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def CalcIV(Xvar, Yvar): N_0 = np.sum(Yvar==0)
N_1 = np.sum(Yvar==1)
N_0_group = np.zeros(np.unique(Xvar).shape)
N_1_group = np.zeros(np.unique(Xvar).shape)
for i in range(len(np.unique(Xvar))):
N_0_group = Yvar[(Xvar == np.unique(Xvar)) & (Yvar == 0)].count()
N_1_group = Yvar[(Xvar == np.unique(Xvar)) & (Yvar == 1)].count()
iv = np.sum((N_0_group/N_0 - N_1_group/N_1) * np.log((N_0_group/N_0)/(N_1_group/N_1)))
return iv
def caliv_batch(df, Kvar, Yvar):
df_Xvar = df.drop([Kvar, Yvar], axis=1)
ivlist = []
for col in df_Xvar.columns:
iv = CalcIV(df[col], df[Yvar])
ivlist.append(iv)
names = list(df_Xvar.columns)
iv_df = pd.DataFrame({'Var': names, 'Iv': ivlist}, columns=['Var', 'Iv'])
return iv_df |
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