Merge pull request #6850 from ales-erjavec/numpy2.0

[ENH] Numpy 2.0 compatibility

Author: markotoplak

Orange/classification/__init__.py

@@ -1,5 +1,5 @@
 # Pull members from modules to Orange.classification namespace
-# pylint: disable=wildcard-import
+# pylint: disable=wildcard-import,broad-except
 
 from .base_classification import (ModelClassification as Model,
                                   LearnerClassification as Learner,
@@ -23,7 +23,7 @@
 from .scoringsheet import *
 try:
     from .catgb import *
-except ModuleNotFoundError:
+except Exception:
     pass
 from .gb import *
 try:

Orange/data/io_base.py

@@ -381,7 +381,7 @@ def get_arrays(self) -> Tuple[np.ndarray, np.ndarray,
                  (self.cols_W, float))
         X, Y, M, W = [self._list_into_ndarray(lst, dt) for lst, dt in lists]
         if X is None:
-            X = np.empty((self.data.shape[0], 0), dtype=np.float_)
+            X = np.empty((self.data.shape[0], 0), dtype=np.float64)
         return X, Y, M, W
 
     @staticmethod
@@ -393,7 +393,7 @@ def _list_into_ndarray(lst: List, dtype=None) -> Optional[np.ndarray]:
         if dtype is not None:
             array.astype(dtype)
         else:
-            assert array.dtype == np.float_
+            assert array.dtype == np.float64
         return array
 
 

Orange/data/util.py

@@ -50,7 +50,7 @@ def scale(values, min=0, max=1):
     """Return values scaled to [min, max]"""
     if len(values) == 0:
         return np.array([])
-    minval = np.float_(bn.nanmin(values))
+    minval = np.float64(bn.nanmin(values))
     ptp = bn.nanmax(values) - minval
     if ptp == 0:
         return np.clip(values, min, max)

Orange/preprocess/_relieff.pyx

@@ -371,7 +371,7 @@ cdef tuple prepare(X, y, is_discrete, contingencies):
     is_defined = np.logical_not(np.isnan(y))
     X = X[is_defined]
     y = y[is_defined]
-    attr_stats = np.row_stack((np.nanmean(X, 0), np.nanstd(X, 0)))
+    attr_stats = np.vstack((np.nanmean(X, 0), np.nanstd(X, 0)))
     is_discrete = np.asarray(is_discrete, dtype=np.int8)
     contingency_tables(X, y, is_discrete, contingencies)
     return X, y, attr_stats, is_discrete

Orange/preprocess/discretize.py

@@ -44,7 +44,7 @@ def transform(self, c):
         if sp.issparse(c):
             return self.digitize(c, self.points)
         elif c.size:
-            return np.where(np.isnan(c), np.NaN, self.digitize(c, self.points))
+            return np.where(np.isnan(c), np.nan, self.digitize(c, self.points))
         else:
             return np.array([], dtype=int)
 

Orange/preprocess/remove.py

@@ -233,7 +233,7 @@ def remove_unused_values(var, data):
     if len(unique) == len(var.values):
         return var
     used_values = [var.values[i] for i in unique]
-    translation_table = np.array([np.NaN] * len(var.values))
+    translation_table = np.array([np.nan] * len(var.values))
     translation_table[unique] = range(len(used_values))
     return DiscreteVariable(var.name, values=used_values, sparse=var.sparse,
                             compute_value=Lookup(var, translation_table))

Orange/regression/__init__.py

@@ -17,7 +17,7 @@
 from ..classification.simple_tree import *
 try:
     from .catgb import *
-except ModuleNotFoundError:
+except Exception:
     pass
 from .gb import *
 try:

Orange/tests/test_classification.py

@@ -347,7 +347,7 @@ def test_multinomial(self):
     def test_nan_columns(self):
         data = Orange.data.Table("iris")
         with data.unlocked():
-            data.X[:, (1, 3)] = np.NaN
+            data.X[:, (1, 3)] = np.nan
         lr = LogisticRegressionLearner()
         cv = CrossValidation(k=2, store_models=True)
         res = cv(data, [lr])

Orange/tests/test_domain.py

@@ -430,7 +430,7 @@ def test_preprocessor_chaining(self):
         domain = Domain([DiscreteVariable("a", values="01"),
                          DiscreteVariable("b", values="01")],
                         DiscreteVariable("y", values="01"))
-        table = Table.from_list(domain, [[0, 1], [1, np.NaN]], [0, 1])
+        table = Table.from_list(domain, [[0, 1], [1, np.nan]], [0, 1])
         pre1 = Continuize()(Impute()(table))
         pre2 = table.transform(pre1.domain)
         np.testing.assert_almost_equal(pre1.X, pre2.X)

Orange/tests/test_preprocess.py

@@ -80,15 +80,15 @@ class TestRemoveNaNColumns(unittest.TestCase):
     def test_column_filtering(self):
         data = Table("iris")
         with data.unlocked():
-            data.X[:, (1, 3)] = np.NaN
+            data.X[:, (1, 3)] = np.nan
 
         new_data = RemoveNaNColumns()(data)
         self.assertEqual(len(new_data.domain.attributes),
                          len(data.domain.attributes) - 2)
 
         data = Table("iris")
         with data.unlocked():
-            data.X[0, 0] = np.NaN
+            data.X[0, 0] = np.nan
         new_data = RemoveNaNColumns()(data)
         self.assertEqual(len(new_data.domain.attributes),
                          len(data.domain.attributes))