJz0dZE" + }, + "id": "GgismcJz0dZE" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "vsQ0vF42cH_r", + "metadata": { + "id": "vsQ0vF42cH_r" + }, + "outputs": [], + "source": [ + "def average_vectorizer_Word2Vec(doc):\n", + " # Initializing a feature vector for the sentence\n", + " feature_vector = np.zeros((vec_size,), dtype=\"float64\")\n", + "\n", + " # Creating a list of words in the sentence that are present in the model vocabulary\n", + " words_in_vocab = [word for word in doc.split() if word in words]\n", + "\n", + " # adding the vector representations of the words\n", + " for word in words_in_vocab:\n", + " feature_vector += np.array(word_vector_dict[word])\n", + "\n", + " # Dividing by the number of words to get the average vector\n", + " if len(words_in_vocab) != 0:\n", + " feature_vector /= len(words_in_vocab)\n", + "\n", + " return feature_vector" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Jtxc1yVHcJjV", + "metadata": { + "id": "Jtxc1yVHcJjV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a0ab0ac1-7d5a-4cb0-d761-af1e7f93a323" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Time taken 8.816098928451538\n" + ] + } + ], + "source": [ + "# creating a dataframe of the vectorized documents\n", + "start = time.time()\n", + "\n", + "X_train_wv = pd.DataFrame(X_train['News'].apply(average_vectorizer_Word2Vec).tolist(), columns=['Feature '+str(i) for i in range(vec_size)])\n", + "X_test_wv = pd.DataFrame(X_test['News'].apply(average_vectorizer_Word2Vec).tolist(), columns=['Feature '+str(i) for i in range(vec_size)])\n", + "\n", + "end = time.time()\n", + "print('Time taken ', (end-start))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8IrY8tZjA4VZ", + "metadata": { + "id": "8IrY8tZjA4VZ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ded041ed-e998-442e-edc6-4bf5abe65675" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(347, 300) (71, 300)\n" + ] + } + ], + "source": [ + "print(X_train_wv.shape, X_test_wv.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "a3GUvne0hyPx", + "metadata": { + "id": "a3GUvne0hyPx" + }, + "source": [ + "### **Generating Text Embeddings using Sentence Transformer**" + ] + }, + { + "cell_type": "markdown", + "id": "51ITQezWi9VE", + "metadata": { + "id": "51ITQezWi9VE" + }, + "source": [ + "#### **Defining the model**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3EQ7eQIpYSyz", + "metadata": { + "id": "3EQ7eQIpYSyz" + }, + "outputs": [], + "source": [ + "#Defining the model\n", + "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" + ] + }, + { + "cell_type": "markdown", + "id": "Lll4MLfzKfBa", + "metadata": { + "id": "Lll4MLfzKfBa" + }, + "source": [ + "#### **Encoding the dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "q1BaGKThKcX3", + "metadata": { + "id": "q1BaGKThKcX3", + "colab": { + "base_uri": "https://localhost:8080/", + "referenced_widgets": [ + "1230a037e0b9479caa9db62c5f9ecb6a", + "ed6c19298c4747a59992a79d99cdaaa7", + "e010222da3cf4751995a51ffc82560ef", + "9f3e3b616bcf482d9fd91a2b54d8d82a", + "6838e428d6d54a3f80d34638812441e6", + "991c2589b56f444486443a31bef569d5", + "4ed01d32996f47f38fbaba687cee45ae", + "a0ce999dbcfe427ba08202bc989b1c33", + "f598184dc72f443ab0ada8de6cf076ad", + "96e9e320eec74a2e9094935af065b254", + "fb854fb10f3e415c9c4c0ac176fb74b4", + "2fb4071397a049f888159e2cbec3ec99", + "280899c6e305423a8d6f20dd395b4e10", + "f68b5d3640c54560b38a29f32deb33a8", + "115335a31d874aba99efb63fa2830e09", + "7b371d0574e04f98bf87a88f722b8477", + "9095b2b09d4a45928fbc3cf45eb35cbb", + "971a53d397494d76b8b5c4a2abb954f7", + "54dd267783314434a5389477c97974e5", + "5bfd23c3586e4615909878610be8e24b", + "4eda58c3e66e40db98ea40fc40ebb109", + "99ee6edbe0574c778200ae65b87d7e0f" + ] + }, + "outputId": "03ca294e-285e-4fe1-f930-d22aaa9f87dc" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Batches: 0%| | 0/11 [00:00#sk-container-id-1 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: #000;\n", + " --sklearn-color-text-muted: #666;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-1 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-1 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", + " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", + " so we also need the `!important` here to be able to override the\n", + " default hidden behavior on the sphinx rendered scikit-learn.org.\n", + " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", + " display: inline-block !important;\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-text-repr-fallback {\n", + " display: none;\n", + "}\n", + "\n", + "div.sk-parallel-item,\n", + "div.sk-serial,\n", + "div.sk-item {\n", + " /* draw centered vertical line to link estimators */\n", + " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", + " background-size: 2px 100%;\n", + " background-repeat: no-repeat;\n", + " background-position: center center;\n", + "}\n", + "\n", + "/* Parallel-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item::after {\n", + " content: \"\";\n", + " width: 100%;\n", + " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", + " flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-1 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: flex;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + " align-items: start;\n", + " justify-content: space-between;\n", + " gap: 0.5em;\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label .caption {\n", + " font-size: 0.6rem;\n", + " font-weight: lighter;\n", + " color: var(--sklearn-color-text-muted);\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-1 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-1 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-1 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 0.5em;\n", + " text-align: center;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `` HTML tag */\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "" + ] + }, + "metadata": {}, + "execution_count": 40 + } + ], + "source": [ + "# Building the model\n", + "rf_word2vec = RandomForestClassifier(n_estimators = 100, max_depth = 3, random_state = 42)\n", + "\n", + "\n", + "# Fitting on train data\n", + "rf_word2vec.fit(X_train_wv, y_train)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**\n" + ], + "metadata": { + "id": "95O3167WbBnd" + }, + "id": "95O3167WbBnd" + }, + { + "cell_type": "code", + "source": [ + "# Predicting on train data\n", + "y_pred_train = rf_word2vec.predict(X_train_wv)\n", + "\n", + "# Predicting on test data\n", + "y_pred_test = rf_word2vec.predict(X_test_wv)" + ], + "metadata": { + "id": "TtQlY8DlzadF" + }, + "id": "TtQlY8DlzadF", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "ycl7jAX7cZuj" + }, + "id": "ycl7jAX7cZuj" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a_AW25srClm-", + "metadata": { + "id": "a_AW25srClm-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "7c3d30ac-13eb-4053-ff9a-6718a9fbf3c7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_confusion_matrix(y_train,y_pred_train)" + ] + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test,y_pred_test)" + ], + "metadata": { + "id": "sp4-2sLEDcM3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "6046c9f3-6602-421d-8738-7c000827c3a1" + }, + "id": "sp4-2sLEDcM3", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "E1jLbrZAidAB" + }, + "id": "E1jLbrZAidAB" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8rV_bYhqClm_", + "metadata": { + "id": "8rV_bYhqClm_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cb772720-3f55-4fe6-97c6-75f6fe7384e6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.755043 0.755043 0.778891 0.720565\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "rf_train_wv = model_performance_classification_sklearn(y_train,y_pred_train)\n", + "print(\"Training performance:\\n\", rf_train_wv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "_AA2cSvzClm_", + "metadata": { + "id": "_AA2cSvzClm_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d063296f-2448-4515-f5c1-575671bcbd48" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.746479 0.746479 0.687934 0.680114\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "rf_test_wv = model_performance_classification_sklearn(y_test, y_pred_test)\n", + "print(\"Testing performance:\\n\",rf_test_wv)" + ] + }, + { + "cell_type": "markdown", + "id": "P2OnPdLRF2M9", + "metadata": { + "id": "P2OnPdLRF2M9" + }, + "source": [ + "* The model is slightly overfitting, as there is a little difference between its performance on the training set and the test set." + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Building a Random Forest Model using text embeddings obtained from the Sentence Transformer**" + ], + "metadata": { + "id": "uijWj2Nl2jyK" + }, + "id": "uijWj2Nl2jyK" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "04W4gkoZ2jyK", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "outputId": "364a1ac8-b4b6-403a-85d4-133186c89aa9" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestClassifier(max_depth=3, random_state=42)" + ], + "text/html": [ + "
RandomForestClassifier(max_depth=3, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "# Building the model\n", + "rf_st = RandomForestClassifier(n_estimators = 100, max_depth = 3, random_state = 42)\n", + "\n", + "\n", + "# Fitting on train data\n", + "rf_st.fit(X_train_st, y_train)" + ], + "id": "04W4gkoZ2jyK" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**" + ], + "metadata": { + "id": "BTWSvJfC2jyL" + }, + "id": "BTWSvJfC2jyL" + }, + { + "cell_type": "code", + "source": [ + "# Predicting on train data\n", + "y_pred_train = rf_st.predict(X_train_st)\n", + "\n", + "# Predicting on test data\n", + "y_pred_test = rf_st.predict(X_test_st)" + ], + "metadata": { + "id": "QPI_ePlJ2jyL" + }, + "execution_count": null, + "outputs": [], + "id": "QPI_ePlJ2jyL" + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "vskhvTGm2jyL" + }, + "id": "vskhvTGm2jyL" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9P_tYSn92jyM", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "f404f8af-0142-4662-a7eb-8281eb953a13" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_confusion_matrix(y_train,y_pred_train)" + ], + "id": "9P_tYSn92jyM" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test,y_pred_test)" + ], + "metadata": { + "id": "LBzzMFHJDolN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "d4e2c9a6-e164-45ff-ef5a-5c70c78c1736" + }, + "id": "LBzzMFHJDolN", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "sSvRSDit2jyM" + }, + "id": "sSvRSDit2jyM" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_kEV9XZD2jyM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eb0311c5-dc0f-4aec-a189-a8358f68a7de" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.801153 0.801153 0.831835 0.775232\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "rf_train_st = model_performance_classification_sklearn(y_train,y_pred_train)\n", + "print(\"Training performance:\\n\", rf_train_st)" + ], + "id": "_kEV9XZD2jyM" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QoFxAES32jyM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "74f810ea-08e2-4c99-a9c2-9187bfbd2357" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.71831 0.71831 0.551745 0.624105\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "rf_test_st = model_performance_classification_sklearn(y_test, y_pred_test)\n", + "print(\"Testing performance:\\n\",rf_test_st)" + ], + "id": "QoFxAES32jyM" + }, + { + "cell_type": "markdown", + "id": "ZmPPcdrHE9K2", + "metadata": { + "id": "ZmPPcdrHE9K2" + }, + "source": [ + "* The model is highly overfitting, as there is a significant difference between its performance on the training set and the test set." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DHgj_cCm2pIn" + }, + "source": [ + "### **Building Neural Network Models using different text embeddings**" + ], + "id": "DHgj_cCm2pIn" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Building a Neural Network Model using text embeddings obtained from the Word2Vec**" + ], + "metadata": { + "id": "LpasFYQriueC" + }, + "id": "LpasFYQriueC" + }, + { + "cell_type": "code", + "source": [ + "# Convert the labels\n", + "label_mapping = {1: 2, -1: 0, 0: 1}\n", + "y_train_mapped_wv = [label_mapping[label] for label in y_train]\n", + "y_test_mapped_wv = [label_mapping[label] for label in y_test]\n", + "\n", + "# Convert your features DataFrame to a NumPy array\n", + "X_train_wv_np = np.array(X_train_wv)\n", + "X_test_wv_np = np.array(X_test_wv)\n", + "y_train_mapped_wv = np.array(y_train_mapped_wv)\n", + "y_test_mapped_wv = np.array(y_test_mapped_wv)" + ], + "metadata": { + "id": "xIeKB-P4nYFi" + }, + "id": "xIeKB-P4nYFi", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import gc\n", + "\n", + "# Clear previous sessions\n", + "tf.keras.backend.clear_session()\n", + "gc.collect()\n", + "\n", + "# Model definition\n", + "model = Sequential()\n", + "model.add(Dense(128, activation='relu', input_shape=(X_train_wv_np.shape[1],))) # Use the shape of the Word2Vec embeddings\n", + "model.add(Dropout(0.3))\n", + "model.add(Dense(64, activation='relu'))\n", + "model.add(Dense(3, activation='softmax')) # 3 output classes\n", + "\n", + "# Compile\n", + "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy', metrics =['accuracy'])\n", + "\n", + "# Summary\n", + "model.summary()" + ], + "metadata": { + "id": "pPoM2BhyXvBv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 257 + }, + "outputId": "46ff0a8e-280d-40a4-df68-ce369e6c8029" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"sequential\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m38,528\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m195\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ], + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                     Output Shape                  Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (Dense)                   │ (None, 128)            │        38,528 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (Dropout)               │ (None, 128)            │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (Dense)                 │ (None, 64)             │         8,256 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (Dense)                 │ (None, 3)              │           195 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m46,979\u001b[0m (183.51 KB)\n" + ], + "text/html": [ + "
 Total params: 46,979 (183.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m46,979\u001b[0m (183.51 KB)\n" + ], + "text/html": [ + "
 Trainable params: 46,979 (183.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ], + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+              "
\n" + ] + }, + "metadata": {} + } + ], + "id": "pPoM2BhyXvBv" + }, + { + "cell_type": "markdown", + "source": [ + "**Note:**\n", + "- During training, we use accuracy as a metric to monitor how well the model is learning to distinguish between classes in each batch.\n", + "- Accuracy is fast and reliable during training and gives us a quick view of model progress.\n", + "- It reflects how often the model is predicting the correct label out of all predictions made.\n", + "\n" + ], + "metadata": { + "id": "kIxFfSYLQNlT" + }, + "id": "kIxFfSYLQNlT" + }, + { + "cell_type": "code", + "source": [ + "# Fitting the model\n", + "history = model.fit(\n", + " X_train_wv_np, y_train_mapped_wv,\n", + " epochs=10,\n", + " batch_size=32\n", + ")" + ], + "metadata": { + "id": "bgHeOMfpnobV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6b648ba6-870f-4dfc-a2a7-b38a97c50cef" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.5349 - loss: 0.9062\n", + "Epoch 2/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6190 - loss: 0.7523 \n", + "Epoch 3/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5989 - loss: 0.7214 \n", + "Epoch 4/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5874 - loss: 0.7687 \n", + "Epoch 5/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6741 - loss: 0.7038 \n", + "Epoch 6/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6298 - loss: 0.7276 \n", + "Epoch 7/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6655 - loss: 0.7134 \n", + "Epoch 8/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6025 - loss: 0.7213 \n", + "Epoch 9/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6321 - loss: 0.7183 \n", + "Epoch 10/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6456 - loss: 0.7322 \n" + ] + } + ], + "id": "bgHeOMfpnobV" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**" + ], + "metadata": { + "id": "IX11-Hmx8_E1" + }, + "id": "IX11-Hmx8_E1" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on training data\n", + "y_train_pred_probs = model.predict(X_train_wv_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_train_preds_wv = tf.argmax(y_train_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "ZpEpHWni87cO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "156eca77-fc90-4df0-87ac-daf08655f0b6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n" + ] + } + ], + "id": "ZpEpHWni87cO" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on test data\n", + "y_test_pred_probs = model.predict(X_test_wv_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_test_preds_wv = tf.argmax(y_test_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "hBMMkZBk9Jkz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d66d1a0d-3bb4-4fa3-d68e-ded8a15c2696" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n" + ] + } + ], + "id": "hBMMkZBk9Jkz" + }, + { + "cell_type": "code", + "source": [ + "# Convert back to [-1, 0, 1] to match utility function expectations\n", + "label_mapping = {2: 1, 0: -1, 1: 0}\n", + "y_train_preds_wv = np.array([label_mapping[index] for index in y_train_preds_wv])\n", + "y_test_preds_wv = np.array([label_mapping[index] for index in y_test_preds_wv])" + ], + "metadata": { + "id": "wCPqMh0nwryB" + }, + "id": "wCPqMh0nwryB", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "Jbeyf8dzk3MP" + }, + "id": "Jbeyf8dzk3MP" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_train, y_train_preds_wv)" + ], + "metadata": { + "id": "lIh2fXcwxJ0G", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "711755dc-7004-4b9d-cb30-b06839893f72" + }, + "id": "lIh2fXcwxJ0G", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test, y_test_preds_wv)" + ], + "metadata": { + "id": "djUVsYwYYBJd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "b02647ff-0825-4c59-8c4b-bdc26cc7a9e1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "id": "djUVsYwYYBJd" + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "1NqSOfNd1UmS" + }, + "id": "1NqSOfNd1UmS" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5qzE4NHS1UmS", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "68bd5ff5-8663-49d2-cf32-0907384a849a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.636888 0.636888 0.664626 0.516128\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "NN_train_wv = model_performance_classification_sklearn(y_train,y_train_preds_wv)\n", + "print(\"Training performance:\\n\", NN_train_wv)" + ], + "id": "5qzE4NHS1UmS" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4Nr34HI31UmT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b07a23fe-a40e-4497-8df2-05e18239480e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.760563 0.760563 0.804628 0.669911\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "NN_test_wv = model_performance_classification_sklearn(y_test, y_test_preds_wv)\n", + "print(\"Testing performance:\\n\",NN_test_wv)" + ], + "id": "4Nr34HI31UmT" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Building a Neural Network Model using text embeddings obtained from the Sentence Transformer**" + ], + "metadata": { + "id": "bcXtMsPu3JfI" + }, + "id": "bcXtMsPu3JfI" + }, + { + "cell_type": "code", + "source": [ + "# Convert the labels\n", + "label_mapping = {1: 2, -1: 0, 0: 1}\n", + "y_train_mapped_st = [label_mapping[label] for label in y_train]\n", + "y_test_mapped_st = [label_mapping[label] for label in y_test]\n", + "\n", + "# Convert your features DataFrame to a NumPy array\n", + "X_train_st_np = np.array(X_train_st)\n", + "X_test_st_np = np.array(X_test_st)\n", + "y_train_mapped_st = np.array(y_train_mapped_st)\n", + "y_test_mapped_st = np.array(y_test_mapped_st)" + ], + "metadata": { + "id": "FUfjCAua4A2-" + }, + "execution_count": null, + "outputs": [], + "id": "FUfjCAua4A2-" + }, + { + "cell_type": "code", + "source": [ + "import gc\n", + "\n", + "# Clear previous sessions\n", + "tf.keras.backend.clear_session()\n", + "gc.collect()\n", + "\n", + "# Define the model\n", + "model = Sequential()\n", + "model.add(Dense(128, activation='relu', input_shape=(X_train_st.shape[1],)))\n", + "model.add(Dropout(0.3))\n", + "model.add(Dense(64, activation='relu'))\n", + "model.add(Dense(3, activation='softmax')) # 3 classes (positive, negative, neutral)\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['Accuracy'])\n", + "\n", + "# Summary\n", + "model.summary()" + ], + "metadata": { + "id": "ziE6DVHA4A2-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 257 + }, + "outputId": "55b5e48b-d3dd-4986-ce74-027c5f902891" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"sequential\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m49,280\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m195\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ], + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                     Output Shape                  Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (Dense)                   │ (None, 128)            │        49,280 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (Dropout)               │ (None, 128)            │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (Dense)                 │ (None, 64)             │         8,256 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (Dense)                 │ (None, 3)              │           195 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m57,731\u001b[0m (225.51 KB)\n" + ], + "text/html": [ + "
 Total params: 57,731 (225.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m57,731\u001b[0m (225.51 KB)\n" + ], + "text/html": [ + "
 Trainable params: 57,731 (225.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ], + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+              "
\n" + ] + }, + "metadata": {} + } + ], + "id": "ziE6DVHA4A2-" + }, + { + "cell_type": "code", + "source": [ + "# Fitting the model\n", + "history = model.fit(\n", + " X_train_st_np, y_train_mapped_st,\n", + " epochs=15,\n", + " batch_size=32\n", + ")" + ], + "metadata": { + "id": "8J-JncGj4A2_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3faf37bb-8f28-4662-8b16-ca054230d4cd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Accuracy: 0.6300 - loss: 1.0422\n", + "Epoch 2/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6496 - loss: 0.8380 \n", + "Epoch 3/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6464 - loss: 0.7074 \n", + "Epoch 4/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6099 - loss: 0.7004 \n", + "Epoch 5/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6664 - loss: 0.6625 \n", + "Epoch 6/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6915 - loss: 0.6035 \n", + "Epoch 7/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.7356 - loss: 0.5585 \n", + "Epoch 8/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.7714 - loss: 0.5521 \n", + "Epoch 9/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - Accuracy: 0.7655 - loss: 0.5158 \n", + "Epoch 10/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - Accuracy: 0.7861 - loss: 0.4997 \n", + "Epoch 11/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - Accuracy: 0.8121 - loss: 0.4551\n", + "Epoch 12/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - Accuracy: 0.7897 - loss: 0.4756 \n", + "Epoch 13/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - Accuracy: 0.8454 - loss: 0.4200 \n", + "Epoch 14/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - Accuracy: 0.8362 - loss: 0.4135 \n", + "Epoch 15/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - Accuracy: 0.8560 - loss: 0.3473 \n" + ] + } + ], + "id": "8J-JncGj4A2_" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**" + ], + "metadata": { + "id": "rbsZ24gM4A2_" + }, + "id": "rbsZ24gM4A2_" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on training data\n", + "y_train_pred_probs = model.predict(X_train_st_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_train_preds_st = tf.argmax(y_train_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "xaWGws3r4A2_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c3f459d7-9745-44f3-bfd6-0df051411294" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n" + ] + } + ], + "id": "xaWGws3r4A2_" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on test data\n", + "y_test_pred_probs = model.predict(X_test_st_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_test_preds_st = tf.argmax(y_test_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "P8yF-MWH4A2_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0bfe417b-bb4a-446f-8ee0-7fde7e16f62a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n" + ] + } + ], + "id": "P8yF-MWH4A2_" + }, + { + "cell_type": "code", + "source": [ + "# Convert back to [-1, 0, 1] to match utility function expectations\n", + "label_mapping = {2: 1, 0: -1, 1: 0}\n", + "y_train_preds_st = np.array([label_mapping[index] for index in y_train_preds_st])\n", + "y_test_preds_st = np.array([label_mapping[index] for index in y_test_preds_st])" + ], + "metadata": { + "id": "YbwmP-dE4A3A" + }, + "execution_count": null, + "outputs": [], + "id": "YbwmP-dE4A3A" + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "YoVyydZW4A3A" + }, + "id": "YoVyydZW4A3A" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_train, y_train_preds_st)" + ], + "metadata": { + "id": "I2yC2oAB4A3A", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "c678cd56-9ae7-4a9c-becf-e1dfa09973d1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "id": "I2yC2oAB4A3A" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test, y_test_preds_st)" + ], + "metadata": { + "collapsed": true, + "id": "mZLu8fRk4A3A", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "e96330e8-96aa-476e-b47d-e4d5bcc6a561" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "id": "mZLu8fRk4A3A" + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "eDqgpX_a4A3B" + }, + "id": "eDqgpX_a4A3B" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Jq_ES16g4A3B", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "95db0a8b-3659-4319-ecab-82c051217d1f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.878963 0.878963 0.864731 0.871679\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "NN_train_st = model_performance_classification_sklearn(y_train,y_train_preds_st)\n", + "print(\"Training performance:\\n\", NN_train_st)" + ], + "id": "Jq_ES16g4A3B" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7MUEidM44A3B", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b3ebc9fb-7278-4621-cb18-5f53190b284a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.788732 0.788732 0.780908 0.784708\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "NN_test_st = model_performance_classification_sklearn(y_test, y_test_preds_st)\n", + "print(\"Testing performance:\\n\",NN_test_st)" + ], + "id": "7MUEidM44A3B" + }, + { + "cell_type": "markdown", + "id": "gsmrYpkrFY2A", + "metadata": { + "id": "gsmrYpkrFY2A" + }, + "source": [ + "### **Model Performance Summary and Final Model Selection**" + ] + }, + { + "cell_type": "code", + "source": [ + "# Concatenate the training performance metrics from different models into a single DataFrame\n", + "models_train_comp_df = pd.concat(\n", + " [\n", + " rf_train_wv.T, # Random Forest using Word2Vec embeddings\n", + " NN_train_wv.T, # Neural Network using Word2Vec embeddings\n", + " rf_train_st.T, # Random Forest using Sentence Transformer embeddings\n", + " NN_train_st.T # Neural Network using Sentence Transformer embeddings\n", + " ],\n", + " axis=1 # Concatenate along columns (i.e., each model's metrics form one column)\n", + ")\n", + "\n", + "# Assigning meaningful column names for each model for clarity in the output DataFrame\n", + "models_train_comp_df.columns = [\n", + " \"Word2Vec (Random Forest)\",\n", + " \"Word2Vec (Neural Network)\",\n", + " \"Sentence Transformer (Random Forest)\",\n", + " \"Sentence Transformer (Neural Network)\"\n", + "]\n", + "\n", + "# Print the training performance comparison table\n", + "print(\"Training performance comparison:\")\n", + "models_train_comp_df" + ], + "metadata": { + "id": "FmgvAlKBWjR-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 + }, + "outputId": "68dd172d-e821-4c30-82e3-8df928e36a4c" + }, + "id": "FmgvAlKBWjR-", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance comparison:\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Word2Vec (Random Forest) Word2Vec (Neural Network) \\\n", + "Accuracy 0.755043 0.636888 \n", + "Recall 0.755043 0.636888 \n", + "Precision 0.778891 0.664626 \n", + "F1 0.720565 0.516128 \n", + "\n", + " Sentence Transformer (Random Forest) \\\n", + "Accuracy 0.801153 \n", + "Recall 0.801153 \n", + "Precision 0.831835 \n", + "F1 0.775232 \n", + "\n", + " Sentence Transformer (Neural Network) \n", + "Accuracy 0.878963 \n", + "Recall 0.878963 \n", + "Precision 0.864731 \n", + "F1 0.871679 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Word2Vec (Random Forest)Word2Vec (Neural Network)Sentence Transformer (Random Forest)Sentence Transformer (Neural Network)
Accuracy0.7550430.6368880.8011530.878963
Recall0.7550430.6368880.8011530.878963
Precision0.7788910.6646260.8318350.864731
F10.7205650.5161280.7752320.871679
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "models_train_comp_df", + "summary": "{\n \"name\": \"models_train_comp_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Word2Vec (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02400850605954694,\n \"min\": 0.720564904885155,\n \"max\": 0.7788911179618219,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7550432276657061,\n 0.7788911179618219,\n 0.720564904885155\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Word2Vec (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06630504860294313,\n \"min\": 0.516128150662598,\n \"max\": 0.6646264228575315,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6368876080691642,\n 0.6646264228575315,\n 0.516128150662598\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02314897229764395,\n \"min\": 0.7752322113738629,\n \"max\": 0.8318353116624009,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.8011527377521613,\n 0.8318353116624009,\n 0.7752322113738629\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.006828124944117901,\n \"min\": 0.8647306583906008,\n \"max\": 0.8789625360230547,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.8789625360230547,\n 0.8647306583906008,\n 0.8716785041639248\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 72 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Concatenate the testing performance metrics from different models into a single DataFrame\n", + "models_test_comp_df = pd.concat(\n", + " [\n", + " rf_test_wv.T, # Random Forest using Word2Vec embeddings\n", + " NN_test_wv.T, # Neural Network using Word2Vec embeddings\n", + " rf_test_st.T, # Random Forest using Sentence Transformer embeddings\n", + " NN_test_st.T # Neural Network using Sentence Transformer embeddings\n", + " ],\n", + " axis=1 # Concatenate along columns so each model's test metrics appear as one column\n", + ")\n", + "\n", + "# Set descriptive column names for clarity in the resulting comparison table\n", + "models_test_comp_df.columns = [\n", + " \"Word2Vec (Random Forest)\",\n", + " \"Word2Vec (Neural Network)\",\n", + " \"Sentence Transformer (Random Forest)\",\n", + " \"Sentence Transformer (Neural Network)\"\n", + "]\n", + "\n", + "# Print the testing performance comparison table\n", + "print(\"Testing performance comparison:\")\n", + "models_test_comp_df" + ], + "metadata": { + "id": "APzbgeHrWjOj", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 + }, + "outputId": "2095fa14-43c0-42dc-e342-c8229f7750b9" + }, + "id": "APzbgeHrWjOj", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance comparison:\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Word2Vec (Random Forest) Word2Vec (Neural Network) \\\n", + "Accuracy 0.746479 0.760563 \n", + "Recall 0.746479 0.760563 \n", + "Precision 0.687934 0.804628 \n", + "F1 0.680114 0.669911 \n", + "\n", + " Sentence Transformer (Random Forest) \\\n", + "Accuracy 0.718310 \n", + "Recall 0.718310 \n", + "Precision 0.551745 \n", + "F1 0.624105 \n", + "\n", + " Sentence Transformer (Neural Network) \n", + "Accuracy 0.788732 \n", + "Recall 0.788732 \n", + "Precision 0.780908 \n", + "F1 0.784708 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Word2Vec (Random Forest)Word2Vec (Neural Network)Sentence Transformer (Random Forest)Sentence Transformer (Neural Network)
Accuracy0.7464790.7605630.7183100.788732
Recall0.7464790.7605630.7183100.788732
Precision0.6879340.8046280.5517450.780908
F10.6801140.6699110.6241050.784708
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "models_test_comp_df", + "summary": "{\n \"name\": \"models_test_comp_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Word2Vec (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03619949158384784,\n \"min\": 0.6801140174379611,\n \"max\": 0.7464788732394366,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7464788732394366,\n 0.687933571578726,\n 0.6801140174379611\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Word2Vec (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05661832323426673,\n \"min\": 0.6699110653078362,\n \"max\": 0.8046277665995976,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7605633802816901,\n 0.8046277665995976,\n 0.6699110653078362\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.08086640854457602,\n \"min\": 0.5517452541334966,\n \"max\": 0.7183098591549296,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7183098591549296,\n 0.5517452541334966,\n 0.6241052874624798\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0037559350807984376,\n \"min\": 0.7809076682316118,\n \"max\": 0.7887323943661971,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7887323943661971,\n 0.7809076682316118,\n 0.7847082494969819\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 73 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Model Performance Summary:**" + ], + "metadata": { + "id": "X0yz_T4j6uJc" + }, + "id": "X0yz_T4j6uJc" + }, + { + "cell_type": "markdown", + "source": [ + " **Model Selection: Sentence Transformer + Neural Network**\n", + "\n", + "**Rationale:**\n", + "\n", + "1. **Best Generalization**:\n", + " The Sentence Transformer + Neural Network model achieves the highest F1 score on the test set (0.788), indicating strong generalization and better handling of both precision and recall on unseen data.\n", + "\n", + "2. **Balanced Performance**:\n", + " Training F1 = 0.87 and testing F1 = 0.78 show a minimal gap, meaning the model learned meaningful representations without significant overfitting.\n", + "\n", + "3. **Superior Feature Encoding**:\n", + " Sentence Transformers capture semantic meaning more effectively than Word2Vec, which explains the performance boost across both Random Forest and Neural Network setups.\n", + "\n", + "4. **Neural Network Suitability**:\n", + " While Word2Vec + NN struggles due to sparse and less informative vectors, combining powerful embeddings (Sentence Transformer) with flexible learning (NN) achieves the best synergy.\n", + "\n", + "##### **Why Other Models Were Not Chosen?**\n", + "\n", + "* **Word2Vec + RF**:\n", + " Training F1 = 0.730, Test F1 = 0.685. Although the gap is small (shows some stability), the absolute test performance is lower than Sentence Transformer + NN.\n", + "\n", + "* **Word2Vec + NN**:\n", + " Low performance in both training (F1 = 0.516) and testing (F1 = 0.67) indicates underfitting and ineffective learning due to weak input representations.\n", + "\n", + "* **Sentence Transformer + RF**:\n", + " Strong training F1 = 0.775, but test F1 = 0.624 is slightly lower than the NN version, suggesting mild overfitting and less flexibility in modeling complex patterns." + ], + "metadata": { + "id": "wI7woP0xHrwW" + }, + "id": "wI7woP0xHrwW" + }, + { + "cell_type": "markdown", + "id": "HiOLoD7BO3L-", + "metadata": { + "id": "HiOLoD7BO3L-" + }, + "source": [ + "## **Conclusions and Recommendations**" + ] + }, + { + "cell_type": "markdown", + "source": [ + "* The daily opening, high, low, and closing prices of the stock exhibit similar distributions individually, when compared across different sentiment polarities, and negative sentiment news resulted in a lower value for each price.\n", + "\n", + "* The minimum variation also resulted in the prices exhibiting perfect correlation amongst them, while exhibiting a very low negative correlation with volume, which might be due to selling pressure during periods of negative sentiment.\n", + "\n", + "* The stock price gradually increased over time from ~40 to ~50 in the period for which the data is available while exhibiting a monthly trend.\n", + "\n", + "* We predicted the sentiment of market news by encoding them via different ML models.\n", + "\n", + "* The models largely overfit the data, with only **the Sentence Transformer + Neural Network model** yielding comparatively better performance than the others (train F1 = 0.876, test F1 = 0.788).\n", + "\n", + " * The predominance of neutral news also suggests a cautious market sentiment in this period. As such, a wider period should be considered for data collection to ensure volume and diversity in news sentiment polarities.\n", + "\n", + "* Integrating real-time sentiment analysis systems can allow financial analysts to make informed decisions and quickly respond to market sentiment changes to optimize investment strategies.\n", + "\n", + "* One can explore combining news sentiments with technical and fundamental indicators of the stock and introduce data of other similar stocks for a more comprehensive market analysis." + ], + "metadata": { + "id": "NMR7mKugPFme" + }, + "id": "NMR7mKugPFme" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mQvaNDqQ3BJa" + }, + "source": [ + " Power Ahead \n", + "___" + ], + "id": "mQvaNDqQ3BJa" + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "1230a037e0b9479caa9db62c5f9ecb6a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ed6c19298c4747a59992a79d99cdaaa7", + "IPY_MODEL_e010222da3cf4751995a51ffc82560ef", + "IPY_MODEL_9f3e3b616bcf482d9fd91a2b54d8d82a" + ], + "layout": "IPY_MODEL_6838e428d6d54a3f80d34638812441e6" + } + }, + "ed6c19298c4747a59992a79d99cdaaa7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_991c2589b56f444486443a31bef569d5", + "placeholder": "​", + "style": "IPY_MODEL_4ed01d32996f47f38fbaba687cee45ae", + "value": "Batches: 100%" + } + }, + "e010222da3cf4751995a51ffc82560ef": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a0ce999dbcfe427ba08202bc989b1c33", + "max": 11, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_f598184dc72f443ab0ada8de6cf076ad", + "value": 11 + } + }, + "9f3e3b616bcf482d9fd91a2b54d8d82a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_96e9e320eec74a2e9094935af065b254", + "placeholder": "​", + "style": "IPY_MODEL_fb854fb10f3e415c9c4c0ac176fb74b4", + "value": " 11/11 [00:44<00:00,  3.41s/it]" + } + }, + "6838e428d6d54a3f80d34638812441e6": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "991c2589b56f444486443a31bef569d5": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4ed01d32996f47f38fbaba687cee45ae": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "a0ce999dbcfe427ba08202bc989b1c33": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f598184dc72f443ab0ada8de6cf076ad": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "96e9e320eec74a2e9094935af065b254": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fb854fb10f3e415c9c4c0ac176fb74b4": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2fb4071397a049f888159e2cbec3ec99": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_280899c6e305423a8d6f20dd395b4e10", + "IPY_MODEL_f68b5d3640c54560b38a29f32deb33a8", + "IPY_MODEL_115335a31d874aba99efb63fa2830e09" + ], + "layout": "IPY_MODEL_7b371d0574e04f98bf87a88f722b8477" + } + }, + "280899c6e305423a8d6f20dd395b4e10": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9095b2b09d4a45928fbc3cf45eb35cbb", + "placeholder": "​", + "style": "IPY_MODEL_971a53d397494d76b8b5c4a2abb954f7", + "value": "Batches: 100%" + } + }, + "f68b5d3640c54560b38a29f32deb33a8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_54dd267783314434a5389477c97974e5", + "max": 3, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_5bfd23c3586e4615909878610be8e24b", + "value": 3 + } + }, + "115335a31d874aba99efb63fa2830e09": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4eda58c3e66e40db98ea40fc40ebb109", + "placeholder": "​", + "style": "IPY_MODEL_99ee6edbe0574c778200ae65b87d7e0f", + "value": " 3/3 [00:09<00:00,  2.81s/it]" + } + }, + "7b371d0574e04f98bf87a88f722b8477": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9095b2b09d4a45928fbc3cf45eb35cbb": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "971a53d397494d76b8b5c4a2abb954f7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "54dd267783314434a5389477c97974e5": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5bfd23c3586e4615909878610be8e24b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "4eda58c3e66e40db98ea40fc40ebb109": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "99ee6edbe0574c778200ae65b87d7e0f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 2130bd15977f139a23c4203b60ec52326ff92e5c Mon Sep 17 00:00:00 2001 From: biplob <110578485+bks1984@users.noreply.github.com> Date: Mon, 10 Nov 2025 08:55:34 +0530 Subject: [PATCH 3/4] Created using Colab --- stock_market__news_sentiment_analysisf.ipynb | 6747 ++++++++++++++++++ 1 file changed, 6747 insertions(+) create mode 100644 stock_market__news_sentiment_analysisf.ipynb diff --git a/stock_market__news_sentiment_analysisf.ipynb b/stock_market__news_sentiment_analysisf.ipynb new file mode 100644 index 0000000..14376df --- /dev/null +++ b/stock_market__news_sentiment_analysisf.ipynb @@ -0,0 +1,6747 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "inNE1fy-ISPj", + "metadata": { + "id": "inNE1fy-ISPj" + }, + "source": [ + "

\n", + " \n", + "

\n", + "\n", + "
Stock Market News Sentiment Analysis
" + ] + }, + { + "cell_type": "markdown", + "id": "EvCcfwuSU-fz", + "metadata": { + "id": "EvCcfwuSU-fz" + }, + "source": [ + "## **Problem Statement**" + ] + }, + { + "cell_type": "markdown", + "id": "6QR_RHvIVHT2", + "metadata": { + "id": "6QR_RHvIVHT2" + }, + "source": [ + "### Business Context" + ] + }, + { + "cell_type": "markdown", + "id": "pl3dmH-EnJGl", + "metadata": { + "id": "pl3dmH-EnJGl" + }, + "source": [ + "The prices of the stocks of companies listed under a global exchange are influenced by a variety of factors, with the company's financial performance, innovations and collaborations, and market sentiment being factors that play a significant role. News and media reports can rapidly affect investor perceptions and, consequently, stock prices in the highly competitive financial industry. With the sheer volume of news and opinions from a wide variety of sources, investors and financial analysts often struggle to stay updated and accurately interpret its impact on the market. As a result, investment firms need sophisticated tools to analyze market sentiment and integrate this information into their investment strategies." + ] + }, + { + "cell_type": "markdown", + "id": "Vn6bbxSwVKl3", + "metadata": { + "id": "Vn6bbxSwVKl3" + }, + "source": [ + "### Problem Definition" + ] + }, + { + "cell_type": "markdown", + "id": "jCIswL3zobj6", + "metadata": { + "id": "jCIswL3zobj6" + }, + "source": [ + "With an ever-rising number of news articles and opinions, an investment startup aims to leverage artificial intelligence to address the challenge of interpreting stock-related news and its impact on stock prices. They have collected historical daily news for a specific company listed under NASDAQ, along with data on its daily stock price and trade volumes.\n", + "\n", + "As a member of the Data Science and AI team in the startup, you have been tasked with developing an AI-driven sentiment analysis system that will automatically process and analyze news articles to gauge market sentiment, and summarizing the news at a weekly level to enhance the accuracy of their stock price predictions and optimize investment strategies. This will empower their financial analysts with actionable insights, leading to more informed investment decisions and improved client outcomes." + ] + }, + { + "cell_type": "markdown", + "id": "ZJOtDHVSF5hu", + "metadata": { + "id": "ZJOtDHVSF5hu" + }, + "source": [ + "### Data Dictionary" + ] + }, + { + "cell_type": "markdown", + "id": "ZlkjI8V5F9RK", + "metadata": { + "id": "ZlkjI8V5F9RK" + }, + "source": [ + "* `Date` : The date the news was released\n", + "* `News` : The content of news articles that could potentially affect the company's stock price\n", + "* `Open` : The stock price (in \\$) at the beginning of the day\n", + "* `High` : The highest stock price (in \\$) reached during the day\n", + "* `Low` : The lowest stock price (in \\$) reached during the day\n", + "* `Close` : The adjusted stock price (in \\$) at the end of the day\n", + "* `Volume` : The number of shares traded during the day\n", + "* `Label` : The sentiment polarity of the news content\n", + " * 1: positive\n", + " * 0: neutral\n", + " * -1: negative" + ] + }, + { + "cell_type": "markdown", + "id": "VrFQHcW5mYgv", + "metadata": { + "id": "VrFQHcW5mYgv" + }, + "source": [ + "## **Installing and Importing the necessary libraries**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "A-E2-iaumpo8", + "metadata": { + "id": "A-E2-iaumpo8", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5f12e599-de14-4e2b-adb4-de180cdd4fba" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: numpy==1.26.4 in /usr/local/lib/python3.12/dist-packages (1.26.4)\n", + "Requirement already satisfied: scikit-learn==1.6.1 in /usr/local/lib/python3.12/dist-packages (1.6.1)\n", + "Requirement already satisfied: scipy==1.13.1 in /usr/local/lib/python3.12/dist-packages (1.13.1)\n", + "Requirement already satisfied: gensim==4.3.3 in /usr/local/lib/python3.12/dist-packages (4.3.3)\n", + "Requirement already satisfied: sentence-transformers==3.4.1 in /usr/local/lib/python3.12/dist-packages (3.4.1)\n", + "Requirement already satisfied: pandas==2.2.2 in /usr/local/lib/python3.12/dist-packages (2.2.2)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.6.1) (1.5.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn==1.6.1) (3.6.0)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.12/dist-packages (from gensim==4.3.3) (7.3.1)\n", + "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (4.56.1)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (4.67.1)\n", + "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (2.8.0+cu126)\n", + "Requirement already satisfied: huggingface-hub>=0.20.0 in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (0.35.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from sentence-transformers==3.4.1) (11.3.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas==2.2.2) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas==2.2.2) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas==2.2.2) (2025.2)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (3.19.1)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2025.3.0)\n", + "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (25.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (6.0.2)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2.32.4)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (4.15.0)\n", + "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (1.1.10)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas==2.2.2) (1.17.0)\n", + "Requirement already satisfied: wrapt in /usr/local/lib/python3.12/dist-packages (from smart-open>=1.8.1->gensim==4.3.3) (1.17.3)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (75.2.0)\n", + "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (1.13.3)\n", + "Requirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (3.5)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (3.1.6)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.77)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.77)\n", + "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.80)\n", + "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (9.10.2.21)\n", + "Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.4.1)\n", + "Requirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (11.3.0.4)\n", + "Requirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (10.3.7.77)\n", + "Requirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (11.7.1.2)\n", + "Requirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.5.4.2)\n", + "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (0.7.1)\n", + "Requirement already satisfied: nvidia-nccl-cu12==2.27.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (2.27.3)\n", + "Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.77)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (12.6.85)\n", + "Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (1.11.1.6)\n", + "Requirement already satisfied: triton==3.4.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers==3.4.1) (3.4.0)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers==3.4.1) (2024.11.6)\n", + "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers==3.4.1) (0.22.0)\n", + "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers==3.4.1) (0.6.2)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.11.0->sentence-transformers==3.4.1) (1.3.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers==3.4.1) (3.0.2)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (3.4.3)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (3.10)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers==3.4.1) (2025.8.3)\n" + ] + } + ], + "source": [ + "# installing the sentence-transformers and gensim libraries for word embeddings\n", + "!pip install numpy==1.26.4 \\\n", + " scikit-learn==1.6.1 \\\n", + " scipy==1.13.1 \\\n", + " gensim==4.3.3 \\\n", + " sentence-transformers==3.4.1 \\\n", + " pandas==2.2.2" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Note:\n", + "- After running the above cell, kindly restart the runtime (for Google Colab) or notebook kernel (for Jupyter Notebook), and run all cells sequentially from the next cell.\n", + "- On executing the above line of code, you might see a warning regarding package dependencies. This error message can be ignored as the above code ensures that all necessary libraries and their dependencies are maintained to successfully execute the code in this notebook." + ], + "metadata": { + "id": "Su4_EiqL5aIZ" + }, + "id": "Su4_EiqL5aIZ" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "179a2a45", + "metadata": { + "id": "179a2a45" + }, + "outputs": [], + "source": [ + "# To manipulate and analyze data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# To visualize data\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# To used time-related functions\n", + "import time\n", + "\n", + "# To build, tune, and evaluate ML models\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, precision_score, recall_score\n", + "\n", + "# To load/create word embeddings\n", + "from gensim.models import Word2Vec\n", + "\n", + "# To work with transformer models\n", + "import torch\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "# Import TensorFlow and Keras for deep learning model building.\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout\n", + "\n", + "# To implement progress bar related functionalities\n", + "from tqdm import tqdm\n", + "tqdm.pandas()\n", + "\n", + "# To ignore unnecessary warnings\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "wQ46zPgumfjF", + "metadata": { + "id": "wQ46zPgumfjF" + }, + "source": [ + "## **Loading the Dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yu_7XbWQWma8", + "metadata": { + "id": "yu_7XbWQWma8" + }, + "outputs": [], + "source": [ + "# # uncomment and run the following code if Google Colab is being used and the dataset is in Google Drive\n", + "# from google.colab import drive\n", + "# drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62a33eef", + "metadata": { + "id": "62a33eef" + }, + "outputs": [], + "source": [ + "# Read the CSV file named 'stock_news' into a pandas DataFrame named 'stock'\n", + "stock_news = pd.read_csv(\"/content/02. Dataset - stock_news.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1xFSwCCer1uA", + "metadata": { + "id": "1xFSwCCer1uA" + }, + "outputs": [], + "source": [ + "#Creating a copy of the dataset\n", + "stock = stock_news.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "EvFNfrvGWthn", + "metadata": { + "id": "EvFNfrvGWthn" + }, + "source": [ + "## **Data Overview**" + ] + }, + { + "cell_type": "markdown", + "id": "GW4rkWI1WzBb", + "metadata": { + "id": "GW4rkWI1WzBb" + }, + "source": [ + "#### **Displaying the first few rows of the dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd2f105b", + "metadata": { + "id": "dd2f105b", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4fce6c84-28cc-4aee-e18c-154c97c9e849" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Date News Open \\\n", + "0 01-02-2019 The dollar minutes ago tumbled to 106 67 from... 38.72 \n", + "1 01-02-2019 By Wayne Cole and Swati Pandey SYDNEY Reuters... 38.72 \n", + "2 01-02-2019 By Stephen Culp NEW YORK Reuters Wall Stre... 38.72 \n", + "3 01-02-2019 By Wayne Cole SYDNEY Reuters The Australia... 38.72 \n", + "4 01-02-2019 Investing com Asian equities fell in morning... 38.72 \n", + "\n", + " High Low Close Volume Label \n", + "0 39.71 38.56 39.48 130672400 1 \n", + "1 39.71 38.56 39.48 130672400 -1 \n", + "2 39.71 38.56 39.48 130672400 0 \n", + "3 39.71 38.56 39.48 130672400 -1 \n", + "4 39.71 38.56 39.48 130672400 1 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DateNewsOpenHighLowCloseVolumeLabel
001-02-2019The dollar minutes ago tumbled to 106 67 from...38.7239.7138.5639.481306724001
101-02-2019By Wayne Cole and Swati Pandey SYDNEY Reuters...38.7239.7138.5639.48130672400-1
201-02-2019By Stephen Culp NEW YORK Reuters Wall Stre...38.7239.7138.5639.481306724000
301-02-2019By Wayne Cole SYDNEY Reuters The Australia...38.7239.7138.5639.48130672400-1
401-02-2019Investing com Asian equities fell in morning...38.7239.7138.5639.481306724001
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "stock", + "summary": "{\n \"name\": \"stock\",\n \"rows\": 418,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 73,\n \"samples\": [\n \"01-08-2019\",\n \"04-15-2019\",\n \"01-30-2019\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"News\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 418,\n \"samples\": [\n \" Reuters Apple Inc NASDAQ AAPL is expected to unveil a new video streaming service and a news subscription platform at an event on Monday at its California headquarters The iPhone maker is banking on growing its services business to offset a dip in smartphone sales While the Wall Street Journal plans to join Apple s new subscription news service other major publishers including the New York Times and the Washington Post have declined according to a New York Times report Apple has also partnered with Hollywood celebrities to make a streaming debut with a slate of original content taking a page out of Netflix NASDAQ NFLX Inc s playbook Below are some of the shows curated from media reports and Apple s own announcements which are part of the iPhone maker s content library SHOWS CONFIRMED BY APPLE UNTITLED DRAMA SERIES WITH REESE WITHERSPOON AND JENNIFER ANISTON Two seasons of a drama series starring Reese Witherspoon and Jennifer Aniston that looks at the lives of people working on a morning television show REVIVAL OF STEVEN SPIELBERG S 1985 AMAZING STORIES The tech giant has also struck a deal with director Steven Spielberg to make new episodes of Amazing Stories a science fiction and horror anthology series that ran on NBC in the 1980s A NEW THRILLER BY M NIGHT SHYAMALAN Plot of the story has not been disclosed ARE YOU SLEEPING A MYSTERY SERIES A drama featuring Octavia Spencer based on a crime novel by Kathleen Barber AN ANTHOLOGY SERIES CALLED LITTLE AMERICA Focuses on stories of immigrants coming to the United States AN ANIMATED CARTOON MUSICAL CALLED CENTRAL PARK The animated musical comedy is about a family of caretakers who end up saving the park and the world DICKINSON AN EMILY DICKINSON COMEDY A half hour comedy series that is set during American poet Emily Dickinson s era with a modern sensibility and tone OPRAH WINFREY PARTNERSHIP Apple in June last year announced a multi year deal with Oprah Winfrey to create original programming SHOWS REPORTED BY MEDIA TIME BANDITS A FANTASY SERIES The potential series is an adaptation of Terry Gilliam s 1981 fantasy film of the same name about a young boy who joins a group of renegade time traveling dwarves Deadline reported https UNTITLED CAPTAIN MARVEL STAR BRIE LARSON S CIA PROJECT The new series looks at a young woman s journey in the CIA reported Variety https DEFENDING JACOB STARRING CAPTAIN AMERICA CHRIS EVANS This limited series is based on the novel of the same name and is about an assistant district attorney who is investigating the murder of a 14 year old boy according to Deadline https FOR ALL MANKIND A SCI FI SERIES A space drama from producer Ronald Moore according to Deadline https MY GLORY WAS I HAD SUCH FRIENDS A series featuring Jennifer Garner is based on the 2017 memoir of the same name by Amy Silverstein reported Variety https SEE A FANTASY EPIC STARRING JASON MOMOA The show poses the question about the fate of humanity if everyone lost their sight Variety reported https FOUNDATION A SCI FI ADAPTATION An adaptation of the iconic novel series from famed sci fi author Isaac Asimov Deadline reported The book series follows a mathematician who predicts the collapse of humanity A COMEDY SHOW BY ROB MCELHENNEY AND CHARLIE DAY The sitcom comedy based on the lives of a diverse group of people who work together in a video game development studio Variety reported https AN UNSCRIPTED SERIES HOME FROM THE DOCUMENTARY FILMMAKER MATT TYRNAUER The series will offer viewers a never before seen look inside the world s most extraordinary homes and feature interviews with people who built them according to Variety https UNTITLED RICHARD GERE SERIES Based on an Israeli series Nevelot the show is about two elderly Vietnam vets whose lives are changed when a woman they both love is killed in a car accident Deadline reported J J ABRAMS PRODUCED LITTLE VOICE Singer and actress Sara Bareilles is writing the music and could possibly star in the J J Abrams produced half hour show which explores the journey of finding one s authentic voice in early 20s according to Variety THE PEANUTS GANG Apple has acquired the rights to the famous characters and the first series will be a science and math oriented short featuring Snoopy as an astronaut according to Hollywood Reporter ON THE ROCKS A feature film directed by Sofia Coppola starring Bill Murray is about a young mother who reconnects with her larger than life playboy father on an adventure through New York Variety reported https LOSING EARTH Apple has acquired the rights to a TV series based on Nathaniel Rich s 70 page New York Times Magazine story Losing Earth New York Times reported THE ELEPHANT QUEEN Apple has acquired the rights to Victoria Stone and Mark Deeble s documentary The Elephant Queen Deadline reported WOLFWALKERS An Irish animation about a young hunter who comes to Ireland with her father to destroy a pack of evil wolves but instead befriends a wild native girl who runs with them first reported by Bloomberg PACHINKO Apple has secured the rights to develop Min Jin Lee s best selling novel about four generations of a Korean immigrant family into a series reported Variety CALLS Apple has bought the rights to make an English language version of the French original short form series according to Variety SHANTARAM Apple has won the rights to develop the hit novel Shantaram as a drama series reported Variety https SWAGGER A DRAMA SERIES BASED ON KEVIN DURANT A drama series based on the early life and career of NBA superstar Kevin Durant according to Variety https YOU THINK IT I LL SAY IT Apple has ordered a 10 episode half hour run of the comedy show which is an adaptation of Curtis Sittenfeld s short story collection by the same name Variety reported https WHIPLASH DIRECTOR DAMIEN CHAZELLE DRAMA SERIES According to Variety Apple has ordered a whole season of a series without first shooting a pilot but no other details are known about the show \\n Apple may offer cut priced bundles with video offering The Information reported on Thursday \",\n \"Investing com Stocks in focus in premarket trade Monday \\n Viacom NASDAQ VIAB jumped 4 2 by 8 04 AM ET 12 04 GMT as the company announced that it had renewed its contract with AT T NYSE T avoiding a blackout of MTV Nickelodeon and Comedy Central for DirecTV users \\n Nike NYSE NKE fell 0 3 after European Union antitrust regulators fined the company 12 5 million euros 14 14 million for restricting cross border sales of merchandising products \\n Apple NASDAQ AAPL dropped 0 3 while markets geared up for a company presentation that is expected to lift the curtain on Monday on a secretive years long effort to build a video streaming prodduct \\n Boeing NYSE BA gained 0 4 as the company preps to brief more than 200 global airline pilots technical leaders and regulators on Wednesday over software and training updates for its 737 MAX aircraft \\n CalAmp NASDAQ CAMP fell 2 4 after JP Morgan downgraded it to neutral from overweight according to Briefing com \\n Winnebago Industries NYSE WGO stock declined 0 9 after the company s fiscal second quarter revenue was lower than expected although earnings per share beat expectations\\n Thermo Fisher Scientific NYSE TMO stock could see movement in the regular session after the company announced that it would acquire Brammer Bio for approximately 1 7 billion in cash \\n Biogen NASDAQ BIIB bounced 1 5 after announcing a new 5 billion buyback The stock had fallen by nearly one third last week after saying it had halted the development of a drug it had been developing to treat Alzheimer s \",\n \"By Yimou Lee TAIPEI Reuters Terry Gou chairman of Apple NASDAQ AAPL supplier Foxconn said on Wednesday he will contest Taiwan s 2020 presidential election shaking up the political landscape at a time of heightened tension between the self ruled island and Beijing Gou Taiwan s richest person with a net worth of 7 6 billion according to Forbes said he would join the already competitive race and take part in the opposition China friendly Kuomintang KMT primaries His decision capped a flurry of news this week that began when Gou told Reuters on Monday he planned to step down from the world s largest contract manufacturer to pave the way for younger talent to move up the company s ranks He announced on Tuesday he was considering a presidential bid and hinted he was close to a decision when he told more than 100 people packed into a temple he would follow the instruction of a sea goddess who had told him to run in the presidential race Peace stability and Taiwan s economy future are my core values Gou said later at the KMT s headquarters in Taipei He urged the party to rediscover the spirit of the KMT the honor of KMT members and the KMT s lost support of the youth Gou s bid which requires KMT approval comes at a delicate time for cross strait relations and delivers a blow to the ruling pro independence Democratic Progressive NYSE PGR Party which is struggling in opinion polls China Taiwan relations have deteriorated since the island s president Tsai Ing wen of the independence leaning DPP swept to power in 2016 China suspects Tsai is pushing for the island s formal independence That is a red line for China which has never renounced the use of force to bring Taiwan under its control Tsai says she wants to maintain the status quo with China but will defend Taiwan s security and democracy VERY PRO CHINA A senior adviser to Tsai told Reuters he thought Gou s bid could create problems given his extensive business ties with China This is problematic to Taiwan s national security the adviser Yao Chia wen said He s very pro China and he represents the class of the wealthy people Will that gain support from Taiwanese Yao said adding he believed Gou would face a tough battle in the KMT primary Tension between Taipei and Beijing escalated again on Monday as Chinese bombers and warships conducted drills around the island prompting Taiwan to scramble jets and ships to monitor the Chinese forces The KMT which once ruled China before fleeing to Taiwan at the end of a civil war with the Communists in 1949 said in February it could sign a peace treaty with Beijing if it won the hotly contested presidential election Zhang Baohui a regional security analyst at Hong Kong s Lingnan University said Gou s run could mark the start of the most unusual election race in Taiwan history This is something entirely fresh for Taiwan politics here is a candidate who sees everything through the pragmatic angle of a businessman rather than raw politics or ideology Zhang told Reuters He has no baggage and that will be a fascinating scenario Gou s news comes as Tsai is grappling with a series of unpopular domestic reform initiatives from a pension scheme to labour law which have come under intense voter scrutiny The KMT said this week Gou had been a party member for more than 50 years and had given it an interest free loan of T 45 million 1 5 million in 2016 under the name of his mother which had signalled his loyalty to the party Foxconn said on Tuesday Gou would remain chairman of Foxconn though he planned to withdraw from daily operations \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.947134201503234,\n \"min\": 35.99,\n \"max\": 51.84,\n \"num_unique_values\": 69,\n \"samples\": [\n 43.22,\n 38.72,\n 48.83\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.947413441172774,\n \"min\": 36.43,\n \"max\": 52.12,\n \"num_unique_values\": 67,\n \"samples\": [\n 43.87,\n 39.08,\n 37.96\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.967879507972434,\n \"min\": 35.5,\n \"max\": 51.76,\n \"num_unique_values\": 66,\n \"samples\": [\n 49.54,\n 50.97,\n 38.56\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.999867403609388,\n \"min\": 35.55,\n \"max\": 51.87,\n \"num_unique_values\": 68,\n \"samples\": [\n 48.77,\n 39.08,\n 37.69\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 45745495,\n \"min\": 45448000,\n \"max\": 365248800,\n \"num_unique_values\": 73,\n \"samples\": [\n 216071600,\n 70146400,\n 244439200\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": -1,\n \"max\": 1,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n -1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "stock.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "y2ewB36LL9Cz", + "metadata": { + "id": "y2ewB36LL9Cz" + }, + "source": [ + "#### **Understanding the shape of the dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "wWx6wqN0MTPw", + "metadata": { + "id": "wWx6wqN0MTPw", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "edd77532-57d5-4ab5-ebc3-b8a4fa737b0a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(418, 8)" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "stock.shape" + ] + }, + { + "cell_type": "markdown", + "id": "yQjb8QOTivg3", + "metadata": { + "id": "yQjb8QOTivg3" + }, + "source": [ + "**Observations:**\n", + "* There are a total of 418 records with 8 attributes each." + ] + }, + { + "cell_type": "markdown", + "id": "fPLJXhFcMA7N", + "metadata": { + "id": "fPLJXhFcMA7N" + }, + "source": [ + "#### **Checking the data types of the columns**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Gc_eAiMdMVe2", + "metadata": { + "id": "Gc_eAiMdMVe2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8c61445b-ef88-4a92-b810-0e15bb3000f5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 418 entries, 0 to 417\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 418 non-null object \n", + " 1 News 418 non-null object \n", + " 2 Open 418 non-null float64\n", + " 3 High 418 non-null float64\n", + " 4 Low 418 non-null float64\n", + " 5 Close 418 non-null float64\n", + " 6 Volume 418 non-null int64 \n", + " 7 Label 418 non-null int64 \n", + "dtypes: float64(4), int64(2), object(2)\n", + "memory usage: 26.3+ KB\n" + ] + } + ], + "source": [ + "stock.info()" + ] + }, + { + "cell_type": "markdown", + "id": "i1CgPxT5mxEf", + "metadata": { + "id": "i1CgPxT5mxEf" + }, + "source": [ + "Let's convert the Date column to pandas `datetime` type." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ZD5fstuv6ery", + "metadata": { + "id": "ZD5fstuv6ery" + }, + "outputs": [], + "source": [ + "# Convert the 'Date' column in the 'stocks' DataFrame to datetime format\n", + "stock['Date'] = pd.to_datetime(stock['Date'])" + ] + }, + { + "cell_type": "markdown", + "id": "8dORemydMDfR", + "metadata": { + "id": "8dORemydMDfR" + }, + "source": [ + "#### **Checking the statistical summary**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gUazWjegMeQl", + "metadata": { + "id": "gUazWjegMeQl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cd88b728-318a-4f67-d74e-71373400a55b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Date Open High Low \\\n", + "count 418 418.000000 418.000000 418.000000 \n", + "mean 2019-02-14 12:24:06.889952256 42.308852 42.787321 41.923732 \n", + "min 2019-01-02 00:00:00 35.990000 36.430000 35.500000 \n", + "25% 2019-01-11 00:00:00 38.130000 38.420000 37.720000 \n", + "50% 2019-01-31 00:00:00 41.530000 42.250000 41.140000 \n", + "75% 2019-03-21 00:00:00 47.190000 47.427500 46.480000 \n", + "max 2019-04-29 00:00:00 51.840000 52.120000 51.760000 \n", + "std NaN 4.947134 4.947413 4.967880 \n", + "\n", + " Close Volume Label \n", + "count 418.000000 4.180000e+02 418.000000 \n", + "mean 42.418517 1.294225e+08 0.308612 \n", + "min 35.550000 4.544800e+07 -1.000000 \n", + "25% 38.270000 1.029072e+08 -1.000000 \n", + "50% 41.610000 1.156272e+08 1.000000 \n", + "75% 47.032500 1.511252e+08 1.000000 \n", + "max 51.870000 3.652488e+08 1.000000 \n", + "std 4.999867 4.574550e+07 0.943473 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DateOpenHighLowCloseVolumeLabel
count418418.000000418.000000418.000000418.0000004.180000e+02418.000000
mean2019-02-14 12:24:06.88995225642.30885242.78732141.92373242.4185171.294225e+080.308612
min2019-01-02 00:00:0035.99000036.43000035.50000035.5500004.544800e+07-1.000000
25%2019-01-11 00:00:0038.13000038.42000037.72000038.2700001.029072e+08-1.000000
50%2019-01-31 00:00:0041.53000042.25000041.14000041.6100001.156272e+081.000000
75%2019-03-21 00:00:0047.19000047.42750046.48000047.0325001.511252e+081.000000
max2019-04-29 00:00:0051.84000052.12000051.76000051.8700003.652488e+081.000000
stdNaN4.9471344.9474134.9678804.9998674.574550e+070.943473
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"stock\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1970-01-01 00:00:00.000000418\",\n \"max\": \"2019-04-29 00:00:00\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"418\",\n \"2019-02-14 12:24:06.889952256\",\n \"2019-03-21 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.29734162506347,\n \"min\": 4.947134201503234,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 42.308851674641154,\n 47.19,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.18648944299875,\n \"min\": 4.947413441172774,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 42.78732057416268,\n 47.427499999999995,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.4078256172517,\n \"min\": 4.967879507972434,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 41.923732057416274,\n 46.48,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135.30548063571206,\n \"min\": 4.999867403609388,\n \"max\": 418.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 42.41851674641149,\n 47.0325,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 111473859.17448182,\n \"min\": 418.0,\n \"max\": 365248800.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 129422491.86602871,\n 151125200.0,\n 418.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 147.67411440583984,\n \"min\": -1.0,\n \"max\": 418.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.30861244019138756,\n 0.9434730920044713,\n -1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "stock.describe()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Observations:**\n", + "\n", + "- **Date Range and Trading Period**:\n", + " - The data covers a period from January 2, 2019, to April 29, 2019, indicating a span of approximately four months.\n", + "\n", + "- **Price Overview**:\n", + " - **Average Prices**: The average opening price is approximately \\$42.30, while the average closing price is about \\$42.41.\n", + " - **Price Variability**: The prices range from a minimum of around \\$35.99 for opening to a maximum of \\$51.84 for opening, reflecting significant volatility during this period.\n", + "\n", + "- **Trading Volume**:\n", + " - The average trading volume is approximately 129.42 million shares, with fluctuations from around 45.45 million to 365.24 million, highlighting varying market activity levels." + ], + "metadata": { + "id": "0wZ7x_5W77tD" + }, + "id": "0wZ7x_5W77tD" + }, + { + "cell_type": "markdown", + "id": "lXRpNWnQMGIY", + "metadata": { + "id": "lXRpNWnQMGIY" + }, + "source": [ + "#### **Checking the duplicate values**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ti4UpPi6M5kM", + "metadata": { + "id": "ti4UpPi6M5kM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "66fad013-2ec1-4ec3-9cfe-842656486d7c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "stock.duplicated().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "XkwHzJH6k_jx", + "metadata": { + "id": "XkwHzJH6k_jx" + }, + "source": [ + "**Observations:**\n", + "* There are no duplicate values." + ] + }, + { + "cell_type": "markdown", + "id": "fxghULa0MOY-", + "metadata": { + "id": "fxghULa0MOY-" + }, + "source": [ + "#### **Checking for missing values**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yItWheKoNGkf", + "metadata": { + "id": "yItWheKoNGkf", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ff1d5335-a019-465d-e7f8-546c97e5873f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Date 0\n", + "News 0\n", + "Open 0\n", + "High 0\n", + "Low 0\n", + "Close 0\n", + "Volume 0\n", + "Label 0\n", + "dtype: int64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
Date0
News0
Open0
High0
Low0
Close0
Volume0
Label0
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "stock.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "qg7TsQTclDUS", + "metadata": { + "id": "qg7TsQTclDUS" + }, + "source": [ + "**Observations:**\n", + "* There are no missing values." + ] + }, + { + "cell_type": "markdown", + "id": "hGHBK8-QeKOB", + "metadata": { + "id": "hGHBK8-QeKOB" + }, + "source": [ + "## **Exploratory Data Analysis**" + ] + }, + { + "cell_type": "markdown", + "id": "Q0UlMQnyegl7", + "metadata": { + "id": "Q0UlMQnyegl7" + }, + "source": [ + "### **Univariate Analysis**" + ] + }, + { + "cell_type": "markdown", + "id": "RrznHeBaLu0W", + "metadata": { + "id": "RrznHeBaLu0W" + }, + "source": [ + "#### **Countplot on Label**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "meVjTKoxLpmA", + "metadata": { + "id": "meVjTKoxLpmA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dcd02675-45e6-4016-fb12-10ce1aef7018" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.countplot(data=stock, x='Label', stat=\"percent\");" + ] + }, + { + "cell_type": "markdown", + "id": "nXPvfQr-Avd7", + "metadata": { + "id": "nXPvfQr-Avd7" + }, + "source": [ + "**Observations:**\n", + "* The dataset is imbalanced for the sentiment polarities.\n", + "* There is more news content with positive polarity compared to other types." + ] + }, + { + "cell_type": "markdown", + "id": "dpGHhbGeeoF8", + "metadata": { + "id": "dpGHhbGeeoF8" + }, + "source": [ + "#### **Density Plot of Price (Open, High, Low, Close)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "BKqgbg0_v5EM", + "metadata": { + "id": "BKqgbg0_v5EM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0a50f216-e030-496b-de8c-cc3a2b8153ca" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Plot KDE for the 'Open', 'High', 'Low', 'Close' columns of the 'stock' DataFrame.\n", + "sns.displot(data=stock[['Open','High','Low','Close']], kind='kde', palette=\"tab10\"); # Create a KDE plot with a color palette." + ] + }, + { + "cell_type": "markdown", + "id": "l5jX1Kp-lbD5", + "metadata": { + "id": "l5jX1Kp-lbD5" + }, + "source": [ + "**Observations:**\n", + "* The distributions of the prices are quite similar, with the high price showing a slight variation than the others." + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Histogram on Volume**" + ], + "metadata": { + "id": "1wBKKuVIaWnl" + }, + "id": "1wBKKuVIaWnl" + }, + { + "cell_type": "code", + "source": [ + "sns.histplot(stock, x='Volume');" + ], + "metadata": { + "id": "FMDJ_m6maaoK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fa63eb8f-f159-41cc-c969-fce63404ff4b" + }, + "id": "FMDJ_m6maaoK", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Observations:**\n", + "* In a large portion of the time considered, 80 to 175 million shares of the stock were traded, with occasional days where the volume rose to more than 200 million." + ], + "metadata": { + "id": "gNzyLLIgfnz6" + }, + "id": "gNzyLLIgfnz6" + }, + { + "cell_type": "markdown", + "id": "9GVt_AAbe29X", + "metadata": { + "id": "9GVt_AAbe29X" + }, + "source": [ + "#### **Histogram and statistical summary on News Length**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0kwZSJvwOUpa", + "metadata": { + "id": "0kwZSJvwOUpa", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9c9e9158-a8e5-4f51-9321-d54fb2ed6675" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 418.000000\n", + "mean 525.662679\n", + "std 303.584080\n", + "min 44.000000\n", + "25% 304.250000\n", + "50% 480.000000\n", + "75% 700.500000\n", + "max 2142.000000\n", + "Name: news_len, dtype: float64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
news_len
count418.000000
mean525.662679
std303.584080
min44.000000
25%304.250000
50%480.000000
75%700.500000
max2142.000000
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "#Calculating the total number of words present in the news content.\n", + "stock['news_len'] = stock['News'].apply(lambda x: len(x.split(' ')))\n", + "stock['news_len'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "NWn03B4Xey5d", + "metadata": { + "id": "NWn03B4Xey5d", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "abfb1de2-8239-4a5d-d9e3-71b30ad328ef" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.histplot(data=stock,x='news_len');" + ] + }, + { + "cell_type": "markdown", + "id": "VWLWG2X8mrCw", + "metadata": { + "id": "VWLWG2X8mrCw" + }, + "source": [ + "**Observations:**\n", + "* Most of the news have between 50 - 1000 words, with an average of 525 words\n", + " * The shortest news has 44 words\n", + "\n", + "* This indicates that these are likely to be news summaries rather than the actual news content itself." + ] + }, + { + "cell_type": "markdown", + "id": "hLE0s7OFKilB", + "metadata": { + "id": "hLE0s7OFKilB" + }, + "source": [ + "### **Bivariate Analysis**" + ] + }, + { + "cell_type": "markdown", + "id": "Yn_9wfzxL-r1", + "metadata": { + "id": "Yn_9wfzxL-r1" + }, + "source": [ + "#### **Correlation**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gOBaxNZeKllB", + "metadata": { + "id": "gOBaxNZeKllB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c46aaa76-2682-4469-c36e-44097287cc6e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "cols = ['Open','High','Low','Close','Volume','news_len']\n", + "sns.heatmap(\n", + " stock[cols].corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "15UHbBu8Cucj", + "metadata": { + "id": "15UHbBu8Cucj" + }, + "source": [ + "**Observations:**\n", + "* The prices are all perfectly correlated.\n", + " * This might be due to the minimum variation between the different prices.\n", + "\n", + "* There is a negative correlation, albeit very low, between volume and prices.\n", + " * This might be due to selling pressure during periods of negative sentiment." + ] + }, + { + "cell_type": "markdown", + "id": "h-Hz7CpdMAi3", + "metadata": { + "id": "h-Hz7CpdMAi3" + }, + "source": [ + "#### **Label vs Price (Open, High, Low, Close)**" + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(10, 8))\n", + "\n", + "for i, variable in enumerate(['Open', 'High', 'Low', 'Close']):\n", + " plt.subplot(2, 2, i + 1)\n", + " sns.boxplot(data=stock, x=\"Label\", y=variable)\n", + " plt.tight_layout(pad=2)\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "lCVHNWhgMElU", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b838000a-b88f-43ac-90ee-499cf11fa5ee" + }, + "id": "lCVHNWhgMElU", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Observations:**\n", + "* The median for all prices is significantly lower for negative sentiment news as compared to both positive and neutral sentiment news, indicating that negative news likely triggers investor sell-offs which drive the stock prices down.\n", + "\n", + "* The boxplot for the open price under neutral sentiment displays a notably higher upper whisker relative to positive sentiment. This suggests that the market's opening often covers a wider range of prices when news is neutral.\n", + " * This variability might be attributed to different interpretations of seemingly neutral news, which leads some investors to react more aggressively and drive the opening price to higher levels." + ], + "metadata": { + "id": "axyzmidFWaNS" + }, + "id": "axyzmidFWaNS" + }, + { + "cell_type": "markdown", + "id": "cY9P2rdBMH-h", + "metadata": { + "id": "cY9P2rdBMH-h" + }, + "source": [ + "#### **Label vs Volume**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mzCxLFg1LCPk", + "metadata": { + "id": "mzCxLFg1LCPk", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "13536009-bc63-4dff-b422-df723211166c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.boxplot(\n", + " data=stock, x=\"Label\", y=\"Volume\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "LFipGtxhOa8g", + "metadata": { + "id": "LFipGtxhOa8g" + }, + "source": [ + "**Observations:**\n", + "* The median trading volume for the stock is approximately the same across all sentiment polarities.\n", + "* The volume distribution for positive sentiment news shows a wider spread compared to other sentiment categories.\n", + " - This wider range might indicate that even positive news leads to diverse interpretations among investors, contributing to varied trading activities and reactions." + ] + }, + { + "cell_type": "markdown", + "id": "9ySUmJUyQ0vi", + "metadata": { + "id": "9ySUmJUyQ0vi" + }, + "source": [ + "#### **Date vs Price (Open, High, Low, Close)**" + ] + }, + { + "cell_type": "markdown", + "id": "tq0NL64DQ0v1", + "metadata": { + "id": "tq0NL64DQ0v1" + }, + "source": [ + "- The data is at the level of news, and we might have more than one news in a day. However, the prices are at daily level\n", + "- So, we can aggregate the data at a daily level by taking the mean of the attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bECqvVQtwheA", + "metadata": { + "id": "bECqvVQtwheA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "89dd8d7b-2323-418b-9e11-544405d0396b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Open High Low Close Volume\n", + "Date \n", + "2019-01-02 38.72 39.71 38.56 39.48 130672400.0\n", + "2019-01-03 35.99 36.43 35.50 35.55 103544800.0\n", + "2019-01-04 36.13 37.14 35.95 37.06 111448000.0\n", + "2019-01-07 37.17 37.21 36.47 36.98 109012000.0\n", + "2019-01-08 37.39 37.96 37.13 37.69 216071600.0" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
OpenHighLowCloseVolume
Date
2019-01-0238.7239.7138.5639.48130672400.0
2019-01-0335.9936.4335.5035.55103544800.0
2019-01-0436.1337.1435.9537.06111448000.0
2019-01-0737.1737.2136.4736.98109012000.0
2019-01-0837.3937.9637.1337.69216071600.0
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "stock_daily", + "summary": "{\n \"name\": \"stock_daily\",\n \"rows\": 73,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2019-01-02 00:00:00\",\n \"max\": \"2019-04-29 00:00:00\",\n \"num_unique_values\": 73,\n \"samples\": [\n \"2019-01-08 00:00:00\",\n \"2019-04-15 00:00:00\",\n \"2019-01-30 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.522032992144083,\n \"min\": 35.99,\n \"max\": 51.84,\n \"num_unique_values\": 69,\n \"samples\": [\n 43.22,\n 38.71999999999999,\n 48.830000000000005\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.515838152119043,\n \"min\": 36.43,\n \"max\": 52.12,\n \"num_unique_values\": 68,\n \"samples\": [\n 49.080000000000005,\n 39.08,\n 37.96\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.522815819552585,\n \"min\": 35.5,\n \"max\": 51.76,\n \"num_unique_values\": 66,\n \"samples\": [\n 49.54,\n 50.97,\n 38.56\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.5189565039202,\n \"min\": 35.55,\n \"max\": 51.86999999999999,\n \"num_unique_values\": 68,\n \"samples\": [\n 48.77,\n 39.08,\n 37.68999999999999\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49858245.96607382,\n \"min\": 45448000.0,\n \"max\": 365248800.0,\n \"num_unique_values\": 73,\n \"samples\": [\n 216071600.0,\n 70146400.0,\n 244439200.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "stock_daily = stock.groupby('Date').agg(\n", + " {\n", + " 'Open': 'mean',\n", + " 'High': 'mean',\n", + " 'Low': 'mean',\n", + " 'Close': 'mean',\n", + " 'Volume': 'mean',\n", + " }\n", + ").reset_index() # Group the 'stocks' DataFrame by the 'Date' column\n", + "\n", + "stock_daily.set_index('Date', inplace=True)\n", + "stock_daily.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ORSmC3lxrwy", + "metadata": { + "id": "7ORSmC3lxrwy", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "119a6be1-50f4-47be-d5d4-f3cdd077f128" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAABMwAAAHACAYAAABeTvS/AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd0FdXexvHvaTnpPSEJBEjovQlI71IUqSIoKoodsWD3VQEviF0vguhVFAtiQ1AUUYoggnTpPXRSSe857f3jaCAmICUQyvNZ66ycmdmz5zeD4s1z995jcLlcLkRERERERERERAQAY0UXICIiIiIiIiIicjFRYCYiIiIiIiIiInICBWYiIiIiIiIiIiInUGAmIiIiIiIiIiJyAgVmIiIiIiIiIiIiJ1BgJiIiIiIiIiIicgIFZiIiIiIiIiIiIidQYCYiIiIiIiIiInICc0UXUB6cTifx8fH4+flhMBgquhwREREREREREalALpeL7OxsoqKiMBrPfLzYZRGYxcfHEx0dXdFliIiIiIiIiIjIReTw4cNUqVLljM+7LAIzPz8/wP0Q/P39K7gaERERERERERGpSFlZWURHRxdnRmfqsgjM/p6G6e/vr8BMREREREREREQAznrpLi36LyIiIiIiIiIicgIFZiIiIiIiIiIiIic4o8Bs3LhxGAyGEp+6desCkJaWxujRo6lTpw5eXl5UrVqVBx98kMzMzFP2OWLEiFJ99urV6+zvSERERERERERE5Byc8RpmDRo0YNGiRcc7MLu7iI+PJz4+ntdee4369etz8OBB7r33XuLj4/nmm29O2WevXr346KOPiretVuuZlvWvXC4Xdrsdh8NR7n1fqUwmE2az+aznA4uIiIiIiIiIXIzOODAzm81ERESU2t+wYUNmz55dvF2jRg0mTpzI8OHDsdvtxcFaWaxWa5l9lpeioiISEhLIy8s7b9e4Unl7exMZGYmHh0dFlyIiIiIiIiIiUi7OODDbs2cPUVFReHp60qZNGyZNmkTVqlXLbJuZmYm/v/8pwzKApUuXEh4eTlBQEF27dmXChAmEhISctH1hYSGFhYXF21lZWSdt63Q62b9/PyaTiaioKDw8PDQiqhy4XC6KiopISUlh//791KpVC6NRS+KJiIiIiIiIyKXP4HK5XKfb+KeffiInJ4c6deqQkJDA+PHjOXr0KFu3bsXPz69E22PHjtGiRQuGDx/OxIkTT9rnF198gbe3NzExMcTFxfHMM8/g6+vLH3/8gclkKvOccePGMX78+FL7/w7oTlRQUMD+/fupVq0a3t7ep3urcpry8vI4ePAgMTExeHp6VnQ5IiIiIiIiIiJkZWUREBBQZlZ0Os4oMPunjIwMqlWrxhtvvMHIkSNLFNWjRw+Cg4P5/vvvsVgsp93nvn37qFGjBosWLaJbt25ltilrhFl0dPQpAzMFOueHnq+IiIiIiIiIXGzONTA7pzl0gYGB1K5dm7179xbvy87OplevXvj5+TFnzpwzCssAYmNjCQ0NLdHnP1mtVvz9/Ut8REREREREREREysM5BWY5OTnExcURGRkJuNO7a665Bg8PD77//vuzGnF05MgRUlNTi/sUERERERERERG5kM4oMHvsscdYtmwZBw4cYOXKlQwYMACTycSwYcOKw7Lc3FymT59OVlYWiYmJJCYm4nA4ivuoW7cuc+bMAdyB2+OPP86qVas4cOAAixcvpl+/ftSsWZOePXuW752KiIiIiIiIiIichjMKzI4cOcKwYcOoU6cOQ4YMISQkhFWrVhEWFsaGDRtYvXo1W7ZsoWbNmkRGRhZ/Dh8+XNzHrl27yMzMBMBkMrF582auv/56ateuzciRI2nRogXLly/HarWW751eog4fPswdd9xR/IbPatWq8dBDD5GamlrRpYmIiIiIiIiIXJbMZ9L4iy++OOmxzp07czrvDzixjZeXFz///POZlHBF2bdvH23atKF27drMmjWLmJgYtm3bxuOPP85PP/3EqlWrCA4OrugyRUREREREREQuK+e0htmlyuVykVdkr5DPmbyUdNSoUXh4ePDLL7/QqVMnqlatSu/evVm0aBFHjx7l//7v/wCoXr06//nPfxg2bBg+Pj5UrlyZqVOnlugrIyODO++8k7CwMPz9/enatSubNm0qPj5u3DiaNm3Kp59+SvXq1QkICGDo0KFkZ2eXz0MXERERERERuZzZi2DLN/D5jfDdA+B0VnRFcg7OaITZ5SLf5qD+8xUzsm37Cz3x9vj3x56WlsbPP//MxIkT8fLyKnEsIiKCm2++mS+//JJ33nkHgFdffZVnnnmG8ePH8/PPP/PQQw9Ru3ZtevToAcANN9yAl5cXP/30EwEBAbz33nt069aN3bt3F49Si4uLY+7cufzwww+kp6czZMgQXnrpJSZOnFjOT0FERERERETkMpEVD+s+gvUzIDfZva/OtWD8a4ySwwZOO1i8TtqFXHyuyMDsUrBnzx5cLhf16tUr83i9evVIT08nJSUFgHbt2vHUU08BULt2bVasWMGbb75Jjx49+P3331mzZg3JycnFa8O99tprzJ07l2+++Ya7774bAKfTyYwZM/Dz8wPglltuYfHixQrMRERERERERE7kcsHBFbDmf7DjB3D99bJD3whoMACaDD3edvt3MP8xaHYLtBwJQdUrpGQ5M1dkYOZlMbH9hYp5C6eXxXRG7U93CmebNm1Kbb/11lsAbNq0iZycHEJCQkq0yc/PJy4urni7evXqxWEZQGRkJMnJyWdUr4iIiIiIiMhl78Dv8PF1x7ertYNWd0Hd68BkKdl2xzzIT4eVk2Hl21C7p7ttbNfjo9DkonNFBmYGg+G0pkVWpJo1a2IwGNixYwcDBgwodXzHjh0EBQURFhb2r33l5OQQGRnJ0qVLSx0LDAws/m6xlPyX2mAw4NScaxEREREREZGSqrWDiEZQpSW0vBMqNTh528Efwu4bYe37ELcEdi9wf2p0g5u/UWh2kbq4U6MrWEhICD169OCdd97hkUceKbGOWWJiIjNnzuTWW2/FYDAAsGrVqhLnr1q1qng6Z/PmzUlMTMRsNlO9evULdg8iIiIiIiIilwWXC9e8MaTv88MVEItnvbpYh3yP+a81wU/JaIK6fdyfY3th7Qfu9c7iFsP6j9zTNOWio8DsIjZlyhTatm1Lz549mTBhAjExMWzbto3HH3+cypUrl1hbbMWKFbzyyiv079+fhQsX8vXXX/Pjjz8C0L17d9q0aUP//v155ZVXqF27NvHx8fz4448MGDCAq666qqJuUUREREREROTit/07Mr76gqR1gSV2Bw0fTsSz/wdA3tq15PoFcd+SRFwGI93qhdO9XiVqhfsWD3YhtCb0fsm9jtmCJ91TNJvfBibFMxcb/YlcxGrVqsW6desYO3YsQ4YMIS0tjYiICPr378/YsWOL324J8Oijj7Ju3TrGjx+Pv78/b7zxBj17utdpMxgMzJ8/n//7v//j9ttvJyUlhYiICDp27EilSpUq6vZERERERERELn6F2Ti+e5qUze41v72aNsWelobt0CEsVSoXNzvy8CM4UlN52mzli9rdeOVgV15ZsIuqwd50qxdOj3qVaBkTjMVkdK9hlp/u/qmw7KJkcJ3uqvIXsaysLAICAsjMzMTf37/EsYKCAvbv309MTAyenp4VVOH5Vb16dR5++GEefvjhC37tK+H5ioiIiIiIyBXs5//DvnQqyTsqU2CPJWbOtxgsFhw5OeByYfLzw5mby86bbqVoz24SfYLZ+8RLrM8xsSIulSL78bXB/TzNdK4TTve/Rp/5WBWWnS+nyopOh/5kRERERERERETKkrgVVk3DbHUR9ep/cUa3x/DXC/NMvr7FzQze3jzf61F2dH+VUI803uxopXLWQV5sGctmQyUWbU9iyc5kUnOLmLcpnnmb4qke4s0397Ul1FIEf0yFdg+BxetklcgFpsBMREREREREROSfnE5c8x4mY48V/2u6YqrVnZO9z/K7jfGsP3IIn9hksix5fLP7G4q+/o7aC51c9cIL9LzhBhxOFxsPZ7BoRxKz1x/hQGoeI2esZY51LMaj68Bhg27PXdBblJNTYHYZOHDgQEWXICIiIiIiInJ5+fNTctZsJnFdCMeOHKHGjYUYrdZSzXIK7bw4fwcuhx93Vv8freplEmAN4Kec+RhdhSSPHYdHSAh+XbvSoloQLaoFcUOLKgyctpJNRzJ5t9p13M86WPFfaDwEwupUwM3KP50sHBURERERERERuWI5bTaSNgYBENC3X5lhGcCUJXtJdW4hKiKe+zrVpW1UWxqENOD2qYsJGDQQnE4OP/wweevWFZ8TG+bLB7dehYfZyCsHa7HLvx04bfDDI3DpLzV/WVBgJiIiIiIiIiLyT81uIeCmO7BERxNyzz1lNtl/LJfpK3bjGTGH7KDJLDn8S/GxEK8QIsaN41CTShiKbMTdfScFu3YVH7+qejBv3dgUg8HAHclDsBk94eAK2DjzvN8a4A7msuJh98+wdvqFueYlRIGZiIiIiIiIiMjfnA4AjFYrYY88SY35P2Ly9Smz6QvztmFz2qhkaUKkTySdozuXOG60WIh/fCg7ow2Y8wrJW726xPE+jSL5vz71OEoYrxYOcO/85TnITS3fe3LYwF50fHvJBHi1BrxRDz4fAj89AbaC8r3mJU5rmImIiIiIiIiIgDtYmt6D9IRq+N4+FkvV2OK3Yv7Tkp1J/LorBYvJiw+unURUkBlPs2epdiNa3MO+j9oSuukw3r2uYVfaLuoEH1+nbGT7GI6k5/Phyt4MNK+gbv4hWPgc9H/n7O6hINP9ds+krZC4GRK3QPIOuGEG1L3W3cblgrxUMJggtDZENIKiHLCUrv9KpcBMRERERERERARg1TTyt2wlcWEShjmDqLnwF8xhYaWaFdodvDBvOx6hC2lY1YS/bys8zaFldmkwGKhRpTG5lWpw3+L7OHB4C2/vak3t51/E5OuLwWDguevqczQjn6d3jGS2dRyFSXvxsheCuex10wB36GUwHN/+ZiQcWQsZB8tun7T9eGDW/Bb39/B6YPE63adzRVFgdombMWMGDz/8MBkZGad9zogRI8jIyGDu3LnnrS4RERERERGRS0rmEVy/vkTShgAA/Hv2LDMsA5j++34OZKTgV/M39uTb2JJyHV2qdjll92ajGbvTzn1f58KBhRzae5SI8ePwatQIk9HA5KHNGPp+ITcceZ6UjMbMzocwv79OthdByk73aLHELe7RY0nb4JFt4OHtbpO693hYFhDtHjUW0QgqNXT/DKx2vJig6u6PnJQCs4vYyYKtpUuX0qVLF9LT07nxxhvp06dPxRQoIiIiIiIicqmz5UPmUfjlWbL2OslP9cDo7U3Yo2PKbJ6YWcCUJXvB4cPtNSeQb/mz1NplZbGarEzuOpmj/ssxPjSegu3bKTpyBK9GjQDw8jAx/barGPhOEYfS8njqw/m8V+UnzMnbIGWX+y2a/5S8A6q0cH/vPhaMFqjUALyDz/ZpyF8UmF3ivLy88PLS8EkRERERERGRUpxOyE2GzCOQedj9s0ZXd6gEsPgFWP66u6nNQPKmcABC7r0XS3h4mV1O+mkH+c50mlaN4JH2nTEYTn8Qi7+HP/5tr2XHp4Es+984gqpnciOQt3Yt2UuXEnrPPcy4vSUDp63kz4QszOlfFp+bb/In3a82+SH1cYY3xKNKU/z96xLocmEwGNz3JeVGgdklrqwpmRMmTGDy5Mnk5+dz4403EhoayoIFC9i4cWOJc1977TVef/11ioqKGDp0KG+99RaWkyxmKCIiIiIiInLRKcyG7CQIrXl83/cPQmqcOyDLii89MqvPa8cDM+8Q90+LDxlHw7HnF2KJjib4tlvLvNzaA2l8tzEerypzSAtOYVWCJ22i2pxx2Rs5xNQmiURu+5CBtQaSNOklCrZvJ3P2t4SOfoAPburBLR9vYKLtJva7ItnurEY8IZBrgERgG8AxYCkWk4FQXythflbC/v7pd3w79O+fvh74WM0YDAacubkU7tuPMzcHZ04OzvwCAvped8b3cTm7sgOzotxTHzdZwfTXI7IXlT388W8G4/GF8lwusOWVbuNR9mtoy9PMmTOZOHEi77zzDu3ateOLL77g9ddfJyYmpkS7X3/9lcjISH799Vf27t3LjTfeSNOmTbnrrrvOe40iIiIiIiIipy07EQ78fnyEWPHnsPuNkAYTPJt8/Pf3A8shbV/x6S6nEbs5ErsxHEuVKpgDq+HMzyf51VexJydiP9aLqh9MJ8jTE8M332CJisJoLb3Yvs3hZOx328CYh59/CllF6UT4RJzVLd1Q+wYSchK4qd5NmE0Wwh56kKRXXqUoLo6k/0wgKGYmi0Y/TFyN8dTNKaJVaiZZScfIScskyW5kr8GPlJxCGu5ZR2h+Jt72ArxtBXjbC/G2F7A4ugVrI+oDMH3hJFxFeeTYCnio91PYo6JpmXGAkbNfOf6MDAbWxrSgfZ1wPC2ms7qny82VHZi9GHXq4zfMgAYD3N+XvAAr3z5526hmcPdS9/e8VHi1Ruk24zLPuMQffvgBX1/fEvscDsdJ27/99tuMHDmS22+/HYDnn3+eX375hZycnBLtgoKCmDJlCiaTibp163LttdeyePFiBWYiIiIiIiJyYRRkQvpByDpacspk5hGI7QxdnnG3S9oKs0eW2YUt10RhQQD2rz/Ho3ZDvJs3hy7/x9E3v6LgSCqOjGwcWdmAC0gicsIDBNa+BoPDQfoXX7qnbAL21FQ8qlUjaOjQsku1ORg1cwPbE7Lw9/Rnbr/vOJi7jZiAmDLb/xuz0cyYq9xrpB3OPoyxRU1iv5tLxtdfk/L2FIr276dozEO0+nY2ni3qk/zaF6R+MB2AwCFDiHxhPABxA9+jaPv2Uv0HNG5EaMMIUrILCbDl42PLB8CYl8fB1DwMmXau8wok32wlz+JJntmTFz5ezdrxfRSY/eXKDswuAV26dGHatGkl9q1evZrhw4eX2X7Xrl3cf//9Jfa1atWKJUuWlNjXoEEDTKbj/xJERkayZcuWcqpaRERERERE5BTmPQQbPgGXs3iXy+VeR8zk4QKvYAr37Sf39+XYD+3Bvq0O9kIz9nww+fpS7ZUnIKAKae9/Tdqnn8PCSQQOG+oOzBoNpijzC4oOxR+/nsWCOSSkeNNgMhH24IMYfX0xh4ZiCj75IvnZBTbu/Hgdq/en4ekXx73daxIV4E9UwJlPxSz1GOLmMXblWNpGtWVKtykEDRuG/3XXkfq/97ElJOBZ3z1KzOjri8HDw/3zhNFvfh06YKtRA6OvDyYfH4y+vhh9fOnfvBnDGrinnRZ0/gyD1UqBxZP/WXw4VuAgJbuQQ7f2ICW7sPjTILeIAC8t0/S3Kzsweyb+1MdNJwzB7Po8dH765G0NxuPfvUP+ve/T5OPjQ82aNUvsO3LkyDn3+8+1ygwGA06n8yStRURERERERM6Bww6OwuKligpSDXjYnRj9w7CbIznwRRb2PDsum5M6n4/DWLkhBWu2kfTipFJdmQKcUKs7AB6xNbHWro05NBTrCUsRhT/xBLic7jAsJARTQAAGo7FEP6H33vOvZafmFDLio7VsOZqJn9VIpVoLmLbzMJHBhQyoNeBcnggADUMb4nK5KHIUkW/Px8vshcnPj/BHx+ByuYrbhdx9N6H33lvq/PBHHv7Xa3jWqweAFQgAyhoTt/XYVhqGtjy7m7hMXdmB2ZmsKWb2ADxOr63BcEHWKytLnTp1WLt2LbfeenyBwrVr11ZILSIiIiIiInKFy89wjyRb/R6uxjeSY2hH2oyPyVu7lsinXyTwtlGYbDZs7zUuPsUe3h6PSpXxiAW/3r0wh4ZhDg11f8LcP/8WNGwYQcOGlbqsT+tW51x6QmY+wz9YTVxKLsE+HnxyRytWpR3l0+2fck31a865f4CYgBhmXz+bmIAY95suT3Di9j/DvnOVkJPA/P3zqR5QnW5VuxGXEUc1/2r4efiV63UuZVd2YHYZGj16NHfddRdXXXUVbdu25csvv2Tz5s3ExsZWdGkiIiIiIiJypUjbB6vfgw2f4szLI2O/F2lffoktc5b7uNmMLdM9y8lgsVD9668wBQVjDg3B6OkJgFeDBlR5880KKX//sVyGf7Caoxn5RAQVcn2HOBpEdadB1EiuqXYNPpbyGyQTGxiLzWHj852f4+/hXy4j18qSlJtEnj2PmIAYfjrwE29teItWEa3oVrUbHat0JKMgQ4HZCRSYXWZuvvlm9u3bx2OPPUZBQQFDhgxhxIgRrFmzpqJLExERERERkSvB5q9hzj3gcr+wLiWuOmkbiwAnxoAAgoYMIejmm7BEHH/DpFejRhVUbGnb47O49cPVHMspIibUA2v1qczac5gQHyv3NLmHqv5Vy/2aP+7/kdfWvUagNZBu1brh7+FfLv26XC4MBgNf7fqKCasm0LVqV97q8hY9q/dk5dGV9IntA0CQZxBBnkHlcs3LhcF14qTYS1RWVhYBAQFkZmbi71/yH6qCggL2799PTEwMnn+l1FeaHj16EBERwaefflrufev5ioiIiIiISLFDq3B8cD1Ja72o1L8Rpu5jKDTEcGTUKIJuGU5g//4Yvb3JKbQzZcleCmwOvDxMeFtMeHmY8LSY8PYw4fXXtpfFhLeHGS8P41/HzHhZTHhajKWmMJaHdQfSuH3GWrIL7NSP9OeTka1YnjCfD7Z8wLs93iXaL7rcrwlgd9q5b9F99Inpw/U1rsdkPLc3Vf6Z/CfvbHyHyr6VGdd2HLvSdjF43mBaR7bmfz3+h9FQvlM8L0anyopOh0aYXWby8vJ499136dmzJyaTiVmzZrFo0SIWLlxY0aWJiIiIiIjI5czlgoXPc2yTlcz93ngYrie0RlesQOz8H0sEXG8v2cN7y/ad0+VODNW8PNxBW4nArUToZsKzVDBnLtHmQGouY77aSIHNSf3YRGrX/JUA76sZUGsAvWN642k+f4NEzEYz71/zPgBOl5OsoqwzGmWWa8tlyaEl2J12BtQagMPpYFXCKvw8/Pi/q/+P2kG1WTR4EZV8Kp2vW7jsKDC7zBgMBubPn8/EiRMpKCigTp06zJ49m+7du1d0aSIiIiIiInI5Mxhw9J1OxuQ+gB1zWPgJh46HZZl5Nj774yAAN14VjY/VTL7NTn6Rg7wiB/k2B/l//zzhe16RgyK7s7ifv4+Xt/Z1fIizfsDiwznM3N6QEQ1HnNew7ES70nYxbuU4QrxCmNJtyinb5tnySMhNoEZgDdYkrOGZ358h3DucfjX70bxScx676jG6RnfFYrQAKCw7QwrMLjNeXl4sWrSoossQERERERGRK4XTARjAaAQPPwJvuoX8P/8koN/1ZTb/+I8D5BYVUS36AC8N6oPdaWfBgQV4mDzoXrU7JqOJTSmbMGCgVlAtvMxeHMs/hsvlwtfij8NpIq/QTqHdWRyw5RXZKfgrVMv/Z+h2QuBWvF3kIM/moKBEGOegV6MwJvRrysqESczfN5+b6t10QR+lh8mDnWk78TB5kJCTQKRvZJnt1iWu4/7F9xPhE8F3/b6jXeV2NAptRPvK7Sl0FOJl9uK2Brdd0NovNwrMREREREREROTs/fx/kHUUBryLKTCQSk8+UbzY/D/lFtr5cMV+LMErMYVswWB4kBxbDs/8/gwAm27dBMCDSx4krSCN2dfPpnZQbZ787UnWJK7hlY6v0DumN//982W+3v019zS5h/ua3Me8uHlM3TaVTlU68XTrp9mVtouX1rxEVf+qjO8+njxbHhNWTcDX5MHzbZ7HaDAyfct0bE4bN9S+gRCvEN5c/ybrktZhd71P5+jOdI7ufCGfIgAxATFM6jiJqypdRahXaPH+I9lHmLZpGqkFqbzb/V3qBNfB7rRT5CgivTCdYM9gPr/28wte7+VMgZmIiIiIiIiInJ2102H1NAAKQ6/B1LQP5pCQky7IP2vNITLyCgmo8geFLjt/xP9BveB6tI1qi8PpKF6MPtInEi+zF15mr+JzjQYjHkYPAGxOGw6XAyPu9pmFmRzNOUp6YToAaQVprEtaR1ZRFuBe42vevnkYDUbGtR0HwIdbPySrKItrql9DiFcIG5M3sjllM0sPLy1+e2RF6FW9FwAbkzfy/pb3mdptKlaTlXlx83DhIj4nnijfKOb2m0u0X/R5efmBKDATERERERERkbMR9yvMf9z9veuzJLy7gIKtrxH1+mv49+hRqnmh3cH/ftsHGBlZ8z/kWlfQLLwZnmZP3uvxXom2X1z3RYnt6T2nA+ByuQB4vOXj3N/0/uJArVdMLxqFNcLPww+AWkG1eLXTq3ibvQHwtnjzaItHsbvsxX0OrDWQHFsOgdZAAJqGN2Vo3aH0jul9bs+lHGxI2sDIn0did9lJL0gnzDuMx1s+ToOQBkT4RABQ1b9qBVd5eVNgJiIiIiIiIiJn5tge+Po2cDmg8Y3kB/Ykf8OHYLHg1bhJmafMXn+U5OxCKgUYuKdtO6zmjmd82b9HU/lYfPCx+BTvD/UKLTGFMdQrtHik1t/tRzQcUaKvR696tMT2Iy0eOeN6zpfGYY0ZWncohY5CCh2FANxS/5YKrurKosBMRERERERERE5fXhp8PgQKMiG6NfSdTNpTzwIQ0Kc3lkrhpU6xO5y8uywOo+cR7JWnM3XjTh5p8YimE56E2WjmyVZPVnQZVzRjRRcgIiIiIiIiIpcIexF8dSuk7YPAqnDjTGypGWT9/DMAwbeV/WbGH7ckcCgtD9/grRS58knOT1ZYJhc1jTATERERERERkdNTkAFp+8HDD4Z9Cb5hpL/3OtjteLdsiWf9+qVOcTpdvPNrHAB31h/NVfWGUcmn0gUuXOTMKDC7iI0YMYKMjAzmzp1b0aWIiIiIiIhIeSnMgfT97lFatgJocmNFV3T6fMNh+GzIjodK7nDMEhWFOSKC4BFljy5bvDOZXUnZ+PqmcvPVXQnxrX0hKxY5KwrMRERERERERM6XXQsgaYt7VFbaPvcnJ6n48NaIAYTH9CPc37MCi/wXx/bCplnQ9VkwGCC8rvvzl6BhwwgcPBhMplKnulwupvy6Fww2PKu+y00LZvL+Ne/rDY9y0dMaZpeoZcuW0apVK6xWK5GRkTz11FPY7e7X4/7www8EBgbicDgA2LhxIwaDgaeeeqr4/DvvvJPhw4dXSO0iIiIiIiKXO2duLnlr1+L6/b+wZAJsnAmH/igOy5xewWwz1OLhg20Y/O4fHE7Lg01fwm+vuUegXSwOr4HpPWD5a7DqnRKHXE4nRQcPAmCwWDAYS0cMf8SlsulwBp7eKXha3IFalG/U+a9b5BydUWA2btw4DAZDiU/dusdT5YKCAkaNGkVISAi+vr4MGjSIpKSkU/ToTpuff/55IiMj8fLyonv37uzZs+fs7uYM5dnyyLPl4XK5AMi355Nny8PhdAdNhY5C8mx52Jw2AGwOG3m2PIocRQDYnXbybHkU2AsAcLqcxX2e7Brl4ejRo/Tp04eWLVuyadMmpk2bxvTp05kwYQIAHTp0IDs7mz///BNwh2uhoaEsXbq0uI9ly5bRuXPncqtJREREREREjkuc+CIHb7mV+KVGXI2GuUdnDf4Q7l6K/fEDDA+exbX549nrqsKhtDyGv7sM+8KxsOQ/8N/GsGIyFOX9+4XOpx0/wMd9IT8NoppDoyElDucsXUpcr97EP/nUSTqAqUv3AjCkcRsW3bCQKd2mYDZqsptc/M54hFmDBg1ISEgo/vz+++/Fxx555BHmzZvH119/zbJly4iPj2fgwIGn7O+VV15h8uTJvPvuu6xevRofHx969uxJQUHBmd/NGWr9eWtaf96a9MJ0AIb9MIzWn7dmQ/IGAJ5e/jStP2/NN7u/AeD9Le/T+vPWvLL2FQAWH1pM689bc9+i+wDYl7GP1p+3ptfsXie9Rnl45513iI6OZsqUKdStW5f+/fszfvx4Xn/9dZxOJwEBATRt2rQ4IFu6dCmPPPIIf/75Jzk5ORw9epS9e/fSqVOncqtJRERERERE3Jz715I1fz4AWWv3kZ7XHjo+Dg0HQVQzXl6awMq4VLw9THw6shU1wnw4nGVnbN6NFPlXh7xUWPgcTG4Kq98De+GFv4k178NXt4C9AGr3ghE/gG9YiSZpH38CLhfmsNAyu/jzUDor9qZi8UqgY6M8PM2e1AqqdSGqFzlnZxyYmc1mIiIiij+hoe5/MTIzM5k+fTpvvPEGXbt2pUWLFnz00UesXLmSVatWldmXy+Xirbfe4tlnn6Vfv340btyYTz75hPj4eC10fwo7duygTZs2JV7B265dO3Jycjhy5AgAnTp1YunSpbhcLpYvX87AgQOpV68ev//+O8uWLSMqKopatfQXlYiIiIiISHnLfmcMroICTP4+BPTrR9CwocXHvtt4lPeX7wfg9Rua0KFWGF/e04baEQHMzGtFm+yXONLxNQio6p6++dMT8FYjmHVTyYss/g/89qo7UNv4OeyYB3G/wpH1kLL77Kd1Op2wcCzMfwxcTmgxAm6cCR4+JZo5cnKwHzsGJhNBN99cZlfvLHW/GbNK7G88snwkH2396OxqEqkAZzwOcs+ePURFReHp6UmbNm2YNGkSVatWZf369dhsNrp3717ctm7dulStWpU//viDq6++ulRf+/fvJzExscQ5AQEBtG7dmj/++IOhQ4eWOgegsLCQwsLjCXtWVtaZ3gYAq29aDYCX2QuAWdfNwuVyYTVZAZjUYRIT2k3AYrIAcFejuxjRYETx8NFuVbux+qbVGA3u3DE2MLa4z5Nd40Lp3LkzH374IZs2bcJisVC3bl06d+7M0qVLSU9P1+gyERERERGR8+HYHrLWHwE8CRzUn/AnnwXAZbez8/W3GZdeHfDgvs416N0oEoBQXytf3H01t320lk2HM+i9rCozbl1Ii7Qf3WuaZcdD8vbj13A63GuKncrgj6DhXzO+lr4EGz4Fq98/Pr5g9YeQmtBypLvtT0/A2vfd37s8Cx0fcy/0/w8mX19i531P4a5dWCIjSx3flZjNwu1JGAxOGkdGsSJxO12rdj2DBylSsc4oMGvdujUzZsygTp06JCQkMH78eDp06MDWrVtJTEzEw8ODwMDAEudUqlSJxMTEMvv7e3+lSpVO+xyASZMmMX78+DMpvUzeFu8S2/8MtawmK5zwkg+LyVIcngGYjeYSc6+NBmOpPv+5XR7q1avH7NmzcblcxaPMVqxYgZ+fH1WqVAGOr2P25ptvFodjnTt35qWXXiI9PZ1HH3203OsSERERERG54m34BP+q+bi8QgkYeGPx7kP/mQhffsHYwGh+HP4kj11Tp8Rpgd4efDayFSM/Xsea/WkMn7GRD27rR7sHb3a/LMDlPN7YaYc2D0BhFhRml/HJAk//4+1zkiDryMlrrtr2eGDWcKD7jZi9X4FmZY8cczkcGEwmDEYjnvXqldlm2l9rl/VqEMV/u79EdlE2fh5+p3hwIheXMwrMevfuXfy9cePGtG7dmmrVqvHVV1/h5XXhRlA9/fTTjBkzpng7KyuL6OjoC3b9CykzM5ONGzeW2Hf33Xfz1ltvMXr0aB544AF27drF2LFjGTNmDMa/3koSFBRE48aNmTlzJlOmTAGgY8eODBkyBJvNphFmIiIiIiIi5c1eBJtmEVA9n4CnxsFfy+A4nC6meNZnkIcPdTIO0yz1D0zGbqVO9/O08PHtrbj703Us33OM22es5d3hzelat0vJhmYr9Jx4+nV1fAKa3VJ2sFaYDQFVjret1hYe2gw+IWV2lZFXxIZxL+G1fTN7r7uZlNj6FDlc2BzO4k+h3cn3m+LBmE9QlV+Iz4nUmzHlknNOr6YIDAykdu3a7N27lx49elBUVERGRkaJUWZJSUlERESUef7f+5OSkog8YQhnUlISTZs2Pel1rVYrVqv1XEq/ZCxdupRmzZqV2Ddy5Ejmz5/P448/TpMmTQgODmbkyJE8++yzJdp16tSJjRs3Fr8NMzg4mPr165OUlESdOiX/3wwRERERERE5R7sXUBifjkdUJQy1rine/cbCXXyd6c2fXUYxuehPop987KRdeHmY+OC2q3jg8z9ZuD2Juz9Zz+RhzejTqPS0x9PmH+n+nAbb0aM4cnOBVMwhIZhD3MFZ6o7dfLvmIN+tP8yzS78joCiPBSt2suKAx0n7qlt7B/MOfsWe7A183ffrs69fpAKcU2CWk5NDXFwct9xyCy1atMBisbB48WIGDRoEwK5duzh06BBt2rQp8/yYmBgiIiJYvHhxcUCWlZXF6tWrue+++86ltMvCjBkzmDFjxkmPr1mz5pTnv/XWW7z11lsl9v1ztJqIiIiIiIiUD9uy6ez7KQxLiC+x9xdh9LGwYGsCU391L34/+vZrqN/0dsD9Erxj06YR0LcvHv+YMWU1m3jn5uY8+tUmvt8Uz6jPNxAT4kNUoBeRAZ5EBXpROdCLqEAvogLd256W4+sJOfPzKdy3D2dmJs6iIvz+GkSRs2wZWb/8giMzE2dGJo5M98ezXj2i33sXgOTXXydr/k8AhI4ahffd9zJj5QGajrmF9vlZtP/rGtmBYVTr15saFjMWkxGLyYiHyeD+bjbiZTERGRHAV3sP0iemz3l86iLnxxkFZo899hh9+/alWrVqxMfHM3bsWEwmE8OGDSMgIICRI0cyZswYgoOD8ff3Z/To0bRp06bEgv9169Zl0qRJDBgwAIPBwMMPP8yECROoVasWMTExPPfcc0RFRdG/f//yvlcRERERERGR8yPzCNm/rQUCMEdVxejjw56kbB79ahMAI9vH0K9p5eLmaR9+xLHJb5M+axY+LVth9PHG6O2NtW49Agf0x2IyMrF6IfX3xPNrXDpxuZXZdywQD4eN27f9SHJRHvm2PJKL8omz5eFvy+OD/mMw1KhNg+yjXPP2UwC4/PwIWfwbYb5WCvfsIXP2t6VKt4Uen35pjozEFBKCy+VibWI+z77yK2m5Rbxj8cGMCx8PM1YvK3WfepIXezX9l4dSnWti2+Nyuc716YpccGcUmB05coRhw4aRmppKWFgY7du3Z9WqVYSFhQHw5ptvYjQaGTRoEIWFhfTs2ZN33nmnRB+7du0iMzOzePuJJ54gNzeXu+++m4yMDNq3b8+CBQvw9PQsh9sTERERERERuQBsBRR51ARS8O83iKwCG/d8up7cIgdXxwbzdO+6JZr7972OjDnfUrQ3jqz584v3+11zDYED+gOQPn06nX7/nU5A3sNPcbBVExJSc+g67+kyS0g+ksymwgA25WbS3NOfbA9vsjx8eGriIixmI20KDbRqNxBrUCDeocH4h4cQGBFKeHQEOYV2fK1mAh8Zw09tBzN1aRwp2YWQW0T1EG+K3vuUq5pEYTKWfmNmWZ5a/hQNQxoysNbA8/IyPJHzzeC6DKLerKwsAgICyMzMxN/fv8SxgoIC9u/fT0xMjEK480DPV0RERERE5DhbYgJGH1+eXbifWWsOExngybzR7Qn1Lb0OtyMnh5zFi91TJPPycObmYa1Zg4B+/QBIevkV8jdvxlVYSMjIO/D/60V8KW9PwejtjSkwAKO/PwVevqQaPUnwDuZovoujGQXEZ+QTn5FPQmYBiVkFOJz//qu/v6cZk9FAep4NgMqBXjzUvRYDm1XGbDLidDkxGtwvmpu4aiLZtmwebfEoYd5hvLr2VXal7+K+JvdR1a8qDyx5gN3pu/l50M+Ee4eX1+MVOW2nyopOxzmtYSYiIiIiIiIix1kiIknJLmT2+qMAvHlj0+KwLCUvhfVJ67GarHSp2oUNOTt4ovAtJnSbQLvK7Ur1VenJJ8q8RtjoB0ps+wPhQL2T1GR3OEnOLiQ+I5+jGfnE/xWoJWTmF4drmfk2sgrsAET4e/JA15oMuSoaD7ORjIIMJq2YxOaUzcwbMA+z0czPB34mvTCdOxveSZh3GNtTt7MuaR2Daw+mRaUWNAptRO2g2grL5JKlwExERERERETkHLiWvcKR91fi2aIjQSNu57N1KRQ5nNSqfohZB35hZ14zRjQcwdrEtTy5/EmahTejS9Uu+Hv4cyz/GGNXjuWngT9hMVnOS31mk/GvFwR4cdVJ2uQU2knIyCcj30adCC9WJizj5bWf8OzVz+Lr4cvK+JVkFGawIWkDrSJbcV/T+yhyFBHsFQzAXY3uYnDtwTQObQzArfVvpZJPpfNyPyIXggIzERERERERkbOVm0rh3NfJWRdM7qY4vG+9nc9WHQSgdS0z3x1egs1pY0TDEdQOqk2TsCY0DG0IQPWA6rSv3J7HrnrsvIVlp8toLCLB9icdq3ekwF7A8yufJ9+ez8BaA2kQ2oBnr36WSt6VaBzmDsSG1R1W4vy2lduW2K7qX/WC1S5yPigwExERERERETlbm78g64A77PLt3Jkf9mWTmltI5UBvbm/ejXpR3jQIaQBAzaCafNbns+JTrSYr07pPAyAxN5FFBxcxvP7wC1a6w+nAZDSRb8+n+zfdyS7KZl7/eVQPqM6Q2kMwG80EeQYB0LN6zwtWl8jFQIGZiIiIiIhcGvYthfmPQ2xn6PUyGI0VXZFc6VwuXOs/JuugFwB+ffow/ff9mAPW4119C7vS7+Dmejf/azdZRVnc+MONpBWkEewZTJ/YPue1bLvTzourX2TxocV80/cbwrzDaBzWmIOZB0nKS6J6QHUea/nYea1B5GKn/8KIiIiIiMjFzeWCFf+FTwfAsd2w5n+welpFVyUCR9aRv3MftjwzRm9vNkc3YndSDp6Bm0ks3MWRnCOn1Y2/hz+Daw+mXnA9GoU1KvcyXS4X21O38/q618kpysFsNLMrfRdpBWksPrQYgJc7vMz8gfNpHdm63K8vcinSCLNLnMFgYM6cOfTv37+iSxERERERKX+FOfD9A7Btjnu7egcwGKHZLRVblwjAho/JOugNgF+P7kxaGw/AtZGP0LjOQTpV6XTaXd3f5H7ubnw3VpOVw1mHCfEKwdvifU7lHcg8QGXfypiNZp787UkOZB2gVlAtrq9xPaObjQagZaWWAARYA87pWiKXG40wu8glJiYyevRoYmNjsVqtREdH07dvXxYvXlzRpYmIiIiInF+pcTC9hzssM1rg2tfhtnlwy1zw9K/o6uRKV5gNW78ltEE2lUbdTE7P61m6KwWDwcn9HZpxc72bqeJX5bS7MxlNWE1WfjvyG0N+GMK4P8bhcrnOurzRi0fTd25fVsSvwGAwMKDWAK6pdg3RftEAXB15NVdHXo3JaDrra4hczhSYXcQOHDhAixYtWLJkCa+++ipbtmxhwYIFdOnShVGjRlV0eSIiIiIi54/LBV+PgOTt4FsJRvwILe8Eg+H42mWFOTBrGMQtqdBS5Qq19Vuw5WKuHEvwA//HjEx3iFu57kwmbXiUXWm7zqpbPw8/8u35JOYmkm/PP+3z5sXN4/YFt7MhaQMA0f7RmAwm9mbsBeCOhnfweufXaRbe7KzqErnSKDC7iN1///0YDAbWrFnDoEGDqF27Ng0aNGDMmDGsWrWqzHO2bNlC165d8fLyIiQkhLvvvpucnJzi40uXLqVVq1b4+PgQGBhIu3btOHjwYPHx7777jubNm+Pp6UlsbCzjx4/Hbref93sVERERESnBYID+77gX+L97GVQtY12llW/Drvnw5S0Qv/FCVyhXuvrXkxV2NwXVbyMtz8bs9UcwmHLJYgcr4lfgY/E5q26bhTfj/WveZ3rP6XhbvHE4HWW2y7Pl8eO+HzmUdQiA1QmrWZe0jvn75wNwe4PbWTJkCXc2uvPs7k/kCndFr2HmzMsrc7/BasVgMuFyOnEVFBTvN3q754+7bDZcNlsZJxowernfjuIsKoK/gyazGaOHxxnVlpaWxoIFC5g4cSI+PqX/og0MDCy1Lzc3l549e9KmTRvWrl1LcnIyd955Jw888AAzZszAbrfTv39/7rrrLmbNmkVRURFr1qzBYDAAsHz5cm699VYmT55Mhw4diIuL4+677wZg7NixZ1S/iIiIiMgZK8iEDZ9Cm1HuwCyiEdz63cnbdxgDh/6A/ctg5mAY+QsEx164euXfrZrm/jP1CXWPFPQN/+tnJfANc/8MigGPv9bqcjqBf5mG+PcUQpcLXM5TtzUY3f8sAZwkeDqh8fHRi6fRt9PgRcJHS3Hm5LD2iRoU2qFh5Uje7j+X9Unrz2g65j+1jHCvKzYvbh7Tt0zn494fE2ANwOawYXPa8LZ489yK5/jl4C/c2ehOHmr+EINrDyY2MJbe1XsDEOYddtbXF5ErPDDb1bxFmfurfvwxPq1bYTt0iLhe7r9sMJupt3ULAOlffUXSfyaUOs+jZg1q/PADAMmvvUb6J58CEHTrLUQ888wZ1bZ3715cLhd169Y97XM+//xzCgoK+OSTT4pDtilTptC3b19efvllLBYLmZmZXHfdddSoUQOAevXqFZ8/fvx4nnrqKW677TYAYmNj+c9//sMTTzyhwExEREREzq/8dPigO6TudQcV7R7893PMVrjxM5jRBxK3wKcDYeRCdxAjF4cmQ2Hnj+5Q82Ru+hpqX+P+/t39sGnWydvW6gk3f+X+nrwDprU59fVHrYGwOu7vnw+BvYtO3rbpze5RjQAHlsPHfU/ZdU6Lj3Dm5GCqFMHUeAtgY0TbKsQExBATEHPquk5DoaOQdze9y6HsQ8zcMRM/Dz/e2/weIxuO5PaGt9Ojeg92pO0g1CvUXX54U5qGNz3n64qI2xUdmF3MzmZxxx07dtCkSZMSI9LatWuH0+lk165ddOzYkREjRtCzZ0969OhB9+7dGTJkCJGRkQBs2rSJFStWMHHixOLzHQ4HBQUF5OXl4e19bm9oERERERE5qa3fusMyv0io3v70z/P0h5tnu18OkL7fPdJsxI9g9T1/tcrp8wqCnhMheSfkJLk/uSl/fU92//QNr+gqz4rBYsGzUSOOVqtHUo6NsMB8Xt05lDW5nXix/YuYjef267bVZOWNzm+w5PAS7m50N9/u/ZbMwkxWJaxyB2ZVe9CzWs/iGUMiUr6u6MCszob1Ze43WK0AWKpWLbNN0JAhBA4YUMaJx/+iCn/sMcIffti9YT7zx1yrVi0MBgM7d+4843NP5aOPPuLBBx9kwYIFfPnllzz77LMsXLiQq6++mpycHMaPH8/AgQNLnefp6VmudYiIiIiIlHBgufvnVXdA5eZndq5fJbhljjs0S9gIX90Cw74E85ktiyLlJH6je325vv91B5eRTdyf09HnNej54smPnxhChdaGJ/afuj/rCW9THfwROE+xPrPphH9eqrb51779PAPx7Xkdo//7GyTm0LJ+IsvT80jKTTrnsOxvdYLrUCfYPULummrXUNWvKldVuspdrt5uKXJeXdGBmfFfRkwZjEYMZbQxWCwYLJZT9+3hAWe4btmJgoOD6dmzJ1OnTuXBBx8stY5ZRkZGqXXM6tWrx4wZM8jNzS1uv2LFCoxGI3Xq1Clu16xZM5o1a8bTTz9NmzZt+Pzzz7n66qtp3rw5u3btombNmmddt4iIiIjIGXO54MDv7u9nMrrsRCE14OavYUZfOLYHchIhsGr51SinJyseZg2F7ATwCYPeL520qcvpxHbkCAU7dmJPSgIgaNhQDFZfCvftI/f3FQCYw8Pw79ULgJwVKyiK21eqL48asfi2a+cuYcEC7MkpAPi0b4c1NhaXzUb6V2Wvh+ffpzfm0FDsaWlkfeleVsfgYSFo6FAA8rduI3/Dhn/U7iDohhtYnVjA1sQcPC1GJna7i6P5nSlyFJ324zoTAdYAWkeW8fILETkvrujA7GI3depU2rVrR6tWrXjhhRdo3LgxdrudhQsXMm3aNHbs2FGi/c0338zYsWO57bbbGDduHCkpKYwePZpbbrmFSpUqsX//fv73v/9x/fXXExUVxa5du9izZw+33norAM8//zzXXXcdVatWZfDgwRiNRjZt2sTWrVuZMKH0mm0iIiIiIuXi2G73ND2zJ1Que53h01K5Bdz0pTs8848qv/rk9BTlwaxh7rAsrC50ebr4kLOgAIPZjMFspmDXbhJfeIHCnTtx5uaW6CJw8CAMFgsF27aR9KJ7pJlXs2bFgVnWvB/InDu31KUD+vcvDszSPv6E/D//BCDq1VeKA7O/+/snr2ZN3YFZUlJxG6OfX3Fglrd6FcmvvlbqvMzvv+enFoOBAPo1C8PPy0RDn4Zn8MBE5GKmwOwiFhsby4YNG5g4cSKPPvooCQkJhIWF0aJFC6ZNm1aqvbe3Nz///DMPPfQQLVu2xNvbm0GDBvHGG28UH9+5cycff/wxqampREZGMmrUKO655x4AevbsyQ8//MALL7xQ/JKAunXrcuedeg2xiIiIiJxHf0/HjG7lXsj/JJxOF5+uOkiDKH+uqh5cdqOYDse/u1xweDVUvboci5UyOZ0w5x73lFjvEBj2BS6LL6nvvkvWjz9SuG8/1T79BO/mzTH5+pC/3r30jcHDA2utWliqRmMwGMHknmZoiYrCv08fADyqVy++jFeTxriKSo/g8mrSuPi7T5s2WP5ap9kS9VdwajIV9/dPpoAAAIx+/sVtDF7Hl6TxiIkt89xcD2++TfMAC4RGraXrV2O4p8k93Fzv5tN5YiJykTO4zmZ1+YtMVlYWAQEBZGZm4u/vX+JYQUEB+/fvJyYmRutwnQd6viIiIiJyzr4eAdvmUFh/NJbrnjnp0ikLtiZw72cbMBjggS41eahbLcwmY9l9Op0wbzT8+RkM/AAa33D+6hdY/B9Y/pp7HbBbv4dqbXDm5bF/yBCK9sYBEDFuHEFDb8TlcpH1w49Y69TGGhPzr8vdXKzGfreVj/84SNe64Rij3mN1wmqeb/M8N9TWP2siF4NTZUWnQyPMRERERESkYtW5FluGjUP/XYx1cQpV33uvzGY/bkkE3APH3l6yl9X70vjvsKZEBniVbmwwgIef+/vc+8AnBGp0PV93cGXb9KU7LAPoO5kiovDAvWZ0lcmTyVuzBt8uXTGHhwFgMBgI6HtdxdVbDjLzbHy9/ggAI9vH0Dr2XdYmrqV+SP0KrkxEyosCMxERERERqViNb8BY7RoMXwwid9lvFOzahecJL60CKLA5WLLDvTD8/Z1r8MkfB1lzII3e/13Oa4Ob0L1+pZJ9Ggzuty3mJMG2b+HLW2DEjxDV9ALd1BXCXgRL3et+udo9TPqmIpJevY7I8eMIHDQIa2ws1tjYk57udLqYtiyOn7clcinNfcousJFX5KBuhB+1o1yYDCbaRLWp6LJEpBwpMBMRERERkQpnCgjAs2FDbEePkv7ZTCL/80KJ48v3HCO3yEFUgCeP96zDkKuiGT3rT7YczeTOT9ZxR7sYnuxdB6vZdPwkoxEGvAt5x2D/bzBzMIz8BYJPHuDIGTJ7wO0LcPz2DgnzMsj+xf1WzLz1GwgcNOiUp9ocTp6cvZlvNxy9EJWeF/d2qsHdC+8m357P651fp0FIg4ouSUTKiQIzERERERGpOGvex+kwk/DFegwe3vj37UvgkCGlmv20NQGAng0jMBgMVA/14Zv72vDyT7v4cMV+Plyxn7UH0phyUzOqhfgcP9FshRtnwow+kLgFPh0IIxeCb9iFusPLXkFCNkenrKPowAGwWKj05JME3XzTKc/JK7IzauYGft2Vgslo4OnedakR7nthCi4n/p4WokNtvLj9KDanjWi/6IouSUTKkQIzERERERGpGC4XLJ1E/r5sspaGYo6KpObixRgMhhLNiuxOFm13T8fs3TCyeL/VbOL5vvVpUyOEx7/ZxJajmVw7+XdeHNiI65tEHe/A0x9ung3Te0D6fvjqFrj9J/e0TTk78X/Crp9w1OjHweG348zOxhwZSZU338CradNTnpqRV8TtM9by56EMPC1Gpt7UnG71Kp3ynIvZshuXsSN1B/4eZ76ouIhcvE7ySpnLz2XwMtCLkp6riIiIiJy1lJ2Ql0ruMfeIMJ+WLTEYDDjz88n87jtcDgcAf+xLJavATpiflRbVgkp106N+JeY/2IGW1YPIKbTz4Kw/eWr2ZvKLHMcb+VWC4d9CSE3oNlZh2blaOx2WvYxp3WTCRo/Gp107Yr6d/a9hWXxGPoPf/YM/D2UQ4GVh5p2tL9mwzOVycSjrEF5mL5pXal7R5YhIObvsAzPLX68ozsvLq+BKLk9/P1fLJfoqaBERERGpQAd+ByAvwx2CebdqhcvpZF+//sQ/+RQ5v/0GwE9b/pqO2aASJmPZQVdUoBez7rqa0V1rYjDAF2sP02/q7+xOyj7eKLQm3L8aqmlx9nNSmI1jw7c4HUCL2wi6ZTjR/3sPc1DpMPNEe5OzGTRtJXuTc4jw9+Tre9vQolrwhan5PNiVvotr51zLzT/ejNPlrOhyRKScXfZTMk0mE4GBgSQnJwPg7e1daoi3nDmXy0VeXh7JyckEBgZiMpn+/SQRERERkRMdWI7TbqAgsQgA75YtMRiN+HXvTtqHH5L+2Uy8OnbilzKmY5bFbDLy6DV1uDo2hIe+2MjupByun/I74/o24MaW0e7fA0wn/Aq07kNIPwg9xp+3W7wsbf2W1E1GMg5GEd48mcBqBviX3wc2HErnjhlrycizERvmw6cjW1M50OsCFXx+7EjdgdlgJtw7HKPhsh+LInLFuewDM4CIiAiA4tBMyk9gYGDx8xUREREROW1OJxz4nfxUCy67E3NEBJZo96LpQcOGkvbRR+SuWMG63zeSlltEkLeF1jGnNxqpXc1QfnqoA2O+2sjyPcd46tstrIhL5cUBDfHz/GtmROJW+OER93ffcGgz6nzc5WXJueYTMvZ54ygCU8C/r9v1665k7vtsPQU2J02jA/lwREuCfTwuQKXn14BaA+gS3YVsW/a/NxaRS84VEZgZDAYiIyMJDw/HZrNVdDmXDYvFopFlIiIiInJ2/lq/zOzrQ8gdIzD4+BbPBPGIjsa3c2dyfv2VpI8/hfDu9KhfCbPp9EfxhPlZ+fj2Vrz32z5e+2UX8zbFs/lIBm8Pa0bjKoEQ0RC6j4NF4+DnZ8C3EjQafF5u9bKStJ2CrZtxOkKwREbg26nTKZvP+fMwT8xdgs3hS6fa0bx+Y11M5nyKHOBhunRDs+Q892CMcO9wAj0DK7YYETkvrqhxoyaTCU9PT33K6aOwTERERETO2l/rl1kbtST8iScJG1VyhFfQ8JsBiF7zK962gn+djlkWo9HAfZ1r8NU9bagc6MXB1DwGTVvJ9N/3u19e1e5haH2fu/GceyHu13O6pSvCn5/iHVZEzUebUPmttzCc4neC3+OO8uyau/CMfY3WDRL44Lar+HD7u7T/oj3vbHwHgDl75tD80+Y8sewJADYmb6TVzFYM/t4dXmYWZtJqZitazWyF3WkHoM+3fWg1sxW703cDMGrxKFrNbMWCAwsAeH3d67Sa2Yppm6YVX6PVzFbleo1Hfn2Ebl93Y9rGaeX0YEXkYnNFBWYiIiIiInKR8I/CFdMNZ3SXMg/7tG2Lo15D5ldrTYCHgbY1Q876Ui2qBTH/wQ70bFAJm8PFf37Yzl2frCM9zwY9X4QGA8Bpgy+HQ8KmM+t854/w58yzru2SYi+ETbMAMLcfiVeTJidtmpRVwMNfbMOWXR+Ake3qYDEZsTncM34sJvfU2CJHETanjUJHIQBOl5N8ez4FjoLivvLt+eTb84u3C+wF5Nvz3aEnUOgoJN+ej8PpfiuqzWkj355ffC27y06+Pb9cr9EorBEGDLSo1OLMnqGIXDIMrr//BriEZWVlERAQQGZmJv7+/z6HXkREREREKl7u6jUcuuMOfDt3JnrqlFLH/zNvG9NXHGBAs8q8eWPTc76ey+Xikz8OMvHHHRQ5nEQGePLfoc1oFe0DMwfD/t/AJxxG/gLBMSfvyOkEoxFcLkjdC+92gLsWQ6UG51zjRc3pJPGJ+yDjACHjpmOpUqXMZkV2J/0++IwdB4KpU8mfT+9uTLhPIOD+M7C73KO4LEYLebY8soqyMBvNhHqFUugoJCUvBbPRTIRPBA6ng4Rc91tSK/tWxmAwkJCTgMPlINw7HA+TByl5KRQ6Cgn2DMbb4k1GQQY5thz8PPwIsAaQU5RDRmEGnmbPcr0GoOmYIhexc82Krog1zERERERE5OKTt2YNOBwYPT1LHXO5XCzY5n47Zp9oT4oOHcKjatVzup7BYOC2ttVpUS2I0bP+ZP+xXIb+7w8e6V6b+4d8hmnGtZC0FQ6vKTswK8qFVe/Atu/cAZnZCsvfAIMBFj4Pw2efU30XO3taGhk//4HLZiMgPeOkgdm9387giNeb+EU3550hbxPuc/wXVYPBgMVgKd72tnjjbfEu3raarFTxO96vyWgqsQ0Q6Vtyem6Yd1iJ7UDPwBJBlq+HL74evuV+DRG5vGlKpoiIiIiIXFgHV8KOHyjY9CcA3i1blmqy5WgmRzPy6Zmwkeh7byRp0kvldvmGlQOYN7o9A5tVxumC1xfu5pbPdnCs30wYNgua3FjyBIcN1k6Hyc1gyQRI2gJbvnEf6/S4+/jeRZf3GmhOJxnffIPLZsOzSWO8GjUss9mXaw+xfN8BXC4jnWpWo0aYZgCJyKVJgZmIiIiIiFxYq6bBlzdT5Za6xHw7G79repRq8tPWRABCmzfBVVREztKlFB0+XG4l+FrNvHFjU167oQleFhMr41LpOX03ywxXHW+UuAU2fw1TW8OPYyAnCQKrwcAPoMkwd5vgWGh5p/v7wufc0zUvQ64lL5I+3T1tNvimm8pss2p/PM/N3YItozUDKk3izR7PXcgSRUTKlaZkioiIiIjIheN0Fr8h0xDbCc/o+qWauFwuftriXlOqTZfm+GxuR8HuXRQdPIRHdHS5ljO4RRWaRgfywOcb2JmYzW0fruHeTjV4rHoc5i9PCIa8Q6HTE9DidjB7lOyk4+OwceZfAduX0HRYudZY4ZwOsr+biT0bTP4++PXqVapJYmYu9/zyIKZIA139RjO+ZweMRkMFFCsiUj4UmImIiIiIyIWTsgPy08hO9Mex+hA+HtWxVAov0WRnYjYHUvOwmo10qROOx4svYg4KxODhcZJOz03NcF/mjmrHhB+389mqQ7y7LA7bzj08a7RgMFuhzQPQ9gGw+uFyucjKt5GSXUhKdiGpuYU0iAogpsMYWDTOPWWzQX+weJ2XWitE3BIyt+cDXgTeOBSj1VrisM3h5K4v5+HwiMNsNfJgz1CFZSJyyVNgJiIiIiIiF85fo8vSDoST9+xzRIx9nqBhJUdk/T0ds2PtMHysZjghUHPZbBgsFsqbp8XEhP6NaFsjlCdnb2Z6Ui1+85xMu5rVOHrYSsoHm90hWU4hRfaS0y4DvS0sefAOgtd8AFlHYPW70P6Rcq+xwmz4mMpt0smytMXnpptLHX5x/g627PPD1+8Bnro+kqYRpUcNiohcahSYiYiIiIjIhXNgOU4H5B8tBMpe8H/BVvd0zN4NI4r32Y4eJenlVyg6cpiY2bMxGM7PCKY+jSJpVDmA0bP+ZONh2LMho8x2fp5mwnytZObbSM0t4vVfDzGx23PwxxSo3OK81FYhcpJh108YzRB477NQqeTbI6eu+JUvDk3BYBrC6/2vpWeDiJN0JCJyaVFgJiIiIiIiF4bTCQdWUJDmgcvmwBQcjEeNGiWa7E3OYXdSDhaTgW71KhXvN/r6krN8Oa78fPLXrSszaCsv0cHefH1vG75Ye5hj2YWE+VmPf3zdPz0tJgBW70vlxv+t4vM1hxjWsicN7x4Cxsvn3WqOVR+T9IcvQW2r4RlejxNjys1H0nln24uYfRNp1uQPeja48aT9iIhcai6fv8lFREREROTilrwd8tPIO+YLuEeX/XOk2N+jy9rVDCXA6/jUS1NAAAF9+wKQMG48WT/9hMtuP2+lWkxGbrm6Go/0qM3wq6vRs0EEzasGER3sXRyWAbSODeH6JlG4XDBu3g5cJ96PreC81XdBuFxkfvkZmfu9if+15DTUX3cmc/tH68g/chOBrqZ8eP0LFVSkiMj5ocBMREREREQujKJcqNKSvKxQALxblR4l9vf6ZSdOx/ybdfgN2L2tFMXFcfSRMcT16o0zL+/81nwanu5TFy+LiXUH0/luYzwc2wOfDoA591R0aefElRpH+qZ8AIJuuwODwUCh3cH4edu4a/ZHpBWmUC+0Jj8M+ZBAT/8KrlZEpHydU2D20ksvYTAYePjhhwE4cOAABoOhzM/XX3990n5GjBhRqn2vMl5VLCIiIiIil7CqreHORXh2uxFr7dr4tGpV4vCh1Dy2xWdhMhroUb9kYHY05yj9N9zPmHvMGO4Yiik4GGudOhi9vQHIXbmSosOHL9itnCgywIsHutYE3Avg5+XnQdyvsH0uHF5bITWVi+BYwv5vEj5XNSBg0BD2JmfTf+pKPl67Gs+oWQTVnsxbw6sQ4F3+L2EQEaloZx2YrV27lvfee4/GjRsX74uOjiYhIaHEZ/z48fj6+tK7d+9T9terV68S582aNetsSxMRERERkYtY+GOPEfv9d1hr1Sqxf8E293TM1jHBBPt44HK5WHl0JXannSifKGICYqhT82piH30avx9m8W2/UIocRbiKioh/6mnievbiyOjRFB06dMHvaWT7GKqFeJOcXcjb26zQ9K+3Sf7yLLhcF7ye8mAwGvHvN4joT7/mq+1pXPf27+xIyCLQy5NqfjVpHdWMmkHVK7pMEZHz4qwCs5ycHG6++Wbef/99goKCivebTCYiIiJKfObMmcOQIUPw9fU9ZZ9Wq7XEeSf2KyIiIiIil7iseNj/G66i/JM2+ed0zCd+e4J7Ft3D7N3ut2K+1uk13uzyJhajhcdXPcMHCbP5z6r/YM/IwFq7Njid5CxdhtHLCwBHTi4um+383xvgaTHx3LX1Afhg+T4ONX0EzF5weBXs/OGC1FCebFuXk7t0IRm5Rdw/cwNPf7uFApuTdrX8WDBqIN8N/IKXOrx03t5WKiJS0c7qLZmjRo3i2muvpXv37kyYMOGk7davX8/GjRuZOnXqv/a5dOlSwsPDCQoKomvXrkyYMIGQkJCzKU9ERERERC4227+DBU9xZENdnAF1CH/sUbyaNCk+nJCZz5+HMjBasqhftRCA5pWas/jQYrJt2QCEeYcVt7+/6f1MXDWRuxvfjcUvnNBp/6XSwXjyN2/BHOZul/reu2TO+4Ggm28iaMgQTAEB5/UWu9ULp3OdMJbuSmHc0nQ+bDMKlr8GC8dC7V5gOoupi04HHF4Nu+bj2rUAQ7Z7FF5hppHCTPf4B8s1j+A18GEA8t5/FPv6b0p1Y/V3Yg10QlgdcuqMw1lYCLZ8vFfdj9nThaMIchOP/3qYcwAyD3izq0Ytfmp0DxaTgds6m/gl9VlWpzzOdX7XEWA9v89TRKQinXFg9sUXX7BhwwbWrv33ufjTp0+nXr16tG3b9pTtevXqxcCBA4mJiSEuLo5nnnmG3r1788cff2AymUq1LywspLCwsHg7KyvrTG9DREREREQupP3LcTkh92AersK1GDw9Sxz+Ys1hzL7b8K7yJW9vacRHUR8xuPZgOlbpSGXfyqW6axvVlu/6f4fZaOZg1kFu/elWHmr+EAMHDgTA5XSSvXgJ9sREUl5/g2PvTCNwwACCb70Fj+rVz8stGgwGnruuPiv2/saSncksa3ETnbxnQFocLHweer4IpzMiy5YPFvcoORw2XJ8MJnWzgbxjHkR3zMFggOx9vqRscc/iSYn/mc9y2gNw7+cr8UrwLtVlaMMswhrmcDQ5jfj3nsQ3JwOAat0LMIfasGeaObo0vNR5P4Y0o3qIN5OHNePLA6+SXpjO0sNLuS72urN5RCIil4wzCswOHz7MQw89xMKFC/H8x3/g/ik/P5/PP/+c55577l/7HTp0aPH3Ro0a0bhxY2rUqMHSpUvp1q1bqfaTJk1i/PjxZ1K6iIiIiIhUFKcTDq6gIM2Cq9COKSCgxPplBTYHn646iKOwMiYj2J12soqyCLAGlBmW/c1sdP86883ub0grSGPOnjlcX+N6zEYzBqORmLlzyPrhR9JmzKBw927SP/+c9FmzqDJ1Kn5du5yXW60R5ssd7WJ477d9jF1wiIUdn8Cy4AlY/a47MPvbl7eAhy8EVHF//Cu7g7VdP8GhVTBmO3gHk3I4heSllTEezQFg9MFRbI6sydWWrXQI3gzAemMMv2xPAqC+Zy1qBMeXqutjSy9WFjbCVmDmLt/v8fcIBGC8cwTxhWGEODK4I3h+iXPi/KMI6N2PHwY2xtdqpn7UeGoF1qJfzX6aiikilz2Dy3X6K1DOnTuXAQMGlBj15XA4MBgMGI1GCgsLi499+umnjBw5kqNHjxIWFnayLk8qLCyMCRMmcM89pV/FXNYIs+joaDIzM/H31+uMRUREREQuCk4HxP/pno65cjIusy/5vb/DnpKKf6+exc1mrj7I/83ZSuVALz68qwq1g2udUSDjdDn5dPunXBt7LaFeofwR/wd1gusQ7BkMgMvlIm/VKlJnzKBg4yZqLlmM0ceHwrg4CrZuxb93bwweHuV22zmFdrq8tpSU7EKeuiaWe9NehZxkGPHXWmb2QphQejTXiXL7fci3C3Oo89lk/IryyDNb+aHzzcTeNLjMWTjnQ0yoD21qhLAxeSOHsg/RN7avgjIRuWRkZWUREBBw1lnRGQVm2dnZHDx4sMS+22+/nbp16/Lkk0/SsGHD4v2dO3cmNDSUb74pPX/+3xw5coSqVasyd+5crr/++n9tf64PQUREREREyllOMkxtBfnpx/fV6ws3flaimdPposubC0jxe42rK3Xgvev+D8vZrPX1l51pO7ll/i0EeQbxca+PifSNLHHckZlZvJZZ/NPPkDlnDuawMMIeHUNg//5nfd1/+nbDEcZ8tQlvDxNLHu1MRMAJM3TshbBtLmQehswjkHXU/dMrmILY7nyV1ZAdMxdz68bvADgUXh3P8RPo2Ln5BQ+s8mx53DDvBg5lH+LpVk9zU72bLuj1RUTO1rlmRWc0JdPPz69EKAbg4+NDSEhIif179+7lt99+Y/78+f/sAoC6desyadIkBgwYQE5ODuPHj2fQoEFEREQQFxfHE088Qc2aNenZs2eZ54uIiIiIyEXCYYdDK2HPQkjaCsO/da/T5RMGVn9wOSG2M67qXchOCMA7JaV4UX6ARTuSOFq4Hq/QZJKdG4qnWZ4tD6MHlXwqUcW3CuHepUdxnbjwv7VWLcxhYdhTUjAY3Qvou1wubEeO4BEdfU51DGhWmc9WHWTDoQwm/bSD/w5tdvyg2QpNbizRPq/Izid/HOTdZXFk5BURFVqXIZafKezTnx4vPIXRaj2nes6Wh8mD/jX7M2fvHPrW6FshNYiIVIRz+6/RSXz44YdUqVKFa665pszju3btIjMzEwCTycTmzZv5+OOPycjIICoqimuuuYb//Oc/WCvoPwoiIiIiInIKLhccWQdbvoZtcyA3+fixlJ0QXs8dmt32PfhXAZOZgi1bOPrkEIwBAdT+Y2VxQPXB8v3YsxvQ3n8M/ZpFnvMIqtjAWD6/9nNcLhcmo4mvdn3FkewjPNT8IUzGklMZQ+64neDhN5P18y/493T/7pK/bh0Hb7kV306dCB5xG95XX31WNRkMBsZf35Drp/7Odxvj6d+0MmF+VuIz8t2fzILj3zMKSMrKp2/cCtoajexs2YOHhzWjwbi+eISGnNPzOBfH8o8R4hnCXY3v4rYGt+FhKr9pqyIiF7szmpJ5sdKUTBERERGRC6AwG35/E7Z8AxknLNXiFQS1e0PNblDrGvAs/b/JU6dPJ/nV1/Dt2pXod6YCsPFwBv2n/o7FZGT5E11LTlssBwk5CfSZ0we7087LHV6mT2yffz3n2P/eJ+XNN92hIODToQNVpk7BeJZrnD397WZmrTlcYp/VXkhkbhqZVh/SPf2xOGy88dsUamYexWmxEPP993jHVD+r65WX7KJsBnw3gAYhDXih3QsEWAP+/SQRkYvIBZ2SKSIiIiIiV5jsRPCLcH83e8H6jyHvGFh8oO610OgGqNEF/mXdsbw1awHwbtWyeN/7y/dhDthAWOU1rEopon9A/3ItPdI3kkntJ7EifgW9Y3rjcrlIyE0gyjfqpOeE3n0X/tf0IO2TT8mYPZvc5ctJeOb/iHrl5eJRcWfi0R61KZj/I9UP7aBqQRpROcfwz3PPttk/5E4MN3QmKtAL67JJ4OFB5OOP41W9Wql+juUf4/3N79MpuhNto9ry5c4vWXhoIb2r92ZQ7UEsP7Kcj7d/TJOwJoxuNpq4jDgmrZlEmFcYkzpMIs+Wx4O/PgjAtG7TsJgsPLHsCdIK03ju6ueo5l+NN9e/ybbUbdzR8A5sDhupBansydiDxXj2a8qJiFyqFJiJiIiIiIhbQab7rZZHN8DR9e6f2fHw8FYIjAaTGbo9B1Y/qN0LPHzYk5TNI++s4oYW0dzWtnqZ3bocDvLWrwfAu6U7MDuclsdPWxKwVt5CtusgibmJ5+WWesX0oldMLwA+2PIB729+n0kdJtG1ateTnuNRvToRzz+Hb7euHL7nXrJ+/JGgYUPxbtHijK8f5Cjg/riF2BMSSuw3BgTQKtqf0EbulxJkv/wS1lq18KhSpVQfLpeLUYtHsT11Ow1D3WtHH8w+yOqE1TQMcW+n5KewOmE1XiYvd39F2axOWE0VX3d/TpeT1Qmr3f3hHj23IXkDSXlJ5NpyAfcLE1YnrKZfjX70rdGXL679ApvThrfF+4zvW0TkUqfATERERETkSlaQBfMfh/gNcGx3iUMuF9hyLVj2r8DQbKh7Z4sRxcdtDicPf7mRbfFZbD26jcqBXnSvX6nUJfLWrMGZk4PRzw/PunUBmP77fpwuaOp1D4PaZNA6svV5u0UAh9PBqvhV5NnzOJZ/7LTO8W3XjsgJ/8Ho6XVWYRmAKTCQap98TPpnn+HZqDEe1ariER2NKTCwRDu/Ll1Knbv8yHKsJiutIltxX5P7eHfTu9QNdj+/62Kvo2FIQ2oE1gCgZaWWvNzh5eIXHVTzr8bLHV4uDrusZisvd3jZXZPBvZbbM62focBeQGXfygCMbDiSfjX60TisMQB1guuc1T2LiFwOtIaZiIiIiMjlzumAY3vco8biN7inWQ6d+dcxJ7xcDQqz3NuB1bD5NCRzn4XMNQcpOpqEd5urqfrhh6UWv//voj28ueh4yOZrNTN3VFtqhvuVaGdLTubg8FvwatiQym+8TmaejTYvLSavqIhP7mhDx9phXAg2p43FBxfTK6YXDqeDD7d+yE31bsLH4nN65ycnU7T/AD6tW52yncvpJGnii3hf1QL/3r3PqtY5e+bw/MrnqeZfjTnXz8FsNOPChdFw5tNCRUSuRFrDTERERERESspPh/2/HZ9WGb8RirJLtslLA+9gMBqh10vgE0bOITupn31N3urVxYveAwQOHlwqLNsWn8nbS/YA8MaQJnyx9jBr9qdx1yfrmTuqHQFeFuxpaZiDg7GEh1Pt4xkYvd2jnWauOUhekYPQmjOZsW8uISGP0iCkwXl9JAAWo6V4eubbf77N9K3TWXJoCTOvnfmvQZTt6FEO3HILjoxMqn3yCV4Ny67X5XSSOHYsGV9/Q8ZXX+HVtCmWyMjTqi/XlstvR36jd0xvelTrwTub3qFzlc7YnDYsJgsGzu0NoiIicvoUmImIiIiIXMry0tzrjuGCmt3d+5J3wle3lmxn8YbIplC5OVRuAWYrLqeT/I2b8Gp2EwaDAfuW2eStWgWAd+vWBAzoj8/VV2OJcC/6X7hvP67CAoy16vDY15uxO130ahDBgGaV6Vg7jOvf/p39x3J56Is/ebu2nfjRo4kY+zwB/foVh0ZFdiczVhwAYwE2yy7WJjnws5QckXYhdKnahXlx87itwW0YDUYcTgcmo+mk7c3h4Virx5C7ciWH772XGj/MKzWtEsCekED2wkVgNBI5ccJph2U5RTn0m9uP5PxkIn0iaRrelHn95+FpLt83h4qIyOlRYCYiIiIicilx2GHDx3DoD/cIsrR97v2VrzoemEU2cYdjUc2OB2ShddyL9uNeRP7Y1HfInDMH29GjVP/qS7waN8avZy/sycn4970ejyqVS1zWnpLC4bvuwpGezqrbn2RHgg/BPh5MGNAQg8FAqK+V/916FYPfXUn2b8s5+PrHmOw2MufPx//664tHqH2/KZ7k7EIq+QfwZb95/Jmyjqr+VS/U0yvWJKwJ8wbMw9viTZGjiLt+uYsu0V24rcFtpUbTARgsFipP/i8Hb7mVwAEDygzLACyVK1P1w+kUHTx4WtMx1yaupXZQbQKsAbSr3I71SeuxO+0ACstERCqQAjMRERERkUvJz0/Dmv+V3BccC2F13dMoDQbw8IZ7lpVo4sjJxXZ0H551amMwGMhbvRrb0aMYfXwoOngQr8aNMfn6EHrffWVe1uDpiaVKFWxHj9Js2guEdX+KsTd1JdTXWtymYeUAXh7UmFfeTyTLZMXStCV13n67OIByuVx8sNwd8A2/OorqgdFUD4wux4dzZv5eEH/BgQVsSN7Anow99IntU7xw/j+ZfH2J+fILDB4egPuZGjwsGAwGEp4fS0C/fvhc3RrP+vXxrF//X6//xro3+GjbRwyvN5wnWz3J4y0fx2qy4mHyKL+bFBGRs6LATERERETkUpEaB+s+dH9v/whU7+AeReYdXGZzl9NJ3urVZMyZQ/bCRVgiIoid/yMGg4GQe+8hcMgQ/Lp3w+jl9a+XNvn5ETHtXb4ecjcbrJVo1bo+1zWOKtHGlpREv6aV2X7d1Ywxmsj2D+bLlHwaVnYHQMv3HGNnYjY+Xnl8En8be5e05bVOr2ExWc7tuZyjvrF9ySnKoap/VcK9wzmSfQSTwUSkb+nplH+HZbakJA7fcy/WGjVwFRWRvXAh2YsXU3PxIkx+J59iml6QTnxOPA1CG3B15NV8sv0TwB0m+nlc+KmpIiJSNgVmIiIiIiKXitXvgtPunnrZfdxJm9lTU0n79FMyv/see0JCiWOO1FTMoaH4tmt3xpef8vtB3m4whBAfD37p1wB7Sgrps2YRev/9ZHz9NUmTXqLK1Ck80bM9OxOyWbY7hXs+Xc/3D7QjxNfK+3+NLmvd4BhrcwtIzkuu8LAMwGAwcFO9mwDIs+Xx4K8PkpqfypSuU2gU1qjMc4r2H6Bw714Kd+5092GxEPXyS6cMyzalbOK+hffh5+HH9wO+p23ltswfOJ8o36iTniMiIhVDgZmIiIiIyKXimgnu6ZfV25c65MjKAqcTU2AgLoeD1P+9D04nRn9//Pv0JnDAADwbNy5zfa7TsflIBu8sjQODgQkDGhHsZebA7aMo2LKF7CW/FgdHuX+swrdDByYPbUa/qb9zIDWP+2du4Nlr67N8zzGMBhjX5VYKjN3IKco5p8dxPuTYcjAZTBgwnHRqJoDP1a2J/M9/SHj6aQxWK1WmTMG3Q+k/F3AHZU3CmlA7qDZeFi/8PPxIzksm2i9aYZmIyEXK4HKd8L7oS1RWVhYBAQFkZmbi7+9f0eWIiIiIiFwweRs2kP7ZTLIXLyb4jtsJf+ghAFLenoK1Zg18u3bFaLX+Sy+nVmh30Pft39mdlEPfJlG8PawZAFm//EL8Y4/jKioCIOSuuwgb80hxKLcnKZv+U1eQW+QgwMtCZr6N3o2C+e/QFhf1Ol359nyOZh+lZlBN4nPi2Z+5n3aVyx6Rl/fnn5gCA7HGxJR5/Id9P/D08qf5sOeHtIxoyeGsw0T5Rp3yjZwiInLuzjUrMp6HmkREREREpDwlboX0g8WbRUeOFn/P/3MjWfPn4yospGDbtuL9YaMfwL9373MOywD+u2gPu5NyCPX1YPz1DYr3+19zDVVnfIRX06aEP/Uk4Y+OKTGCrVYlP94a6g7XMvNtAMTGbKPzl535YMsH51zX+eJl9qJmUE0OZx/mph9v4uFfH2brsa1ltvVu1uykYZnL5WLZYffLF9YnrQcg2j9aYZmIyCVAUzJFRERERC5mLhd8/wAkbYNB00nfnE/i+PHEzv8Ra0wMAdf3xRYfT0D//ng2bPDv/Z2hjYczeHdZHAAT+jci2KfkyDCvZs2I/vyzk4ZAPepXYkyP2ryxcDdtYkM4UvAL2bZsrKZzD/LOt0ifSOqH1CcpL4lQr9AzOrfAXoCn2ZOXOrxE26i29KvZ7zxVKSIi54OmZIqIiIiIXMy2zYGvR4CHLwV9ZnPg1ntwFRURMfZ5goYNO2+XzS20s/bQYf7vly+Jj4/l+ka1aNRgPZtSNnFr/VtpGdGSN9a/wcztM7mj0R2MajrqpH25XC7W7E+jToQf/l5mNiZvpJp/NUK8Qs5b/eUl15YLgI/Fh30Z+wjzDvvXt1mm5KVwy0+3cFuD2xhW9/z9GYmIyMmda1akEWYiIiIiIhcrhw0WvwCA86r7iH9uIq6iInw7dSJw6NByuYTL5SIpq5AdCVlsT8hi29FMdiRmcyAtE5+aEzH45hMc+BDj+jbg+VUfsvTIUjpV6UTLiJaYDWaKnEVkFmYCsPzIchwuB52jO5e4hsFgoHVsCMfyjwHBNK/UvFxqvxB8LD4ArE5YzcO/Pkyj0EZM7T4Vi/Hkb/f8Lu47juYc5fMdn9O/Zn+8zF4XqlwRESknCsxERERERC5WGz6BtH3gHUryyiIK9+zFFBpK5IsTz+ptlzaHk30puWxPyGR7fBY7ErLZnpBFWm4R4MISvByPwDXkZY7A5QzFXNAQq3cSz13XlCAfD26ocwMdozsWB17D6w/nhto3EOgZyLH8Yzy74lnSCtJ4o/Mb9KjWo9T1H/r1IRJyEnil4ytcFXHVuT6dC8rPww+Hy0Gho5ACewEWj9KBmcvlwmAwMLLhSCxGC12rdlVYJiJyiVJgJiIiIiJyMSrKhWUvA5AbOpT0V74GIGrSJMwh/z6VMavAxs6EbLbHZ7L9r9Fju5NyKLI7T2jlxOSzF5O5CrHB4djCjpDqPMagTvE8dfVQ/Ly6l1hrrGOVjiWuEewZXPzdaDByXex1rElcQ6cqnQA4nH2YaL9oALKLstmfuZ+cohyq+Vc7q0dSkeqH1OfDnh9SO6g2HiYPjuUfK7GumcvlYvwf4wnzDuP+JvdzW4PbKrBaERE5VwrMREREREQuRqumQU4SBFbD68anCEr0wWAw4tuhfYlmLpeLoxn5J4wYcwdkh9Pyy+zW12qmXqQf9SP92WqfzO6cP3i42aOMbNyJtYneHMk+Qs/qPfG2nNmi/FaTlcdbPk6hoxAPkweLDi7isWWPcW+Te7m3yb34efixdMhSth7bSph32Fk/lorUMLQhAAv2L+C5Fc/xYocXi0fSrUtax+w9szEajHSO7kyDkPJ/AYOIiFw4CsxERERERC42hTmwYjIuF9Dl/zD6+BHxzDO4XC7yiuws2JrIlqOZ7nXH4rPIKrCX2U3lQK/icKx+lD82yx4WHvmGXjG96BPblq939+DNdVsxGN2jzlpGtKRlRMtzKv3vEWlrEtfgcDkosBcAcCDzANX8q11S65edzIbkDRQ4Clh4YGFxYNYyoiVj24zF7rQrLBMRuQzoLZkiIiIiIhejw2tImzyRvMLqRP7nBfDzZ/aGI7z+yy6SsgpLNLWYDNQMdwdj9SL9qB/lT/1IfwK9PUjMTaTAXkD1gOq8t+k9pmycQvPw5nzc+2MKHYU4Xc7zts7W0sNLaRfVjtSCVHp804MonyjmDZiHh8njvFzvQrE77czdO5cBNQewPmk9Cw4s4JnWz2A2ajyCiMjFQm/JFBERERG5DBXkBZC8YD8u224S6zbjeVsNtidkAVAlyItr6kcUB2M1w33xMBtL9fHVrq+YuHoi3ap2443Ob9C/Zn8yCjMYXHswQIn1yc6Hv9+WeSDrAEDx+l+XOrPRXPwM5+2bx9y9c4n2i+b2hrdXcGUiIlJeFJiJiIiIiFxMEjZBRGPSZ83CZbOxt2YzRh8MBUMWfp5mRnetyW1tq2M1m0qdmpibyFe7vsJsNHN/0/tpGt4Up8tJri0Xh9NBJZ9KPNnqyQt+S60iWvFp70+pGVjzgl/7fMouymZ3+m66V+3OTfVuquhyRESkHGlKpoiIiIjIxWLfMvikH7aoq3gxeBIZX81mSeVm5Hr5MfzqajzYrRbBPiVHaNkcNg5nHyY2MJbVCau585c78bX4sviGxXhbvInPiSfKN6qCbkhERKRiaEqmiIiIiMilzOWCvYvh9zfh4O8UZplY5jDw0b5kiO1A93qVeLpPXWqE+ZY6dVfaLu5eeDcWo4WfB/1My4iWXF/jejpW6YjFZAFQWCYiInIWFJiJiIiIiFQEhx22z4Xf34KkLQAU5FvYs6gSOX5GWveFh25oTdsaoSVO23ZsG9/s+Ybnr36e6gHVcbqcOFwODmcfpnpAdSa2n3jh70VEROQyo8BMRERERORCczrhvY6QvA0Al8WH+YbuOH+Jp2bRUar7mJn5cHfMniUX5U/JS+HWn26lyFnEoFqDaBjakI96fkS1gGpYjJaKuBMREZHLUulX6YiIiIiISPnLSYHCHPd3oxFiO4N3CPntn2K43wccXmyjZtpRXL5+NHh3Somw7HDWYTILMwnzDuO2BrdxTbVrqORdCYCaQTUVlomIiJQzLfovIiIiInK+2Ath9wLYOAv2/AJdn4UOY9zH8jM4VuBi+MdbaL3kK4buXozLbKba9On4tG5V3MUvB37h2RXP0jm6M690fAWny4kBAwaDoYJuSkRE5OKnRf9FRERERC4mLhfEb3CHZFu/gfz048eOrCv+mljkyU0friLlaDK9D60BIOo//ykOy1wuFwaDgQifCIocRSTnJZNvz8fL7HVBb0dERORKpMBMRERERKS87JwPi8dDys7j+/wiofGN0PQmCKsDwOG0PG76YBWH0/KJCgsh7NOZBG1eQ+CA/gD8duQ3pm6cyv96/I/GYY35qNdHNA5tjMloqoCbEhERufIoMBMRERERKS8GozssM3tC3eug6TCI7QInBF1xKTkM/2A15iMH6WUp4NknR1AlyBsa1QLA5rTx6tpXOZB1gA+2fMCjVz1Ks/BmFXVHIiIiVyQFZiIiIiIiZ8vlgoMroXo793bN7nD9FKh/PXgGlGq+MzGL4R+sxpaaypQ1HxGal05A3zrQrRs5RTkcyz9G9YDqTOowiR/3/ciopqMu8A2JiIgIKDATERERETk7TicseArWvAd9XoNWd4HJDM1vKbP5tvhMbv5gNRl5Nv675StCs49hqVoVr+bNySzM5J6F95Ccl8zHvT6mYWhDGoY2vMA3JCIiIn8zVnQBIiIiIiKXHKcDfnjIHZZhKDHlsizpuUXc/cl6MvJsNKsaSNuXx2KtVZPod9/FHBSEy+Wi0FGIzWkjx5ZzYe5BRERETkojzEREREREzoTDDnPvhS1fu9cs6/eOe62yk3A6XYz5aiNHM/Jp5GVjxu2tCPCyEPrddyTlJ5OcspnGYY15/5r3ySzMpEZgjQt4MyIiIlIWBWYiIiIiIqfLXgSz74Ad88BohkEfQIMBpzxl2rI4dq7fwaN7l9AtaSvGToHQuTNJ+cmMWDCCzMJMpvecTv2Q+oR6hV6Y+xAREZFTUmAmIiIiInI6bPnw1a2w5xcwecCQT6BO71OesnrBCsyvvs37RzdjwgVAwdZt+HXuTIA1gAifCEwGE4HWwAtwAyIiInK6FJiJiIiIiJyOgythz0Iwe8Gwz6FG11M23//K6/h/+AEd/9r26dSR0Dvv5HANf7YfXkan6E5M7TaVPFseYd5h579+EREROW1a9F9ERERE5HTU7Ab9psLwb8oMy1wOB9mLF+Oy27E5nLyTGYTDYGRdrVZEfTObqu+9R3LtMO74+Q4eWfoI65PW42PxUVgmIiJyEdIIMxERERGRk8lLg5SdUK2te7vZzWU2y12zhsTnnqfo4EGiXn+NdwyxfG0PZ/V1z/Pxk9cREOoDQBW/KrSObE1yXjK1gmpdqLsQERGRM6TATERERESkLDkp8Gl/SI1zjyqr3r7MZi6Xi5TJkyk6eBBjQABb9iTwXiJgMPD0rR2JCfVh8aHFpOanMqTOEF7u8DI2pw1vi/cFvR0RERE5fec0JfOll17CYDDw8MMPF+/r3LkzBoOhxOfee+89ZT8ul4vnn3+eyMhIvLy86N69O3v27DmX0kREREREzl5WPMzoA0lbwdMfvE/+9kqDwUD0lCkE33EH1q+/576MaABGto+hd6NItqVuY8zSMUxYNYH1SeuxmCwKy0RERC5yZx2YrV27lvfee4/GjRuXOnbXXXeRkJBQ/HnllVdO2dcrr7zC5MmTeffdd1m9ejU+Pj707NmTgoKCsy1PREREROTspB+Ej3rDsd3gXwVu/wnC65Zqlr14MakffgSAKTCQgEfGcP+cXWQX2GlRLYgne9XB5XJRP7g+g2sNZkCtATQJa3Kh70ZERETOwllNyczJyeHmm2/m/fffZ8KECaWOe3t7ExERcVp9uVwu3nrrLZ599ln69esHwCeffEKlSpWYO3cuQ4cOPZsSRURERETOXGocfHw9ZB2BoOpw2zwIrFqqWdbPv3D00UfBbscjpjp+Xbowft42tidkEezjweRhTXht/csEWAMY1XQUz7R+BqPBiMFguPD3JCIiImfsrEaYjRo1imuvvZbu3buXeXzmzJmEhobSsGFDnn76afLy8k7a1/79+0lMTCzRV0BAAK1bt+aPP/4o85zCwkKysrJKfEREREREzknyDvfIsqwjEFrbPbKsrLBs/nyOjhkDdjv+112Hd7v2TP11L7PWHMZggP8ObcrBvE3M2jmL9za9x+703ZiMJoVlIiIil5AzHmH2xRdfsGHDBtauXVvm8Ztuuolq1aoRFRXF5s2befLJJ9m1axfffvttme0TExMBqFSpUon9lSpVKj72T5MmTWL8+PFnWrqIiIiIyMltmws5SVCpIdwyF3zDSjXJ/P574p96GpxOAvr3x/jks9z2yQZ+33sMgAe6RtOmRhBmY1sebPYgVfyqUDuo9oW9DxERETlnZxSYHT58mIceeoiFCxfi6elZZpu77767+HujRo2IjIykW7duxMXFUaNGjXOr9i9PP/00Y8aMKd7OysoiOjq6XPoWERERkStU56fAwweaDQfv4FKHiw4dIv7pZ8DpJPCGwWwech9Pvb2C9DwbnhYjj/aqzKL0iUxYVY+xbcZyV+O7KuAmREREpDyc0ZTM9evXk5ycTPPmzTGbzZjNZpYtW8bkyZMxm804HI5S57Ru3RqAvXv3ltnn32udJSUlldiflJR00nXQrFYr/v7+JT4iIiIiImfs4Er3umUABgO0e7DMsAzAo2pVIp5/Ht8bb2Ryk8HcM/NP0vNsNKzszw+jO1CnWgY703ay5NASkvKSyuxDRERELg1nNMKsW7dubNmypcS+22+/nbp16/Lkk09iMplKnbNx40YAIiMjy+wzJiaGiIgIFi9eTNOmTQH3iLHVq1dz3333nUl5IiIiIiKnb+9i+OJm8A6Bkb9AQOUym2V+/z3eV1+NJTyco+178uCRcPatO4LBAHd3jKVPcwdRQUZqWjoysf1EGoY0JMLn9F6AJSIiIhenMwrM/Pz8aNiwYYl9Pj4+hISE0LBhQ+Li4vj888/p06cPISEhbN68mUceeYSOHTvSuHHj4nPq1q3LpEmTGDBgAAaDgYcffpgJEyZQq1YtYmJieO6554iKiqJ///7lcpMiIiIiIiXsnA9f3waOIqjUwB2alSHtk09IenESHrGx/PrgS7y8/DA2h4tK/lbeHNKUPMtG7vjlSdpXbs+bXd7kutjrLvCNiIiIyPlwxov+n4qHhweLFi3irbfeIjc3l+joaAYNGsSzzz5bot2uXbvIzMws3n7iiSfIzc3l7rvvJiMjg/bt27NgwYKTrpMmIiIiInLWtn4L394FTjvUux4GTQezR6lm+Zs2kfTiJAB+Da3LhF8PgsFAzwaVeGlgY4J8PPgzOQQXLpw4KXIU4WnW/34VERG5HBhcLperoos4V1lZWQQEBJCZman1zERERETk5DZ+Dt+NApcTGg2B/tPAdPL/D/mP519m8fYEPqjRHS8PM2P71mdg8wimbZ7GiAYjCLAGsC11G3WD6mIyll6eRERERCrGuWZF5TrCTERERETkorV2Ovz415vWm98K170FZYRcGd98g7lLNyYsPcwXRQ2hZkMaVQ7graFNqRHmy9PLn+aHfT+w5dgW3u/xPg1CGlzY+xAREZHzToGZiIiIiFz+nE7Y+aP7e6t7oNdLYCz5wniXy0XK5MmkTnuXuPCP+PrqezCYTNzTsQZjetTG/Fe2dkfDO1ibuJYRDUZgMBgu8I2IiIjIhaDATEREREQuf0Yj3PgZbP4SWoyAfwRdLpeL5NdeJ236dACWhNUnLNCHN25sQtsaoaxLXMeLa17knW7vUCuoFvMHzsfDVHrdMxEREbk8GP+9iYiIiIhc8VwusBdVdBVnxuVyT8MsyHJve3jDVbeXGZbtf2FicVg2rVE/8gcMZcHDHWhbIxSny8kra19hT/oe3tn4jrsrhWUiIiKXNQVmIiIiInJyLhdsmwNTroLXasLBlRVd0elxueDnZ9xrln1+IzjsJ236x9hXKJw1E4D3mg+m3RP3887NzQnwsnA46zBGg5E3u7zJ4NqDebr10xfqDkRERKQCaUqmiIiIiJRt31JYNA7i/zy+b+YNcOdiCK9bUVX9O6fTHZSt/8i93XBgmW/CzC2088K87SxNj+RlryCWX92XB18YTWyYL4WOQp5b8Ry/H/2dz/p8RmxALGPbjL3ANyIiIiIVRYGZiIiIiJSUsMkdlMUtcW97+EKbB+DQSvAOgZAaFVreKTns8N0o2PwFYIB+U6DZ8FLNNh1I5a13vudXYzgG3xDWjZ/Gk30a4WE+PgEjPieefFs+u9N3ExsQewFvQkRERCqaAjMRERERKWnvIndYZrTAVXdAx8fB9//Zu+swqao3gOPf6Zmd2U62l4alu7tLUkVUwFYM7O7Wn4hBWZRYhIJFSinN0g0Lm2z37vTc3x+DiysY5KK+n+eZZ+69c8657x0Ql5dz3hMKjgrQ6M86W+uK4HLAolth/zeg0sDwD6DxyCpN3B6FGWsOo7z2Ig+m7cCn601c//A42tcKBiC5KJn0snS6RHfhne7vkFycTOuI1tXwMEIIIYSoTlfoTztCCCGEEOKyKcuBjO1Qr7/3vO2dUJwBHe6BoITT7fQ+v+mTCwtvgn6vQ3jDyxvv2ThtMH8sHF7qTeqNnAkNBlVpkllk5YGvdtLi648YnLodj0rNU/3qUuM3ybLrf7gep8fJrH6zSAxJJNgUXB1PI4QQQohqJgkzIYQQQoj/KlsJbHgPNk4BxQP37QTfCG9ibNCkP++7/Ck4vg7mDIFxP0Bo3csS8h+yl0DeYdAa4Zp5UKdXlY9/2HOSxxftodjqpKR2B/oVHCD+hefw69unsk2sXyzNwppR6iilhqXGZX4AIYQQQlxJVIqiKNUdxIUqKSnB39+f4uJi/Pz8qjscIYQQQogrm8sO2z6BdW9CRb73WlRLuGoKhDX4e2NYC2H2YMjaA5YIGP9D9dc2K0qDolSI71h5qdzu4rkl+/h66wniSrKxNGrI5GubE+ejQu3jg0fxMDlpMjG+MYyqO4oKZwUatQaDxlCNDyKEEEKIC3WhuSKZYSaEEEII8V/hccOe+bD6ZW9iCSC4NvR8BhoMAZXq749lCoQbFsPsQZCz35s8G/8DBMZfktDPyloIWz+GTg+AWg0BMd7XKTvTipj4xQ4ycop5esscWhQmEzduBn4h5so2y1OWM3PvTLQqLe1rtCfaN/ryxS+EEEKIK5YkzIQQQggh/isWT4Bdn3uPLRHQ/XFodv35F/E3B8ONS2DWAO9yyFmnkma/SVpdMuV5MHeod4abrRj6vFj5kdujMH3tMd5ecRi108HL2+fQOPsAKoMBtcsFQLG9GD+9H33j+rKxzkZaR7SWZJkQQgghKqn/uokQQgghhADAXuqd1fRP4rSdPm46Ggz+0PNZuHcHtBx3wTte2gxBHO73GWXmOChOpXxqN0pLLvF3VJoFswZ6k2XmMO9znZJZZGX0h5t4c9khNA477+/9lMaZB1CZTMTMmI6lU0cOFx5m1Lej+HDPh6hUKp7v8DyDag76kxsKIYQQ4r9GZpgJIYQQQvwVWzGsnwSbpoFaC48cA52puqP6c7mHYdXz3iTfjYu9yy1rdoX794Lxz+t4KIqCMyMTV3YWPi1bApD56usULVpERXQ8WSExHPGrwXZtKFsUfxxqLRE8xJf6F7HZ9Nw+YyfvX9eCRjV8vTHU7ApxnUCrv/DnKkrzbjRQkAy+kTB2CYTUAeC73Zk8sWgPJTYXgWo3Hx77AvPxfah9fIj5YAY+rVoBsDNnJyfLT/Jd8nfc2PBGjFrjhcclhBBCiH8VSZgJIYQQQvwRtwu2z4Q1r54ujh/T5nSyzFYCX1wH9QdCg8HgfwUs6SvJ9Ma741PvzpcqNeQePF3M/3fJMo/Dgf3IEQzx8ajNZqx795E6fjye0lI0gYFErVnL51vSOL6vlOtLSzAf2E0tdlML6AfsCa7Jy33uIzqsNj8UPUJGdjonHBUMn7qBtzu5GLh5MvwyGQx+ULsn1O0PdXqDT9C5P1tBMsweAsVpEBALY7+FwHjKThX2X7A9HYCmMQFMHtUEw6SNlKYeI+bDD/Fp0Zw1aWvoHNWZq+tdDUDf+L6SLBNCCCHEWckumUIIIYQQv6cocGQ5LH/KW5sLIKQu9H4RanUH7akdFPcsgIU3n+4X1QoaDoE6fSC4zgUvdzwn1kL4+W3YPANcp5Zh1hvoLegfVv/UYylUbN6M7cBB7AcPeN+Tk8HlIubDD7F07oSrsJAj7TuAVktpRAz3d7iDDIcGH6eVuu5i2lBIg7IsIvPSsaQn49N/APEvPItKpeLENddi3bWL5FpNeSmhP2ZfG8+HrqG9ayvqitzTsWoM0GkidLr/78/Uyz3kTZaVZXk3KrhxCfhHsSO1kIlf7iQlvwKVCu5rF8ldfRuiNxpQ3G4cqakYEhJ4e/vbfLL3E65vcD2Ptnn04n73QgghhLjiyC6ZQgghhBAXk6LA59fC4aXec59g6Pb4qXpfuqpt4ztBv9dg/xJI3QgZ27yvFc+A1uidjTb229Pt7WVgsFz8mDdOhbWveZeOAsS2x9nkXqx5GmyfLcWYmIZf796oVCoyH34EV25ule4af3/cRUUAlOjNrH30HaYfc1LkAhwQF+zDnV0bM7xFNHrt6RK4iqKgOByoTu2uqYuNxbp/PzWP7eK+UTfy9BEYk3M9sYE38/EQDXUK18PBHyD3AKx9HQ5+D7evA7Xmr5/RXgqOMghrCDcuxu0TyrSfjvD2yiO4PQpRASYm9U8g7KVHyfkpkqi3/odKq8WQkABAw+CGqFVqgk3BKIpSGbMQQgghxNlIwkwIIcSls+5N2PIRJHSBptd6l2MJcaVTqbxJmWM/Qbs7ofODYPQ/e1vfCG+bdnd6C9Ef+Nb7ytjuTe64nafbWovg9XgIjIeIxqeWb/4uadPpfrCEeo+3fgz5x/44zuhW0Gg4HqsV+84kbPucuLU1CXnsVajbl8K33iL/o48B8Bs8GL/evQGwdOuGu7gYY4P6GOrXx1i/PtqICHJK7bz73X4+25yK1ekGoG64hQndazOwcQ20mjP3ilKpVKgMhsrzqDffIOSuOylfv55rbxxA/bQiPn71E2wpxQwsbs1jA25g/J1PoTqwGJY+Dk2u/nvJsl+f98bFEJhAusPEAx9sYsuJAgAGNanBiz3jKLzzVmz7D+BMS8OZmUlhsIEZu2fwWJvH6Bvfl7qBdUnwT/h79xNCCCHEf5osyRRCCHFpFKXCuy3Acyph0OpmGDTJe2wr8dZVuhQzbYQ4V45y2PAemAKh7e3ea7YS7xLHwLjzG9PjgcLj3qRZjabea6mbsL89EACDv8t7aXUwtsLfzFoz+oNKTciEuwhiESSv5ui3YXic3mRVbPc8jIEubIVaUtdHoeh88JSWeu8HoNVSP2k7Kr2ekqXLyP/oI4wN6mNu3x6/AQPOGmpaQQXT1x5j/rZ0HG7vOI2j/JnQvTZ9GoajVp//TCxPeTlH+g3Ak5vDwcAYpjceSlynNrw5sin+Gpt3Ft6vs/Z+fts7u6/93ac3B0jZCNl7oc2tlWMu2ZXJk1/vodTmwqzX8MJVjRjeIgrF6ST9nnuw7d1H7MxP0NWuxYglIzhWfIzR9UfzRNsnzvs5hBBCCPHPI0syhRBCXJnWv+VNlkW3hpi20GDI6c+2z4LVL0PtXtDwKqjb949n8AhxqXjcsOtz+OklKD3pLUrfeJS3GL3R7y93kvxTajUE16o8LVmxgtzJ7+A4FoZvpxZEj2oHFfm4N67A7Sg43c9R6g3NboeWwyGyGe4lX+NxOABQmo2BmCCU1ALcy1YA3iWYmqAgjA0aYKhfD4/djkavx69fX/z69f3DEI/lljF19TG+2ZmB2+P999PW8YFM6F6brnVDL8qSRZVOR8i4seRNnUr9wjQmr3uPlcdbMvroCG4Y1JLhLaIwgHfny9Wvgtvu/TUZ8D9Agc9Hg7MC/CIpS+jLM4v3sigpA4DmsQFMvqYZ0To3KApqvZ7od9/FlZ2NKqoGGrWGR9s8yqTtkxibOPaCn0UIIYQQ/y0yw0wIIcTFV5gC77UAjwvGL4W49lU//+pG2L/49LlGDzW7e5Nn9fqf3+55QpyL5DWw7CnI3uM9D4iD3s9Dw6HeJZkXQFEUbHv3UbpqJSF33YVar6d09WrS77wLdDp8e/UkatIkVCoVjvR0FLv9jDE0QUFoAwMBsCcf9+52CeiiolAbjXhsNpwZ3sSR2tcXbejfT3Dtzyxhyuqj/LD3JL/+FNi5Tgh3d69N25rB5/W8hwsPUy+oHna3nQGLBtAstBm3NL6FBsHenTldubnkTHqb4q+/BqBca+Se7hNxRURxc6cErmsTg9/hRd5NFspP1VdTa71/htTuxY4O73PfgoOkFlSgVsHd3WtzT886kJ9H6rjxmNu1JfzppwGYu38u3yZ/y8y+M7HoLbg9bjR/d9mnEEIIIf41LjRXJAkzIYQQF9+SeyBpDkpCV5RrvkJtNFb9XFG8y6z2L/a+ft2FELx/Se72GHR5+PLGLP4bcg/DiqdPF/Q3+EOXh7xLMbWGP+8L5Hz2BfaDB9HYbVh69aysC3bi+utxZefgqajAU16OYvPuUhkzYzqWrl3xOByULl2KpVs3NNX0s0pSaiFTfjrKqoM5ldd6Nwzn7u61aRoTcE5j/Vo03+VxMWzxME6UnGDx0MXU9K/JI2sf4ccTPzK3/1yahTVj/uH5GDVGusd0R3MwmcznX+BI/TY8b27ByRJvstDXoGVMuzhubhlI6Lb/wdaPQPGg1BvI1JAnmbQ6pbKw/9vXNKNNQhDOrCxSx47DkZKCtkYNEhbMp9yiZejioeRZ83iq7VNcU/+ai/kVCiGEEOIfRJZkCiGEuLIUnoCdnwFwckcNSt5qS/iTTxJ4zdWn26hU3qLnEY2hx1OQc9CbODuwxJtIC659uu2Rld5aUA0GewusC3G+FAXmj4Ocfd7EbKuboeujYD5zVpXH4cC2Zw8VW7eiuNyox93CB+uTCZj9Pd1StgGwzaonJKE5LeICcWZk4jp5srK/ymTC0rUrmoAAANR6Pf5Dhpxxn0tNURQ2JuczZfVRfjma741NBYOaRDKhey3qR5zbD48excObW99kZepKPu3/KeHmcKJ9o8muyOZo4VES/BIYmziWhIAEmoQ2we1xM2XHFPJt+UzvNZ2OTTpim/4s7f1iWavx5dstycxcd5S9RS6mrz3GJz+rGdHyBiZcczWm/H1M2F2TTbtOADCkaSQvDm2Ev0mHMyODlHHjcaaloYuKIvjD98nUlRNriOX9Hu+zPXs7V9e7+s8fRgghhBDiT8gMMyGEEBeXywE756GkbSVrZxhFX34JQOiDDxBy661/0RnvroC+NUDv4z2fNwqOLAeNAcbMh5pdL2Hw4l/HaQN76emdJw8thaTZ0PsFCKlTpakjLY3ib7+lYstWrDt3Vs4Sc5gsjB74PBUuhVauOTTJO0y5O4E9xu7sD66JXqtmiDaPxlEBNKodQf2EcEw1wlEb/nrG2qWiKAprDuXy/uqjbE8pBECrVjGseRR3dqtFzdC/v+HGrL2z2JK1hWfbP0u4OZwbf7yRHTk7eKzNY4xpMIaTZScJMAZg0prO6FvhrOCTvZ+w8eRGZvWdhVatZeDXA8mz5jGl2UuEPP8xah8fDj/wEtN+PkFSahHgTeoZtRqsTjcWg5YXhyYytFlU5TLW1BvH4szMRBcTg8+0N5mw7zmsLivzBswj2HTuy0qFEEII8e8jM8yEEEJcWbR6aDUeVavxRAxVQK2i6PMvyH1rEp6SEkIfeODPay39plA64N0YoOSkt9bUsifg9vXegupC/BlFgb0LYeXz3pmMo72zHqnXD+r1w2O3Y92yhYqtWwm6/no0/v4409PJe/e9yiFsFj+2B8ayJ8YDvj/S1HwNwzo8wGt7bqGebyj1nJ3JS04jt7yEBbYgFhwDjuVg0uXTrmYWPRuE06tBOBH+xrPHeAl4PArL9mXx/uqj7MssAUCvVXNt6xhu61KT6ECfP++veJi+azrbs7czqdsk/A3+/HjiR/bn7ycpJ4n+Cf25o8kdOD1O2kW2A6CGpcYfjuej8+Hu5ndzd/O7ASi0FaJCRZhPGKFWPbajR1EqKkj8ahoLXniBbSlFTF97jJ8O5mB1umkRG8Dka5oTG3w6bmdqKq7cXPRxccTOmU2Zvx77bjsOt4N8W74kzIQQQghxUcgMMyGEEBdPRQH4BFG2fj2GOnXQRXiXUOZ/9BE5/3sLgKCxNxL++OPnPu47TcFeAiM+hsYjL3bk4t8kdTMsfxLSt3rP/aLwjF+Fx6VFGxqKoigc7dkTV6Z3CWX01Cn49uiBx2rl+COPsckSx8wKf44aw9GYj+IT9zFalY5lI5YSaArkx+M/UiegDg2CGzBt5zQ+2PMhHYOuQynuwaZj+eSXO6qE0zjKn54NwujVIJzESL+Lsvvk77ncHpbsymTqmmMczSkDwEev4fp2cdzSKYEwv7Mn7ZxuJ+/ueJddubuY0XsGJq2JwV8P5kTJCd7v8T5dY7ryzdFvKHOU0T22O1GWqAuO1e62U+GsINAYyLHvv8Tx8AsEXnsN4U8+iUrjLc5/KKuUozll9E0MR6vxJsh/rZsGUL5hA5uMmYTE1KFpaFNSSlIwaAxEmGXZthBCCCG8ZIaZEEKIK0P+MZjWAVfNoWS8vQPF5SL+s3kYGzQg+JZbUPv5kf3Kq5i7dDn3sX2CoMM9sPpl76vhVaDRXfxnEJePrRhWvQCB8dD6VtBdhFlYBcdh5XOw/xs8LrAW+1Gh70TFETXWWf3w7dWLqElvoVKpMDVpSoXDiblNazQBAeSU2Ji+9jjzAnqg+K9GG7+Ppq4nmdhzND/l5dMxqiPBpmA0ag1Dap2uRXag4AAuj5OBDRPpn9CCLSe3klPsITUrmFUHckhKLWRPRjF7MoqZvPIINfyN9GwQRs8G4TSO8kd9gckzj6KwfF8209ceI7WgAgA/o5ZxHeIZ3zGBQLO+SvsKZwVvb3+b/fn7md1/Nlq1lh+O/0BORQ57cvfQpkYbxjcaj1txV+5wObT20AuK8fcMGgMGjYF9+fu4s3QKDe6O5rXx91QmywDqRfhSL8K38tx+9CgZDz1M1Fv/w1CrFhujKrh/zfMEHQniq0FfEecXd1FjFEIIIYSQGWZCCCEujkW3w+4vyDyYSPHOQowNGxI//6sqfwl25uSgCwsDwH7sGLrISNSmM+senZW91DvLrCIfBr8LLcdeiqcQl4PTBp+OgJSfvef+sdDrWWg0wlu86lwpCsrSp1FtmwFuBwVHLOTs9EdxV/0Rx9iwIQmLFgLgLitHbfYht9TOtLXH+GzrYewOPaicBNT9H251Ma90eoXBtQb/5e135uykUUgjNCoNw5cM52jRUWb0nkGHyA7szjrGnlSFdYeKWHc4D6vTfe7P9zcFm/Xc3DmBG9rF4Wv0JpSLbEVM2j6Jo0VHmTdgHgoKnb/oTImjhM8Hfk6jkEYsOLwAg8ZAp6hOBBoDL1l8v5dVnsX1P1xPiCmEKT2nEOgxkvnkUwSPG4upWbPKdrZDh0kdPx53QQHmzp2I/fBDKpwVjF06lpbhLXm41cNo1Jo/vpEQQggh/pNkhpkQQojql3cE9nwFgE/vUZSlLyL86afIq3CxMTmbCD8j8cE+hIZ6C6/bk5NJuf4G9AkJxEyfhubv/A/M4AudH/TWMVv7OjS55uLMShKXX+4ByNwBel/vr2txKiy8GTZNhb6vQGy7vx5DUcDjwl1aTuZjj1O+YR11BztR1+uOrukwlO2voQ0Lw6dNG3zatMbcpg26uNOzkHLdGqZ/u5/PtqSC/0r08Wup6ZzAEz0GUa41olKp6B3X+289TrOwZgCUO8tpGNyQlJIUGgY1BODVbU9xuPAw7/R4h3eu7c2PBw6y5aiD1QcLyCqxnfNXdzaR/kZu6VyT0W1iKXXl8+KWJ8ksy+TTAZ9i1pn58fiP2Nw2kouTqRVQi4ktJ+Kv96+clTWybvUscY4wR/BJ308IMYXgo/Ph8ItP4l66lIotW4j/6kv00dEA2I8ewV1YiK5BPf43wMXVGT/TKaoTs/vNxkf35zXZhBBCCCHOl8wwE0IIceEW3gJ75kO9ATD6czx2O2qDgRs+3sz6I3mVzUw6DXHBPrSzZjD8y/+hs1XgqtOAgJmziQyyoFH/xewipw3mjYSW4yBxuBT//ydL3w4uK0S2gI1T4Oe3wVnu/ezPZhAWp8OuL2DnZyhNbyBlxjasSUkAxL40AfOICXhsNlzZ2eji4s6oF5ZVbGPamqN8vv0gTrcbxW0hpvaPFOnWMrz2cJ7v+PwFP1qBrYAgYxBOt5OBXw/kZPlJVoxcQYQ5gluW3cLuvN283vl1usV0Y/7h+aSUpDAgYQANgxvyU+pPJOUk0TaiLZ2jO7MrdxcrUlZQO6A2Q2sPJb00nS8OfUGAIYBbGt+C1WXl8fWPU2QvYnb/2VQ4K+j4eUdciotlI5YRaYlk/uH5hPuE0yq81RWbYFqfvp4nVjzA65/rCEwtRF+rFvGfzUPj7w9A6apVzNUlMe34HMJMYfww4gcMmurbhVQIIYQQVz6ZYSaEEKJ65R6CPQsoyzSg6X4NJkBtMFBQ7uCXo95kWUyQiYxCK1anm4NZpRzEj7Xt7+DFjR/xkV9z1vxvHXqNmpggE/HBZmKDfYgPNhN36j0q0IROo/bOKBv3XfU+78XkccPJXRCeCNr/wF/+s/Z4d6wEiG55+nrXh6HFjd76dPsXQ/2Bpz/zeMBlg4Pfwc55kLwW8P5bn+rwt/j2ugH7sWPEfjADY5MmoFKhNpnQx8dXuXVmkZVpa47x5dY0PJatGOMXE66047Wuz1MzogX78ofSI7bHRXnMIGMQADqNjmUjlpFemk6EOQK3x01KaQpWl5U4f28yb0XKCjad3ETD4IYkhiSyNXsr8w7Mw6Ax0CWmC0eKjjBn/xx6xPRgWJ1h5NnymLN/DnF+cdza5FacHic/pf0EnE7UPdHuCeJ84wgxhQAwqu6oi/Jcl5LdbadYbWf+LQ25c8oJHMeOcbhtO2qvWU2+L0T07Mmt7i6kqQoYlzhOkmVCCCGEuORkhpkQQogLs+Am3EmLOLYsBneFm5gPPsDSuRPzt6Xx8ILdNKzhxw/3dcbh8pBRZOVEfjkpeeWcyK/gZFYBR0rdpBVUEFySh9bjJt037IxbaNQqaoaYmXZ9C2qHnS4E/uuunP9YPz4Km6dDWCKM/BjCGlR3RJfOpmmw9DHo9xq0u/OP21mLwBTgPbaVwPutwFoI7tM7TyqxHVESr0HdbDgYfHEVFqINPHvtrcwiK1PXHOWr7Ydwa7NxW+NpVDOPFMP/aBTSiLn956JVX75/P/QoHo4VHaN2QG1UKhULDy8ktTSV/gn9qR9Un9Wpq9mZu5M2EW3oGNWR3bm7WZW6itoBtRlcazAZZRl8degrAg2BjGs0DqvLyhcHv6BFeAsSgxMv67NcbBsyN9A6vDXuw0c5ft0YsFop7dWKO9vu5/mOzzOo5qDqDlEIIYQQ/yAXmiuShJkQQojzl3MAprYne4cvBYcs6OPjqblkMSq9nltmb2V9wUf0r92BNwaMZn3GetrXaH/WJWH27ByOj74Od1kZhx58iYO+UZzIKyclv4KUgnJsTg8AvRqE8dHY1t4dFr+dCMdWwb07/7lJs4Lj8G4z77HW6K3f1eqm8yt8fyXbPR8W3eI97vE0dHno7/Xb8D4sf9J7HBgPTa+DpteQ99VySpctI2bGdLSn6uL9XkaRlamrj/LVtjTculR84j5EpzIxucNXdK0bydasrbQKb3XGkk1R/b499i1zFj3LI+r+bGjrx0fHPqV/fH/e6PpGdYcmhBBCiH8QWZIphBCi+qjU2AM6U3DkKADhTz6BSq/H6nDzc+oe9LEbWFe0jV25Dbl/9f2Em8NZOGQhfvqq/8PS6LQYAgOwZWaQ+Obj9Js2FZ8BrQFQFIW9GSVcNeVnVh7IYXd6EU0i/bxLQW3FsOFd6PXc5X7y81eQDAFxoNZAUAJM3AvfTYSjK+H7B+DYTzDkvX9uEvD3jqyEb+7wHre9w7txA1CyfDk5r7+BvlZNwh99FEOtWmf2bXsHhNYDvcW7EYBKRdHX35D79tsAlK1dS8DIqgXr0wsrmLrmGPN3HADzTpzuDrSJSyTbFEi4OYiaEd5dKltHtL50zywuyKaTmzgY5uT7Ojqeaf8wCTUayOwyIYQQQlx2Ui1ZCCHEeVNC6pK9Jxw8YOneHUvnzgCsO5KL3W7AUN6TkXVGABBpiaRleEv89H5UOCtILkquHEcbFETs7Fn4tG6Np6yM1FtupXTNGgBUKhWNo/0Z2iwKgMkrj3iL/fd82tt503Qozb58D30hjq6E6V1gxTOnrwXEwHXzvbPL1Dpvra5pHeH4+vO7h8d9cWK9GNK3wVc3gMcFjUZC31crZ8+58vJwZmRQvm49yVcNJfvVV3GXlFR2tbvcLDuYxwM7Qpm40cgbyw6x5IOFZD75FACWceOrJMvSCip4fNFuuv9vDZ9tOYY+bhLGiG95ZpSWr27rzJeDP+XLQV8S4xdzeb8Dcc6e6/Acz7Z/lmfaP4NapWZIrSGoVfIjqxBCCCEuL1mSKYQQ4twoijfx43ZQURZOyvU3oNLpqPn9d+hjYwF44KudLErK4OZOCTw9qCEANpcNu9uOv8GfydsnM3vfbO5rcR/jGo2rHNpjs5Ex8X7K1qwBrZaYadOwdO4EwPG8cnpNWovbo/DNhI40i/aHj3tD+lZocxsMePNyfxPnZttM+P5BUNwQ3xmuX3hmof+Tu2DBzZB/BPyi4d6kP94MwFoIJ36B3APQ5eHT199p6t1JUmvybpJQ+W7wHre5FZpc7W17fD3sXXCWtqfeDX6QOPT02Jk7QaPzLh/Vmbzvv75+v2Np7iH4pK83zlo9Ua79nOIl32Nq2gRD7dooHg9FX35J2fqfKfvJW7ReExRE4d2PslAfx497syi1uSqHq1uYyms/T8fkdrAqugVvtbwWi0lPVICJILOeLakpaALX4ihsR4e4uoTHLyfTtp9HWj9Ci/AW5/drJoQQQggh/rFkSaYQQojLJ3UTrHweUjeAXxSmu7cRPXUKzvSMymSZy+1h5dGdGGqsJTZyDOBNmBm1RoxaI4qikFaahktxEe8fD0BuRS4hphDURiPR771L5hNP4jh2DFPzZpW3TggxM7RZFAuT0pm88jCzxreBns/A7MHeZFSHeyAg9jJ/IX+DxwOrnoNf3vGeN70OBr8DWj2p+RV8tS2Noc2jqB1mgRpN4fa13uL4jUdVTZbZS73f//G1cHwdnNzNr7tF0mIsWE5tluC0eWd0OUq9r99reNXp4+x9sH3WH8duDq2aMPukr3fHyrPRGOCqKdBklDepuuE9b7IsqiWOjq+RdftdlG/YgKlpU+I+m4dKoyFw9GgCrr2W3V8vxTb5LSy5mTy2Ppvj/hoAIvyMDG5agxCznoYvz8DkdnAougEzO45BsSuU2lwczPI+ozF6ETrf/fRI9GVa3xuxu5ujV+ulRpkQQgghhDgvkjATQgjx17L2wk8vwuGl3nONAVdMf7SKG98ePao03XKiAJthG4aAbewsDmQsvap8rlKpeKvbW+zO3U3jkMY43A5uWnYTQcYgXun8ClGWKCJffw1PeTkaiwWA8i1b8Gndmnt71uabnRmsOZTL9pRCWiZ0gZrdIHkNrHkdhk65DF/GOXBa4evbYf9i73n3J72zwVQqfthzkkcX7KbU7mLe5hTm3tyWRlH+oDd7a5j91pfXw4HvqEyQ/SqkLsR35sjJAl7fkEKYn5G2HRfSLMJArJ8alcvmTaC5rKffwxqe7h/dGro9UfXz374bfrMjqaJ4k3LO37TxnJ4Bhtt+epaZSuWtwRZSD66bj33Lbso3bEBlMODb2/v7we5yM31NMguT0kkt8KBpdy+N85MprBHHdY1rMCJzK+HJ+wlv9TC68HBciR+TM+ltBj/5JEMtZsrtLr45vJScUiuRuraojffwTeo0rm80BACD5g9m5gkhhBBCCPE3yJJMIYQQf6wgGVa/AnsWAAqoNCjNxpB7MIzCRd8T/+lcDHXqVOny3JJ9zNmxlvq1D/FY1xF0ie7yp7fYmbOT21bcho/Wh2+HfYuv3he7216Z8Mj/+BNy3nyToLFjCX3gfh779iBfbUunc50Q5t7cFtK3w0c9QKWGuzZDaN1L9W14KYp3lpWj/HevMu9yy9qnEoQeD3zQBbL2gEZ/avbV1dicbl7+/gBzN6UAYNJpsDrd+Bm1fHpLW5pEB1S93+Hl8Nko73FgvHc5Z0JXiO8EfjXYcDSP2+dup9TuqtItwEdH85gAWsQG0jw2kKYx/vgadRf3u3C7qibZTEFgsKAoCo4d6/CozJiatwIg74MP8e3dC0NCAkUVDm6bu50txwtQaYswqgLpk1iDIU0j6VwnFK3bydEePXHn52OoX5/4eZ+iNpur3HpVyiomrpmIn96PJUOXEGwKRlEUmVEmhBBCCCEAWZIphBDiUvG4Yc5QKPImdkgcBt2fInvaFxTOmwdA+abNVRJmiqKwYn82HmscDzQfQZfo8L+8TbOwZiwZuoS00jR89b7syt3FvT/dy8QWExlWZxhovLOWCmbPRhcVyT2DRrIoKYNNB0+yPaWAlnEtof6gU7W19Gc+w++TWo5yiO94us2OT6Es+8w2jnJwVEDLsZU1vzwbPsC9+HE8DnDb1bgdalRaBUuEHYDC1FDK/QfjKS7Gf+hQ/POTwRiAZ+hM1PV7cDyvnAnzkth/0lvc/s5utbitc01unr2VpNQixny0mTk3taF5bGBleE5TA8ojn0YXXxtTx95VEkeLd2bw0PxdON0KreO9ibGklEL2ZBRTVOFk9aFcVh/KBbyTvuqF+9I8NoDmsYG0iA2kZogZtfrPE0yK2w0eDyqdDsXlomzdOtyFRbiLCgm87jrUJl8q9h0lZ9Jbp64X4S4uBpcLXUwMNRd/g9rHh5DbbgUgJb+ccTM3czzPiq9BQ0TDr3Cryhjf7S2ahZ36/aI1EDNjBtkvv4x1xw6KlywhcPRo3B43q9NW0zO2J11jutI4pDHtarTDrDOfekZJlgkhhBBCiItDEmZCCCFOsxZ6k0zmEFBroPMDsH+Jt1ZYZDMAfFq3pvCrr4h46ikCr7m6Svd9mSXkqJdijignOrw+8NcJM4AIcwQR5ggA5h2YR4GtgKScJIbVGUbQ2LFog4LIeWsSvr16ERTkw02xavq/9wzJh5pR545rsQyYitrP3zvYkZXeZZCOcu+sp99RFPA8cAJPWQXu4mLcn7+JOy8Lc4QdjV7BUaYhb78Fj8ObqItOOA5A3vQZ5E6eDIRVGc8U5sFS3wB6C9b9Lko3eJetGhs3gfYTUBJHcWz0HRSHz2GypSkHgmoTZDEy6eqmdKvnHWvOzW25aeZWtpwo4IaPtzBrfGvqHt5G4WefU7F5szdoAM1TBI0bS9hDDzF9bTLvfrsTp87IwCY1eGtUU4w6b+0vh8vDgZMlJKUWsiO1iKTUQtILrRzMKuVgVikr1u4hojyfCOwE1Ywlqk0LWsQFED//Y0hN8Sa9Cgu97yUlRLzwPIGjRoFKRfqEuyvj8e3TB31MDIrDjnXb9jO+a33NBDwVFah9fADYnlLALZ+uwR78EaGa7rw7bASPbCqgwlFBvF88AO/veJ8GQQ3o0rALcZ/No2LzZvSxsXgUDzcvv5nt2dt5vfPrDKg5gDn956BVy48yQgghhBDi4rugnzJfe+01Hn/8ce677z4mT55MQUEBzz77LMuXLyc1NZXQ0FCGDh3Kiy++iL+//x+OM27cOGbPnl3lWt++fVm6dOmFhCeEEOLvcpTD5unewvQNh8KQd73XW4yFluNwl5XjTk1FHxuLX7++mBo3QhcVdcYwy/aeRB+0AbWumIyy4dQPrnXOobzc6WUahzSmf0J/AN7a9hZlIWXcu3QROlMQANco6TjcThoe3krGA1tRGQyYO3Ui8NprsdRQQUUeBYfM2AoDcDvUBNatwBJnwKM2c2g28GWH3901iLh7O+FTLwYlz0HxdwsBUOm0KA2HogLUp+qpoVaj8fNF4x+AOsAfQ916MPEFAPya/IIxORmNvx+G+g2gXl2KN23BlXkSc+ZJnmQz+f5hRI8dQ0x468q7WwxaZo5ryWNvf8e3xXDjJ1v4zPYLhk2bADA1bYozNwdX5kk0YeE8u2QfczacYO6qNzGajUR62qLuEg7RUVj37KH4228JKSqiZ2ER3Yq8s74UP3+SX3iPpNRCwuZOp0PScgC+zejA2yXeZ3tv9UpqF2ec8WviLiryfh8aDeb27UCrRRMQgErr/THCUK8eUZPfRhMQgCYw0PseEIDacLqO2He7M3ngq13g/wsGUxrmwB9oFn03q0atYn/+fgKMAWSUZfDB7g9QUPhh+A/E+MZQ2jgevUZPkEpN+xrtOVRwCLfiBpBkmRBCCCGEuGTO+yfNrVu3MmPGDJo0aVJ5LTMzk8zMTP73v//RsGFDUlJSuOOOO8jMzGTBggV/Ol6/fv2YOXNm5bnBIMV6hRDiknM5IGk2rHvTuywRIDPJe12rB5UKV34+abfdjquggPjPP0MXEXHWZBnA8gNZ2MsH067RSTpGdTxrm7+iU+u4oeENAORU5DDvwDxciovecb0rx6x5x8285QyhbMUKeuXuJbgoh7JVq1Cp1Vj+9zLcuZGyR1+l/MQ2AMw3vQjXX49KUVDNa4bicKDS6U4ldvxR+/uj6nwvNG6MtrSU0MJoNP7+aPz9IcS75DRg+DD8hwxGbbGg+rW4/e9YOnWETqef+2hOGXdvslHS61EGHd/IoMztBBfnYH33bY7N/oTa69aiOF0UzJxJ8eLFTMjNxXrbJFamWXnUGs+rY26m0fjR6KO933dZajpP/niExRtTCLaXEGwvQWUrpmTJEsIfeRgAx4kTFM6Ze0ZsmqBy+iZG0DcxgoLclhQUHMbh40vTxPpclRjJjtQiFtTphsllp0RvpkTvQ4nejMfPn1pKDZqtOEyLuECavT8Df5+qtdC0gYH49et31u9EURSmrT3Gmys3o7gC6Bl2DTXrhXJdg2swaU2Ad1kueAv1j280nuyKbGJ8Yyi2F9N7QW+6Rnfl/Z7vc1OjmxheZzihPqF//RtJCCGEEEKIC3BeCbOysjLGjBnDhx9+yEsvvVR5vVGjRixcuLDyvFatWrz88stcf/31uFwutNo/vp3BYCAiIuJ8whFCCHGuPG5vIf/VL5+uURYQ593FsfFI73JMwJmRQerNt+A4cQJNUBDuwkJ0f/BndUp+OYeyytGoG/N+7/sx/r6e2HkI8wnj474fszZ9LR2jOqIoCvm2fEJMIYy+sS/dsvXMcvXnqz6hJOzbjOJweHd2DG9IwNWjMXfqitrfH58WLQBvjataK1ag8fNFZTSeteaVxteXkDvuOOP6r8sK/0ix1cnRnDKO5pRyJLuMo7llbE4uwOp0ExIRRZf7XqZRlA/F331H4bzPMDZsiNpgQFGpKJw3D3dREWqzmTeb+3C/j4U1h2C0PZIPK/R0AQrLHdz8XQpJqRXoNWpeuKk79V4cjHXnLuyHD6MNCQG8s72Cb70FTcCpmV6BAd7jwIDKWINuvJGgG28EoCHQ99T13NIO7EgtZEdaEUkphRxLL8bqdJOeXMTa5KLK/rXDLLT4TS20OmGWs9ZCc7o9PP3NXuYfWI651mc0s1zHByMGoFG3Oet3GGIK4f6W91eeb8vahgoVhwoPkW/NJ9gULMkyIYQQQghxWZxXwmzChAkMHDiQXr16VUmYnc2vuxH8WbIMYM2aNYSFhREYGEiPHj146aWXCA4OPmtbu92O3W6vPC8pKTn3hxBCiP8qWwl80g9y9nnPzWHQ9RHv8svfJLnsR46QevMtuHJy0EVGEvPxRxgSEv5w2OX7TmKK+ZhIY0O02i7AhSfMAFqEt6BFeAucHifPbXiOLVlbmNt/LlEBEVzTOoZPN6Xy1lEPX957b5V+fgMGnHU8XXjYWa//Xflldo7mlHEkp+zUuzdBllNqP2v79jWDeefaZoT6GlCpVJiGD2FVUxVZBalMUBRUej3Z1/fC5BdI4+G3YLD4MaONmwnzklh5IIdbZm/juSGJfLQ+meS8cvyMWj68sRVta3r/H2np3AlL506V9zPWrYvxwQfP69lCfQ30SYygT6I3KepyeziYVcqO1EKSTtVCS8mvOJUYLOOrbekA+Bq0NI0J8CbR4gJpHhOAWq1iwrwk1h/JwxCchUrtxC8wGZVKAf5ecf6ecT1ZffVqfPW+6DUX5/eTEEIIIYQQf8c5J8y++OILkpKS2Lp161+2zcvL48UXX+S2227703b9+vVj+PDhJCQkcOzYMZ544gn69+/Pxo0b0Wg0Z7R/9dVXef755881dCGEEABGP/CLhJJ06HgftL0D9OYqTWyHDpFy41g8xcUY6tQm5qOP0IX/eQH/xQc3oLUcoUidgV7zzEUPu8JZwZ68PeRW5LI7dzcR5ggmdK/NV1vT2Xy8gA3H8uhQK+Si3EtRFHJK7RzJ9ibEfpsgKyh3/GG/CD8jtcINhAYVERGgonfN9gT6FXHrT9fgVtx8N+w71Co1L2x6EQWFa5uNJcgYxDNhP5NnzWNGcTs6WDpwtPggr4yshWaRimX7snni6z0ARPobmXVTG+qEWUgvTSfKEoVKpeKen+5he9Z2Xur0Ej1iezBl5xRWp65mTIMxDKszjHXp6/gu+Ttahbfi6npXk1aSxvfHv6eGuQZX1b4Km8vGuvR1+Oh86BTlTb6dKD6BXqOnfo0wGkX5c307BZVKRX6ZvXIjgR2pRexKL6LU7uLno3n8fDSv8rvwNWiw6ndi0jXl3QEPYTN2pV9CP9Sqsy9n/SPBprP/45kQQgghhBCX0jklzNLS0rjvvvtYsWIFRqPxT9uWlJQwcOBAGjZsyHPPPfenba+99trK48aNG9OkSRNq1arFmjVr6Nmz5xntH3/8cR544IEq94qJiTmXRxFCiP+Ok7tg1QvexFid3t5rgyZ5ly6aAs/aRR8djT4+DpVKTcz0aWgCAv70FnlldvanmFFbRnJb76hLMhvI3+DP9F7TOVZ0jM7RnXF6nARbNFzbJoY5G1OYvOII7WsGn3WZ5R/xeBQyiqwczS3j6Knk2K+JsVKb6w/7xQSZqB1qoU64L4H+xRSxi9rB4YyqP5CfUn/ivtWPU09Tj8fiBlJgg+TiZABsLhtGrZHBtQZj0VlQFAWry0rX6K5sz95Oq4hWKIrCI2sfIb0snWk9Z6BV1+CHAweJiyjlyxvHEuqrp8/CPmSVZ/HdsO+I84vD7rJT6ixFp/bWFssozeBQ4SGK7cUAHCk8wo/Hf0Sv1nN1vatJLk5mys4pJAYnclXtq8i35fPg2gcxaAxsu95b9+3q767G6rJWFt+//ofrOVJ0hEndJtGrYScO2uejuH7m1e7XUdvclfn7l7MhaxWlhfGczGiM1bgZU+QCropX0zuxPzD4PH/lhRBCCCGEuPzOKWG2fft2cnJyaHGqFgyA2+1m3bp1vP/++9jtdjQaDaWlpfTr1w9fX1++/vprdDrdn4x6ppo1axISEsLRo0fPmjAzGAyyKYAQQvyV/GPw00uwb5H3vKLgdMIsIPasXax79mJslIjabCZ2xgxUev1f1u4C+OlADh63kURLT+5p1ekv25+vSEskkZZIKpwVPLT2IUxaEw92fZEvtqax5UQBG47l07H2mbPM3B6F1IIKjmR7E2LHfjNjzOp0n/VeahXEB5upHWahdpgP8aFGGkYEk27fynfHF9E6ojVjE8fy7bFvmfrz+7QoacGo+kOpH1QfP70fwaZgFEUh0BDIB70/IMY3pjKR+HKnl6vc67kOz6Eo3hlcxfZijFojGpWGpmGNaTvah5SvXyal7DAq7TDUqjDCfcLJs+aRUpJCnF8c97e8n4fVDxPj6/3Ho1sa38KgmoOI8fOet4tsx6OaR0nw9y6pDTeHM6LOCGqYa3ifFTUtwlpU7jqpKApGjRG3x11ZmL/CVYHVZa1sk1KSwr78fZQ6S0iM9Ofn3AJOZvzCiJYR3HtTb77a52T6wa9pVqPmBf2aCyGEEEIIUR1UiqIof7dxaWkpKSkpVa6NHz+e+vXr8+ijj9KoUSNKSkro27cvBoOBH374AZ+/8Ret30tPTyc2NpZvvvmGIUOG/GX7kpIS/P39K+ulCSHEf1pxBqx9HXZ8CoobUHkL+Xd/AoL+OHlRMGcu2a+8QujEiYTccfs53XLUrE/Za/2CnlGDeH/ImQXzL7adOTsZv2w8WpWWTwd8yuc/u5i14QQt4wJ5bXhjjuSUVVlOmZxXjsPlOetYOo2KmiGWU4kxC/GhOvSmHJpFxhHlG8GUnVOYs28Otza5lVsa38IXB7/g5c0v0zmqM1N7TeV48XHeTXqXZmHNGJs4ll//t3ouM93OpsBWQJAxiCJbEX0W9iHCHMFrnV+jYXBDssqzCDIGXda6XoW2Qsqd5QSbgjFpTRwqOER2RTYJ/gnE+MawK3cXSdlJ1AmsQ6eoThwsOEiZo4xWEa0uW4xCCCGEEEL86kJzRec0w8zX15dGjRpVuWY2mwkODq5MlvXp04eKigo+/fRTSkpKKgvyh4aGVtYjq1+/Pq+++irDhg2jrKyM559/nhEjRhAREcGxY8d45JFHqF27Nn379j0jBiGE+E9SFFh0Kxz4DjR60Oi8736RcOuq0+0+6Q+ZSeCyec/r9oMeT0NEo7OPe0rhF1+S/corALgK8itnO/0d5XYX+4rXoQ1IQeOT8tcdLoJmYc14vfPrhPmEUS+oHrd1LefzLalsTymk99vrztrHoFVTO8xCnTDvUspaoRbqhFvIdexjf+E++sb3JcoSxd2r7mZt+lqeaPsEo+uPxqgxUuGq4HDhYQDa1WjH420ep3FIYwAS/BN4u/vblfe50ETZr4KMQQAEGAPYMmZLlc8izJd/V+lAYyCBxtNLeOsF1aNeUL3K86ahTWka2rTyvH5Q/csanxBCCCGEEBfTee2S+UeSkpLYvHkzALVr167y2fHjx4mPjwfg0KFDFBd766poNBp2797N7NmzKSoqIjIykj59+vDiiy/KskshhPjV3oWwZ7732GU9fV39m41RFAVSN3iPYztAr2chtt3fGt63dy8KZs3Cf+hVBN9++zklfdYdzsWa240wTQTjmwz/2/0uVJ/4PgAU2Yp49Jd76NWqC99visRi0FLr18RYmDcplhDiQ2yQBY1axdSdU9mTt4dh8U8TabHw4tJpbM/eTqgplChLFHUD67I7dzcOt7e4/+Bag+ka3ZV4/3gA4v3jK4+FEEIIIYQQ/04XnDBbs2ZN5XG3bt34Oys8f9vGZDKxbNmyCw1DCCH+vWwlsOxJ73GnB6D59eB2eF/8LrF1zTywhEF0a/iLpJfH4aB8/Xp8e/ZEGxxMwqKFf6te2e8t25eF4vLnqpqjaRbW4Jz7X6iFRxayM3cnwcY0Nj6xGD+jltTSVBoEN8DpcTJu6TiObD/CshHLCDQGsjptNQcLDnKg4ACRlkg6RXUixBRCqE8oAHc2vZN7mt9TmTQM8wkjzCfssj+XEEIIIYQQovpc1BlmQgghLoGD30FZFgQmQNdHQfcHuxSrVNBg0N8a0l1WTvrdd1OxaRM1Xn6ZgBHDzytZ5nR7WJUzA1N0PglR9wCXP2E2vtF48qx5jKo7Cr3ORfvPO6NSqdh83WaMWiOFtkKsLisHCw7SPrI91ze4HrvbTsOghoC3QP5v6TTntlGNEEIIIYQQ4t9HEmZCCHGla3Yd+EaAWvfHybJz4MrPJ+2227Ht24faxwddZI3zHmtjci4e8w602gpiQ6on0aRWqXm0zaOAdwZzoDEQjUpDVnkW8f7xvNzpZQIMAcT6encGvar2VdUSpxBCCCGEEOKfQxJmQgjxT1Crx0UZxpmRQerNt+A4cQJNYCAxH3yAqfGfbwjwZ1buz8WacjstGmTQtkbrixLjhVCpVHw/7HssekvlteZhzasxIiGEEEIIIcQ/kSTMhBDiSnXwe1Broe7F2THYXVbOiTHX48rKQhtZg9iPPsZQM+G8x1MUhWX7TuJxhHNX84Fo1VfG/1J+mywTQgghhBBCiPOhru4AhBBCnIW1CL69Dz67GvZ9fVGG1FjMBI0di752LeI/++yCkmUAO9LyKAt7AUv0lyRGS90vIYQQQgghxL/HlTEdQAghRFWrX4HyXAiuA/UGXtBQ5Rs3YqhTB21ICMHjxxF47TWoTaa/3V9RFErtLgrKHOSX28krc5Bf5mDhgVWodUXodcmE+PhfUIxCCCGEEEIIcSWRhJkQQlxpTu6CrR96jwf+D7T68x6q+NvvyHz8cQx16hA3dw4aiwW1yYTN6Sa/3EF+mZ38Msfp43IHeZXXTr2XOXC4PadG9KA2pqP1OY6joDNq412M7haERq258OcWQgghhBBCiCuEJMyEEOJK4vHA9w+B4oHE4VCz2zl1L7Y62ZtRTF6ZHdvefTR56WEAdqoDeGTmdnJtHvLLHJTZXX9zRAW1PgedJRW9tR1BFh3FYR+hqBz0iu9Cs4g+3Nyp5rk9oxBCCCGEEEJc4SRhJoQQV5Kd8yB9C+gt0Pflv90tr8zOR+uPM3fjCcodbu9FReGW2l3RetzMqD0EJaOsSh+dRkWw2UCwRU+wxUCIWU+wRY/BWEqF6jidI3tgNrq4bd0A3IqbH4bfTIxvDI+sW4nL4+L2JnWoF1TrYj69EEIIIYQQQlwRJGEmhBBXiooCWPms97jbY+AX+ZddckpszFiXzLzNKdicHtSKh5syNnC0XW98gwJwtp+An8XAS74Ggs0GQk4lx4ItenwNWlQqFSWOEg4VHKJ1RDOK7cV0/qIzCgp3te9FiCmSNofbeMNzVgDwRpc3LtlXIIQQQgghhBBXAkmYCSHElSJ7L7idENoA2t5xxscVW7dSvnEjaLU4rxvPjLXHOLnga7of38xrLgcBKhchih1NUQFmYx4xH36ASqU6Yxy7205y0VEaGBuQZ82j5/yeqFCx/tr1+Bv8aRDcAI1KQ741nxBTCDN6zzjrOEIIIYQQQgjxbyUJMyGEuFIkdIG7t0FFHmh0VT4qXbmS9LvvAcDm48vVGfE43QrXlBXSLO9YlbYqvZ6AkSMqk1xuj5vM8kxifGM4WXaSwd8MBuCX0b8QYgohxjcGtUrNyfKT+Op9+XTAp+jUp+8vyTIhhBBCCCHEf40kzIQQ4kriG+59naIoCiqVCnOXLpREJ7ATf9LNITjdCu1qBjGs92girT1Rm31Q+5hR+/igi4qixEcBIK0kjdE/jMbj8bDu2nVEmCPw0/sBkF6aTq2AWnw56EvMOnPlPX+bLBNCCCGEEEKI/yJJmAkhRHWzFsH+byCqFUQ0AkBxucj/8ENshw4T9fYktqaXckPz23BodHSuE8JXPerQJiHojKFWpqzkjZ8ewOlx8tOon4i0ROJRPHjwkF6aTrx/PF8O+pIQU0jlzLHfJsuEEEIIIYQQQkjCTAghql/6Nvj2PgiqBfcm4UhNJfORR7Hu3AlA6ciRPLvNjUOj47q2sbwyrPEZQ7g9bjRqDa0jWqNVa8m15pJZnkmUJYrPBnxGtG80WrX3j/xQn9DL+XRCCCGEEEII8Y+jru4AhBDiPy9jGwBKZAuKFizg+NBhWHfuRG2xEPn6ayykBgezSgnw0fFwn3pndD9UcIhhS4aRlJ2Ev8Gf2f1m88u1vxBliQIg3j++MlkmhBBCCCGEEOKvScJMCCGqW8Z2AHI22Dj51NN4KirwadWKmou/wdGjH5NWHAHg4b71CDTrz+g+d/9cjhcfZ9L2SSiKQqhPKD46n8v6CEIIIYQQQgjxbyJTDoQQojopindJJuA/dCRFaw4QctedBI0fj0qj4fX5uyi1u2gc5c+1rWMru7k8LrZmbaV9ZHueaPsEJq2JCc0myI6WQgghhBBCCHERSMJMCCGqU+FxsBaARo+xQ39qr+6Oxt8fgO0phSzYng7A81clolF7k2F2t527Vt7F1qytTOk5hc7RnXmy3ZPV9ghCCCGEEEII8W8jSzKFEKI6ZSThKNVQkFmTil17K5Nlbo/CM4v3AnB1q2haxAZWdjFoDMT6xWLSmnB6nNUSthBCCCGEEEL8m0nCTAghqlP6Nipy9WSvLiFvytTKy59vSWVfZgl+Ri2P9KsPwI/Hf+S9He8B8Hibx/ly0Jf0iO1RLWELIYQQQgghxL+ZLMkUQojqpNFhr/AFQF+rFgAF5Q7eXHYIgAf71CPEYqDMUcazG57F6rLSrkY7Wke0Jt4/vrqiFkIIIYQQQoh/NUmYCSFEderzIvqCRHwsSzE1aQzAm8sOUWx1Uj/ClzFtTxf6v6PpHWzN2krL8JbVFa0QQgghhBBC/CeoFEVRqjuIC1VSUoK/vz/FxcX4+flVdzhCCHHedqcXcdWUX1AUmH9He1rHB1V3SEIIIYQQQgjxj3OhuSKpYSaEENWl8ASUZFaeejwKTy/eh6LAsOZRlcmyffn7uGfVPfyc8XM1BSqEEEIIIYQQ/y2yJFMIIarLmtewrZ3PyYOJ+HTtz5peY9iVVoTFoOXx/vUrmy04vIA16Wvw0fnQKapTNQYshBBCCCGEEP8NkjATQojqkrEde5EO24lcPEH7eN3lLfQ/sVcdwvyMlc1ubHgjZq2ZXnG9qitSIYQQQgghhPhPkYSZEEJUB2sR5B3GXuLdIXOfPoiCcgd1wiyM7RBf2UxRFBL8E3io9UPVE6cQQgghhBBC/AdJDTMhhKgOmUkA2K3e4pNrrGYAnhuSiE5z+o/mm5ffzLMbniWrPOvyxyiEEEIIIYQQ/1GSMBNCiOqQsR0AR6kBgGRzGCEWAx1qBVc2OVx4mK1ZW1lybAl6jb5awhRCCCGEEEKI/yJZkimEENUhfTseNzgK7ACk+YbTrmYQKpWqskmdgDrM6jeLw4WHCTIGVVekQgghhBBCCPGfIwkzIYS43BTFO8NMgRr338SC9SfJN/rRLuF0UszpdoIKWoa3pGV4y2oMVgghhBBCCCH+e2RJphBCXG7luWArRq3X4nPDXfwvrheoVLRJOL0c87vk7+izoA/zDsyrxkCFEEIIIYQQ4r9JZpgJIcTlZgmDx9Nxp+xmT2Y5NqeHQB8ddcIslU1Wpq4kz5qH1WWtxkCFEEIIIYQQ4r9JEmZCCFEdtHpOvj8P44qV9Gk8HNWAwajVp+uXTe42mTXpa2ge1rwagxRCCCGEEEKI/yZZkimEEJeby1vo33EsGbXLRaHBQtvfLMfMKMtAq9bSO643IaaQ6opSCCGEEEIIIf6zZIaZEEJcTm4nvFELxT8WR0oFAKm+4bQ5VfDf5rIx6ttRhJnCmN57OhHmiOqMVgghhBBCCCH+k2SGmRBCXE45B8BRiqokHcfCH5nQ7X6sQaE0qOEHwMGCg7g8LqwuK2E+YdUcrBBCCCGEEEL8N8kMMyGEuJwytnnfo5qzOdNKckAUPRNC0JyqX9YsrBmrRq0itTQVtUr+TUMIIYQQQgghqoP8bUwIIS6njO0AVFhjKfr+e8IqCmhb07scM7s8mxPFJ/DV+5IYnFidUQohhBBCCCHEf5okzIQQ4nJK9ybMCnYUMmjxVHqkJdHmVMH/2ftnM/ibwby3473qjFAIIYQQQggh/vMuKGH22muvoVKpmDhxYuU1m83GhAkTCA4OxmKxMGLECLKzs/90HEVReOaZZ6hRowYmk4levXpx5MiRCwlNCCGuPLYSyD0IQGl6MQDZgTVoFOmtX1ZsL0atUtM0tGm1hSiEEEIIIYQQ4gISZlu3bmXGjBk0adKkyvX777+fb7/9lvnz57N27VoyMzMZPnz4n471xhtv8O677zJ9+nQ2b96M2Wymb9++2Gy28w1PCCGuPCd3AgqKXwzu1DQA/BvURavx/lH8cqeXWTZiGR0jO1ZfjEIIIYQQQgghzi9hVlZWxpgxY/jwww8JDAysvF5cXMzHH3/MpEmT6NGjBy1btmTmzJls2LCBTZs2nXUsRVGYPHkyTz31FFdddRVNmjRhzpw5ZGZm8s0335zXQwkhxBUp3Vvw32VphMZhx6nSULd5fQB+SP6BAlsBEeYINGpNdUYphBBCCCGEEP9555UwmzBhAgMHDqRXr15Vrm/fvh2n01nlev369YmNjWXjxo1nHev48eNkZWVV6ePv70/btm3/sI/dbqekpKTKSwghrnitxsOYhdiCegKQYQmhTe0wCm2FPLb+MXrN70WxvbiagxRCCCGEEEIIcc4Jsy+++IKkpCReffXVMz7LyspCr9cTEBBQ5Xp4eDhZWVlnHe/X6+Hh4X+7z6uvvoq/v3/lKyYm5lwfQwghLj9TINTpRaY6ihO+4aQERtEkOoBcay4NgxtS078m/gb/6o5SCCGEEEIIIf7ztOfSOC0tjfvuu48VK1ZgNBovVUx/6fHHH+eBBx6oPC8pKZGkmRDiH2NLZCJP93yYDjWDuEOrpm5gXb4Y9AUOt6O6QxNCCCGEEEIIwTnOMNu+fTs5OTm0aNECrVaLVqtl7dq1vPvuu2i1WsLDw3E4HBQVFVXpl52dTURExFnH/PX673fS/LM+BoMBPz+/Ki8hhLiiHV4GswbBlg/ZnJwPQNuaIXgUD6tSV1HiKEGv0VdzkEIIIYQQQggh4BwTZj179mTPnj3s3Lmz8tWqVSvGjBlTeazT6Vi1alVln0OHDpGamkr79u3POmZCQgIRERFV+pSUlLB58+Y/7COEEP84Kb/AifV4Tu6h5ey3uGnfd7QN1XG48DATV0+k38J+uDyu6o5SCCGEEEIIIQTnuCTT19eXRo0aVblmNpsJDg6uvH7zzTfzwAMPEBQUhJ+fH/fccw/t27enXbt2lX3q16/Pq6++yrBhw1CpVEycOJGXXnqJOnXqkJCQwNNPP01kZCRDhw698CcUQogrQfp2AHLVtWiXsoLWqKhZ6y12lewiwT+BON84tOpz+iNZCCGEEEIIIcQlctH/dvb222+jVqsZMWIEdrudvn37MnXq1CptDh06RHHx6Z3gHnnkEcrLy7ntttsoKiqiU6dOLF26tFrrpAkhxEXjcUPmDgD2pempARQFhOJj8aG9pT1Lhi7B5rJVb4xCCCGEEEIIISqpFEVRqjuIC1VSUoK/vz/FxcX/nnpmh5ZC9h5oOAxCald3NEKIC5G9D6Z1AL2FZwM+wrpyBe3qRtDvxbvZkbODluEtMWrlHwiEEEIIIYQQ4mK50FyRrP+5Um18H06sB/8YSZgJ8U+Xvs37HtmclVkGMur2ZPDNbdh0chP3rb6POoF1WDRkUfXGKIQQQgghhBCi0jkV/ReXUWCc970wpXrjEEJcuAxv/bKS4Kbk5pegVatoGRdIubOcMJ8wWoW3quYAhRBCCCGEEEL8lswwu1IFxnvfiyRhJsQ5URT4eRJYiyBxGEQ2B5WqemPKPQjAXlUdZi5/BY1Wi+aWBgyuNZhBNQdhc0v9MiGEEEIIIYS4kkjC7EoVEO99LzxRnVGIfxpbMRSne18lmWAJg9D63gSsWlPd0V0eR1fCqhe8xxvehaCa0GgkdLgbjP7VE9PIT+DTEazLCuIqeynYodhPQ0bODhqHNMakNVVPXEIIIYQQQgghzkoSZleogtUHsW0KIMh5AikFLqrweODIMsjZDx3vB/WpldVT2lbOZDqD1gTdHoVO93vPy/PBUeatkaf+d63MVta9RfZ2P1yeAMwhxQR4klFtngFdHjrdqDwfzMGXIRjFO7vNPxpuX8fhR+cA4A4NZ0Xuel7b8hrdYrrxXo/3Ln0sQgghhBBCCCH+NkmYXaFKtx6g4oQP5ogCjC47aA3VHZK4EigKfDcRkmZ7z5vf4J1FBmAM8L6bAlH8okl3+WN25BJQfhy1y4pi8KNyYeKe+bD0UdCZIbQehDWEsPoQ2gDCGoBfZPUvYzwfqZuo2L6dwiMhgAtrSTwBD0wAWxEVu/fjKS/Hp34s6mnNIboNNB7h3Yn2UiTPNn/g3el20GRQa8gqV9BnpALgU6cWdrcdf4O/1C8TQgghhBBCiCuQJMyuULrIGGAHLqvGu7wuuFZ1hySqm6LAsie8yTKV2rvM0OM+/fnVs8HgC3ozH6w9xqs/emebqfEQp8pG+cGf0O0bqB1mYVTZcZqpdaid5ZCZ5H39VkQTuGO999jjhuPrvIk0S/iVnUjbv5iSVO/yRmNiIr59+6Jqeg0ABffcQ+mKlaj0WsIb6wn0bIDUDfDjo1CzOzQeBfUHeL/DC7XjU/jxYe9xrZ6QOJTNx/OJLc0GwFynDjc1uomxDcfi9Dgv/H5CCCGEEEIIIS4qSZhdobQREQA4VeHgrKjmaMQVYfUrsGmq93jIe9D8+qqf+3p/z5TanExfewyAFrEB5Jc7OFGgRrHCiROFbD1RyOd0QUsH4lTZNNGfpLU5m0RtBrGuFPytqdh8amD0KKjVKig4DnOHeu9hCjw1C62+d1ZaaH1vIs0ccpm+hD+n9HyB0udWAaWETpyIpXOnys90kVFoIyJwZWWhG/M++Gfg2TGf47Oz8Anbgnn1esxR96FpOhiGf3D+icG9C2HJPd7jdhOg4VUAbEouoGFpDgDl0UEcKjhE3cC6GLWy6FoIIYQQQgghrjSSMLtCaSPCAXCFdoSIxtUcjah2P78N697wHvd/88xk2W988vMJCiuc1Aw189Xt7dFq1Nicbo7llnE0x/s6kl3GkZxSTuTrOGaP4mv76f56nFj2W6l4dim1Qi308k1lvCkWf2s6Kmuhd1ZW6oaqN713JwQleI+PrAC92ZtM8wm6uN/DX6jYvh13USmagADM7dpW+Sz88ccIe+xRHEePoouJAaORCldTHKV34CjVUnTMDCoF06YkLLkzCBo3DrXaA+lbIL7z39s04dCPsOg2UDzQchz0fbky8bbleD4rmo9iUmtfdoZu58NvRzKmwRgea/PYJfgmhBBCCCGEEEJcCEmYXaF0v84wy8qu5khEtdvyIax8znvc6zloe9sfNi2qcDBz7RGGHPuZW37ZRFZKE0LunoCxbl0SI/1JjKy6S6TD5SElv5wjv0miHc0pIzm3HIfTw77MEvYRwDu8hgEHtVSZNNRk0MqcTUNtJnGuE5hdRRyzBxHvcmPQauCHh07v7mqJqDobrW7fyplwF1VxOpScxNSiBdHTpuIuKECl053RTKVSYahTp/Lcp00bYj6YQdnPP1O+/mccx49jTa3AMWcuwbfdBvu/pmLqHTjdQZh79kfb4QaIanH22WfHVsNXY8HjgsZXw8BJle1yS+0cyy1H5RNAk+G9+WXPPkx5JlqEtbj434UQQgghhBBCiAsmCbMrlD4+Ad/+/TAkxEJRKgTEVndIorr4RYFGDx3vO73L5R/4YF0yI3csYcTRtQCULs+idMUK/AYMIGTCBAw1E6q012vV1An3pU64L/xmIqPL7SGt0MqR7FKO5JRxLKeMIzllHM0xsd8Zz4Li020NOLC/8zMatYqEIAOTPOHE6W34O7Kg7NQreY23sSkIrl8AUS0vxjdz2vpJsO1j1B0n4tv7+b/dTW0yYenSBUuXLgA40jMo//lnFLsNlVoN1iIKkwMoOaGGtcsIbTyfkE6h0HgkSuIIVOENvAOlboIvrgO3HeoPgqHTqsxI23K8gABbKR31ZVhsZTze9nEebPXgRf0KhBBCCCGEEEJcPCpFUZTqDuJClZSU4O/vT3FxMX5+ftUdzsVzeBl8djXUaAq3r6vuaER1yjsCwbX/sK6Wbf9+SkIi6freJizFeXyS9BGRN4/DunMnpcuWARB6372E3HnnBYXh8ShkFFlPL+3M8SbUjmaXUWp3VWlrxkodVQb11Gm0MGXRWbWLcE82mhu/gbgOFxRHFWU58HYjbHlunB1ewDLsVlR6/UUbPm/GdEoWL8CenEF013J8axTjccOxb8Mx1gzDcvU9WOr6oVt2G0S3htGfn7Gr7TOL95L55QIeTPoSVcsmmKf9jxi/mIsWoxBCCCGEEEKIqi40VyQzzK5k/tHe98KU6o1DXH6HlnpnK50qGE9InbM2c2Zlkfv2ZIqXLGFfr6uxmltTt14CiW+tQH1qSaLtwAHyP/6EwBtuALyzqPI/+pCQ229HV6PGOYWlVquICfIhJsiH7vXDKq8rikJOqb1yWeeRylppfuysqM2XZWBmKImaNB7y1KfNeXwlf2jTVHDbKcioTfGz7xN4pIiIp568aMOH3H4HIbffgSsvD7VeBSmrsS6eicuWTNn+fMqeew61jw/Rr7yFucegymSZoigcySlj47F8lu3LYsCpgv97LUU8//UAHmn9CDc0vOGixSmEEEIIIYQQ4uKRhNkVLPOtWdjWhlKjTREmaxGYAqo7JHE5JK+Br24EjxPGfgfxHf+waeHnX1C8eDEAaUdToGlrHupTF7VOx778fdQw1yCoQQOi/vdmZZ+8aVMpXriI4oWLCJkwgZA7br/gkFUqFeF+RsL9jHSqU3XHzPwyO0dyypj1ywmW7jNx92dJfH9vZ0IPfQY6EzS99vxvbCuGrR+juKH0uHeyrG+vXhfyKH9IG3LquRqPxCdxOPH9NlK+cQslK9ZgP3yYtEdfQfdyIJtD67ExOZ+Nx/LJKzu9m4JarULl709+hA9alZbmYc0vSZxCCCGEEEIIIS6cJMyuYI6Mk9iLdTjLNZiKUiRh9l9wchd8Pvp0LayYqnOxFKeTooWLsHTrii4iguBbb8F+9Cjz6/Xk3Uw9bRKC6FQ7hMVHF/PUL0/xSd9PCIoIwul2otN4Z5wFDB+OMzWNiq1b0YYEV47tLitHYzFf9EcKthgIthhoEu1P8pQyDmeX8d7seTyf9wAqFG/Sq+15Ju22fgz2Etx+dTF3bI9t/wF8Wre6uA8AuD0KpTYnJVYXJTYnJVYnJcaalLSLpTSxP1GTXyTucBKfT1vAR42HVPYz6tS0iguifa1g+j7QhVqhFup5PIxxW/HR+Vz0OIUQQgghhBBCXBySMLuC6cLDsQLOCo1318EaTas7JHEpOa2w8FZwVkDNbjDyEziV5FIUhdJly8h9ezKOlBQCRo2kxosvorFYUF54g6lvrQEUHupTDwWFT/Z+AoBBY8DutjP0m6F0jOrIvS3uxa9lS2LnzKZi61Z8mntnOVn37CV17FgCb7iB4PHj0AQEXPTH89FrmTqmJUPe/5m56aFcFT+KlllfwY+PeJNmXR7+wxptZ+W0epdjAto+DxLd9FoUtxuVRnNGU5fbQ6nt12TXb5JeZ5y7zkyM2VyU/a4+2+9p6l9Db5+a/FSzLW3igmifEESH2iE0iw3w7hx6ysGCg4SYQggxhfzJaEIIIYQQQgghqpskzK5ggdePwS/gGMbSdVLH7L9g1QuQdwgs4TDik8paWOUbN5Lz1iRse/cCoAkKwtCgQWW3d1YdweVR6FI3lDYJQQDM6T+Hzw5+RsPghqxKXUV6WTpr0tbwSOtHUBSF7IpsItqcnr1WvGQJnooK8mfMoHDePILGjSNo3Fg0FstFfcTaYRZeG9GEez/fwYgTV/FTq3Bq7n0PVr8M1iLo8xKo1X9vsJ3zoDwXxTcGGgxFBag0GooqHLz43QH2ZhRXJr3KHe6LEr9Jp8HPpMXPqMPPpMPPqMXPpCMqwET7Wu15Oy4IA27S774b3969MdQcBYDjxAnyP/6YJcpG5tbJZnL3yfSM7XlRYhJCCCGEEEIIcfFJwuwK5tOiBRS2gPWrvTPMxL9X8trK2VJcNQXM3qWSpStXkn73PQCofXwIGj+eoPHjK5dOHs0p4+sd6QA82LsuSdlJ6DV6GoU04s6m3h0x+8T14ZO+n1DqKEWv0bPp5CbuWHEHI+uO5Kl2TwEQ/sTjmNu2Ifedd7EfOULe++9TOHcu4U8+gf+QIVxMQ5pGsv1EAbM3pjBsXxfWdAklcN0zsGkK2Ipg8Lug+Rt/NNXuDa1voSzTxMnuvQi89lpyR9zI7Z9uI63AetYuZr3mVKJLh++pZJdf5bvud8mwque+Ri06zV8n8wrnL6J83XrK161HsVoJuvFGrPv2UTR/Ac3izcyrq6ZpqMwWFUIIIYQQQogrmSTMrmCKouDRheMu1aAvkhlm/1r2MvjmLu9xy/E4DPUgJQV9XByWrl3R16qFuX17Qu68A21wcJWub688jEeB3g3DqVvDwLDFT5BZlsnb3d6mZ5x3BpNKpaJ1ROvKPhszN+JW3GhU3qWCWeVZlDvLqdWrF5YePShdupTc96fgSE6uXJqpKAqKw4HaYLgoj/zEwAbsTC9mV1oRY/e3ZOGQqei+vcc7a0ylhqve/+tBAuNg4FuUPPwI7vx8Dief5OZpv2BzeogN8uHpQQ2J8DNWJr18jVq0fyPhdaECRo7EcfwEBZ98gnXnTpQbbsBxLBmAxq37s+6ah/A3+F/yOIQQQgghhBBCnD9JmF2BFEXhzWWHKEnayZg5k9GGNaTO89OrOyxxqejN0O1R2PIhJaruZAwchKVDB2JmTEel01Hz60Wo9Pozuu3PLOH73SdRqeDBPnVxuB00D2uOW3HTPrL9H97u/pb30yu2F+HmcACm75rOoiOLuLfFvdzS+Bb8BgzAt29fytauw9y5MwBlq1eT9dzzBN9xOwEjR6I+SzznwqDVMOW65gx672d2pxfzQmpTXrxmLswfD5HNTjfMOQDWQohtf7q+maKA2wlaPR6bjdJVqwB4sTwSm8FD17qhvHNtMwJ8LizG86VSqQh7+CGMDRvi17cPKpUK+5HD3tDjoyVZJoQQQgghhBD/AJIwuwKpVCp+2HOS0mwPYwBXQTGKKYhzKIcu/klUKmhxIzZ9SzJHjwanE8XpwGOzoTYaz5osA5i0wpuEGdQkktphPmjVWl7t/CrF9uK/3IGxcWhjwJucrXBWoKDQMrwlAKtTVxNmDiOxR/fK9kVfzceVk0P2Cy9i27ePyJdfvuDHjg704e1rmjF+5lbmbkqhVXwzrrpnGxgDTjf65R3Y9TkExkPT66Dptd4k2vcPQJeHyTwZgVJRQbYpgIOBcdzdvTb3966LRl29/7WoVCr8Bw0EQPF4KF2xEoDHU6fwSH4nEoMTqzM8IYQQQgghhBB/4dKvTxLnpVaohUKjL4paDS4Xrvz86g5JXGylWZC+rfLU0KA+/kOH4tu3LzEffojaaPzDrjtSC1l5IBu1Cu7pWZPxS8czafskrC7rOc1gUqlUvNH1DX4Y9gPNw5pjdVl5fuPzXPvdtfyS8Utlu6h33yH8mafRhocTNGYMAIrDQckPP6C4z7+gfvd6YdzTozYAjy3cwxF7IBj9Tjcw+IHe4q3ht+YVeKcJLLoVSjLITjnIiinzANgc25wZN7biob71qj1Z9nsVW72/xh4VZEUYqBtQt5ojEkIIIYQQQgjxVyRhdoWqFWbBo1Jj9Q0EwPXhNZCxvZqjEheNosDiu+HjPjhXvIficqHSaIh44Xmi/vcmKo3mT7v/OrtsRItoTtp3sjN3JwsOLaDUUXpe4cT4xQBQ4aygfWR7YnxjaFOjDYqi8OrmV9lRuIeg666j9soVGBs2BKBo0ddkPPAgyYOHeBNnHs953Xtir7p0rB2M1enmjk+3U253nf5wwBvw0GEY9gEkdAVUYC/BrdYzcltj6mceBGDYA2PpmxhxxthZ5VksPrqY3bm7zyu2i8Hctg2xMz8h9r33+GL8cnQaXbXFIoQQQgghhBDi75ElmVeokIByND5HyPfxxac4H+fx/ZhyD0NUy+oOTVwM22fC0RU47UZSXl6A4euDRE16yzurTPfHCRWn28OU1UdZfyQPnUbFvT3rEBPkw3s93qPcWU6YT9gFhRVsCubVzq9ic9nQqXWsT1/PZwc/4+ujX7Ni5Iqqs9fUKjT+/jiSk8l44EEMMz4g9J67sfTsiUr192d5adQq3rm2OQPfXc+x3HJ6T1pLoFmPTqNGr1Gj06rQaRLQaZ4hPD6XBkWr+TEniDQlkE/vncyzNUoJ7dKmcrz5h+eTU5HDbY1vY0fODp765SlahrdkVr9ZpJakcuvyW6kVUIupvaaiKApLji2hhrkGzcObo1Nf/GTW3ry97ApIZmSbkfhqLs6mCUIIIYQQQgghLi1JmF2hlmZPwiduDydN0cQArgoNyE6ZXh6PdzfFvQvBNwJC60FofajZHXR/vIzxipF/DJY9iduhIm1rHZxZOWA4iqei4k+XYe5JL+bhBbs4mOWdRXZzpwR2F60m3L833WK6XdQQjVpvHLUCajGq7ij8Df74G/w5UXyCZzc8y21NbqPj1VfjN2AABbNnUzBzFvZDh0i/+x5C77uXkDvvPKf7hVgMTLmuBWM+2kxZTj6Zep/TRf7P0BWVCh7qU5e7utVG/ZslmHnWPN7c+iZWl5UY3xgCDYG0r9GeBsENAEgvSyezPBOT1gRAiaOEp355CoCtY7aiU+u48ccbUavUvNDhBWL9Ytl8cjMKCg2CGvyt5a778vexLn0dbSPa0iK8BctPLGfmvpm8k/QOS0csJcgYdE7fjRBCCCGEEEKIy08SZleo+sF1OJibxfSmzRl1SxtMOyd76zj912XvR/nufgpW7qHoqJmQhpvwT7B6P3ss7XTCbNWLoDVAWAOo3Qt0puqL+bfcLvj6djzWCtK21MSeno8mNITYjz9GG3T2RIrN6WbyyiN8uD4Zt0ch0EfHc0MS8QnYzwNrH+PjvR/z5aAvL8nsqEhLJM+0f6by/JO9n5CUk8TnBz+nY1RHPD4GQu66i6AxY8ifOYuiL7/Ef+hQ76OWlGDbtw+fdu3+cMaZMzOTkh9+wNioEa3atWPtXa3IG30NjnqJFNw6EYfRB6fbg8PlwelWcHm8xy3DDNT48mOsPn3xadumcvxgYzAvdHiB5SnLGVRzEGqVms7RnSvv1zS0KXP7z8XpcQJgdVnpENkBq8uKUWvE5XGxO3c3bsVdmTSctH0S+/P3816P9+gW0413kt4hKTuJGxreQK+4XiQXJfNT2k+0j2xPYnAiXx/5mi8PfUlR/SJahLegS3QX8qx59I3vK8kyIYQQQgghhPiHkITZFeqVLs+zbO0KTioO8kMKiFHz306YuZ3w00uw8X1wuihJCcNRqiVzcyDq2u3xjed0sXhFgc0z4Nd6Xj4h0O4OaH0LmAKr6wm8fnkbJXUrGZvCsKbbUPv6EvvRR+ijo8/afOuJAh5dsJvkvHIABjWpwXNDEgmxGFiTdoRgYzDdortdkmTZ2dzT/B4segsDEgYAMHvfbJafWM79Le+n/f0TCbnzjspZcgWzZpM3dSo+rVsTev9EfFq0AMBVWEjp0qUUf/c91u3euny+fftibtcO8/5dFOblYMo+SULKERKWLEZtOHMZY8kPP5Dx2eeU/fwLtZYtBWBP7h7qB9enX0I/+iX0O2v8Zp2ZZmHNKs8jzBHM6D2j8lyFitn9Z5NRmkGIKQSAWN9YKpwVRFu8v0b78vaRlJPEsDrDAPg2+Vs+2vMR5c5yEoMT6RrdlSJ7UeWuo60iWtEqotX5feFCCCGEEEIIIaqFJMyuYPGhamzl6aTtsRKYacDi+x9ekqnWoqQlgduFKnEQMTc/TvK1t2KoXx/zozNArz/d1u2ELg9B7iE4sR6K07zJtp8nQ8tx0H4C+EVe/mfISII1r+Eo1VJRYEZlgJhpUzHWq3dG03K7izeWHmTOphQUBcJ8Dbw0tBF9ThW2VxSFbjHdWDx0MYbLWBcr1CeUR1o/AoBH8bDg8AIyyjLIs+YBkOsuJsSjQ6PWoLjdqHQ6KrZupfyXDfi0aEHBnDlkv/EmuE4V9lep8GnVCkvXrgD49upF3KdzyXzoYfyHXnXWZBlAyY8/AuDXrx8qlYq0kjRuXn4zcX5xTO81nWBT8Hk9n0atoWloU5qGNq289mbXN6u0ebDVgxwvOU6TkCYAWHQWesb2pH5QfQA6R3euMqtNCCGEEEIIIcQ/jyTMrlD78vdx2DSReiYTge+VkmX2p3ZkJjht/4w6XRdDcQbYSyCsAa6iItJ/1GFpcjMh105CC9TdtBHF4UCl16N4PBR++ikBo0ahNpmg00TvGG4n7PvamyzL2eedobblA5i4x1v/7HLK8+5saWg3gLg7nsKVlYVPqzNnHq0/ksvji/aQXmgFFLo3K6BzQxWd6nYA4Nblt3Ki5ARvdX2LJqFNLucTVKFWqfli4BcsOrqI/gn9URSFu3+6G7vbzhtd3qD+/RMJvPYa8j+ZSdC4sQAY6tYDlwtjYiJ+gwbhN6A/uvDwKuP6NG9OwuJvvL+OgDM7m+yXXib8qScr2/r27YfHbsdvQH/AW5tMr9Hjq/cl0HhpZxHWC6pHvaDTSc6bG998Se8nhBBCCCGEEOLyk4TZFerX5V/5vt7aTC6rBkUBVXEahNSpztAuPbcLtsyA1a9AYAKOgfNIu/1OHMePY0/JIGBCQWW9L9WpmWW5775L/vQZFH//PTHTpqENCkJRFCpcKsxNrobGo+DoSvj5bdBbTifLPG7I2g2RzS/5Y5VXxKHt9iGGNj0xGv3hLDPLvkraxzM/v4I68CSRyhO8NrwpTyWNYNuOQjpEt6Z+UH10ah1Z5VksO7GsWhNmAAHGAG5qdBMAqSWpZJRl4Pa4qWGuAUASqbR4/GE0p5aM+rRuRc0ff8CQkPCn42oslsrjrOeep2z1aiq2bKHGKy/j27Mn/oMG4j9oYGWb9pHt+eaqb3B5XKhV6ov9mEIIIYQQQggh/mNUiqIo1R3EhSopKcHf35/i4mL8/PyqO5yL5sf9J7hn1i5mrX6T6FAVsRO6o+50O/hHVXdol076Nvh2ImTvAcCmb07q907c+QVoa9Qg9oMZGOqcmTCsSEoi9c67UIqLKQ+twafD7mdduZGCcgfNYwMY3TqWQU1r4KPXgqMC9D7ejvuXwFc3QFwn76y02r3+ZHfG81RwHI8pgmP9B+DKySFmxgwsnTud0azU5qTb+x9gC5yLWlvO14O/p3ZQLI+vf5wKZwV3NbuLekH1OFF8AoPGQA1LjYsb50VQ5ijjQMEBWke0JrkomaGLhxJpiWThkIWYdebzGtN+/DiZDz2Mbd8+AMwd2hM9ZQpqk4nUklSe/uVpnmz3JHUD617MRxFCCCGEEEII8Q92obkiSZhdwdIKKuj8xkr0WjjwwmA06oucyLmSWAth1QuwbSaggDGAsvDxZLz3LZ6KCgz16hHzwYzKJXkej8L21EJ2pBayK72YXWlFkJrCixs/JKKikCK9mWfb38zhwNjKW1gMWq5qFsnoNrE0ivL3Xlw/yTuT7dSuiYQ3ho73QeIw0FyECZjbZsKPj5DnHEHuV2vRRUZS88cfzlqb67kl+5i14QRxofDIUDU947pX7tT4T7QufR3P/PIMzcKaMbn7ZEocJSw9vpRhdYad8yYFisNBzuR3KPjkE+8FrZY669Zy/45nWZO+hk5RnZjWa9oleAohhBBCCCGEEP9EkjDj35swe33LG8zd/xn2nD6suOlZ4kPOb4bOFW/vQvjxUSjP9Z43vQ76vEjJuq1k3P8APu3bEf3uu2h8fQEos7u4+7Mk1hzKPWOoZmYXD/40nZCTJ1ACAvH7+ju+PVTIF1tTScmvqGzXOMqf0W1iGdIsEostGzZN9Sa3nN7dKAmIhfb3QPPrT89GO1cpG2H2YPA4KQ0ZS853Rwi5++4qSwl/tSstn2sWPYijsB1zxoykU52Q87vnFcbmslHqKCXUJ5Rpu6YxdedUOkd1Zmqvqec1XtnPv3Dy6acx1K5NzAczyLPm8ebWN5nYciKRlmrYyEEIIYQQQgghxBVJEmb8exNmU3ZOYfqu6bTdUpMJrihqd4jBt00jaDC4ukO7uFa/Cmtfg5C6KAPewh3QqLJGWdm6dZjbtausVXay2MpNs7Zx4GQJBq2abvVCaRoTQNPoABpH++Nn1OEuKyfzwQcJGH0tvt26oSgK9hMpJLktfLYllWX7snC6vb/tffQahjSN5No2sTQN9qDa+jFsngYV+d7Yej1/egOBc1GcDh908yYBGw6FUbNQXC7QaFCpq9bYcnsUen34KnnGz9FiYcOYnzBpTef5ZV65vj7yNZOTJvNUu6foHdebrVlbcXqcdIjscE7jKIpCWmkadredOoH/8np+QgghhBBCCCHOiyTM+PcmzPKseTy2aAd1Pv2CIcc3ENywlLA+8XDHz9Ud2oVx2ryF9mPanD7fPgul2Q1kvfIG5T//TNwXn6MLC6vSbV9mMTfN2kp2iZ0Qi56Px7amaUzAWW+hKAqqU7XIyn75hbSbb8HctQvB48Zha9yCRUkZfL41leTc8so+DWr4MbpNDFclBuJ/8CvYPgvGfQemU7suHvoRIhqDf/RfPJ8VPukHJ3di1zXA2ellLF17/mHzTzel8PR3GzBHLOO+Tj25tdkNfz7+P1iFswKT1oRbcTNiyQiSi5N5rv1zjKg74m+P4fa4Gbt0LPvz9/NK51foF9/vEkYshBBCCCGEEOKf6EJzRbKd3BUsxBRCYlgceaYAAFwVGihMrd6gLtTJXTCtA8wZCkVp3ms6I0qb20h/4BGKvvoK58mTWLdtq9Jt9cEcRk3fSHaJnTphFr6+q+MfJsuAymQZgG33blCpKF+7jvxZswi2GLi1S01W3t+FL29rx7DmUei1ag6cLOGZxfto++YGHjzRhm39lqAYT93DWggLb4F3msI3d0HuobPfWFHg2/vg5E4wBZF9rC5pt99N3vQZZ22eW2rn9eVJKC4/Hmz+7L86WQbgo/NBpVLhcDvoENmBUFMofeL7ADDvwDxSSlLO2k9RFPKseaSWpFLmLCPQEIheo6dpSNPLGb4QQgghhBBCiP8ImWF2BatwVjDym7HEbjzGfT9Y8Qm3E9c9Hx49cXrW0z+JywHT2kP+UbBEwKiZEHd6OV7B7NnkTHqbyP+9iV/v3pXX5248wbNL9uFRoGPtYKaOaYm/6dyKxjtOnKBgzlx8+/bF3LYNisvFiTFjsHTpQuDo0ZQZLXy9I4PPt6RyOLussl+dMAuj28QyMt6G34qHIOU3s/vqDfQu1/x1phzAhvdh+ZOg0lDe+FVSn5gMWi21vvsWfXz8GXHd9vm3bLA9i5+zK6vHv4FBd27P9U9nc9kwao3sy9/Htd9di1atZfmI5ZQ7yxm7dCxGjZFlI5fhUTw0neNNjq29Zi2BhkDSS9OJ8Yup5icQQgghhBBCCHElutBc0UXYBlBcKiatiTx7Kr4BdgBcNm8dLwpP/DMTZls/8ibLzKFw10bwCcKRmorKaEQXFkbQ2LFYevZCHx0FeHfCfPXHA3y4/jgAo1pG8/Kwxui1fz0xssJZwXMbn2Nv3l4ebPkgPeN7EvHM05Wfl/70E7Zdu7Ht2k3+jA/wv2oI1914I+MmdiEptYjPt6Ty3e5MjuSU8cJ3+3lNq2ZAo5e4pX8Bicc/QXXwezh06hXXETpOhDq9we39tVJ6v0T26z8CEDh69FmTZRuP5bMmYyWGUCf1oh3/uWQZULkLqI/Why7RXfDV+xLqE4qjzEGBrQCjxvu5WqXGrPNuemFz2VAZVZIsE0IIIYQQQghxycgMsyvc2tRfePKdDXz4/SeodCrqDc9AdfVsSBxa3aGdm/J8eK852Iph8LvQcizW3btJu+NOtOHhxM2dg8ZiqWxudbiZ+OUOlu3LBuChPnWZ0L12laWWv5dcnMwPyT9wW5Pb0Kl1DF8ynOPFx1k6YikR5gjm7p9L3cC6tIloAy4XJcuWUzBrFra9eyvHsPToQfSU91GpVJTYnCzekcHnW9LYf7Kksk3NEDO3J7q4qnwhxv3zweOEyBZw60+gUkHmToo2HuPkk0+h9vOj1rKlaAOrJjgdLg8D3l3P0ZwyerXI59n+3YnxlQSQ0+1Ep9Hh9Dg5Xnwck9ZU+b38ti6dEEIIIYQQQgjxZ2SG2b9c19iO6IJK+TmyMb0jc0DJ8M4w+6dZ84o3WRbeGJpfT+nq1WTc/wCKzYYuIgLFZoNTCbPcUju3zNnGrrQi9Bo1b45qwlXNos46bLG9GF+9LwC3Lr+VnIoc6gXVo3dcbx5p/Qg2l40IcwQ5FTlM2jYJl+JiweAF1Auqh9/AAfgNHIA1KYmCWbMpXbkSjb9/ZVJGd+QgY1rU5fp2cezJKObzLaks2ZlJcl45j66FpzSDGVV3KBOMy6nRpCfqU/2UkIbkvjsRgJA77jgjWQYwfd0hUpzLCbZ04q1Bo/H3+e/NLjsbncb7PejUOuoG1q3ymSTLhBBCCCGEEEJcLpIwu8KtSVuDI2o2bw6Ion1EHKqjO6Do7IXRr1jZ+2HbJ97jfq9SOH8hWc8/Dx4P5k6diJo8GY3Fu9zuSHYp42dtJb3QSoCPjg9vbEXr+KAzhrS6rDz9y9OsTl3N9N7TaR3RmqG1h3Ko4BAhphAA2ke2r2yvQsXIuiNJK02jXlA9rC4r131/HX3j+zKu6Tii33sXR1oaqLzLPT3l5aTedDMqnY7Aa68lcfw4Xh3ehCcHNuTbXZl8sSWVXenFfHbAxWf0IDbFxDVZRxnaPIqoABNRk9+mcO5cAq8fc0bsaQUVTN8xE2PEj9QwJ+Pv0/9if+NCCCGEEEIIIYS4AJIwu8IV2gopUe1FY7KT7GpLI8M/cMlpyi/e9waDKfglleyXXgLAf/hwajz/HKpTtbs2HM3j9k+3U2pzER/sw8zxbUgIMVcZyu1x41bcmLQmLDoLDo+DjZkbaR3Rmrub3f2Hs5BCfUJ5st2T/LoC+cfjP3K06CjWo1ZubXwrHsVDhp+LBP8EABwpKah9LbgyT1IwezZBN40HwKyB0W1iGd0mln2ZxXyxJY1vdmSQWlDBm8sO8eayQzSN9qd/4xr0f/JF1Hp9lTgUReG5Jftw2ALwVfy5vfl1F+c7FkIIIYQQQgghxEVzTjXMpk2bxrRp0zhx4gQAiYmJPPPMM/Tv358TJ06QkJBw1n5fffUVo0aNOutn48aNY/bs2VWu9e3bl6VLl/7dsP7VNczSStJ4b+NS9B/8TO+cTJo9fA/+Q4ZUd1jnLmsvGHyxZVvJev4FfNq0IXTifZUJrvnb0nh80R5cHoVWcYF8cGMrgsxVk01ppWk89fNTNAxuyKNtHqXcWU5aaRr1g+qfczhOj5MVJ1agUWvoG9+XdenrmLBqAv3i+/Fm1zcBUFwuSleswFVQQNAY70yxk888izM9jaCxYzF37oxKrabC4eL73SeZvz2d+j98Rro5lNUxzVFUahrU8GNAowj6N46gdpgvy/dlcdvcbWjVahZNaEmjyFDUqr/exEAIIYQQQgghhBB/32WtYRYdHc1rr71GnTp1UBSF2bNnc9VVV7Fjxw7q16/PyZMnq7T/4IMPePPNN+nf/8+XnPXr14+ZM2dWnhsMhnMJ618txi+GEXWGs758A2G56ThSUqs7pPMT0QgAYyDEzfsUVCpUKhWKojBpxWHe++koAIObRvLmyCYYdZozhjhefJyknCQOFx7m1ia3EmQMOq9kGXhrZA2oOaDy/ED+AVSoCPcJB+BY0TF25e5iYJ+B+Gm8vx89FRWUfPcdnooKyjdsRF+zJkE33oj/VUMY1SqGwb4VHJ+8ChSFoIb1+LrClwMnSzhwsoS3VhymTpiFAvdRfOLn0z/ydppEhZ9X7EIIIYQQQgghhLi0zilhNnjw4CrnL7/8MtOmTWPTpk0kJiYSERFR5fOvv/6aq6++Gstvdj88G4PBcEZfcdrGgq8oCkuFE2A7eRIc5aDWgVb/l32rVdJc73uzMZRt3IgzPYOAUSNRqb0zquwuN48s2M3inZkATOheiwd710OtPr2sMrcilxm7Z/BQq4foEt2FR1s/SreYbgQZz6xrdiFub3o7A2oOwKQ1ATBz70wWH1vMjpwdvNjxRQDUPj4kLFlC4aefUrRgAY7kZLKee47cyZOJnT2L3Elvg6Lg268f/3vqGp4od7ByfzY/7D3JL0fzOJJThinme7SmDHQBW4FhF/UZhBBCCCGEEEIIcXGcdw0zt9vN/PnzKS8vp3379md8vn37dnbu3MmUKVP+cqw1a9YQFhZGYGAgPXr04KWXXiI4OPgP29vtdux2e+V5SUnJ+T3EP8TajOVYm2aTYujF+z5r4ZX3Ydz3EN+pukP7Y+X5sPxJsBXjQU/W85/gTE3FY60geNw4Cssd3D53O1tOFKBVq3hlWGOubh1TZQhFUbh95e0cKTyCTq3j0TaPcn3D6y9ZyDG+p+/fILgBW7K2MLLuSAA+3P0hqaWpjG80npqPPUrI3RMoXriQgjlz+X97dx4dRZX+f/zd2fcEyE4gECAgEhYjIgiKghBEEAZFcEH4OiIIo6goPxwVl8GMIIroiDOjgo4iAgOuILIYENkxkR0BWSUhGMhOOkvf3x8ZepIhQMKSpNOf1zl9IFW3nro3z6178LG6yhQX4xkTQ9izf8bF15eQcY8BUN/Xg8EdG3HXtVGsOPQjpzKiWPvr41BvGU9e+6crNg4RERERERERuTRVeoYZwLZt2+jcuTMFBQX4+fkxZ84cbrvttrPaPfLIIyQlJbFz587zxps7dy4+Pj40bdqU/fv388wzz+Dn58e6detwdT37a3kAL7zwAi+++OJZ2+viM8wAPtv9Ge/+sJODh2PYGDGXkPS1cMc70OHsNzDWGt88CZveg7A4fjf3cGLGW7iFhhKzeDFHCmDE7E0c+D0Pf083Zt4XT9cWwfZDs6xZZJzOICYohh+O/sBbyW8xuetkWtRrUa1DKLYV4+biRmFJIT3n9+SU9RSv3fQavZv0Jj0/nWDvYCw2Q9GRI3g0aXLOOKOXj2bNb2uY3HUy/Zs54PPnRERERERERBxMtT7DDKBly5akpKSQlZXFggULeOCBB1i1ahWtW7e2tzl9+jRz5szhueeeu2C8IUOG2P8eFxdH27ZtadasGUlJSfTo0aPCYyZOnMgTTzxh/zk7O5tGjRpV2LYuuLvV3WzZ/jPHd+8jLTOAEIBTB2u4V+dxfCds/qD07wmJBPrEYt23H78ePVhxKIcJ/97KqfwiGgZ5M2tER2LD/O2H7srYxdiVY/Fx82Fev3l0i+pGl8guuLpUXDy9ktxcSi8PD1cPZtwyg6/2f0WPxj0wxjDyu5HYsDH1xqm0bNKy3HEFxQV8se8LWjdoTVxIHPFh8Ww5voVTBaeqfQwiIiIiIiIiUnVVLph5eHjQvHlzAOLj49m0aRNvvvkmf//73+1tFixYQH5+PsOGDatyh2JiYggODmbfvn3nLJh5eno61YsBfsv9jQLbt/z7m9kAlAyy4Jp5qGY7dS7GwNKJYGxwVT9o2g03Y9g36v/x+rJf+PmHLQC0jQrkvQeuJdTfC4ASWwmuLq5E+kWCAYMhPT+d6IDoGimW/a/2oe1pH9oegEPZhziefxybsZX2F1i0dxFdG3YlxCeEN396k493fcxNUTfxdo+3GdpqKHe2uJMgr6CaG4CIiIiIiIiIVNpFP8PsDJvNVu55YgDvv/8+/fv3JyQkpMrxjh49SkZGBhEREZfatTrjSM4Rvj/9McM8LPgWGopPu+JaW+8w+2Up/JoErh7k+NzOvkmJTA2+nrW/5QPg7e7KA12a8GiP5vh4lE6/n0/8zPM/Ps9LN7xEu5B2zLx1JlF+Ufi4+9TgQM4tOiCaZXcuY9fJXfh7+LPv1D6eX/s8CU0SmHrTVIa0GkLSkSS6RHbBGIOvuy+413SvRURERERERKSyqlQwmzhxIn369KFx48bk5OQwZ84ckpKSWLp0qb3Nvn37WL16NYsXL64wRqtWrUhMTGTgwIHk5uby4osvMmjQIMLDw9m/fz9PP/00zZs3p3fv3pc2sjokJjCGGyJ6kOGzBt/CPIrzXfA8VQvvMCsuhKXPAJDafBgHX36boOwMGl+Vzuare3H/9dGMuqkZIf7l7w78bPdn/Jr1KzN+msH7vd8ntl5sTfS+Svw8/OgY3hEAa4mVDqEdSDqSxKmCU0QHRPPNH77BxeJSs50UERERERERkYtSpYJZeno6w4YNIzU1lcDAQNq2bcvSpUu59dZb7W0++OADoqKi6NWrV4Ux9uzZQ1ZWFgCurq5s3bqVDz/8kMzMTCIjI+nVqxcvv/yyU33l8kJCfUJ5u8frzPvLH2jMHoryXSE3DYpOg7v35T9hsRVcPcBiKf35ty2QewKsOWDN/s+fZT4tekKbQXDqAIUFeZx2qcd7n+ZxZ3YGJ7yD8Bp6L6t7tyE80Mt+ir2n9pKcnszgloOZcN0E/D38eaT9I5d/LNXg6uCr+ajPR+QV5eHjVnpXnIplIiIiIiIiIo6rym/JrI0u9c0HjmBXxi7WPDSBrjv30qBdAaFXnYQxGyGk5fkPzDkO+1dCQCTE3FS67cQeWD21TNHrf4pgJYXw9AHwqV/a/h/d4Vjyuc/ReSzWHi/x4lc7WbThFzrl7eSJFZ/hbivBK3EqTQfeXq75kewjDPhiAMWmmA8TPrQ/G0xERERERERE5HKo9rdkSs34W8rfCK23D4BDnp0IHfEYBF7gzaAlRfDRHXBiV+kdYGcKZtYc2Db//Mdas/9bMAttDVjA0/8/n4Ayf/fnVL04HvzHen46nIkFT+79dTvuthJ8u3Wj0YC+Z4VuFNCIXk16kV2YTUO/hlX8TYiIiIiIiIiIXFkqmDmItiFtWXr9cb4Nbckt1wyiY3SHCx+0/h04sYv87PpkfXcK14Ov4eIfgKunBZfA4bg1aIBvfBx4+GOzeYCXP5aABli8AkqLYmcMeOecp0g5ksnD/9rM8exM/L3c+FvPhoSvPorN3Z3wZ/+M5czXOoGNqRvZm7mXIS2H8EKXF/Bw8Si3X0RERERERESkNlDBzEGMbDuS0JK+jNufwi8nCy58QNZvkPQqANbIP5D53mJYvbtcE/dGjWg+7HkAfp/2Ohn//Ce4u1P/nnsIm/j/AEh7+S8UZ2Tg6u9XWmwL8MfF3x+fjh35OseHZxZtI/RUGvHh9Zg6vBNNoxpgW7KY/ORkPKKj7ec6XXyaSWsncTT3KIUlhYxoM+Iy/WZERERERERERC4vFcwcRJGtCE+3Y3TIXkWHjYUQ/jH4R8Itf674gKUToSgPGl2PV8cHCPaOwZadQ0luTumfOTm4BQfbm5fk5vznREXg5mrfnvfjjxQePHhW+C0D/8izphUYw7vfT8PVVoL1EyhcvBjPmKb4d+9err2nqycj2ozg092fclfsXZf66xARERERERERuWJUMHMQablpvPjjCGatLAHAFnkMl8i4igtm+1bAzi/IPuxDgXtHAry8CBkz5rzxw59/nrDx4ynJycHi4WHfHvL44xSfOIEtJ5uS7BzyT2aSvPMI89NdIQQevzEaj1X+lOTkQEkJrgH+Z8X+/fTv1Peqz+CWgxnUYhCuLq5ntRERERERERERqS1UMHMQEX4RuAcGUeB2Eq9iQ3G+Kx6nDoIxUPY5YMVWWPwUAJkZLchb+29cI5vjFRt73vgWiwWLry8uvr7ltgf07mX/+85j2Yz812aOXn0aHw9X3h3cnoQ24dB3HcYYTH4+Fm/vcscXlhTyx6V/JMgriMSuiUT4RVzaL0JERERERERE5ApzqekOSOW4ubjxw9A1HI+IZXe9RhibBQpz4PSp8g2Ngbg7sfk2Iv9AFgB+3bpe8vm/3nqMQTPXcvTUaRrX92HRIzeUFsv+w2Kx4OLri8Wl/JTafXI3x/KOcSDrAN5u3v8bVkRERERERESk1tEdZg7EGMP3Y8Yzb9NRevrsx7Podzh1AHzq/7eRuxfc/Aymw8OE1P+cgh078GjW7KLPWWIzTPtuD+8k7QegW4tg3hragSAfjwscWaptSFsW3bGIY7nHCPIKuuh+iIiIiIiIiIhUFxXMHMiUTVNYnPUx7vW7k1YYhj+/w6lD0DC+tEH+SXvxzDWoAQ0efPCSzpddUMS4uSms3J0OwMgbY3i6d0vcXC98Y2KxrZipm6ZyX+v7aOTfiIZ+DS+pLyIiIiIiIiIi1UVfyXQgkX6RxB2EMT9sI32Xe+nGUwdL//xlKUyPg/UzAShKTb2kc+1Lz2XA2z+ycnc6nm4uTL+7Pc/cdlWlimUAn+z6hDm75zB8yXCKSoouqS8iIiIiIiIiItVJd5g5kDtj7+SGhsVYP51CemQ9iAEyD0HR6dIH/RfmQvYxCg8eZH9CHzxbtqTpwn9jca3aWylX7DrOuLkp5FiLiQz04u/3X0tcVGCVYtzS6BZWH11N35i+uLu6V+lYEREREREREZGapIKZA/F28ya4aWN+A0ryLeQ064d/w3hYM720cOYfCTdNIHfe5wC4BgVVqVhmjOFv3+9j2rJfMAaua1Kfd+67hmA/z0rHsBkbJ/JP0CigEe/1eq9qAxQRERERERERqQX0lUwHUmwr5rEdLwHgWVDImvZToUlXWPNGaYOEV8DTj9wfVgPgd2O3SsfOsxYzZs5PvPZdabHsvusb8/EfO1WpWAaw4JcF9Pu8Hwt+WYDFYsFisVTpeBERERERERGRmqY7zByIm4sbWQGlKQsqyCXl2En4eRKUWCGmO7QegK2ggPwNGwHw7Va5gtnhjHxG/mszu9NycHe18NIdbRh6XeMq988YQ9KRJE4Xn6aguKDKx4uIiIiIiIiI1Aa6w8zBTO3/d0rc3HABYrfMgH3LSnfc9hpYLORv2oSxWnELC8OzRYsLxluz93f6/20Nu9NyCPbz5NOHrq9ysWzPyT0kbkgkryiPt3u8zZQbpzC01dCLGJ2IiIiIiIiISM3THWYOpln95my8OYHv951idNaX4A20uweCS4tjBbt3A6Vfxzzf1yGNMby/5gCvLN6FzUC7qED+fv+1hAd6VaofBcUFuLm44Wpx5anVT3Eg6wDNgpoxuOVg+jTtc8njFBERERERERGpKSqYOZgVh1bwevdf2Ne8PvszHuPvXa1Ybnravj/4oYcIvOMOTGHROWMUFJXwzKJtLPzpNwAGXRPF5IFt8HKv3AsC3v35XT7a+REvd3mZHtE9GNpqKJvTNtOqfqtLG5yIiIiIiIiISC2ggpkDOpz7K26eRXxXeCdpHW8hwt273H730NBzHpuadZqH/7WFrUezcHWx8OfbrmLEDU3OezdaYUkhyw4to3lQc1rWb0lBcQE5hTmsOLzCXjDTVzBFREREREREpK5QwczBdAjrwAfF93Hy46WsbrCU/emdiAgsLZjlLF+Ode9e/G+9Fc/mzc86dvPBk4z6+Cd+z7US5OPO3+65hhuaB5/zXDZjw8XiwpRNU/hsz2f0i+nHK91eYUirIcSHxdMlsssVG6eIiIiIiIiISE1RwczB1PeqD+6R+P2eSmOPEPafyKVri9KiV+aCf5OblASubmcVzOZsOMykL7dTVGJoFe7PP4ddS6P6PhWeY8vxLcz8eSat67fmiWufoH+z/iQdSaJZUDMAwn3DCfcNv5LDFBERERERERGpMSqYOaDFeRuIB0KsJ0hOzwXAZrWSt2EDUPrA/zMKi228+NUOPtlwGIC+cRFMvastPh7lU3805yjH848THxZPtjWbDakb2HtqL49e8yhxwXEsHbQUV5fKPeNMRERERERERMSRqWDmgNbVy2D1ABcOFbWnwYnSgpkpKiL44ZGc3roNtxaxrP81gyXbUvl2RxrHs61YLDC+V0se6d7srOeVrT66mrErxtI4oDFfDviSblHd+FOHP9E3pi9uLqVTxNWiYpmIiIiIiIiIOAcVzBzQXTc8zM5mJ1j2RRGhZwpm3j7s7nEni8Nu4LvEFfyeW2hvH+TjzuuD23FLqzAAMgsymbN7Dvsz9zOt+zSuDbsWP3c/In0jybJmUc+rHiPbjqyRsYmIiIiIiIiI1DQVzBxQ90bdae+XxSenFnLCmsv4+T+zKXkfh2xe9jaB3u7c2jqMPm3C6doiGDcX2JGxg6sbXE2hrZB/bP0HJaaEX079Qmy9WJYMWkKgZ2ANjkpEREREREREpHZQwcwBHck+wom+A3nvZB7jbvwTq3/I4cPvXuFA/SjWPD6FhHZRdG7WAHdXFwCyC7Pp//ldHM8/znd3fkeoTygPt32YJoFNaBLQBEDFMhERERERERGR/1DBzAHlFOVwxCuPWKC1h5X2vlYArooO4ba7OtjbnS4+jbebNwEeAYT5hpFblMu+U/sI9QlldPvRNdR7EREREREREZHaTQUzBxQdEE1qwyZw7CDPXteA/A0byQX8b7rR3uZUwSlGfDuC22JuY2TbkbzS9RWCvYPxcvM6Z1wREREREREREQGXmu6AVJ2vuy9tWpcWx4qOHiV/3brS7d262dusOLyC/Vn7+WzPZ2RZs4jyj1KxTERERERERESkEnSHmYP6zceKN3Dym6+w5Ofj2qABXlddhbXEioeLB3fG3kmRrYhOEZ30fDIRERERERERkSrQHWYOalPJfgAsGZkA+HXtSqEpYszyMUzeMBmbsTG01VBiAmNqsJciIiIiIiIiIo5Hd5g5qIgeffmiaQM6t72dLl5XYYqL2ZC6kY1pG9n2+zbub30/0QHRNd1NERERERERERGHYzHGmJruxKXKzs4mMDCQrKwsAgICaro71a7YVgyAm4sb3/z6DaE+oXQM71jDvRIRERERERERqRmXWivSHWYOylpiZcdfnsGy7idW3n8VRyM9mXLjFPrG9K3promIiIiIiIiIODQ9w8xBnS46jfeni/E6mEbI3CS+P/I9W3/fWtPdEhERERERERFxeLrDzEEFegayvH0QLXdkE/bE40yOjiA+LL6muyUiIiIiIiIi4vBUMHNQFouFP3y0ClteHnH16tV0d0RERERERERE6gwVzByYi4cHLh4eNd0NEREREREREZE6Rc8wExERERERERERKUMFMxERERERERERkTJUMBMRERERERERESlDBTMREREREREREZEyVDATEREREREREREpo0oFs5kzZ9K2bVsCAgIICAigc+fOLFmyxL6/e/fuWCyWcs3mUkIAABW1SURBVJ9Ro0adN6Yxhueff56IiAi8vb3p2bMne/fuvbjRiIiIiIiIiIiIXKIqFcyioqL461//ypYtW9i8eTO33HILd9xxBzt27LC3eeihh0hNTbV/pkyZct6YU6ZMYcaMGbz77rts2LABX19fevfuTUFBwcWNSERERERERERE5BK4VaVxv379yv08efJkZs6cyfr167n66qsB8PHxITw8vFLxjDFMnz6dZ599ljvuuAOAjz76iLCwMD7//HOGDBlSle6JiIiIiIiIiIhcsot+hllJSQlz584lLy+Pzp0727d/8sknBAcH06ZNGyZOnEh+fv45Yxw4cIC0tDR69uxp3xYYGEinTp1Yt27dOY+zWq1kZ2eX+4iIiIiIiIiIiFwOVbrDDGDbtm107tyZgoIC/Pz8WLRoEa1btwbgnnvuITo6msjISLZu3cqECRPYs2cPCxcurDBWWloaAGFhYeW2h4WF2fdVJDExkRdffLGqXRcREREREREREbmgKhfMWrZsSUpKCllZWSxYsIAHHniAVatW0bp1a0aOHGlvFxcXR0REBD169GD//v00a9bssnV64sSJPPHEE/afs7OzadSo0WWLLyIiIiIiIiIizqvKX8n08PCgefPmxMfHk5iYSLt27XjzzTcrbNupUycA9u3bV+H+M886O378eLntx48fP+9z0Dw9Pe1v6jzzERERERERERERuRwu+hlmZ9hsNqxWa4X7UlJSAIiIiKhwf9OmTQkPD2fFihX2bdnZ2WzYsKHcc9FERERERERERESqS5W+kjlx4kT69OlD48aNycnJYc6cOSQlJbF06VL279/PnDlzuO2222jQoAFbt27l8ccf58Ybb6Rt27b2GK1atSIxMZGBAwdisVgYN24cf/nLX2jRogVNmzblueeeIzIykgEDBlzusYqIiIiIiIiIiFxQlQpm6enpDBs2jNTUVAIDA2nbti1Lly7l1ltv5ciRIyxfvpzp06eTl5dHo0aNGDRoEM8++2y5GHv27CErK8v+89NPP01eXh4jR44kMzOTrl278u233+Ll5XV5RigiIiIiIiIiIlIFFmOMqelOXKqsrCyCgoI4cuSInmcmIiIiIiIiIuLkzrwgMjMzk8DAwCofX+W3ZNZGOTk5AHpTpoiIiIiIiIiI2OXk5FxUwaxO3GFms9k4duwY/v7+WCyWmu7OBZ2pcuqOOOej3MuVpjnmvJR756S8Oy/lXq40zTHnpdzLpahN88cYQ05ODpGRkbi4VP2dl3XiDjMXFxeioqJquhtVFhAQUOMTSGqGci9XmuaY81LunZPy7ryUe7nSNMecl3Ivl6K2zJ+LubPsjKqX2EREREREREREROowFcxERERERERERETKUMGsBnh6ejJp0iQ8PT1ruitSzZR7udI0x5yXcu+clHfnpdzLlaY55ryUe7kUdWn+1ImH/ouIiIiIiIiIiFwuusNMRERERERERESkDBXMREREREREREREylDBTEREREREREREpAwVzERERERERERERMqo0wWzxMREOnbsiL+/P6GhoQwYMIA9e/aUa1NQUMCYMWNo0KABfn5+DBo0iOPHj5dr8+ijjxIfH4+npyft27ev8Fzz5s2jffv2+Pj4EB0dzdSpUyvVx/nz59OqVSu8vLyIi4tj8eLF5fYvXLiQXr160aBBAywWCykpKZWKe/LkSe69914CAgIICgriwQcfJDc3t9y4hw8fTlxcHG5ubgwYMKBScR2BM+d98uTJdOnSBR8fH4KCgipsY7FYzvrMnTu3UvHlvxx9nhUVFTFhwgTi4uLw9fUlMjKSYcOGcezYsQvGPXz4MH379sXHx4fQ0FCeeuopiouL7ftTU1O55557iI2NxcXFhXHjxlWqv47CmXN/oT4fPHiwwjVm/fr1lep3beboeQd44YUXaNWqFb6+vtSrV4+ePXuyYcOGC8Z15mvemfPuzNd7dasL86ysUaNGYbFYmD59+gXjan1xzrxrfbk86sIcGj58+Fl5TkhIuGDc6lo76nTBbNWqVYwZM4b169ezbNkyioqK6NWrF3l5efY2jz/+OF999RXz589n1apVHDt2jD/84Q9nxfq///s/7r777grPs2TJEu69915GjRrF9u3beeedd3jjjTd4++23z9u/tWvXMnToUB588EGSk5MZMGAAAwYMYPv27fY2eXl5dO3alVdffbVKY7/33nvZsWMHy5Yt4+uvv2b16tWMHDnSvr+kpARvb28effRRevbsWaXYtZ0z572wsJC77rqL0aNHn7fdrFmzSE1NtX/qUsG0ujj6PMvPz+enn37iueee46effmLhwoXs2bOH/v37nzduSUkJffv2pbCwkLVr1/Lhhx8ye/Zsnn/+eXsbq9VKSEgIzz77LO3atTtvPEfkrLmvTJ/PWL58ebk1Jj4+vlKxazNHzztAbGwsb7/9Ntu2bWPNmjU0adKEXr16ceLEiXPGdfZr3lnzXpk+n1EXr/fqVhfm2RmLFi1i/fr1REZGXnDcWl+cM++V6fMZWl/Or67MoYSEhHJ5/vTTT88bt1rXDuNE0tPTDWBWrVpljDEmMzPTuLu7m/nz59vb7Nq1ywBm3bp1Zx0/adIk065du7O2Dx061Nx5553lts2YMcNERUUZm812zv4MHjzY9O3bt9y2Tp06mYcffvistgcOHDCASU5OPt8QjTHG7Ny50wBm06ZN9m1LliwxFovF/Pbbb2e1f+CBB8wdd9xxwbiOylnyXtasWbNMYGBghfsAs2jRoirFkwtz5Hl2xsaNGw1gDh06dM42ixcvNi4uLiYtLc2+bebMmSYgIMBYrdaz2t90003mscceO2e8usBZcl+ZPl/smuWI6kLes7KyDGCWL19+zja65stzlrxXps/OdL1XN0edZ0ePHjUNGzY027dvN9HR0eaNN9447zi1vpTnLHmvTJ+1vlwcR5xDF1OLqM61o07fYfa/srKyAKhfvz4AW7ZsoaioqNwdVq1ataJx48asW7eu0nGtViteXl7ltnl7e3P06FEOHTp0zuPWrVt31t1dvXv3rtK5zxU3KCiIa6+91r6tZ8+euLi4VOoW/LrGWfJeFWPGjCE4OJjrrruODz74AGNMtZ27rqoL8ywrKwuLxXLOr/OeiRsXF0dYWFi5uNnZ2ezYseMCo6mbnCX3VdG/f39CQ0Pp2rUrX3755WWJWds4et4LCwv5xz/+QWBg4Hn/z6uu+fKcJe9V4QzXe3VzxHlms9m4//77eeqpp7j66qsr1R+tL+U5S96rQutL1TjiHAJISkoiNDSUli1bMnr0aDIyMs7bn+pcO5ymYGaz2Rg3bhw33HADbdq0ASAtLQ0PD4+z/gMhLCyMtLS0Ssfu3bs3CxcuZMWKFdhsNn755RemTZsGlH539lzS0tLKJflizn2uuKGhoeW2ubm5Ub9+/UuO7WicKe+V9dJLLzFv3jyWLVvGoEGDeOSRR3jrrbeq5dx1VV2YZwUFBUyYMIGhQ4cSEBBQ5bhn9jkbZ8p9Zfj5+TFt2jTmz5/PN998Q9euXRkwYECd+0euI+f966+/xs/PDy8vL9544w2WLVtGcHBwleOe2edMnCnvleEs13t1c9R59uqrr+Lm5sajjz5a6f5offkvZ8p7ZWh9qTpHnUMJCQl89NFHrFixgldffZVVq1bRp08fSkpKqhz3zL7LyWkKZmPGjGH79u1X5OHmDz30EGPHjuX222/Hw8OD66+/niFDhgDg4uLC4cOH8fPzs39eeeWVy3buUaNGlYst5SnvZ3vuuee44YYb6NChAxMmTODpp5+u9EMbpWKOPs+KiooYPHgwxhhmzpxp396nTx973Cvxfw3rAuW+vODgYJ544gk6depEx44d+etf/8p9991X59YYR877zTffTEpKCmvXriUhIYHBgweTnp4O6Jq/EOW9PGe53qubI86zLVu28OabbzJ79mwsFkuFbbS+nJ/yXp7Wl6pzxDkEMGTIEPr3709cXBwDBgzg66+/ZtOmTSQlJQE1v3a4VfsZa8DYsWPtD76Pioqybw8PD6ewsJDMzMxyVdfjx48THh5e6fgWi4VXX32VV155hbS0NEJCQlixYgUAMTEx1KtXr9xbDs/cIhkeHn7WGyqqeu6XXnqJ8ePHl9sWHh5u/0fQGcXFxZw8ebJKsR2ds+X9YnXq1ImXX34Zq9WKp6fnZYnpTBx9np0pmBw6dIiVK1eWu8Povffe4/Tp0wC4u7vb427cuPGsuGf2ORNny/3F6tSpE8uWLbukGLWJo+fd19eX5s2b07x5c66//npatGjB+++/z8SJE3XNn4ez5f1i1bXrvbo56jz74YcfSE9Pp3Hjxvb9JSUlPPnkk0yfPp2DBw9qfTkPZ8v7xdL6cm6OOocqEhMTQ3BwMPv27aNHjx41vnbU6TvMjDGMHTuWRYsWsXLlSpo2bVpuf3x8PO7u7vZkA+zZs4fDhw/TuXPnKp/P1dWVhg0b4uHhwaeffkrnzp0JCQnBzc3N/o+U5s2b2ydQ586dy50bYNmyZVU6d2hoaLnYZ+JmZmayZcsWe7uVK1dis9no1KlTlcflaJw17xcrJSWFevXqqVhWRXVhnp0pmOzdu5fly5fToEGDcu0bNmxojxsdHW2Pu23btnJF+WXLlhEQEEDr1q2rPC5H5Ky5v1gpKSlERERcUozaoC7kvSI2mw2r1Qromq+Is+b9YtWV6726Ofo8u//++9m6dSspKSn2T2RkJE899RRLly4FtL5UxFnzfrG0vpzN0edQRY4ePUpGRoY91zW+dlzUqwIcxOjRo01gYKBJSkoyqamp9k9+fr69zahRo0zjxo3NypUrzebNm03nzp1N586dy8XZu3evSU5ONg8//LCJjY01ycnJJjk52f4GhhMnTpiZM2eaXbt2meTkZPPoo48aLy8vs2HDhvP278cffzRubm7mtddeM7t27TKTJk0y7u7uZtu2bfY2GRkZJjk52XzzzTcGMHPnzjXJyckmNTX1vLETEhJMhw4dzIYNG8yaNWtMixYtzNChQ8u12bFjh0lOTjb9+vUz3bt3t4/L0Tlz3g8dOmSSk5PNiy++aPz8/Ox9zsnJMcYY8+WXX5p//vOfZtu2bWbv3r3mnXfeMT4+Pub555+v0u9YHH+eFRYWmv79+5uoqCiTkpJSbgwVvV3mjOLiYtOmTRvTq1cvk5KSYr799lsTEhJiJk6cWK7dmXHEx8ebe+65xyQnJ5sdO3ZU6XdcWzlr7ivT59mzZ5s5c+aYXbt2mV27dpnJkycbFxcX88EHH1T591zbOHrec3NzzcSJE826devMwYMHzebNm82IESOMp6en2b59+znjOvs176x5r0yf6/L1Xt0cfZ5VpDJvS9T64px5r0yftb5UjqPPoZycHDN+/Hizbt06c+DAAbN8+XJzzTXXmBYtWpiCgoJzxq3OtaNOF8yACj+zZs2ytzl9+rR55JFHTL169YyPj48ZOHDgWUWJm266qcI4Bw4cMMaUTqDrr7/e+Pr6Gh8fH9OjRw+zfv36SvVx3rx5JjY21nh4eJirr77afPPNN+X2z5o1q8JzT5o06bxxMzIyzNChQ42fn58JCAgwI0aMsBdNzoiOjq4wtqNz5rw/8MADFR73/fffG2OMWbJkiWnfvr3x8/Mzvr6+pl27dubdd981JSUlleq3/Jejz7Mzr+s+33w5l4MHD5o+ffoYb29vExwcbJ588klTVFR0wd9PdHR0pfpd2zlz7i/U59mzZ5urrrrK+Pj4mICAAHPdddeVe5W5I3P0vJ8+fdoMHDjQREZGGg8PDxMREWH69+9vNm7ceMG4znzNO3Penfl6r26OPs8qUtnCidYX58y71pfLw9HnUH5+vunVq5cJCQkx7u7uJjo62jz00EMmLS3tgnGra+2w/CeQiIiIiIiIiIiIUMefYSYiIiIiIiIiIlJVKpiJiIiIiIiIiIiUoYKZiIiIiIiIiIhIGSqYiYiIiIiIiIiIlKGCmYiIiIiIiIiISBkqmImIiIiIiIiIiJShgpmIiIiIiIiIiEgZKpiJiIiIiIiIiIiUoYKZiIiISC0zfPhwLBYLFosFd3d3wsLCuPXWW/nggw+w2WyVjjN79myCgoKuXEdFRERE6igVzERERERqoYSEBFJTUzl48CBLlizh5ptv5rHHHuP222+nuLi4prsnIiIiUqepYCYiIiJSC3l6ehIeHk7Dhg255ppreOaZZ/jiiy9YsmQJs2fPBuD1118nLi4OX19fGjVqxCOPPEJubi4ASUlJjBgxgqysLPvdai+88AIAVquV8ePH07BhQ3x9fenUqRNJSUk1M1ARERGRWkgFMxEREREHccstt9CuXTsWLlwIgIuLCzNmzGDHjh18+OGHrFy5kqeffhqALl26MH36dAICAkhNTSU1NZXx48cDMHbsWNatW8fcuXPZunUrd911FwkJCezdu7fGxiYiIiJSm1iMMaamOyEiIiIi/zV8+HAyMzP5/PPPz9o3ZMgQtm7dys6dO8/at2DBAkaNGsXvv/8OlD7DbNy4cWRmZtrbHD58mJiYGA4fPkxkZKR9e8+ePbnuuut45ZVXLvt4RERERByNW013QEREREQqzxiDxWIBYPny5SQmJrJ7926ys7MpLi6moKCA/Px8fHx8Kjx+27ZtlJSUEBsbW2671WqlQYMGV7z/IiIiIo5ABTMRERERB7Jr1y6aNm3KwYMHuf322xk9ejSTJ0+mfv36rFmzhgcffJDCwsJzFsxyc3NxdXVly5YtuLq6ltvn5+dXHUMQERERqfVUMBMRERFxECtXrmTbtm08/vjjbNmyBZvNxrRp03BxKX0s7bx588q19/DwoKSkpNy2Dh06UFJSQnp6Ot26dau2vouIiIg4EhXMRERERGohq9VKWloaJSUlHD9+nG+//ZbExERuv/12hg0bxvbt2ykqKuKtt96iX79+/Pjjj7z77rvlYjRp0oTc3FxWrFhBu3bt8PHxITY2lnvvvZdhw4Yxbdo0OnTowIkTJ1ixYgVt27alb9++NTRiERERkdpDb8kUERERqYW+/fZbIiIiaNKkCQkJCXz//ffMmDGDL774AldXV9q1a8frr7/Oq6++Sps2bfjkk09ITEwsF6NLly6MGjWKu+++m5CQEKZMmQLArFmzGDZsGE8++SQtW7ZkwIABbNq0icaNG9fEUEVERERqHb0lU0REREREREREpAzdYSYiIiIiIiIiIlKGCmYiIiIiIiIiIiJlqGAmIiIiIiIiIiJShgpmIiIiIiIiIiIiZahgJiIiIiIiIiIiUoYKZiIiIiIiIiIiImWoYCYiIiIiIiIiIlKGCmYiIiIiIiIiIiJlqGAmIiIiIiIiIiJShgpmIiIiIiIiIiIiZahgJiIiIiIiIiIiUoYKZiIiIiIiIiIiImX8f2fWjs52VPf4AAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "sns.lineplot(stock_daily.drop('Volume', axis=1));" + ] + }, + { + "cell_type": "markdown", + "id": "5EZ5L0-UQ0v2", + "metadata": { + "id": "5EZ5L0-UQ0v2" + }, + "source": [ + "**Observations:**\n", + "* The stock price has gradually increased over time from ~\\$40 to ~\\$50 in the period for which the data is available." + ] + }, + { + "cell_type": "markdown", + "id": "KG4y9NK1Ng1-", + "metadata": { + "id": "KG4y9NK1Ng1-" + }, + "source": [ + "#### **Volume vs Close Price**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0WMHYw6w0TM6", + "metadata": { + "id": "0WMHYw6w0TM6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8517bfee-4d34-40e8-a1cd-567350afe1bb" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Create a figure and axis\n", + "fig, ax1 = plt.subplots(figsize=(15,5))\n", + "\n", + "# Lineplot on primary y-axis\n", + "sns.lineplot(data=stock_daily.reset_index(), x='Date', y='Close', ax=ax1, color='blue', marker='o', label='Close Price')\n", + "\n", + "# Create a secondary y-axis\n", + "ax2 = ax1.twinx()\n", + "\n", + "# Lineplot on secondary y-axis\n", + "sns.lineplot(data=stock_daily.reset_index(), x='Date', y='Volume', ax=ax2, color='gray', marker='o', label='Volume')\n", + "\n", + "ax1.legend(bbox_to_anchor=(1,1));" + ] + }, + { + "cell_type": "markdown", + "id": "fHU5KgCGNOX5", + "metadata": { + "id": "fHU5KgCGNOX5" + }, + "source": [ + "**Observations:**\n", + "- There is no specific pattern here\n", + " - There have been periods where the price decreased with increasing volumes\n", + " - There have been periods where the price increased with increasing volumes" + ] + }, + { + "cell_type": "markdown", + "id": "N8z4-vOBmwqv", + "metadata": { + "id": "N8z4-vOBmwqv" + }, + "source": [ + "## **Data Preprocessing**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2jIN9NycxtUC", + "metadata": { + "id": "2jIN9NycxtUC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fee181c0-f896-4c4c-cf2c-378747dfb425" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 418\n", + "mean 2019-02-14 12:24:06.889952256\n", + "min 2019-01-02 00:00:00\n", + "25% 2019-01-11 00:00:00\n", + "50% 2019-01-31 00:00:00\n", + "75% 2019-03-21 00:00:00\n", + "max 2019-04-29 00:00:00\n", + "Name: Date, dtype: object" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Date
count418
mean2019-02-14 12:24:06.889952256
min2019-01-02 00:00:00
25%2019-01-11 00:00:00
50%2019-01-31 00:00:00
75%2019-03-21 00:00:00
max2019-04-29 00:00:00
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "stock['Date'].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "0FxlsnepSb5m", + "metadata": { + "id": "0FxlsnepSb5m" + }, + "source": [ + "**Observations:**\n", + "* We see that 75% of the data is till the third week of March 2019.\n", + "* We'll take the data till the end of March 2019 for training, and keep the April 2019 data for test set." + ] + }, + { + "cell_type": "markdown", + "id": "j7KR_HgZRDtk", + "metadata": { + "id": "j7KR_HgZRDtk" + }, + "source": [ + "### Train-test Split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "yXsgpkpeI8UK", + "metadata": { + "id": "yXsgpkpeI8UK" + }, + "outputs": [], + "source": [ + "X_train = stock[stock['Date'] < '2019-04-01'].reset_index()\n", + "X_test = stock[(stock['Date'] >= '2019-04-01')].reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "__2ON8RuI8Q2", + "metadata": { + "id": "__2ON8RuI8Q2" + }, + "outputs": [], + "source": [ + "y_train = X_train['Label'].copy()\n", + "y_test = X_test['Label'].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "imMx6hH0__IB", + "metadata": { + "id": "imMx6hH0__IB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3dbdaa6f-f304-47f0-94a8-32e8a5a7309d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train data shape (347, 10)\n", + "Test data shape (71, 10)\n", + "Train label shape (347,)\n", + "Test label shape (71,)\n" + ] + } + ], + "source": [ + "print(\"Train data shape\",X_train.shape)\n", + "print(\"Test data shape \",X_test.shape)\n", + "\n", + "print(\"Train label shape\",y_train.shape)\n", + "print(\"Test label shape \",y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "uJZqic2Q6YZD", + "metadata": { + "id": "uJZqic2Q6YZD" + }, + "outputs": [], + "source": [ + "# y_train.value_counts(normalize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Xf9R3BaR6bZw", + "metadata": { + "id": "Xf9R3BaR6bZw" + }, + "outputs": [], + "source": [ + "# y_test.value_counts(normalize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0rYgR14ORf7b", + "metadata": { + "id": "0rYgR14ORf7b" + }, + "source": [ + "## **Word Embeddings**" + ] + }, + { + "cell_type": "markdown", + "id": "4IUBFAOTbjju", + "metadata": { + "id": "4IUBFAOTbjju" + }, + "source": [ + "### **Generating Text Embeddings using Word2Vec**" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Defining the model**" + ], + "metadata": { + "id": "bzwPsqJvVbNC" + }, + "id": "bzwPsqJvVbNC" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ZD188ZNsboS4", + "metadata": { + "id": "ZD188ZNsboS4" + }, + "outputs": [], + "source": [ + "# Creating a list of all words in our data\n", + "words_list = [item.split(\" \") for item in stock['News'].values]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eGVgM5iTbwHy", + "metadata": { + "id": "eGVgM5iTbwHy" + }, + "outputs": [], + "source": [ + "# Creating an instance of Word2Vec\n", + "vec_size = 300\n", + "model_W2V = Word2Vec(words_list, vector_size = vec_size, min_count = 1, window=5, workers = 6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lhy6DjNxbzOd", + "metadata": { + "id": "lhy6DjNxbzOd", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6bf055ce-4c91-4cb3-bd4c-673e2c5de694" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Length of the vocabulary is 14577\n" + ] + } + ], + "source": [ + "# Checking the size of the vocabulary\n", + "print(\"Length of the vocabulary is\", len(list(model_W2V.wv.key_to_index)))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Encoding the datasets**" + ], + "metadata": { + "id": "ZYCiT-7GVNaH" + }, + "id": "ZYCiT-7GVNaH" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "F_4ldXPzcF7y", + "metadata": { + "id": "F_4ldXPzcF7y" + }, + "outputs": [], + "source": [ + "# Retrieving the words present in the Word2Vec model's vocabulary\n", + "words = list(model_W2V.wv.key_to_index.keys())\n", + "\n", + "# Retrieving word vectors for all the words present in the model's vocabulary\n", + "wvs = model_W2V.wv[words].tolist()\n", + "\n", + "# Creating a dictionary of words and their corresponding vectors\n", + "word_vector_dict = dict(zip(words, wvs))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Averaging the word vectors to get sentence encodings**" + ], + "metadata": { + "id": "GgismcJz0dZE" + }, + "id": "GgismcJz0dZE" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "vsQ0vF42cH_r", + "metadata": { + "id": "vsQ0vF42cH_r" + }, + "outputs": [], + "source": [ + "def average_vectorizer_Word2Vec(doc):\n", + " # Initializing a feature vector for the sentence\n", + " feature_vector = np.zeros((vec_size,), dtype=\"float64\")\n", + "\n", + " # Creating a list of words in the sentence that are present in the model vocabulary\n", + " words_in_vocab = [word for word in doc.split() if word in words]\n", + "\n", + " # adding the vector representations of the words\n", + " for word in words_in_vocab:\n", + " feature_vector += np.array(word_vector_dict[word])\n", + "\n", + " # Dividing by the number of words to get the average vector\n", + " if len(words_in_vocab) != 0:\n", + " feature_vector /= len(words_in_vocab)\n", + "\n", + " return feature_vector" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Jtxc1yVHcJjV", + "metadata": { + "id": "Jtxc1yVHcJjV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a0ab0ac1-7d5a-4cb0-d761-af1e7f93a323" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Time taken 8.816098928451538\n" + ] + } + ], + "source": [ + "# creating a dataframe of the vectorized documents\n", + "start = time.time()\n", + "\n", + "X_train_wv = pd.DataFrame(X_train['News'].apply(average_vectorizer_Word2Vec).tolist(), columns=['Feature '+str(i) for i in range(vec_size)])\n", + "X_test_wv = pd.DataFrame(X_test['News'].apply(average_vectorizer_Word2Vec).tolist(), columns=['Feature '+str(i) for i in range(vec_size)])\n", + "\n", + "end = time.time()\n", + "print('Time taken ', (end-start))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8IrY8tZjA4VZ", + "metadata": { + "id": "8IrY8tZjA4VZ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ded041ed-e998-442e-edc6-4bf5abe65675" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(347, 300) (71, 300)\n" + ] + } + ], + "source": [ + "print(X_train_wv.shape, X_test_wv.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "a3GUvne0hyPx", + "metadata": { + "id": "a3GUvne0hyPx" + }, + "source": [ + "### **Generating Text Embeddings using Sentence Transformer**" + ] + }, + { + "cell_type": "markdown", + "id": "51ITQezWi9VE", + "metadata": { + "id": "51ITQezWi9VE" + }, + "source": [ + "#### **Defining the model**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3EQ7eQIpYSyz", + "metadata": { + "id": "3EQ7eQIpYSyz" + }, + "outputs": [], + "source": [ + "#Defining the model\n", + "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" + ] + }, + { + "cell_type": "markdown", + "id": "Lll4MLfzKfBa", + "metadata": { + "id": "Lll4MLfzKfBa" + }, + "source": [ + "#### **Encoding the dataset**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "q1BaGKThKcX3", + "metadata": { + "id": "q1BaGKThKcX3", + "colab": { + "base_uri": "https://localhost:8080/", + "referenced_widgets": [ + "1230a037e0b9479caa9db62c5f9ecb6a", + "ed6c19298c4747a59992a79d99cdaaa7", + "e010222da3cf4751995a51ffc82560ef", + "9f3e3b616bcf482d9fd91a2b54d8d82a", + "6838e428d6d54a3f80d34638812441e6", + "991c2589b56f444486443a31bef569d5", + "4ed01d32996f47f38fbaba687cee45ae", + "a0ce999dbcfe427ba08202bc989b1c33", + "f598184dc72f443ab0ada8de6cf076ad", + "96e9e320eec74a2e9094935af065b254", + "fb854fb10f3e415c9c4c0ac176fb74b4", + "2fb4071397a049f888159e2cbec3ec99", + "280899c6e305423a8d6f20dd395b4e10", + "f68b5d3640c54560b38a29f32deb33a8", + "115335a31d874aba99efb63fa2830e09", + "7b371d0574e04f98bf87a88f722b8477", + "9095b2b09d4a45928fbc3cf45eb35cbb", + "971a53d397494d76b8b5c4a2abb954f7", + "54dd267783314434a5389477c97974e5", + "5bfd23c3586e4615909878610be8e24b", + "4eda58c3e66e40db98ea40fc40ebb109", + "99ee6edbe0574c778200ae65b87d7e0f" + ] + }, + "outputId": "03ca294e-285e-4fe1-f930-d22aaa9f87dc" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Batches: 0%| | 0/11 [00:00#sk-container-id-1 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: #000;\n", + " --sklearn-color-text-muted: #666;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-1 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-1 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", + " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", + " so we also need the `!important` here to be able to override the\n", + " default hidden behavior on the sphinx rendered scikit-learn.org.\n", + " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", + " display: inline-block !important;\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-text-repr-fallback {\n", + " display: none;\n", + "}\n", + "\n", + "div.sk-parallel-item,\n", + "div.sk-serial,\n", + "div.sk-item {\n", + " /* draw centered vertical line to link estimators */\n", + " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", + " background-size: 2px 100%;\n", + " background-repeat: no-repeat;\n", + " background-position: center center;\n", + "}\n", + "\n", + "/* Parallel-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item::after {\n", + " content: \"\";\n", + " width: 100%;\n", + " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", + " flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-1 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: flex;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + " align-items: start;\n", + " justify-content: space-between;\n", + " gap: 0.5em;\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label .caption {\n", + " font-size: 0.6rem;\n", + " font-weight: lighter;\n", + " color: var(--sklearn-color-text-muted);\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-1 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-1 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-1 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 0.5em;\n", + " text-align: center;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `` HTML tag */\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "" + ] + }, + "metadata": {}, + "execution_count": 40 + } + ], + "source": [ + "# Building the model\n", + "rf_word2vec = RandomForestClassifier(n_estimators = 100, max_depth = 3, random_state = 42)\n", + "\n", + "\n", + "# Fitting on train data\n", + "rf_word2vec.fit(X_train_wv, y_train)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**\n" + ], + "metadata": { + "id": "95O3167WbBnd" + }, + "id": "95O3167WbBnd" + }, + { + "cell_type": "code", + "source": [ + "# Predicting on train data\n", + "y_pred_train = rf_word2vec.predict(X_train_wv)\n", + "\n", + "# Predicting on test data\n", + "y_pred_test = rf_word2vec.predict(X_test_wv)" + ], + "metadata": { + "id": "TtQlY8DlzadF" + }, + "id": "TtQlY8DlzadF", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "ycl7jAX7cZuj" + }, + "id": "ycl7jAX7cZuj" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a_AW25srClm-", + "metadata": { + "id": "a_AW25srClm-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "7c3d30ac-13eb-4053-ff9a-6718a9fbf3c7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_confusion_matrix(y_train,y_pred_train)" + ] + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test,y_pred_test)" + ], + "metadata": { + "id": "sp4-2sLEDcM3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "6046c9f3-6602-421d-8738-7c000827c3a1" + }, + "id": "sp4-2sLEDcM3", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "E1jLbrZAidAB" + }, + "id": "E1jLbrZAidAB" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8rV_bYhqClm_", + "metadata": { + "id": "8rV_bYhqClm_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cb772720-3f55-4fe6-97c6-75f6fe7384e6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.755043 0.755043 0.778891 0.720565\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "rf_train_wv = model_performance_classification_sklearn(y_train,y_pred_train)\n", + "print(\"Training performance:\\n\", rf_train_wv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "_AA2cSvzClm_", + "metadata": { + "id": "_AA2cSvzClm_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d063296f-2448-4515-f5c1-575671bcbd48" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.746479 0.746479 0.687934 0.680114\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "rf_test_wv = model_performance_classification_sklearn(y_test, y_pred_test)\n", + "print(\"Testing performance:\\n\",rf_test_wv)" + ] + }, + { + "cell_type": "markdown", + "id": "P2OnPdLRF2M9", + "metadata": { + "id": "P2OnPdLRF2M9" + }, + "source": [ + "* The model is slightly overfitting, as there is a little difference between its performance on the training set and the test set." + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Building a Random Forest Model using text embeddings obtained from the Sentence Transformer**" + ], + "metadata": { + "id": "uijWj2Nl2jyK" + }, + "id": "uijWj2Nl2jyK" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "04W4gkoZ2jyK", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "outputId": "364a1ac8-b4b6-403a-85d4-133186c89aa9" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestClassifier(max_depth=3, random_state=42)" + ], + "text/html": [ + "
RandomForestClassifier(max_depth=3, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "# Building the model\n", + "rf_st = RandomForestClassifier(n_estimators = 100, max_depth = 3, random_state = 42)\n", + "\n", + "\n", + "# Fitting on train data\n", + "rf_st.fit(X_train_st, y_train)" + ], + "id": "04W4gkoZ2jyK" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**" + ], + "metadata": { + "id": "BTWSvJfC2jyL" + }, + "id": "BTWSvJfC2jyL" + }, + { + "cell_type": "code", + "source": [ + "# Predicting on train data\n", + "y_pred_train = rf_st.predict(X_train_st)\n", + "\n", + "# Predicting on test data\n", + "y_pred_test = rf_st.predict(X_test_st)" + ], + "metadata": { + "id": "QPI_ePlJ2jyL" + }, + "execution_count": null, + "outputs": [], + "id": "QPI_ePlJ2jyL" + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "vskhvTGm2jyL" + }, + "id": "vskhvTGm2jyL" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9P_tYSn92jyM", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "f404f8af-0142-4662-a7eb-8281eb953a13" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_confusion_matrix(y_train,y_pred_train)" + ], + "id": "9P_tYSn92jyM" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test,y_pred_test)" + ], + "metadata": { + "id": "LBzzMFHJDolN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "d4e2c9a6-e164-45ff-ef5a-5c70c78c1736" + }, + "id": "LBzzMFHJDolN", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "sSvRSDit2jyM" + }, + "id": "sSvRSDit2jyM" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_kEV9XZD2jyM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eb0311c5-dc0f-4aec-a189-a8358f68a7de" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.801153 0.801153 0.831835 0.775232\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "rf_train_st = model_performance_classification_sklearn(y_train,y_pred_train)\n", + "print(\"Training performance:\\n\", rf_train_st)" + ], + "id": "_kEV9XZD2jyM" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QoFxAES32jyM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "74f810ea-08e2-4c99-a9c2-9187bfbd2357" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.71831 0.71831 0.551745 0.624105\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "rf_test_st = model_performance_classification_sklearn(y_test, y_pred_test)\n", + "print(\"Testing performance:\\n\",rf_test_st)" + ], + "id": "QoFxAES32jyM" + }, + { + "cell_type": "markdown", + "id": "ZmPPcdrHE9K2", + "metadata": { + "id": "ZmPPcdrHE9K2" + }, + "source": [ + "* The model is highly overfitting, as there is a significant difference between its performance on the training set and the test set." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DHgj_cCm2pIn" + }, + "source": [ + "### **Building Neural Network Models using different text embeddings**" + ], + "id": "DHgj_cCm2pIn" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Building a Neural Network Model using text embeddings obtained from the Word2Vec**" + ], + "metadata": { + "id": "LpasFYQriueC" + }, + "id": "LpasFYQriueC" + }, + { + "cell_type": "code", + "source": [ + "# Convert the labels\n", + "label_mapping = {1: 2, -1: 0, 0: 1}\n", + "y_train_mapped_wv = [label_mapping[label] for label in y_train]\n", + "y_test_mapped_wv = [label_mapping[label] for label in y_test]\n", + "\n", + "# Convert your features DataFrame to a NumPy array\n", + "X_train_wv_np = np.array(X_train_wv)\n", + "X_test_wv_np = np.array(X_test_wv)\n", + "y_train_mapped_wv = np.array(y_train_mapped_wv)\n", + "y_test_mapped_wv = np.array(y_test_mapped_wv)" + ], + "metadata": { + "id": "xIeKB-P4nYFi" + }, + "id": "xIeKB-P4nYFi", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import gc\n", + "\n", + "# Clear previous sessions\n", + "tf.keras.backend.clear_session()\n", + "gc.collect()\n", + "\n", + "# Model definition\n", + "model = Sequential()\n", + "model.add(Dense(128, activation='relu', input_shape=(X_train_wv_np.shape[1],))) # Use the shape of the Word2Vec embeddings\n", + "model.add(Dropout(0.3))\n", + "model.add(Dense(64, activation='relu'))\n", + "model.add(Dense(3, activation='softmax')) # 3 output classes\n", + "\n", + "# Compile\n", + "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy', metrics =['accuracy'])\n", + "\n", + "# Summary\n", + "model.summary()" + ], + "metadata": { + "id": "pPoM2BhyXvBv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 257 + }, + "outputId": "46ff0a8e-280d-40a4-df68-ce369e6c8029" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"sequential\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m38,528\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m195\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ], + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                     Output Shape                  Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (Dense)                   │ (None, 128)            │        38,528 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (Dropout)               │ (None, 128)            │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (Dense)                 │ (None, 64)             │         8,256 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (Dense)                 │ (None, 3)              │           195 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m46,979\u001b[0m (183.51 KB)\n" + ], + "text/html": [ + "
 Total params: 46,979 (183.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m46,979\u001b[0m (183.51 KB)\n" + ], + "text/html": [ + "
 Trainable params: 46,979 (183.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ], + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+              "
\n" + ] + }, + "metadata": {} + } + ], + "id": "pPoM2BhyXvBv" + }, + { + "cell_type": "markdown", + "source": [ + "**Note:**\n", + "- During training, we use accuracy as a metric to monitor how well the model is learning to distinguish between classes in each batch.\n", + "- Accuracy is fast and reliable during training and gives us a quick view of model progress.\n", + "- It reflects how often the model is predicting the correct label out of all predictions made.\n", + "\n" + ], + "metadata": { + "id": "kIxFfSYLQNlT" + }, + "id": "kIxFfSYLQNlT" + }, + { + "cell_type": "code", + "source": [ + "# Fitting the model\n", + "history = model.fit(\n", + " X_train_wv_np, y_train_mapped_wv,\n", + " epochs=10,\n", + " batch_size=32\n", + ")" + ], + "metadata": { + "id": "bgHeOMfpnobV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6b648ba6-870f-4dfc-a2a7-b38a97c50cef" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.5349 - loss: 0.9062\n", + "Epoch 2/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6190 - loss: 0.7523 \n", + "Epoch 3/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5989 - loss: 0.7214 \n", + "Epoch 4/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5874 - loss: 0.7687 \n", + "Epoch 5/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6741 - loss: 0.7038 \n", + "Epoch 6/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6298 - loss: 0.7276 \n", + "Epoch 7/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6655 - loss: 0.7134 \n", + "Epoch 8/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6025 - loss: 0.7213 \n", + "Epoch 9/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6321 - loss: 0.7183 \n", + "Epoch 10/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6456 - loss: 0.7322 \n" + ] + } + ], + "id": "bgHeOMfpnobV" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**" + ], + "metadata": { + "id": "IX11-Hmx8_E1" + }, + "id": "IX11-Hmx8_E1" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on training data\n", + "y_train_pred_probs = model.predict(X_train_wv_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_train_preds_wv = tf.argmax(y_train_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "ZpEpHWni87cO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "156eca77-fc90-4df0-87ac-daf08655f0b6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n" + ] + } + ], + "id": "ZpEpHWni87cO" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on test data\n", + "y_test_pred_probs = model.predict(X_test_wv_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_test_preds_wv = tf.argmax(y_test_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "hBMMkZBk9Jkz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d66d1a0d-3bb4-4fa3-d68e-ded8a15c2696" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n" + ] + } + ], + "id": "hBMMkZBk9Jkz" + }, + { + "cell_type": "code", + "source": [ + "# Convert back to [-1, 0, 1] to match utility function expectations\n", + "label_mapping = {2: 1, 0: -1, 1: 0}\n", + "y_train_preds_wv = np.array([label_mapping[index] for index in y_train_preds_wv])\n", + "y_test_preds_wv = np.array([label_mapping[index] for index in y_test_preds_wv])" + ], + "metadata": { + "id": "wCPqMh0nwryB" + }, + "id": "wCPqMh0nwryB", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "Jbeyf8dzk3MP" + }, + "id": "Jbeyf8dzk3MP" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_train, y_train_preds_wv)" + ], + "metadata": { + "id": "lIh2fXcwxJ0G", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "711755dc-7004-4b9d-cb30-b06839893f72" + }, + "id": "lIh2fXcwxJ0G", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test, y_test_preds_wv)" + ], + "metadata": { + "id": "djUVsYwYYBJd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "b02647ff-0825-4c59-8c4b-bdc26cc7a9e1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "id": "djUVsYwYYBJd" + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "1NqSOfNd1UmS" + }, + "id": "1NqSOfNd1UmS" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5qzE4NHS1UmS", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "68bd5ff5-8663-49d2-cf32-0907384a849a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.636888 0.636888 0.664626 0.516128\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "NN_train_wv = model_performance_classification_sklearn(y_train,y_train_preds_wv)\n", + "print(\"Training performance:\\n\", NN_train_wv)" + ], + "id": "5qzE4NHS1UmS" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4Nr34HI31UmT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b07a23fe-a40e-4497-8df2-05e18239480e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.760563 0.760563 0.804628 0.669911\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "NN_test_wv = model_performance_classification_sklearn(y_test, y_test_preds_wv)\n", + "print(\"Testing performance:\\n\",NN_test_wv)" + ], + "id": "4Nr34HI31UmT" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Building a Neural Network Model using text embeddings obtained from the Sentence Transformer**" + ], + "metadata": { + "id": "bcXtMsPu3JfI" + }, + "id": "bcXtMsPu3JfI" + }, + { + "cell_type": "code", + "source": [ + "# Convert the labels\n", + "label_mapping = {1: 2, -1: 0, 0: 1}\n", + "y_train_mapped_st = [label_mapping[label] for label in y_train]\n", + "y_test_mapped_st = [label_mapping[label] for label in y_test]\n", + "\n", + "# Convert your features DataFrame to a NumPy array\n", + "X_train_st_np = np.array(X_train_st)\n", + "X_test_st_np = np.array(X_test_st)\n", + "y_train_mapped_st = np.array(y_train_mapped_st)\n", + "y_test_mapped_st = np.array(y_test_mapped_st)" + ], + "metadata": { + "id": "FUfjCAua4A2-" + }, + "execution_count": null, + "outputs": [], + "id": "FUfjCAua4A2-" + }, + { + "cell_type": "code", + "source": [ + "import gc\n", + "\n", + "# Clear previous sessions\n", + "tf.keras.backend.clear_session()\n", + "gc.collect()\n", + "\n", + "# Define the model\n", + "model = Sequential()\n", + "model.add(Dense(128, activation='relu', input_shape=(X_train_st.shape[1],)))\n", + "model.add(Dropout(0.3))\n", + "model.add(Dense(64, activation='relu'))\n", + "model.add(Dense(3, activation='softmax')) # 3 classes (positive, negative, neutral)\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['Accuracy'])\n", + "\n", + "# Summary\n", + "model.summary()" + ], + "metadata": { + "id": "ziE6DVHA4A2-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 257 + }, + "outputId": "55b5e48b-d3dd-4986-ce74-027c5f902891" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"sequential\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m49,280\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m195\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ], + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                     Output Shape                  Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (Dense)                   │ (None, 128)            │        49,280 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (Dropout)               │ (None, 128)            │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (Dense)                 │ (None, 64)             │         8,256 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (Dense)                 │ (None, 3)              │           195 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m57,731\u001b[0m (225.51 KB)\n" + ], + "text/html": [ + "
 Total params: 57,731 (225.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m57,731\u001b[0m (225.51 KB)\n" + ], + "text/html": [ + "
 Trainable params: 57,731 (225.51 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ], + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+              "
\n" + ] + }, + "metadata": {} + } + ], + "id": "ziE6DVHA4A2-" + }, + { + "cell_type": "code", + "source": [ + "# Fitting the model\n", + "history = model.fit(\n", + " X_train_st_np, y_train_mapped_st,\n", + " epochs=15,\n", + " batch_size=32\n", + ")" + ], + "metadata": { + "id": "8J-JncGj4A2_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3faf37bb-8f28-4662-8b16-ca054230d4cd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Accuracy: 0.6300 - loss: 1.0422\n", + "Epoch 2/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6496 - loss: 0.8380 \n", + "Epoch 3/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6464 - loss: 0.7074 \n", + "Epoch 4/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6099 - loss: 0.7004 \n", + "Epoch 5/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6664 - loss: 0.6625 \n", + "Epoch 6/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.6915 - loss: 0.6035 \n", + "Epoch 7/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.7356 - loss: 0.5585 \n", + "Epoch 8/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Accuracy: 0.7714 - loss: 0.5521 \n", + "Epoch 9/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - Accuracy: 0.7655 - loss: 0.5158 \n", + "Epoch 10/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - Accuracy: 0.7861 - loss: 0.4997 \n", + "Epoch 11/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - Accuracy: 0.8121 - loss: 0.4551\n", + "Epoch 12/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - Accuracy: 0.7897 - loss: 0.4756 \n", + "Epoch 13/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - Accuracy: 0.8454 - loss: 0.4200 \n", + "Epoch 14/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - Accuracy: 0.8362 - loss: 0.4135 \n", + "Epoch 15/15\n", + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - Accuracy: 0.8560 - loss: 0.3473 \n" + ] + } + ], + "id": "8J-JncGj4A2_" + }, + { + "cell_type": "markdown", + "source": [ + "#### **Checking Training and Test Performance**" + ], + "metadata": { + "id": "rbsZ24gM4A2_" + }, + "id": "rbsZ24gM4A2_" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on training data\n", + "y_train_pred_probs = model.predict(X_train_st_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_train_preds_st = tf.argmax(y_train_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "xaWGws3r4A2_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c3f459d7-9745-44f3-bfd6-0df051411294" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n" + ] + } + ], + "id": "xaWGws3r4A2_" + }, + { + "cell_type": "code", + "source": [ + "# Predict class probabilities on test data\n", + "y_test_pred_probs = model.predict(X_test_st_np)\n", + "\n", + "# Convert probabilities to class labels\n", + "y_test_preds_st = tf.argmax(y_test_pred_probs, axis=1).numpy()" + ], + "metadata": { + "id": "P8yF-MWH4A2_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0bfe417b-bb4a-446f-8ee0-7fde7e16f62a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n" + ] + } + ], + "id": "P8yF-MWH4A2_" + }, + { + "cell_type": "code", + "source": [ + "# Convert back to [-1, 0, 1] to match utility function expectations\n", + "label_mapping = {2: 1, 0: -1, 1: 0}\n", + "y_train_preds_st = np.array([label_mapping[index] for index in y_train_preds_st])\n", + "y_test_preds_st = np.array([label_mapping[index] for index in y_test_preds_st])" + ], + "metadata": { + "id": "YbwmP-dE4A3A" + }, + "execution_count": null, + "outputs": [], + "id": "YbwmP-dE4A3A" + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion Matrix**" + ], + "metadata": { + "id": "YoVyydZW4A3A" + }, + "id": "YoVyydZW4A3A" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_train, y_train_preds_st)" + ], + "metadata": { + "id": "I2yC2oAB4A3A", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "c678cd56-9ae7-4a9c-becf-e1dfa09973d1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "id": "I2yC2oAB4A3A" + }, + { + "cell_type": "code", + "source": [ + "plot_confusion_matrix(y_test, y_test_preds_st)" + ], + "metadata": { + "collapsed": true, + "id": "mZLu8fRk4A3A", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "outputId": "e96330e8-96aa-476e-b47d-e4d5bcc6a561" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "id": "mZLu8fRk4A3A" + }, + { + "cell_type": "markdown", + "source": [ + "**Classification Report**" + ], + "metadata": { + "id": "eDqgpX_a4A3B" + }, + "id": "eDqgpX_a4A3B" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Jq_ES16g4A3B", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "95db0a8b-3659-4319-ecab-82c051217d1f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.878963 0.878963 0.864731 0.871679\n" + ] + } + ], + "source": [ + "#Calculating different metrics on training data\n", + "NN_train_st = model_performance_classification_sklearn(y_train,y_train_preds_st)\n", + "print(\"Training performance:\\n\", NN_train_st)" + ], + "id": "Jq_ES16g4A3B" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7MUEidM44A3B", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b3ebc9fb-7278-4621-cb18-5f53190b284a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance:\n", + " Accuracy Recall Precision F1\n", + "0 0.788732 0.788732 0.780908 0.784708\n" + ] + } + ], + "source": [ + "#Calculating different metrics on test data\n", + "NN_test_st = model_performance_classification_sklearn(y_test, y_test_preds_st)\n", + "print(\"Testing performance:\\n\",NN_test_st)" + ], + "id": "7MUEidM44A3B" + }, + { + "cell_type": "markdown", + "id": "gsmrYpkrFY2A", + "metadata": { + "id": "gsmrYpkrFY2A" + }, + "source": [ + "### **Model Performance Summary and Final Model Selection**" + ] + }, + { + "cell_type": "code", + "source": [ + "# Concatenate the training performance metrics from different models into a single DataFrame\n", + "models_train_comp_df = pd.concat(\n", + " [\n", + " rf_train_wv.T, # Random Forest using Word2Vec embeddings\n", + " NN_train_wv.T, # Neural Network using Word2Vec embeddings\n", + " rf_train_st.T, # Random Forest using Sentence Transformer embeddings\n", + " NN_train_st.T # Neural Network using Sentence Transformer embeddings\n", + " ],\n", + " axis=1 # Concatenate along columns (i.e., each model's metrics form one column)\n", + ")\n", + "\n", + "# Assigning meaningful column names for each model for clarity in the output DataFrame\n", + "models_train_comp_df.columns = [\n", + " \"Word2Vec (Random Forest)\",\n", + " \"Word2Vec (Neural Network)\",\n", + " \"Sentence Transformer (Random Forest)\",\n", + " \"Sentence Transformer (Neural Network)\"\n", + "]\n", + "\n", + "# Print the training performance comparison table\n", + "print(\"Training performance comparison:\")\n", + "models_train_comp_df" + ], + "metadata": { + "id": "FmgvAlKBWjR-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 + }, + "outputId": "68dd172d-e821-4c30-82e3-8df928e36a4c" + }, + "id": "FmgvAlKBWjR-", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training performance comparison:\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Word2Vec (Random Forest) Word2Vec (Neural Network) \\\n", + "Accuracy 0.755043 0.636888 \n", + "Recall 0.755043 0.636888 \n", + "Precision 0.778891 0.664626 \n", + "F1 0.720565 0.516128 \n", + "\n", + " Sentence Transformer (Random Forest) \\\n", + "Accuracy 0.801153 \n", + "Recall 0.801153 \n", + "Precision 0.831835 \n", + "F1 0.775232 \n", + "\n", + " Sentence Transformer (Neural Network) \n", + "Accuracy 0.878963 \n", + "Recall 0.878963 \n", + "Precision 0.864731 \n", + "F1 0.871679 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Word2Vec (Random Forest)Word2Vec (Neural Network)Sentence Transformer (Random Forest)Sentence Transformer (Neural Network)
Accuracy0.7550430.6368880.8011530.878963
Recall0.7550430.6368880.8011530.878963
Precision0.7788910.6646260.8318350.864731
F10.7205650.5161280.7752320.871679
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "models_train_comp_df", + "summary": "{\n \"name\": \"models_train_comp_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Word2Vec (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02400850605954694,\n \"min\": 0.720564904885155,\n \"max\": 0.7788911179618219,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7550432276657061,\n 0.7788911179618219,\n 0.720564904885155\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Word2Vec (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06630504860294313,\n \"min\": 0.516128150662598,\n \"max\": 0.6646264228575315,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6368876080691642,\n 0.6646264228575315,\n 0.516128150662598\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02314897229764395,\n \"min\": 0.7752322113738629,\n \"max\": 0.8318353116624009,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.8011527377521613,\n 0.8318353116624009,\n 0.7752322113738629\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.006828124944117901,\n \"min\": 0.8647306583906008,\n \"max\": 0.8789625360230547,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.8789625360230547,\n 0.8647306583906008,\n 0.8716785041639248\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 72 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Concatenate the testing performance metrics from different models into a single DataFrame\n", + "models_test_comp_df = pd.concat(\n", + " [\n", + " rf_test_wv.T, # Random Forest using Word2Vec embeddings\n", + " NN_test_wv.T, # Neural Network using Word2Vec embeddings\n", + " rf_test_st.T, # Random Forest using Sentence Transformer embeddings\n", + " NN_test_st.T # Neural Network using Sentence Transformer embeddings\n", + " ],\n", + " axis=1 # Concatenate along columns so each model's test metrics appear as one column\n", + ")\n", + "\n", + "# Set descriptive column names for clarity in the resulting comparison table\n", + "models_test_comp_df.columns = [\n", + " \"Word2Vec (Random Forest)\",\n", + " \"Word2Vec (Neural Network)\",\n", + " \"Sentence Transformer (Random Forest)\",\n", + " \"Sentence Transformer (Neural Network)\"\n", + "]\n", + "\n", + "# Print the testing performance comparison table\n", + "print(\"Testing performance comparison:\")\n", + "models_test_comp_df" + ], + "metadata": { + "id": "APzbgeHrWjOj", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 + }, + "outputId": "2095fa14-43c0-42dc-e342-c8229f7750b9" + }, + "id": "APzbgeHrWjOj", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing performance comparison:\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Word2Vec (Random Forest) Word2Vec (Neural Network) \\\n", + "Accuracy 0.746479 0.760563 \n", + "Recall 0.746479 0.760563 \n", + "Precision 0.687934 0.804628 \n", + "F1 0.680114 0.669911 \n", + "\n", + " Sentence Transformer (Random Forest) \\\n", + "Accuracy 0.718310 \n", + "Recall 0.718310 \n", + "Precision 0.551745 \n", + "F1 0.624105 \n", + "\n", + " Sentence Transformer (Neural Network) \n", + "Accuracy 0.788732 \n", + "Recall 0.788732 \n", + "Precision 0.780908 \n", + "F1 0.784708 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Word2Vec (Random Forest)Word2Vec (Neural Network)Sentence Transformer (Random Forest)Sentence Transformer (Neural Network)
Accuracy0.7464790.7605630.7183100.788732
Recall0.7464790.7605630.7183100.788732
Precision0.6879340.8046280.5517450.780908
F10.6801140.6699110.6241050.784708
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "models_test_comp_df", + "summary": "{\n \"name\": \"models_test_comp_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Word2Vec (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03619949158384784,\n \"min\": 0.6801140174379611,\n \"max\": 0.7464788732394366,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7464788732394366,\n 0.687933571578726,\n 0.6801140174379611\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Word2Vec (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05661832323426673,\n \"min\": 0.6699110653078362,\n \"max\": 0.8046277665995976,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7605633802816901,\n 0.8046277665995976,\n 0.6699110653078362\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Random Forest)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.08086640854457602,\n \"min\": 0.5517452541334966,\n \"max\": 0.7183098591549296,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7183098591549296,\n 0.5517452541334966,\n 0.6241052874624798\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentence Transformer (Neural Network)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0037559350807984376,\n \"min\": 0.7809076682316118,\n \"max\": 0.7887323943661971,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7887323943661971,\n 0.7809076682316118,\n 0.7847082494969819\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 73 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Model Performance Summary:**" + ], + "metadata": { + "id": "X0yz_T4j6uJc" + }, + "id": "X0yz_T4j6uJc" + }, + { + "cell_type": "markdown", + "source": [ + " **Model Selection: Sentence Transformer + Neural Network**\n", + "\n", + "**Rationale:**\n", + "\n", + "1. **Best Generalization**:\n", + " The Sentence Transformer + Neural Network model achieves the highest F1 score on the test set (0.788), indicating strong generalization and better handling of both precision and recall on unseen data.\n", + "\n", + "2. **Balanced Performance**:\n", + " Training F1 = 0.87 and testing F1 = 0.78 show a minimal gap, meaning the model learned meaningful representations without significant overfitting.\n", + "\n", + "3. **Superior Feature Encoding**:\n", + " Sentence Transformers capture semantic meaning more effectively than Word2Vec, which explains the performance boost across both Random Forest and Neural Network setups.\n", + "\n", + "4. **Neural Network Suitability**:\n", + " While Word2Vec + NN struggles due to sparse and less informative vectors, combining powerful embeddings (Sentence Transformer) with flexible learning (NN) achieves the best synergy.\n", + "\n", + "##### **Why Other Models Were Not Chosen?**\n", + "\n", + "* **Word2Vec + RF**:\n", + " Training F1 = 0.730, Test F1 = 0.685. Although the gap is small (shows some stability), the absolute test performance is lower than Sentence Transformer + NN.\n", + "\n", + "* **Word2Vec + NN**:\n", + " Low performance in both training (F1 = 0.516) and testing (F1 = 0.67) indicates underfitting and ineffective learning due to weak input representations.\n", + "\n", + "* **Sentence Transformer + RF**:\n", + " Strong training F1 = 0.775, but test F1 = 0.624 is slightly lower than the NN version, suggesting mild overfitting and less flexibility in modeling complex patterns." + ], + "metadata": { + "id": "wI7woP0xHrwW" + }, + "id": "wI7woP0xHrwW" + }, + { + "cell_type": "markdown", + "id": "HiOLoD7BO3L-", + "metadata": { + "id": "HiOLoD7BO3L-" + }, + "source": [ + "## **Conclusions and Recommendations**" + ] + }, + { + "cell_type": "markdown", + "source": [ + "* The daily opening, high, low, and closing prices of the stock exhibit similar distributions individually, when compared across different sentiment polarities, and negative sentiment news resulted in a lower value for each price.\n", + "\n", + "* The minimum variation also resulted in the prices exhibiting perfect correlation amongst them, while exhibiting a very low negative correlation with volume, which might be due to selling pressure during periods of negative sentiment.\n", + "\n", + "* The stock price gradually increased over time from ~40 to ~50 in the period for which the data is available while exhibiting a monthly trend.\n", + "\n", + "* We predicted the sentiment of market news by encoding them via different ML models.\n", + "\n", + "* The models largely overfit the data, with only **the Sentence Transformer + Neural Network model** yielding comparatively better performance than the others (train F1 = 0.876, test F1 = 0.788).\n", + "\n", + " * The predominance of neutral news also suggests a cautious market sentiment in this period. As such, a wider period should be considered for data collection to ensure volume and diversity in news sentiment polarities.\n", + "\n", + "* Integrating real-time sentiment analysis systems can allow financial analysts to make informed decisions and quickly respond to market sentiment changes to optimize investment strategies.\n", + "\n", + "* One can explore combining news sentiments with technical and fundamental indicators of the stock and introduce data of other similar stocks for a more comprehensive market analysis." + ], + "metadata": { + "id": "NMR7mKugPFme" + }, + "id": "NMR7mKugPFme" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mQvaNDqQ3BJa" + }, + "source": [ + " Power Ahead \n", + "___" + ], + "id": "mQvaNDqQ3BJa" + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "1230a037e0b9479caa9db62c5f9ecb6a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ed6c19298c4747a59992a79d99cdaaa7", + "IPY_MODEL_e010222da3cf4751995a51ffc82560ef", + "IPY_MODEL_9f3e3b616bcf482d9fd91a2b54d8d82a" + ], + "layout": "IPY_MODEL_6838e428d6d54a3f80d34638812441e6" + } + }, + "ed6c19298c4747a59992a79d99cdaaa7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_991c2589b56f444486443a31bef569d5", + "placeholder": "​", + "style": "IPY_MODEL_4ed01d32996f47f38fbaba687cee45ae", + "value": "Batches: 100%" + } + }, + "e010222da3cf4751995a51ffc82560ef": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a0ce999dbcfe427ba08202bc989b1c33", + "max": 11, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_f598184dc72f443ab0ada8de6cf076ad", + "value": 11 + } + }, + "9f3e3b616bcf482d9fd91a2b54d8d82a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_96e9e320eec74a2e9094935af065b254", + "placeholder": "​", + "style": "IPY_MODEL_fb854fb10f3e415c9c4c0ac176fb74b4", + "value": " 11/11 [00:44<00:00,  3.41s/it]" + } + }, + "6838e428d6d54a3f80d34638812441e6": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "991c2589b56f444486443a31bef569d5": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4ed01d32996f47f38fbaba687cee45ae": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "a0ce999dbcfe427ba08202bc989b1c33": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f598184dc72f443ab0ada8de6cf076ad": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "96e9e320eec74a2e9094935af065b254": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fb854fb10f3e415c9c4c0ac176fb74b4": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2fb4071397a049f888159e2cbec3ec99": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_280899c6e305423a8d6f20dd395b4e10", + "IPY_MODEL_f68b5d3640c54560b38a29f32deb33a8", + "IPY_MODEL_115335a31d874aba99efb63fa2830e09" + ], + "layout": "IPY_MODEL_7b371d0574e04f98bf87a88f722b8477" + } + }, + "280899c6e305423a8d6f20dd395b4e10": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9095b2b09d4a45928fbc3cf45eb35cbb", + "placeholder": "​", + "style": "IPY_MODEL_971a53d397494d76b8b5c4a2abb954f7", + "value": "Batches: 100%" + } + }, + "f68b5d3640c54560b38a29f32deb33a8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_54dd267783314434a5389477c97974e5", + "max": 3, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_5bfd23c3586e4615909878610be8e24b", + "value": 3 + } + }, + "115335a31d874aba99efb63fa2830e09": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4eda58c3e66e40db98ea40fc40ebb109", + "placeholder": "​", + "style": "IPY_MODEL_99ee6edbe0574c778200ae65b87d7e0f", + "value": " 3/3 [00:09<00:00,  2.81s/it]" + } + }, + "7b371d0574e04f98bf87a88f722b8477": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9095b2b09d4a45928fbc3cf45eb35cbb": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "971a53d397494d76b8b5c4a2abb954f7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "54dd267783314434a5389477c97974e5": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5bfd23c3586e4615909878610be8e24b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "4eda58c3e66e40db98ea40fc40ebb109": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "99ee6edbe0574c778200ae65b87d7e0f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From c2bb62bc2815133aef7102b5316b0810bf735176 Mon Sep 17 00:00:00 2001 From: biplob <110578485+bks1984@users.noreply.github.com> Date: Tue, 2 Dec 2025 11:23:34 +0530 Subject: [PATCH 4/4] Created using Colab --- Copy_of_Logistic_Regression.ipynb | 726 ++++++++++++++++++++++++++++++ 1 file changed, 726 insertions(+) create mode 100644 Copy_of_Logistic_Regression.ipynb diff --git a/Copy_of_Logistic_Regression.ipynb b/Copy_of_Logistic_Regression.ipynb new file mode 100644 index 0000000..41ae87a --- /dev/null +++ b/Copy_of_Logistic_Regression.ipynb @@ -0,0 +1,726 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **What is Logistic Regression**\n", + "\n", + "As depicted in this ML algorithm chart, Logistic Regression is part of [**Supervised Machine Learning**](https://colab.research.google.com/drive/1WHMXFxU8DK-lgTgaHFZ2oLtkpFM25bDm?usp=sharing). Logistic Regression is used for **Classification** problem." + ], + "metadata": { + "id": "qC487XkXHW2R" + } + }, + { + "cell_type": "markdown", + "source": [ + "![image.png](data:image/png;base64,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)" + ], + "metadata": { + "id": "Abdg7AmMHXZJ" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DEkuSfNBY6g3" + }, + "source": [ + "In Linear Regression, we used to determine the value of a continuous dependent variable. **Logistic Regression** is generally used for **classification** purposes. Unlike Linear Regression, the dependent variable can take a limited number of values only i.e, the **dependent variable is categorical**.\n", + "\n", + "**Classification techniques** are an essential part of machine learning and data mining applications.\n", + "\n", + "**Approximately 70% of problems in Data Science are classification problems.**\n", + "\n", + "There are lots of classification problems that are available, but the **logistics regression is common and is a useful regression method for solving the binary classification problem**.\n", + "\n", + "\n", + "**Types of Logistic Regression:**\n", + "\n", + "\n", + "**Binary Logistic Regression**\n", + "When the number of possible outcomes is only two it is called Binary Logistic Regression.The target variable has only two possible outcomes such as Spam or Not Spam, Cancer or No Cancer.\n", + "\n", + "**Multinomial Logistic Regression**: The target variable has three or more nominal categories such as predicting the type of Wine.\n", + "Other example, **IRIS dataset** a very famous example of multi-class classification. Other examples are classifying article/blog/document category.\n", + "\n", + "**Ordinal Logistic Regression:** the target variable has three or more ordinal categories such as restaurant or product rating from 1 to 5.\n", + "\n", + "\n", + "Logistic Regression is one of the most simple and commonly used Machine Learning algorithms for **two-class classification**. It is easy to implement and can be used as the baseline for any binary classification problem.\n", + "\n", + "Famous examples :\n", + "\n", + "**spam detection**.\n", + "\n", + "**Diabetes prediction**,\n", + "\n", + "**Churn prediction** etc\n", + "\n", + "Its basic fundamental concepts are also constructive in deep learning. Logistic regression describes and estimates the relationship between one dependent binary variable and independent variables.\n", + "\n", + "\n", + "Let’s look at how logistic regression can be used for classification tasks.\n", + "In Linear Regression, the output is the weighted sum of inputs.\n", + "\n", + "Logistic Regression is a generalized Linear Regression in the sense that we don’t output the weighted sum of inputs directly, but we pass it through a function that can map any real value between 0 and 1.\n", + "![Capture.PNG](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g5M2KZjGY6jX" + }, + "source": [ + "The activation function that is used is known as the sigmoid function. The plot of the sigmoid function looks like\n", + "\n", + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FJ7jd-bPvvQe" + }, + "source": [ + "##Linear Regression Vs. Logistic Regression\n", + "Linear regression gives you a continuous output, but logistic regression provides a constant output. An example of the continuous output is house price and stock price. Example's of the discrete output is predicting whether a patient has cancer or not, predicting whether the customer will churn. Linear regression is estimated using Ordinary Least Squares (OLS) while logistic regression is estimated using Maximum Likelihood Estimation (MLE) approach." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pA5A5bOhvnVg" + }, + "source": [ + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3uP7Itp8y9OK" + }, + "source": [ + "##Example : Lets build logistic regression model using Scikit-learn for diabetes prediction\n", + "\n", + "Here, we are going to predict diabetes using Logistic Regression Classifier." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aTcJK4nUyrJ5" + }, + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": "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", + "ok": true, + "headers": [ + [ + "content-type", + "application/javascript" + ] + ], + "status": 200, + "status_text": "" + } + }, + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "id": "kTMLmgKeY_mo", + "outputId": "8c9898f1-2913-4546-bd10-4460027b1cea" + }, + "source": [ + "#To upload from your local drive, start with the following code:\n", + "from google.colab import files\n", + "uploaded = files.upload()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving diabetes.csv to diabetes.csv\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z1GFG4bMyVwx" + }, + "source": [ + "#to import it into a dataframe (make sure the filename matches the name of the uploaded file)\n", + "import io\n", + "import pandas as pd\n", + "df = pd.read_csv(io.BytesIO(uploaded['diabetes.csv']))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "gdDKwmWza66t", + "outputId": "68197960-2482-42d1-ab6e-3f388df710b4" + }, + "source": [ + "df.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
061487235033.60.627501
11856629026.60.351310
28183640023.30.672321
318966239428.10.167210
40137403516843.12.288331
\n", + "
" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure ... DiabetesPedigreeFunction Age Outcome\n", + "0 6 148 72 ... 0.627 50 1\n", + "1 1 85 66 ... 0.351 31 0\n", + "2 8 183 64 ... 0.672 32 1\n", + "3 1 89 66 ... 0.167 21 0\n", + "4 0 137 40 ... 2.288 33 1\n", + "\n", + "[5 rows x 9 columns]" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lum0Cxna1aIJ" + }, + "source": [ + "Selecting Feature\n", + "Here, you need to divide the given columns into two types of variables dependent(or target variable) and independent variable(or feature variables)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sj-Renl5zI_j" + }, + "source": [ + "#split dataset in features and target variable\n", + "feature_cols = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']\n", + "X = df[feature_cols] # Features\n", + "y = df.Outcome # Target variable" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vb8siCfQ3Umz" + }, + "source": [ + "### Splitting Data\n", + "To understand model performance, dividing the dataset into a training set and a test set is a good strategy.\n", + "\n", + "Let's split dataset by using **function train_test_split()**. You need to pass 3 parameters features, target, and test_set size. Additionally, you can use random_state to select records randomly." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2wJFeZ2M1jOh" + }, + "source": [ + "# split X and y into training and testing sets\n", + "from sklearn.model_selection import train_test_split\n", + "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nY978WTH38Uv" + }, + "source": [ + "Here, the Dataset is broken into two parts in a ratio of 75:25. It means 75% data will be used for model training and 25% for model testing." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PylEEeof3-D5" + }, + "source": [ + "### Model Development and Prediction\n", + "First, import the Logistic Regression module and create a Logistic Regression classifier object using **LogisticRegression()** function.\n", + "\n", + "Then, fit your model on the train set using fit() and perform prediction on the test set using predict()." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QCP2RFoa3fML", + "outputId": "6e4c9f1f-2f26-4191-bd7a-ad73c8eec17a" + }, + "source": [ + "# import the class\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# instantiate the model (using the default parameters)\n", + "logreg = LogisticRegression()\n", + "\n", + "# fit the model with data\n", + "logreg.fit(X_train,y_train)\n", + "\n", + "#\n", + "y_pred=logreg.predict(X_test)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LbT8Y8VL4cDx" + }, + "source": [ + "# **Model Evaluation using Confusion Matrix**\n", + "\n", + "A **confusion matrix** is a table that is used to evaluate the performance of a classification model. You can also visualize the performance of an algorithm. The fundamental of a confusion matrix is the number of correct and incorrect predictions are summed up class-wise.\n", + "![CM.PNG](data:image/png;base64,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)\n", + "![CM2.PNG](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# IMPORTANT\n", + "\n", + "Confusion matrix accuracy is not meaningful for **unbalanced classification**.\n", + "In such cases where class variable is imbalanced it is recommended to measure the accuracy using the Area Under the Precision-Recall Curve (AUPRC).\n", + "\n", + "❗ Example of **unbalanced classification**\n", + "\n", + "Suppose we have a dataset of credit card transactions where:\n", + "\n", + "Total transactions are : 284,807\n", + "\n", + "Fraud transaction (Class variable) : 492\n", + "\n", + "This dataset is highly unbalanced, because the positive class (frauds) account only for 0.172% of all transactions.\n" + ], + "metadata": { + "id": "svvPVFGFFanw" + } + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zYYrt_Ku4Vhx", + "outputId": "a15506a2-d458-42f0-e7ae-aeba519dc930" + }, + "source": [ + "# import the metrics class\n", + "from sklearn import metrics\n", + "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n", + "cnf_matrix" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[115, 15],\n", + " [ 25, 37]])" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vCtLXypF4pWd" + }, + "source": [ + "Here, you can see the confusion matrix in the form of the array object. The dimension of this matrix is 2*2 because this model is binary classification. You have two classes 0 and 1. Diagonal values represent accurate predictions, while non-diagonal elements are inaccurate predictions. In the output, 119 and 36 are actual predictions, and 26 and 11 are incorrect predictions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XM9qUNYl4tpk" + }, + "source": [ + "### Visualizing Confusion Matrix using Heatmap\n", + "Let's visualize the results of the model in the form of a confusion matrix using matplotlib and seaborn.\n", + "\n", + "Here, you will visualize the confusion matrix using Heatmap." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lXr1xgSJ4j_5" + }, + "source": [ + "# import required modules\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "%matplotlib inline" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 342 + }, + "id": "fR-1Aq9h4y5I", + "outputId": "694eb98d-fc22-4d63-832e-20b93795164e" + }, + "source": [ + "class_names=[0,1] # name of classes\n", + "fig, ax = plt.subplots()\n", + "tick_marks = np.arange(len(class_names))\n", + "plt.xticks(tick_marks, class_names)\n", + "plt.yticks(tick_marks, class_names)\n", + "# create heatmap\n", + "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n", + "ax.xaxis.set_label_position(\"top\")\n", + "plt.tight_layout()\n", + "plt.title('Confusion matrix', y=1.1)\n", + "plt.ylabel('Actual label')\n", + "plt.xlabel('Predicted label')\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 257.44, 'Predicted label')" + ] + }, + "metadata": {}, + "execution_count": 24 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7cc8Gwr65MN1" + }, + "source": [ + "### Confusion Matrix Evaluation Metrics\n", + "Let's evaluate the model using model evaluation metrics such as accuracy, precision, and recall." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k9p9fJmP46tw", + "outputId": "6e7e9c88-2c7a-4d2b-8a10-abcf28c8acfb" + }, + "source": [ + "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", + "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", + "print(\"Recall:\",metrics.recall_score(y_test, y_pred))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy: 0.7916666666666666\n", + "Precision: 0.7115384615384616\n", + "Recall: 0.5967741935483871\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U-WHC8za5Z5J" + }, + "source": [ + "**We got a classification rate of 80%, considered as good accuracy.**\n", + "\n", + "**Precision**: Precision is about being precise, i.e., how accurate your model is. In other words, you can say, when a model makes a prediction, how often it is correct. In your prediction case, when your Logistic Regression model predicted patients are going to suffer from diabetes, that patients have 76% of the time.\n", + "\n", + "**Recall**: If there are patients who have diabetes in the test set and your Logistic Regression model can identify it 58% of the time." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6-crRCXI58oG" + }, + "source": [ + "## ROC Curve\n", + "Receiver Operating Characteristic(ROC) curve is a plot of the true positive rate against the false positive rate. It shows the tradeoff between sensitivity and specificity." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "I4C7a8AE5Rfw", + "outputId": "950434de-211a-452b-eb7e-f89d56529940" + }, + "source": [ + "y_pred_proba = logreg.predict_proba(X_test)[::,1]\n", + "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba)\n", + "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n", + "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n", + "plt.legend(loc=4)\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sm7cDn8V6M1i" + }, + "source": [ + "AUC score for the case is 0.86. AUC score 1 represents perfect classifier, and 0.5 represents a worthless classifier.\n", + "\n", + "**Advantages**\n", + "Because of its efficient and straightforward nature, doesn't require high computation power, easy to implement, easily interpretable, used widely by data analyst and scientist. Also, it doesn't require scaling of features. Logistic regression provides a probability score for observations.\n", + "\n", + "**Disadvantages**\n", + "Logistic regression is not able to handle a large number of categorical features/variables. It is vulnerable to overfitting. Also, can't solve the non-linear problem with the logistic regression that is why it requires a transformation of non-linear features. Logistic regression will not perform well with independent variables that are not correlated to the target variable and are very similar or correlated to each other." + ] + } + ] +} \ No newline at end of file