class BirchImpl(): def __init__(self, threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True): self._hyperparams = { 'threshold': threshold, 'branching_factor': branching_factor, 'n_clusters': n_clusters, 'compute_labels': compute_labels, 'copy': copy } def fit(self, X, y=None): self._sklearn_model = SKLModel(**self._hyperparams) if (y is not None): self._sklearn_model.fit(X, y) else: self._sklearn_model.fit(X) return self def transform(self, X): return self._sklearn_model.transform(X) def predict(self, X): return self._sklearn_model.predict(X)
def test_birch_predict(): """Test the predict method predicts the nearest centroid.""" rng = np.random.RandomState(0) X = generate_clustered_data(n_clusters=3, n_features=3, n_samples_per_cluster=10) # n_samples * n_samples_per_cluster shuffle_indices = np.arange(30) rng.shuffle(shuffle_indices) X_shuffle = X[shuffle_indices, :] brc = Birch(n_clusters=4, threshold=1.0) brc.fit(X_shuffle) centroids = brc.subcluster_centers_ assert_array_equal(brc.labels_, brc.predict(X_shuffle)) nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids) assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)
def test_birch_predict(): """Test the predict method predicts the nearest centroid.""" rng = np.random.RandomState(0) X = generate_clustered_data(n_clusters=3, n_features=3, n_samples_per_cluster=10) # n_samples * n_samples_per_cluster shuffle_indices = np.arange(30) rng.shuffle(shuffle_indices) X_shuffle = X[shuffle_indices, :] brc = Birch(n_clusters=4, threshold=1.) brc.fit(X_shuffle) centroids = brc.subcluster_centers_ assert_array_equal(brc.labels_, brc.predict(X_shuffle)) nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids) assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)