<commit_before>`frame_learn` - `pandas` adapters for `scikit-learn`
===================================================

<NAME>, <NAME>, and <NAME>

.. image:: https://travis-ci.org/atavory/frame_learn.svg?branch=master  
    :target: https://travis-ci.org/atavory/frame_learn


`scikit-learn <http://scikit-learn.org/stable/>`_

`pandas <http://pandas.pydata.org/>`_


TL;DR
-----

    >>> import pandas as pd
    >>> from sklearn import linear_model
    >>> from frame_learn import *

A `pd.DataFrame` and 

    >>> x = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4]})                       
    >>> y = pd.Series([1, 2, 3])                                                                                                                                       

sdf
																					
    >>> prd = frame(preprocessing.StandardScaler()) | \                          
    ...     apply() + apply({'sqrt_a': np.sqrt, 'sqr_a': lambda x: x ** 2}, columns='a') | \
    ...     frame(linear_model.LinearRegression())                                  
    >>> y_hat = prd.fit(x, y).predict(x)   

API
---
<commit_msg>coverage-badge<commit_after>`frame_learn` - `pandas` adapters for `scikit-learn`
===================================================

<NAME>, <NAME>, and <NAME>

.. image:: https://travis-ci.org/atavory/frame_learn.svg?branch=master  
    :target: https://travis-ci.org/atavory/frame_learn

.. image:: https://coveralls.io/repos/github/atavory/frame_learn/badge.svg?branch=master
	:target: https://coveralls.io/github/atavory/frame_learn?branch=master

    

`scikit-learn <http://scikit-learn.org/stable/>`_

`pandas <http://pandas.pydata.org/>`_


TL;DR
-----

    >>> import pandas as pd
    >>> from sklearn import linear_model
    >>> from frame_learn import *

A `pd.DataFrame` and 

    >>> x = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4]})                       
    >>> y = pd.Series([1, 2, 3])                                                                                                                                       

sdf
																					
    >>> prd = frame(preprocessing.StandardScaler()) | \                          
    ...     apply() + apply({'sqrt_a': np.sqrt, 'sqr_a': lambda x: x ** 2}, columns='a') | \
    ...     frame(linear_model.LinearRegression())                                  
    >>> y_hat = prd.fit(x, y).predict(x)   

API
---

{'fraction_non_alphanumeric': 0.20317725752508362, 'fraction_numerical': 0.008361204013377926, 'mean_word_length': 1.5926327193932828, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 55, 'https://': 6, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 12, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1908208', 'n_tokens_mistral': 765, 'n_tokens_neox': 723, 'n_words': 128}