Merge branch 'dev' of https://github.com/mljar/mljar-supervised into dev

tests/tests_automl/test_report_dir_change.py → tests/tests_automl/test_dir_change.py

@@ -5,12 +5,14 @@
 import tempfile
 import numpy as np
 import pandas as pd
+from sklearn import datasets
+from numpy.testing import assert_almost_equal
 
 from supervised import AutoML
 from supervised.exceptions import AutoMLException
 
 
-class AutoMLReportDirChangeTest(unittest.TestCase):
+class AutoMLDirChangeTest(unittest.TestCase):
 
     automl_dir_a = "automl_testing_A"
     automl_dir_b = "automl_testing_B"
@@ -49,3 +51,41 @@ def test_create_report_after_dir_change(self):
             results_path=path_b,
         )
         automl2.report()
+
+    def test_compute_predictions_after_dir_change(self):
+        #
+        # test for https://github.com/mljar/mljar-supervised/issues/384
+        #
+        self.create_dir(self.automl_dir_a)
+        self.create_dir(self.automl_dir_b)
+
+        path_a = os.path.join(self.automl_dir_a, self.automl_dir)
+        path_b = os.path.join(self.automl_dir_b, self.automl_dir)
+
+        X, y = datasets.make_regression(
+            n_samples=100,
+            n_features=5,
+            n_informative=4,
+            n_targets=1,
+            shuffle=False,
+            random_state=0,
+        )
+
+        automl = AutoML(
+            results_path=path_a,
+            explain_level=0,
+            ml_task="regression",
+            total_time_limit=10,
+        )
+        automl.fit(X, y)
+        p = automl.predict(X[:3])
+
+        shutil.move(path_a, path_b)
+
+        automl2 = AutoML(
+            results_path=path_b,
+        )
+        p2 = automl2.predict(X[:3])
+
+        for i in range(3):
+            assert_almost_equal(p[i], p2[i])

tests/tests_utils/test_metric.py

@@ -59,20 +59,16 @@ def test_mape_metric(self):
         score = m(y_true, y_predicted)
         self.assertEqual(score, 0.0)
 
-
     def test_user_defined_metric(self):
-
-        def custom(x,y,sample_weight=None):
-            return np.sum(x+y)
+        def custom(x, y, sample_weight=None):
+            return np.sum(x + y)
 
         UserDefinedEvalMetric().set_metric(custom)
 
         params = {"name": "user_defined_metric"}
         m = Metric(params)
 
-        a = np.array([1,1,1])
+        a = np.array([1, 1, 1])
 
-        score = m(a,a)
+        score = m(a, a)
         self.assertEqual(score, 6)
-
-