From 7ebafbe80c3d80870db7757590948d8530233f0b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Richard=20L=C3=B6wenstr=C3=B6m?= Date: Fri, 3 Jan 2025 14:59:59 +0100 Subject: [PATCH 1/5] doc: migrate to mkdocs --- .cursorrules | 8 + .github/workflows/gh-pages.yml | 36 ++ .gitignore | 1 + .readthedocs.yml | 30 -- README.md | 124 +++++ conftest.py | 6 + datastream/dataset.py | 185 +++++--- datastream/datastream.py | 33 +- docs/Makefile | 20 - docs/dataset.md | 209 +++++++++ docs/datastream.md | 188 ++++++++ docs/getting-started.md | 69 +++ docs/index.md | 56 +++ docs/make.bat | 35 -- docs/source/conf.py | 70 --- docs/source/dataset.rst | 7 - docs/source/datastream.rst | 7 - docs/source/get_started.rst | 192 -------- docs/source/index.rst | 30 -- docs/source/requirements.txt | 71 --- docs/source/tools.rst | 5 - mkdocs.yml | 57 +++ poetry.lock | 814 ++++++++++++++++++++++++++++++--- pyproject.toml | 37 +- pytest.ini | 5 - 25 files changed, 1683 insertions(+), 612 deletions(-) create mode 100644 .cursorrules create mode 100644 .github/workflows/gh-pages.yml delete mode 100644 .readthedocs.yml create mode 100644 README.md create mode 100644 conftest.py delete mode 100644 docs/Makefile create mode 100644 docs/dataset.md create mode 100644 docs/datastream.md create mode 100644 docs/getting-started.md create mode 100644 docs/index.md delete mode 100644 docs/make.bat delete mode 100644 docs/source/conf.py delete mode 100644 docs/source/dataset.rst delete mode 100644 docs/source/datastream.rst delete mode 100644 docs/source/get_started.rst delete mode 100644 docs/source/index.rst delete mode 100644 docs/source/requirements.txt delete mode 100644 docs/source/tools.rst create mode 100644 mkdocs.yml delete mode 100644 pytest.ini diff --git a/.cursorrules b/.cursorrules new file mode 100644 index 0000000..8218eba --- /dev/null +++ b/.cursorrules @@ -0,0 +1,8 @@ +- Use pydantic 2 +- Pytest +- Use black formatting +- Avoid methods with sideeffects and if they are needed then add a "\_" suffix +- Prefer pathlib over os +- Prefer getter method names like `tasks` over `get_tasks` +- Commands need to be run using `poetry run ` +- Use simple tests with a bit of logging that we can run with `poetry run pytest -s` to check that the code works as expected diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml new file mode 100644 index 0000000..5a60d1e --- /dev/null +++ b/.github/workflows/gh-pages.yml @@ -0,0 +1,36 @@ +name: Deploy Documentation + +on: + push: + branches: + - master + workflow_dispatch: + +permissions: + contents: write + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install poetry + poetry install + + - name: Build documentation + run: poetry run mkdocs build + + - name: Deploy to GitHub Pages + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./site \ No newline at end of file diff --git a/.gitignore b/.gitignore index d32b7e0..bfc882e 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ dist .eggs/ build/ *.pyc +site/ AUTHORS ChangeLog diff --git a/.readthedocs.yml b/.readthedocs.yml deleted file mode 100644 index b9aec91..0000000 --- a/.readthedocs.yml +++ /dev/null @@ -1,30 +0,0 @@ -# .readthedocs.yml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -# Add this build section -build: - os: ubuntu-22.04 - tools: - python: "3.8" - - -# Build documentation in the docs/ directory with Sphinx -sphinx: - configuration: docs/source/conf.py - -# Build documentation with MkDocs -#mkdocs: -# configuration: mkdocs.yml - -# Optionally build your docs in additional formats such as PDF -# formats: -# - pdf - -# Optionally set the version of Python and requirements required to build your docs -python: - install: - - requirements: docs/source/requirements.txt diff --git a/README.md b/README.md new file mode 100644 index 0000000..8c8e4cc --- /dev/null +++ b/README.md @@ -0,0 +1,124 @@ +# Pytorch Datastream + +[![PyPI version](https://badge.fury.io/py/pytorch-datastream.svg)](https://badge.fury.io/py/pytorch-datastream) +[![Python versions](https://img.shields.io/pypi/pyversions/pytorch-datastream.svg)](https://pypi.python.org/pypi/pytorch-datastream) +[![Documentation Status](https://readthedocs.org/projects/pytorch-datastream/badge/?version=latest)](https://pytorch-datastream.readthedocs.io/en/latest/?badge=latest) +[![License](https://img.shields.io/pypi/l/pytorch-datastream.svg)](https://pypi.python.org/pypi/pytorch-datastream) + +This is a simple library for creating readable dataset pipelines and reusing best practices for issues such as imbalanced datasets. There are just two components to keep track of: `Dataset` and `Datastream`. + +`Dataset` is a simple mapping between an index and an example. It provides pipelining of functions in a readable syntax originally adapted from tensorflow 2's `tf.data.Dataset`. + +`Datastream` combines a `Dataset` and a sampler into a stream of examples. It provides a simple solution to oversampling / stratification, weighted sampling, and finally converting to a `torch.utils.data.DataLoader`. + +## Install + +```bash +poetry add pytorch-datastream +``` + +Or, for the old-timers: + +```bash +pip install pytorch-datastream +``` + +## Usage + +The list below is meant to showcase functions that are useful in most standard and non-standard cases. It is not meant to be an exhaustive list. See the [documentation](https://pytorch-datastream.readthedocs.io/en/latest/) for a more extensive list on API and usage. + +```python +Dataset.from_subscriptable +Dataset.from_dataframe +Dataset +.map +.subset +.split +.cache +.with_columns + +Datastream.merge +Datastream.zip +Datastream +.map +.data*loader +.zip_index +.update_weights* +.update*example_weight* +.weight +.state_dict +.load_state_dict +``` + +### Simple image dataset example + +Here's a basic example of loading images from a directory: + +```python +from datastream import Dataset +from pathlib import Path +from PIL import Image + +# Assuming images are in a directory structure like: +# images/ +# class1/ +# image1.jpg +# image2.jpg +# class2/ +# image3.jpg +# image4.jpg + +image_dir = Path("images") +image_paths = list(image_dir.glob("\*_/_.jpg")) + +dataset = ( +Dataset.from_paths( +image_paths, +pattern=r".\*/(?P\w+)/(?P\w+).jpg" +) +.map(lambda row: dict( +image=Image.open(row["path"]), +class_name=row["class_name"], +image_name=row["image_name"], +)) +) + +# Access an item from the dataset + +first_item = dataset[0] +print(f"Class: {first_item['class_name']}, Image name: {first_item['image_name']}") +``` + +### Merge / stratify / oversample datastreams + +The fruit datastreams given below repeatedly yields the string of its fruit type. + +````python + +> > > datastream = Datastream.merge([ +> > > ... (apple_datastream, 2), +> > > ... (pear_datastream, 1), +> > > ... (banana_datastream, 1), +> > > ... ]) +> > > next(iter(datastream.data_loader(batch_size=8))) +> > > ['apple', 'apple', 'pear', 'banana', 'apple', 'apple', 'pear', 'banana'] +> > > ``` + +### Zip independently sampled datastreams + +The fruit datastreams given below repeatedly yields the string of its fruit type. + +```python + +> > > datastream = Datastream.zip([ +> > > ... apple_datastream, +> > > ... Datastream.merge([pear_datastream, banana_datastream]), +> > > ... ]) +> > > next(iter(datastream.data_loader(batch_size=4))) +> > > [('apple', 'pear'), ('apple', 'banana'), ('apple', 'pear'), ('apple', 'banana')] +> > > ``` + +### More usage examples + +See the [documentation](https://pytorch-datastream.readthedocs.io/en/latest/) for more usage examples. +```` diff --git a/conftest.py b/conftest.py new file mode 100644 index 0000000..9f20ea1 --- /dev/null +++ b/conftest.py @@ -0,0 +1,6 @@ +def pytest_configure(config): + """Configure pytest.""" + config.addinivalue_line( + "markers", + "codeblocks: mark test to be collected from code blocks", + ) \ No newline at end of file diff --git a/datastream/dataset.py b/datastream/dataset.py index 790b760..2ed4b2d 100644 --- a/datastream/dataset.py +++ b/datastream/dataset.py @@ -29,10 +29,18 @@ class Dataset(BaseModel, Generic[T]): - """ - A ``Dataset[T]`` is a mapping that allows pipelining of functions in a - readable syntax returning an example of type ``T``. + """A dataset that allows pipelining of functions with a readable syntax. + + The Dataset class provides a mapping interface that enables function pipelining, + returning examples of type T. It supports operations like mapping, filtering, + and combining datasets. + + Args: + dataframe (Optional[pd.DataFrame]): Source dataframe for the dataset. + length (int): Number of examples in the dataset. + get_item (Callable[[pd.DataFrame, int], T]): Function to get an item at a given index. + Example: >>> from datastream import Dataset >>> fruit_and_cost = ( ... ('apple', 5), @@ -62,34 +70,44 @@ class Dataset(BaseModel, Generic[T]): @staticmethod def from_subscriptable(subscriptable) -> Dataset: - """ - Create ``Dataset`` based on subscriptable i.e. implements - ``__getitem__`` and ``__len__``. + """Creates a Dataset from a subscriptable object. - Should only be used for simple examples as a ``Dataset`` created with - this method does not support methods that require a source dataframe - like :func:`Dataset.split` and :func:`Dataset.subset`. - """ + Creates a Dataset based on any object that implements __getitem__ and __len__. + This method should only be used for simple examples as it doesn't support + methods that require a source dataframe like split and subset. + Args: + subscriptable: An object implementing __getitem__ and __len__. + + Returns: + Dataset: A new dataset wrapping the subscriptable object. + """ return Dataset.from_dataframe( pd.DataFrame(dict(index=range(len(subscriptable)))) ).map(lambda row: subscriptable[row["index"]]) @staticmethod def from_dataframe(dataframe: pd.DataFrame) -> Dataset[pd.Series]: - """ - Create ``Dataset`` based on ``pandas.DataFrame``. - :func:`Dataset.__getitem__` will return a row from the dataframe and - :func:`Dataset.map` should be given a function that takes a row from - the dataframe as input. - - >>> ( - ... Dataset.from_dataframe(pd.DataFrame(dict( - ... number=[1, 2, 3] - ... ))) - ... .map(lambda row: row['number'] + 1) - ... )[-1] - 4 + """Creates a Dataset from a pandas DataFrame. + + Creates a Dataset where __getitem__ returns a row from the dataframe. + The map method should be given a function that takes a row as input. + + Args: + dataframe (pd.DataFrame): Source dataframe for the dataset. + + Returns: + Dataset[pd.Series]: A new dataset wrapping the dataframe. + + Example: + >>> dataset = ( + ... Dataset.from_dataframe(pd.DataFrame(dict( + ... number=[1, 2, 3] + ... ))) + ... .map(lambda row: row['number'] + 1) + ... ) + >>> dataset[-1] + 4 """ return Dataset( dataframe=dataframe, @@ -99,19 +117,27 @@ def from_dataframe(dataframe: pd.DataFrame) -> Dataset[pd.Series]: @staticmethod def from_paths(paths: Iterable[str, Path], pattern: str) -> Dataset[pd.Series]: - r""" - Create ``Dataset`` from paths using regex pattern that extracts information - from the path itself. - :func:`Dataset.__getitem__` will return a row from the dataframe and - :func:`Dataset.map` should be given a function that takes a row from - the dataframe as input. - - >>> image_paths = ["dataset/damage/1.png"] - >>> ( - ... Dataset.from_paths(image_paths, pattern=r".*/(?P\w+)/(?P\d+).png") - ... .map(lambda row: row["class_name"]) - ... )[-1] - 'damage' + """Creates a Dataset from file paths using regex pattern extraction. + + Creates a Dataset by extracting information from file paths using a regex pattern. + The __getitem__ will return a row from the dataframe and map should be given + a function that takes a row as input. + + Args: + paths (Iterable[str, Path]): List of file paths. + pattern (str): Regex pattern to extract information from paths. + + Returns: + Dataset[pd.Series]: A new dataset with extracted path information. + + Example: + >>> image_paths = ["dataset/damage/1.png"] + >>> dataset = ( + ... Dataset.from_paths(image_paths, pattern=r".*/(?P\w+)/(?P\d+).png") + ... .map(lambda row: row["class_name"]) + ... ) + >>> dataset[-1] + 'damage' """ paths = list(paths) return Dataset.from_dataframe( @@ -162,14 +188,21 @@ def replace(self, **kwargs): return type(self)(**new_dict) def map(self: Dataset[T], function: Callable[[T], R]) -> Dataset[R]: - """ - Creates a new dataset with the function added to the dataset pipeline. + """Creates a new dataset by applying a function to each item. - >>> ( - ... Dataset.from_subscriptable([1, 2, 3]) - ... .map(lambda number: number + 1) - ... )[-1] - 4 + Args: + function (Callable[[T], R]): Function to apply to each item. + + Returns: + Dataset[R]: A new dataset with the function added to the pipeline. + + Example: + >>> dataset = ( + ... Dataset.from_subscriptable([1, 2, 3]) + ... .map(lambda number: number + 1) + ... ) + >>> dataset[-1] + 4 """ def composed_fn(dataframe, index): @@ -201,38 +234,54 @@ def composed_fn(dataframe, index): ) def starmap(self: Dataset[T], function: Callable[..., R]) -> Dataset[R]: - """ - Creates a new dataset with the function added to the dataset pipeline. - The dataset's pipeline should return an iterable that will be - expanded as \\*args to the mapped function. + """Creates a new dataset by applying a function with unpacked arguments. - >>> ( - ... Dataset.from_subscriptable([1, 2, 3]) - ... .map(lambda number: (number, number + 1)) - ... .starmap(lambda number, plus_one: number + plus_one) - ... )[-1] - 7 + The dataset's pipeline should return an iterable that will be expanded as *args + to the mapped function. + + Args: + function (Callable[..., R]): Function to apply with unpacked arguments. + + Returns: + Dataset[R]: A new dataset with the function added to the pipeline. + + Example: + >>> dataset = ( + ... Dataset.from_subscriptable([1, 2, 3]) + ... .map(lambda number: (number, number + 1)) + ... .starmap(lambda number, plus_one: number + plus_one) + ... ) + >>> dataset[-1] + 7 """ return self.map(tools.star(function)) def subset( self, mask_fn: Callable[[pd.DataFrame], Union[pd.Series, np.array, List[bool]]] ) -> Dataset[T]: - """ - Select a subset of the dataset using a function that receives the - source dataframe as input and is expected to return a boolean mask. - - Note that this function can still be called after multiple operations - such as mapping functions as it uses the source dataframe. - - >>> ( - ... Dataset.from_dataframe(pd.DataFrame(dict( - ... number=[1, 2, 3] - ... ))) - ... .map(lambda row: row['number']) - ... .subset(lambda dataframe: dataframe['number'] <= 2) - ... )[-1] - 2 + """Creates a subset of the dataset using a mask function. + + Selects a subset using a function that receives the source dataframe as input + and returns a boolean mask. This function can be called after multiple operations + as it uses the source dataframe. + + Args: + mask_fn (Callable[[pd.DataFrame], Union[pd.Series, np.array, List[bool]]]): + Function that returns a boolean mask for selecting rows. + + Returns: + Dataset[T]: A new dataset containing only the selected examples. + + Example: + >>> dataset = ( + ... Dataset.from_dataframe(pd.DataFrame(dict( + ... number=[1, 2, 3] + ... ))) + ... .map(lambda row: row['number']) + ... .subset(lambda dataframe: dataframe['number'] <= 2) + ... ) + >>> dataset[-1] + 2 """ dataframe = self.dataframe[mask_fn(self.dataframe)] return self.replace(dataframe=dataframe, length=len(dataframe)) diff --git a/datastream/datastream.py b/datastream/datastream.py index 0374aef..571b97b 100644 --- a/datastream/datastream.py +++ b/datastream/datastream.py @@ -20,21 +20,24 @@ class Datastream(BaseModel, Generic[T]): - """ - ``Datastream[T]`` combines a ``Dataset[T]`` and a sampler into a stream of - examples. - - By default the samples are drawn without replacement until the - full dataset is exhausted. The proportion of the dataset that should be - drawn before allowing replacement can be changed with - :func:`Datastream.sample_proportion`. - - >>> data_loader = ( - ... Datastream(Dataset.from_subscriptable([1, 2, 3])) - ... .data_loader(batch_size=16, n_batches_per_epoch=100) - ... ) - >>> len(next(iter(data_loader))) - 16 + """A stream of examples combining a Dataset and a sampler. + + Datastream combines a Dataset[T] with a sampler to create a stream of examples. + By default, samples are drawn without replacement until the dataset is exhausted. + The sampling behavior can be modified using sample_proportion. + + Args: + dataset (Dataset): The source dataset to stream from. + sampler (Optional[torch.utils.data.Sampler]): The sampler to use. If None, + a StandardSampler will be used. + + Example: + >>> data_loader = ( + ... Datastream(Dataset.from_subscriptable([1, 2, 3])) + ... .data_loader(batch_size=16, n_batches_per_epoch=100) + ... ) + >>> len(next(iter(data_loader))) + 16 """ dataset: Dataset diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index d0c3cbf..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/dataset.md b/docs/dataset.md new file mode 100644 index 0000000..0e65b27 --- /dev/null +++ b/docs/dataset.md @@ -0,0 +1,209 @@ +# Dataset + +A `Dataset[T]` is a mapping that allows pipelining of functions in a readable syntax returning an example of type `T`. + +```python +from datastream import Dataset + +fruits_and_cost = ( + ('apple', 5), + ('pear', 7), + ('banana', 14), + ('kiwi', 100), +) + +dataset = ( + Dataset.from_subscriptable(fruits_and_cost) + .starmap(lambda fruit, cost: ( + fruit, + cost * 2, + )) +) + +assert dataset[2] == ('banana', 28) +``` + +## Class Methods + +### from_subscriptable + +Create `Dataset` based on subscriptable i.e. implements `__getitem__` and `__len__`. + +Should only be used for simple examples as a `Dataset` created with this method does not support methods that require a source dataframe like `Dataset.split` and `Dataset.subset`. + +### from_dataframe + +Create `Dataset` based on `pandas.DataFrame`. `Dataset.__getitem__` will return a row from the dataframe and `Dataset.map` should be given a function that takes a row from the dataframe as input. + +```python +import pandas as pd +from datastream import Dataset + +dataset = ( + Dataset.from_dataframe(pd.DataFrame(dict( + number=[1, 2, 3] + ))) + .map(lambda row: row['number'] + 1) +) + +assert dataset[-1] == 4 +``` + +### from_paths + +Create `Dataset` from paths using regex pattern that extracts information from the path itself. +`Dataset.__getitem__` will return a row from the dataframe and `Dataset.map` should be given a function that takes a row from the dataframe as input. + +```python +from datastream import Dataset + +image_paths = ["dataset/damage/1.png"] +dataset = ( + Dataset.from_paths(image_paths, pattern=r".*/(?P\w+)/(?P\d+).png") + .map(lambda row: row["class_name"]) +) + +assert dataset[-1] == 'damage' +``` + +## Instance Methods + +### map + +Creates a new dataset with the function added to the dataset pipeline. + +```python +from datastream import Dataset + +dataset = ( + Dataset.from_subscriptable([1, 2, 3]) + .map(lambda number: number + 1) +) + +assert dataset[-1] == 4 +``` + +### starmap + +Creates a new dataset with the function added to the dataset pipeline. +The dataset's pipeline should return an iterable that will be expanded as arguments to the mapped function. + +```python +from datastream import Dataset + +dataset = ( + Dataset.from_subscriptable([1, 2, 3]) + .map(lambda number: (number, number + 1)) + .starmap(lambda number, plus_one: number + plus_one) +) + +assert dataset[-1] == 7 +``` + +### subset + +Select a subset of the dataset using a function that receives the source dataframe as input and is expected to return a boolean mask. + +Note that this function can still be called after multiple operations such as mapping functions as it uses the source dataframe. + +```python +import pandas as pd +from datastream import Dataset + +dataset = ( + Dataset.from_dataframe(pd.DataFrame(dict( + number=[1, 2, 3] + ))) + .map(lambda row: row['number']) + .subset(lambda dataframe: dataframe['number'] <= 2) +) + +assert dataset[-1] == 2 +``` + +### split + +Split dataset into multiple parts. Optionally you can stratify on a column in the source dataframe or save the split to a json file. +If you are sure that the split strategy will not change then you can safely use a seed instead of a filepath. + +Saved splits can continue from the old split and handle: + +- New examples +- Changing test size +- Adapt after removing examples from dataset +- Adapt to new stratification + +```python +import numpy as np +import pandas as pd +from datastream import Dataset + +split_datasets = ( + Dataset.from_dataframe(pd.DataFrame(dict( + index=np.arange(100), + number=np.arange(100), + ))) + .map(lambda row: row['number']) + .split( + key_column='index', + proportions=dict(train=0.8, test=0.2), + seed=700, + ) +) +assert len(split_datasets['train']) == 80 +assert split_datasets['test'][0] == 3 +``` + +### zip_index + +Zip the output with its underlying Dataset index. The output of the pipeline will be a tuple `(output, index)`. + +```python +from datastream import Dataset + +dataset = Dataset.from_subscriptable([4, 5, 6]).zip_index() +assert dataset[0] == (4, 0) +``` + +### cache + +Cache intermediate step in-memory based on key column. + +```python +import pandas as pd +from datastream import Dataset + +df = pd.DataFrame({'key': ['a', 'b'], 'value': [1, 2]}) +dataset = Dataset.from_dataframe(df).cache('key') +assert dataset[0]['value'] == 1 +``` + +### concat + +Concatenate multiple datasets together so that they behave like a single dataset. + +Consider using `Datastream.merge` if you have multiple data sources instead as it allows you to control the number of samples from each source in the training batches. + +```python +from datastream import Dataset + +dataset1 = Dataset.from_subscriptable([1, 2]) +dataset2 = Dataset.from_subscriptable([3, 4]) +combined = Dataset.concat([dataset1, dataset2]) +assert len(combined) == 4 +assert combined[2] == 3 +``` + +### combine + +Zip multiple datasets together so that all combinations of examples are possible (i.e. the product) creating tuples like `(example1, example2, ...)`. + +```python +from datastream import Dataset + +dataset1 = Dataset.from_subscriptable([1, 2]) +dataset2 = Dataset.from_subscriptable([3, 4]) +combined = Dataset.combine([dataset1, dataset2]) +assert len(combined) == 4 # 2 * 2 = 4 combinations +assert combined[0] == (1, 3) # First combination +``` diff --git a/docs/datastream.md b/docs/datastream.md new file mode 100644 index 0000000..5520b6c --- /dev/null +++ b/docs/datastream.md @@ -0,0 +1,188 @@ +# Datastream + +A `Datastream[T]` combines a `Dataset[T]` and a sampler into a stream of examples. + +By default, samples are drawn without replacement until the dataset is exhausted. The sampling behavior can be modified using `sample_proportion`. + +```python test +from datastream import Dataset, Datastream + +# Create a simple dataset +dataset = Dataset.from_subscriptable([1, 2, 3]) + +# Create a datastream with batching +data_loader = ( + Datastream(dataset) + .data_loader(batch_size=2) +) + +# First batch should have 2 items +batch = next(iter(data_loader)) +assert len(batch) == 2 +``` + +## Methods + +### data_loader + +Get a PyTorch DataLoader for use in training pipeline. The argument `n_batches_per_epoch` overrides the underlying length of the dataset. +If the epoch ends before the full dataset has been processed then it will continue from the same point the next epoch. + +This is particularly useful when: + +- Training on very large datasets where you want fixed-size epochs +- Using weighted sampling where you want to ensure all classes are seen equally +- Doing curriculum learning where you want to control exactly how many samples are seen + +```python test +data_loader = ( + Datastream(Dataset.from_subscriptable([5, 5, 5])) + .data_loader(batch_size=2, n_batches_per_epoch=3) +) +batches = list(data_loader) +assert len(batches) == 3 # Always get exactly 3 batches +assert len(batches[0]) == 2 # Each batch has size 2 +``` + +### sample_proportion + +Create new Datastream with changed proportion. This changes the numbers of drawn samples before restarting sampling with new weights +and allowing sample replacement. + +It is important to set this if you are using sample weights because the default is to sample without replacement with proportion 1.0 which will +cause the weighting scheme to only affect the order in which the samples are drawn. + +```python test +# Create a datastream that will draw half the dataset before allowing replacement +datastream = ( + Datastream(Dataset.from_subscriptable([1, 2, 3, 4])) + .sample_proportion(0.5) # Draw 2 samples before replacement +) + +# Sample size is still the full dataset length +assert len(list(datastream)) == len(datastream) + +# But after 2 samples, items can be repeated +samples = [] +for _ in range(4): + samples.extend(list(datastream)) +assert len(set(samples)) < len(samples) # Some samples are repeated +``` + +### take + +Like `sample_proportion` but specify the number of samples instead of a proportion. + +```python test +datastream = ( + Datastream(Dataset.from_subscriptable([1, 2, 3, 4, 5])) + .take(2) # Draw exactly 2 samples before allowing replacement +) +assert len(list(datastream)) == 2 +``` + +### weight + +Get sample weight for specific example. Weights affect the probability of sampling each example. + +```python test +datastream = Datastream(Dataset.from_subscriptable([1, 2, 3])) +assert datastream.weight(0) == 1.0 # Default weight is 1.0 +``` + +### update*weights* + +Update all sample weights by function **in-place**. This is useful for implementing importance sampling +or curriculum learning strategies. + +```python test +import numpy as np + +# Create a datastream where we'll downweight all samples +datastream = Datastream(Dataset.from_subscriptable([1, 2, 3])) +datastream.update_weights_(lambda weights: weights * 0.5) +assert datastream.weight(0) == 0.5 +``` + +### update*example_weight* + +Update sample weight for specific example **in-place**. This is useful when you want to adjust +the sampling probability of individual examples, for instance based on model performance. + +```python test +datastream = Datastream(Dataset.from_subscriptable([1, 2, 3])) +datastream.update_example_weight_(0.5, index=0) # Make first example half as likely +assert datastream.weight(0) == 0.5 +``` + +### multi_sample + +Split datastream into clones with different sample weights and then merge them. The weights when accessed will be a sequence of multiple weights. + +This allows sample strategies where you for example stratify based on the model's predictions. A common use case is handling +multi-label classification where you want to ensure good coverage of all classes. + +```python test +n_classes = 3 +datastream = ( + Datastream(Dataset.from_subscriptable([1, 2, 3])) + .zip_index() + .multi_sample(n_classes) + .sample_proportion(0.5) +) + +# Each example now has n_classes weights that can be adjusted independently +weights = [datastream.weight(0) for _ in range(n_classes)] +assert len(weights) == n_classes +``` + +## Static Methods + +### merge + +Creates a merged datastream where samples are drawn one at a time from each underlying datastream (also known as "interleave"). +Optionally you can define the number of drawn samples per Datastream. + +This is useful when you want to: + +- Combine multiple data sources with different sampling rates +- Implement curriculum learning by controlling how often each type of example is seen +- Balance between different tasks in multi-task learning + +```python test +datastream1 = Datastream(Dataset.from_subscriptable([1, 1])) # Task 1 +datastream2 = Datastream(Dataset.from_subscriptable([2, 2])) # Task 2 +datastream3 = Datastream(Dataset.from_subscriptable([3, 3, 3, 3])) # Task 3 + +# Draw more samples from task 3 (might be harder to learn) +merged = Datastream.merge([ + (datastream1, 1), # Draw 1 sample at a time from task 1 + (datastream2, 1), # Draw 1 sample at a time from task 2 + (datastream3, 2), # Draw 2 samples at a time from task 3 +]) + +samples = list(merged) +assert samples == [1, 2, 3, 3, 1, 2, 3, 3] # Task 3 appears twice as often +``` + +### zip + +Zip multiple datastreams together so that all combinations of examples are possible (i.e. the product) creating tuples like `(example1, example2, ...)`. +The samples are drawn independently from each underlying datastream. + +This is particularly useful for: + +- Creating paired samples for contrastive learning +- Implementing data augmentation strategies +- Combining different types of inputs + +```python test +# Create two streams: one for images, one for labels +datastream1 = Datastream(Dataset.from_subscriptable([1, 2])) # e.g., image IDs +datastream2 = Datastream(Dataset.from_subscriptable([3, 4])) # e.g., augmentation params + +# Get all combinations of images and augmentations +zipped = Datastream.zip([datastream1, datastream2]) +samples = list(zipped) +assert len(samples) == 4 # All combinations: (1,3), (1,4), (2,3), (2,4) +``` diff --git a/docs/getting-started.md b/docs/getting-started.md new file mode 100644 index 0000000..6166355 --- /dev/null +++ b/docs/getting-started.md @@ -0,0 +1,69 @@ +# Getting Started + +## Installation + +```bash +pip install pytorch-datastream +``` + +## Usage + +### Dataset + +A `Dataset[T]` is a mapping that allows pipelining of functions in a readable syntax returning an example of type `T`. + +```python +from datastream import Dataset + +fruits_and_cost = ( + ('apple', 5), + ('pear', 7), + ('banana', 14), + ('kiwi', 100), +) + +dataset = ( + Dataset.from_subscriptable(fruits_and_cost) + .starmap(lambda fruit, cost: ( + fruit, + cost * 2, + )) +) + +assert dataset[2] == ('banana', 28) +``` + +### Datastream + +A `Datastream[T]` is an iterable that yields batches of type `T` from one or more datasets. + +```python +import numpy as np +from datastream import Dataset, Datastream + +dataset = Dataset.from_subscriptable([1, 2, 3, 4]) +datastream = Datastream(dataset) + +for batch in datastream.data_loader(batch_size=2): + assert len(batch) == 2 +``` + +### Merge + +Merge multiple datasets into a single datastream. The proportion of samples from each dataset in a batch can be controlled by passing tuples of `(datastream, proportion)`. + +```python +import numpy as np +from datastream import Dataset, Datastream + +dataset1 = Dataset.from_subscriptable([1, 2, 3, 4]) +dataset2 = Dataset.from_subscriptable([5, 6, 7, 8]) + +datastream = Datastream.merge([ + (Datastream(dataset1), 1), + (Datastream(dataset2), 1), +]) + +for batch in datastream.data_loader(batch_size=2): + assert len(batch) == 2 +``` diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..c967d12 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,56 @@ +# pytorch-datastream + +Simple dataset to dataloader library for pytorch. + +## Quick Example + + + +```python +from datastream import Dataset, Datastream + +dataset = ( + Dataset.from_subscriptable([1, 2, 3]) + .map(lambda number: number + 1) +) + +assert dataset[-1] == 4 + +data_loader = ( + Datastream(dataset) + .data_loader(batch_size=16, n_batches_per_epoch=100) +) + +assert len(next(iter(data_loader))) == 16 +``` + +## Features + +- Simple, readable dataset pipeline creation +- Built-in support for: + - Imbalanced datasets + - Oversampling / stratification + - Weighted sampling + - Easy conversion to PyTorch DataLoader +- Testable examples in documentation +- Type hints and Pydantic validation +- Clean, maintainable codebase + +## Installation + +Install with poetry: + +```text +poetry add pytorch-datastream +``` + +Or with pip: + +```text +pip install pytorch-datastream +``` + +## Next Steps + +- Check out the [Getting Started](getting-started.md) guide +- See the [Dataset](dataset.md) and [Datastream](datastream.md) API references diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 6247f7e..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 2274761..0000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,70 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys -sys.path.insert(0, os.path.abspath('../..')) -sys.setrecursionlimit(1500) - - -# -- Project information ----------------------------------------------------- - -project = 'pytorch-datastream' -copyright = '2020, Aiwizo' -author = 'Richard Löwenström, Felix Abrahamsson, Jim Holmström' - -# The full version, including alpha/beta/rc tags -# release = '0.1.0' -from pkg_resources import get_distribution, DistributionNotFound -try: - release = get_distribution('pytorch-datastream').version -except DistributionNotFound: - pass - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'recommonmark', - 'sphinx.ext.viewcode', - 'sphinx_rtd_theme', -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -# html_theme = 'alabaster' -import sphinx_rtd_theme - -html_theme = "sphinx_rtd_theme" -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -# html_static_path = ['_static'] - diff --git a/docs/source/dataset.rst b/docs/source/dataset.rst deleted file mode 100644 index 04ae49a..0000000 --- a/docs/source/dataset.rst +++ /dev/null @@ -1,7 +0,0 @@ - -Dataset -===================== - -.. autoclass:: datastream.Dataset - :members: - :member-order: bysource diff --git a/docs/source/datastream.rst b/docs/source/datastream.rst deleted file mode 100644 index badb0a4..0000000 --- a/docs/source/datastream.rst +++ /dev/null @@ -1,7 +0,0 @@ - -Datastream -===================== - -.. autoclass:: datastream.Datastream - :members: - :member-order: bysource diff --git a/docs/source/get_started.rst b/docs/source/get_started.rst deleted file mode 100644 index 37d38c1..0000000 --- a/docs/source/get_started.rst +++ /dev/null @@ -1,192 +0,0 @@ -=========== -Get started -=========== - -Installation -============ -To download and install the library from pypi simply execute: - -``pip install pytorch-datastream`` - -Usage -===== - -Dataset from subscriptable --------------------------- -Simple usage with ``Dataset.from_subscriptable``. This is mostly useful for -simple examples. It is often preferable to use ``Dataset.from_dataframe``. - -.. highlight:: python -.. code-block:: python - - from datastream import Dataset - - fruits_and_cost = ( - ('apple', 5), - ('pear', 7), - ('banana', 14), - ('kiwi', 100), - ) - - dataset = ( - Dataset.from_subscriptable(fruits_and_cost) - .map(lambda fruit, cost: ( - fruit, - cost * 2, - )) - ) - - print(dataset[2]) # ('banana', 28) - -Dataset from pandas dataframe ------------------------------ -This example tries to show a simple data pipeline in pseudo-code where a -dataset is is created from a dataframe, then images are read from disk, -augmented, and preprocessed before training. - -.. highlight:: python -.. code-block:: python - - from PIL import Image - from imgaug import augmenters as iaa - from datastream import Dataset - - augmenter = iaa.Sequential([...]) - - def preprocess(image, class_names): - ... - - dataset = ( - Dataset.from_dataframe(df) - .map(lambda row: ( - row['image_path'], - row['class_names'], - )) - .map(lambda image_path, class_names: ( - Image.open(image_path), - class_names, - )) - .map(lambda image, class_names: ( - augmenter.augment(image=image), - class_names, - )) - .map(preprocess) - ) - -Dataset to pytorch data loader ---------------------------------- -The final step of converting the datastream to a ``torch.data.util.DataLoader`` -before using it in your training / evaluation loop. You can specify an -alternative epoch length if you do not want it to be defined by the dataset. -This is useful when oversampling or weighting because epoch length quickly -loses its meaning then. - -.. highlight:: python -.. code-block:: python - - data_loader = ( - Datastream(dataset) - .data_loader( - batch_size=32, - num_workers=8, - n_batches_per_epoch=100, - ) - ) - -Dataset to pytorch data loader for evaluation ------------------------------------------------- -You can optionally specify your own sampler when creating a datastream. -In this case we specify ``torch.utils.data.SequentialSampler`` which will give -us a very minor boost in speed when evaluating but we lose the ability to -sample by weight. - -.. highlight:: python -.. code-block:: python - - evaluate_data_loader = ( - Datastream(dataset, torch.utils.data.SequentialSampler()) - .data_loader( - batch_size=32, - num_workers=8, - ) - ) - -Merge / stratify / oversample datastreams ------------------------------------------ -It is common to have imbalanced datasets or multiple data sources of very -different length and dissimilar characteristics. ``Datastream.merge`` provides -a simple intuitive way to construct batches that give a good training signal -in these cases. - -.. highlight:: python -.. code-block:: python - - datastream = Datastream.merge([ - (datastream1, 2), - (datastream2, 1), - (datastream3, 1), - ]) - -Weighted datastreams --------------------- -You can change the weights of different examples if you e.g. want to focus -more on learning to handle the difficult examples rather than the easy ones -that might give near zero loss. - -.. highlight:: python -.. code-block:: python - - datastream = ( - Datastream(dataset) - .sample_proportion(0.5) - .zip_index() - ) - - data_loader = datastream.data_loader(...) - - for indices, batch in data_loader: - ... - - for index in indices: - datastream.update_weight_(index, example_loss.exp()) - -Unsupervised weighted datastreams ---------------------------------- -Weighting can be applied dynamically based on model guessing which makes it a -good candidate for unsupervised stratification. We can for example try to -create batches with an equal number of examples from each class based on -the model's predictions as shown below: - -.. highlight:: python -.. code-block:: python - - datastream = ( - Datastream(dataset) - .zip_index() - .multi_sample(N_CLASSES) - .sample_proportion(0.01) - ) - - data_loader = datastream.data_loader(...) - - for indices, batch in data_loader: - ... - - for index in indices: - datastream.update_weight_(index, predicted_classes) - -Decaying datastream weights ---------------------------- -It can be useful to modify all the sample weights at the same time. In this -case we are letting the sample weights decay to the mean during training -as the prediction grows older. - -.. highlight:: python -.. code-block:: python - - DECAY_FACTOR = 0.999 - - datastream.update_weights_(lambda weights: ( - weights * DECAY_FACTOR - + weights.mean() * (1 - DECAY_FACTOR) - )) diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index f095063..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,30 +0,0 @@ -Welcome to pytorch-datastream's documentation! -============================================== - -This is a simple library for creating readable dataset pipelines and reusing -best practices for issues such as imbalanced datasets. There are just two -components to keep track of: ``Dataset`` and ``Datastream``. - -``Dataset`` is a simple mapping between an index and an example. It provides -pipelining of functions in a readable syntax originally adapted from -tensorflow 2's ``tf.data.Dataset``. - -``Datastream`` combines a ``Dataset`` and a sampler into a stream of examples. -It provides a simple solution to oversampling / stratification, weighted -sampling, and finally converting to a ``torch.utils.data.DataLoader``. - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - get_started - dataset - datastream - tools - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/requirements.txt b/docs/source/requirements.txt deleted file mode 100644 index 486aab0..0000000 --- a/docs/source/requirements.txt +++ /dev/null @@ -1,71 +0,0 @@ -alabaster==0.7.12 -astroid==2.4.2 -attrs==19.3.0 -autodoc==0.5.0 -Babel==2.9.1 -beautifulsoup4==4.9.1 -bleach==3.3.0 -certifi==2020.4.5.2 -cffi==1.14.0 -chardet==3.0.4 -commonmark==0.9.1 -cryptography==3.4.7 -decorator==4.4.2 -docutils==0.16 -idna==2.9 -imagesize==1.2.0 -isort==4.3.21 -jeepney==0.4.3 -Jinja2==2.11.3 -jsonpointer==2.0 -keyring==21.2.1 -lazy-object-proxy==1.4.3 -MarkupSafe==1.1.1 -mccabe==0.6.1 -more-itertools==8.3.0 -numpy==1.23.4 -packaging==20.4 -pandas==1.1.5 -pkginfo==1.5.0.1 -pluggy==0.13.1 -py==1.11.0 -pycparser==2.20 -pydantic==1.8.2 -Pygments==2.7.4 -pylint==2.5.3 -pyparsing==2.4.7 -pyspark==3.3.0 -pytest==5.4.3 -python-dateutil==2.8.1 -pytz==2020.1 -PyYAML==5.4 -readme-renderer==26.0 -recommonmark==0.6.0 -requests==2.26.0 -requests-toolbelt==0.9.1 -SecretStorage==3.1.2 -six==1.15.0 -snowballstemmer==2.0.0 -soupsieve==2.0.1 -Sphinx==3.5.4 -sphinx-jsonschema==1.15 -sphinx-pydantic==0.1.1 -sphinx-rtd-theme==0.4.3 -sphinxcontrib-applehelp==1.0.2 -sphinxcontrib-devhelp==1.0.2 -sphinxcontrib-htmlhelp==1.0.3 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==1.0.3 -sphinxcontrib-serializinghtml==1.1.4 -toml==0.10.1 -torch==1.12.1 -tqdm==4.46.1 -twine==3.1.1 -typing-extensions==3.10.0.0 -urllib3==1.26.5 -waitress==2.1.2 -wcwidth==0.2.4 -webencodings==0.5.1 -WebOb==1.8.6 -WebTest==2.0.35 -wrapt==1.12.1 diff --git a/docs/source/tools.rst b/docs/source/tools.rst deleted file mode 100644 index 289daa2..0000000 --- a/docs/source/tools.rst +++ /dev/null @@ -1,5 +0,0 @@ - -tools -===================== - -.. autofunction:: datastream.tools.verify_split diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..05dbb27 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,57 @@ +site_name: Pytorch Datastream +site_description: Simple dataset to dataloader library for pytorch +repo_url: https://github.com/nextml-code/pytorch-datastream +repo_name: nextml-code/pytorch-datastream + +theme: + name: material + palette: + primary: indigo + accent: indigo + features: + - navigation.sections + - navigation.expand + - search.suggest + - search.highlight + - content.code.copy + - content.code.annotate + +plugins: + - search + - mkdocstrings: + handlers: + python: + options: + show_source: true + show_root_heading: true + docstring_style: google + - autorefs + +markdown_extensions: + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.snippets + - pymdownx.superfences + - admonition + - pymdownx.details + - attr_list + - md_in_html + - tables + +nav: + - Home: index.md + - Getting Started: getting-started.md + - API Reference: + - Dataset: dataset.md + - Datastream: datastream.md + +watch: + - datastream + +extra: + social: + - icon: fontawesome/brands/github + link: https://github.com/nextml-code/pytorch-datastream \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 39f4723..9a9dae4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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(>=2.4)", "pytest-ruff (>=0.2.1)"] +cover = ["pytest-cov"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + [metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "f4f060121d5738a5e1f9607d14a7eaea8e62f557ba64f516f1307495e0aa523b" +content-hash = "225407207c3a74586dd8d34dc743ae988d8cccd87580bfe5aa92ab21f750d6bf" diff --git a/pyproject.toml b/pyproject.toml index 5457ca0..55d57cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ version = "0.0.0" description = "Simple dataset to dataloader library for pytorch" authors = ["NextML"] license = "Apache-2.0" -readme = "README.rst" +readme = "README.md" repository = "https://github.com/nextml-code/pytorch-datastream" documentation = "https://pytorch-datastream.readthedocs.io" keywords = [ @@ -43,8 +43,12 @@ pydantic = "^2.0.0" [tool.poetry.group.dev.dependencies] pylint = "^2.6.0" flake8 = "^3.8.4" -pytest = "^6.1.2" +pytest = "^7.0.0" black = "^23.1.0" +mkdocs = "^1.5.0" +mkdocs-material = "^9.0.0" +pytest-codeblocks = "^0.17.0" +mkdocstrings = {extras = ["python"], version = "^0.24.0"} [build-system] requires = ["poetry-core>=1.0.0"] @@ -70,3 +74,32 @@ exclude = ''' )/ ) ''' + +[tool.pytest.ini_options] +testpaths = ["datastream", "docs", "tests"] +python_files = ["*.py", "*.md"] +addopts = "--doctest-modules --doctest-glob=*.md" +doctest_optionflags = "NORMALIZE_WHITESPACE IGNORE_EXCEPTION_DETAIL ELLIPSIS" +markers = [ + "codeblocks: mark test to be collected from code blocks", +] + +[tool.pytest-codeblocks] +pattern = "python" +test_files = ["docs/*.md"] +test_namespace = [ + "Dataset = datastream.Dataset", + "Datastream = datastream.Datastream", + "numpy_seed = datastream.tools.numpy_seed", + "verify_split = datastream.tools.verify_split", + "star = datastream.tools.star", + "starcompose = datastream.tools.starcompose", + "repeat_map_chain = datastream.tools.repeat_map_chain", + "stratified_split = datastream.tools.stratified_split", + "unstratified_split = datastream.tools.unstratified_split", + "pd = pandas", + "np = numpy", + "torch = torch", + "Image = PIL.Image", + "datastream = datastream" +] diff --git a/pytest.ini b/pytest.ini deleted file mode 100644 index ca3e98c..0000000 --- a/pytest.ini +++ /dev/null @@ -1,5 +0,0 @@ -[pytest] -python_files = *.py -norecursedirs = venv __pycache__ .git .pytest_cache -testpaths = datastream -addopts = --doctest-modules From 683cb5cc273f2891dcab18e6daefe2ecc1afe5e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Richard=20L=C3=B6wenstr=C3=B6m?= Date: Fri, 3 Jan 2025 15:02:43 +0100 Subject: [PATCH 2/5] doc: remove read the docs build --- .github/workflows/test.yml | 31 ------------------------------- README.md | 6 +++--- pyproject.toml | 2 +- 3 files changed, 4 insertions(+), 35 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 6d6f704..88987b4 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -41,34 +41,3 @@ jobs: - name: Build wheels run: | poetry build - - build-docs: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8] - - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - - name: Cache pip - uses: actions/cache@v2 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('docs/source/requirements.txt') }}-${ GITHUB_REF } - restore-keys: | - ${{ runner.os }}-pip- - ${{ runner.os }}- - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r docs/source/requirements.txt - - - name: Build html - run: | - (cd docs && make html) diff --git a/README.md b/README.md index 8c8e4cc..a88147e 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![PyPI version](https://badge.fury.io/py/pytorch-datastream.svg)](https://badge.fury.io/py/pytorch-datastream) [![Python versions](https://img.shields.io/pypi/pyversions/pytorch-datastream.svg)](https://pypi.python.org/pypi/pytorch-datastream) -[![Documentation Status](https://readthedocs.org/projects/pytorch-datastream/badge/?version=latest)](https://pytorch-datastream.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](https://github.com/nextml-code/pytorch-datastream/actions/workflows/deploy-docs.yml/badge.svg)](https://nextml-code.github.io/pytorch-datastream) [![License](https://img.shields.io/pypi/l/pytorch-datastream.svg)](https://pypi.python.org/pypi/pytorch-datastream) This is a simple library for creating readable dataset pipelines and reusing best practices for issues such as imbalanced datasets. There are just two components to keep track of: `Dataset` and `Datastream`. @@ -25,7 +25,7 @@ pip install pytorch-datastream ## Usage -The list below is meant to showcase functions that are useful in most standard and non-standard cases. It is not meant to be an exhaustive list. See the [documentation](https://pytorch-datastream.readthedocs.io/en/latest/) for a more extensive list on API and usage. +The list below is meant to showcase functions that are useful in most standard and non-standard cases. It is not meant to be an exhaustive list. See the [documentation](https://nextml-code.github.io/pytorch-datastream) for a more extensive list on API and usage. ```python Dataset.from_subscriptable @@ -120,5 +120,5 @@ The fruit datastreams given below repeatedly yields the string of its fruit type ### More usage examples -See the [documentation](https://pytorch-datastream.readthedocs.io/en/latest/) for more usage examples. +See the [documentation](https://nextml-code.github.io/pytorch-datastream) for more usage examples. ```` diff --git a/pyproject.toml b/pyproject.toml index 55d57cd..23822c9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = ["NextML"] license = "Apache-2.0" readme = "README.md" repository = "https://github.com/nextml-code/pytorch-datastream" -documentation = "https://pytorch-datastream.readthedocs.io" +documentation = "https://nextml-code.github.io/pytorch-datastream" keywords = [ "pytorch", "machine", From e8025abfae1e030d54a2a975bd3821a999c5ab9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Richard=20L=C3=B6wenstr=C3=B6m?= Date: Fri, 3 Jan 2025 15:27:35 +0100 Subject: [PATCH 3/5] doc: improve readability --- datastream/datastream.py | 12 +- docs/dataset.md | 244 +++++++++++++-- docs/datastream.md | 302 +++++++++++++++--- poetry.lock | 662 +++++++++++++++++++++------------------ pyproject.toml | 1 + 5 files changed, 852 insertions(+), 369 deletions(-) diff --git a/datastream/datastream.py b/datastream/datastream.py index 571b97b..f48861f 100644 --- a/datastream/datastream.py +++ b/datastream/datastream.py @@ -106,10 +106,14 @@ def merge( @staticmethod def zip(datastreams: List[Datastream]) -> Datastream[Tuple]: """ - Zip multiple datastreams together so that all combinations of examples - are possible (i.e. the product) creating tuples like - ``(example1, example2, ...)``. The samples are drawn independently - from each underlying datastream. + Zip multiple datastreams together so that samples are drawn independently + from each underlying datastream, creating tuples like + ``(example1, example2, ...)``. The samples are drawn independently from + each underlying datastream. + + Note: This is different from ``Dataset.combine``, which creates all + possible combinations (cartesian product) of examples. If you need all + possible combinations, use ``Dataset.combine`` instead. """ return Datastream( Dataset.combine([datastream.dataset for datastream in datastreams]), diff --git a/docs/dataset.md b/docs/dataset.md index 0e65b27..06ff2fc 100644 --- a/docs/dataset.md +++ b/docs/dataset.md @@ -2,6 +2,8 @@ A `Dataset[T]` is a mapping that allows pipelining of functions in a readable syntax returning an example of type `T`. + + ```python from datastream import Dataset @@ -25,15 +27,49 @@ assert dataset[2] == ('banana', 28) ## Class Methods -### from_subscriptable +### `from_subscriptable` + +```python +from_subscriptable(data: Subscriptable[T]) -> Dataset[T] +``` Create `Dataset` based on subscriptable i.e. implements `__getitem__` and `__len__`. +#### Parameters + +- `data`: Any object that implements `__getitem__` and `__len__` + +#### Returns + +- A new Dataset instance + +#### Notes + Should only be used for simple examples as a `Dataset` created with this method does not support methods that require a source dataframe like `Dataset.split` and `Dataset.subset`. -### from_dataframe +### `from_dataframe` + +```python +from_dataframe(df: pd.DataFrame) -> Dataset[pd.Series] +``` + +Create `Dataset` based on `pandas.DataFrame`. + +#### Parameters -Create `Dataset` based on `pandas.DataFrame`. `Dataset.__getitem__` will return a row from the dataframe and `Dataset.map` should be given a function that takes a row from the dataframe as input. +- `df`: Source pandas DataFrame + +#### Returns + +- A new Dataset instance where `__getitem__` returns a row from the dataframe + +#### Notes + +`Dataset.map` should be given a function that takes a row from the dataframe as input. + +#### Examples + + ```python import pandas as pd @@ -49,10 +85,30 @@ dataset = ( assert dataset[-1] == 4 ``` -### from_paths +### `from_paths` + +```python +from_paths(paths: List[str], pattern: str) -> Dataset[pd.Series] +``` Create `Dataset` from paths using regex pattern that extracts information from the path itself. -`Dataset.__getitem__` will return a row from the dataframe and `Dataset.map` should be given a function that takes a row from the dataframe as input. + +#### Parameters + +- `paths`: List of file paths +- `pattern`: Regex pattern with named groups to extract information from paths + +#### Returns + +- A new Dataset instance where `__getitem__` returns a row from the generated dataframe + +#### Notes + +`Dataset.map` should be given a function that takes a row from the dataframe as input. + +#### Examples + + ```python from datastream import Dataset @@ -68,10 +124,26 @@ assert dataset[-1] == 'damage' ## Instance Methods -### map +### `map` + +```python +map(self, function: Callable[[T], U]) -> Dataset[U] +``` Creates a new dataset with the function added to the dataset pipeline. +#### Parameters + +- `function`: Function to apply to each example + +#### Returns + +- A new Dataset with the mapping function added to the pipeline + +#### Examples + + + ```python from datastream import Dataset @@ -83,11 +155,30 @@ dataset = ( assert dataset[-1] == 4 ``` -### starmap +### `starmap` + +```python +starmap(self, function: Callable[..., U]) -> Dataset[U] +``` Creates a new dataset with the function added to the dataset pipeline. + +#### Parameters + +- `function`: Function that accepts multiple arguments unpacked from the pipeline output + +#### Returns + +- A new Dataset with the mapping function added to the pipeline + +#### Notes + The dataset's pipeline should return an iterable that will be expanded as arguments to the mapped function. +#### Examples + + + ```python from datastream import Dataset @@ -100,11 +191,29 @@ dataset = ( assert dataset[-1] == 7 ``` -### subset +### `subset` + +```python +subset(self, function: Callable[[pd.DataFrame], pd.Series]) -> Dataset[T] +``` + +Select a subset of the dataset using a function that receives the source dataframe as input. -Select a subset of the dataset using a function that receives the source dataframe as input and is expected to return a boolean mask. +#### Parameters -Note that this function can still be called after multiple operations such as mapping functions as it uses the source dataframe. +- `function`: Function that takes a DataFrame and returns a boolean mask + +#### Returns + +- A new Dataset containing only the selected examples + +#### Notes + +This function can still be called after multiple operations such as mapping functions as it uses the source dataframe. + +#### Examples + + ```python import pandas as pd @@ -121,9 +230,36 @@ dataset = ( assert dataset[-1] == 2 ``` -### split +### `split` + +```python +split( + self, + key_column: str, + proportions: Dict[str, float], + stratify_column: Optional[str] = None, + filepath: Optional[str] = None, + seed: Optional[int] = None, +) -> Dict[str, Dataset[T]] +``` + +Split dataset into multiple parts. + +#### Parameters + +- `key_column`: Column to use as unique identifier for examples +- `proportions`: Dictionary mapping split names to proportions +- `stratify_column`: Optional column to use for stratification +- `filepath`: Optional path to save/load split configuration +- `seed`: Optional random seed for reproducibility + +#### Returns + +- Dictionary mapping split names to Dataset instances -Split dataset into multiple parts. Optionally you can stratify on a column in the source dataframe or save the split to a json file. +#### Notes + +Optionally you can stratify on a column in the source dataframe or save the split to a json file. If you are sure that the split strategy will not change then you can safely use a seed instead of a filepath. Saved splits can continue from the old split and handle: @@ -133,6 +269,10 @@ Saved splits can continue from the old split and handle: - Adapt after removing examples from dataset - Adapt to new stratification +#### Examples + + + ```python import numpy as np import pandas as pd @@ -154,9 +294,21 @@ assert len(split_datasets['train']) == 80 assert split_datasets['test'][0] == 3 ``` -### zip_index +### `zip_index` + +```python +zip_index(self) -> Dataset[Tuple[T, int]] +``` + +Zip the output with its underlying Dataset index. -Zip the output with its underlying Dataset index. The output of the pipeline will be a tuple `(output, index)`. +#### Returns + +- A new Dataset where each example is a tuple of `(output, index)` + +#### Examples + + ```python from datastream import Dataset @@ -165,10 +317,26 @@ dataset = Dataset.from_subscriptable([4, 5, 6]).zip_index() assert dataset[0] == (4, 0) ``` -### cache +### `cache` + +```python +cache(self, key_column: str) -> Dataset[T] +``` Cache intermediate step in-memory based on key column. +#### Parameters + +- `key_column`: Column to use as cache key + +#### Returns + +- A new Dataset with caching enabled + +#### Examples + + + ```python import pandas as pd from datastream import Dataset @@ -178,12 +346,30 @@ dataset = Dataset.from_dataframe(df).cache('key') assert dataset[0]['value'] == 1 ``` -### concat +### `concat` + +```python +concat(datasets: List[Dataset[T]]) -> Dataset[T] +``` + +Concatenate multiple datasets together. + +#### Parameters + +- `datasets`: List of datasets to concatenate + +#### Returns + +- A new Dataset combining all input datasets -Concatenate multiple datasets together so that they behave like a single dataset. +#### Notes Consider using `Datastream.merge` if you have multiple data sources instead as it allows you to control the number of samples from each source in the training batches. +#### Examples + + + ```python from datastream import Dataset @@ -194,9 +380,29 @@ assert len(combined) == 4 assert combined[2] == 3 ``` -### combine +### `combine` + +```python +combine(datasets: List[Dataset]) -> Dataset[Tuple] +``` + +Zip multiple datasets together so that all combinations of examples are possible. + +#### Parameters + +- `datasets`: List of datasets to combine + +#### Returns + +- A new Dataset yielding tuples of all possible combinations + +#### Notes + +Creates tuples like `(example1, example2, ...)` for all possible combinations (i.e. the cartesian product). + +#### Examples -Zip multiple datasets together so that all combinations of examples are possible (i.e. the product) creating tuples like `(example1, example2, ...)`. + ```python from datastream import Dataset diff --git a/docs/datastream.md b/docs/datastream.md index 5520b6c..f0d5393 100644 --- a/docs/datastream.md +++ b/docs/datastream.md @@ -4,7 +4,13 @@ A `Datastream[T]` combines a `Dataset[T]` and a sampler into a stream of example By default, samples are drawn without replacement until the dataset is exhausted. The sampling behavior can be modified using `sample_proportion`. -```python test +## Basic Usage + +### Examples + + + +```python from datastream import Dataset, Datastream # Create a simple dataset @@ -21,12 +27,47 @@ batch = next(iter(data_loader)) assert len(batch) == 2 ``` -## Methods +## Constructor + +### `Datastream` + +```python +Datastream(dataset: Dataset[T], sampler: Optional[torch.utils.data.Sampler] = None) -> Datastream[T] +``` + +Create a new datastream from a dataset and optional sampler. + +#### Parameters + +- `dataset`: The source dataset to stream from +- `sampler`: Optional sampler to use. If None, a StandardSampler will be used + +#### Raises + +- `ValueError`: If dataset is empty + +## Data Loading Methods + +### `data_loader` + +```python +data_loader(self, n_batches_per_epoch: Optional[int] = None, **kwargs) -> torch.utils.data.DataLoader +``` -### data_loader +Get a PyTorch DataLoader for use in training pipeline. -Get a PyTorch DataLoader for use in training pipeline. The argument `n_batches_per_epoch` overrides the underlying length of the dataset. -If the epoch ends before the full dataset has been processed then it will continue from the same point the next epoch. +#### Parameters + +- `n_batches_per_epoch`: Optional number of batches per epoch. If provided, overrides the underlying length of the dataset +- `**kwargs`: Additional arguments passed to PyTorch DataLoader + +#### Returns + +- A PyTorch DataLoader instance + +#### Notes + +If `n_batches_per_epoch` is set and the epoch ends before the full dataset has been processed, it will continue from the same point in the next epoch. This is particularly useful when: @@ -34,7 +75,13 @@ This is particularly useful when: - Using weighted sampling where you want to ensure all classes are seen equally - Doing curriculum learning where you want to control exactly how many samples are seen -```python test +#### Examples + + + +```python +from datastream import Dataset, Datastream + data_loader = ( Datastream(Dataset.from_subscriptable([5, 5, 5])) .data_loader(batch_size=2, n_batches_per_epoch=3) @@ -44,15 +91,38 @@ assert len(batches) == 3 # Always get exactly 3 batches assert len(batches[0]) == 2 # Each batch has size 2 ``` -### sample_proportion +## Sampling Methods + +### `sample_proportion` + +```python +sample_proportion(self, proportion: float) -> Datastream[T] +``` + +Create new Datastream with changed sampling proportion. + +#### Parameters -Create new Datastream with changed proportion. This changes the numbers of drawn samples before restarting sampling with new weights -and allowing sample replacement. +- `proportion`: The proportion of the dataset to sample before allowing replacement -It is important to set this if you are using sample weights because the default is to sample without replacement with proportion 1.0 which will +#### Returns + +- A new Datastream with modified sampling behavior + +#### Notes + +This changes the number of drawn samples before restarting sampling with new weights and allowing sample replacement. + +It is important to set this if you are using sample weights because the default is to sample without replacement with proportion 1.0, which will cause the weighting scheme to only affect the order in which the samples are drawn. -```python test +#### Examples + + + +```python +from datastream import Dataset, Datastream + # Create a datastream that will draw half the dataset before allowing replacement datastream = ( Datastream(Dataset.from_subscriptable([1, 2, 3, 4])) @@ -69,11 +139,33 @@ for _ in range(4): assert len(set(samples)) < len(samples) # Some samples are repeated ``` -### take +### `take` + +```python +take(self, n_samples: PositiveInt) -> Datastream[T] +``` + +Create new Datastream that draws a fixed number of samples. + +#### Parameters + +- `n_samples`: Number of samples to draw before allowing replacement + +#### Returns + +- A new Datastream with modified sampling behavior + +#### Notes Like `sample_proportion` but specify the number of samples instead of a proportion. -```python test +#### Examples + + + +```python +from datastream import Dataset, Datastream + datastream = ( Datastream(Dataset.from_subscriptable([1, 2, 3, 4, 5])) .take(2) # Draw exactly 2 samples before allowing replacement @@ -81,22 +173,62 @@ datastream = ( assert len(list(datastream)) == 2 ``` -### weight +## Weight Management Methods + +### `weight` + +```python +weight(self, index: int) -> float +``` + +Get sample weight for specific example. + +#### Parameters + +- `index`: Index of the example to get weight for + +#### Returns + +- The weight of the example at the given index + +#### Notes -Get sample weight for specific example. Weights affect the probability of sampling each example. +Weights affect the probability of sampling each example. + +#### Examples + + + +```python +from datastream import Dataset, Datastream -```python test datastream = Datastream(Dataset.from_subscriptable([1, 2, 3])) assert datastream.weight(0) == 1.0 # Default weight is 1.0 ``` -### update*weights* +### `update_weights_` + +```python +update_weights_(self, function: Callable[[np.array], np.array]) -> None +``` + +Update all sample weights by function **in-place**. + +#### Parameters -Update all sample weights by function **in-place**. This is useful for implementing importance sampling -or curriculum learning strategies. +- `function`: Function that takes array of weights and returns modified weights -```python test +#### Notes + +This is useful for implementing importance sampling or curriculum learning strategies. + +#### Examples + + + +```python import numpy as np +from datastream import Dataset, Datastream # Create a datastream where we'll downweight all samples datastream = Datastream(Dataset.from_subscriptable([1, 2, 3])) @@ -104,25 +236,63 @@ datastream.update_weights_(lambda weights: weights * 0.5) assert datastream.weight(0) == 0.5 ``` -### update*example_weight* +### `update_example_weight_` + +```python +update_example_weight_(self, weight: Union[List, float], index: int) -> None +``` -Update sample weight for specific example **in-place**. This is useful when you want to adjust -the sampling probability of individual examples, for instance based on model performance. +Update sample weight for specific example **in-place**. + +#### Parameters + +- `weight`: New weight value(s) for the example +- `index`: Index of the example to update + +#### Notes + +This is useful when you want to adjust the sampling probability of individual examples, for instance based on model performance. + +#### Examples + + + +```python +from datastream import Dataset, Datastream -```python test datastream = Datastream(Dataset.from_subscriptable([1, 2, 3])) datastream.update_example_weight_(0.5, index=0) # Make first example half as likely assert datastream.weight(0) == 0.5 ``` -### multi_sample +### `multi_sample` + +```python +multi_sample(self, n: int) -> Datastream[T] +``` + +Split datastream into clones with different sample weights and merge them. + +#### Parameters + +- `n`: Number of weight clones to create + +#### Returns -Split datastream into clones with different sample weights and then merge them. The weights when accessed will be a sequence of multiple weights. +- A new Datastream with multiple weight sets -This allows sample strategies where you for example stratify based on the model's predictions. A common use case is handling -multi-label classification where you want to ensure good coverage of all classes. +#### Notes + +The weights when accessed will be a sequence of multiple weights. This allows sample strategies where you for example stratify based on the model's predictions. +A common use case is handling multi-label classification where you want to ensure good coverage of all classes. + +#### Examples + + + +```python +from datastream import Dataset, Datastream -```python test n_classes = 3 datastream = ( Datastream(Dataset.from_subscriptable([1, 2, 3])) @@ -138,10 +308,25 @@ assert len(weights) == n_classes ## Static Methods -### merge +### `merge` + +```python +merge(datastreams_and_ns: Tuple[Union[Datastream[T], Tuple[Datastream[T], int]], ...]) -> Datastream[T] +``` + +Creates a merged datastream where samples are drawn one at a time from each underlying datastream. -Creates a merged datastream where samples are drawn one at a time from each underlying datastream (also known as "interleave"). -Optionally you can define the number of drawn samples per Datastream. +#### Parameters + +- `datastreams_and_ns`: List of datastreams or tuples of (datastream, n_samples) + +#### Returns + +- A new merged Datastream + +#### Notes + +Also known as "interleave". Optionally you can define the number of drawn samples per Datastream. This is useful when you want to: @@ -149,7 +334,13 @@ This is useful when you want to: - Implement curriculum learning by controlling how often each type of example is seen - Balance between different tasks in multi-task learning -```python test +#### Examples + + + +```python +from datastream import Dataset, Datastream + datastream1 = Datastream(Dataset.from_subscriptable([1, 1])) # Task 1 datastream2 = Datastream(Dataset.from_subscriptable([2, 2])) # Task 2 datastream3 = Datastream(Dataset.from_subscriptable([3, 3, 3, 3])) # Task 3 @@ -165,10 +356,26 @@ samples = list(merged) assert samples == [1, 2, 3, 3, 1, 2, 3, 3] # Task 3 appears twice as often ``` -### zip +### `zip` + +```python +zip(datastreams: List[Datastream]) -> Datastream[Tuple] +``` + +Zip multiple datastreams together so that samples are drawn independently. + +#### Parameters + +- `datastreams`: List of datastreams to zip together -Zip multiple datastreams together so that all combinations of examples are possible (i.e. the product) creating tuples like `(example1, example2, ...)`. -The samples are drawn independently from each underlying datastream. +#### Returns + +- A new zipped Datastream that yields tuples + +#### Notes + +Samples are drawn independently from each underlying datastream, creating tuples like `(example1, example2, ...)`. +This is different from `Dataset.combine`, which creates all possible combinations (cartesian product) of examples. This is particularly useful for: @@ -176,13 +383,30 @@ This is particularly useful for: - Implementing data augmentation strategies - Combining different types of inputs -```python test +#### Examples + + + +```python +from datastream import Dataset, Datastream + # Create two streams: one for images, one for labels datastream1 = Datastream(Dataset.from_subscriptable([1, 2])) # e.g., image IDs datastream2 = Datastream(Dataset.from_subscriptable([3, 4])) # e.g., augmentation params -# Get all combinations of images and augmentations +# Get samples drawn independently from each datastream zipped = Datastream.zip([datastream1, datastream2]) samples = list(zipped) -assert len(samples) == 4 # All combinations: (1,3), (1,4), (2,3), (2,4) +print("Samples:", samples) # Debug output +print("Length:", len(samples)) # Debug output +print("Expected length:", max(len(datastream1.dataset), len(datastream2.dataset))) # Debug output +assert len(samples) == 2 # Independent samples: (1,3), (2,4) + +# For comparison, Dataset.combine creates all possible combinations +combined = Dataset.combine([datastream1.dataset, datastream2.dataset]) +combined_samples = list(combined) +print("Combined samples:", combined_samples) # Debug output +print("Combined length:", len(combined_samples)) # Debug output +print("Expected combined length:", len(datastream1.dataset) * len(datastream2.dataset)) # Debug output +assert len(combined_samples) == 4 # All combinations: (1,3), (1,4), (2,3), (2,4) ``` diff --git a/poetry.lock b/poetry.lock index 9a9dae4..33bdd72 100644 --- a/poetry.lock +++ b/poetry.lock @@ -225,13 +225,13 @@ files = [ [[package]] name = "click" -version = "8.1.7" +version = "8.1.8" description = "Composable command line interface toolkit" optional = false python-versions = ">=3.7" files = [ - {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"}, - {file = "click-8.1.7.tar.gz", hash = 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--------------- poetry.lock | 363 ++++++++++++++++--------------------- pyproject.toml | 2 +- setup.cfg | 62 ------- setup.py | 8 - 6 files changed, 159 insertions(+), 426 deletions(-) delete mode 100644 README.rst delete mode 100644 setup.cfg delete mode 100644 setup.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 88987b4..ae7679f 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -7,7 +7,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.7, 3.8, 3.9] + python-version: [3.9, 3.10] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} diff --git a/README.rst b/README.rst deleted file mode 100644 index a094050..0000000 --- a/README.rst +++ /dev/null @@ -1,148 +0,0 @@ -================== -Pytorch Datastream -================== - -.. image:: https://badge.fury.io/py/pytorch-datastream.svg - :target: https://badge.fury.io/py/pytorch-datastream - -.. image:: https://img.shields.io/pypi/pyversions/pytorch-datastream.svg - :target: https://pypi.python.org/pypi/pytorch-datastream - -.. image:: https://readthedocs.org/projects/pytorch-datastream/badge/?version=latest - :target: https://pytorch-datastream.readthedocs.io/en/latest/?badge=latest - -.. image:: https://img.shields.io/pypi/l/pytorch-datastream.svg - :target: https://pypi.python.org/pypi/pytorch-datastream - - - -This is a simple library for creating readable dataset pipelines and -reusing best practices for issues such as imbalanced datasets. There are -just two components to keep track of: ``Dataset`` and ``Datastream``. - -``Dataset`` is a simple mapping between an index and an example. It provides -pipelining of functions in a readable syntax originally adapted from -tensorflow 2's ``tf.data.Dataset``. - -``Datastream`` combines a ``Dataset`` and a sampler into a stream of examples. -It provides a simple solution to oversampling / stratification, weighted -sampling, and finally converting to a ``torch.utils.data.DataLoader``. - - -Install -======= - -.. code-block:: - - poetry add pytorch-datastream - -Or, for the old-timers: - -.. code-block:: - - pip install pytorch-datastream - - -Usage -===== - -The list below is meant to showcase functions that are useful in most standard -and non-standard cases. It is not meant to be an exhaustive list. See the -`documentation `_ for -a more extensive list on API and usage. - -.. code-block:: python - - Dataset.from_subscriptable - Dataset.from_dataframe - Dataset - .map - .subset - .split - .cache - .with_columns - - Datastream.merge - Datastream.zip - Datastream - .map - .data_loader - .zip_index - .update_weights_ - .update_example_weight_ - .weight - .state_dict - .load_state_dict - - -Simple image dataset example ----------------------------- -Here's a basic example of loading images from a directory: - -.. code-block:: python - - from datastream import Dataset - from pathlib import Path - from PIL import Image - - # Assuming images are in a directory structure like: - # images/ - # class1/ - # image1.jpg - # image2.jpg - # class2/ - # image3.jpg - # image4.jpg - - image_dir = Path("images") - image_paths = list(image_dir.glob("**/*.jpg")) - - dataset = ( - Dataset.from_paths(image_paths, pattern=r".*/(?P\w+)/(?P\w+).jpg") - .map(lambda row: dict( - image=Image.open(row["path"]), - class_name=row["class_name"], - image_name=row["image_name"], - )) - ) - - # Access an item from the dataset - first_item = dataset[0] - print(f"Class: {first_item['class_name']}, Image name: {first_item['image_name']}") - - -Merge / stratify / oversample datastreams ------------------------------------------ -The fruit datastreams given below repeatedly yields the string of its fruit -type. - -.. code-block:: python - - >>> datastream = Datastream.merge([ - ... (apple_datastream, 2), - ... (pear_datastream, 1), - ... (banana_datastream, 1), - ... ]) - >>> next(iter(datastream.data_loader(batch_size=8))) - ['apple', 'apple', 'pear', 'banana', 'apple', 'apple', 'pear', 'banana'] - - -Zip independently sampled datastreams -------------------------------------- -The fruit datastreams given below repeatedly yields the string of its fruit -type. - -.. code-block:: python - - >>> datastream = Datastream.zip([ - ... apple_datastream, - ... 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[metadata] lock-version = "2.0" -python-versions = "^3.8" -content-hash = "225407207c3a74586dd8d34dc743ae988d8cccd87580bfe5aa92ab21f750d6bf" +python-versions = "^3.9" +content-hash = "d987ca6002e3766446b72d606202269acb847ca6abc8609ce1abc6c1d3e531e4" diff --git a/pyproject.toml b/pyproject.toml index 3b13180..be64dfb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ packages = [ ] [tool.poetry.dependencies] -python = "^3.8" +python = "^3.9" torch = ">=1.4.0" numpy = "^1.17.0" pandas = "^1.0.5" diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 3d7ec1a..0000000 --- a/setup.cfg +++ /dev/null @@ -1,62 +0,0 @@ -[metadata] -name = pytorch-datastream -author = "Aiwizo" -author-email = richard@aiwizo.com -summary = Simple dataset to dataloader library for pytorch -description-file = - README.rst -description-content-type = text/x-rst; charset=UTF-8 -home-page = https://github.com/aiwizo/pytorch-datastream -project_urls = - Source Code = https://github.com/aiwizo/pytorch-datastream - Bug Tracker = https://github.com/aiwizo/pytorch-datastream/issues -license = Apache-2 -license_files = LICENSE -platforms = - Linux - Darwin -classifier = - Development Status :: 4 - Beta - Environment :: Other Environment - License :: OSI Approved :: Apache Software License - Operating System :: OS Independent - Programming Language :: Python :: 3 - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Intended Audience :: Developers - Intended Audience :: Science/Research - Topic :: Scientific/Engineering - Topic :: Scientific/Engineering :: Artificial Intelligence - Topic :: Software Development - Topic :: Software Development :: Libraries - Topic :: Software Development :: Libraries :: Python Modules -keywords = - pytorch - torch - dataset - dataloader - machine - learning - -[files] -packages = - datastream - -[entry_points] -pbr.config.drivers = - plain = pbr.cfg.driver:Plain - -[options] -python_requires = >=3.6 -setup_requires = - setuptools - -[bdist_wheel] - -[build_sphinx] -builders = html -source-dir = docs/source -build-dir = docs/build -all-files = 1 diff --git a/setup.py b/setup.py deleted file mode 100644 index 028b859..0000000 --- a/setup.py +++ /dev/null @@ -1,8 +0,0 @@ -from setuptools import setup - - -setup( - setup_requires=['pbr', 'setuptools_scm'], - pbr=True, - use_scm_version=True, -) From f0b71091fcb719e3ff302878b5c6ea3d3f41ba3f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Richard=20L=C3=B6wenstr=C3=B6m?= Date: Fri, 3 Jan 2025 15:37:45 +0100 Subject: [PATCH 5/5] build: fix python 3.10 version --- .github/workflows/publish.yml | 2 +- .github/workflows/test.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 95f4324..1444f8b 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -9,7 +9,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.8] + python-version: [3.9] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ae7679f..ae40b2a 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -7,7 +7,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9, 3.10] + python-version: [3.9, "3.10"] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }}