8000 TST Add tests for the new assert helpers · scikit-learn/scikit-learn@3341afb · GitHub
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TST Add tests for the new assert helpers
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‎sklearn/utils/tests/test_testing.py

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@@ -1,9 +1,9 @@
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import warnings
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import unittest
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import sys
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import numpy as np
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from nose.tools import assert_raises
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from sklearn.utils.testing import (
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_assert_less,
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_assert_greater,
@@ -13,10 +13,15 @@
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assert_no_warnings,
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assert_equal,
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set_random_state,
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assert_raise_message)
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assert_raise_message,
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assert_same_model,
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assert_not_same_model)
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.lda import LDA
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from sklearn.qda import QDA
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from sklearn.datasets import make_blobs
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from sklearn.svm import LinearSVC
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from sklearn.cluster import KMeans
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try:
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from nose.tools import assert_less
@@ -96,10 +101,53 @@ def _no_raise():
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"test", _no_raise)
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def test_assert_same_not_same_model():
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X1, y1 = make_blobs(n_samples=200, n_features=5, center_box=(-200, -150),
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centers=2, random_state=0)
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X2, y2 = make_blobs(n_samples=100, n_features=5, center_box=(-1, 1),
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centers=3, random_state=1)
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X3, y3 = make_blobs(n_samples=50, n_features=5, center_box=(-100, -50),
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centers=4, random_state=2)
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# Checking both non-transductive and transductive algorithms
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# By testing for transductive algorithms we also eventually test
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# the assert_fitted_attributes_equal helper.
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for Estimator in (LinearSVC, KMeans):
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assert_same_model(X3, Estimator(random_state=0).fit(X1, y1),
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Estimator(random_state=0).fit(X1, y1))
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assert_raises(AssertionError, assert_not_same_model, X3,
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Estimator(random_state=0).fit(X1, y1),
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Estimator(random_state=0).fit(X1, y1))
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assert_raises(AssertionError, assert_same_model, X3,
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Estimator(random_state=0).fit(X1, y1),
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Estimator(random_state=0).fit(X2, y2))
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assert_not_same_model(X3, Estimator(random_state=0).fit(X1, y1),
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Estimator(random_state=0).fit(X2, y2))
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def test_qda_same_model():
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# NRT to make sure the rotations_ attribute is correctly compared
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X = np.array([[0, 0], [-2, -2], [-2, -1], [-1, -1], [-1, -2],
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[1, 3], [1, 2], [2, 1], [2, 2]])
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y = np.array([1, 1, 1, 1, 1, 2, 2, 2, 2])
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X1 = np.array([[-3, -1], [-2, 0], [-1, 0], [-11, 0], [0, 0], [1, 0],
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[1, 5], [2, 0], [3, 4]])
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y1 = np.array([1, 1, 1, 1, 2, 2, 2, 2, 2])
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X2 = np.array([[-1, -3], [0, -2], [0, -1], [0, -5], [0, 0], [10, 1],
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[0, 11], [0, 22], [0, 33]])
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clf1 = QDA().fit(X, y)
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clf2 = QDA().fit(X, y)
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assert_same_model(X1, clf1, clf2)
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clf3 = QDA().fit(X1, y1)
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assert_not_same_model(X2, clf1, clf3)
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# This class is inspired from numpy 1.7 with an alteration to check
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# the reset warning filters after calls to assert_warns.
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# This assert_warns behavior is specific to scikit-learn because
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#`clean_warning_registry()` is called internally by assert_warns
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# `clean_warning_registry()` is called internally by assert_warns
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# and clears all previous filters.
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class TestWarns(unittest.TestCase):
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def test_warn(self):

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