import streamlit as st import pandas as pd import plotly.express as px import plotly.graph_objects as go from Eda_functions import format_numbers import numpy as np import pickle from st_aggrid import AgGrid from st_aggrid import GridOptionsBuilder,GridUpdateMode from utilities import set_header,load_local_css from st_aggrid import GridOptionsBuilder import time import itertools import statsmodels.api as sm import numpy as np import re import itertools from sklearn.metrics import mean_absolute_error, r2_score,mean_absolute_percentage_error from sklearn.preprocessing import MinMaxScaler import os import matplotlib.pyplot as plt from statsmodels.stats.outliers_influence import variance_inflation_factor st.set_option('deprecation.showPyplotGlobalUse', False) from datetime import datetime import seaborn as sns from Data_prep_functions import * st.set_page_config( page_title="Model Build", page_icon=":shark:", layout="wide", initial_sidebar_state='collapsed' ) load_local_css('styles.css') set_header() st.title('1. Build Your Model') # media_data=pd.read_csv('Media_data_for_model.csv') media_data=pd.read_csv('Media_data_for_model_dma_level.csv') date=media_data['Date'] st.session_state['date']=date revenue=media_data['Total Approved Accounts - Revenue'] media_data.drop(['Total Approved Accounts - Revenue'],axis=1,inplace=True) media_data.drop(['Date'],axis=1,inplace=True) media_data.reset_index(drop=True,inplace=True) dma=st.selectbox('Select the Level of data ',[ col for col in media_data.columns if col.lower() in ['dma','panel']]) dma_dict={ dm:media_data[media_data[dma]==dm] for dm in media_data[dma].unique()} # st.write(dma_dict) st.markdown('## Select the Range of Transformations') columns = st.columns(2) old_shape=media_data.shape if "old_shape" not in st.session_state: st.session_state['old_shape']=old_shape with columns[0]: slider_value_adstock = st.slider('Select Adstock Range (only applied to media)', 0.0, 1.0, (0.2, 0.4), step=0.1, format="%.2f") with columns[1]: slider_value_lag = st.slider('Select Lag Range (applied to media, seasonal, macroeconomic variables)', 1, 7, (1, 3), step=1) # with columns[2]: # slider_value_power=st.slider('Select Power range (only applied to media )',0,4,(1,2),step=1) # with columns[1]: # st.number_input('Select the range of half saturation point ',min_value=1,max_value=5) # st.number_input('Select the range of ') def lag(X, features, min_lag=0,max_lag=6): for i in features: for lag in range(min_lag, max_lag + 1): X[f'{i}_lag{lag}'] = X[i].shift(periods=lag) return X.fillna(method='bfill') def adstock_variable(X,variable_name,decay): adstock = [0] * len(X[variable_name]) for t in range(len(X[variable_name])): if t == 0: adstock[t] = X[variable_name][t] else: adstock[t] = X[variable_name][t] + adstock[t-1] * decay return adstock if 'media_data' not in st.session_state: st.session_state['media_data']=pd.DataFrame() variables_to_be_transformed=[col for col in media_data.columns if col.lower() not in ['dma','panel'] ] # st.write(variables_to_be_transformed) with columns[0]: if st.button('Apply Transformations'): for dm in dma_dict.keys(): dma_dict[dm].reset_index(drop=True,inplace=True) dma_dict[dm]=lag(dma_dict[dm],variables_to_be_transformed,min_lag=slider_value_lag[0],max_lag=slider_value_lag[1]) for dm in dma_dict.keys(): for i in dma_dict[dm].drop(['DMA','Panel'],axis=1).columns: for j in np.arange(slider_value_adstock[0],slider_value_adstock[1]+0.1,0.1):#adding adstock dma_dict[dm][f'{i}_adst.{np.round(j,2)}']=adstock_variable(dma_dict[dm],i,j) st.write(dma_dict) st.session_state['media_data']=media_data with st.spinner('Applying Transformations'): time.sleep(2) st.success("Transformations complete!") if st.session_state['media_data'].shape[1]>old_shape[1]: with columns[0]: st.write(f'Total no.of variables before transformation: {old_shape[1]}, Total no.of variables after transformation: {st.session_state["media_data"].shape[1]}') #st.write(f'Total no.of variables after transformation: {st.session_state["media_data"].shape[1]}') bucket=['paid_search', 'kwai','indicacao','infleux', 'influencer','FB: Level Achieved - Tier 1 Impressions', ' FB: Level Achieved - Tier 2 Impressions','paid_social_others', ' GA App: Will And Cid Pequena Baixo Risco Clicks', 'digital_tactic_others',"programmatic" ] with columns[1]: if st.button('Create Combinations of Variables'): top_3_correlated_features=[] for col in st.session_state['media_data'].columns[:19]: corr_df=pd.concat([st.session_state['media_data'].filter(regex=col), revenue],axis=1).corr()['Total Approved Accounts - Revenue'].iloc[:-1] top_3_correlated_features.append(list(corr_df.sort_values(ascending=False).head(2).index)) flattened_list = [item for sublist in top_3_correlated_features for item in sublist] all_features_set={var:[col for col in flattened_list if var in col] for var in bucket} channels_all=[values for values in all_features_set.values()] st.session_state['combinations'] = list(itertools.product(*channels_all)) # if 'combinations' not in st.session_state: # st.session_state['combinations']=combinations_all st.session_state['final_selection']=st.session_state['combinations'] revenue.reset_index(drop=True,inplace=True) if 'Model_results' not in st.session_state: st.session_state['Model_results']={'Model_object':[], 'Model_iteration':[], 'Feature_set':[], 'MAPE':[], 'R2':[], 'ADJR2':[] } #if st.button('Build Model'): if 'iterations' not in st.session_state: st.session_state['iterations']=1 save_path = r"Model" with columns[1]: if "final_selection" in st.session_state: st.write(f'Total combinations created {format_numbers(len(st.session_state["final_selection"]))}') st.success('Done') if st.checkbox('Build all iterations'): iterations=len(st.session_state['final_selection']) else: iterations = st.number_input('Select the number of iterations to perform', min_value=1, step=100, value=st.session_state['iterations']) st.session_state['iterations']=iterations st.session_state['media_data']=st.session_state['media_data'].fillna(method='ffill') if st.button("Build Models"): st.markdown('Data Split -- Training Period: May 9th, 2023 - October 5th,2023 , Testing Period: October 6th, 2023 - November 7th, 2023 ') progress_bar = st.progress(0) # Initialize the progress bar #time_remaining_text = st.empty() # Create an empty space for time remaining text start_time = time.time() # Record the start time progress_text = st.empty() #time_elapsed_text = st.empty() for i, selected_features in enumerate(st.session_state["final_selection"][40000:40000+int(iterations)]): df = st.session_state['media_data'] fet = [var for var in selected_features if len(var) > 0] X = df[fet] y = revenue ss = MinMaxScaler() X = pd.DataFrame(ss.fit_transform(X), columns=X.columns) X = sm.add_constant(X) X_train=X.iloc[:150] X_test=X.iloc[150:] y_train=y.iloc[:150] y_test=y.iloc[150:] model = sm.OLS(y_train, X_train).fit() # st.write(fet) positive_coeff=X.columns negetive_coeff=[] coefficients=model.params.to_dict() model_possitive=[col for col in coefficients.keys() if coefficients[col]>0] # st.write(positive_coeff) # st.write(model_possitive) pvalues=[var for var in list(model.pvalues) if var<=0.06] if (len(model_possitive)/len(selected_features))>0.9 and (len(pvalues)/len(selected_features))>=0.8: predicted_values = model.predict(X_train) mape = mean_absolute_percentage_error(y_train, predicted_values) adjr2 = model.rsquared_adj r2 = model.rsquared filename = os.path.join(save_path, f"model_{i}.pkl") with open(filename, "wb") as f: pickle.dump(model, f) # with open(r"C:\Users\ManojP\Documents\MMM\simopt\Model\model.pkl", 'rb') as file: # model = pickle.load(file) st.session_state['Model_results']['Model_object'].append(filename) st.session_state['Model_results']['Model_iteration'].append(i) st.session_state['Model_results']['Feature_set'].append(fet) st.session_state['Model_results']['MAPE'].append(mape) st.session_state['Model_results']['R2'].append(r2) st.session_state['Model_results']['ADJR2'].append(adjr2) current_time = time.time() time_taken = current_time - start_time time_elapsed_minutes = time_taken / 60 completed_iterations_text = f"{i + 1}/{iterations}" progress_bar.progress((i + 1) / int(iterations)) progress_text.text(f'Completed iterations: {completed_iterations_text},Time Elapsed (min): {time_elapsed_minutes:.2f}') st.write(f'Out of {st.session_state["iterations"]} iterations : {len(st.session_state["Model_results"]["Model_object"])} valid models') pd.DataFrame(st.session_state['Model_results']).to_csv('model_output.csv') def to_percentage(value): return f'{value * 100:.1f}%' st.title('2. Select Models') if 'tick' not in st.session_state: st.session_state['tick']=False if st.checkbox('Show results of top 10 models (based on MAPE and Adj. R2)',value=st.session_state['tick']): st.session_state['tick']=True st.write('Select one model iteration to generate performance metrics for it:') data=pd.DataFrame(st.session_state['Model_results']) data.sort_values(by=['MAPE'],ascending=False,inplace=True) data.drop_duplicates(subset='Model_iteration',inplace=True) top_10=data.head(10) top_10['Rank']=np.arange(1,len(top_10)+1,1) top_10[['MAPE','R2','ADJR2']]=np.round(top_10[['MAPE','R2','ADJR2']],4).applymap(to_percentage) top_10_table = top_10[['Rank','Model_iteration','MAPE','ADJR2','R2']] #top_10_table.columns=[['Rank','Model Iteration Index','MAPE','Adjusted R2','R2']] gd=GridOptionsBuilder.from_dataframe(top_10_table) gd.configure_pagination(enabled=True) gd.configure_selection(use_checkbox=True) gridoptions=gd.build() table = AgGrid(top_10,gridOptions=gridoptions,update_mode=GridUpdateMode.SELECTION_CHANGED) selected_rows=table.selected_rows # if st.session_state["selected_rows"] != selected_rows: # st.session_state["build_rc_cb"] = False st.session_state["selected_rows"] = selected_rows if 'Model' not in st.session_state: st.session_state['Model']={} if len(selected_rows)>0: st.header('2.1 Results Summary') model_object=data[data['Model_iteration']==selected_rows[0]['Model_iteration']]['Model_object'] features_set=data[data['Model_iteration']==selected_rows[0]['Model_iteration']]['Feature_set'] with open(str(model_object.values[0]), 'rb') as file: model = pickle.load(file) st.write(model.summary()) st.header('2.2 Actual vs. Predicted Plot') df=st.session_state['media_data'] X=df[features_set.values[0]] X = sm.add_constant(X) y=revenue X_train=X.iloc[:150] X_test=X.iloc[150:] y_train=y.iloc[:150] y_test=y.iloc[150:] ss = MinMaxScaler() X_train = pd.DataFrame(ss.fit_transform(X_train), columns=X_train.columns) st.session_state['X']=X_train st.session_state['features_set']=features_set.values[0] metrics_table,line,actual_vs_predicted_plot=plot_actual_vs_predicted(date, y_train, model.predict(X_train), model,target_column='Revenue') st.plotly_chart(actual_vs_predicted_plot,use_container_width=True) st.markdown('## 2.3 Residual Analysis') columns=st.columns(2) with columns[0]: fig=plot_residual_predicted(y_train,model.predict(X_train),X_train) st.plotly_chart(fig) with columns[1]: st.empty() fig = qqplot(y_train,model.predict(X_train)) st.plotly_chart(fig) with columns[0]: fig=residual_distribution(y_train,model.predict(X_train)) st.pyplot(fig) vif_data = pd.DataFrame() # X=X.drop('const',axis=1) vif_data["Variable"] = X_train.columns vif_data["VIF"] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])] vif_data.sort_values(by=['VIF'],ascending=False,inplace=True) vif_data=np.round(vif_data) vif_data['VIF']=vif_data['VIF'].astype(float) st.header('2.4 Variance Inflation Factor (VIF)') #st.dataframe(vif_data) color_mapping = { 'darkgreen': (vif_data['VIF'] < 3), 'orange': (vif_data['VIF'] >= 3) & (vif_data['VIF'] <= 10), 'darkred': (vif_data['VIF'] > 10) } # Create a horizontal bar plot fig, ax = plt.subplots() fig.set_figwidth(10) # Adjust the width of the figure as needed # Sort the bars by descending VIF values vif_data = vif_data.sort_values(by='VIF', ascending=False) # Iterate through the color mapping and plot bars with corresponding colors for color, condition in color_mapping.items(): subset = vif_data[condition] bars = ax.barh(subset["Variable"], subset["VIF"], color=color, label=color) # Add text annotations on top of the bars for bar in bars: width = bar.get_width() ax.annotate(f'{width:}', xy=(width, bar.get_y() + bar.get_height() / 2), xytext=(5, 0), textcoords='offset points', va='center') # Customize the plot ax.set_xlabel('VIF Values') #ax.set_title('2.4 Variance Inflation Factor (VIF)') #ax.legend(loc='upper right') # Display the plot in Streamlit st.pyplot(fig) with st.expander('Results Summary Test data'): ss = MinMaxScaler() X_test = pd.DataFrame(ss.fit_transform(X_test), columns=X_test.columns) st.header('2.2 Actual vs. Predicted Plot') metrics_table,line,actual_vs_predicted_plot=plot_actual_vs_predicted(date, y_test, model.predict(X_test), model,target_column='Revenue') st.plotly_chart(actual_vs_predicted_plot,use_container_width=True) st.markdown('## 2.3 Residual Analysis') columns=st.columns(2) with columns[0]: fig=plot_residual_predicted(revenue,model.predict(X_test),X_test) st.plotly_chart(fig) with columns[1]: st.empty() fig = qqplot(revenue,model.predict(X_test)) st.plotly_chart(fig) with columns[0]: fig=residual_distribution(revenue,model.predict(X_test)) st.pyplot(fig) value=False if st.checkbox('Save this model to tune',key='build_rc_cb'): mod_name=st.text_input('Enter model name') if len(mod_name)>0: st.session_state['Model'][mod_name]={"Model_object":model,'feature_set':st.session_state['features_set'],'X_train':X_train} st.session_state['X_train']=X_train st.session_state['X_test']=X_test st.session_state['y_train']=y_train st.session_state['y_test']=y_test with open("best_models.pkl", "wb") as f: pickle.dump(st.session_state['Model'], f) st.success('Model saved!, Proceed next page to tune the model') value=False