Gaussian Transforms

gauss_transform_1.py

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()

gauss_transform_2.py

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()

gauss_transform_3.py

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()

gauss_transform_4.py

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()

gauss_transform_5.py

boxcox_transform = PowerTransformer(method="box-cox")
boxcox_transform.fit(X)
boxcox_transform.lambdas_

gauss_transform_6.py

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()

gauss_transform_7.py

yeo_transform = PowerTransformer(method="yeo-johnson")
yeo_transform.fit(X)
yeo_transform.lambdas_