158 lines
4.4 KiB
Python
158 lines
4.4 KiB
Python
import unittest
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import sys
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import numpy as np
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from sklearn.mixture import DPGMM, VBGMM
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from sklearn.mixture.dpgmm import log_normalize
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from sklearn.datasets import make_blobs
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from sklearn.utils.testing import assert_array_less, assert_equal
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from sklearn.mixture.tests.test_gmm import GMMTester
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from sklearn.externals.six.moves import cStringIO as StringIO
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np.seterr(all='warn')
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def test_class_weights():
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# check that the class weights are updated
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# simple 3 cluster dataset
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X, y = make_blobs(random_state=1)
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for Model in [DPGMM, VBGMM]:
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dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50)
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dpgmm.fit(X)
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# get indices of components that are used:
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indices = np.unique(dpgmm.predict(X))
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active = np.zeros(10, dtype=np.bool)
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active[indices] = True
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# used components are important
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assert_array_less(.1, dpgmm.weights_[active])
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# others are not
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assert_array_less(dpgmm.weights_[~active], .05)
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def test_verbose_boolean():
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# checks that the output for the verbose output is the same
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# for the flag values '1' and 'True'
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# simple 3 cluster dataset
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X, y = make_blobs(random_state=1)
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for Model in [DPGMM, VBGMM]:
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dpgmm_bool = Model(n_components=10, random_state=1, alpha=20,
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n_iter=50, verbose=True)
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dpgmm_int = Model(n_components=10, random_state=1, alpha=20,
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n_iter=50, verbose=1)
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old_stdout = sys.stdout
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sys.stdout = StringIO()
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try:
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# generate output with the boolean flag
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dpgmm_bool.fit(X)
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verbose_output = sys.stdout
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verbose_output.seek(0)
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bool_output = verbose_output.readline()
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# generate output with the int flag
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dpgmm_int.fit(X)
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verbose_output = sys.stdout
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verbose_output.seek(0)
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int_output = verbose_output.readline()
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assert_equal(bool_output, int_output)
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finally:
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sys.stdout = old_stdout
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def test_verbose_first_level():
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# simple 3 cluster dataset
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X, y = make_blobs(random_state=1)
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for Model in [DPGMM, VBGMM]:
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dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50,
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verbose=1)
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old_stdout = sys.stdout
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sys.stdout = StringIO()
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try:
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dpgmm.fit(X)
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finally:
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sys.stdout = old_stdout
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def test_verbose_second_level():
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# simple 3 cluster dataset
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X, y = make_blobs(random_state=1)
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for Model in [DPGMM, VBGMM]:
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dpgmm = Model(n_components=10, random_state=1, alpha=20, n_iter=50,
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verbose=2)
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old_stdout = sys.stdout
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sys.stdout = StringIO()
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try:
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dpgmm.fit(X)
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finally:
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sys.stdout = old_stdout
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def test_log_normalize():
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v = np.array([0.1, 0.8, 0.01, 0.09])
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a = np.log(2 * v)
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assert np.allclose(v, log_normalize(a), rtol=0.01)
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def do_model(self, **kwds):
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return VBGMM(verbose=False, **kwds)
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class DPGMMTester(GMMTester):
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model = DPGMM
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do_test_eval = False
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def score(self, g, train_obs):
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_, z = g.score_samples(train_obs)
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return g.lower_bound(train_obs, z)
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class TestDPGMMWithSphericalCovars(unittest.TestCase, DPGMMTester):
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covariance_type = 'spherical'
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setUp = GMMTester._setUp
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class TestDPGMMWithDiagCovars(unittest.TestCase, DPGMMTester):
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covariance_type = 'diag'
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setUp = GMMTester._setUp
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class TestDPGMMWithTiedCovars(unittest.TestCase, DPGMMTester):
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covariance_type = 'tied'
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setUp = GMMTester._setUp
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class TestDPGMMWithFullCovars(unittest.TestCase, DPGMMTester):
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covariance_type = 'full'
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setUp = GMMTester._setUp
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class VBGMMTester(GMMTester):
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model = do_model
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do_test_eval = False
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def score(self, g, train_obs):
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_, z = g.score_samples(train_obs)
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return g.lower_bound(train_obs, z)
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class TestVBGMMWithSphericalCovars(unittest.TestCase, VBGMMTester):
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covariance_type = 'spherical'
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setUp = GMMTester._setUp
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class TestVBGMMWithDiagCovars(unittest.TestCase, VBGMMTester):
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covariance_type = 'diag'
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setUp = GMMTester._setUp
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class TestVBGMMWithTiedCovars(unittest.TestCase, VBGMMTester):
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covariance_type = 'tied'
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setUp = GMMTester._setUp
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class TestVBGMMWithFullCovars(unittest.TestCase, VBGMMTester):
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covariance_type = 'full'
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setUp = GMMTester._setUp
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