Metadata-Version: 1.2
Name: scikit-nni
Version: 0.1.1
Summary: Hyper parameters search for scikit-learn components using Microsoft NNI
Home-page: https://github.com/ksachdeva/scikit-nni
Author: Kapil Sachdeva
Author-email: not@anemail.com
License: Apache Software License 2.0
Description: ==========
        scikit-nni
        ==========
        
        
        .. image:: https://img.shields.io/pypi/v/scikit-nni.svg
                :target: https://pypi.python.org/pypi/scikit-nni
        
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                :target: https://travis-ci.org/ksachdeva/scikit-nni
        
        .. image:: https://readthedocs.org/projects/scikit-nni/badge/?version=latest
                :target: https://scikit-nni.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        Hyper parameters search for scikit-learn components using Microsoft NNI
        
        
        * Free software: Apache Software License 2.0
        * Documentation: https://scikit-nni.readthedocs.io.
        
        
        Features
        --------
        
        * Hyperparameters search for scikit-learn pipelines using Microsoft NNI
        * No code required to define the pipelines
        * Builtin datasource reader for reading npz files for classification
        * Support for using custom datasource reader
        * Single configuration file to define NNI configuration and search space
        
        I plan to add more datasource readers (e.g. CSV, libSVM format files etc). Contributions are always welcome !
        
        Usage
        -----
        
        Step 1 - Write specification file
        #################################
        
        The specification file is essentially a YAML file but with extension `.nni.yml`
        
        There are 4 parts (sections) in the configuration file.
        
        ******************
        Datasource Section
        ******************
        
        This is where you will specify the (python) callable that `sknni` would invoking to the training and
        test dataset.
        
        The callable should return 2 values where each value is a `tuple` of two items. The first tuple
        consists of training data `(X_train, y_train)` and the second tuple consists of test data `(X_test, y_test)`.
        
        An example callable would look like this::
        
            import numpy as np
        
            from sklearn.datasets import load_digits
            from sklearn.model_selection import train_test_split
        
            class ACustomDataSource(object):
                def __init__(self):
                    pass
        
                def __call__(self, test_size:float=0.25):
                    digits = load_digits()
                    X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=test_size)
        
                    return (X_train, y_train), (X_test, y_test)
        
        In the above example, the callable generates the train and test dataset. The callable can even have paramaters for e.g. in this
        example you could optionally pass the fraction of data to be used for testing purposes.
        
        Now let's see how you would specify in the specification file.
        
        .. code-block:: yaml
        
            # Datasource is how you specify which callable
            # sknni will invoke to get the data
            dataSource:
                reader: yourmodule.ACustomDataSource
                params:
                    test_size: 0.30
        
        Make sure that during the exeuction of the experiment your datasource (i.e. in this case `yourmodule.ACustomDataSource`)
        is available in the PYTHONPATH.
        
        Here is an additional example showing the usage of an built-in datasource reader
        
        .. code-block:: yaml
        
            dataSource:
                reader: sknni.datasource.NpzClassificationSource
                params:
                    dir_path: /Users/ksachdeva/Desktop/Dev/myoss/scikit-nni/examples/data/multiclass-classification
        
        
        **************************
        Pipline definition Section
        **************************
        
        Below is the example of the section. You simply specify the list of steps of your typical scikit-learn Pipeline.
        
        Note - The sequence of steps is very important.
        
        What you **MUST** ensure is that the full qualified name of your scikit-learn preprocessors, transformers and
        estimators is correctly specified. `sknni` uses reflection and introspection to create the instances so if you have a
        typo in the names and/or they are not available in your PYTHONPATH you will get an error at experiment execution time.
        
        .. code-block:: yaml
        
            sklearnPipeline:
                name: normalizer_svc
                steps:
                    normalizer: sklearn.preprocessing.Normalizer
                    svc: sklearn.svm.SVC
        
        In above example, there are 2 steps. The first step is to normalize the data and the second step is train a classifier using Support
        Vector Machine.
        
        ********************
        Search Space Section
        ********************
        
        This section corresponds to the search space for your hyperparameters. When you ```nnictrl``` this is typically
        specified in search-space.json file.
        
        Here are the important things to note about this section -
        
        - The syntax is the same (except we are using YAML here instead of JSON) for specifiying parameter types and ranges.
        - You **MUST** specifiy the parameters corresponding to the step in your scikit pipeline.
        - You **MUST** use the names of the parameters that are same as the ones accepted by scikit-learn components (i.e. preprocessors, estimators etc).
        
        
        Below is an example of this section.
        
        .. code-block:: yaml
        
            nniConfigSearchSpace:
                - normalizer:
                    norm:
                        _type: choice
                        _value: [l2, l1]
                - svc:
                    C:
                        _type: uniform
                        _value: [0.1,0.0]
                    kernel:
                        _type: choice
                        _value: [linear,rbf,poly,sigmoid]
                    degree:
                        _type: choice
                        _value: [1,2,3,4]
                    gamma:
                        _type: uniform
                        _value: [0.01,0.1]
                    coef0:
                        _type: uniform
                        _value: [0.01,0.1]
        
        Note that `sklearn.svm.SVC` takes C, kernel, degree, gamman and coef0 is the paramaters and hence we have used here
        the same names (keys) in the search space specification. You can add as many or as little parameters to search for.
        
        ******************
        NNI Config Section
        ******************
        
        This is the simplest of all sections as there is nothing new here from sknni perspective. You just copy-paste
        here your NNI's config.yaml here. You do not have to specify `codedir` and `command` field in the `trial` subsection as
        this is added by the sknni in the generated configuration files.
        
        Here is an example.
        
        
        .. code-block:: yaml
        
            # This is exactly same as the one that of NNI
            # except that you do not have to specify the command
            # and code fields. They are automatically added by the sknni generator
            nniConfig:
                authorName: default
                experimentName: example_sklearn-classification
                trialConcurrency: 1
                maxExecDuration: 1h
                maxTrialNum: 100
                trainingServicePlatform: local
                useAnnotation: false
                tuner:
                    builtinTunerName: TPE
                    classArgs:
                        optimize_mode: maximize
                trial:
                    gpuNum: 0
        
        You can look at the various examples in the repository to learn how to define your own specification file.
        
        
        Step 2 - Generate your experiment
        #################################
        
        .. code-block:: bash
        
            sknni generate-experiment --spec example/basic_svc.nni.yml --output-dir experiments
        
        
        Above command will create a directory experiments/svc-classification will following files
        
            - The original specification file i.e. basic_svc.nni.yml (used during experiment run as well)
            - Generated Microsoft NNI's config.yml
            - Generated Microsoft NNI's search-space.json
        
        
        Step 3 - Run your experiment
        #################################
        
        This is same as running `nnitctl`
        
        .. code-block:: bash
        
            nnictl create --config experiments/svc-classification/config.yml
        
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        =======
        History
        =======
        
        0.1.1 (2019-10-20)
        ------------------
        
        * First release on PyPI.
        
Keywords: sknni,scikit-nni
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*
