48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
import numpy as np
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from numpy.testing import assert_array_equal, assert_array_almost_equal
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from nose.tools import assert_equal, assert_true
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from .. import lda
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# Data is just 6 separable points in the plane
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X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
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y = np.array([1, 1, 1, 2, 2, 2])
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y3 = np.array([1, 1, 2, 2, 3, 3])
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# Degenerate data with 1 feature (still should be separable)
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X1 = np.array([[-2, ], [-1, ], [-1, ], [1, ], [1, ], [2, ]])
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def test_lda_predict():
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"""
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LDA classification.
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This checks that LDA implements fit and predict and returns
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correct values for a simple toy dataset.
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"""
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clf = lda.LDA()
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y_pred = clf.fit(X, y).predict(X)
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assert_array_equal(y_pred, y)
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# Assure that it works with 1D data
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y_pred1 = clf.fit(X1, y).predict(X1)
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assert_array_equal(y_pred1, y)
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# Test probas estimates
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y_proba_pred1 = clf.predict_proba(X1)
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assert_array_equal((y_proba_pred1[:, 1] > 0.5) + 1, y)
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y_log_proba_pred1 = clf.predict_log_proba(X1)
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assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1, 8)
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# Primarily test for commit 2f34950 -- "reuse" of priors
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y_pred3 = clf.fit(X, y3).predict(X)
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# LDA shouldn't be able to separate those
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assert_true(np.any(y_pred3 != y3))
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def test_lda_transform():
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clf = lda.LDA()
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X_transformed = clf.fit(X, y).transform(X)
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assert_equal(X_transformed.shape[1], 1)
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