diff --git a/src/data_handling/iterator.py b/src/data_handling/iterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..51073286810f4eb6ef02beb47727932008cb9b7c
--- /dev/null
+++ b/src/data_handling/iterator.py
@@ -0,0 +1,162 @@
+
+__author__ = 'Lukas Leufen'
+__date__ = '2020-07-07'
+
+from collections import Iterator, Iterable
+import keras
+import numpy as np
+import math
+import os
+import shutil
+import pickle
+from typing import Tuple, List
+
+
+class StandardIterator(Iterator):
+
+ _position: int = None
+
+ def __init__(self, collection: list):
+ assert isinstance(collection, list)
+ self._collection = collection
+ self._position = 0
+
+ def __next__(self):
+ """Return next element or stop iteration."""
+ try:
+ value = self._collection[self._position]
+ self._position += 1
+ except IndexError:
+ raise StopIteration()
+ return value
+
+
+class DataCollection(Iterable):
+
+ def __init__(self, collection: list):
+ assert isinstance(collection, list)
+ self._collection = collection
+
+ def __iter__(self) -> Iterator:
+ return StandardIterator(self._collection)
+
+
+class KerasIterator(keras.utils.Sequence):
+
+ def __init__(self, collection: DataCollection, batch_size: int, path: str, shuffle: bool = False):
+ self._collection = collection
+ self._path = os.path.join(path, "%i.pickle")
+ self.batch_size = batch_size
+ self.shuffle = shuffle
+ self.indexes: list = []
+ self._cleanup_path(path)
+ self._prepare_batches()
+
+ def __len__(self) -> int:
+ return len(self.indexes)
+
+ def __getitem__(self, index: int) -> Tuple[np.ndarray, np.ndarray]:
+ """Get batch for given index."""
+ return self.__data_generation(self.indexes[index])
+
+ def __data_generation(self, index: int) -> Tuple[np.ndarray, np.ndarray]:
+ """Load pickle data from disk."""
+ file = self._path % index
+ with open(file, "rb") as f:
+ data = pickle.load(f)
+ return data["X"], data["Y"]
+
+ @staticmethod
+ def _concatenate(new: List[np.ndarray], old: List[np.ndarray]) -> List[np.ndarray]:
+ """Concatenate two lists of data along axis=0."""
+ return list(map(lambda n1, n2: np.concatenate((n1, n2), axis=0), old, new))
+
+ 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))
+
+ def _prepare_batches(self) -> None:
+ """
+ Prepare all batches as locally stored files.
+
+ Walk through all elements of collection and split (or merge) data according to the batch size. Too long data
+ sets are divided into multiple batches. Not fully filled batches are merged with data from the next collection
+ element. If data is remaining after the last element, it is saved as smaller batch. All batches are enumerated
+ beginning from 0. A list with all batch numbers is stored in class's parameter indexes.
+ """
+ index = 0
+ remaining = None
+ for data in self._collection:
+ X, Y = data.get_X(), data.get_Y()
+ if remaining is not None:
+ X, Y = self._concatenate(X, remaining[0]), self._concatenate(Y, remaining[1])
+ length = X[0].shape[0]
+ batches = self._get_number_of_mini_batches(length)
+ for b in range(batches):
+ batch_X, batch_Y = self._get_batch(X, b), self._get_batch(Y, b)
+ self._save_to_pickle(X=batch_X, Y=batch_Y, index=index)
+ index += 1
+ if (batches * self.batch_size) < length: # keep remaining to concatenate with next data element
+ remaining = (self._get_batch(X, batches), self._get_batch(Y, batches))
+ else:
+ remaining = None
+ if remaining is not None: # add remaining as smaller batch
+ self._save_to_pickle(X=remaining[0], Y=remaining[1], index=index)
+ index += 1
+ self.indexes = np.arange(0, index).tolist()
+
+ def _save_to_pickle(self, X: List[np.ndarray], Y: List[np.ndarray], index: int) -> None:
+ """Save data as pickle file with variables X and Y and given index as <index>.pickle ."""
+ data = {"X": X, "Y": Y}
+ file = self._path % index
+ with open(file, "wb") as f:
+ pickle.dump(data, f)
+
+ def _get_number_of_mini_batches(self, number_of_samples: int) -> int:
+ """Return number of mini batches as the floored ration of number of samples to batch size."""
+ return math.floor(number_of_samples / self.batch_size)
+
+ @staticmethod
+ def _cleanup_path(path: str, create_new: bool = True) -> None:
+ """First remove existing path, second create empty path if enabled."""
+ if os.path.exists(path):
+ shutil.rmtree(path)
+ if create_new is True:
+ os.makedirs(path)
+
+ def on_epoch_end(self) -> None:
+ """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)
\ No newline at end of file
diff --git a/test/test_data_handling/test_iterator.py b/test/test_data_handling/test_iterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f1cf683d627495cf958b6c2376a5c42a4c6e61f
--- /dev/null
+++ b/test/test_data_handling/test_iterator.py
@@ -0,0 +1,201 @@
+
+from src.data_handling.iterator import DataCollection, StandardIterator, KerasIterator
+from src.helpers.testing import PyTestAllEqual
+
+import numpy as np
+import pytest
+import os
+import shutil
+
+
+class TestStandardIterator:
+
+ @pytest.fixture
+ def collection(self):
+ return list(range(10))
+
+ def test_blank(self):
+ std_iterator = object.__new__(StandardIterator)
+ assert std_iterator._position is None
+
+ def test_init(self, collection):
+ std_iterator = StandardIterator(collection)
+ assert std_iterator._collection == list(range(10))
+ assert std_iterator._position == 0
+
+ def test_next(self, collection):
+ std_iterator = StandardIterator(collection)
+ for i in range(10):
+ assert i == next(std_iterator)
+ with pytest.raises(StopIteration):
+ next(std_iterator)
+ std_iterator = StandardIterator(collection)
+ for e, i in enumerate(iter(std_iterator)):
+ assert i == e
+
+
+class TestDataCollection:
+
+ @pytest.fixture
+ def collection(self):
+ return list(range(10))
+
+ def test_init(self, collection):
+ data_collection = DataCollection(collection)
+ assert data_collection._collection == collection
+
+ def test_iter(self, collection):
+ data_collection = DataCollection(collection)
+ assert isinstance(iter(data_collection), StandardIterator)
+ for e, i in enumerate(data_collection):
+ assert i == e
+
+
+class DummyData:
+
+ 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]
+
+
+class TestKerasIterator:
+
+ @pytest.fixture
+ def collection(self):
+ coll = []
+ for i in range(3):
+ coll.append(DummyData(50 + i))
+ data_coll = DataCollection(collection=coll)
+ return data_coll
+
+ @pytest.fixture
+ def path(self):
+ p = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata")
+ shutil.rmtree(p, ignore_errors=True) if os.path.exists(p) else None
+ yield p
+ shutil.rmtree(p, ignore_errors=True)
+
+ def test_init(self, collection, path):
+ iterator = KerasIterator(collection, 25, path)
+ assert isinstance(iterator._collection, DataCollection)
+ assert iterator._path == os.path.join(path, "%i.pickle")
+ assert iterator.batch_size == 25
+ assert iterator.shuffle is False
+
+ def test_cleanup_path(self, path):
+ assert os.path.exists(path) is False
+ iterator = object.__new__(KerasIterator)
+ iterator._cleanup_path(path, create_new=False)
+ assert os.path.exists(path) is False
+ iterator._cleanup_path(path)
+ assert os.path.exists(path) is True
+ iterator._cleanup_path(path, create_new=False)
+ assert os.path.exists(path) is False
+
+ def test_get_number_of_mini_batches(self):
+ iterator = object.__new__(KerasIterator)
+ iterator.batch_size = 36
+ assert iterator._get_number_of_mini_batches(30) == 0
+ assert iterator._get_number_of_mini_batches(40) == 1
+ assert iterator._get_number_of_mini_batches(72) == 2
+
+ def test_len(self):
+ iterator = object.__new__(KerasIterator)
+ iterator.indexes = [0, 1, 2, 3, 4, 5]
+ assert len(iterator) == 6
+
+ def test_concatenate(self):
+ arr1 = DummyData(10).get_X()
+ arr2 = DummyData(50).get_X()
+ iterator = object.__new__(KerasIterator)
+ new_arr = iterator._concatenate(arr2, arr1)
+ test_arr = [np.concatenate((arr1[0], arr2[0]), axis=0),
+ np.concatenate((arr1[1], arr2[1]), axis=0),
+ np.concatenate((arr1[2], arr2[2]), axis=0)]
+ for i in range(3):
+ assert PyTestAllEqual([new_arr[i], test_arr[i]])
+
+ def test_get_batch(self):
+ arr = DummyData(20).get_X()
+ iterator = object.__new__(KerasIterator)
+ iterator.batch_size = 19
+ batch1 = iterator._get_batch(arr, 0)
+ assert batch1[0].shape[0] == 19
+ batch2 = iterator._get_batch(arr, 1)
+ assert batch2[0].shape[0] == 1
+
+ def test_save_to_pickle(self, path):
+ os.makedirs(path)
+ d = DummyData(20)
+ X, Y = d.get_X(), d.get_Y()
+ iterator = object.__new__(KerasIterator)
+ iterator._path = os.path.join(path, "%i.pickle")
+ assert os.path.exists(iterator._path % 2) is False
+ iterator._save_to_pickle(X=X, Y=Y, index=2)
+ assert os.path.exists(iterator._path % 2) is True
+
+ def test_prepare_batches(self, collection, path):
+ iterator = object.__new__(KerasIterator)
+ iterator._collection = collection
+ iterator.batch_size = 50
+ iterator.indexes = []
+ iterator._path = os.path.join(path, "%i.pickle")
+ os.makedirs(path)
+ iterator._prepare_batches()
+ assert len(os.listdir(path)) == 4
+ assert len(iterator.indexes) == 4
+ assert len(iterator) == 4
+ assert iterator.indexes == [0, 1, 2, 3]
+
+ def test_prepare_batches_no_remaining(self, path):
+ iterator = object.__new__(KerasIterator)
+ iterator._collection = DataCollection([DummyData(50)])
+ iterator.batch_size = 50
+ iterator.indexes = []
+ iterator._path = os.path.join(path, "%i.pickle")
+ os.makedirs(path)
+ iterator._prepare_batches()
+ assert len(os.listdir(path)) == 1
+ assert len(iterator.indexes) == 1
+ assert len(iterator) == 1
+ assert iterator.indexes == [0]
+
+ def test_data_generation(self, collection, path):
+ iterator = KerasIterator(collection, 50, path)
+ X, Y = iterator._KerasIterator__data_generation(0)
+ expected = next(iter(collection))
+ assert PyTestAllEqual([X, expected.get_X()])
+ assert PyTestAllEqual([Y, expected.get_Y()])
+
+ def test_getitem(self, collection, path):
+ iterator = KerasIterator(collection, 50, path)
+ X, Y = iterator[0]
+ expected = next(iter(collection))
+ assert PyTestAllEqual([X, expected.get_X()])
+ assert PyTestAllEqual([Y, expected.get_Y()])
+ reversed(iterator.indexes)
+ X, Y = iterator[3]
+ assert PyTestAllEqual([X, expected.get_X()])
+ assert PyTestAllEqual([Y, expected.get_Y()])
+
+ def test_on_epoch_end(self):
+ iterator = object.__new__(KerasIterator)
+ iterator.indexes = [0, 1, 2, 3, 4]
+ iterator.shuffle = False
+ iterator.on_epoch_end()
+ assert iterator.indexes == [0, 1, 2, 3, 4]
+ iterator.shuffle = True
+ while iterator.indexes == sorted(iterator.indexes):
+ iterator.on_epoch_end()
+ assert iterator.indexes != [0, 1, 2, 3, 4]
+ assert sorted(iterator.indexes) == [0, 1, 2, 3, 4]
\ No newline at end of file