TST Improve SelectFromModel tests (#9733)

Should fix one of the issues in #9393

sklearn/feature_selection/tests/test_from_model.py

@@ -40,7 +40,6 @@ def test_input_estimator_unchanged():
     assert_true(transformer.estimator is est)
 
 
-@skip_if_32bit
 def test_feature_importances():
     X, y = datasets.make_classification(
         n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
@@ -59,17 +58,33 @@ def test_feature_importances():
         feature_mask = np.abs(importances) > func(importances)
         assert_array_almost_equal(X_new, X[:, feature_mask])
 
+
+def test_sample_weight():
+    # Ensure sample weights are passed to underlying estimator
+    X, y = datasets.make_classification(
+        n_samples=100, n_features=10, n_informative=3, n_redundant=0,
+        n_repeated=0, shuffle=False, random_state=0)
+
     # Check with sample weights
     sample_weight = np.ones(y.shape)
     sample_weight[y == 1] *= 100
 
-    est = RandomForestClassifier(n_estimators=50, random_state=0)
+    est = LogisticRegression(random_state=0, fit_intercept=False)
     transformer = SelectFromModel(estimator=est)
+    transformer.fit(X, y, sample_weight=None)
+    mask = transformer._get_support_mask()
     transformer.fit(X, y, sample_weight=sample_weight)
-    importances = transformer.estimator_.feature_importances_
+    weighted_mask = transformer._get_support_mask()
+    assert not np.all(weighted_mask == mask)
     transformer.fit(X, y, sample_weight=3 * sample_weight)
-    importances_bis = transformer.estimator_.feature_importances_
-    assert_almost_equal(importances, importances_bis)
+    reweighted_mask = transformer._get_support_mask()
+    assert np.all(weighted_mask == reweighted_mask)
+
+
+def test_coef_default_threshold():
+    X, y = datasets.make_classification(
+        n_samples=100, n_features=10, n_informative=3, n_redundant=0,
+        n_repeated=0, shuffle=False, random_state=0)
 
     # For the Lasso and related models, the threshold defaults to 1e-5
     transformer = SelectFromModel(estimator=Lasso(alpha=0.1))
@@ -80,7 +95,7 @@ def test_feature_importances():
 
 
 @skip_if_32bit
-def test_feature_importances_2d_coef():
+def test_2d_coef():
     X, y = datasets.make_classification(
         n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
         n_repeated=0, shuffle=False, random_state=0, n_classes=4)