mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 05:07:47 +00:00
Mark Aron Szulyovszky
1cd0119589
* fix(FeatureExtractor): apply log to transform some series to normality * feat(DataLoader): add ability of not returning returns when they're not needed (exogenous data), applied log to certain features * feat(FeatureExtractors): added standard scaling for exogenous data * feat(FeatureSelection): scale data with the passed in scaler before doing feature-selection * fix(Config): sweep config * feat(Models): output probability, store it * feat(Core): added caching to select_features() and load_data() * fix(Dependencies): added diskcache * fix(Training): error when creating results DF * feat(Models): added xgboost, fixed tests * refactor(Cache): moved hashing to a separate function, created wrapper functions to separate business logic and caching * fix(Tests): new syntax * fix(Model): XGboost can't handle -1 class, so we'll use the deprecated label_encoder fornow * fix(Model): XGBoost config * feat(Cache): add run_clear_cache script * fix(Pipeline) accidentally re-instatiating all_predictions for each asset
30 lines
784 B
Python
30 lines
784 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_scaling = 'unscaled'
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only_column = 'model_'
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feature_selection = 'off'
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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' |