MAINT Add fixture for dataset generation in common tests (scikit-lear…

…n-contrib#932)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

imblearn/utils/estimator_checks.py

@@ -59,6 +59,22 @@
 sklearn_version = parse_version(sklearn.__version__)
 
 
+def sample_dataset_generator():
+    X, y = make_classification(
+        n_samples=1000,
+        n_classes=3,
+        n_informative=4,
+        weights=[0.2, 0.3, 0.5],
+        random_state=0,
+    )
+    return X, y
+
+
+@pytest.fixture(name="sample_dataset_generator")
+def sample_dataset_generator_fixture():
+    return sample_dataset_generator()
+
+
 def _set_checking_parameters(estimator):
     params = estimator.get_params()
     name = estimator.__class__.__name__
@@ -233,13 +249,7 @@ def check_samplers_fit(name, sampler_orig):
 
 def check_samplers_fit_resample(name, sampler_orig):
     sampler = clone(sampler_orig)
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     target_stats = Counter(y)
     X_res, y_res = sampler.fit_resample(X, y)
     if isinstance(sampler, BaseOverSampler):
@@ -269,13 +279,7 @@ def check_samplers_fit_resample(name, sampler_orig):
 def check_samplers_sampling_strategy_fit_resample(name, sampler_orig):
     sampler = clone(sampler_orig)
     # in this test we will force all samplers to not change the class 1
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     expected_stat = Counter(y)[1]
     if isinstance(sampler, BaseOverSampler):
         sampling_strategy = {2: 498, 0: 498}
@@ -298,13 +302,7 @@ def check_samplers_sparse(name, sampler_orig):
     sampler = clone(sampler_orig)
     # check that sparse matrices can be passed through the sampler leading to
     # the same results than dense
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     X_sparse = sparse.csr_matrix(X)
     X_res_sparse, y_res_sparse = sampler.fit_resample(X_sparse, y)
     sampler = clone(sampler)
@@ -318,13 +316,7 @@ def check_samplers_pandas(name, sampler_orig):
     pd = pytest.importorskip("pandas")
     sampler = clone(sampler_orig)
     # Check that the samplers handle pandas dataframe and pandas series
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     X_df = pd.DataFrame(X, columns=[str(i) for i in range(X.shape[1])])
     y_df = pd.DataFrame(y)
     y_s = pd.Series(y, name="class")
@@ -351,13 +343,7 @@ def check_samplers_pandas(name, sampler_orig):
 def check_samplers_list(name, sampler_orig):
     sampler = clone(sampler_orig)
     # Check that the can samplers handle simple lists
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     X_list = X.tolist()
     y_list = y.tolist()
 
@@ -374,13 +360,7 @@ def check_samplers_list(name, sampler_orig):
 def check_samplers_multiclass_ova(name, sampler_orig):
     sampler = clone(sampler_orig)
     # Check that multiclass target lead to the same results than OVA encoding
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     y_ova = label_binarize(y, classes=np.unique(y))
     X_res, y_res = sampler.fit_resample(X, y)
     X_res_ova, y_res_ova = sampler.fit_resample(X, y_ova)
@@ -391,27 +371,15 @@ def check_samplers_multiclass_ova(name, sampler_orig):
 
 def check_samplers_2d_target(name, sampler_orig):
     sampler = clone(sampler_orig)
-    X, y = make_classification(
-        n_samples=100,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
 
     y = y.reshape(-1, 1)  # Make the target 2d
     sampler.fit_resample(X, y)
 
 
 def check_samplers_preserve_dtype(name, sampler_orig):
     sampler = clone(sampler_orig)
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     # Cast X and y to not default dtype
     X = X.astype(np.float32)
     y = y.astype(np.int32)
@@ -422,13 +390,7 @@ def check_samplers_preserve_dtype(name, sampler_orig):
 
 def check_samplers_sample_indices(name, sampler_orig):
     sampler = clone(sampler_orig)
-    X, y = make_classification(
-        n_samples=1000,
-        n_classes=3,
-        n_informative=4,
-        weights=[0.2, 0.3, 0.5],
-        random_state=0,
-    )
+    X, y = sample_dataset_generator()
     sampler.fit_resample(X, y)
     sample_indices = sampler._get_tags().get("sample_indices", None)
     if sample_indices: