mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-07-19 11:27:25 +08:00
117 lines
4.0 KiB
Python
117 lines
4.0 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 functools import lru_cache
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from typing import Callable, List, cast
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import numpy as np
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import pandas as pd
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from pandas.tseries.offsets import Tick
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from .common import DataEntry
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class ProcessStartField:
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def __init__(self, name: str, freq: str) -> None:
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self.name = name
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self.freq = freq
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def __call__(self, data: DataEntry) -> DataEntry:
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try:
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value = ProcessStartField.process(data[self.name], self.freq)
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except (TypeError, ValueError) as e:
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raise Exception(f'Error "{e}" occurred when reading field "{self.name}"')
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data[self.name] = value
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return data
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@staticmethod
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@lru_cache(maxsize=10000)
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def process(string: str, freq: str) -> pd.Timestamp:
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timestamp = pd.Timestamp(string, freq=freq)
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# operate on time information (days, hours, minute, second)
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if isinstance(timestamp.freq, Tick):
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return pd.Timestamp(timestamp.floor(timestamp.freq), timestamp.freq)
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# since we are only interested in the data piece, we normalize the
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# time information
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timestamp = timestamp.replace(
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hour=0, minute=0, second=0, microsecond=0, nanosecond=0
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)
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return timestamp.freq.rollforward(timestamp)
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class ProcessTimeSeriesField:
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def __init__(self, name, is_required: bool, is_static: bool, is_cat: bool) -> None:
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self.name = name
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self.is_required = is_required
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self.req_ndim = 1 if is_static else 2
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self.dtype = np.int64 if is_cat else np.float32
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def __call__(self, data: DataEntry) -> DataEntry:
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value = data.get(self.name, None)
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if value is not None:
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value = np.asarray(value, dtype=self.dtype)
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dim_diff = self.req_ndim - value.ndim
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if dim_diff == 1:
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value = np.expand_dims(a=value, axis=0)
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elif dim_diff != 0:
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raise Exception(
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f"JSON array has bad shape - expected {self.req_ndim} dimensions got {dim_diff}"
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)
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data[self.name] = value
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return data
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elif not self.is_required:
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return data
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else:
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raise Exception(f"JSON object is missing a required field `{self.name}`")
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class ProcessDataEntry:
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def __init__(self, freq: str, one_dim_target: bool = True) -> None:
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self.trans = cast(
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List[Callable[[DataEntry], DataEntry]],
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[
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ProcessStartField("start", freq=freq),
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ProcessTimeSeriesField(
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"target", is_required=True, is_cat=False, is_static=one_dim_target
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),
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ProcessTimeSeriesField(
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"feat_dynamic_cat", is_required=False, is_cat=True, is_static=False
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),
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ProcessTimeSeriesField(
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"feat_dynamic_real",
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is_required=False,
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is_cat=False,
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is_static=False,
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),
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ProcessTimeSeriesField(
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"feat_static_cat", is_required=False, is_cat=True, is_static=True
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),
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ProcessTimeSeriesField(
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"feat_static_real", is_required=False, is_cat=False, is_static=True
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),
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],
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)
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def __call__(self, data: DataEntry) -> DataEntry:
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for t in self.trans:
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data = t(data)
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return data
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