Resolve "release v1.2.0"

Closes #257 (closed)

mlair/data_handler/iterator.py

@@ -37,7 +37,7 @@ class DataCollection(Iterable):
         if collection is None:
             collection = []
         assert isinstance(collection, list)
-        self._collection = collection
+        self._collection = collection.copy()
         self._mapping = {}
         self._set_mapping()
         self._name = name
@@ -119,9 +119,10 @@ class KerasIterator(keras.utils.Sequence):
 
     def _get_batch(self, data_list: List[np.ndarray], b: int) -> List[np.ndarray]:
         """Get batch according to batch size from data list."""
-        return list(map(lambda data: data[b * self.batch_size:(b+1) * self.batch_size, ...], data_list))
+        return list(map(lambda data: data[b * self.batch_size:(b + 1) * self.batch_size, ...], data_list))
 
-    def _permute_data(self, X, Y):
+    @staticmethod
+    def _permute_data(X, Y):
         p = np.random.permutation(len(X[0]))  # equiv to .shape[0]
         X = list(map(lambda x: x[p], X))
         Y = list(map(lambda x: x[p], Y))
@@ -184,35 +185,3 @@ class KerasIterator(keras.utils.Sequence):
         """Randomly shuffle indexes if enabled."""
         if self.shuffle is True:
             np.random.shuffle(self.indexes)
-
-
-class DummyData:  # pragma: no cover
-
-    def __init__(self, number_of_samples=np.random.randint(100, 150)):
-        self.number_of_samples = number_of_samples
-
-    def get_X(self):
-        X1 = np.random.randint(0, 10, size=(self.number_of_samples, 14, 5))  # samples, window, variables
-        X2 = np.random.randint(21, 30, size=(self.number_of_samples, 10, 2))  # samples, window, variables
-        X3 = np.random.randint(-5, 0, size=(self.number_of_samples, 1, 2))  # samples, window, variables
-        return [X1, X2, X3]
-
-    def get_Y(self):
-        Y1 = np.random.randint(0, 10, size=(self.number_of_samples, 5, 1))  # samples, window, variables
-        Y2 = np.random.randint(21, 30, size=(self.number_of_samples, 5, 1))  # samples, window, variables
-        return [Y1, Y2]
-
-
-if __name__ == "__main__":
-
-    collection = []
-    for _ in range(3):
-        collection.append(DummyData(50))
-
-    data_collection = DataCollection(collection=collection)
-
-    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
-    iterator = KerasIterator(data_collection, 25, path, shuffle=True)
-
-    for data in data_collection:
-        print(data)
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