Metadata-Version: 2.1
Name: mlprimitives
Version: 0.2.5
Summary: Pipelines and primitives for machine learning and data science.
Home-page: https://github.com/HDI-Project/MLPrimitives
Author: MIT Data To AI Lab
Author-email: dailabmit@gmail.com
License: MIT license
Description: <p align="left">
        <img width=25% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt=“DAI-Lab” />
        <i>An open source project from Data to AI Lab at MIT.</i>
        </p>
        
        
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        # MLPrimitives
        
        Pipelines and primitives for machine learning and data science.
        
        * License: [MIT](https://github.com/hdi-project/MLPrimitives/blob/master/LICENSE)
        * Development Status: [Pre-Alpha](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
        * Documentation: https://hdi-project.github.io/MLPrimitives
        * Homepage: https://github.com/hdi-project/MLPrimitives
        
        # Overview
        
        This repository contains primitive annotations to be used by the MLBlocks library, as well as
        the necessary Python code to make some of them fully compatible with the MLBlocks API requirements.
        
        There is also a collection of custom primitives contributed directly to this library, which either
        combine third party tools or implement new functionalities from scratch.
        
        ## Why did we create this library?
        
        * Too many libraries in a fast growing field
        * Huge societal need to build machine learning apps
        * Domain expertise resides at several places (knowledge of math)
        * No documented information about hyperparameters, behavior...
        
        # Installation
        
        ## Requirements
        
        **MLPrimitives** has been developed and tested on [Python 3.5, 3.6 and 3.7](https://www.python.org/downloads/)
        
        Also, although it is not strictly required, the usage of a
        [virtualenv](https://virtualenv.pypa.io/en/latest/) is highly recommended in order to avoid
        interfering with other software installed in the system where **MLPrimitives** is run.
        
        ## Install with pip
        
        The easiest and recommended way to install **MLPrimitives** is using [pip](https://pip.pypa.io/en/stable/):
        
        ```bash
        pip install mlprimitives
        ```
        
        This will pull and install the latest stable release from [PyPi](https://pypi.org/).
        
        If you want to install from source or contribute to the project please read the
        [Contributing Guide](https://hdi-project.github.io/MLPrimitives/community/welcome.html).
        
        # Quickstart
        
        This section is a short series of tutorials to help you getting started with MLPrimitives.
        
        In the following steps you will learn how to load and run a primitive on some data.
        
        Later on you will learn how to evaluate and improve the performance of a primitive by tuning
        its hyperparameters.
        
        ## Running a Primitive
        
        In this first tutorial, we will be executing a single primitive for data transformation.
        
        ### 1. Load a Primitive
        
        The first step in order to run a primitive is to load it.
        
        This will be done using the `mlprimitives.load_primitive` function, which will
        load the indicated primitive as an [MLBlock Object from MLBlocks](https://hdi-project.github.io/MLBlocks/api/mlblocks.html#mlblocks.MLBlock)
        
        In this case, we will load the `mlprimitives.custom.feature_extraction.CategoricalEncoder`
        primitive.
        
        ```python3
        from mlprimitives import load_primitive
        
        primitive = load_primitive('mlprimitives.custom.feature_extraction.CategoricalEncoder')
        ```
        
        ### 2. Load some data
        
        The CategoricalEncoder is a transformation primitive which applies one-hot encoding to all the
        categorical columns of a `pandas.DataFrame`.
        
        So, in order to be able to run our primitive, we will first load some data that contains
        categorical columns.
        
        This can be done with the `mlprimitives.datasets.load_census` function:
        
        ```python3
        from mlprimitives.datasets import load_census
        
        dataset = load_census()
        ```
        
        This dataset object has an attribute `data` which contains a table with several categorical
        columns.
        
        We can have a look at this table by executing `dataset.data.head()`, which will return a
        table like this:
        
        ```
                                     0                    1                   2
        age                         39                   50                  38
        workclass            State-gov     Self-emp-not-inc             Private
        fnlwgt                   77516                83311              215646
        education            Bachelors            Bachelors             HS-grad
        education-num               13                   13                   9
        marital-status   Never-married   Married-civ-spouse            Divorced
        occupation        Adm-clerical      Exec-managerial   Handlers-cleaners
        relationship     Not-in-family              Husband       Not-in-family
        race                     White                White               White
        sex                       Male                 Male                Male
        capital-gain              2174                    0                   0
        capital-loss                 0                    0                   0
        hours-per-week              40                   13                  40
        native-country   United-States        United-States       United-States
        ```
        
        ### 3. Fit the primitive
        
        In order to run our pipeline, we first need to fit it.
        
        This is the process where it analyzes the data to detect which columns are categorical
        
        This is done by calling its `fit` method and assing the `dataset.data` as `X`.
        
        ```python3
        primitive.fit(X=dataset.data)
        ```
        
        ### 4. Produce results
        
        Once the pipeline is fit, we can process the data by calling the `produce` method of the
        primitive instance and passing agin the `data` as `X`.
        
        ```python3
        transformed = primitive.produce(X=dataset.data)
        ```
        
        After this is done, we can see how the transformed data contains the newly generated
        one-hot vectors:
        
        ```
                                                        0      1       2       3       4
        age                                            39     50      38      53      28
        fnlwgt                                      77516  83311  215646  234721  338409
        education-num                                  13     13       9       7      13
        capital-gain                                 2174      0       0       0       0
        capital-loss                                    0      0       0       0       0
        hours-per-week                                 40     13      40      40      40
        workclass= Private                              0      0       1       1       1
        workclass= Self-emp-not-inc                     0      1       0       0       0
        workclass= Local-gov                            0      0       0       0       0
        workclass= ?                                    0      0       0       0       0
        workclass= State-gov                            1      0       0       0       0
        workclass= Self-emp-inc                         0      0       0       0       0
        ...                                             ...    ...     ...     ...     ...
        ```
        
        ## Tuning a Primitive
        
        In this short tutorial we will teach you how to evaluate the performance of a primitive
        and improve its performance by modifying its hyperparameters.
        
        To do so, we will load a primitive that can learn from the transformed data that we just
        generated and later on make predictions based on new data.
        
        ### 1. Load another primitive
        
        Firs of all, we will load the `xgboost.XGBClassifier` primitive that we will use afterwards.
        
        ```python3
        primitive = load_primitive('xgboost.XGBClassifier')
        ```
        
        ### 2. Split the dataset
        
        Before being able to evaluate the primitive perfomance, we need to split the data in two
        parts: train, which will be used for the primitive to learn, and test, which will be used
        to make the predictions that later on will be evaluated.
        
        In order to do this, we will get the first 75% of rows from the transformed data that we
        obtained above and call it `X_train`, and then set the next 25% of rows as `X_test`.
        
        ```python3
        train_size = int(len(transformed) * 0.75)
        X_train = transformed.iloc[:train_size]
        X_test = transformed.iloc[train_size:]
        ```
        
        Similarly, we need to obtain the `y_train` and `y_test` variables containing the corresponding
        output values.
        
        ```python3
        y_train = dataset.target[:train_size]
        y_test = dataset.target[train_size:]
        ```
        
        ### 3. Fit the new primitive
        
        Once we have have splitted the data, we can fit the primitive by passing `X_train` and `y_train`
        to its `fit` method.
        
        ```python3
        primitive.fit(X=X_train, y=y_train)
        ```
        
        ### 4. Make predictions
        
        Once the primitive has been fitted, we can produce predictions using the `X_test` data as input.
        
        ```python3
        predictions = primitive.produce(X=X_test)
        ```
        
        ### 5. Evalute the performance
        
        We can now evaluate how good the predictions from our primitive are by using the `score`
        method from the `dataset` object on both the expected output and the real output from the
        primitive:
        
        ```python3
        dataset.score(y_test, predictions)
        ```
        
        This will output a float value between 0 and 1 indicating how good the predicitons are, being
        0 the worst score possible and 1 the best one.
        
        In this case we will obtain a score around 0.866
        
        ### 6. Set new hyperparameter values
        
        In order to improve the performance of our primitive we will try to modify a couple of its
        hyperparameters.
        
        First we will see which hyperparameter values the primitive has by calling its
        `get_hyperparameters` method.
        
        ```python3
        primitive.get_hyperparameters()
        ```
        
        which will return a dictionary like this:
        
        ```python
        {
            "n_jobs": -1,
            "n_estimators": 100,
            "max_depth": 3,
            "learning_rate": 0.1,
            "gamma": 0,
            "min_child_weight": 1
        }
        ```
        
        Next, we will see which are the valid values for each one of those hyperparameters by calling its
        `get_tunable_hyperparameters` method:
        
        ```python3
        primitive.get_tunable_hyperparameters()
        ```
        
        For example, we will see that the `max_depth` hyperparameter has the following specification:
        
        ```python
        {
            "type": "int",
            "default": 3,
            "range": [
                3,
                10
            ]
        }
        ```
        
        Next, we will choose a valid value, for example 7, and set it into the pipeline using the
        `set_hyperparameters` method:
        
        ```python3
        primitive.set_hyperparameters({'max_depth': 7})
        ```
        
        ### 7. Re-evaluate the performance
        
        Once the new hyperparameter value has been set, we repeat the fit/train/score cycle to
        evaluate the performance of this new hyperparameter value:
        
        ```python3
        primitive.fit(X=X_train, y=y_train)
        predictions = primitive.produce(X=X_test)
        dataset.score(y_test, predictions)
        ```
        
        This time we should see that the performance has improved to a value around 0.724
        
        ## What's Next?
        
        Do you want to [learn more about how the project](https://hdi-project.github.io/MLPrimitives/getting_started/concepts.html),
        about [how to contribute to it](https://hdi-project.github.io/MLPrimitives/community/contributing.html)
        or browse the [API Reference](https://hdi-project.github.io/MLPrimitives/api/mlprimitives.html)?
        Please check the corresponding sections of the [documentation](https://hdi-project.github.io/MLPrimitives/)!
        
        
        # History
        
        ## 0.2.5 - 2020-07-29
        
        ### Primitive Improvements
        
        * Accept timedelta `window_size` in `cutoff_window_sequences` - [Issue #239](https://github.com/HDI-Project/MLPrimitives/issues/239) by @joanvaquer
        
        ### Bug Fixes
        
        * ImportError: Keras requires TensorFlow 2.2 or higher. Install TensorFlow via `pip install tensorflow` - [Issue #237](https://github.com/HDI-Project/MLPrimitives/issues/237) by @joanvaquer
        
        ### New Primitives
        
        + Add `pandas.DataFrame.set_index` primitive - [Issue #222](https://github.com/HDI-Project/MLPrimitives/issues/222) by @JDTheRipperPC
        
        ## 0.2.4 - 2020-01-30
        
        ### New Primitives
        
        * Add RangeScaler and RangeUnscaler primitives - [Issue #232](https://github.com/HDI-Project/MLPrimitives/issues/232) by @csala
        
        ### Primitive Improvements
        
        * Extract input_shape from X in keras.Sequential - [Issue #223](https://github.com/HDI-Project/MLPrimitives/issues/223) by @csala
        
        ### Bug Fixes
        
        * mlprimitives.custom.text.TextCleaner fails if text is empty - [Issue #228](https://github.com/HDI-Project/MLPrimitives/issues/228) by @csala
        * Error when loading the reviews dataset - [Issue #230](https://github.com/HDI-Project/MLPrimitives/issues/230) by @csala
        * Curate dependencies: specify an explicit prompt-toolkit version range - [Issue #224](https://github.com/HDI-Project/MLPrimitives/issues/224) by @csala
        
        ## 0.2.3 - 2019-11-14
        
        ### New Primitives
        
        * Add primitive to make window_sequences based on cutoff times - [Issue #217](https://github.com/HDI-Project/MLPrimitives/issues/217) by @csala
        * Create a keras LSTM based TimeSeriesClassifier primitive - [Issue #218](https://github.com/HDI-Project/MLPrimitives/issues/218) by @csala
        * Add pandas DataFrame primitives - [Issue #214](https://github.com/HDI-Project/MLPrimitives/issues/214) by @csala
        * Add featuretools.EntitySet.normalize_entity primitive - [Issue #209](https://github.com/HDI-Project/MLPrimitives/issues/209) by @csala
        
        ### Primitive Improvements
        
        * Make featuretools.EntitySet.entity_from_dataframe entityset arg optional - [Issue #208](https://github.com/HDI-Project/MLPrimitives/issues/208) by @csala
        
        * Add text regression dataset - [Issue #206](https://github.com/HDI-Project/MLPrimitives/issues/206) by @csala
        
        ### Bug Fixes
        
        * pandas.DataFrame.resample crash when grouping by integer columns - [Issue #211](https://github.com/HDI-Project/MLPrimitives/issues/211) by @csala
        
        ## 0.2.2 - 2019-10-08
        
        ### New Primitives
        
        * Add primitives for GAN based time-series anomaly detection - [Issue #200](https://github.com/HDI-Project/MLPrimitives/issues/200) by @AlexanderGeiger
        * Add `numpy.reshape` and `numpy.ravel` primitives - [Issue #197](https://github.com/HDI-Project/MLPrimitives/issues/197) by @AlexanderGeiger
        * Add feature selection primitive based on Lasso - [Issue #194](https://github.com/HDI-Project/MLPrimitives/issues/194) by @csala
        
        ### Primitive Improvements
        
        * `feature_extraction.CategoricalEncoder` support dtype category - [Issue #196](https://github.com/HDI-Project/MLPrimitives/issues/196) by @csala
        
        ## 0.2.1 - 2019-09-09
        
        ### New Primitives
        
        * Timeseries Intervals to Mask Primitive - [Issue #186](https://github.com/HDI-Project/MLPrimitives/issues/186) by @AlexanderGeiger
        * Add new primitive: Arima model - [Issue #168](https://github.com/HDI-Project/MLPrimitives/issues/168) by @AlexanderGeiger
        
        ### Primitive Improvements
        
        * Curate PCA primitive hyperparameters - [Issue #190](https://github.com/HDI-Project/MLPrimitives/issues/190) by @AlexanderGeiger
        * Add option to drop rolling window sequences - [Issue #186](https://github.com/HDI-Project/MLPrimitives/issues/186) by @AlexanderGeiger
        
        ### Bug Fixes
        
        * scikit-image==0.14.3 crashes when installed on Mac - [Issue #188](https://github.com/HDI-Project/MLPrimitives/issues/188) by @csala
        
        ## 0.2.0
        
        ### New Features
        
        * Publish the pipelines as an `entry_point`
        [Issue #175](https://github.com/HDI-Project/MLPrimitives/issues/175) by @csala
        
        ### Primitive Improvements
        
        * Improve pandas.DataFrame.resample primitive [Issue #177](https://github.com/HDI-Project/MLPrimitives/issues/177) by @csala
        * Improve `feature_extractor` primitives [Issue #183](https://github.com/HDI-Project/MLPrimitives/issues/183) by @csala
        * Improve `find_anomalies` primitive [Issue #180](https://github.com/HDI-Project/MLPrimitives/issues/180) by @AlexanderGeiger
        
        ### Bug Fixes
        
        * Typo in the primitive keras.Sequential.LSTMTimeSeriesRegressor [Issue #176](https://github.com/HDI-Project/MLPrimitives/issues/176) by @DanielCalvoCerezo
        
        
        ## 0.1.10
        
        ### New Features
        
        * Add function to run primitives without a pipeline [Issue #43](https://github.com/HDI-Project/MLPrimitives/issues/43) by @csala
        
        ### New Pipelines
        
        * Add pipelines for all the MLBlocks examples [Issue #162](https://github.com/HDI-Project/MLPrimitives/issues/162) by @csala
        
        ### Primitive Improvements
        
        * Add Early Stopping to `keras.Sequential.LSTMTimeSeriesRegressor` primitive [Issue #156](https://github.com/HDI-Project/MLPrimitives/issues/156) by @csala
        * Make FeatureExtractor primitives accept Numpy arrays [Issue #165](https://github.com/HDI-Project/MLPrimitives/issues/165) by @csala
        * Add window size and pruning to the `timeseries_anomalies.find_anomalies` primitive [Issue #160](https://github.com/HDI-Project/MLPrimitives/issues/160) by @csala
        
        
        ## 0.1.9
        
        ### New Features
        
        * Add a single table binary classification dataset [Issue #141](https://github.com/HDI-Project/MLPrimitives/issues/141) by @csala
        
        ### New Primitives
        
        * Add Multilayer Perceptron (MLP) primitive for binary classification [Issue #140](https://github.com/HDI-Project/MLPrimitives/issues/140) by @Hector-hedb12
        * Add primitive for Sequence classification with LSTM [Issue #150](https://github.com/HDI-Project/MLPrimitives/issues/150) by @Hector-hedb12
        * Add VGG-like convnet primitive [Issue #149](https://github.com/HDI-Project/MLPrimitives/issues/149) by @Hector-hedb12
        * Add Multilayer Perceptron (MLP) primitive for multi-class softmax classification [Issue #139](https://github.com/HDI-Project/MLPrimitives/issues/139) by @Hector-hedb12
        * Add primitive to count feature matrix columns [Issue #146](https://github.com/HDI-Project/MLPrimitives/issues/146) by @csala
        
        ### Primitive Improvements
        
        * Add additional fit and predict arguments to keras.Sequential [Issue #161](https://github.com/HDI-Project/MLPrimitives/issues/161) by @csala
        * Add suport for keras.Sequential Callbacks [Issue #159](https://github.com/HDI-Project/MLPrimitives/issues/159) by @csala
        * Add fixed hyperparam to control keras.Sequential verbosity [Issue #143](https://github.com/HDI-Project/MLPrimitives/issues/143) by @csala
        
        ## 0.1.8
        
        ### New Primitives
        
        * mlprimitives.custom.timeseries_preprocessing.time_segments_average - [Issue #137](https://github.com/HDI-Project/MLPrimitives/issues/137)
        
        ### New Features
        
        * Add target_index output in timseries_preprocessing.rolling_window_sequences - [Issue #136](https://github.com/HDI-Project/MLPrimitives/issues/136)
        
        ## 0.1.7
        
        ### General Improvements
        
        * Validate JSON format in `make lint` -  [Issue #133](https://github.com/HDI-Project/MLPrimitives/issues/133)
        * Add demo datasets - [Issue #131](https://github.com/HDI-Project/MLPrimitives/issues/131)
        * Improve featuretools.dfs primitive - [Issue #127](https://github.com/HDI-Project/MLPrimitives/issues/127)
        
        ### New Primitives
        
        * pandas.DataFrame.resample - [Issue #123](https://github.com/HDI-Project/MLPrimitives/issues/123)
        * pandas.DataFrame.unstack - [Issue #124](https://github.com/HDI-Project/MLPrimitives/issues/124)
        * featuretools.EntitySet.add_relationship - [Issue #126](https://github.com/HDI-Project/MLPrimitives/issues/126)
        * featuretools.EntitySet.entity_from_dataframe - [Issue #126](https://github.com/HDI-Project/MLPrimitives/issues/126)
        
        ### Bug Fixes
        
        * Bug in timeseries_anomalies.py - [Issue #119](https://github.com/HDI-Project/MLPrimitives/issues/119)
        
        ## 0.1.6
        
        ### General Improvements
        
        * Add Contributing Documentation
        * Remove upper bound in pandas version given new release of `featuretools` v0.6.1
        * Improve LSTMTimeSeriesRegressor hyperparameters
        
        ### New Primitives
        
        * mlprimitives.candidates.dsp.SpectralMask
        * mlprimitives.custom.timeseries_anomalies.find_anomalies
        * mlprimitives.custom.timeseries_anomalies.regression_errors
        * mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences
        * mlprimitives.custom.timeseries_preprocessing.time_segments_average
        * sklearn.linear_model.ElasticNet
        * sklearn.linear_model.Lars
        * sklearn.linear_model.Lasso
        * sklearn.linear_model.MultiTaskLasso
        * sklearn.linear_model.Ridge
        
        ## 0.1.5
        
        ### New Primitives
        
        * sklearn.impute.SimpleImputer
        * sklearn.preprocessing.MinMaxScaler
        * sklearn.preprocessing.MaxAbsScaler
        * sklearn.preprocessing.RobustScaler
        * sklearn.linear_model.LinearRegression
        
        ### General Improvements
        
        * Separate curated from candidate primitives
        * Setup `entry_points` in setup.py to improve compaitibility with MLBlocks
        * Add a test-pipelines command to test all the existing pipelines
        * Clean sklearn example pipelines
        * Change the `author` entry to a `contributors` list
        * Change the name of `mlblocks_primitives` folder
        * Pip install `requirements_dev.txt` fail documentation
        
        ### Bug Fixes
        
        * Fix LSTMTimeSeriesRegressor primitive. Issue #90
        * Fix timeseries primitives. Issue #91
        * Negative index anomalies in `timeseries_errors`. Issue #89
        * Keep pandas version below 0.24.0. Issue #87
        
        ## 0.1.4
        
        ### New Primitives
        
        * mlprimitives.timeseries primitives for timeseries data preprocessing
        * mlprimitives.timeseres_error primitives for timeseries anomaly detection
        * keras.Sequential.LSTMTimeSeriesRegressor
        * sklearn.neighbors.KNeighbors Classifier and Regressor
        * several sklearn.decomposition primitives
        * several sklearn.ensemble primitives
        
        ### Bug Fixes
        
        * Fix typo in mlprimitives.text.TextCleaner primitive
        * Fix bug in index handling in featuretools.dfs primitive
        * Fix bug in SingleLayerCNNImageClassifier annotation
        * Remove old vlaidation tags from JSON annotations
        
        ## 0.1.3
        
        ### New Features
        
        * Fix and re-enable featuretools.dfs primitive.
        
        ## 0.1.2
        
        ### New Features
        
        * Add pipeline specification language and Evaluation utilities.
        * Add pipelines for graph, text and tabular problems.
        * New primitives ClassEncoder and ClassDecoder
        * New primitives UniqueCounter and VocabularyCounter
        
        ### Bug Fixes
        
        * Fix TrivialPredictor bug when working with numpy arrays
        * Change XGB default learning rate and number of estimators
        
        
        ## 0.1.1
        
        ### New Features
        
        * Add more keras.applications primitives.
        * Add a Text Cleanup primitive.
        
        ### Bug Fixes
        
        * Add keywords to `keras.preprocessing` primtives.
        * Fix the `image_transform` method.
        * Add `epoch` as a fixed hyperparameter for `keras.Sequential` primitives.
        
        ## 0.1.0
        
        * First release on PyPI.
        
Keywords: mlblocks mlprimitives pipelines primitives machine learning data science
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
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5,<3.8
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
