Merge branch 'lukas_issue143_feat_implement-iterator' into 'lukas_issue135_feat_decouple-join'

Resolve "Implement Iterator"

See merge request toar/machinelearningtools!117

src/data_handling/iterator.py

+
+__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

test/test_data_handling/test_iterator.py

+
+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