split_data.py

from __future__ import print_function, division

__author__ = 'amritanshu.agrawal'

import sys

sys.dont_write_bytecode = True

import numpy as np
import random
random.seed(1)
np.random.seed(1)

##See this book Hands-On Machine Learning with Scikit-Learn and TensorFlow on safarionlinebooks

housing=[]

def split_train_test(data, test_ratio):
    shuffled_indices = np.random.permutation(len(data))
    test_set_size = int(len(data) * test_ratio)
    test_indices = shuffled_indices[:test_set_size]
    train_indices = shuffled_indices[test_set_size:]
    return data.iloc[train_indices], data.iloc[test_indices]

def stratified_sampling_based_on_attribute(housing):
    housing["income_cat"] = np.ceil(housing["median_income"] / 1.5)
    housing["income_cat"].where(housing["income_cat"] < 5, 5.0, inplace=True)
    from sklearn.model_selection import StratifiedShuffleSplit

    split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
    for train_index, test_index in split.split(housing, housing["income_cat"]):
        strat_train_set = housing.loc[train_index]
        strat_test_set = housing.loc[test_index]

from sklearn.base import BaseEstimator, TransformerMixin

rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6

class CombinedAttributesAdder(BaseEstimator, TransformerMixin):
    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs
        self.add_bedrooms_per_room = add_bedrooms_per_room
    def fit(self, X, y=None):
        return self  # nothing else to do
    def transform(self, X, y=None):
        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]
        population_per_household = X[:, population_ix] / X[:, household_ix]
        if self.add_bedrooms_per_room:
            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]
            return np.c_[X, rooms_per_household, population_per_household,
                         bedrooms_per_room]
        else:
            return np.c_[X, rooms_per_household, population_per_household]

attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)
housing_extra_attribs = attr_adder.transform(housing.values)