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scikit-learn/sklearn/preprocessing/tests/test_discretization.py

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from __future__ import absolute_import
import pytest
import numpy as np
import scipy.sparse as sp
import warnings
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils.testing import (
assert_array_equal,
assert_raises,
assert_raise_message,
assert_warns_message
)
X = [[-2, 1.5, -4, -1],
[-1, 2.5, -3, -0.5],
[0, 3.5, -2, 0.5],
[1, 4.5, -1, 2]]
@pytest.mark.parametrize(
'strategy, expected',
[('uniform', [[0, 0, 0, 0], [1, 1, 1, 0], [2, 2, 2, 1], [2, 2, 2, 2]]),
('kmeans', [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2]]),
('quantile', [[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [2, 2, 2, 2]])])
def test_fit_transform(strategy, expected):
est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy=strategy)
est.fit(X)
assert_array_equal(expected, est.transform(X))
def test_valid_n_bins():
KBinsDiscretizer(n_bins=2).fit_transform(X)
KBinsDiscretizer(n_bins=np.array([2])[0]).fit_transform(X)
assert KBinsDiscretizer(n_bins=2).fit(X).n_bins_.dtype == np.dtype(np.int)
def test_invalid_n_bins():
est = KBinsDiscretizer(n_bins=1)
assert_raise_message(ValueError, "KBinsDiscretizer received an invalid "
"number of bins. Received 1, expected at least 2.",
est.fit_transform, X)
est = KBinsDiscretizer(n_bins=1.1)
assert_raise_message(ValueError, "KBinsDiscretizer received an invalid "
"n_bins type. Received float, expected int.",
est.fit_transform, X)
def test_invalid_n_bins_array():
# Bad shape
n_bins = np.full((2, 4), 2.)
est = KBinsDiscretizer(n_bins=n_bins)
assert_raise_message(ValueError,
"n_bins must be a scalar or array of shape "
"(n_features,).", est.fit_transform, X)
# Incorrect number of features
n_bins = [1, 2, 2]
est = KBinsDiscretizer(n_bins=n_bins)
assert_raise_message(ValueError,
"n_bins must be a scalar or array of shape "
"(n_features,).", est.fit_transform, X)
# Bad bin values
n_bins = [1, 2, 2, 1]
est = KBinsDiscretizer(n_bins=n_bins)
assert_raise_message(ValueError,
"KBinsDiscretizer received an invalid number of bins "
"at indices 0, 3. Number of bins must be at least 2, "
"and must be an int.",
est.fit_transform, X)
# Float bin values
n_bins = [2.1, 2, 2.1, 2]
est = KBinsDiscretizer(n_bins=n_bins)
assert_raise_message(ValueError,
"KBinsDiscretizer received an invalid number of bins "
"at indices 0, 2. Number of bins must be at least 2, "
"and must be an int.",
est.fit_transform, X)
@pytest.mark.parametrize(
'strategy, expected',
[('uniform', [[0, 0, 0, 0], [0, 1, 1, 0], [1, 2, 2, 1], [1, 2, 2, 2]]),
('kmeans', [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 2, 2, 2]]),
('quantile', [[0, 0, 0, 0], [0, 1, 1, 1], [1, 2, 2, 2], [1, 2, 2, 2]])])
def test_fit_transform_n_bins_array(strategy, expected):
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode='ordinal',
strategy=strategy).fit(X)
assert_array_equal(expected, est.transform(X))
# test the shape of bin_edges_
n_features = np.array(X).shape[1]
assert est.bin_edges_.shape == (n_features, )
for bin_edges, n_bins in zip(est.bin_edges_, est.n_bins_):
assert bin_edges.shape == (n_bins + 1, )
def test_invalid_n_features():
est = KBinsDiscretizer(n_bins=3).fit(X)
bad_X = np.arange(25).reshape(5, -1)
assert_raise_message(ValueError,
"Incorrect number of features. Expecting 4, "
"received 5", est.transform, bad_X)
@pytest.mark.parametrize('strategy', ['uniform', 'kmeans', 'quantile'])
def test_same_min_max(strategy):
warnings.simplefilter("always")
X = np.array([[1, -2],
[1, -1],
[1, 0],
[1, 1]])
est = KBinsDiscretizer(strategy=strategy, n_bins=3, encode='ordinal')
assert_warns_message(UserWarning,
"Feature 0 is constant and will be replaced "
"with 0.", est.fit, X)
assert est.n_bins_[0] == 1
# replace the feature with zeros
Xt = est.transform(X)
assert_array_equal(Xt[:, 0], np.zeros(X.shape[0]))
def test_transform_1d_behavior():
X = np.arange(4)
est = KBinsDiscretizer(n_bins=2)
assert_raises(ValueError, est.fit, X)
est = KBinsDiscretizer(n_bins=2)
est.fit(X.reshape(-1, 1))
assert_raises(ValueError, est.transform, X)
def test_numeric_stability():
X_init = np.array([2., 4., 6., 8., 10.]).reshape(-1, 1)
Xt_expected = np.array([0, 0, 1, 1, 1]).reshape(-1, 1)
# Test up to discretizing nano units
for i in range(1, 9):
X = X_init / 10**i
Xt = KBinsDiscretizer(n_bins=2, encode='ordinal').fit_transform(X)
assert_array_equal(Xt_expected, Xt)
def test_invalid_encode_option():
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode='invalid-encode')
assert_raise_message(ValueError, "Valid options for 'encode' are "
"('onehot', 'onehot-dense', 'ordinal'). "
"Got encode='invalid-encode' instead.",
est.fit, X)
def test_encode_options():
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3],
encode='ordinal').fit(X)
Xt_1 = est.transform(X)
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3],
encode='onehot-dense').fit(X)
Xt_2 = est.transform(X)
assert not sp.issparse(Xt_2)
assert_array_equal(OneHotEncoder(
categories=[np.arange(i) for i in [2, 3, 3, 3]],
sparse=False)
.fit_transform(Xt_1), Xt_2)
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3],
encode='onehot').fit(X)
Xt_3 = est.transform(X)
assert sp.issparse(Xt_3)
assert_array_equal(OneHotEncoder(
categories=[np.arange(i) for i in [2, 3, 3, 3]],
sparse=True)
.fit_transform(Xt_1).toarray(),
Xt_3.toarray())
def test_invalid_strategy_option():
est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], strategy='invalid-strategy')
assert_raise_message(ValueError, "Valid options for 'strategy' are "
"('uniform', 'quantile', 'kmeans'). "
"Got strategy='invalid-strategy' instead.",
est.fit, X)
@pytest.mark.parametrize(
'strategy, expected_2bins, expected_3bins',
[('uniform', [0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 2, 2]),
('kmeans', [0, 0, 0, 0, 1, 1], [0, 0, 1, 1, 2, 2]),
('quantile', [0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 2, 2])])
def test_nonuniform_strategies(strategy, expected_2bins, expected_3bins):
X = np.array([0, 0.5, 2, 3, 9, 10]).reshape(-1, 1)
# with 2 bins
est = KBinsDiscretizer(n_bins=2, strategy=strategy, encode='ordinal')
Xt = est.fit_transform(X)
assert_array_equal(expected_2bins, Xt.ravel())
# with 3 bins
est = KBinsDiscretizer(n_bins=3, strategy=strategy, encode='ordinal')
Xt = est.fit_transform(X)
assert_array_equal(expected_3bins, Xt.ravel())
@pytest.mark.parametrize('strategy', ['uniform', 'kmeans', 'quantile'])
@pytest.mark.parametrize('encode', ['ordinal', 'onehot', 'onehot-dense'])
def test_inverse_transform(strategy, encode):
X = np.random.RandomState(0).randn(100, 3)
kbd = KBinsDiscretizer(n_bins=3, strategy=strategy, encode=encode)
Xt = kbd.fit_transform(X)
X2 = kbd.inverse_transform(Xt)
X2t = kbd.fit_transform(X2)
if encode == 'onehot':
assert_array_equal(Xt.todense(), X2t.todense())
else:
assert_array_equal(Xt, X2t)
if 'onehot' in encode:
Xt = kbd._encoder.inverse_transform(Xt)
X2t = kbd._encoder.inverse_transform(X2t)
assert_array_equal(Xt.max(axis=0) + 1, kbd.n_bins_)
assert_array_equal(X2t.max(axis=0) + 1, kbd.n_bins_)
@pytest.mark.parametrize('strategy', ['uniform', 'kmeans', 'quantile'])
def test_transform_outside_fit_range(strategy):
X = np.array([0, 1, 2, 3])[:, None]
kbd = KBinsDiscretizer(n_bins=4, strategy=strategy, encode='ordinal')
kbd.fit(X)
X2 = np.array([-2, 5])[:, None]
X2t = kbd.transform(X2)
assert_array_equal(X2t.max(axis=0) + 1, kbd.n_bins_)
assert_array_equal(X2t.min(axis=0), [0])
def test_overwrite():
X = np.array([0, 1, 2, 3])[:, None]
X_before = X.copy()
est = KBinsDiscretizer(n_bins=3, encode="ordinal")
Xt = est.fit_transform(X)
assert_array_equal(X, X_before)
Xt_before = Xt.copy()
Xinv = est.inverse_transform(Xt)
assert_array_equal(Xt, Xt_before)
assert_array_equal(Xinv, np.array([[0.5], [1.5], [2.5], [2.5]]))
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