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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
d3d7184ea4
* feat(Models): added StaticAverageModel for average ensembling * feat(Models): made sure we only pipe in predictions to StaticAverageModel, added StaticNaiveModel as potential baseline * chore(Models): removed unnecessary commented out code
24 lines
529 B
Python
24 lines
529 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_format = 'dataframe'
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data_scaling = 'unscaled'
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only_column = 'model_'
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def fit(self, X, y):
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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[0])
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return np.array([prediction])
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def clone(self):
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return self |