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binarizer_test.py
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import unittest
import numpy as np
import pandas as pd
from features_binarizer import FeaturesBinarizer
from sklearn.preprocessing import OneHotEncoder
class Test(unittest.TestCase):
def test_FeaturesBinarizer(self):
"""Test FeaturesBinarizer
"""
# Create a features matrix
values = np.array([[0.00902084, 0.54159776, 0., 3.],
[0.46599565, -0.71875887, 0., 2.],
[0.52091721, -0.83803094, 1., 2.],
[0.47315496, 0.0730993, 1., 1.],
[0.08180209, -1.11447889, 0., 0.],
[0.45011727, -0.57931684, 0., 0.],
[2.04347947, -0.10127498, 1., 7.],
[-0.98909384, 1.36281079, 0., 0.],
[-0.30637613, -0.19147753, 1., 1.],
[0.27110903, 0.44583304, 0., 0.]])
columns = ['c:continuous', 'a:continuous', 'd', 'b:discrete']
features = pd.DataFrame(values, columns=columns)
# 1. Test method='quantile'
# Get the correct result with remove_first=False
values_res = np.array([[0, 3, 0, 3],
[2, 0, 0, 2],
[3, 0, 1, 2],
[2, 2, 1, 1],
[1, 0, 0, 0],
[2, 1, 0, 0],
[3, 2, 1, 4],
[0, 3, 0, 0],
[0, 1, 1, 1],
[1, 2, 0, 0]])
columns_res = ['c#0', 'c#1', 'c#2', 'c#3', 'a#0', 'a#1', 'a#2', 'a#3',
'd', 'b#0', 'b#1', 'b#2', 'b#3', 'b#4']
enc = OneHotEncoder(sparse=True)
X_bin_res = enc.fit_transform(values_res)
# Create the FeatureBinarizer
n_cuts = 3
for get_type in ["auto", "column_names"]:
binarizer = FeaturesBinarizer(method='quantile', n_cuts=n_cuts,
get_type=get_type,
remove_first=False)
# Apply it on the features matrix
features_bin = binarizer.fit(features)
X_bin = features_bin.transform(features)
X_bin_fit_transform = binarizer.fit_transform(features)
self.assertTrue((X_bin != X_bin_res).nnz == 0)
self.assertTrue((X_bin_fit_transform != X_bin_res).nnz == 0)
np.testing.assert_equal(columns_res, features_bin._columns_names)
self.assertTrue(binarizer.blocks_start == [0, 4])
self.assertTrue(binarizer.blocks_length == [4, 4])
np.testing.assert_equal(
np.around(binarizer.bins_boundaries['a:continuous'], 4),
np.array([-np.inf, -0.7188, -0.1915, 0.4458, np.inf]))
np.testing.assert_equal(
np.around(binarizer.bins_boundaries['c:continuous'], 4),
np.array([-np.inf, 0.009, 0.2711, 0.4732, np.inf]))
# Get the correct result with remove_first=True
values_res = np.array([[0, 2, 0, 2],
[1, 0, 0, 1],
[2, 0, 1, 1],
[1, 1, 1, 0],
[0, 0, 0, 0],
[1, 0, 0, 0],
[2, 1, 1, 3],
[0, 2, 0, 0],
[0, 0, 1, 0],
[0, 1, 0, 0]])
columns_res = ['c#0', 'c#1', 'c#2', 'a#0', 'a#1', 'a#2', 'd', 'b#0',
'b#1', 'b#2', 'b#3']
enc = OneHotEncoder(sparse=True)
X_bin_res = enc.fit_transform(values_res)
# Create the FeatureBinarizer
n_cuts = 3
for get_type in ["auto", "column_names"]:
binarizer = FeaturesBinarizer(method='quantile', n_cuts=n_cuts,
get_type=get_type,
remove_first=True)
# Apply it on the features matrix
features1, features2 = features.copy(), features.copy()
features_bin = binarizer.fit(features1)
X_bin = features_bin.transform(features1)
X_bin_fit_transform = binarizer.fit_transform(features2)
self.assertTrue((X_bin_fit_transform != X_bin_res).nnz == 0)
self.assertTrue((X_bin != X_bin_res).nnz == 0)
np.testing.assert_equal(columns_res,
features_bin._columns_names)
# 2. Test method='linspace'
# Get the correct result with remove_first=False
values_res = np.array([[1, 2, 0, 3],
[1, 0, 0, 2],
[1, 0, 1, 2],
[1, 1, 1, 1],
[1, 0, 0, 0],
[1, 0, 0, 0],
[3, 1, 1, 4],
[0, 3, 0, 0],
[0, 1, 1, 1],
[1, 2, 0, 0]])
columns_res = ['c#0', 'c#1', 'c#2', 'c#3', 'a#0', 'a#1', 'a#2', 'a#3',
'd', 'b#0', 'b#1', 'b#2', 'b#3', 'b#4']
enc = OneHotEncoder(sparse=True)
X_bin_res = enc.fit_transform(values_res)
# Create the FeatureBinarizer
n_cuts = 3
for get_type in ["auto", "column_names"]:
binarizer = FeaturesBinarizer(method='linspace', n_cuts=n_cuts,
get_type=get_type,
remove_first=False)
# Apply it on the features matrix
features_bin = binarizer.fit(features)
X_bin = features_bin.transform(features)
X_bin_fit_transform = binarizer.fit_transform(features)
self.assertTrue((X_bin != X_bin_res).nnz == 0)
self.assertTrue((X_bin_fit_transform != X_bin_res).nnz == 0)
np.testing.assert_equal(columns_res,
features_bin._columns_names)
self.assertTrue(binarizer.blocks_start == [0, 4])
self.assertTrue(binarizer.blocks_length == [4, 4])
np.testing.assert_equal(
np.around(binarizer.bins_boundaries['a:continuous'], 4),
np.array([-np.inf, -0.4952, 0.1242, 0.7435, np.inf]))
np.testing.assert_equal(
np.around(binarizer.bins_boundaries['c:continuous'], 4),
np.array([-np.inf, -0.231, 0.5272, 1.2853, np.inf]))
# Get the correct result with remove_first=True
values_res = np.array([[0, 1, 0, 2],
[0, 0, 0, 1],
[0, 0, 1, 1],
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[2, 0, 1, 3],
[0, 2, 0, 0],
[0, 0, 1, 0],
[0, 1, 0, 0]])
columns_res = ['c#0', 'c#1', 'c#2', 'a#0', 'a#1', 'a#2', 'd', 'b#0',
'b#1', 'b#2', 'b#3']
enc = OneHotEncoder(sparse=True)
X_bin_res = enc.fit_transform(values_res)
# Create the FeatureBinarizer
n_cuts = 3
for get_type in ["auto", "column_names"]:
binarizer = FeaturesBinarizer(method='linspace', n_cuts=n_cuts,
get_type=get_type,
remove_first=True)
# Apply it on the features matrix
features1, features2 = features.copy(), features.copy()
features_bin = binarizer.fit(features1)
X_bin = features_bin.transform(features1)
X_bin_fit_transform = binarizer.fit_transform(features2)
self.assertTrue((X_bin_fit_transform != X_bin_res).nnz == 0)
self.assertTrue((X_bin != X_bin_res).nnz == 0)
np.testing.assert_equal(columns_res,
features_bin._columns_names)
return
if __name__ == "main":
unittest.main()