Metadata-Version: 1.0
Name: pandas-ml
Version: 0.6.1
Summary: pandas, scikit-learn and xgboost integration
Home-page: http://pandas-ml.readthedocs.org/en/stable
Author: sinhrks
Author-email: sinhrks@gmail.com
License: BSD
Description: pandas-ml
        =========
        
        .. image:: https://img.shields.io/pypi/v/pandas_ml.svg
          :target: https://pypi.python.org/pypi/pandas_ml/
        .. image:: https://readthedocs.org/projects/pandas-ml/badge/?version=latest
          :target: http://pandas-ml.readthedocs.org/en/latest/
          :alt: Latest Docs
        .. image:: https://travis-ci.org/pandas-ml/pandas-ml.svg?branch=master
          :target: https://travis-ci.org/pandas-ml/pandas-ml
        .. image:: https://codecov.io/gh/pandas-ml/pandas-ml/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/pandas-ml/pandas-ml
        
        Overview
        ~~~~~~~~
        
        `pandas <http://pandas.pydata.org/>`_, `scikit-learn <http://scikit-learn.org/>`_
        and `xgboost <http://xgboost.readthedocs.org/en/latest/index.html>`_ integration.
        
        Installation
        ~~~~~~~~~~~~
        
        .. code-block::
        
            $ pip install pandas_ml
        
        
        Documentation
        ~~~~~~~~~~~~~
        
        http://pandas-ml.readthedocs.org/en/stable/
        
        Example
        ~~~~~~~
        
        .. code-block:: python
        
            >>> import pandas_ml as pdml
            >>> import sklearn.datasets as datasets
        
            # create ModelFrame instance from sklearn.datasets
            >>> df = pdml.ModelFrame(datasets.load_digits())
            >>> type(df)
            <class 'pandas_ml.core.frame.ModelFrame'>
        
            # binarize data (features), not touching target
            >>> df.data = df.data.preprocessing.binarize()
            >>> df.head()
               .target  0  1  2  3  4  5  6  7  8 ...  54  55  56  57  58  59  60  61  62  63
            0        0  0  0  1  1  1  1  0  0  0 ...   0   0   0   0   1   1   1   0   0   0
            1        1  0  0  0  1  1  1  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
            2        2  0  0  0  1  1  1  0  0  0 ...   1   0   0   0   0   1   1   1   1   0
            3        3  0  0  1  1  1  1  0  0  0 ...   1   0   0   0   1   1   1   1   0   0
            4        4  0  0  0  1  1  0  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
            [5 rows x 65 columns]
        
            # split to training and test data
            >>> train_df, test_df = df.model_selection.train_test_split()
        
            # create estimator (accessor is mapped to sklearn namespace)
            >>> estimator = df.svm.LinearSVC()
        
            # fit to training data
            >>> train_df.fit(estimator)
        
            # predict test data
            >>> test_df.predict(estimator)
            0     4
            1     2
            2     7
            ...
            448    5
            449    8
            Length: 450, dtype: int64
        
            # Evaluate the result
            >>> test_df.metrics.confusion_matrix()
            Predicted   0   1   2   3   4   5   6   7   8   9
            Target
            0          52   0   0   0   0   0   0   0   0   0
            1           0  37   1   0   0   1   0   0   3   3
            2           0   2  48   1   0   0   0   1   1   0
            3           1   1   0  44   0   1   0   0   3   1
            4           1   0   0   0  43   0   1   0   0   0
            5           0   1   0   0   0  39   0   0   0   0
            6           0   1   0   0   1   0  35   0   0   0
            7           0   0   0   0   2   0   0  42   1   0
            8           0   2   1   0   1   0   0   0  33   1
            9           0   2   1   2   0   0   0   0   1  38
        
        
        Supported Packages
        ~~~~~~~~~~~~~~~~~~
        
        - ``scikit-learn``
        - ``patsy``
        - ``xgboost``
        
Platform: UNKNOWN
