auto_optimizer.py

import pandas as pd
import numpy as np
import streamlit as st
from sklearn.impute import KNNImputer,SimpleImputer,IterativeImputer
import best_tts, evaluationer,models
from sklearn.experimental import enable_iterative_imputer
from sklearn.model_selection import train_test_split as tts
from collections import Counter
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import root_mean_squared_error
import seaborn as sns
from sklearn.decomposition import PCA
import grid_search_cv
import matplotlib.pyplot as plt
import outliers,best_tts
import feature_selections
def Auto_optimizer(X,y,eva,model,model_name,test= None):
    if st.button("Train Regression Model"):
        num_cols = X.select_dtypes(exclude = "O").columns
        cat_cols = X.select_dtypes(include = "O").columns
        st.write("Num_cols",tuple(num_cols))
        st.write("cat_cols",tuple(cat_cols))
        
    # check for Duplicate and drop duplicated in X
        
        if len(X.isnull().sum()[(X.isnull().sum()/len(X)*100) >40]) >0:
            X = X.drop(columns = X.isnull().sum()[(X.isnull().sum()/len(X)*100) >40].index)
            st.write("Columns with more than 40% null values removed")
        # st.write("csx",X)

        len_null = X.isnull().sum().sum() 

        st.write(f"There are {len_null} null values in Train")

        knn_imputed_num_X = X.copy()
        si_mean_imputed_num_X = X.copy()
        # st.write("sf",si_mean_imputed_num_X)
        si_median_imputed_num_X = X.copy()
        si_most_frequent_imputed_num_X = X.copy()
        iter_imputed_num_X = X.copy()
        knn_imputed_X_cat_dropped = knn_imputed_num_X.copy()
        si_mean_imputed_X_cat_dropped = si_mean_imputed_num_X.copy()
        si_median_imputed_X_cat_dropped = si_median_imputed_num_X.copy()
        si_most_frequent_imputed_X_cat_dropped = si_most_frequent_imputed_num_X.copy()
        iter_imputed_X_cat_dropped = iter_imputed_num_X.copy()
        if len_null >0:
        
            if X[num_cols].isnull().sum().sum() >0:

                knn_imputer = KNNImputer(n_neighbors = 5) 
                knn_imputed_num_X[num_cols] = knn_imputer.fit_transform(knn_imputed_num_X[num_cols])
                si_imputer = SimpleImputer(strategy = "mean")
                si_mean_imputed_num_X[num_cols] = si_imputer.fit_transform(si_mean_imputed_num_X[num_cols])
                si_imputer = SimpleImputer(strategy = "median")
                si_median_imputed_num_X[num_cols] = si_imputer.fit_transform(si_median_imputed_num_X[num_cols])
                si_imputer = SimpleImputer(strategy = "most_frequent")
                si_most_frequent_imputed_num_X[num_cols] = si_imputer.fit_transform(si_most_frequent_imputed_num_X[num_cols])
                iter_imputer = IterativeImputer(max_iter = 200,random_state= 42)
                iter_imputed_num_X[num_cols] = iter_imputer.fit_transform(iter_imputed_num_X[num_cols])
            knn_imputed_X_cat_dropped = knn_imputed_num_X.copy()
            si_mean_imputed_X_cat_dropped = si_mean_imputed_num_X.copy()
            si_median_imputed_X_cat_dropped = si_median_imputed_num_X.copy()
            si_most_frequent_imputed_X_cat_dropped = si_most_frequent_imputed_num_X.copy()
            iter_imputed_X_cat_dropped = iter_imputed_num_X.copy()

            if X[cat_cols].isnull().sum().sum() >0:
                # treating missing values in categorical columns
                # st.write("si_mean_imputed_num_X",si_mean_imputed_num_X)
                si_imputer = SimpleImputer(strategy = "most_frequent")
                
                knn_imputed_num_X[cat_cols] = si_imputer.fit_transform(knn_imputed_num_X[cat_cols])
                si_imputer = SimpleImputer(strategy = "most_frequent")
                si_mean_imputed_num_X.loc[:,cat_cols] = si_imputer.fit_transform(si_mean_imputed_num_X.loc[:,cat_cols])
                # st.write("si_mean_imputed_num_X",si_mean_imputed_num_X)
                si_median_imputed_num_X[cat_cols] = si_imputer.fit_transform(si_median_imputed_num_X[cat_cols])
                si_most_frequent_imputed_num_X[cat_cols] = si_imputer.fit_transform(si_most_frequent_imputed_num_X[cat_cols])
                iter_imputed_num_X[cat_cols] = si_imputer.fit_transform(iter_imputed_num_X[cat_cols])

                knn_imputed_X_cat_dropped = knn_imputed_X_cat_dropped.dropna()
                si_mean_imputed_X_cat_dropped =si_mean_imputed_X_cat_dropped.dropna()
                si_median_imputed_X_cat_dropped =si_median_imputed_X_cat_dropped.dropna()
                si_most_frequent_imputed_X_cat_dropped =si_most_frequent_imputed_X_cat_dropped.dropna()
                iter_imputed_X_cat_dropped =iter_imputed_X_cat_dropped.dropna()
                
                                                            
        miss_val_dropped_X = X.dropna()
            
            # list of dataframes
            
        list_X_after_missing_values= [knn_imputed_num_X,
                                si_mean_imputed_num_X,
                                si_median_imputed_num_X,
                                si_most_frequent_imputed_num_X,
                                iter_imputed_num_X,
                                knn_imputed_X_cat_dropped,
                                si_mean_imputed_X_cat_dropped,
                                si_median_imputed_X_cat_dropped,
                                si_most_frequent_imputed_X_cat_dropped,
                                iter_imputed_X_cat_dropped,
                                miss_val_dropped_X]
        list_X_after_missing_values_names= ["knn_imputed_num_X",
                                "si_mean_imputed_num_X",
                                "si_median_imputed_num_X",
                                "si_most_frequent_imputed_num_X",
                                "iter_imputed_num_X",
                                "knn_imputed_X_cat_dropped",
                                "si_mean_imputed_X_cat_dropped",
                                "si_median_imputed_X_cat_dropped",
                                "si_most_frequent_imputed_X_cat_dropped",
                                "iter_imputed_X_cat_dropped",
                                "miss_val_dropped_X"]
        # st.write("si_most_frequent_imputed_num_X",si_most_frequent_imputed_num_X,)   
        ord_enc_cols = []
        ohe_enc_cols = []

        if len(cat_cols) == 0:
            st.write("No Categorical Columns in Train")
        else:
            st.write("Select Columns for Ordinal Encoding")
            for column in cat_cols:
                selected = st.checkbox(column)
                if selected:
                    st.write(f"No. of Unique value in {column} column are", X[column].nunique())
                    ord_enc_cols.append(column)
        ohe_enc_cols = set(cat_cols) -set(ord_enc_cols)
        ohe_enc_cols = list(ohe_enc_cols)
        
        if len(ord_enc_cols)>0:
                    st.write("ordinal encoded columns" ,tuple(ord_enc_cols))
        if len(ohe_enc_cols)>0:
            st.write("one hot encoded columns" ,tuple(ohe_enc_cols))
        
        if len(ord_enc_cols)>0:
            
            ordinal_order_vals = []

            for column in ord_enc_cols:
                unique_vals = X.dropna()[column].unique()
                # st.write(f"No. of Unique value in {column} column are", len(unique_vals))
                                
                ordered_unique_vals = st.multiselect("Select values in order for Ordinal Encoding",unique_vals,unique_vals)
                ordinal_order_vals.append(ordered_unique_vals)
            
            st.write("order of values for Ordinal Encoding",tuple(ordinal_order_vals))  

            if len_null > 0: 

                for df_name, df in enumerate(list_X_after_missing_values):
                    # st.write(f"{list_X_after_missing_values_names[df_name]}",df)
                    from sklearn.preprocessing import OrdinalEncoder
                    ord = OrdinalEncoder(categories=ordinal_order_vals,handle_unknown= "use_encoded_value",unknown_value = -1 )
                    df[ord_enc_cols] = ord.fit_transform(df[ord_enc_cols])
                    # st.write(f"{list_X_after_missing_values_names[df_name]}",df)
            else :  
                from sklearn.preprocessing import OrdinalEncoder
                ord = OrdinalEncoder(categories=ordinal_order_vals,handle_unknown= "use_encoded_value",unknown_value = -1 )
                X[ord_enc_cols] = ord.fit_transform(X[ord_enc_cols])

            st.write("Ordinal Encoding Completed ✅")

        if len(ohe_enc_cols)>0:
            if len_null > 0:
                for df_name, df in enumerate(list_X_after_missing_values):
                    from sklearn.preprocessing import OneHotEncoder
                    ohe = OneHotEncoder(sparse_output = False,handle_unknown = "ignore")
                    pd.options.mode.chained_assignment = None
                    df.loc[:, ohe.get_feature_names_out()] = ohe.fit_transform(df[ohe_enc_cols])
                    df.drop(columns = ohe_enc_cols,inplace = True)
                    pd.options.mode.chained_assignment = 'warn'
            else:
                from sklearn.preprocessing import OneHotEncoder
                ohe = OneHotEncoder(sparse_output = False,handle_unknown = "ignore")
                pd.options.mode.chained_assignment = None
                X.loc[:, ohe.get_feature_names_out()] = ohe.fit_transform(X[ohe_enc_cols])
                X.drop(columns = ohe_enc_cols,inplace = True)
                pd.options.mode.chained_assignment = 'warn'
            st.write("OneHot Encoding Completed ✅")


        if len(ohe_enc_cols)>0:
            if len_null > 0:
                for name,df in enumerate(list_X_after_missing_values):
                    X_train,X_test,y_train,y_test = tts(df,y[df.index],test_size =.2 ,random_state = 42)
                    #  best_tts.best_tts(df,y,model,eva)
                    evaluationer.evaluation(f"{list_X_after_missing_values_names[name]}",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva)
            else:
                X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size =.2 ,random_state = 42)
                #  best_tts.best_tts(X,y,model,eva)
                    
                evaluationer.evaluation(f"baseline_model",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva)

        if len_null >0:
            for name,df in enumerate(list_X_after_missing_values):
                X_train,X_test,y_train,y_test = tts(df,y[df.index],test_size =.2 ,random_state = 42)
                
                evaluationer.evaluation(f"{list_X_after_missing_values_names[name]}",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva)

        if eva == "class":
            counter = Counter(y)
            total = sum(counter.values())
            balance_ratio = {cls: count / total for cls, count in counter.items()}
            num_classes = len(balance_ratio)
            ideal_ratio = 1 / num_classes
            a = all(abs(ratio - ideal_ratio) <= 0.1 * ideal_ratio for ratio in balance_ratio.values())
            if a == True:
                st.write("Balanced Dataset ✅")
                st.write("Using accuracy for Evaluation")
                value = "test_acc"
            else:
                st.write("Unbalanced Dataset ❌")
                st.write("Using F1 score for Evaluation")
                value = "test_f1"
            
            evaluationer.classification_evaluation_df.sort_values(by = value,inplace= True)
            name = str(evaluationer.classification_evaluation_df.iloc[-1,0])
            st.write("df name",evaluationer.classification_evaluation_df.iloc[-1,0])
            if len_null >0:
                b = list_X_after_missing_values_names.index(name)
                
                st.write("df",list_X_after_missing_values[b])
                X = list_X_after_missing_values[b]
        if eva == "reg":
            st.write("Using R2 score for Evaluation",evaluationer.reg_evaluation_df)
            value = "test_r2"
            evaluationer.reg_evaluation_df.sort_values(by = value,inplace= True)
            
            name = str(evaluationer.reg_evaluation_df.iloc[-1,0])
            
            if len_null >0:
                b = list_X_after_missing_values_names.index(name)
                
                st.write("df",list_X_after_missing_values[b])
                X = list_X_after_missing_values[b]
        

        # Create a figure and axes
        num_plots = len(num_cols)
        cols = 2  # Number of columns in the subplot grid
        rows = (num_plots + cols - 1) // cols  # Calculate the number of rows needed

        fig, axes = plt.subplots(rows, cols, figsize=(15, 5 * rows))

        # Flatten the axes array for easy iteration, and remove any excess subplots
        axes = axes.flatten()
        for ax in axes[num_plots:]:
            fig.delaxes(ax)

        for i, col in enumerate(num_cols):
            sns.histplot(X[col], ax=axes[i],kde = True,color=sns.color_palette('Oranges', as_cmap=True)(0.7))
            axes[i].set_title(col)

        # Adjust layout
        plt.tight_layout()

        # Show the plot in Streamlit
        st.pyplot(fig)

        # Create a figure and axes
        num_plots = len(num_cols)
        cols = 3  # Number of columns in the subplot grid
        rows = (num_plots + cols - 1) // cols  # Calculate the number of rows needed

        fig, axes = plt.subplots(rows, cols, figsize=(15, 5 * rows))

        # Flatten the axes array for easy iteration, and remove any excess subplots
        axes = axes.flatten()
        for ax in axes[num_plots:]:
            fig.delaxes(ax)

        for i, col in enumerate(num_cols):
            sns.boxplot(y=X[col], ax=axes[i],palette="magma")
            axes[i].set_title(col)

        # Adjust layout
        plt.tight_layout()

        # Show the plot in Streamlit
        st.pyplot(fig)
        
        outlier_cols = st.multiselect("De-Select columns for Detecting Outliers", num_cols,default= list(num_cols))

        st.write("Checking for Outliers")
        outliers_df_X,outlier_indexes = outliers.detect_outliers(X,list(outlier_cols))
        st.write("Outliers in Dataframe Summary",outliers_df_X)
        st.write("Columns for Outliers handling",tuple(outliers_df_X["columns name"]))

        select_outlier_cols = st.multiselect("Select columns for Outlier Handling",tuple(outliers_df_X["columns name"]),default =tuple(outliers_df_X["columns name"]))
        resultant,outlier_handled_df,outlier_handled_df_name= outliers.outlier_handling(X,y,model,outlier_indexes = outlier_indexes,outlier_cols = select_outlier_cols ,method = root_mean_squared_error,test_size = 0.2, random_state = 42,eva = "reg")
        st.write("outlier handling with methods",resultant)
        st.write("Best method with outlier handling",resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])
        try :
            st.write("Best X Data Index No.",outlier_handled_df_name.index(resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0]))
        
            st.write("Best X DataFrame after outlier handling ",outlier_handled_df[outlier_handled_df_name.index(resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])])
            X = outlier_handled_df[outlier_handled_df_name.index(resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])]
        except :
            "evaluation of baseline model is better continuing with baseline model"
        
        
        X_train,X_test,y_train,y_test = tts(X,y[X.index],random_state = 42,test_size = 0.2)
        st.write("result_df",X)
        
        

        try:
            result_df_1 , feature_col, feature_col_name = feature_selections.feature_selection(X_train,X_test,y_train,y_test,model,alpha = 0.05)
            X = X.drop(columns = feature_col[feature_col_name.index(result_df_1.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])])
        except:
            "evaluation by feature selection is not better than previous"

        try:
            result,X_train_b,X_test_b,y_train_b,y_test_b = best_tts.best_tts(X,y,model,eva)
            st.write("result_df",result)
        except:
            X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size =0.2,random_state = 42)


        

        st.write("cheking with polynomial features")
        poly = PolynomialFeatures(degree=(2))
        X_train_poly = poly.fit_transform(X_train)
        X_test_poly = poly.transform(X_test)
        result_df_2 = evaluationer.evaluation("polynomial features degree 2",X_train_poly,X_test_poly,y_train,y_test,model,root_mean_squared_error,eva)
        st.write("after polynomial features degree 2",evaluationer.reg_evaluation_df)
        poly1 = PolynomialFeatures(degree=(3))
        X_train_poly1 = poly.fit_transform(X_train)
        X_test_poly1 = poly.transform(X_test)
        evaluationer.evaluation("polynomial features degree 3",X_train_poly1,X_test_poly1,y_train,y_test,model,root_mean_squared_error,eva)
        st.write("after polynomial features degree 3",evaluationer.reg_evaluation_df)

        pca = PCA(n_components=0.95)
        X_train_pca = pca.fit_transform(X_train)
        X_test_pca = pca.transform(X_test)
        evaluationer.evaluation("PCA",X_train_pca,X_test_pca,y_train,y_test,model,root_mean_squared_error,eva)
        st.write("After PCA",evaluationer.reg_evaluation_df)

        grid_search_cv.perform_grid_search(model,model_name,X_train,X_test,y_train,y_test,eva)
        st.write("best param",evaluationer.reg_evaluation_df)
        st.sidebar.button("click to clear evaluation metrics",evaluationer.reg_evaluation_df.drop(index = evaluationer.reg_evaluation_df.index))