[MRG+1] Add DBSCAN support for additional metric params (scikit-learn…

…#8139)

* Add DBSCAN support for additional metric params

sklearn/cluster/dbscan_.py

@@ -20,7 +20,7 @@
 from ._dbscan_inner import dbscan_inner
 
 
-def dbscan(X, eps=0.5, min_samples=5, metric='minkowski',
+def dbscan(X, eps=0.5, min_samples=5, metric='minkowski', metric_params=None,
            algorithm='auto', leaf_size=30, p=2, sample_weight=None, n_jobs=1):
     """Perform DBSCAN clustering from vector array or distance matrix.
 
@@ -50,6 +50,11 @@ def dbscan(X, eps=0.5, min_samples=5, metric='minkowski',
         must be square. X may be a sparse matrix, in which case only "nonzero"
         elements may be considered neighbors for DBSCAN.
 
+    metric_params : dict, optional
+        Additional keyword arguments for the metric function.
+
+        .. versionadded:: 0.19
+
     algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
         The algorithm to be used by the NearestNeighbors module
         to compute pointwise distances and find nearest neighbors.
@@ -130,7 +135,8 @@ def dbscan(X, eps=0.5, min_samples=5, metric='minkowski',
     else:
         neighbors_model = NearestNeighbors(radius=eps, algorithm=algorithm,
                                            leaf_size=leaf_size,
-                                           metric=metric, p=p,
+                                           metric=metric,
+                                           metric_params=metric_params, p=p,
                                            n_jobs=n_jobs)
         neighbors_model.fit(X)
         # This has worst case O(n^2) memory complexity
@@ -184,6 +190,11 @@ class DBSCAN(BaseEstimator, ClusterMixin):
         .. versionadded:: 0.17
            metric *precomputed* to accept precomputed sparse matrix.
 
+    metric_params : dict, optional
+        Additional keyword arguments for the metric function.
+
+        .. versionadded:: 0.19
+
     algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
         The algorithm to be used by the NearestNeighbors module
         to compute pointwise distances and find nearest neighbors.
@@ -237,10 +248,12 @@ class DBSCAN(BaseEstimator, ClusterMixin):
     """
 
     def __init__(self, eps=0.5, min_samples=5, metric='euclidean',
-                 algorithm='auto', leaf_size=30, p=None, n_jobs=1):
+                 metric_params=None, algorithm='auto', leaf_size=30, p=None,
+                 n_jobs=1):
         self.eps = eps
         self.min_samples = min_samples
         self.metric = metric
+        self.metric_params = metric_params
         self.algorithm = algorithm
         self.leaf_size = leaf_size
         self.p = p

sklearn/cluster/tests/test_dbscan.py

@@ -133,6 +133,34 @@ def test_dbscan_callable():
     assert_equal(n_clusters_2, n_clusters)
 
 
+def test_dbscan_metric_params():
+    # Tests that DBSCAN works with the metrics_params argument.
+    eps = 0.8
+    min_samples = 10
+    p = 1
+
+    # Compute DBSCAN with metric_params arg
+    db = DBSCAN(metric='minkowski', metric_params={'p': p}, eps=eps,
+                min_samples=min_samples, algorithm='ball_tree').fit(X)
+    core_sample_1, labels_1 = db.core_sample_indices_, db.labels_
+
+    # Test that sample labels are the same as passing Minkowski 'p' directly
+    db = DBSCAN(metric='minkowski', eps=eps, min_samples=min_samples,
+                algorithm='ball_tree', p=p).fit(X)
+    core_sample_2, labels_2 = db.core_sample_indices_, db.labels_
+
+    assert_array_equal(core_sample_1, core_sample_2)
+    assert_array_equal(labels_1, labels_2)
+
+    # Minkowski with p=1 should be equivalent to Manhattan distance
+    db = DBSCAN(metric='manhattan', eps=eps, min_samples=min_samples,
+                algorithm='ball_tree').fit(X)
+    core_sample_3, labels_3 = db.core_sample_indices_, db.labels_
+
+    assert_array_equal(core_sample_1, core_sample_3)
+    assert_array_equal(labels_1, labels_3)
+
+
 def test_dbscan_balltree():
     # Tests the DBSCAN algorithm with balltree for neighbor calculation.
     eps = 0.8