102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
import numpy as np
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import pytest
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from sklearn.metrics import euclidean_distances
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from sklearn.neighbors import KNeighborsTransformer, RadiusNeighborsTransformer
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from sklearn.neighbors._base import _is_sorted_by_data
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from sklearn.utils._testing import assert_array_equal
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def test_transformer_result():
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# Test the number of neighbors returned
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n_neighbors = 5
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n_samples_fit = 20
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n_queries = 18
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n_features = 10
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rng = np.random.RandomState(42)
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X = rng.randn(n_samples_fit, n_features)
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X2 = rng.randn(n_queries, n_features)
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radius = np.percentile(euclidean_distances(X), 10)
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# with n_neighbors
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for mode in ["distance", "connectivity"]:
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add_one = mode == "distance"
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nnt = KNeighborsTransformer(n_neighbors=n_neighbors, mode=mode)
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Xt = nnt.fit_transform(X)
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assert Xt.shape == (n_samples_fit, n_samples_fit)
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assert Xt.data.shape == (n_samples_fit * (n_neighbors + add_one),)
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assert Xt.format == "csr"
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assert _is_sorted_by_data(Xt)
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X2t = nnt.transform(X2)
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assert X2t.shape == (n_queries, n_samples_fit)
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assert X2t.data.shape == (n_queries * (n_neighbors + add_one),)
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assert X2t.format == "csr"
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assert _is_sorted_by_data(X2t)
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# with radius
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for mode in ["distance", "connectivity"]:
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add_one = mode == "distance"
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nnt = RadiusNeighborsTransformer(radius=radius, mode=mode)
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Xt = nnt.fit_transform(X)
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assert Xt.shape == (n_samples_fit, n_samples_fit)
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assert not Xt.data.shape == (n_samples_fit * (n_neighbors + add_one),)
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assert Xt.format == "csr"
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assert _is_sorted_by_data(Xt)
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X2t = nnt.transform(X2)
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assert X2t.shape == (n_queries, n_samples_fit)
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assert not X2t.data.shape == (n_queries * (n_neighbors + add_one),)
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assert X2t.format == "csr"
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assert _is_sorted_by_data(X2t)
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def _has_explicit_diagonal(X):
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"""Return True if the diagonal is explicitly stored"""
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X = X.tocoo()
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explicit = X.row[X.row == X.col]
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return len(explicit) == X.shape[0]
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def test_explicit_diagonal():
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# Test that the diagonal is explicitly stored in the sparse graph
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n_neighbors = 5
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n_samples_fit, n_samples_transform, n_features = 20, 18, 10
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rng = np.random.RandomState(42)
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X = rng.randn(n_samples_fit, n_features)
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X2 = rng.randn(n_samples_transform, n_features)
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nnt = KNeighborsTransformer(n_neighbors=n_neighbors)
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Xt = nnt.fit_transform(X)
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assert _has_explicit_diagonal(Xt)
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assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
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Xt = nnt.transform(X)
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assert _has_explicit_diagonal(Xt)
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assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
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# Using transform on new data should not always have zero diagonal
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X2t = nnt.transform(X2)
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assert not _has_explicit_diagonal(X2t)
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@pytest.mark.parametrize("Klass", [KNeighborsTransformer, RadiusNeighborsTransformer])
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def test_graph_feature_names_out(Klass):
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"""Check `get_feature_names_out` for transformers defined in `_graph.py`."""
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n_samples_fit = 20
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n_features = 10
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rng = np.random.RandomState(42)
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X = rng.randn(n_samples_fit, n_features)
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est = Klass().fit(X)
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names_out = est.get_feature_names_out()
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class_name_lower = Klass.__name__.lower()
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expected_names_out = np.array(
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[f"{class_name_lower}{i}" for i in range(est.n_samples_fit_)],
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dtype=object,
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)
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assert_array_equal(names_out, expected_names_out)
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