mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 19:27:47 +00:00
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
42 lines
850 B
Python
42 lines
850 B
Python
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from typing import Literal, Optional
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from sklearn.base import clone
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from abc import ABC, abstractmethod, abstractproperty
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class Model(ABC):
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# data_format: Literal['dataframe', 'numpy']
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data_scaling: Literal["scaled", "unscaled"]
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# data_format: Literal["wide", "narrow"]
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only_column: Optional[str]
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@abstractmethod
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def fit(self, X, y):
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pass
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@abstractmethod
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def predict(self, X):
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pass
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@abstractmethod
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def clone(self):
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pass
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class SKLearnModel(Model):
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# data_format = 'numpy'
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data_scaling = 'scaled'
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only_column = None
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def __init__(self, model):
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self.model = model
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def fit(self, X, y):
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self.model.fit(X, y)
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def predict(self, X):
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return self.model.predict(X)
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def clone(self):
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return SKLearnModel(clone(self.model)) |