From a9ea61153fc39905c160d30edd2d2eeef1256352 Mon Sep 17 00:00:00 2001
From: Kashif Rasul <kashif.rasul@zalando.de>
Date: Tue, 29 Dec 2020 16:16:31 +0100
Subject: [PATCH] removed all transforms

---
 pts/transform/transform.py | 157 -------------------------------------
 1 file changed, 157 deletions(-)
 delete mode 100644 pts/transform/transform.py

diff --git a/pts/transform/transform.py b/pts/transform/transform.py
deleted file mode 100644
index 7d7eb13..0000000
--- a/pts/transform/transform.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License").
-# You may not use this file except in compliance with the License.
-# A copy of the License is located at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# or in the "license" file accompanying this file. This file is distributed
-# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
-# express or implied. See the License for the specific language governing
-# permissions and limitations under the License.
-
-
-from abc import ABC, abstractmethod
-from functools import reduce
-from typing import Callable, Iterator, Iterable, List
-
-from gluonts.core.component import validated
-from pts.dataset import DataEntry
-
-MAX_IDLE_TRANSFORMS = 100
-
-
-class Transformation(ABC):
-    @abstractmethod
-    def __call__(
-        self, data_it: Iterable[DataEntry], is_train: bool
-    ) -> Iterator[DataEntry]:
-        pass
-
-    def estimate(self, data_it: Iterable[DataEntry]) -> Iterator[DataEntry]:
-        return data_it  # default is to pass through without estimation
-
-    def chain(self, other: "Transformation") -> "Chain":
-        return Chain([self, other])
-
-    def __add__(self, other: "Transformation") -> "Chain":
-        return self.chain(other)
-
-
-class Chain(Transformation):
-    """
-    Chain multiple transformations together.
-    """
-
-    @validated()
-    def __init__(self, trans: List[Transformation]) -> None:
-        self.transformations = []
-        for transformation in trans:
-            # flatten chains
-            if isinstance(transformation, Chain):
-                self.transformations.extend(transformation.transformations)
-            else:
-                self.transformations.append(transformation)
-
-    def __call__(
-        self, data_it: Iterable[DataEntry], is_train: bool
-    ) -> Iterator[DataEntry]:
-        tmp = data_it
-        for t in self.transformations:
-            tmp = t(tmp, is_train)
-        return tmp
-
-    def estimate(self, data_it: Iterator[DataEntry]) -> Iterator[DataEntry]:
-        return reduce(lambda x, y: y.estimate(x), self.transformations, data_it)
-
-
-class Identity(Transformation):
-    def __call__(
-        self, data_it: Iterable[DataEntry], is_train: bool
-    ) -> Iterator[DataEntry]:
-        return data_it
-
-
-class MapTransformation(Transformation):
-    """
-    Base class for Transformations that returns exactly one result per input in the stream.
-    """
-
-    def __call__(self, data_it: Iterable[DataEntry], is_train: bool) -> Iterator:
-        for data_entry in data_it:
-            try:
-                yield self.map_transform(data_entry.copy(), is_train)
-            except Exception as e:
-                raise e
-
-    @abstractmethod
-    def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
-        pass
-
-
-class SimpleTransformation(MapTransformation):
-    """
-    Element wise transformations that are the same in train and test mode
-    """
-
-    def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
-        return self.transform(data)
-
-    @abstractmethod
-    def transform(self, data: DataEntry) -> DataEntry:
-        pass
-
-
-class AdhocTransform(SimpleTransformation):
-    """
-    Applies a function as a transformation
-    This is called ad-hoc, because it is not serializable.
-    It is OK to use this for experiments and outside of a model pipeline that
-    needs to be serialized.
-    """
-
-    def __init__(self, func: Callable[[DataEntry], DataEntry]) -> None:
-        self.func = func
-
-    def transform(self, data: DataEntry) -> DataEntry:
-        return self.func(data.copy())
-
-
-class FlatMapTransformation(Transformation):
-    """
-    Transformations that yield zero or more results per input, but do not combine
-    elements from the input stream.
-    """
-
-    def __call__(self, data_it: Iterable[DataEntry], is_train: bool) -> Iterator:
-        num_idle_transforms = 0
-        for data_entry in data_it:
-            num_idle_transforms += 1
-            try:
-                for result in self.flatmap_transform(data_entry.copy(), is_train):
-                    num_idle_transforms = 0
-                    yield result
-            except Exception as e:
-                raise e
-            if num_idle_transforms > MAX_IDLE_TRANSFORMS:
-                raise Exception(
-                    f"Reached maximum number of idle transformation calls.\n"
-                    f"This means the transformation looped over "
-                    f"MAX_IDLE_TRANSFORMS={MAX_IDLE_TRANSFORMS} "
-                    f"inputs without returning any output.\n"
-                    f"This occurred in the following transformation:\n{self}"
-                )
-
-    @abstractmethod
-    def flatmap_transform(self, data: DataEntry, is_train: bool) -> Iterator[DataEntry]:
-        pass
-
-
-class FilterTransformation(FlatMapTransformation):
-    def __init__(self, condition: Callable[[DataEntry], bool]) -> None:
-        self.condition = condition
-
-    def flatmap_transform(self, data: DataEntry, is_train: bool) -> Iterator[DataEntry]:
-        if self.condition(data):
-            yield data