Created
February 16, 2021 04:36
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Gaussian Transforms
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from sklearn.preprocessing import FunctionTransformer, ColumnTransformer | |
log_transform = FunctionTransformer(lambda x: np.log(x)) | |
ct = ColumnTransformer(transformers=[['log_transform',log_transform,list(range(len(X.columns)))]],remainder='passthrough') | |
log_X = ct.fit_transform(X).copy() | |
log_X = pd.DataFrame(log_X,columns=[0,1,2,3]).copy() |
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reciprocal_transform = FunctionTransformer(lambda x: 1/x) | |
ct = ColumnTransformer(transformers=[['reciprocal_transform',reciprocal_transform,list(range(len(X.columns)))]],remainder='passthrough') | |
reci_X = ct.fit_transform(X).copy() | |
reci_X = pd.DataFrame(reci_X,columns=[0,1,2,3]).copy() |
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square_transform = FunctionTransformer(lambda x: x ** 2) | |
ct = ColumnTransformer(transformers=[['square_transform',square_transform,list(range(len(X.columns)))]],remainder='passthrough') | |
square_X = ct.fit_transform(X).copy() | |
square_X = pd.DataFrame(square_X,columns=[0,1,2,3]).copy() |
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from sklearn.preprocessing import PowerTransformer | |
boxcox_transform = PowerTransformer(method="box-cox") | |
ct = ColumnTransformer(transformers=[['boxcox_transform',boxcox_transform,list(range(len(X.columns)))]],remainder='passthrough') | |
boxcox_X = ct.fit_transform(X).copy() | |
boxcox_X = pd.DataFrame(boxcox_X,columns=[0,1,2,3]).copy() |
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boxcox_transform = PowerTransformer(method="box-cox") | |
boxcox_transform.fit(X) | |
boxcox_transform.lambdas_ |
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yeo_transform = PowerTransformer(method="yeo-johnson") | |
ct = ColumnTransformer(transformers=[['yeo_transform',yeo_transform,list(range(len(X.columns)))]],remainder='passthrough') | |
yeo_X = ct.fit_transform(X).copy() | |
yeo_X = pd.DataFrame(yeo_X,columns=[0,1,2,3]).copy() |
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yeo_transform = PowerTransformer(method="yeo-johnson") | |
yeo_transform.fit(X) | |
yeo_transform.lambdas_ |
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