mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 13:17:47 +00:00
Mark Aron Szulyovszky
1856fcad22
* feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference() * fix(WalkForward): use Dataframes to call Transformation.fit_transform() * feat(WalkForward): restored option for models to recieve unscaled data * fix(Transformations): output DataFrame as expected * fix(Tests): missing new property
35 lines
942 B
Python
35 lines
942 B
Python
from __future__ import annotations
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from statsmodels.tsa.base.tsa_model import TimeSeriesModel
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from models.base import Model
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import numpy as np
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from copy import deepcopy
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class StatsModel(Model):
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# This is work in progress
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data_transformation = 'transformed'
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only_column = None
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feature_selection = 'on'
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model_type = 'ml'
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predict_window_size = 'single_timestamp'
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def __init__(self, model: TimeSeriesModel):
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self.model = model
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def fit(self, X: np.ndarray, y: np.ndarray) -> None:
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self.model.fit(X, y)
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def predict(self, X) -> tuple[float, np.ndarray]:
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pred = self.model.predict(X).item()
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return (pred, np.array([0]))
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def clone(self) -> StatsModel:
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return StatsModel(deepcopy(self.model))
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def get_name(self) -> str:
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return self.model.__class__.__name__
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def initialize_network(self, input_dim:int, output_dim:int):
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pass
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