86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
# Author: Christian Osendorfer <osendorf@gmail.com>
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# Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD3
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import numpy as np
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from sklearn.utils.testing import assert_warns
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_less
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.decomposition import FactorAnalysis
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from sklearn.utils.testing import ignore_warnings
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# Ignore warnings from switching to more power iterations in randomized_svd
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@ignore_warnings
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def test_factor_analysis():
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# Test FactorAnalysis ability to recover the data covariance structure
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rng = np.random.RandomState(0)
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n_samples, n_features, n_components = 20, 5, 3
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# Some random settings for the generative model
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W = rng.randn(n_components, n_features)
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# latent variable of dim 3, 20 of it
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h = rng.randn(n_samples, n_components)
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# using gamma to model different noise variance
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# per component
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noise = rng.gamma(1, size=n_features) * rng.randn(n_samples, n_features)
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# generate observations
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# wlog, mean is 0
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X = np.dot(h, W) + noise
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assert_raises(ValueError, FactorAnalysis, svd_method='foo')
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fa_fail = FactorAnalysis()
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fa_fail.svd_method = 'foo'
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assert_raises(ValueError, fa_fail.fit, X)
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fas = []
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for method in ['randomized', 'lapack']:
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fa = FactorAnalysis(n_components=n_components, svd_method=method)
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fa.fit(X)
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fas.append(fa)
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X_t = fa.transform(X)
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assert_equal(X_t.shape, (n_samples, n_components))
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assert_almost_equal(fa.loglike_[-1], fa.score_samples(X).sum())
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assert_almost_equal(fa.score_samples(X).mean(), fa.score(X))
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diff = np.all(np.diff(fa.loglike_))
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assert_greater(diff, 0., 'Log likelihood dif not increase')
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# Sample Covariance
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scov = np.cov(X, rowvar=0., bias=1.)
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# Model Covariance
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mcov = fa.get_covariance()
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diff = np.sum(np.abs(scov - mcov)) / W.size
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assert_less(diff, 0.1, "Mean absolute difference is %f" % diff)
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fa = FactorAnalysis(n_components=n_components,
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noise_variance_init=np.ones(n_features))
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assert_raises(ValueError, fa.fit, X[:, :2])
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f = lambda x, y: np.abs(getattr(x, y)) # sign will not be equal
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fa1, fa2 = fas
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for attr in ['loglike_', 'components_', 'noise_variance_']:
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assert_almost_equal(f(fa1, attr), f(fa2, attr))
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fa1.max_iter = 1
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fa1.verbose = True
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assert_warns(ConvergenceWarning, fa1.fit, X)
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# Test get_covariance and get_precision with n_components == n_features
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# with n_components < n_features and with n_components == 0
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for n_components in [0, 2, X.shape[1]]:
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fa.n_components = n_components
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fa.fit(X)
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cov = fa.get_covariance()
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precision = fa.get_precision()
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assert_array_almost_equal(np.dot(cov, precision),
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np.eye(X.shape[1]), 12)
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