160 lines
5.5 KiB
Python
160 lines
5.5 KiB
Python
"""
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Tests for the birch clustering algorithm.
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"""
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from scipy import sparse
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import numpy as np
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from sklearn.cluster.tests.common import generate_clustered_data
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from sklearn.cluster.birch import Birch
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from sklearn.cluster.hierarchical import AgglomerativeClustering
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from sklearn.datasets import make_blobs
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from sklearn.linear_model import ElasticNet
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from sklearn.metrics import pairwise_distances_argmin, v_measure_score
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from sklearn.utils.testing import assert_greater_equal
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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_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_warns
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def test_n_samples_leaves_roots():
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# Sanity check for the number of samples in leaves and roots
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X, y = make_blobs(n_samples=10)
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brc = Birch()
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brc.fit(X)
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n_samples_root = sum([sc.n_samples_ for sc in brc.root_.subclusters_])
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n_samples_leaves = sum([sc.n_samples_ for leaf in brc._get_leaves()
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for sc in leaf.subclusters_])
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assert_equal(n_samples_leaves, X.shape[0])
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assert_equal(n_samples_root, X.shape[0])
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def test_partial_fit():
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# Test that fit is equivalent to calling partial_fit multiple times
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X, y = make_blobs(n_samples=100)
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brc = Birch(n_clusters=3)
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brc.fit(X)
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brc_partial = Birch(n_clusters=None)
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brc_partial.partial_fit(X[:50])
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brc_partial.partial_fit(X[50:])
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assert_array_equal(brc_partial.subcluster_centers_,
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brc.subcluster_centers_)
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# Test that same global labels are obtained after calling partial_fit
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# with None
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brc_partial.set_params(n_clusters=3)
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brc_partial.partial_fit(None)
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assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_)
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def test_birch_predict():
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# Test the predict method predicts the nearest centroid.
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rng = np.random.RandomState(0)
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X = generate_clustered_data(n_clusters=3, n_features=3,
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n_samples_per_cluster=10)
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# n_samples * n_samples_per_cluster
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shuffle_indices = np.arange(30)
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rng.shuffle(shuffle_indices)
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X_shuffle = X[shuffle_indices, :]
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brc = Birch(n_clusters=4, threshold=1.)
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brc.fit(X_shuffle)
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centroids = brc.subcluster_centers_
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assert_array_equal(brc.labels_, brc.predict(X_shuffle))
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nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
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assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)
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def test_n_clusters():
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# Test that n_clusters param works properly
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X, y = make_blobs(n_samples=100, centers=10)
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brc1 = Birch(n_clusters=10)
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brc1.fit(X)
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assert_greater(len(brc1.subcluster_centers_), 10)
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assert_equal(len(np.unique(brc1.labels_)), 10)
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# Test that n_clusters = Agglomerative Clustering gives
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# the same results.
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gc = AgglomerativeClustering(n_clusters=10)
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brc2 = Birch(n_clusters=gc)
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brc2.fit(X)
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assert_array_equal(brc1.subcluster_labels_, brc2.subcluster_labels_)
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assert_array_equal(brc1.labels_, brc2.labels_)
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# Test that the wrong global clustering step raises an Error.
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clf = ElasticNet()
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brc3 = Birch(n_clusters=clf)
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assert_raises(ValueError, brc3.fit, X)
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# Test that a small number of clusters raises a warning.
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brc4 = Birch(threshold=10000.)
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assert_warns(UserWarning, brc4.fit, X)
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def test_sparse_X():
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# Test that sparse and dense data give same results
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X, y = make_blobs(n_samples=100, centers=10)
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brc = Birch(n_clusters=10)
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brc.fit(X)
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csr = sparse.csr_matrix(X)
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brc_sparse = Birch(n_clusters=10)
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brc_sparse.fit(csr)
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assert_array_equal(brc.labels_, brc_sparse.labels_)
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assert_array_equal(brc.subcluster_centers_,
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brc_sparse.subcluster_centers_)
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def check_branching_factor(node, branching_factor):
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subclusters = node.subclusters_
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assert_greater_equal(branching_factor, len(subclusters))
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for cluster in subclusters:
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if cluster.child_:
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check_branching_factor(cluster.child_, branching_factor)
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def test_branching_factor():
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# Test that nodes have at max branching_factor number of subclusters
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X, y = make_blobs()
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branching_factor = 9
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# Purposefully set a low threshold to maximize the subclusters.
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brc = Birch(n_clusters=None, branching_factor=branching_factor,
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threshold=0.01)
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brc.fit(X)
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check_branching_factor(brc.root_, branching_factor)
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brc = Birch(n_clusters=3, branching_factor=branching_factor,
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threshold=0.01)
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brc.fit(X)
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check_branching_factor(brc.root_, branching_factor)
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# Raises error when branching_factor is set to one.
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brc = Birch(n_clusters=None, branching_factor=1, threshold=0.01)
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assert_raises(ValueError, brc.fit, X)
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def check_threshold(birch_instance, threshold):
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"""Use the leaf linked list for traversal"""
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current_leaf = birch_instance.dummy_leaf_.next_leaf_
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while current_leaf:
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subclusters = current_leaf.subclusters_
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for sc in subclusters:
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assert_greater_equal(threshold, sc.radius)
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current_leaf = current_leaf.next_leaf_
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def test_threshold():
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# Test that the leaf subclusters have a threshold lesser than radius
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X, y = make_blobs(n_samples=80, centers=4)
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brc = Birch(threshold=0.5, n_clusters=None)
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brc.fit(X)
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check_threshold(brc, 0.5)
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brc = Birch(threshold=5.0, n_clusters=None)
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brc.fit(X)
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check_threshold(brc, 5.)
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