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213 lines
7.1 KiB
213 lines
7.1 KiB
"""Test truncated SVD transformer."""
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import numpy as np
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import pytest
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import scipy.sparse as sp
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from sklearn.decomposition import PCA, TruncatedSVD
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import assert_allclose, assert_array_less
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SVD_SOLVERS = ["arpack", "randomized"]
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@pytest.fixture(scope="module")
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def X_sparse():
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# Make an X that looks somewhat like a small tf-idf matrix.
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rng = check_random_state(42)
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X = sp.random(60, 55, density=0.2, format="csr", random_state=rng)
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X.data[:] = 1 + np.log(X.data)
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return X
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@pytest.mark.parametrize("solver", ["randomized"])
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@pytest.mark.parametrize("kind", ("dense", "sparse"))
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def test_solvers(X_sparse, solver, kind):
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X = X_sparse if kind == "sparse" else X_sparse.toarray()
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svd_a = TruncatedSVD(30, algorithm="arpack")
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svd = TruncatedSVD(30, algorithm=solver, random_state=42, n_oversamples=100)
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Xa = svd_a.fit_transform(X)[:, :6]
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Xr = svd.fit_transform(X)[:, :6]
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assert_allclose(Xa, Xr, rtol=2e-3)
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comp_a = np.abs(svd_a.components_)
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comp = np.abs(svd.components_)
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# All elements are equal, but some elements are more equal than others.
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assert_allclose(comp_a[:9], comp[:9], rtol=1e-3)
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assert_allclose(comp_a[9:], comp[9:], atol=1e-2)
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@pytest.mark.parametrize("n_components", (10, 25, 41, 55))
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def test_attributes(n_components, X_sparse):
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n_features = X_sparse.shape[1]
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tsvd = TruncatedSVD(n_components).fit(X_sparse)
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assert tsvd.n_components == n_components
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assert tsvd.components_.shape == (n_components, n_features)
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@pytest.mark.parametrize(
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"algorithm, n_components",
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[
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("arpack", 55),
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("arpack", 56),
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("randomized", 56),
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],
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)
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def test_too_many_components(X_sparse, algorithm, n_components):
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tsvd = TruncatedSVD(n_components=n_components, algorithm=algorithm)
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with pytest.raises(ValueError):
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tsvd.fit(X_sparse)
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@pytest.mark.parametrize("fmt", ("array", "csr", "csc", "coo", "lil"))
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def test_sparse_formats(fmt, X_sparse):
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n_samples = X_sparse.shape[0]
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Xfmt = X_sparse.toarray() if fmt == "dense" else getattr(X_sparse, "to" + fmt)()
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tsvd = TruncatedSVD(n_components=11)
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Xtrans = tsvd.fit_transform(Xfmt)
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assert Xtrans.shape == (n_samples, 11)
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Xtrans = tsvd.transform(Xfmt)
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assert Xtrans.shape == (n_samples, 11)
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@pytest.mark.parametrize("algo", SVD_SOLVERS)
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def test_inverse_transform(algo, X_sparse):
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# We need a lot of components for the reconstruction to be "almost
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# equal" in all positions. XXX Test means or sums instead?
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tsvd = TruncatedSVD(n_components=52, random_state=42, algorithm=algo)
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Xt = tsvd.fit_transform(X_sparse)
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Xinv = tsvd.inverse_transform(Xt)
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assert_allclose(Xinv, X_sparse.toarray(), rtol=1e-1, atol=2e-1)
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def test_integers(X_sparse):
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n_samples = X_sparse.shape[0]
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Xint = X_sparse.astype(np.int64)
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tsvd = TruncatedSVD(n_components=6)
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Xtrans = tsvd.fit_transform(Xint)
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assert Xtrans.shape == (n_samples, tsvd.n_components)
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@pytest.mark.parametrize("kind", ("dense", "sparse"))
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@pytest.mark.parametrize("n_components", [10, 20])
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@pytest.mark.parametrize("solver", SVD_SOLVERS)
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def test_explained_variance(X_sparse, kind, n_components, solver):
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X = X_sparse if kind == "sparse" else X_sparse.toarray()
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svd = TruncatedSVD(n_components, algorithm=solver)
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X_tr = svd.fit_transform(X)
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# Assert that all the values are greater than 0
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assert_array_less(0.0, svd.explained_variance_ratio_)
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# Assert that total explained variance is less than 1
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assert_array_less(svd.explained_variance_ratio_.sum(), 1.0)
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# Test that explained_variance is correct
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total_variance = np.var(X_sparse.toarray(), axis=0).sum()
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variances = np.var(X_tr, axis=0)
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true_explained_variance_ratio = variances / total_variance
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assert_allclose(
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svd.explained_variance_ratio_,
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true_explained_variance_ratio,
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)
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@pytest.mark.parametrize("kind", ("dense", "sparse"))
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@pytest.mark.parametrize("solver", SVD_SOLVERS)
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def test_explained_variance_components_10_20(X_sparse, kind, solver):
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X = X_sparse if kind == "sparse" else X_sparse.toarray()
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svd_10 = TruncatedSVD(10, algorithm=solver, n_iter=10).fit(X)
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svd_20 = TruncatedSVD(20, algorithm=solver, n_iter=10).fit(X)
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# Assert the 1st component is equal
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assert_allclose(
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svd_10.explained_variance_ratio_,
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svd_20.explained_variance_ratio_[:10],
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rtol=5e-3,
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)
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# Assert that 20 components has higher explained variance than 10
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assert (
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svd_20.explained_variance_ratio_.sum() > svd_10.explained_variance_ratio_.sum()
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)
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@pytest.mark.parametrize("solver", SVD_SOLVERS)
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def test_singular_values_consistency(solver, global_random_seed):
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# Check that the TruncatedSVD output has the correct singular values
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rng = np.random.RandomState(global_random_seed)
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n_samples, n_features = 100, 80
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X = rng.randn(n_samples, n_features)
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pca = TruncatedSVD(n_components=2, algorithm=solver, random_state=rng).fit(X)
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# Compare to the Frobenius norm
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X_pca = pca.transform(X)
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assert_allclose(
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np.sum(pca.singular_values_**2.0),
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np.linalg.norm(X_pca, "fro") ** 2.0,
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rtol=1e-2,
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)
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# Compare to the 2-norms of the score vectors
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assert_allclose(
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pca.singular_values_, np.sqrt(np.sum(X_pca**2.0, axis=0)), rtol=1e-2
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)
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@pytest.mark.parametrize("solver", SVD_SOLVERS)
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def test_singular_values_expected(solver, global_random_seed):
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# Set the singular values and see what we get back
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rng = np.random.RandomState(global_random_seed)
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n_samples = 100
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n_features = 110
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X = rng.randn(n_samples, n_features)
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pca = TruncatedSVD(n_components=3, algorithm=solver, random_state=rng)
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X_pca = pca.fit_transform(X)
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X_pca /= np.sqrt(np.sum(X_pca**2.0, axis=0))
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X_pca[:, 0] *= 3.142
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X_pca[:, 1] *= 2.718
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X_hat_pca = np.dot(X_pca, pca.components_)
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pca.fit(X_hat_pca)
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assert_allclose(pca.singular_values_, [3.142, 2.718, 1.0], rtol=1e-14)
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def test_truncated_svd_eq_pca(X_sparse):
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# TruncatedSVD should be equal to PCA on centered data
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X_dense = X_sparse.toarray()
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X_c = X_dense - X_dense.mean(axis=0)
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params = dict(n_components=10, random_state=42)
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svd = TruncatedSVD(algorithm="arpack", **params)
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pca = PCA(svd_solver="arpack", **params)
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Xt_svd = svd.fit_transform(X_c)
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Xt_pca = pca.fit_transform(X_c)
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assert_allclose(Xt_svd, Xt_pca, rtol=1e-9)
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assert_allclose(pca.mean_, 0, atol=1e-9)
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assert_allclose(svd.components_, pca.components_)
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@pytest.mark.parametrize(
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"algorithm, tol", [("randomized", 0.0), ("arpack", 1e-6), ("arpack", 0.0)]
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)
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@pytest.mark.parametrize("kind", ("dense", "sparse"))
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def test_fit_transform(X_sparse, algorithm, tol, kind):
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# fit_transform(X) should equal fit(X).transform(X)
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X = X_sparse if kind == "sparse" else X_sparse.toarray()
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svd = TruncatedSVD(
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n_components=5, n_iter=7, random_state=42, algorithm=algorithm, tol=tol
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)
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X_transformed_1 = svd.fit_transform(X)
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X_transformed_2 = svd.fit(X).transform(X)
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assert_allclose(X_transformed_1, X_transformed_2)
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