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Merge pull request #1191 from mindsdb/new_ts_defaults
[ENH] New ts defaults
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| from typing import Dict, List, Union, Optional | ||
| from copy import deepcopy | ||
|
|
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| import numpy as np | ||
| import pandas as pd | ||
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||
| from lightwood.helpers.log import log | ||
| from lightwood.encoder.base import BaseEncoder | ||
| from lightwood.mixer.base import BaseMixer | ||
| from lightwood.mixer.xgboost import XGBoostMixer | ||
| from lightwood.api.types import PredictionArguments, TimeseriesSettings | ||
| from lightwood.data.encoded_ds import EncodedDs, ConcatedEncodedDs | ||
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| class XGBoostArrayMixer(BaseMixer): | ||
| """XGBoost-based model, intended for usage in forecasting tasks.""" | ||
| models: List[XGBoostMixer] | ||
| submodel_stop_after: float | ||
| target: str | ||
| supports_proba: bool | ||
| ts_analysis: Dict | ||
| tss: TimeseriesSettings | ||
|
|
||
| def __init__( | ||
| self, | ||
| stop_after: float, | ||
| target: str, | ||
| dtype_dict: Dict[str, str], | ||
| input_cols: List[str], | ||
| fit_on_dev: bool, | ||
| target_encoder: BaseEncoder, | ||
| ts_analysis: Dict[str, object], | ||
| use_stl: bool, | ||
| tss: TimeseriesSettings | ||
| ): | ||
| super().__init__(stop_after) | ||
| self.tss = tss | ||
| self.horizon = tss.horizon | ||
| self.submodel_stop_after = stop_after / self.horizon | ||
| self.target = target | ||
| self.offset_pred_cols = [f'{self.target}_timestep_{i}' for i in range(1, self.horizon)] | ||
| if set(input_cols) != {self.tss.order_by}: | ||
| input_cols.remove(self.tss.order_by) | ||
| for col in self.offset_pred_cols: | ||
| dtype_dict[col] = dtype_dict[self.target] | ||
| self.models = [XGBoostMixer(self.submodel_stop_after, | ||
| target_col, | ||
| dtype_dict, | ||
| input_cols, | ||
| False, # fit_on_dev, | ||
| False, # use_optuna | ||
| target_encoder) | ||
| for _, target_col in zip(range(self.horizon), [target] + self.offset_pred_cols)] | ||
| self.ts_analysis = ts_analysis | ||
| self.supports_proba = False | ||
| self.use_stl = False | ||
| self.stable = False | ||
|
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| def _fit(self, train_data: EncodedDs, dev_data: EncodedDs, submodel_method='fit') -> None: | ||
| original_train = deepcopy(train_data.data_frame) | ||
| original_dev = deepcopy(dev_data.data_frame) | ||
|
|
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| for timestep in range(self.horizon): | ||
| getattr(self.models[timestep], submodel_method)(train_data, dev_data) | ||
|
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||
| # restore dfs | ||
| train_data.data_frame = original_train | ||
| dev_data.data_frame = original_dev | ||
|
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| def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: | ||
| log.info('Started fitting LGBM models for array prediction') | ||
| self._fit(train_data, dev_data, submodel_method='fit') | ||
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| def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args: Optional[dict] = None) -> None: | ||
| log.info('Updating array of LGBM models...') | ||
| self._fit(train_data, dev_data, submodel_method='partial_fit') | ||
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| def __call__(self, ds: Union[EncodedDs, ConcatedEncodedDs], | ||
| args: PredictionArguments = PredictionArguments()) -> pd.DataFrame: | ||
| if args.predict_proba: | ||
| log.warning('This model does not output probability estimates') | ||
|
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| original_df = deepcopy(ds.data_frame) | ||
| length = sum(ds.encoded_ds_lengths) if isinstance(ds, ConcatedEncodedDs) else len(ds) | ||
| ydf = pd.DataFrame(0, # zero-filled | ||
| index=np.arange(length), | ||
| columns=[f'prediction_{i}' for i in range(self.horizon)]) | ||
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| for timestep in range(self.horizon): | ||
| ydf[f'prediction_{timestep}'] = self.models[timestep](ds, args)['prediction'].values | ||
|
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| if self.models[0].positive_domain: | ||
| ydf = ydf.clip(0) | ||
|
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| ydf['prediction'] = ydf.values.tolist() | ||
| ds.data_frame = original_df | ||
| return ydf[['prediction']] |
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