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import pandas as pd
import streamlit as st
# import simple imputer, iter imputer , knn inputer
from sklearn.model_selection import train_test_split as tts
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer
import evaluationer
# import label, ohe,ordinal encoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder
# creating a function for null_handling with different methods for null value imputing, categorical columns encoding and evaluation
null_value_handling_method_num_cols = ["KNN Imputed","SI Mean Imputed","SI Median Imputed","SI Most Frequent Imputed","Iter Imputed"]
null_value_handling_method_cat_cols = ["SI Most Frequent Imputed (categorical)"]
# dict for null value handling method num cols
dict1 = {"KNN Imputed" :KNNImputer(n_neighbors = 5),"SI Mean Imputed":SimpleImputer(strategy = "mean"),"SI Median Imputed":SimpleImputer(strategy = "median"),
"SI Most Frequent Imputed":SimpleImputer(strategy = "most_frequent"),"Iter Imputed":IterativeImputer(max_iter = 200,random_state= 42)}
dict2 = {"SI Most Frequent Imputed (categorical)":SimpleImputer(strategy = "most_frequent")}
# creating dataframe from dict1 and dict2
num_nvh_method_df = pd.DataFrame(data=dict1.values(), index=dict1.keys())
cat_nvh_method_df = pd.DataFrame(data=dict2.values(), index=dict2.keys())
num_imputed_dict = {"KNN Imputed":[],"SI Mean Imputed":[],"SI Median Imputed":[],"SI Most Frequent Imputed":[],"Iter Imputed":[]}
cat_imputed_dict = {"SI Most Frequent Imputed (categorical)":[],"Iter Imputed":[]}
num_imputed_df = pd.DataFrame(data = num_imputed_dict.values(),index = num_imputed_dict.keys())
cat_imputed_df = pd.DataFrame(data = cat_imputed_dict.values(),index = cat_imputed_dict.keys())
final_df = []
def null_handling(X,clean_num_nvh_df,clean_num_nvh_df_cat):
num_nvh_method = clean_num_nvh_df.columns #KNN Imputed","SI Mean Imputed","SI Media
cat_nvh_method = clean_num_nvh_df_cat.columns
for method in num_nvh_method:
X[clean_num_nvh_df[method].dropna().values] = num_nvh_method_df.loc[method].values[0].fit_transform(X[clean_num_nvh_df[method].dropna().values])
for method in cat_nvh_method:
X[clean_num_nvh_df_cat[method].dropna().values] = cat_nvh_method_df.loc[method].values[0].fit_transform(X[clean_num_nvh_df_cat[method].dropna().values])
final_df = X
return final_df
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