Metadata-Version: 2.1
Name: mlblocks
Version: 0.2.4
Summary: Pipelines and primitives for machine learning and data science.
Home-page: https://github.com/HDI-Project/MLBlocks
Author: MIT Data To AI Lab
Author-email: dailabmit@gmail.com
License: MIT license
Description: <p align="center">
        <img width=30% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/mlblocks-icon.png" alt=“MLBlocks” />
        </p>
        
        <p align="center">
        <i>
        Pipelines and Primitives for Machine Learning and Data Science.
        </i>
        </p>
        
        [![PyPi][pypi-img]][pypi-url]
        [![Travis][travis-img]][travis-url]
        [![CodeCov][codecov-img]][codecov-url]
        
        [pypi-img]: https://img.shields.io/pypi/v/mlblocks.svg
        [pypi-url]: https://pypi.python.org/pypi/mlblocks
        [travis-img]: https://travis-ci.org/HDI-Project/MLBlocks.svg?branch=master
        [travis-url]: https://travis-ci.org/HDI-Project/MLBlocks
        [codecov-img]: https://codecov.io/gh/HDI-Project/MLBlocks/branch/master/graph/badge.svg
        [codecov-url]: https://codecov.io/gh/HDI-Project/MLBlocks
        
        * Free software: MIT license
        * Documentation: https://HDI-Project.github.io/MLBlocks
        
        # Overview
        
        MLBlocks is a simple framework for composing end-to-end tunable Machine Learning Pipelines by
        seamlessly combining tools from any python library with a simple, common and uniform interface.
        
        Features include:
        
        * Build Machine Learning Pipelines combining **any Machine Learning Library in Python**.
        * Access a repository with hundreds of primitives and pipelines ready to be used with little to
          no python code to write, carefully curated by Machine Learning and Domain experts.
        * Extract machine-readable information about which hyperparameters can be tuned and within
          which ranges, allowing automated integration with Hyperparameter Optimization tools like
          [BTB](https://github.com/HDI-Project/BTB).
        * Complex multi-branch pipelines and DAG configurations, with unlimited number of inputs and
          outputs per primitive.
        * Easy save and load Pipelines using JSON Annotations.
        
        # Installation
        
        The simplest and recommended way to install MLBlocks is using `pip`:
        
        ```bash
        pip install mlblocks
        ```
        
        Alternatively, you can also clone the repository and install it from sources
        
        ```bash
        git clone git@github.com:HDI-Project/MLBlocks.git
        cd MLBlocks
        make install
        ```
        
        For development, you can use `make install-develop` instead in order to install all
        the required dependencies for testing and code linting.
        
        # Usage Example
        
        Below there is a short example about how to use MLBlocks to create a simple pipeline, fit it
        using demo data and use it to make predictions.
        
        For advance usage and more detailed explanation about each component, please have a look
        at the [documentation](https://HDI-Project.github.io/MLBlocks)
        
        ## Creating a pipeline
        
        With MLBlocks, creating a pipeline is as simple as specifying a list of primitives and passing
        them to the `MLPipeline` class.
        
        ```python
        >>> from mlblocks import MLPipeline
        ... primitives = [
        ...     'cv2.GaussianBlur',
        ...     'skimage.feature.hog',
        ...     'sklearn.ensemble.RandomForestClassifier'
        ... ]
        >>> pipeline = MLPipeline(primitives)
        ```
        
        Optionally, specific hyperparameters can be also set by specifying them in a dictionary:
        
        ```python
        >>> hyperparameters = {
        ...    'skimage.feature.hog': {
        ...        'multichannel': True,
        ...        'visualize': False
        ...    },
        ...    'sklearn.ensemble.RandomForestClassifier': {
        ...         'n_estimators': 100,
        ...    }
        ... }
        >>> pipeline = MLPipeline(primitives, hyperparameters)
        ```
        
        If you can see which hyperparameters a particular pipeline is using, you can do so by calling
        its `get_hyperparameters` method:
        
        ```python
        >>> import json
        >>> hyperparameters = pipeline.get_hyperparameters()
        >>> print(json.dumps(hyperparameters, indent=4))
        {
            "cv2.GaussianBlur#1": {
                "ksize_width": 3,
                "ksize_height": 3,
                "sigma_x": 0,
                "sigma_y": 0
            },
            "skimage.feature.hog#1": {
                "multichannel": true,
                "visualize": false,
                "orientations": 9,
                "pixels_per_cell_x": 8,
                "pixels_per_cell_y": 8,
                "cells_per_block_x": 3,
                "cells_per_block_y": 3,
                "block_norm": null
            },
            "sklearn.ensemble.RandomForestClassifier#1": {
                "n_jobs": -1,
                "n_estimators": 100,
                "criterion": "entropy",
                "max_features": null,
                "max_depth": 10,
                "min_samples_split": 0.1,
                "min_samples_leaf": 0.1,
                "class_weight": null
            }
        }
        ```
        
        ### Making predictions
        
        Once we have created the pipeline with the desired hyperparameters we can fit it
        and then use it to make predictions on new data.
        
        To do this, we first call the `fit` method passing the training data and the corresponding labels.
        
        In this case in particular, we will be loading the handwritten digit classification dataset
        from USPS using the `mlblocks.datasets.load_usps` method, which returns a dataset object
        ready to be played with.
        
        ```python
        >>> from mlblocks.datasets import load_usps
        >>> dataset = load_usps()
        >>> X_train, X_test, y_train, y_test = dataset.get_splits(1)
        >>> pipeline.fit(X_train, y_train)
        ```
        
        Once we have fitted our model to our data, we can call the `predict` method passing new data
        to obtain predictions from the pipeline.
        
        ```python
        >>> predictions = pipeline.predict(X_test)
        >>> predictions
        array([3, 2, 1, ..., 1, 1, 2])
        ```
        
        ## What's Next?
        
        If you want to learn more about how to tune the pipeline hyperparameters, save and load
        the pipelines using JSON annotations or build complex multi-branched pipelines, please
        check our [documentation](https://HDI-Project.github.io/MLBlocks).
        
        # History
        
        In its first iteration in 2015, MLBlocks was designed for only multi table, multi entity temporal
        data. A good reference to see our design rationale at that time is Bryan Collazo’s thesis:
        * [Machine learning blocks](https://dai.lids.mit.edu/wp-content/uploads/2018/06/Mlblocks_Bryan.pdf).
          Bryan Collazo. Masters thesis, MIT EECS, 2015.
        
        With recent availability of a multitude of libraries and tools, we decided it was time to integrate
        them and expand the library to address other data types: images, text, graph, time series and
        integrate with deep learning libraries.
        
        
        Changelog
        =========
        
        0.2.4 - New Datasets and Unit Tests
        -----------------------------------
        
        * Add a new multi-table dataset.
        * Add Unit Tests up to 50% coverage.
        * Improve documentation.
        * Fix minor bug in newsgroups dataset.
        
        0.2.3 - Demo Datasets
        ---------------------
        
        * Add new methods to Dataset class.
        * Add documentation for the datasets module.
        
        0.2.2 - MLPipeline Load/Save
        ----------------------------
        
        * Implement save and load methods for MLPipelines
        * Add more datasets
        
        0.2.1 - New Documentation
        -------------------------
        
        * Add mlblocks.datasets module with demo data download functions.
        * Extensive documentation, including multiple pipeline examples.
        
        0.2.0 - New MLBlocks API
        ------------------------
        
        A new MLBlocks API and Primitive format.
        
        This is a summary of the changes:
        
        * Primitives JSONs and Python code has been moved to a different repository, called MLPrimitives
        * Optional usage of multiple JSON primitive folders.
        * JSON format has been changed to allow more flexibility and features:
            * input and output arguments, as well as argument types, can be specified for each method
            * both classes and function as primitives are supported
            * multitype and conditional hyperparameters fully supported
            * data modalities and primitive classifiers introduced
            * metadata such as documentation, description and author fields added
        * Parsers are removed, and now the MLBlock class is responsible for loading and reading the
          JSON primitive.
        * Multiple blocks of the same primitive are supported within the same pipeline.
        * Arbitrary inputs and outputs for both pipelines and blocks are allowed.
        * Shared variables during pipeline execution, usable by multiple blocks.
        
        0.1.9 - Bugfix Release
        ----------------------
        
        * Disable some NetworkX functions for incompatibilities with some types of graphs.
        
        0.1.8 - New primitives and some improvements
        --------------------------------------------
        
        * Improve the NetworkX primitives.
        * Add String Vectorization and Datetime Featurization primitives.
        * Refactor some Keras primitives to work with single dimension `y` arrays and be compatible with `pickle`.
        * Add XGBClassifier and XGBRegressor primitives.
        * Add some `keras.applications` pretrained networks as preprocessing primitives.
        * Add helper class to allow function primitives.
        
        0.1.7 - Nested hyperparams dicts
        --------------------------------
        
        * Support passing hyperparams as nested dicts.
        
        0.1.6 - Text and Graph Pipelines
        --------------------------------
        
        * Add LSTM classifier and regressor primitives.
        * Add OneHotEncoder and MultiLabelEncoder primitives.
        * Add several NetworkX graph featurization primitives.
        * Add `community.best_partition` primitive.
        
        0.1.5 - Collaborative Filtering Pipelines
        -----------------------------------------
        
        * Add LightFM primitive.
        
        0.1.4 - Image pipelines improved
        --------------------------------
        
        * Allow passing `init_params` on `MLPipeline` creation.
        * Fix bug with MLHyperparam types and Keras.
        * Rename `produce_params` as `predict_params`.
        * Add SingleCNN Classifier and Regressor primitives.
        * Simplify and improve Trivial Predictor
        
        0.1.3 - Multi Table pipelines improved
        --------------------------------------
        
        * Improve RandomForest primitive ranges
        * Improve DFS primitive
        * Add Tree Based Feature Selection primitives
        * Fix bugs in TrivialPredictor
        * Improved documentation
        
        0.1.2 - Bugfix release
        ----------------------
        
        * Fix bug in TrivialMedianPredictor
        * Fix bug in OneHotLabelEncoder
        
        0.1.1 - Single Table pipelines improved
        ---------------------------------------
        
        * New project structure and primitives for integration into MIT-TA2.
        * MIT-TA2 default pipelines and single table pipelines fully working.
        
        0.1.0
        -----
        
        * First release on PyPI.
        
Keywords: auto machine learning classification regression data science pipeline
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
