mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-07-08 02:41:01 +08:00
Kashif Rasul
258 lines
8.2 KiB
Python
258 lines
8.2 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 List
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import numpy as np
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import pandas as pd
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from pts.dataset import DataEntry
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from pts.feature import TimeFeature
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from .transform import SimpleTransformation, MapTransformation
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from .split import shift_timestamp
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def target_transformation_length(
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target: np.array, pred_length: int, is_train: bool
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) -> int:
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return target.shape[-1] + (0 if is_train else pred_length)
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class AddObservedValuesIndicator(SimpleTransformation):
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"""
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Replaces missing values in a numpy array (NaNs) with a dummy value and adds
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an "observed"-indicator that is ``1`` when values are observed and ``0``
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when values are missing.
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Parameters
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----------
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target_field
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Field for which missing values will be replaced
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output_field
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Field name to use for the indicator
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dummy_value
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Value to use for replacing missing values.
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convert_nans
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If set to true (default) missing values will be replaced. Otherwise
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they will not be replaced. In any case the indicator is included in the
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result.
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"""
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def __init__(
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self,
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target_field: str,
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output_field: str,
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dummy_value: int = 0,
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convert_nans: bool = True,
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dtype: np.dtype = np.float32,
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) -> None:
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self.dummy_value = dummy_value
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self.target_field = target_field
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self.output_field = output_field
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self.convert_nans = convert_nans
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self.dtype = dtype
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def transform(self, data: DataEntry) -> DataEntry:
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value = data[self.target_field]
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nan_indices = np.where(np.isnan(value))
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nan_entries = np.isnan(value)
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if self.convert_nans:
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value[nan_indices] = self.dummy_value
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data[self.target_field] = value
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# Invert bool array so that missing values are zeros and store as float
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data[self.output_field] = np.invert(nan_entries).astype(self.dtype)
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return data
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class AddConstFeature(MapTransformation):
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"""
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Expands a `const` value along the time axis as a dynamic feature, where
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the T-dimension is defined as the sum of the `pred_length` parameter and
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the length of a time series specified by the `target_field`.
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If `is_train=True` the feature matrix has the same length as the `target` field.
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If `is_train=False` the feature matrix has length len(target) + pred_length
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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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target_field
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Field containing the target array. The length of this array will be used.
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pred_length
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Prediction length (this is necessary since
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features have to be available in the future)
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const
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Constant value to use.
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dtype
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Numpy dtype to use for resulting array.
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"""
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def __init__(
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self,
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output_field: str,
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target_field: str,
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pred_length: int,
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const: float = 1.0,
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dtype: np.dtype = np.float32,
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) -> None:
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self.pred_length = pred_length
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self.const = const
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self.dtype = dtype
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self.output_field = output_field
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self.target_field = target_field
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def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
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length = target_transformation_length(
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data[self.target_field], self.pred_length, is_train=is_train
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)
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data[self.output_field] = self.const * np.ones(
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shape=(1, length), dtype=self.dtype
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)
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return data
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class AddTimeFeatures(MapTransformation):
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"""
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Adds a set of time features.
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If `is_train=True` the feature matrix has the same length as the `target` field.
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If `is_train=False` the feature matrix has length len(target) + pred_length
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Parameters
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----------
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start_field
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Field with the start time stamp of the time series
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target_field
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Field with the array containing the time series values
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output_field
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Field name for result.
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time_features
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list of time features to use.
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pred_length
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Prediction length
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"""
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def __init__(
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self,
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start_field: str,
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target_field: str,
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output_field: str,
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time_features: List[TimeFeature],
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pred_length: int,
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) -> None:
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self.date_features = time_features
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self.pred_length = pred_length
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self.start_field = start_field
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self.target_field = target_field
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self.output_field = output_field
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self._min_time_point: pd.Timestamp = None
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self._max_time_point: pd.Timestamp = None
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self._full_range_date_features: np.ndarray = None
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self._date_index: pd.DatetimeIndex = None
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def _update_cache(self, start: pd.Timestamp, length: int) -> None:
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end = shift_timestamp(start, length)
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if self._min_time_point is not None:
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if self._min_time_point <= start and end <= self._max_time_point:
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return
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if self._min_time_point is None:
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self._min_time_point = start
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self._max_time_point = end
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self._min_time_point = min(shift_timestamp(start, -50), self._min_time_point)
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self._max_time_point = max(shift_timestamp(end, 50), self._max_time_point)
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self.full_date_range = pd.date_range(
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self._min_time_point, self._max_time_point, freq=start.freq
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)
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self._full_range_date_features = (
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np.vstack([feat(self.full_date_range) for feat in self.date_features])
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if self.date_features
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else None
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)
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self._date_index = pd.Series(
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index=self.full_date_range, data=np.arange(len(self.full_date_range)),
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)
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def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
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start = data[self.start_field]
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length = target_transformation_length(
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data[self.target_field], self.pred_length, is_train=is_train
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)
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self._update_cache(start, length)
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i0 = self._date_index[start]
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features = (
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self._full_range_date_features[..., i0 : i0 + length]
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if self.date_features
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else None
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)
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data[self.output_field] = features
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return data
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class AddAgeFeature(MapTransformation):
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"""
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Adds an 'age' feature to the data_entry.
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The age feature starts with a small value at the start of the time series
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and grows over time.
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If `is_train=True` the age feature has the same length as the `target`
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field.
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If `is_train=False` the age feature has length len(target) + pred_length
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Parameters
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----------
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target_field
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Field with target values (array) of time series
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output_field
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Field name to use for the output.
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pred_length
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Prediction length
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log_scale
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If set to true the age feature grows logarithmically otherwise linearly
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over time.
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"""
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def __init__(
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self,
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target_field: str,
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output_field: str,
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pred_length: int,
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log_scale: bool = True,
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dtype: np.dtype = np.float32,
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) -> None:
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self.pred_length = pred_length
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self.target_field = target_field
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self.feature_name = output_field
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self.log_scale = log_scale
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self._age_feature = np.zeros(0)
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self.dtype = dtype
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def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
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length = target_transformation_length(
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data[self.target_field], self.pred_length, is_train=is_train
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)
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if self.log_scale:
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age = np.log10(2.0 + np.arange(length, dtype=self.dtype))
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else:
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age = np.arange(length, dtype=self.dtype)
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data[self.feature_name] = age.reshape((1, length))
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return data
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