mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-07 08:07:49 +00:00
Mark Aron Szulyovszky
cc70d3f907
* feat(Selection): added prototype feature selection python script * feat(Utils): added some helpers for the future from Advances in Financial ML book * feat(Selection): added RFECV * feat(Selection): added configurable feature selection step into pipeline * feat(Config): added level_1 & level_2 default config, PCA before feature selection process starts * feat(Selection): added backup feature selector models if current one can't output feature importance, removed unnecessary array for level-2 models * fix(Training): deal with zero first value coming out of static models * feat(Sweep): added feature selection sweep * fix(Sweep): config problem * fix(Sweep): config * chore(Utils): removed unnecessary purged k-fold crossval class * feat(Config): added dimensionality_reduction as a separate flag * fix(Sweep): config updated * fix(Sweep): sweep name * chore(Config): updated level_2 config to the best performing configuation
28 lines
789 B
Python
28 lines
789 B
Python
from models.base import Model
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import numpy as np
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class StaticMomentumModel(Model):
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'''
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Model that uses only one feature: momentum. It's positive if momentum is greater than 0, otherwise it's negative.
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'''
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data_scaling = 'unscaled'
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only_column = 'mom'
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feature_selection = 'off'
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model_type = 'static'
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def __init__(self, allow_short: bool) -> None:
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super().__init__()
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self.allow_short = allow_short
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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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negative_class = -1.0 if self.allow_short == True else 0.0
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prediction = 1.0 if X[-1][0] > 0 else negative_class
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return np.array([prediction])
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def clone(self):
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return self |