diff --git a/copac/__init__.py b/copac/__init__.py index f1c96c9..493f741 100644 --- a/copac/__init__.py +++ b/copac/__init__.py @@ -1,8 +1 @@ -from .copac import COPAC, copac - -__all__ = [ - 'COPAC', - 'copac', - '__version__', -] __version__ = "0.3.0" diff --git a/copac/copac.py b/copac/copac.py index 9dd737d..6519f38 100644 --- a/copac/copac.py +++ b/copac/copac.py @@ -4,7 +4,7 @@ COPAC: Correlation Partition Clustering """ -# Author: Roman Feldbauer +# Author: Roman Feldbauer # Elisabeth Hartel # Jiri Mauritz # Thomas Turic @@ -16,10 +16,7 @@ from scipy.spatial.distance import squareform from sklearn.base import BaseEstimator, ClusterMixin -try: # for sklearn < 0.23 - from sklearn.cluster.dbscan_ import dbscan -except: # for sklearn >= 0.23 - from sklearn.cluster._dbscan import dbscan +from sklearn.cluster import DBSCAN from sklearn.neighbors import NearestNeighbors from sklearn.utils import check_array @@ -41,9 +38,12 @@ def _cdist(P, Q, Mhat_P): return (PQ_diff @ Mhat_P * PQ_diff).sum(axis=1) -def copac(X, k=10, mu=5, eps=0.5, alpha=0.85, metric='euclidean', - metric_params=None, algorithm='auto', leaf_size=30, p=None, - n_jobs=1, sample_weight=None): +def copac(X: np.ndarray, *, + k: int = 10, mu: int = 5, eps: float = 0.5, alpha: float = 0.85, + metric: str = 'euclidean', metric_params=None, + algorithm: str = 'auto', leaf_size: int = 30, p: float = None, + n_jobs: int = 1, sample_weight: np.ndarray=None, + return_core_pts: bool = False): """Perform COPAC clustering from vector array. Parameters @@ -84,12 +84,16 @@ def copac(X, k=10, mu=5, eps=0.5, alpha=0.85, metric='euclidean', n_jobs : int, optional, default=1 Number of parallel processes. Use all cores with n_jobs=-1. sample_weight : None - Currently ignored + Sample weights + return_core_pts : bool + Return clusters labels and core point indices for each correlation dimension. Returns ------- labels : array [n_samples] Cluster labels for each point. Noisy samples are given the label -1. + core_pts_ind : dict[int, array] + Indices of core points for each correlation dimension (only if ``return_core_pts=True``). References ---------- @@ -99,9 +103,9 @@ def copac(X, k=10, mu=5, eps=0.5, alpha=0.85, metric='euclidean', Conference on Data Mining, April 26-28, 2007, Minneapolis, Minnesota, USA (2007), pp. 413–418. """ - X = check_array(X) n, d = X.shape - y = -np.ones(n, dtype=np.int) + data_dtype = X.dtype + y = -np.ones(n, dtype=int) if n_jobs == -1: n_jobs = cpu_count() @@ -141,10 +145,11 @@ def copac(X, k=10, mu=5, eps=0.5, alpha=0.85, metric='euclidean', # Loop over partitions according to local corr. dim. max_label = 0 used_y = np.zeros_like(y, dtype=int) - for D in Ds: + core_pts = {} + for dim, D in enumerate(Ds, start=1): n_D = D.shape[0] - cdist_P = -np.ones(n_D * (n_D - 1) // 2, dtype=np.float) - cdist_Q = -np.ones((n_D, n_D), dtype=np.float) + cdist_P = -np.ones(n_D * (n_D - 1) // 2, dtype=data_dtype) + cdist_Q = -np.ones((n_D, n_D), dtype=data_dtype) start = 0 # Calculate triu part of distance matrix for i in range(0, n_D - 1): @@ -168,9 +173,10 @@ def copac(X, k=10, mu=5, eps=0.5, alpha=0.85, metric='euclidean', # Perform DBSCAN with full distance matrix cdist = squareform(cdist) - clust = dbscan(X=cdist, eps=eps, min_samples=mu, - metric='precomputed', n_jobs=n_jobs) - _, labels = clust + dbscan = DBSCAN(eps=eps, min_samples=mu, metric="precomputed", n_jobs=n_jobs) + labels = dbscan.fit_predict(X=cdist, sample_weight=sample_weight) + core_pts[dim] = dbscan.core_sample_indices_ + # Each DBSCAN run is unaware of previous ones, # so we need to keep track of previous copac IDs y_D = labels + max_label @@ -180,7 +186,11 @@ def copac(X, k=10, mu=5, eps=0.5, alpha=0.85, metric='euclidean', y[D] = y_D used_y[D] += 1 assert np.all(used_y == 1), "Not all samples were handled exactly once!" - return y + + if return_core_pts: + return y, core_pts + else: + return y class COPAC(BaseEstimator, ClusterMixin): @@ -254,7 +264,7 @@ def __init__(self, k=10, mu=5, eps=0.5, alpha=0.85, self.p = p self.n_jobs = n_jobs - def fit(self, X, y=None, sample_weight=None): + def fit(self, X, y=None, sample_weight=None, return_core_pts=False): """Perform COPAC clustering from features. Parameters @@ -268,14 +278,25 @@ def fit(self, X, y=None, sample_weight=None): Note that weights are absolute, and default to 1. CURRENTLY IGNORED. y : Ignored + return_core_pts : bool + Return cluster labels and core points per correlation dimension """ - X = check_array(X) - clust = copac(X, sample_weight=sample_weight, - **self.get_params()) + X: np.ndarray = check_array(X) + result = copac( + X=X, + sample_weight=sample_weight, + return_core_pts=return_core_pts, + **self.get_params(), + ) + if return_core_pts: + clust, core_pts = result + self.core_point_indices_ = core_pts + else: + clust = result self.labels_ = clust return self - def fit_predict(self, X, y=None, sample_weight=None): + def fit_predict(self, X, y=None, sample_weight=None, return_core_pts=False): """Performs clustering on X and returns copac labels. Parameters @@ -289,11 +310,16 @@ def fit_predict(self, X, y=None, sample_weight=None): Note that weights are absolute, and default to 1. CURRENTLY IGNORED. y : Ignored + return_core_pts : bool + Return cluster labels and core points per correlation dimension Returns ------- y : ndarray, shape (n_samples,) copac labels """ - self.fit(X, sample_weight=sample_weight) - return self.labels_ + self.fit(X, sample_weight=sample_weight, return_core_pts=return_core_pts) + if return_core_pts: + return self.labels_, self.core_point_indices_ + else: + return self.labels_ diff --git a/copac/tests/test_copac.py b/copac/tests/test_copac.py index daaba14..ed10ed4 100644 --- a/copac/tests/test_copac.py +++ b/copac/tests/test_copac.py @@ -1,46 +1,40 @@ """ Testing for Clustering methods """ -import unittest +import pytest import numpy as np - from sklearn.metrics.cluster import v_measure_score -from sklearn.utils.testing import assert_equal, assert_array_equal -from sklearn.datasets.samples_generator import make_blobs +from sklearn.datasets import make_blobs from ..copac import COPAC -class TestCopac(unittest.TestCase): - - def setUp(self): - """ Set up very simple data set """ - self.n_clusters = 2 - self.centers = np.array([[3, 3], [-3, -3]]) + 10 - self.X, self.y = make_blobs(n_samples=60, n_features=2, - centers=self.centers, cluster_std=0.4, - shuffle=True, random_state=0) - self.v = v_measure_score(self.y, self.y) - - def tearDown(self): - del self.n_clusters, self.centers, self.X - - def test_copac(self): - """ Minimal test that COPAC runs at all. """ - k = 40 - mu = 10 - eps = 2 - alpha = 0.85 - copac = COPAC(k=k, mu=mu, eps=eps, alpha=alpha) - y_pred = copac.fit_predict(self.X) - v = v_measure_score(self.y, y_pred) - # Must score perfectly on very simple data - assert_equal(self.v, v) - # Check correct labels_ attribute - copac = COPAC(k=k, mu=mu, eps=eps, alpha=alpha) - copac.fit(self.X) - assert_array_equal(copac.labels_, y_pred) - -if __name__ == "__main__": - unittest.main() +@pytest.mark.parametrize("return_core_pts", [True, False]) +def test_copac(return_core_pts): + """ Minimal test that COPAC runs at all. """ + # Set up very simple data set + n_clusters = 2 + centers = np.array([[3, 3], [-3, -3]]) + 10 + X, y = make_blobs(n_samples=60, n_features=2, + centers=centers, cluster_std=0.4, + shuffle=True, random_state=0) + v_true = v_measure_score(y, y) + + k = 40 + mu = 10 + eps = 2 + alpha = 0.85 + + copac = COPAC(k=k, mu=mu, eps=eps, alpha=alpha) + y_pred = copac.fit_predict(X, return_core_pts=return_core_pts) + if return_core_pts: + y_pred, core_pts_ind = y_pred + assert isinstance(core_pts_ind, dict) + v_pred = v_measure_score(y, y_pred) + # Must score perfectly on very simple data + np.testing.assert_equal(v_true, v_pred) + # Check correct labels_ attribute + copac = COPAC(k=k, mu=mu, eps=eps, alpha=alpha) + copac.fit(X) + np.testing.assert_array_equal(copac.labels_, y_pred) diff --git a/setup.cfg b/setup.cfg index a031533..072550e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,7 +3,7 @@ universal = true [metadata] name = COPAC -version = 0.3.0 +version = attr: copac.__init__.__version__ author = Roman Feldbauer author_email = sci@feldbauer.org url = https://github.com/VarIr/copac