136 lines
4.0 KiB
Python
136 lines
4.0 KiB
Python
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.utils._testing import assert_array_equal, assert_allclose
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from sklearn.cluster import BisectingKMeans
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@pytest.mark.parametrize("bisecting_strategy", ["biggest_inertia", "largest_cluster"])
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def test_three_clusters(bisecting_strategy):
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"""Tries to perform bisect k-means for three clusters to check
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if splitting data is performed correctly.
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"""
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# X = np.array([[1, 2], [1, 4], [1, 0],
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# [10, 2], [10, 4], [10, 0],
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# [10, 6], [10, 8], [10, 10]])
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# X[0][1] swapped with X[1][1] intentionally for checking labeling
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X = np.array(
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[[1, 2], [10, 4], [1, 0], [10, 2], [1, 4], [10, 0], [10, 6], [10, 8], [10, 10]]
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)
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bisect_means = BisectingKMeans(
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n_clusters=3, random_state=0, bisecting_strategy=bisecting_strategy
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)
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bisect_means.fit(X)
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expected_centers = [[10, 2], [10, 8], [1, 2]]
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expected_predict = [2, 0]
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expected_labels = [2, 0, 2, 0, 2, 0, 1, 1, 1]
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assert_allclose(expected_centers, bisect_means.cluster_centers_)
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assert_array_equal(expected_predict, bisect_means.predict([[0, 0], [12, 3]]))
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assert_array_equal(expected_labels, bisect_means.labels_)
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def test_sparse():
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"""Test Bisecting K-Means with sparse data.
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Checks if labels and centers are the same between dense and sparse.
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"""
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rng = np.random.RandomState(0)
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X = rng.rand(20, 2)
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X[X < 0.8] = 0
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X_csr = sp.csr_matrix(X)
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bisect_means = BisectingKMeans(n_clusters=3, random_state=0)
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bisect_means.fit(X_csr)
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sparse_centers = bisect_means.cluster_centers_
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bisect_means.fit(X)
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normal_centers = bisect_means.cluster_centers_
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# Check if results is the same for dense and sparse data
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assert_allclose(normal_centers, sparse_centers, atol=1e-8)
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@pytest.mark.parametrize("n_clusters", [4, 5])
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def test_n_clusters(n_clusters):
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"""Test if resulting labels are in range [0, n_clusters - 1]."""
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rng = np.random.RandomState(0)
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X = rng.rand(10, 2)
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bisect_means = BisectingKMeans(n_clusters=n_clusters, random_state=0)
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bisect_means.fit(X)
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assert_array_equal(np.unique(bisect_means.labels_), np.arange(n_clusters))
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def test_one_cluster():
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"""Test single cluster."""
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X = np.array([[1, 2], [10, 2], [10, 8]])
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bisect_means = BisectingKMeans(n_clusters=1, random_state=0).fit(X)
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# All labels from fit or predict should be equal 0
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assert all(bisect_means.labels_ == 0)
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assert all(bisect_means.predict(X) == 0)
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assert_allclose(bisect_means.cluster_centers_, X.mean(axis=0).reshape(1, -1))
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@pytest.mark.parametrize("is_sparse", [True, False])
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def test_fit_predict(is_sparse):
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"""Check if labels from fit(X) method are same as from fit(X).predict(X)."""
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rng = np.random.RandomState(0)
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X = rng.rand(10, 2)
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if is_sparse:
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X[X < 0.8] = 0
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X = sp.csr_matrix(X)
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bisect_means = BisectingKMeans(n_clusters=3, random_state=0)
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bisect_means.fit(X)
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assert_array_equal(bisect_means.labels_, bisect_means.predict(X))
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@pytest.mark.parametrize("is_sparse", [True, False])
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def test_dtype_preserved(is_sparse, global_dtype):
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"""Check that centers dtype is the same as input data dtype."""
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rng = np.random.RandomState(0)
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X = rng.rand(10, 2).astype(global_dtype, copy=False)
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if is_sparse:
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X[X < 0.8] = 0
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X = sp.csr_matrix(X)
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km = BisectingKMeans(n_clusters=3, random_state=0)
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km.fit(X)
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assert km.cluster_centers_.dtype == global_dtype
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@pytest.mark.parametrize("is_sparse", [True, False])
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def test_float32_float64_equivalence(is_sparse):
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"""Check that the results are the same between float32 and float64."""
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rng = np.random.RandomState(0)
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X = rng.rand(10, 2)
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if is_sparse:
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X[X < 0.8] = 0
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X = sp.csr_matrix(X)
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km64 = BisectingKMeans(n_clusters=3, random_state=0).fit(X)
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km32 = BisectingKMeans(n_clusters=3, random_state=0).fit(X.astype(np.float32))
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assert_allclose(km32.cluster_centers_, km64.cluster_centers_)
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assert_array_equal(km32.labels_, km64.labels_)
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