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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))
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