mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-07-07 18:05:24 +08:00
Kashif Rasul
717 lines
22 KiB
Python
717 lines
22 KiB
Python
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License").
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# You may not use this file except in compliance with the License.
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# A copy of the License is located at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# or in the "license" file accompanying this file. This file is distributed
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# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
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# express or implied. See the License for the specific language governing
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# permissions and limitations under the License.
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from typing import Iterator, List, Tuple, Optional
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import numpy as np
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from scipy.special import erf, erfinv
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import torch
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from pts.exception import assert_pts
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from pts.dataset import DataEntry
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from pts.dataset import DataEntry
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from .transform import (
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SimpleTransformation,
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MapTransformation,
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FlatMapTransformation,
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)
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class AsNumpyArray(SimpleTransformation):
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"""
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Converts the value of a field into a numpy array.
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Parameters
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----------
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expected_ndim
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Expected number of dimensions. Throws an exception if the number of
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dimensions does not match.
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dtype
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numpy dtype to use.
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"""
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def __init__(
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self, field: str, expected_ndim: int, dtype: np.dtype = np.float32
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) -> None:
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self.field = field
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self.expected_ndim = expected_ndim
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self.dtype = dtype
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def transform(self, data: DataEntry) -> DataEntry:
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value = data[self.field]
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if not isinstance(value, float):
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# this lines produces "ValueError: setting an array element with a
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# sequence" on our test
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# value = np.asarray(value, dtype=np.float32)
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# see https://stackoverflow.com/questions/43863748/
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value = np.asarray(list(value), dtype=self.dtype)
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else:
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# ugly: required as list conversion will fail in the case of a
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# float
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value = np.asarray(value, dtype=self.dtype)
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assert_pts(
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value.ndim >= self.expected_ndim,
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'Input for field "{self.field}" does not have the required'
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"dimension (field: {self.field}, ndim observed: {value.ndim}, "
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"expected ndim: {self.expected_ndim})",
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value=value,
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self=self,
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)
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data[self.field] = value
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return data
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class ExpandDimArray(SimpleTransformation):
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"""
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Expand dims in the axis specified, if the axis is not present does nothing.
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(This essentially calls np.expand_dims)
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Parameters
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----------
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field
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Field in dictionary to use
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axis
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Axis to expand (see np.expand_dims for details)
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"""
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def __init__(self, field: str, axis: Optional[int] = None) -> None:
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self.field = field
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self.axis = axis
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def transform(self, data: DataEntry) -> DataEntry:
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if self.axis is not None:
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data[self.field] = np.expand_dims(data[self.field], axis=self.axis)
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return data
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class VstackFeatures(SimpleTransformation):
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"""
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Stack fields together using ``np.vstack``.
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Fields with value ``None`` are ignored.
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Parameters
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----------
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output_field
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Field name to use for the output
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input_fields
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Fields to stack together
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drop_inputs
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If set to true the input fields will be dropped.
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"""
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def __init__(
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self, output_field: str, input_fields: List[str], drop_inputs: bool = True,
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) -> None:
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self.output_field = output_field
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self.input_fields = input_fields
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self.cols_to_drop = (
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[]
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if not drop_inputs
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else [fname for fname in self.input_fields if fname != output_field]
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)
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def transform(self, data: DataEntry) -> DataEntry:
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r = [data[fname] for fname in self.input_fields if data[fname] is not None]
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output = np.vstack(r)
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data[self.output_field] = output
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for fname in self.cols_to_drop:
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del data[fname]
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return data
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class ConcatFeatures(SimpleTransformation):
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"""
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Concatenate fields together using ``np.concatenate``.
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Fields with value ``None`` are ignored.
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Parameters
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----------
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output_field
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Field name to use for the output
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input_fields
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Fields to stack together
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drop_inputs
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If set to true the input fields will be dropped.
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"""
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def __init__(
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self, output_field: str, input_fields: List[str], drop_inputs: bool = True,
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) -> None:
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self.output_field = output_field
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self.input_fields = input_fields
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self.cols_to_drop = (
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[]
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if not drop_inputs
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else [fname for fname in self.input_fields if fname != output_field]
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)
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def transform(self, data: DataEntry) -> DataEntry:
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r = [data[fname] for fname in self.input_fields if data[fname] is not None]
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output = np.concatenate(r)
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data[self.output_field] = output
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for fname in self.cols_to_drop:
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del data[fname]
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return data
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class SwapAxes(SimpleTransformation):
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"""
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Apply `np.swapaxes` to fields.
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Parameters
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----------
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input_fields
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Field to apply to
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axes
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Axes to use
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"""
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def __init__(self, input_fields: List[str], axes: Tuple[int, int]) -> None:
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self.input_fields = input_fields
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self.axis1, self.axis2 = axes
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def transform(self, data: DataEntry) -> DataEntry:
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for field in self.input_fields:
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data[field] = self.swap(data[field])
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return data
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def swap(self, v):
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if isinstance(v, np.ndarray):
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return np.swapaxes(v, self.axis1, self.axis2)
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if isinstance(v, list):
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return [self.swap(x) for x in v]
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else:
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raise ValueError(
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f"Unexpected field type {type(v).__name__}, expected "
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f"np.ndarray or list[np.ndarray]"
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)
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class ListFeatures(SimpleTransformation):
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"""
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Creates a new field which contains a list of features.
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Parameters
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----------
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output_field
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Field name for output
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input_fields
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Fields to combine into list
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drop_inputs
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If true the input fields will be removed from the result.
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"""
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def __init__(
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self, output_field: str, input_fields: List[str], drop_inputs: bool = True,
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) -> None:
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self.output_field = output_field
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self.input_fields = input_fields
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self.cols_to_drop = (
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[]
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if not drop_inputs
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else [fname for fname in self.input_fields if fname != output_field]
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)
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def transform(self, data: DataEntry) -> DataEntry:
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data[self.output_field] = [data[fname] for fname in self.input_fields]
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for fname in self.cols_to_drop:
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del data[fname]
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return data
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class TargetDimIndicator(SimpleTransformation):
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"""
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Label-encoding of the target dimensions.
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"""
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def __init__(self, field_name: str, target_field: str) -> None:
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self.field_name = field_name
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self.target_field = target_field
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def transform(self, data: DataEntry) -> DataEntry:
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data[self.field_name] = np.arange(0, data[self.target_field].shape[0])
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return data
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class SampleTargetDim(FlatMapTransformation):
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"""
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Samples random dimensions from the target at training time.
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"""
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def __init__(
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self,
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field_name: str,
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target_field: str,
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observed_values_field: str,
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num_samples: int,
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shuffle: bool = True,
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) -> None:
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self.field_name = field_name
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self.target_field = target_field
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self.observed_values_field = observed_values_field
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self.num_samples = num_samples
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self.shuffle = shuffle
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def flatmap_transform(
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self, data: DataEntry, is_train: bool, slice_future_target: bool = True
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) -> Iterator[DataEntry]:
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if not is_train:
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yield data
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else:
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# (target_dim,)
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target_dimensions = data[self.field_name]
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if self.shuffle:
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np.random.shuffle(target_dimensions)
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target_dimensions = target_dimensions[: self.num_samples]
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data[self.field_name] = target_dimensions
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# (seq_len, target_dim) -> (seq_len, num_samples)
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for field in [
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f"past_{self.target_field}",
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f"future_{self.target_field}",
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f"past_{self.observed_values_field}",
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f"future_{self.observed_values_field}",
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]:
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data[field] = data[field][:, target_dimensions]
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yield data
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class CDFtoGaussianTransform(MapTransformation):
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"""
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Marginal transformation that transforms the target via an empirical CDF
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to a standard gaussian as described here: https://arxiv.org/abs/1910.03002
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To be used in conjunction with a multivariate gaussian to from a copula.
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Note that this transformation is currently intended for multivariate
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targets only.
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"""
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def __init__(
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self,
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target_dim: int,
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target_field: str,
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observed_values_field: str,
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cdf_suffix="_cdf",
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max_context_length: Optional[int] = None,
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) -> None:
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"""
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Constructor for CDFtoGaussianTransform.
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Parameters
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----------
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target_dim
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Dimensionality of the target.
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target_field
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Field that will be transformed.
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observed_values_field
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Field that indicates observed values.
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cdf_suffix
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Suffix to mark the field with the transformed target.
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max_context_length
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Sets the maximum context length for the empirical CDF.
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"""
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self.target_field = target_field
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self.past_target_field = "past_" + self.target_field
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self.future_target_field = "future_" + self.target_field
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self.past_observed_field = f"past_{observed_values_field}"
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self.sort_target_field = f"past_{target_field}_sorted"
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self.slopes_field = "slopes"
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self.intercepts_field = "intercepts"
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self.cdf_suffix = cdf_suffix
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self.max_context_length = max_context_length
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self.target_dim = target_dim
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def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
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self._preprocess_data(data, is_train=is_train)
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self._calc_pw_linear_params(data)
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for target_field in [self.past_target_field, self.future_target_field]:
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data[target_field + self.cdf_suffix] = self.standard_gaussian_ppf(
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self._empirical_cdf_forward_transform(
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data[self.sort_target_field],
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data[target_field],
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data[self.slopes_field],
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data[self.intercepts_field],
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)
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)
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return data
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def _preprocess_data(self, data: DataEntry, is_train: bool):
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"""
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Performs several preprocess operations for computing the empirical CDF.
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1) Reshaping the data.
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2) Normalizing the target length.
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3) Adding noise to avoid zero slopes (training only)
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4) Sorting the target to compute the empirical CDF
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Parameters
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----------
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data
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DataEntry with input data.
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is_train
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if is_train is True, this function adds noise to the target to
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avoid zero slopes in the piece-wise linear function.
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Returns
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-------
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"""
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# (target_length, target_dim)
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past_target_vec = data[self.past_target_field].copy()
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# pick only observed values
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target_length, target_dim = past_target_vec.shape
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# (target_length, target_dim)
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past_observed = (data[self.past_observed_field] > 0) * (
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data["past_is_pad"].reshape((-1, 1)) == 0
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)
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assert past_observed.ndim == 2
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assert target_dim == self.target_dim
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past_target_vec = past_target_vec[past_observed.min(axis=1)]
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assert past_target_vec.ndim == 2
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assert past_target_vec.shape[1] == self.target_dim
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expected_length = (
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target_length
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if self.max_context_length is None
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else self.max_context_length
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)
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if target_length != expected_length:
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# Fills values in the case where past_target_vec.shape[-1] <
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# target_length
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# as dataset.loader.BatchBuffer does not support varying shapes
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past_target_vec = CDFtoGaussianTransform._fill(
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past_target_vec, expected_length
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)
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# sorts along the time dimension to compute empirical CDF of each
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# dimension
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if is_train:
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past_target_vec = self._add_noise(past_target_vec)
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past_target_vec.sort(axis=0)
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assert past_target_vec.shape == (expected_length, self.target_dim)
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data[self.sort_target_field] = past_target_vec
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def _calc_pw_linear_params(self, data: DataEntry):
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"""
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Calculates the piece-wise linear parameters to interpolate between
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the observed values in the empirical CDF.
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Once current limitation is that we use a zero slope line as the last
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piece. Thus, we cannot forecast anything higher than the highest
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observed value.
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Parameters
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----------
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data
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Input data entry containing a sorted target field.
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Returns
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-------
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"""
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sorted_target = data[self.sort_target_field]
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sorted_target_length, target_dim = sorted_target.shape
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quantiles = np.stack(
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[np.arange(sorted_target_length) for _ in range(target_dim)], axis=1,
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) / float(sorted_target_length)
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x_diff = np.diff(sorted_target, axis=0)
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y_diff = np.diff(quantiles, axis=0)
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# Calculate slopes of the pw-linear pieces.
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slopes = np.where(x_diff == 0.0, np.zeros_like(x_diff), y_diff / x_diff)
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zeroes = np.zeros_like(np.expand_dims(slopes[0, :], axis=0))
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slopes = np.append(slopes, zeroes, axis=0)
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# Calculate intercepts of the pw-linear pieces.
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intercepts = quantiles - slopes * sorted_target
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# Populate new fields with the piece-wise linear parameters.
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data[self.slopes_field] = slopes
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data[self.intercepts_field] = intercepts
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def _empirical_cdf_forward_transform(
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self,
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sorted_values: np.ndarray,
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values: np.ndarray,
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slopes: np.ndarray,
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intercepts: np.ndarray,
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) -> np.ndarray:
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"""
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Applies the empirical CDF forward transformation.
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Parameters
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----------
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sorted_values
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Sorted target vector.
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values
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Values (real valued) that will be transformed to empirical CDF
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values.
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slopes
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Slopes of the piece-wise linear function.
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intercepts
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Intercepts of the piece-wise linear function.
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Returns
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-------
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quantiles
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Empirical CDF quantiles in [0, 1] interval with winzorized cutoff.
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"""
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m = sorted_values.shape[0]
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quantiles = self._forward_transform(sorted_values, values, slopes, intercepts)
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quantiles = np.clip(
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quantiles, self.winsorized_cutoff(m), 1 - self.winsorized_cutoff(m)
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)
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return quantiles
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@staticmethod
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def _add_noise(x: np.array) -> np.array:
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scale_noise = 0.2
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std = np.sqrt(
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(np.square(x - x.mean(axis=1, keepdims=True))).mean(axis=1, keepdims=True)
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)
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noise = np.random.normal(
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loc=np.zeros_like(x), scale=np.ones_like(x) * std * scale_noise
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)
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x = x + noise
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return x
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@staticmethod
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def _search_sorted(sorted_vec: np.array, to_insert_vec: np.array) -> np.array:
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"""
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Finds the indices of the active piece-wise linear function.
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Parameters
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----------
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sorted_vec
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Sorted target vector.
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to_insert_vec
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Vector for which the indicies of the active linear functions
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will be computed
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Returns
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-------
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indices
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Indices mapping to the active linear function.
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"""
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indices_left = np.searchsorted(sorted_vec, to_insert_vec, side="left")
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indices_right = np.searchsorted(sorted_vec, to_insert_vec, side="right")
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indices = indices_left + (indices_right - indices_left) // 2
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indices = indices - 1
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indices = np.minimum(indices, len(sorted_vec) - 1)
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indices[indices < 0] = 0
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return indices
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def _forward_transform(
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self,
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sorted_vec: np.array,
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target: np.array,
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slopes: np.array,
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intercepts: np.array,
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) -> np.array:
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"""
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Applies the forward transformation to the marginals of the multivariate
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target. Target (real valued) -> empirical cdf [0, 1]
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Parameters
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----------
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sorted_vec
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Sorted (past) target vector.
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target
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Target that will be transformed.
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slopes
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Slopes of the piece-wise linear function.
|
|
intercepts
|
|
Intercepts of the piece-wise linear function
|
|
|
|
Returns
|
|
-------
|
|
transformed_target
|
|
Transformed target vector.
|
|
"""
|
|
transformed = list()
|
|
for sorted, t, slope, intercept in zip(
|
|
sorted_vec.transpose(),
|
|
target.transpose(),
|
|
slopes.transpose(),
|
|
intercepts.transpose(),
|
|
):
|
|
indices = self._search_sorted(sorted, t)
|
|
transformed_value = slope[indices] * t + intercept[indices]
|
|
transformed.append(transformed_value)
|
|
return np.array(transformed).transpose()
|
|
|
|
@staticmethod
|
|
def standard_gaussian_cdf(x: np.array) -> np.array:
|
|
u = x / (np.sqrt(2.0))
|
|
return (erf(u) + 1.0) / 2.0
|
|
|
|
@staticmethod
|
|
def standard_gaussian_ppf(y: np.array) -> np.array:
|
|
y_clipped = np.clip(y, a_min=1.0e-6, a_max=1.0 - 1.0e-6)
|
|
return np.sqrt(2.0) * erfinv(2.0 * y_clipped - 1.0)
|
|
|
|
@staticmethod
|
|
def winsorized_cutoff(m: np.array) -> np.array:
|
|
"""
|
|
Apply truncation to the empirical CDF estimator to reduce variance as
|
|
described here: https://arxiv.org/abs/0903.0649
|
|
|
|
Parameters
|
|
----------
|
|
m
|
|
Input array with empirical CDF values.
|
|
|
|
Returns
|
|
-------
|
|
res
|
|
Truncated empirical CDf values.
|
|
"""
|
|
res = 1 / (4 * m ** 0.25 * np.sqrt(3.14 * np.log(m)))
|
|
assert 0 < res < 1
|
|
return res
|
|
|
|
@staticmethod
|
|
def _fill(target: np.ndarray, expected_length: int) -> np.ndarray:
|
|
"""
|
|
Makes sure target has at least expected_length time-units by repeating
|
|
it or using zeros.
|
|
|
|
Parameters
|
|
----------
|
|
target : shape (seq_len, dim)
|
|
expected_length
|
|
|
|
Returns
|
|
-------
|
|
array of shape (target_length, dim)
|
|
"""
|
|
|
|
current_length, target_dim = target.shape
|
|
if current_length == 0:
|
|
# todo handle the case with no observation better,
|
|
# we could use dataset statistics but for now we use zeros
|
|
filled_target = np.zeros((expected_length, target_dim))
|
|
elif current_length < expected_length:
|
|
filled_target = np.vstack(
|
|
[target for _ in range(expected_length // current_length + 1)]
|
|
)
|
|
filled_target = filled_target[:expected_length]
|
|
elif current_length > expected_length:
|
|
filled_target = target[-expected_length:]
|
|
else:
|
|
filled_target = target
|
|
|
|
assert filled_target.shape == (expected_length, target_dim)
|
|
|
|
return filled_target
|
|
|
|
|
|
def cdf_to_gaussian_forward_transform(
|
|
input_batch: DataEntry, outputs: torch.Tensor
|
|
) -> np.ndarray:
|
|
"""
|
|
Forward transformation of the CDFtoGaussianTransform.
|
|
|
|
Parameters
|
|
----------
|
|
input_batch
|
|
Input data to the predictor.
|
|
outputs
|
|
Predictor outputs.
|
|
Returns
|
|
-------
|
|
outputs
|
|
Forward transformed outputs.
|
|
|
|
"""
|
|
|
|
def _empirical_cdf_inverse_transform(
|
|
batch_target_sorted: torch.Tensor,
|
|
batch_predictions: torch.Tensor,
|
|
slopes: torch.Tensor,
|
|
intercepts: torch.Tensor,
|
|
) -> np.ndarray:
|
|
"""
|
|
Apply forward transformation of the empirical CDF.
|
|
|
|
Parameters
|
|
----------
|
|
batch_target_sorted
|
|
Sorted targets of the input batch.
|
|
batch_predictions
|
|
Predictions of the underlying probability distribution
|
|
slopes
|
|
Slopes of the piece-wise linear function.
|
|
intercepts
|
|
Intercepts of the piece-wise linear function.
|
|
|
|
Returns
|
|
-------
|
|
outputs
|
|
Forward transformed outputs.
|
|
|
|
"""
|
|
slopes = slopes.cpu().numpy()
|
|
intercepts = intercepts.cpu().numpy()
|
|
|
|
batch_target_sorted = batch_target_sorted.cpu().numpy()
|
|
_, num_timesteps, _ = batch_target_sorted.shape
|
|
indices = np.floor(batch_predictions * num_timesteps)
|
|
# indices = indices - 1
|
|
# for now project into [0, 1]
|
|
indices = np.clip(indices, 0, num_timesteps - 1)
|
|
indices = indices.astype(np.int)
|
|
|
|
transformed = np.where(
|
|
np.take_along_axis(slopes, indices, axis=1) != 0.0,
|
|
(batch_predictions - np.take_along_axis(intercepts, indices, axis=1))
|
|
/ np.take_along_axis(slopes, indices, axis=1),
|
|
np.take_along_axis(batch_target_sorted, indices, axis=1),
|
|
)
|
|
return transformed
|
|
|
|
# applies inverse cdf to all outputs
|
|
_, samples, _, _ = outputs.shape
|
|
for sample_index in range(0, samples):
|
|
outputs[:, sample_index, :, :] = _empirical_cdf_inverse_transform(
|
|
input_batch["past_target_sorted"],
|
|
CDFtoGaussianTransform.standard_gaussian_cdf(
|
|
outputs[:, sample_index, :, :]
|
|
),
|
|
input_batch["slopes"],
|
|
input_batch["intercepts"],
|
|
)
|
|
return outputs
|