MAINT: Moved comput_label test to common tests

MechCoder · MechCoder · commit 36151f6f0696 · 2014-12-09T16:21:45.000+01:00
diff --git a/sklearn/cluster/tests/test_birch.py b/sklearn/cluster/tests/test_birch.py
@@ -158,16 +158,3 @@ def test_threshold():
     brc = Birch(threshold=5.0, n_clusters=None)
     brc.fit(X)
     check_threshold(brc, 5.)
-
-
-def test_compute_label_predict():
-    """Test predict is invariant of the param 'compute_labels'"""
-    X, y = make_blobs(n_samples=80, centers=4)
-    brc1 = Birch(threshold=0.5, n_clusters=None, compute_labels=True)
-    brc1.fit(X)
-    brc1_labels = brc1.predict(X)
-
-    brc2 = Birch(threshold=0.5, n_clusters=None, compute_labels=False)
-    brc2.fit(X)
-    brc2_labels = brc2.predict(X)
-    assert_almost_equal(v_measure_score(brc1_labels, brc2_labels), 1.0)
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -32,6 +32,7 @@
     check_regressors_classifiers_sparse_data,
     check_transformer,
     check_clustering,
+    check_clusterer_compute_labels_predict,
     check_regressors_int,
     check_regressors_train,
     check_regressors_pickle,
@@ -123,6 +124,7 @@ def test_clustering():
     for name, Alg in clustering:
         # test whether any classifier overwrites his init parameters during fit
         yield check_cluster_overwrite_params, name, Alg
+        yield check_clusterer_compute_labels_predict, name, Alg
         if name not in ('WardAgglomeration', "FeatureAgglomeration"):
             # this is clustering on the features
             # let's not test that here.
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -420,6 +420,22 @@ def check_clustering(name, Alg):
     assert_array_equal(pred, pred2)
 
 
+def check_clusterer_compute_labels_predict(name, Clusterer):
+    """Check that predict is invariant of compute_labels"""
+    X, y = make_blobs(n_samples=20, random_state=0)
+    clusterer = Clusterer()
+
+    if hasattr(clusterer, "compute_labels"):
+        # MiniBatchKMeans
+        if hasattr(clusterer, "random_state"):
+            clusterer.set_params(random_state=0)
+
+        X_pred1 = clusterer.fit(X).predict(X)
+        clusterer.set_params(compute_labels=False)
+        X_pred2 = clusterer.fit(X).predict(X)
+        assert_array_equal(X_pred1, X_pred2)
+
+
 def check_classifiers_one_label(name, Classifier):
     error_string_fit = "Classifier can't train when only one class is present."
     error_string_predict = ("Classifier can't predict when only one class is "
