TST Remove Boston dataset in test_mlp (scikit-learn#17337)

sklearn/neural_network/tests/test_mlp.py

@@ -12,17 +12,21 @@
 
 import numpy as np
 
-from numpy.testing import assert_almost_equal, assert_array_equal
+from numpy.testing import (
+    assert_almost_equal,
+    assert_array_equal,
+    assert_allclose,
+)
 
-from sklearn.datasets import load_digits, load_boston, load_iris
+from sklearn.datasets import load_digits, load_iris
 from sklearn.datasets import make_regression, make_multilabel_classification
 from sklearn.exceptions import ConvergenceWarning
 from io import StringIO
 from sklearn.metrics import roc_auc_score
 from sklearn.neural_network import MLPClassifier
 from sklearn.neural_network import MLPRegressor
 from sklearn.preprocessing import LabelBinarizer
-from sklearn.preprocessing import StandardScaler, MinMaxScaler
+from sklearn.preprocessing import MinMaxScaler, scale
 from scipy.sparse import csr_matrix
 from sklearn.utils._testing import ignore_warnings
 
@@ -42,12 +46,10 @@
 classification_datasets = [(X_digits_multi, y_digits_multi),
                            (X_digits_binary, y_digits_binary)]
 
-boston = load_boston()
-
-Xboston = StandardScaler().fit_transform(boston.data)[: 200]
-yboston = boston.target[:200]
-
-regression_datasets = [(Xboston, yboston)]
+X_reg, y_reg = make_regression(n_samples=200, n_features=10, bias=20.,
+                               noise=100., random_state=7)
+y_reg = scale(y_reg)
+regression_datasets = [(X_reg, y_reg)]
 
 iris = load_iris()
 
@@ -252,17 +254,17 @@ def test_lbfgs_classification(X, y):
 
 @pytest.mark.parametrize('X,y', regression_datasets)
 def test_lbfgs_regression(X, y):
-    # Test lbfgs on the boston dataset, a regression problems.
+    # Test lbfgs on the regression dataset.
     for activation in ACTIVATION_TYPES:
         mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50,
                            max_iter=150, shuffle=True, random_state=1,
                            activation=activation)
         mlp.fit(X, y)
         if activation == 'identity':
-            assert mlp.score(X, y) > 0.84
+            assert mlp.score(X, y) > 0.80
         else:
             # Non linear models perform much better than linear bottleneck:
-            assert mlp.score(X, y) > 0.95
+            assert mlp.score(X, y) > 0.98
 
 
 @pytest.mark.parametrize('X,y', classification_datasets)
@@ -287,7 +289,7 @@ def test_lbfgs_regression_maxfun(X, y):
     max_fun = 10
     # regression tests
     for activation in ACTIVATION_TYPES:
-        mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50,
+        mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50, tol=0.0,
                            max_iter=150, max_fun=max_fun, shuffle=True,
                            random_state=1, activation=activation)
         with pytest.warns(ConvergenceWarning):
@@ -400,8 +402,8 @@ def test_partial_fit_unseen_classes():
 def test_partial_fit_regression():
     # Test partial_fit on regression.
     # `partial_fit` should yield the same results as 'fit' for regression.
-    X = Xboston
-    y = yboston
+    X = X_reg
+    y = y_reg
 
     for momentum in [0, .9]:
         mlp = MLPRegressor(solver='sgd', max_iter=100, activation='relu',
@@ -418,9 +420,9 @@ def test_partial_fit_regression():
             mlp.partial_fit(X, y)
 
         pred2 = mlp.predict(X)
-        assert_almost_equal(pred1, pred2, decimal=2)
+        assert_allclose(pred1, pred2)
         score = mlp.score(X, y)
-        assert score > 0.75
+        assert score > 0.65
 
 
 def test_partial_fit_errors():