Bump version to 2.6.0

flaml/version.py

@@ -1 +1 @@
-__version__ = "2.5.0"
+__version__ = "2.6.0"

test/automl/test_constraints.py

@@ -30,9 +30,9 @@ def test_metric_constraints():
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
     except (ArffException, ValueError, URLError):
-        from sklearn.datasets import load_wine
+        from sklearn.datasets import make_classification
 
-        X, y = load_wine(return_X_y=True)
+        X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
     automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
     print(automl.estimator_list)
@@ -59,7 +59,8 @@ def test_metric_constraints():
         metric_constraints=automl.metric_constraints,
         num_samples=5,
     )
-    print(analysis.trials[-1])
+    if analysis.trials:
+        print(analysis.trials[-1])
 
 
 def custom_metric(
@@ -116,10 +117,10 @@ def test_metric_constraints_custom():
 
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
-    except (ArffException, ValueError):
-        from sklearn.datasets import load_wine
+    except (ArffException, ValueError, URLError):
+        from sklearn.datasets import make_classification
 
-        X, y = load_wine(return_X_y=True)
+        X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
     automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
     print(automl.estimator_list)
@@ -156,7 +157,8 @@ def test_metric_constraints_custom():
         metric_constraints=automl.metric_constraints,
         num_samples=5,
     )
-    print(analysis.trials[-1])
+    if analysis.trials:
+        print(analysis.trials[-1])
 
 
 if __name__ == "__main__":

test/automl/test_score.py

@@ -1,3 +1,5 @@
+from urllib.error import URLError
+
 import pandas as pd
 from sklearn.datasets import fetch_california_housing, fetch_openml
 
@@ -181,10 +183,10 @@ def test_rank(self):
         try:
             X, y = fetch_openml(name=dataset, return_X_y=True)
             y = y.cat.codes
-        except (ArffException, ValueError):
-            from sklearn.datasets import load_wine
+        except (ArffException, ValueError, URLError):
+            from sklearn.datasets import make_classification
 
-            X, y = load_wine(return_X_y=True)
+            X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
 
         import numpy as np
 

test/automl/test_split.py

@@ -1,3 +1,5 @@
+from urllib.error import URLError
+
 import numpy as np
 import pandas as pd
 from sklearn.datasets import fetch_openml, load_iris
@@ -26,10 +28,10 @@ def _test(split_type):
 
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
-    except (ArffException, ValueError):
-        from sklearn.datasets import load_wine
+    except (ArffException, ValueError, URLError):
+        from sklearn.datasets import make_classification
 
-        X, y = load_wine(return_X_y=True)
+        X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
     if split_type != "time":
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
     else:
@@ -55,10 +57,10 @@ def test_groups_for_classification_task():
 
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
-    except (ArffException, ValueError):
-        from sklearn.datasets import load_wine
+    except (ArffException, ValueError, URLError):
+        from sklearn.datasets import make_classification
 
-        X, y = load_wine(return_X_y=True)
+        X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
 
     automl = AutoML()
     automl_settings = {
@@ -193,10 +195,10 @@ def test_rank():
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
         y = y.cat.codes
-    except (ArffException, ValueError):
-        from sklearn.datasets import load_wine
+    except (ArffException, ValueError, URLError):
+        from sklearn.datasets import make_classification
 
-        X, y = load_wine(return_X_y=True)
+        X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
     import numpy as np
 
     automl = AutoML()
@@ -230,10 +232,10 @@ def test_object():
 
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
-    except (ArffException, ValueError):
-        from sklearn.datasets import load_wine
+    except (ArffException, ValueError, URLError):
+        from sklearn.datasets import make_classification
 
-        X, y = load_wine(return_X_y=True)
+        X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
 
     import numpy as np
 

test/automl/test_xgboost2d.py

@@ -1,4 +1,5 @@
 import unittest
+from urllib.error import URLError
 
 from sklearn.datasets import fetch_openml
 from sklearn.model_selection import train_test_split
@@ -45,10 +46,10 @@ def test_simple(method=None):
 
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
-    except (ArffException, ValueError):
-        from sklearn.datasets import load_wine
+    except (ArffException, ValueError, URLError):
+        from sklearn.datasets import make_classification
 
-        X, y = load_wine(return_X_y=True)
+        X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, random_state=42)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
     automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
     print(automl.estimator_list)
@@ -80,7 +81,8 @@ def test_simple(method=None):
         metric_constraints=automl.metric_constraints,
         num_samples=5,
     )
-    print(analysis.trials[-1])
+    if analysis.trials:
+        print(analysis.trials[-1])
 
 
 def test_optuna():