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/.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 6d6f704..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: [3.7, 3.8, 3.9] + python-version: [3.9, "3.10"] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} @@ -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/.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..a88147e --- /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://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`. + +`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://nextml-code.github.io/pytorch-datastream) 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://nextml-code.github.io/pytorch-datastream) for more usage examples. +```` 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, - ... 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 `_ -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..f48861f 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 @@ -103,10 +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/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..06ff2fc --- /dev/null +++ b/docs/dataset.md @@ -0,0 +1,415 @@ +# 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` + +```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` + +```python +from_dataframe(df: pd.DataFrame) -> Dataset[pd.Series] +``` + +Create `Dataset` based on `pandas.DataFrame`. + +#### Parameters + +- `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 +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` + +```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. + +#### 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 + +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` + +```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 + +dataset = ( + Dataset.from_subscriptable([1, 2, 3]) + .map(lambda number: number + 1) +) + +assert dataset[-1] == 4 +``` + +### `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 + +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` + +```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. + +#### Parameters + +- `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 +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` + +```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 + +#### 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: + +- New examples +- Changing test size +- Adapt after removing examples from dataset +- Adapt to new stratification + +#### Examples + + + +```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` + +```python +zip_index(self) -> Dataset[Tuple[T, int]] +``` + +Zip the output with its underlying Dataset index. + +#### Returns + +- A new Dataset where each example is a tuple of `(output, index)` + +#### Examples + + + +```python +from datastream import Dataset + +dataset = Dataset.from_subscriptable([4, 5, 6]).zip_index() +assert dataset[0] == (4, 0) +``` + +### `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 + +df = pd.DataFrame({'key': ['a', 'b'], 'value': [1, 2]}) +dataset = Dataset.from_dataframe(df).cache('key') +assert dataset[0]['value'] == 1 +``` + +### `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 + +#### 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 + +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` + +```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 + + + +```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..f0d5393 --- /dev/null +++ b/docs/datastream.md @@ -0,0 +1,412 @@ +# 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`. + +## Basic Usage + +### Examples + + + +```python +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 +``` + +## 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 +``` + +Get a PyTorch DataLoader for use in training pipeline. + +#### 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: + +- 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 + +#### 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) +) +batches = list(data_loader) +assert len(batches) == 3 # Always get exactly 3 batches +assert len(batches[0]) == 2 # Each batch has size 2 +``` + +## Sampling Methods + +### `sample_proportion` + +```python +sample_proportion(self, proportion: float) -> Datastream[T] +``` + +Create new Datastream with changed sampling proportion. + +#### Parameters + +- `proportion`: The proportion of the dataset to sample before allowing replacement + +#### 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. + +#### 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])) + .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` + +```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. + +#### 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 +) +assert len(list(datastream)) == 2 +``` + +## 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 + +Weights affect the probability of sampling each example. + +#### Examples + + + +```python +from datastream import Dataset, Datastream + +datastream = Datastream(Dataset.from_subscriptable([1, 2, 3])) +assert datastream.weight(0) == 1.0 # Default weight is 1.0 +``` + +### `update_weights_` + +```python +update_weights_(self, function: Callable[[np.array], np.array]) -> None +``` + +Update all sample weights by function **in-place**. + +#### Parameters + +- `function`: Function that takes array of weights and returns modified weights + +#### 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])) +datastream.update_weights_(lambda weights: weights * 0.5) +assert datastream.weight(0) == 0.5 +``` + +### `update_example_weight_` + +```python +update_example_weight_(self, weight: Union[List, float], index: int) -> None +``` + +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 + +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` + +```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 + +- A new Datastream with multiple weight sets + +#### 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 + +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` + +```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. + +#### 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: + +- 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 + +#### 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 + +# 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` + +```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 + +#### 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: + +- Creating paired samples for contrastive learning +- Implementing data augmentation strategies +- Combining different types of inputs + +#### 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 samples drawn independently from each datastream +zipped = Datastream.zip([datastream1, datastream2]) +samples = list(zipped) +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/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..687f3ae 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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"more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + [metadata] lock-version = "2.0" -python-versions = "^3.8" -content-hash = "f4f060121d5738a5e1f9607d14a7eaea8e62f557ba64f516f1307495e0aa523b" +python-versions = "^3.9" +content-hash = "d987ca6002e3766446b72d606202269acb847ca6abc8609ce1abc6c1d3e531e4" diff --git a/pyproject.toml b/pyproject.toml index 5457ca0..be64dfb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,9 +4,9 @@ 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" +documentation = "https://nextml-code.github.io/pytorch-datastream" keywords = [ "pytorch", "machine", @@ -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" @@ -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 +74,33 @@ 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", + "Path = pathlib.Path", + "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 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 :: 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