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July 8, 2024 12:03
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Los custom transfomer con los que he trabajado hasta ahora eran desarrollados con una clase. Existe una nueva manera a través de la funcionalidad FunctionTransformer de tal manera que aplicando esta a una función la convierte en un transfomer usable
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import pandas as pd | |
from sklearn.preprocessing import FunctionTransformer | |
from sklearn.pipeline import Pipeline | |
# example | |
from sklearn.linear_model import LogisticRegression | |
# X, y | |
def get_dummies_size(df): | |
return pd.get_dummies(df, columns=['size']) | |
# Using FunctionTransformer to integrate pd.get_dummies | |
dummies_transformer = FunctionTransformer(get_dummies_size) | |
# Creating a pipeline | |
pipeline = Pipeline(steps=[ | |
('dummies', dummies_transformer), | |
('classifier', LogisticRegression()) | |
]) | |
# fit | |
pipeline.fit(X,y) |
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# If you want to check the transformation results you can use named_steps | |
preprocessed_X = pipeline.named_steps['dummies'].fit_transform(X) |
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