def feat2():
from data_pre import data_clean as dc, data_load as dl, X_y_split as splt, data_split_scale as dss
# Mean Features
df=dl() # Loading Dataset into Dataframe
df = dc(df)
df_mean = df[df.columns[:11]]
X , y = splt(df_mean)
return dss(X,y)
def feat3():
from data_pre import data_clean as dc, data_load as dl, X_y_split as splt, data_split_scale as dss
# Squared error Features
df=dl() # Loading Dataset into Dataframe
df = dc(df)
df_se = df.drop(df.columns[1:11], axis=1); df_se = df_se.drop(df_se.columns[11:], axis=1)
X , y = splt(df_se)
return dss(X,y)
def feat4():
from data_pre import data_clean as dc, data_load as dl, X_y_split as splt, data_split_scale as dss
# Worst Features
df=dl() # Loading Dataset into Dataframe
df = dc(df)
df_worst = df.drop(df.columns[1:21], axis=1)
X , y = splt(df_worst)
return dss(X,y)
def feat5():
from data_pre import data_clean as dc, data_load as dl, X_y_split as splt, data_split_scale as dss
# Selected Features
df = dl() # Loading Dataset into Dataframe
df = dc(df)
drop_cols = ['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',
'symmetry_worst', 'fractal_dimension_worst','perimeter_mean','perimeter_se',
'area_mean', 'area_se','concavity_mean','concavity_se', 'concave points_mean',
'concave points_se']
df_sf = df.drop(drop_cols, axis=1)
X , y = splt(df_sf)
return dss(X,y)
def feat1():
from data_pre import data_clean as dc, data_load as dl, X_y_split as splt, data_split_scale as dss
# All Features
df=dl() # Loading Dataset into Dataframe
X , y = splt(dc(df))
return dss(X,y)