reformat operators docs

Author: ahosler

docs/source/user_guide/operators/anomaly_detection_operator/advanced_use_cases.rst

@@ -2,16 +2,14 @@
 Advanced Use Cases
 ==================
 
-**Documentation: Anomaly Detection Science and Model Parameterization**
-
 The Science of Anomaly Detection
 --------------------------------
 
 Anomaly Detection comes in many forms. We will go through some of these and give guidance as to whether this Operator is going to be helpful for each use case.
 
 * Constructive v Destructive v Pre-Processing: This Operator focuses on the Constructive and Pre-Processing use cases. Destructive can work, but more specific parameters may be required.
 * Supervised v Semi-Supervised v Unsupervised: All 3 of these approaches are supported by AutoMLX. AutoTS supports only Unsupervised at this time.
-* Time Series. This Operator is focused on just time-series data.
+* Time Series. This Operator requires time-series data.
 
 
 Data Parameterization
@@ -55,6 +53,9 @@ Data Parameterization
         target_column: y
 
 
+Model Parameterization
+----------------------
+
 **Specify Model Type**
 
 Sometimes users will know which models they want to use. When users know this in advance, they can specify using the ``model_kwargs`` dictionary. In the following example, we will instruct the model to *only* use the ``IsolationForestOD`` model.

docs/source/user_guide/operators/anomaly_detection_operator/index.rst

@@ -1,6 +1,6 @@
-==========================
-Anomaly Detection Operator
-==========================
+=================
+Anomaly Detection
+=================
 
 The Anomaly Detection Operator is a low code tool for integrating Anomaly Detection into any enterprise applicaiton. Specifically, it leverages timeseries constructive anomaly detection in order to flag anomolous moments in your data, by time and by ID.
 

docs/source/user_guide/operators/anomaly_detection_operator/productionize.rst

@@ -72,9 +72,8 @@ The report.html file is designed differently for each model type. Generally, it
 The metrics file includes relevant metrics calculated on the training set.
 
 
-========
 Examples
-========
+--------
 
 **Simple Example**
 

docs/source/user_guide/operators/forecasting_operator/advanced_use_cases.rst

@@ -2,7 +2,6 @@
 Advanced Use Cases
 ==================
 
-
 The Science of Forecasting
 --------------------------
 

docs/source/user_guide/operators/forecasting_operator/index.rst

@@ -1,6 +1,6 @@
-====================
-Forecasting Operator
-====================
+===========
+Forecasting
+===========
 
 The Forecasting Operator leverages historical time series data to generate accurate forecasts for future trends. This operator aims to simplify and expedite the data science process by automating the selection of appropriate models and hyperparameters, as well as identifying relevant features for a given prediction task.
 

docs/source/user_guide/operators/forecasting_operator/productionize.rst

@@ -110,7 +110,7 @@ While local explanations offer granular insights, they may not provide a compreh
 
 
 Examples
-========
+--------
 
 **Simple Example**
 

docs/source/user_guide/operators/pii_operator/index.rst

@@ -1,6 +1,6 @@
-============
-PII Operator
-============
+===
+PII
+===
 
 The PII operator aims to detect and redact Personally Identifiable Information(PII) in datasets by combining pattern match and machine learning solution.
 

pyproject.toml

@@ -179,7 +179,7 @@ forecast = [
   "oracledb",
 ]
 anomaly  = [
-  "tods @ git+https://github.com/datamllab/tods.git",
+  "oracle_ads[opctl]",
   "autots",
   "oracle-automlx[anomaly]==23.2.3",
   "oracledb",