mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-07-10 11:00:51 +08:00
Dr. Kashif Rasul
88 lines
1.9 KiB
Python
88 lines
1.9 KiB
Python
from abc import ABC, abstractmethod
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from typing import Any, Dict, Iterable, NamedTuple, Sized, List, Optional, Iterator
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import pandas as pd
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from pydantic import BaseModel
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DataEntry = Dict[str, Any]
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class SourceContext(NamedTuple):
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source: str
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row: int
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class FieldName:
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"""
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A bundle of default field names to be used by clients when instantiating
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transformer instances.
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"""
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ITEM_ID = "item_id"
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START = "start"
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TARGET = "target"
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FEAT_STATIC_CAT = "feat_static_cat"
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FEAT_STATIC_REAL = "feat_static_real"
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FEAT_DYNAMIC_CAT = "feat_dynamic_cat"
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FEAT_DYNAMIC_REAL = "feat_dynamic_real"
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FEAT_TIME = "time_feat"
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FEAT_CONST = "feat_dynamic_const"
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FEAT_AGE = "feat_dynamic_age"
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OBSERVED_VALUES = "observed_values"
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IS_PAD = "is_pad"
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FORECAST_START = "forecast_start"
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class Dataset(Sized, Iterable[DataEntry], ABC):
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@abstractmethod
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def __iter__(self) -> Iterator[DataEntry]:
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pass
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@abstractmethod
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def __len__(self):
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pass
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class CategoricalFeatureInfo(BaseModel):
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name: str
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cardinality: str
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class BasicFeatureInfo(BaseModel):
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name: str
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class MetaData(BaseModel):
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freq: str = None
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target: Optional[BasicFeatureInfo] = None
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feat_static_cat: List[CategoricalFeatureInfo] = []
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feat_static_real: List[BasicFeatureInfo] = []
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feat_dynamic_real: List[BasicFeatureInfo] = []
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feat_dynamic_cat: List[CategoricalFeatureInfo] = []
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prediction_length: Optional[int] = None
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class TrainDatasets(NamedTuple):
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"""
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A dataset containing two subsets, one to be used for training purposes,
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and the other for testing purposes, as well as metadata.
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"""
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metadata: MetaData
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train: Dataset
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test: Optional[Dataset] = None
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class DateConstants:
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"""
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Default constants for specific dates.
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"""
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OLDEST_SUPPORTED_TIMESTAMP = pd.Timestamp(1800, 1, 1, 12)
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LATEST_SUPPORTED_TIMESTAMP = pd.Timestamp(2200, 1, 1, 12)
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