mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
6b26643ece
* feat(Transformations): removed feature-selection pre-processing step completely * fix(Core): removed unnecessary `original_X` * fix(Transformations): use the X_expanding_window to transform subsequent data * fix(RFE): should check for model correctly * fix(Config): only re-train the model every 40 timestamp * fix(MetaLabeling): pass in the correct X to meta-labeling step * fix(Transformation): PCA should at least keep as many features as sliding_window_size * feat(Transformations): cache transformations across the same asset * fix(Tests): missing preloaded_transformations arg * chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys
32 lines
840 B
Python
32 lines
840 B
Python
from __future__ import annotations
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from models.base import Model
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import numpy as np
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class StaticAverageModel(Model):
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'''
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Model that averages .
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'''
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data_transformation = 'original'
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only_column = 'model_'
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model_type = 'static'
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predict_window_size = 'single_timestamp'
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def fit(self, X: np.ndarray, y: np.ndarray) -> None:
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# This is a static model, it can' learn anything
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pass
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def predict(self, X) -> tuple[float, np.ndarray]:
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# Make sure there's data to average
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assert X.shape[1] > 0
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prediction = np.average(X[-1])
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return (prediction, np.array([]))
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def clone(self) -> StaticAverageModel:
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return self
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def get_name(self) -> str:
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return 'static_average'
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def initialize_network(self, input_dim:int, output_dim:int):
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pass |