1064 lines
33 KiB
Python
1064 lines
33 KiB
Python
import re
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import sys
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import warnings
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from io import StringIO
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import numpy as np
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import pytest
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from scipy import linalg
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from sklearn.base import clone
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from sklearn.decomposition import NMF, MiniBatchNMF, non_negative_factorization
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from sklearn.decomposition import _nmf as nmf # For testing internals
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.utils._testing import (
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assert_allclose,
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assert_almost_equal,
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assert_array_almost_equal,
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assert_array_equal,
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ignore_warnings,
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)
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from sklearn.utils.extmath import squared_norm
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from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
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@pytest.mark.parametrize(
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["Estimator", "solver"],
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[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
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)
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def test_convergence_warning(Estimator, solver):
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convergence_warning = (
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"Maximum number of iterations 1 reached. Increase it to improve convergence."
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)
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A = np.ones((2, 2))
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with pytest.warns(ConvergenceWarning, match=convergence_warning):
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Estimator(max_iter=1, n_components="auto", **solver).fit(A)
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def test_initialize_nn_output():
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# Test that initialization does not return negative values
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rng = np.random.mtrand.RandomState(42)
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data = np.abs(rng.randn(10, 10))
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for init in ("random", "nndsvd", "nndsvda", "nndsvdar"):
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W, H = nmf._initialize_nmf(data, 10, init=init, random_state=0)
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assert not ((W < 0).any() or (H < 0).any())
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# TODO(1.6): remove the warning filter for `n_components`
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@pytest.mark.filterwarnings(
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r"ignore:The multiplicative update \('mu'\) solver cannot update zeros present in"
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r" the initialization",
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"ignore:The default value of `n_components` will change",
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)
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def test_parameter_checking():
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# Here we only check for invalid parameter values that are not already
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# automatically tested in the common tests.
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A = np.ones((2, 2))
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msg = "Invalid beta_loss parameter: solver 'cd' does not handle beta_loss = 1.0"
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with pytest.raises(ValueError, match=msg):
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NMF(solver="cd", beta_loss=1.0).fit(A)
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msg = "Negative values in data passed to"
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with pytest.raises(ValueError, match=msg):
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NMF().fit(-A)
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clf = NMF(2, tol=0.1).fit(A)
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with pytest.raises(ValueError, match=msg):
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clf.transform(-A)
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with pytest.raises(ValueError, match=msg):
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nmf._initialize_nmf(-A, 2, "nndsvd")
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for init in ["nndsvd", "nndsvda", "nndsvdar"]:
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msg = re.escape(
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"init = '{}' can only be used when "
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"n_components <= min(n_samples, n_features)".format(init)
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)
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with pytest.raises(ValueError, match=msg):
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NMF(3, init=init).fit(A)
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with pytest.raises(ValueError, match=msg):
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MiniBatchNMF(3, init=init).fit(A)
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with pytest.raises(ValueError, match=msg):
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nmf._initialize_nmf(A, 3, init)
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def test_initialize_close():
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# Test NNDSVD error
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# Test that _initialize_nmf error is less than the standard deviation of
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# the entries in the matrix.
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rng = np.random.mtrand.RandomState(42)
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A = np.abs(rng.randn(10, 10))
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W, H = nmf._initialize_nmf(A, 10, init="nndsvd")
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error = linalg.norm(np.dot(W, H) - A)
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sdev = linalg.norm(A - A.mean())
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assert error <= sdev
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def test_initialize_variants():
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# Test NNDSVD variants correctness
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# Test that the variants 'nndsvda' and 'nndsvdar' differ from basic
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# 'nndsvd' only where the basic version has zeros.
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rng = np.random.mtrand.RandomState(42)
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data = np.abs(rng.randn(10, 10))
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W0, H0 = nmf._initialize_nmf(data, 10, init="nndsvd")
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Wa, Ha = nmf._initialize_nmf(data, 10, init="nndsvda")
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War, Har = nmf._initialize_nmf(data, 10, init="nndsvdar", random_state=0)
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for ref, evl in ((W0, Wa), (W0, War), (H0, Ha), (H0, Har)):
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assert_almost_equal(evl[ref != 0], ref[ref != 0])
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# ignore UserWarning raised when both solver='mu' and init='nndsvd'
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@ignore_warnings(category=UserWarning)
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@pytest.mark.parametrize(
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["Estimator", "solver"],
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[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
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)
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@pytest.mark.parametrize("init", (None, "nndsvd", "nndsvda", "nndsvdar", "random"))
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@pytest.mark.parametrize("alpha_W", (0.0, 1.0))
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@pytest.mark.parametrize("alpha_H", (0.0, 1.0, "same"))
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def test_nmf_fit_nn_output(Estimator, solver, init, alpha_W, alpha_H):
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# Test that the decomposition does not contain negative values
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A = np.c_[5.0 - np.arange(1, 6), 5.0 + np.arange(1, 6)]
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model = Estimator(
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n_components=2,
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init=init,
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alpha_W=alpha_W,
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alpha_H=alpha_H,
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random_state=0,
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**solver,
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)
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transf = model.fit_transform(A)
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assert not ((model.components_ < 0).any() or (transf < 0).any())
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@pytest.mark.parametrize(
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["Estimator", "solver"],
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[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
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)
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def test_nmf_fit_close(Estimator, solver):
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rng = np.random.mtrand.RandomState(42)
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# Test that the fit is not too far away
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pnmf = Estimator(
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5,
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init="nndsvdar",
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random_state=0,
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max_iter=600,
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**solver,
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)
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X = np.abs(rng.randn(6, 5))
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assert pnmf.fit(X).reconstruction_err_ < 0.1
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def test_nmf_true_reconstruction():
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# Test that the fit is not too far away from an exact solution
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# (by construction)
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n_samples = 15
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n_features = 10
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n_components = 5
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beta_loss = 1
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batch_size = 3
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max_iter = 1000
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rng = np.random.mtrand.RandomState(42)
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W_true = np.zeros([n_samples, n_components])
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W_array = np.abs(rng.randn(n_samples))
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for j in range(n_components):
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W_true[j % n_samples, j] = W_array[j % n_samples]
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H_true = np.zeros([n_components, n_features])
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H_array = np.abs(rng.randn(n_components))
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for j in range(n_features):
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H_true[j % n_components, j] = H_array[j % n_components]
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X = np.dot(W_true, H_true)
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model = NMF(
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n_components=n_components,
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solver="mu",
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beta_loss=beta_loss,
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max_iter=max_iter,
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random_state=0,
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)
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transf = model.fit_transform(X)
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X_calc = np.dot(transf, model.components_)
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assert model.reconstruction_err_ < 0.1
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assert_allclose(X, X_calc)
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mbmodel = MiniBatchNMF(
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n_components=n_components,
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beta_loss=beta_loss,
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batch_size=batch_size,
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random_state=0,
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max_iter=max_iter,
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)
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transf = mbmodel.fit_transform(X)
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X_calc = np.dot(transf, mbmodel.components_)
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assert mbmodel.reconstruction_err_ < 0.1
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assert_allclose(X, X_calc, atol=1)
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@pytest.mark.parametrize("solver", ["cd", "mu"])
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def test_nmf_transform(solver):
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# Test that fit_transform is equivalent to fit.transform for NMF
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# Test that NMF.transform returns close values
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rng = np.random.mtrand.RandomState(42)
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A = np.abs(rng.randn(6, 5))
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m = NMF(
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solver=solver,
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n_components=3,
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init="random",
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random_state=0,
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tol=1e-6,
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)
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ft = m.fit_transform(A)
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t = m.transform(A)
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assert_allclose(ft, t, atol=1e-1)
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def test_minibatch_nmf_transform():
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# Test that fit_transform is equivalent to fit.transform for MiniBatchNMF
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# Only guaranteed with fresh restarts
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rng = np.random.mtrand.RandomState(42)
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A = np.abs(rng.randn(6, 5))
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m = MiniBatchNMF(
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n_components=3,
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random_state=0,
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tol=1e-3,
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fresh_restarts=True,
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)
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ft = m.fit_transform(A)
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t = m.transform(A)
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assert_allclose(ft, t)
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@pytest.mark.parametrize(
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["Estimator", "solver"],
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[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
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)
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def test_nmf_transform_custom_init(Estimator, solver):
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# Smoke test that checks if NMF.transform works with custom initialization
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random_state = np.random.RandomState(0)
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A = np.abs(random_state.randn(6, 5))
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n_components = 4
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avg = np.sqrt(A.mean() / n_components)
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H_init = np.abs(avg * random_state.randn(n_components, 5))
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W_init = np.abs(avg * random_state.randn(6, n_components))
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m = Estimator(
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n_components=n_components, init="custom", random_state=0, tol=1e-3, **solver
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)
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m.fit_transform(A, W=W_init, H=H_init)
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m.transform(A)
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@pytest.mark.parametrize("solver", ("cd", "mu"))
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def test_nmf_inverse_transform(solver):
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# Test that NMF.inverse_transform returns close values
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random_state = np.random.RandomState(0)
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A = np.abs(random_state.randn(6, 4))
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m = NMF(
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solver=solver,
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n_components=4,
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init="random",
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random_state=0,
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max_iter=1000,
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)
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ft = m.fit_transform(A)
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A_new = m.inverse_transform(ft)
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assert_array_almost_equal(A, A_new, decimal=2)
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# TODO(1.6): remove the warning filter
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@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
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def test_mbnmf_inverse_transform():
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# Test that MiniBatchNMF.transform followed by MiniBatchNMF.inverse_transform
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# is close to the identity
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rng = np.random.RandomState(0)
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A = np.abs(rng.randn(6, 4))
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nmf = MiniBatchNMF(
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random_state=rng,
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max_iter=500,
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init="nndsvdar",
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fresh_restarts=True,
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)
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ft = nmf.fit_transform(A)
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A_new = nmf.inverse_transform(ft)
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assert_allclose(A, A_new, rtol=1e-3, atol=1e-2)
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@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
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def test_n_components_greater_n_features(Estimator):
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# Smoke test for the case of more components than features.
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rng = np.random.mtrand.RandomState(42)
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A = np.abs(rng.randn(30, 10))
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Estimator(n_components=15, random_state=0, tol=1e-2).fit(A)
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@pytest.mark.parametrize(
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["Estimator", "solver"],
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[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
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)
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@pytest.mark.parametrize("sparse_container", CSC_CONTAINERS + CSR_CONTAINERS)
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@pytest.mark.parametrize("alpha_W", (0.0, 1.0))
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@pytest.mark.parametrize("alpha_H", (0.0, 1.0, "same"))
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def test_nmf_sparse_input(Estimator, solver, sparse_container, alpha_W, alpha_H):
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# Test that sparse matrices are accepted as input
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rng = np.random.mtrand.RandomState(42)
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A = np.abs(rng.randn(10, 10))
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A[:, 2 * np.arange(5)] = 0
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A_sparse = sparse_container(A)
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est1 = Estimator(
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n_components=5,
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init="random",
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alpha_W=alpha_W,
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alpha_H=alpha_H,
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random_state=0,
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tol=0,
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max_iter=100,
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**solver,
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)
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est2 = clone(est1)
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W1 = est1.fit_transform(A)
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W2 = est2.fit_transform(A_sparse)
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H1 = est1.components_
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H2 = est2.components_
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assert_allclose(W1, W2)
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assert_allclose(H1, H2)
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@pytest.mark.parametrize(
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["Estimator", "solver"],
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[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
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)
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@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
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def test_nmf_sparse_transform(Estimator, solver, csc_container):
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# Test that transform works on sparse data. Issue #2124
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rng = np.random.mtrand.RandomState(42)
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A = np.abs(rng.randn(3, 2))
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A[1, 1] = 0
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A = csc_container(A)
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model = Estimator(random_state=0, n_components=2, max_iter=400, **solver)
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A_fit_tr = model.fit_transform(A)
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A_tr = model.transform(A)
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assert_allclose(A_fit_tr, A_tr, atol=1e-1)
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# TODO(1.6): remove the warning filter
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@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
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@pytest.mark.parametrize("init", ["random", "nndsvd"])
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@pytest.mark.parametrize("solver", ("cd", "mu"))
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@pytest.mark.parametrize("alpha_W", (0.0, 1.0))
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@pytest.mark.parametrize("alpha_H", (0.0, 1.0, "same"))
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def test_non_negative_factorization_consistency(init, solver, alpha_W, alpha_H):
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# Test that the function is called in the same way, either directly
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# or through the NMF class
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max_iter = 500
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rng = np.random.mtrand.RandomState(42)
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A = np.abs(rng.randn(10, 10))
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A[:, 2 * np.arange(5)] = 0
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W_nmf, H, _ = non_negative_factorization(
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A,
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init=init,
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solver=solver,
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max_iter=max_iter,
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alpha_W=alpha_W,
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alpha_H=alpha_H,
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random_state=1,
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tol=1e-2,
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)
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W_nmf_2, H, _ = non_negative_factorization(
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A,
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H=H,
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update_H=False,
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init=init,
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solver=solver,
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max_iter=max_iter,
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alpha_W=alpha_W,
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alpha_H=alpha_H,
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random_state=1,
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tol=1e-2,
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)
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model_class = NMF(
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init=init,
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solver=solver,
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max_iter=max_iter,
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alpha_W=alpha_W,
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alpha_H=alpha_H,
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random_state=1,
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tol=1e-2,
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)
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W_cls = model_class.fit_transform(A)
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W_cls_2 = model_class.transform(A)
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assert_allclose(W_nmf, W_cls)
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assert_allclose(W_nmf_2, W_cls_2)
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def test_non_negative_factorization_checking():
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# Note that the validity of parameter types and range of possible values
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# for scalar numerical or str parameters is already checked in the common
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# tests. Here we only check for problems that cannot be captured by simple
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# declarative constraints on the valid parameter values.
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A = np.ones((2, 2))
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# Test parameters checking in public function
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nnmf = non_negative_factorization
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msg = re.escape("Negative values in data passed to NMF (input H)")
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with pytest.raises(ValueError, match=msg):
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nnmf(A, A, -A, 2, init="custom")
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msg = re.escape("Negative values in data passed to NMF (input W)")
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with pytest.raises(ValueError, match=msg):
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nnmf(A, -A, A, 2, init="custom")
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msg = re.escape("Array passed to NMF (input H) is full of zeros")
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with pytest.raises(ValueError, match=msg):
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nnmf(A, A, 0 * A, 2, init="custom")
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def _beta_divergence_dense(X, W, H, beta):
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"""Compute the beta-divergence of X and W.H for dense array only.
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Used as a reference for testing nmf._beta_divergence.
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"""
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WH = np.dot(W, H)
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if beta == 2:
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return squared_norm(X - WH) / 2
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WH_Xnonzero = WH[X != 0]
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X_nonzero = X[X != 0]
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np.maximum(WH_Xnonzero, 1e-9, out=WH_Xnonzero)
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if beta == 1:
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res = np.sum(X_nonzero * np.log(X_nonzero / WH_Xnonzero))
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res += WH.sum() - X.sum()
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elif beta == 0:
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div = X_nonzero / WH_Xnonzero
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res = np.sum(div) - X.size - np.sum(np.log(div))
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else:
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res = (X_nonzero**beta).sum()
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res += (beta - 1) * (WH**beta).sum()
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res -= beta * (X_nonzero * (WH_Xnonzero ** (beta - 1))).sum()
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res /= beta * (beta - 1)
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return res
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
|
def test_beta_divergence(csr_container):
|
|
# Compare _beta_divergence with the reference _beta_divergence_dense
|
|
n_samples = 20
|
|
n_features = 10
|
|
n_components = 5
|
|
beta_losses = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0]
|
|
|
|
# initialization
|
|
rng = np.random.mtrand.RandomState(42)
|
|
X = rng.randn(n_samples, n_features)
|
|
np.clip(X, 0, None, out=X)
|
|
X_csr = csr_container(X)
|
|
W, H = nmf._initialize_nmf(X, n_components, init="random", random_state=42)
|
|
|
|
for beta in beta_losses:
|
|
ref = _beta_divergence_dense(X, W, H, beta)
|
|
loss = nmf._beta_divergence(X, W, H, beta)
|
|
loss_csr = nmf._beta_divergence(X_csr, W, H, beta)
|
|
|
|
assert_almost_equal(ref, loss, decimal=7)
|
|
assert_almost_equal(ref, loss_csr, decimal=7)
|
|
|
|
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
|
def test_special_sparse_dot(csr_container):
|
|
# Test the function that computes np.dot(W, H), only where X is non zero.
|
|
n_samples = 10
|
|
n_features = 5
|
|
n_components = 3
|
|
rng = np.random.mtrand.RandomState(42)
|
|
X = rng.randn(n_samples, n_features)
|
|
np.clip(X, 0, None, out=X)
|
|
X_csr = csr_container(X)
|
|
|
|
W = np.abs(rng.randn(n_samples, n_components))
|
|
H = np.abs(rng.randn(n_components, n_features))
|
|
|
|
WH_safe = nmf._special_sparse_dot(W, H, X_csr)
|
|
WH = nmf._special_sparse_dot(W, H, X)
|
|
|
|
# test that both results have same values, in X_csr nonzero elements
|
|
ii, jj = X_csr.nonzero()
|
|
WH_safe_data = np.asarray(WH_safe[ii, jj]).ravel()
|
|
assert_array_almost_equal(WH_safe_data, WH[ii, jj], decimal=10)
|
|
|
|
# test that WH_safe and X_csr have the same sparse structure
|
|
assert_array_equal(WH_safe.indices, X_csr.indices)
|
|
assert_array_equal(WH_safe.indptr, X_csr.indptr)
|
|
assert_array_equal(WH_safe.shape, X_csr.shape)
|
|
|
|
|
|
@ignore_warnings(category=ConvergenceWarning)
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
|
def test_nmf_multiplicative_update_sparse(csr_container):
|
|
# Compare sparse and dense input in multiplicative update NMF
|
|
# Also test continuity of the results with respect to beta_loss parameter
|
|
n_samples = 20
|
|
n_features = 10
|
|
n_components = 5
|
|
alpha = 0.1
|
|
l1_ratio = 0.5
|
|
n_iter = 20
|
|
|
|
# initialization
|
|
rng = np.random.mtrand.RandomState(1337)
|
|
X = rng.randn(n_samples, n_features)
|
|
X = np.abs(X)
|
|
X_csr = csr_container(X)
|
|
W0, H0 = nmf._initialize_nmf(X, n_components, init="random", random_state=42)
|
|
|
|
for beta_loss in (-1.2, 0, 0.2, 1.0, 2.0, 2.5):
|
|
# Reference with dense array X
|
|
W, H = W0.copy(), H0.copy()
|
|
W1, H1, _ = non_negative_factorization(
|
|
X,
|
|
W,
|
|
H,
|
|
n_components,
|
|
init="custom",
|
|
update_H=True,
|
|
solver="mu",
|
|
beta_loss=beta_loss,
|
|
max_iter=n_iter,
|
|
alpha_W=alpha,
|
|
l1_ratio=l1_ratio,
|
|
random_state=42,
|
|
)
|
|
|
|
# Compare with sparse X
|
|
W, H = W0.copy(), H0.copy()
|
|
W2, H2, _ = non_negative_factorization(
|
|
X_csr,
|
|
W,
|
|
H,
|
|
n_components,
|
|
init="custom",
|
|
update_H=True,
|
|
solver="mu",
|
|
beta_loss=beta_loss,
|
|
max_iter=n_iter,
|
|
alpha_W=alpha,
|
|
l1_ratio=l1_ratio,
|
|
random_state=42,
|
|
)
|
|
|
|
assert_allclose(W1, W2, atol=1e-7)
|
|
assert_allclose(H1, H2, atol=1e-7)
|
|
|
|
# Compare with almost same beta_loss, since some values have a specific
|
|
# behavior, but the results should be continuous w.r.t beta_loss
|
|
beta_loss -= 1.0e-5
|
|
W, H = W0.copy(), H0.copy()
|
|
W3, H3, _ = non_negative_factorization(
|
|
X_csr,
|
|
W,
|
|
H,
|
|
n_components,
|
|
init="custom",
|
|
update_H=True,
|
|
solver="mu",
|
|
beta_loss=beta_loss,
|
|
max_iter=n_iter,
|
|
alpha_W=alpha,
|
|
l1_ratio=l1_ratio,
|
|
random_state=42,
|
|
)
|
|
|
|
assert_allclose(W1, W3, atol=1e-4)
|
|
assert_allclose(H1, H3, atol=1e-4)
|
|
|
|
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
|
def test_nmf_negative_beta_loss(csr_container):
|
|
# Test that an error is raised if beta_loss < 0 and X contains zeros.
|
|
# Test that the output has not NaN values when the input contains zeros.
|
|
n_samples = 6
|
|
n_features = 5
|
|
n_components = 3
|
|
|
|
rng = np.random.mtrand.RandomState(42)
|
|
X = rng.randn(n_samples, n_features)
|
|
np.clip(X, 0, None, out=X)
|
|
X_csr = csr_container(X)
|
|
|
|
def _assert_nmf_no_nan(X, beta_loss):
|
|
W, H, _ = non_negative_factorization(
|
|
X,
|
|
init="random",
|
|
n_components=n_components,
|
|
solver="mu",
|
|
beta_loss=beta_loss,
|
|
random_state=0,
|
|
max_iter=1000,
|
|
)
|
|
assert not np.any(np.isnan(W))
|
|
assert not np.any(np.isnan(H))
|
|
|
|
msg = "When beta_loss <= 0 and X contains zeros, the solver may diverge."
|
|
for beta_loss in (-0.6, 0.0):
|
|
with pytest.raises(ValueError, match=msg):
|
|
_assert_nmf_no_nan(X, beta_loss)
|
|
_assert_nmf_no_nan(X + 1e-9, beta_loss)
|
|
|
|
for beta_loss in (0.2, 1.0, 1.2, 2.0, 2.5):
|
|
_assert_nmf_no_nan(X, beta_loss)
|
|
_assert_nmf_no_nan(X_csr, beta_loss)
|
|
|
|
|
|
# TODO(1.6): remove the warning filter
|
|
@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
|
|
@pytest.mark.parametrize("beta_loss", [-0.5, 0.0])
|
|
def test_minibatch_nmf_negative_beta_loss(beta_loss):
|
|
"""Check that an error is raised if beta_loss < 0 and X contains zeros."""
|
|
rng = np.random.RandomState(0)
|
|
X = rng.normal(size=(6, 5))
|
|
X[X < 0] = 0
|
|
|
|
nmf = MiniBatchNMF(beta_loss=beta_loss, random_state=0)
|
|
|
|
msg = "When beta_loss <= 0 and X contains zeros, the solver may diverge."
|
|
with pytest.raises(ValueError, match=msg):
|
|
nmf.fit(X)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
["Estimator", "solver"],
|
|
[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
|
|
)
|
|
def test_nmf_regularization(Estimator, solver):
|
|
# Test the effect of L1 and L2 regularizations
|
|
n_samples = 6
|
|
n_features = 5
|
|
n_components = 3
|
|
rng = np.random.mtrand.RandomState(42)
|
|
X = np.abs(rng.randn(n_samples, n_features))
|
|
|
|
# L1 regularization should increase the number of zeros
|
|
l1_ratio = 1.0
|
|
regul = Estimator(
|
|
n_components=n_components,
|
|
alpha_W=0.5,
|
|
l1_ratio=l1_ratio,
|
|
random_state=42,
|
|
**solver,
|
|
)
|
|
model = Estimator(
|
|
n_components=n_components,
|
|
alpha_W=0.0,
|
|
l1_ratio=l1_ratio,
|
|
random_state=42,
|
|
**solver,
|
|
)
|
|
|
|
W_regul = regul.fit_transform(X)
|
|
W_model = model.fit_transform(X)
|
|
|
|
H_regul = regul.components_
|
|
H_model = model.components_
|
|
|
|
eps = np.finfo(np.float64).eps
|
|
W_regul_n_zeros = W_regul[W_regul <= eps].size
|
|
W_model_n_zeros = W_model[W_model <= eps].size
|
|
H_regul_n_zeros = H_regul[H_regul <= eps].size
|
|
H_model_n_zeros = H_model[H_model <= eps].size
|
|
|
|
assert W_regul_n_zeros > W_model_n_zeros
|
|
assert H_regul_n_zeros > H_model_n_zeros
|
|
|
|
# L2 regularization should decrease the sum of the squared norm
|
|
# of the matrices W and H
|
|
l1_ratio = 0.0
|
|
regul = Estimator(
|
|
n_components=n_components,
|
|
alpha_W=0.5,
|
|
l1_ratio=l1_ratio,
|
|
random_state=42,
|
|
**solver,
|
|
)
|
|
model = Estimator(
|
|
n_components=n_components,
|
|
alpha_W=0.0,
|
|
l1_ratio=l1_ratio,
|
|
random_state=42,
|
|
**solver,
|
|
)
|
|
|
|
W_regul = regul.fit_transform(X)
|
|
W_model = model.fit_transform(X)
|
|
|
|
H_regul = regul.components_
|
|
H_model = model.components_
|
|
|
|
assert (linalg.norm(W_model)) ** 2.0 + (linalg.norm(H_model)) ** 2.0 > (
|
|
linalg.norm(W_regul)
|
|
) ** 2.0 + (linalg.norm(H_regul)) ** 2.0
|
|
|
|
|
|
@ignore_warnings(category=ConvergenceWarning)
|
|
@pytest.mark.parametrize("solver", ("cd", "mu"))
|
|
def test_nmf_decreasing(solver):
|
|
# test that the objective function is decreasing at each iteration
|
|
n_samples = 20
|
|
n_features = 15
|
|
n_components = 10
|
|
alpha = 0.1
|
|
l1_ratio = 0.5
|
|
tol = 0.0
|
|
|
|
# initialization
|
|
rng = np.random.mtrand.RandomState(42)
|
|
X = rng.randn(n_samples, n_features)
|
|
np.abs(X, X)
|
|
W0, H0 = nmf._initialize_nmf(X, n_components, init="random", random_state=42)
|
|
|
|
for beta_loss in (-1.2, 0, 0.2, 1.0, 2.0, 2.5):
|
|
if solver != "mu" and beta_loss != 2:
|
|
# not implemented
|
|
continue
|
|
W, H = W0.copy(), H0.copy()
|
|
previous_loss = None
|
|
for _ in range(30):
|
|
# one more iteration starting from the previous results
|
|
W, H, _ = non_negative_factorization(
|
|
X,
|
|
W,
|
|
H,
|
|
beta_loss=beta_loss,
|
|
init="custom",
|
|
n_components=n_components,
|
|
max_iter=1,
|
|
alpha_W=alpha,
|
|
solver=solver,
|
|
tol=tol,
|
|
l1_ratio=l1_ratio,
|
|
verbose=0,
|
|
random_state=0,
|
|
update_H=True,
|
|
)
|
|
|
|
loss = (
|
|
nmf._beta_divergence(X, W, H, beta_loss)
|
|
+ alpha * l1_ratio * n_features * W.sum()
|
|
+ alpha * l1_ratio * n_samples * H.sum()
|
|
+ alpha * (1 - l1_ratio) * n_features * (W**2).sum()
|
|
+ alpha * (1 - l1_ratio) * n_samples * (H**2).sum()
|
|
)
|
|
if previous_loss is not None:
|
|
assert previous_loss > loss
|
|
previous_loss = loss
|
|
|
|
|
|
def test_nmf_underflow():
|
|
# Regression test for an underflow issue in _beta_divergence
|
|
rng = np.random.RandomState(0)
|
|
n_samples, n_features, n_components = 10, 2, 2
|
|
X = np.abs(rng.randn(n_samples, n_features)) * 10
|
|
W = np.abs(rng.randn(n_samples, n_components)) * 10
|
|
H = np.abs(rng.randn(n_components, n_features))
|
|
|
|
X[0, 0] = 0
|
|
ref = nmf._beta_divergence(X, W, H, beta=1.0)
|
|
X[0, 0] = 1e-323
|
|
res = nmf._beta_divergence(X, W, H, beta=1.0)
|
|
assert_almost_equal(res, ref)
|
|
|
|
|
|
# TODO(1.6): remove the warning filter
|
|
@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
|
|
@pytest.mark.parametrize(
|
|
"dtype_in, dtype_out",
|
|
[
|
|
(np.float32, np.float32),
|
|
(np.float64, np.float64),
|
|
(np.int32, np.float64),
|
|
(np.int64, np.float64),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
["Estimator", "solver"],
|
|
[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
|
|
)
|
|
def test_nmf_dtype_match(Estimator, solver, dtype_in, dtype_out):
|
|
# Check that NMF preserves dtype (float32 and float64)
|
|
X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False)
|
|
np.abs(X, out=X)
|
|
|
|
nmf = Estimator(
|
|
alpha_W=1.0,
|
|
alpha_H=1.0,
|
|
tol=1e-2,
|
|
random_state=0,
|
|
**solver,
|
|
)
|
|
|
|
assert nmf.fit(X).transform(X).dtype == dtype_out
|
|
assert nmf.fit_transform(X).dtype == dtype_out
|
|
assert nmf.components_.dtype == dtype_out
|
|
|
|
|
|
# TODO(1.6): remove the warning filter
|
|
@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
|
|
@pytest.mark.parametrize(
|
|
["Estimator", "solver"],
|
|
[[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]],
|
|
)
|
|
def test_nmf_float32_float64_consistency(Estimator, solver):
|
|
# Check that the result of NMF is the same between float32 and float64
|
|
X = np.random.RandomState(0).randn(50, 7)
|
|
np.abs(X, out=X)
|
|
nmf32 = Estimator(random_state=0, tol=1e-3, **solver)
|
|
W32 = nmf32.fit_transform(X.astype(np.float32))
|
|
nmf64 = Estimator(random_state=0, tol=1e-3, **solver)
|
|
W64 = nmf64.fit_transform(X)
|
|
|
|
assert_allclose(W32, W64, atol=1e-5)
|
|
|
|
|
|
# TODO(1.6): remove the warning filter
|
|
@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
|
|
@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
|
|
def test_nmf_custom_init_dtype_error(Estimator):
|
|
# Check that an error is raise if custom H and/or W don't have the same
|
|
# dtype as X.
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((20, 15))
|
|
H = rng.random_sample((15, 15)).astype(np.float32)
|
|
W = rng.random_sample((20, 15))
|
|
|
|
with pytest.raises(TypeError, match="should have the same dtype as X"):
|
|
Estimator(init="custom").fit(X, H=H, W=W)
|
|
|
|
with pytest.raises(TypeError, match="should have the same dtype as X"):
|
|
non_negative_factorization(X, H=H, update_H=False)
|
|
|
|
|
|
@pytest.mark.parametrize("beta_loss", [-0.5, 0, 0.5, 1, 1.5, 2, 2.5])
|
|
def test_nmf_minibatchnmf_equivalence(beta_loss):
|
|
# Test that MiniBatchNMF is equivalent to NMF when batch_size = n_samples and
|
|
# forget_factor 0.0 (stopping criterion put aside)
|
|
rng = np.random.mtrand.RandomState(42)
|
|
X = np.abs(rng.randn(48, 5))
|
|
|
|
nmf = NMF(
|
|
n_components=5,
|
|
beta_loss=beta_loss,
|
|
solver="mu",
|
|
random_state=0,
|
|
tol=0,
|
|
)
|
|
mbnmf = MiniBatchNMF(
|
|
n_components=5,
|
|
beta_loss=beta_loss,
|
|
random_state=0,
|
|
tol=0,
|
|
max_no_improvement=None,
|
|
batch_size=X.shape[0],
|
|
forget_factor=0.0,
|
|
)
|
|
W = nmf.fit_transform(X)
|
|
mbW = mbnmf.fit_transform(X)
|
|
assert_allclose(W, mbW)
|
|
|
|
|
|
def test_minibatch_nmf_partial_fit():
|
|
# Check fit / partial_fit equivalence. Applicable only with fresh restarts.
|
|
rng = np.random.mtrand.RandomState(42)
|
|
X = np.abs(rng.randn(100, 5))
|
|
|
|
n_components = 5
|
|
batch_size = 10
|
|
max_iter = 2
|
|
|
|
mbnmf1 = MiniBatchNMF(
|
|
n_components=n_components,
|
|
init="custom",
|
|
random_state=0,
|
|
max_iter=max_iter,
|
|
batch_size=batch_size,
|
|
tol=0,
|
|
max_no_improvement=None,
|
|
fresh_restarts=False,
|
|
)
|
|
mbnmf2 = MiniBatchNMF(n_components=n_components, init="custom", random_state=0)
|
|
|
|
# Force the same init of H (W is recomputed anyway) to be able to compare results.
|
|
W, H = nmf._initialize_nmf(
|
|
X, n_components=n_components, init="random", random_state=0
|
|
)
|
|
|
|
mbnmf1.fit(X, W=W, H=H)
|
|
for i in range(max_iter):
|
|
for j in range(batch_size):
|
|
mbnmf2.partial_fit(X[j : j + batch_size], W=W[:batch_size], H=H)
|
|
|
|
assert mbnmf1.n_steps_ == mbnmf2.n_steps_
|
|
assert_allclose(mbnmf1.components_, mbnmf2.components_)
|
|
|
|
|
|
def test_feature_names_out():
|
|
"""Check feature names out for NMF."""
|
|
random_state = np.random.RandomState(0)
|
|
X = np.abs(random_state.randn(10, 4))
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nmf = NMF(n_components=3).fit(X)
|
|
|
|
names = nmf.get_feature_names_out()
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|
assert_array_equal([f"nmf{i}" for i in range(3)], names)
|
|
|
|
|
|
# TODO(1.6): remove the warning filter
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|
@pytest.mark.filterwarnings("ignore:The default value of `n_components` will change")
|
|
def test_minibatch_nmf_verbose():
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|
# Check verbose mode of MiniBatchNMF for better coverage.
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|
A = np.random.RandomState(0).random_sample((100, 10))
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|
nmf = MiniBatchNMF(tol=1e-2, random_state=0, verbose=1)
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|
old_stdout = sys.stdout
|
|
sys.stdout = StringIO()
|
|
try:
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|
nmf.fit(A)
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|
finally:
|
|
sys.stdout = old_stdout
|
|
|
|
|
|
# TODO(1.7): remove this test
|
|
@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
|
|
def test_NMF_inverse_transform_Xt_deprecation(Estimator):
|
|
rng = np.random.RandomState(42)
|
|
A = np.abs(rng.randn(6, 5))
|
|
est = Estimator(
|
|
n_components=3,
|
|
init="random",
|
|
random_state=0,
|
|
tol=1e-6,
|
|
)
|
|
X = est.fit_transform(A)
|
|
|
|
with pytest.raises(TypeError, match="Missing required positional argument"):
|
|
est.inverse_transform()
|
|
|
|
with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only"):
|
|
est.inverse_transform(X=X, Xt=X)
|
|
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.simplefilter("error")
|
|
est.inverse_transform(X)
|
|
|
|
with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
|
|
est.inverse_transform(Xt=X)
|
|
|
|
|
|
@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
|
|
def test_nmf_n_components_auto(Estimator):
|
|
# Check that n_components is correctly inferred
|
|
# from the provided custom initialization.
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((6, 5))
|
|
W = rng.random_sample((6, 2))
|
|
H = rng.random_sample((2, 5))
|
|
est = Estimator(
|
|
n_components="auto",
|
|
init="custom",
|
|
random_state=0,
|
|
tol=1e-6,
|
|
)
|
|
est.fit_transform(X, W=W, H=H)
|
|
assert est._n_components == H.shape[0]
|
|
|
|
|
|
def test_nmf_non_negative_factorization_n_components_auto():
|
|
# Check that n_components is correctly inferred from the provided
|
|
# custom initialization.
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((6, 5))
|
|
W_init = rng.random_sample((6, 2))
|
|
H_init = rng.random_sample((2, 5))
|
|
W, H, _ = non_negative_factorization(
|
|
X, W=W_init, H=H_init, init="custom", n_components="auto"
|
|
)
|
|
assert H.shape == H_init.shape
|
|
assert W.shape == W_init.shape
|
|
|
|
|
|
# TODO(1.6): remove
|
|
def test_nmf_n_components_default_value_warning():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((6, 5))
|
|
H = rng.random_sample((2, 5))
|
|
with pytest.warns(
|
|
FutureWarning, match="The default value of `n_components` will change from"
|
|
):
|
|
non_negative_factorization(X, H=H)
|
|
|
|
|
|
def test_nmf_n_components_auto_no_h_update():
|
|
# Tests that non_negative_factorization does not fail when setting
|
|
# n_components="auto" also tests that the inferred n_component
|
|
# value is the right one.
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((6, 5))
|
|
H_true = rng.random_sample((2, 5))
|
|
W, H, _ = non_negative_factorization(
|
|
X, H=H_true, n_components="auto", update_H=False
|
|
) # should not fail
|
|
assert_allclose(H, H_true)
|
|
assert W.shape == (X.shape[0], H_true.shape[0])
|
|
|
|
|
|
def test_nmf_w_h_not_used_warning():
|
|
# Check that warnings are raised if user provided W and H are not used
|
|
# and initialization overrides value of W or H
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((6, 5))
|
|
W_init = rng.random_sample((6, 2))
|
|
H_init = rng.random_sample((2, 5))
|
|
with pytest.warns(
|
|
RuntimeWarning,
|
|
match="When init!='custom', provided W or H are ignored",
|
|
):
|
|
non_negative_factorization(X, H=H_init, update_H=True, n_components="auto")
|
|
|
|
with pytest.warns(
|
|
RuntimeWarning,
|
|
match="When init!='custom', provided W or H are ignored",
|
|
):
|
|
non_negative_factorization(
|
|
X, W=W_init, H=H_init, update_H=True, n_components="auto"
|
|
)
|
|
|
|
with pytest.warns(
|
|
RuntimeWarning, match="When update_H=False, the provided initial W is not used."
|
|
):
|
|
# When update_H is False, W is ignored regardless of init
|
|
# TODO: use the provided W when init="custom".
|
|
non_negative_factorization(
|
|
X, W=W_init, H=H_init, update_H=False, n_components="auto"
|
|
)
|
|
|
|
|
|
def test_nmf_custom_init_shape_error():
|
|
# Check that an informative error is raised when custom initialization does not
|
|
# have the right shape
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((6, 5))
|
|
H = rng.random_sample((2, 5))
|
|
nmf = NMF(n_components=2, init="custom", random_state=0)
|
|
|
|
with pytest.raises(ValueError, match="Array with wrong first dimension passed"):
|
|
nmf.fit(X, H=H, W=rng.random_sample((5, 2)))
|
|
|
|
with pytest.raises(ValueError, match="Array with wrong second dimension passed"):
|
|
nmf.fit(X, H=H, W=rng.random_sample((6, 3)))
|