mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
f762ceed2a
* feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns * fix(Sweep): config * fix(Sweep): name * fix(Sweep): grid * feat(Config): separated sliding_window_size_level1 & sliding_window_size_level2 * feat(Dependencies): added ray, now using it to parallel process feature extraction * fix(Dependencies): added pip explicitly * fix(Dependencies): removed ray from root * fix(Models): average model was probably not taking the right timestamp to average * feat(Config): separated expanding_window_level1 & expanding_window_level2 * fix(Config): set n_features_to_select to the optimal 30
25 lines
566 B
Python
25 lines
566 B
Python
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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def fit(self, X, y, prev_model):
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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):
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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 np.array([prediction])
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def clone(self):
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return self |