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.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+E0DAF720 filter=lfs diff=lfs merge=lfs -text
+Profile_Report.html filter=lfs diff=lfs merge=lfs -text
+raw_data_nov7_combined.xlsx filter=lfs diff=lfs merge=lfs -text
+raw_data_nov7_combined1.xlsx filter=lfs diff=lfs merge=lfs -text
+upf_data_converted.xlsx filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,5 @@
+*ipynb
+Model
+__pycache__
+*out
+*pkl

10_Model_Build_draft.py

@@ -0,0 +1,393 @@
+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,line_plot,summary
+import numpy as np
+from Transformation_functions import check_box
+from Transformation_functions import apply_lag,apply_adstock,top_correlated_feature
+import pickle
+from st_aggrid import AgGrid
+from st_aggrid import GridOptionsBuilder,GridUpdateMode
+from utilities import set_header,initialize_data,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
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_percentage_error  
+from PIL import Image
+import os
+import matplotlib.pyplot as plt
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+st.set_page_config(
+  page_title="Model Build",
+  page_icon=":shark:",
+  layout="wide",
+  initial_sidebar_state='collapsed'
+)
+load_local_css('styles.css')
+set_header()
+
+# logo = Image.open("Full_Logo_Blue.png")
+
+# # Set the logo size
+# logo = logo.resize((100, 100))
+# st.image(logo)
+# st.markdown("""
+#     <style>
+#     .logo {
+#         position: absolute;
+#         top: 10px;
+#         right: 10px;
+#     }
+#     </style>
+#     """,unsafe_allow_html=True)
+
+
+
+# st.image(logo, use_column_width=True, top=0.95, right=0.05)
+
+# Use CSS to position the logo in the top right corner
+# st.write(
+#     """
+#     <style>
+#     .logo {
+#         position: absolute;
+#         top: 10px;
+#         right: 10px;
+#     }
+#     </style>
+#     """
+# )
+
+
+st.title('Model Build')
+with open("filtered_variables.pkl", 'rb') as file:
+    filtered_variables = pickle.load(file)
+
+with open('Categorised_data.pkl', 'rb') as file:
+  Categorised_data = pickle.load(file)
+
+with open("target_column.pkl", 'rb') as file:
+  target_column= pickle.load(file)
+
+with open("df.pkl", 'rb') as file:
+  df= pickle.load(file)
+
+#st.markdown('### Generating all the possible combinations of variables')
+
+if 'final_selection' not in st.session_state:
+    st.session_state['final_selection']=None
+
+keywords = ['Digital (Impressions)', 'Streaming (Impressions)']
+
+  # Use list comprehension to filter columns
+  #drop_columns = [col for col in df.columns if any(keyword in col for keyword in keywords)]
+  #st.write(drop_columns)
+  #df.drop(drop_columns,axis=1,inplace=True)
+if st.button('Create all Possibile combinations of Variables'):
+  with st.spinner('Wait for it'):
+    multiple_col=[col for col in filtered_variables.keys() if Categorised_data[col]['VB']=='Holiday']
+    #st.write(multiple_col)
+
+
+
+
+    for var in multiple_col:  
+      all_combinations_hol = []
+      for r in range(1, len(filtered_variables[var]) + 1):
+          combinations = itertools.combinations(filtered_variables[var], r)
+          all_combinations_hol.extend(combinations)
+      all_combinations_hol.append([])
+      all_combinations_hol = [list(comb) for comb in all_combinations_hol] 
+      filtered_variables[var]=all_combinations_hol
+
+
+    # st.write(filtered_variables)
+    price=[col for col in df.columns if Categorised_data[re.split(r'_adst|_lag', col )[0]]['VB']=='Price']
+    price.append("Non Promo Price")
+    
+    price.append('Promo Price') #tempfix
+    
+    
+    #st.write(price)
+    Distribution=[col for col in df.columns if Categorised_data[re.split(r'_adst|_lag', col )[0]]['VB']=='Distribution']
+    Promotion=[col for col in df.columns if  Categorised_data[re.split(r'_adst|_lag', col )[0]]['VB']=='Promotion']
+    Promotion.remove("Non Promo Price")
+    price.append('')
+    Distribution.append('')
+    
+    
+    Promotion.remove('Promo Price')  #temp fi------
+
+
+    filtered_variables['Price']=price
+    filtered_variables['Distribution']=Distribution
+    filtered_variables['Promotion']=Promotion
+
+    variable_names = list(filtered_variables.keys())
+    variable_values = list(filtered_variables.values())
+
+    combinations = list(itertools.product(*variable_values))
+
+
+    # for combo in combinations:
+    #     flattened_combo = [item for sublist in combo for item in (sublist if isinstance(sublist, list) else [sublist])]
+    #     print(flattened_combo)
+    # st.text(flattened_combo)
+
+
+
+    final_selection=[]
+    for comb in combinations:
+      nested_tuple = comb
+
+      flattened_list = [item for sublist in nested_tuple for item in (sublist if isinstance(sublist, list) else [sublist])]
+      final_selection.append(flattened_list)
+    #st.write(final_selection[:15])
+
+    st.session_state['final_selection']=final_selection
+
+    st.success('Done')
+    st.write(f'Total combinations created {format_numbers(len(final_selection))}')
+
+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'):
+save_path = r"C:\Users\ManojP\Documents\MMM\simopt\Model"
+iterations = st.number_input('Select the number of iterations to perform', min_value=1, step=1, value=1)
+if st.button("Build Model"):
+  
+  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"][:int(iterations)]):
+      df = df.reset_index(drop=True)
+
+      fet = [var for var in selected_features if len(var) > 0]
+      X = df[fet]
+      y = df['Prospects']
+      ss = MinMaxScaler()
+      X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
+      X = sm.add_constant(X)
+      model = sm.OLS(y, X).fit()
+      # st.write(fet)
+      positive_coeff=[col for col in fet if Categorised_data[re.split(r'_adst|_lag', col )[0]]['VB'] in ["Distribution","Promotion	TV"	,"Display",	"Video"	,"Facebook",	"Twitter"	,"Instagram"	,"Pintrest",	"YouTube"	,"Paid Search"	,"OOH	Radio"	,"Audio Streaming",'Digital']]  
+      negetive_coeff=[col for col in fet  if Categorised_data[re.split(r'_adst|_lag', col )[0]]['VB'] in ["Price"]]
+      coefficients=model.params.to_dict()
+      model_possitive=[col for col in coefficients.keys() if coefficients[col]>0]
+      model_negatives=[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 (set(positive_coeff).issubset(set(model_possitive))) and (set(negetive_coeff).issubset(model_negatives)) and (len(pvalues)/len(selected_features))>=0.5:
+
+
+          predicted_values = model.predict(X)
+          mape = mean_absolute_percentage_error(y, 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 {iterations} iterations : {len(st.session_state["Model_results"]["Model_object"])} valid models')
+
+
+def to_percentage(value):
+  return f'{value * 100:.1f}%'   
+
+st.title('Analysis of Results')
+if st.checkbox('Show Results of Top 10 Models'):
+  st.write('Click on the Row to Generate Model Result')
+  data=pd.DataFrame(st.session_state['Model_results'])
+  data.sort_values(by=['MAPE'],ascending=False,inplace=True)
+  top_10=data.head(10)
+  top_10['Row_number']=np.arange(1,11,1)
+  top_10[['MAPE','R2','ADJR2']]=np.round(top_10[['MAPE','R2','ADJR2']],4).applymap(to_percentage)
+
+  gd=GridOptionsBuilder.from_dataframe(top_10[['Row_number','MAPE','R2','ADJR2','Model_iteration']])
+  gd.configure_pagination(enabled=True)
+  gd.configure_selection(use_checkbox=True)
+
+  #gd.configure_columns_auto_size_mode(GridOptionsBuilder.configure_columns)
+  gridoptions=gd.build()
+
+  table = AgGrid(top_10,gridOptions=gridoptions,update_mode=GridUpdateMode.SELECTION_CHANGED)
+
+  selected_rows=table.selected_rows
+  if len(selected_rows)>0:
+    st.header('Model Summary')
+    #st.text(selected_rows[0]['Model_iteration'])
+
+    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']
+    #st.write(features_set.values)
+
+    with open(str(model_object.values[0]), 'rb') as file:
+            model = pickle.load(file)
+    st.write(model.summary())        
+    # st.write(df.index)
+
+
+    def plot_actual_vs_predicted(date, y, predicted_values, model):
+        fig = go.Figure()
+
+        fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='#08083B')))
+        fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='#11B6BD')))
+        
+        # Calculate MAPE
+        mape = mean_absolute_percentage_error(y, predicted_values)
+        
+        # Calculate AdjR2 # Assuming X is your feature matrix
+        adjr2 = model.rsquared_adj
+
+        # Create a table to display the metrics
+        metrics_table = pd.DataFrame({
+            'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+            'Value': [mape, model.rsquared, adjr2]
+        })
+
+        fig.update_layout(
+            xaxis=dict(title='Date'),
+            yaxis=dict(title=target_column),
+            xaxis_tickangle=-30
+        )
+        #metrics_table.set_index(['Metric'],inplace=True)
+        return metrics_table, fig 
+
+  # st.text(features_set.values[0])
+  # st.dataframe(df[features_set.values[0]])
+
+    date=list(df.index)
+    df = df.reset_index(drop=True)
+    X=df[features_set.values[0]]
+    ss = MinMaxScaler()
+    X = pd.DataFrame(ss.fit_transform(X), columns=X.columns)
+    X=sm.add_constant(X)
+#st.write(model.predict(X))
+
+  #st.write(df[target_column])
+    metrics_table,fig=plot_actual_vs_predicted(date, df[target_column], model.predict(X), model)
+
+    st.plotly_chart(fig,use_container_width=True)
+
+    def plot_residual_predicted(actual, predicted, df_):
+        df_['Residuals'] = actual - pd.Series(predicted)
+        df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+        
+        # Create a Plotly scatter plot
+        fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5)
+        
+        # Add horizontal lines
+        fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+        fig.add_hline(y=2, line_color="red")
+        fig.add_hline(y=-2, line_color="red")
+        
+        fig.update_xaxes(title='Predicted')
+        fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+        
+        # Set the same width and height for both figures
+        fig.update_layout(title='Residuals over Predicted values', autosize=False, width=600, height=400)
+        
+        return fig
+
+    def residual_distribution(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        
+        # Create a Plotly histogram and distribution curve with custom colors
+        fig = go.Figure()
+        fig.add_trace(go.Histogram(x=Residuals, name='Residuals', histnorm='probability',
+                                  marker_color="#11B6BD"))
+        fig.add_trace(go.Histogram(x=Residuals, histnorm='probability', showlegend=False,
+                                  marker_color="#11B6BD"))
+        
+        fig.update_layout(title='Distribution of Residuals',title_x=0.5, autosize=False, width=600, height=400)
+        
+        return fig
+    
+    def qqplot(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        Residuals = pd.Series(Residuals)
+        Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+        
+        # Create a QQ plot using Plotly with custom colors
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                mode='markers',
+                                marker=dict(size=5, color="#11B6BD"),
+                                name='QQ Plot'))
+        
+        # Add the 45-degree reference line
+        diagonal_line = go.Scatter(
+            x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+            y=[-2, 2],  # Adjust the y values accordingly
+            mode='lines',
+            line=dict(color='red'),  # Customize the line color and style
+            name=' '
+        )
+        fig.add_trace(diagonal_line)
+        
+        # Customize the layout
+        fig.update_layout(title='QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                          xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+        
+        return fig
+
+    st.markdown('## Residual Analysis')
+    columns=st.columns(2)
+    with columns[0]:
+      fig=plot_residual_predicted(df[target_column],model.predict(X),df)
+      st.plotly_chart(fig)
+    
+    with columns[1]:
+      st.empty()
+      fig = qqplot(df[target_column],model.predict(X))
+      st.plotly_chart(fig)
+
+    with columns[0]:
+      fig=residual_distribution(df[target_column],model.predict(X))
+      st.plotly_chart(fig)
+    
+
+
+    vif_data = pd.DataFrame()
+    X=X.drop('const',axis=1)
+    vif_data["Variable"] = X.columns
+    vif_data["VIF"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
+    vif_data.sort_values(by=['VIF'],ascending=False,inplace=True)
+    st.dataframe(vif_data)

11_Analysis_Of_Result_draft.py

@@ -0,0 +1,118 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+import pickle
+import json
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+import pickle
+import json
+import pandas as pd
+import statsmodels.api as sm
+from sklearn.metrics import mean_absolute_percentage_error
+import sys
+from utilities import (set_header, 
+                       initialize_data,
+                       load_local_css,
+                       create_channel_summary,
+                       create_contribution_pie,
+                       create_contribuion_stacked_plot,
+                       create_channel_spends_sales_plot,
+                       format_numbers,
+                       channel_name_formating,
+                       load_authenticator)
+
+
+
+
+def plot_actual_vs_predicted(date, y, predicted_values, model):
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='#6c757d')))
+    fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='#FF3A3B')))
+    
+    # Calculate MAPE
+    mape = mean_absolute_percentage_error(y, predicted_values)
+    
+    # Calculate AdjR2 # Assuming X is your feature matrix
+    adjr2 = model.rsquared_adj
+
+    # Create a table to display the metrics
+    metrics_table = pd.DataFrame({
+        'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+        'Value': [mape, model.rsquared, adjr2]
+    })
+
+    fig.update_layout(
+        xaxis=dict(title='Date'),
+        yaxis=dict(title='Value'),
+        xaxis_tickangle=-30
+    )
+    #metrics_table.set_index(['Metric'],inplace=True)
+    return metrics_table, fig 
+
+
+X=pd.read_csv('actual_data.csv')
+y=X['total_prospect_id']
+date=X['date']
+X=X.drop(['total_prospect_id','date','Unnamed: 0'],axis=1)
+
+print(X.columns)
+original_stdout = sys.stdout
+sys.stdout = open('temp_stdout.txt', 'w')
+
+# Perform linear regression
+model = sm.OLS(y, X).fit()
+
+
+sys.stdout.close()
+sys.stdout = original_stdout
+
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+st.title('Analysis of Result')
+
+st.write(model.summary(yname='Prospects'))
+
+st.subheader('Actual vs Predicted Plot')
+metrics_table,fig = plot_actual_vs_predicted(date, y, model.predict(X), model)
+
+st.plotly_chart(fig,use_container_width=True)
+#st.plotly_chart(fig)
+
+# Display the metrics table
+
+metrics_table=np.round(metrics_table,2)
+metrics_table_html = metrics_table.to_html(index=False, escape=False)
+
+# Display the metrics table in Streamlit as HTML
+#st.subheader('Model Metrics')
+#st.markdown(metrics_table_html, unsafe_allow_html=True)
+# st.subheader('Model Metrics')
+# st.table(metrics_table)
+
+custom_css = """
+<style>
+table {
+    width: 80%; /* Adjust the table width as needed */
+    border-collapse: collapse;
+}
+th, td {
+    padding: 8px;
+    text-align: left;
+    border-bottom: 1px solid #ddd;
+}
+</style>
+"""
+
+# Display the metrics table in Streamlit as HTML with custom CSS
+st.subheader('Model Metrics')
+st.markdown(custom_css, unsafe_allow_html=True)
+st.markdown(metrics_table_html, unsafe_allow_html=True)

8_Scenario_Planner.py

@@ -0,0 +1,464 @@
+import streamlit as st
+from numerize.numerize import numerize
+import numpy as np
+from functools import partial
+from collections import OrderedDict
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+from utilities import format_numbers,load_local_css,set_header,initialize_data,load_authenticator,send_email,channel_name_formating
+from classes import class_from_dict,class_to_dict
+import pickle
+import streamlit_authenticator as stauth
+import yaml
+from yaml import SafeLoader
+import re
+import pandas as pd
+import plotly.express as px
+target='Revenue'
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+# ======================================================== #
+# ======================= Functions ====================== #
+# ======================================================== #
+
+
+def optimize():
+    """
+    Optimize the spends for the sales    
+    """
+    
+    channel_list = [key for key,value in st.session_state['optimization_channels'].items() if value]
+    print('channel_list')
+    print(channel_list)
+    print('@@@@@@@@')
+    if len(channel_list) > 0 :
+        scenario = st.session_state['scenario']
+        result = st.session_state['scenario'].optimize(st.session_state['total_spends_change'],channel_list)
+        for channel_name, modified_spends in result:
+            st.session_state[channel_name] = numerize(modified_spends * scenario.channels[channel_name].conversion_rate,1)
+            prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends 
+            st.session_state[f'{channel_name}_change'] = round(100*(modified_spends - prev_spends) / prev_spends,2)
+            
+
+def save_scenario(scenario_name):
+    """
+    Save the current scenario with the mentioned name in the session state
+    
+    Parameters
+    ----------
+    scenario_name
+        Name of the scenario to be saved
+    """
+    if 'saved_scenarios' not in st.session_state:
+        st.session_state = OrderedDict()
+        
+    #st.session_state['saved_scenarios'][scenario_name] = st.session_state['scenario'].save()
+    st.session_state['saved_scenarios'][scenario_name] = class_to_dict(st.session_state['scenario'])
+    st.session_state['scenario_input'] = ""
+    print(type(st.session_state['saved_scenarios']))
+    with open('../saved_scenarios.pkl', 'wb') as f:
+        pickle.dump(st.session_state['saved_scenarios'],f)
+        
+def update_all_spends():
+    """
+    Updates spends for all the channels with the given overall spends change
+    """
+    percent_change = st.session_state['total_spends_change']
+    for channel_name in st.session_state['channels_list']:
+        channel = st.session_state['scenario'].channels[channel_name]
+        current_spends =  channel.actual_total_spends
+        modified_spends = (1 + percent_change/100) * current_spends
+        st.session_state['scenario'].update(channel_name, modified_spends)
+        st.session_state[channel_name] = numerize(modified_spends*channel.conversion_rate,1)
+        st.session_state[f'{channel_name}_change'] = percent_change
+
+def extract_number_for_string(string_input):
+    string_input = string_input.upper()
+    if string_input.endswith('K'):
+        return float(string_input[:-1])*10**3
+    elif string_input.endswith('M'):
+        return float(string_input[:-1])*10**6
+    elif string_input.endswith('B'):
+        return float(string_input[:-1])*10**9
+    
+def validate_input(string_input):
+    pattern = r'\d+\.?\d*[K|M|B]$'
+    match = re.match(pattern, string_input)
+    if match is None:
+        return False
+    return True
+
+def update_data_by_percent(channel_name):
+    prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends * st.session_state['scenario'].channels[channel_name].conversion_rate
+    modified_spends = prev_spends * (1 + st.session_state[f'{channel_name}_change']/100)
+    st.session_state[channel_name] = numerize(modified_spends,1)
+    st.session_state['scenario'].update(channel_name, modified_spends/st.session_state['scenario'].channels[channel_name].conversion_rate)
+    
+def update_data(channel_name):
+    """
+    Updates the spends for the given channel
+    """
+    
+    if validate_input(st.session_state[channel_name]):
+        modified_spends = extract_number_for_string(st.session_state[channel_name])
+        prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends * st.session_state['scenario'].channels[channel_name].conversion_rate
+        st.session_state[f'{channel_name}_change'] = round(100*(modified_spends - prev_spends) / prev_spends,2)
+        st.session_state['scenario'].update(channel_name, modified_spends/st.session_state['scenario'].channels[channel_name].conversion_rate)
+    # st.session_state['scenario'].update(channel_name, modified_spends)
+    # else:
+    #     try:
+    #         modified_spends = float(st.session_state[channel_name])
+    #         prev_spends = st.session_state['scenario'].channels[channel_name].actual_total_spends * st.session_state['scenario'].channels[channel_name].conversion_rate
+    #         st.session_state[f'{channel_name}_change'] = round(100*(modified_spends - prev_spends) / prev_spends,2)
+    #         st.session_state['scenario'].update(channel_name, modified_spends/st.session_state['scenario'].channels[channel_name].conversion_rate)
+    #         st.session_state[f'{channel_name}'] = numerize(modified_spends,1)
+    #     except ValueError:
+    #         st.write('Invalid input')
+
+def select_channel_for_optimization(channel_name):
+    """
+    Marks the given channel for optimization
+    """
+    st.session_state['optimization_channels'][channel_name] = st.session_state[f'{channel_name}_selected']
+
+def select_all_channels_for_optimization():
+    """
+    Marks all the channel for optimization
+    """
+    for channel_name in st.session_state['optimization_channels'].keys():
+        st.session_state[f'{channel_name}_selected' ] = st.session_state['optimze_all_channels']
+        st.session_state['optimization_channels'][channel_name] = st.session_state['optimze_all_channels']
+
+def update_penalty():
+    """
+    Updates the penalty flag for sales calculation
+    """
+    st.session_state['scenario'].update_penalty(st.session_state['apply_penalty'])
+  
+def reset_scenario():
+    # print(st.session_state['default_scenario_dict'])
+    # st.session_state['scenario']  = class_from_dict(st.session_state['default_scenario_dict'])
+    # for channel in st.session_state['scenario'].channels.values():
+    #     st.session_state[channel.name] = float(channel.actual_total_spends * channel.conversion_rate)
+    initialize_data()
+    for channel_name in  st.session_state['channels_list']:
+        st.session_state[f'{channel_name}_selected'] = False
+        st.session_state[f'{channel_name}_change'] = 0
+    st.session_state['optimze_all_channels'] = False
+
+def format_number(num):
+    if num >= 1_000_000:
+        return f"{num / 1_000_000:.2f}M"
+    elif num >= 1_000:
+        return f"{num / 1_000:.0f}K"
+    else:
+        return f"{num:.2f}"
+
+def summary_plot(data, x, y, title, text_column):
+    fig = px.bar(data, x=x, y=y, orientation='h',
+                 title=title, text=text_column, color='Channel_name')
+
+    # Convert text_column to numeric values
+    data[text_column] = pd.to_numeric(data[text_column], errors='coerce')
+    
+    # Update the format of the displayed text based on magnitude
+    fig.update_traces(texttemplate='%{text:.2s}', textposition='outside', hovertemplate='%{x:.2s}')
+    
+    fig.update_layout(xaxis_title=x, yaxis_title='Channel Name', showlegend=False)
+    return fig
+
+def s_curve(x,K,b,a,x0):
+    return K / (1 + b*np.exp(-a*(x-x0)))
+
+@st.cache
+def plot_response_curves():
+    cols=4
+    rcs = st.session_state['rcs']
+    shapes = []
+    fig = make_subplots(rows=6, cols=cols,subplot_titles=channels_list)
+    for i in range(0, len(channels_list)):
+        col = channels_list[i]
+        x = st.session_state['actual_df'][col].values
+        spends = x.sum()
+        power = (np.ceil(np.log(x.max()) / np.log(10) )- 3)
+        x = np.linspace(0,3*x.max(),200)
+
+        K = rcs[col]['K']
+        b = rcs[col]['b']
+        a = rcs[col]['a']
+        x0 = rcs[col]['x0']
+        
+        y = s_curve(x/10**power,K,b,a,x0)
+        roi = y/x
+        marginal_roi = a * (y)*(1-y/K)
+        fig.add_trace(
+            go.Scatter(x=52*x*st.session_state['scenario'].channels[col].conversion_rate, 
+                       y=52*y,
+                       name=col, 
+                       customdata = np.stack((roi, marginal_roi),axis=-1),
+                       hovertemplate="Spend:%{x:$.2s}<br>Sale:%{y:$.2s}<br>ROI:%{customdata[0]:.3f}<br>MROI:%{customdata[1]:.3f}"),
+            row=1+(i)//cols , col=i%cols + 1
+        )
+        
+        fig.add_trace(go.Scatter(x=[spends*st.session_state['scenario'].channels[col].conversion_rate], 
+                                 y=[52*s_curve(spends/(10**power*52),K,b,a,x0)],
+                                 name=col,
+                                 legendgroup=col,
+                                 showlegend=False,
+                                 marker=dict(color=['black'])),
+                      row=1+(i)//cols , col=i%cols + 1)
+        
+        shapes.append(go.layout.Shape(type="line", 
+                                      x0=0, 
+                                      y0=52*s_curve(spends/(10**power*52),K,b,a,x0), 
+                                      x1=spends*st.session_state['scenario'].channels[col].conversion_rate, 
+                                      y1=52*s_curve(spends/(10**power*52),K,b,a,x0), 
+                                      line_width=1, 
+                                      line_dash="dash", 
+                                      line_color="black",
+                                      xref= f'x{i+1}',
+                                      yref= f'y{i+1}'))
+        
+        shapes.append(go.layout.Shape(type="line", 
+                                      x0=spends*st.session_state['scenario'].channels[col].conversion_rate, 
+                                      y0=0, 
+                                      x1=spends*st.session_state['scenario'].channels[col].conversion_rate, 
+                                      y1=52*s_curve(spends/(10**power*52),K,b,a,x0), 
+                                      line_width=1, 
+                                      line_dash="dash", 
+                                      line_color="black",
+                                      xref= f'x{i+1}',
+                                      yref= f'y{i+1}'))
+        
+        
+        
+    fig.update_layout(height=1500, width=1000, title_text="Response Curves",showlegend=False,shapes=shapes)
+    fig.update_annotations(font_size=10)
+    fig.update_xaxes(title='Spends')
+    fig.update_yaxes(title=target)
+    return fig
+
+
+
+# ======================================================== #
+# ==================== HTML Components =================== #
+# ======================================================== #
+
+def generate_spending_header(heading):
+    return st.markdown(f"""<h2 class="spends-header">{heading}</h2>""",unsafe_allow_html=True)
+
+
+# ======================================================== #
+# =================== Session variables ================== #
+# ======================================================== #
+
+with open('config.yaml') as file:
+    config = yaml.load(file, Loader=SafeLoader)
+    st.session_state['config'] = config
+    
+authenticator = stauth.Authenticate(
+    config['credentials'],
+    config['cookie']['name'],
+    config['cookie']['key'],
+    config['cookie']['expiry_days'],
+    config['preauthorized']
+)
+st.session_state['authenticator'] = authenticator
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state.get('authentication_status')
+if auth_status == True:
+    authenticator.logout('Logout', 'main')
+    is_state_initiaized = st.session_state.get('initialized',False)
+    if not is_state_initiaized:
+        initialize_data()
+
+
+    channels_list = st.session_state['channels_list']
+        
+
+    # ======================================================== #
+    # ========================== UI ========================== #
+    # ======================================================== #
+    
+    print(list(st.session_state.keys()))
+
+    st.header('Simulation')
+    main_header = st.columns((2,2))
+    sub_header = st.columns((1,1,1,1))
+    _scenario = st.session_state['scenario']
+
+    with main_header[0]:
+        st.subheader('Actual')
+
+    with main_header[-1]:
+        st.subheader('Simulated')
+
+    with sub_header[0]:
+        st.metric(label = 'Spends', value=format_numbers(_scenario.actual_total_spends))
+
+    with sub_header[1]:
+        st.metric(label = target, value=format_numbers(float(_scenario.actual_total_sales),include_indicator=False))
+        
+    with sub_header[2]:
+        st.metric(label = 'Spends', 
+                    value=format_numbers(_scenario.modified_total_spends),
+                    delta=numerize(_scenario.delta_spends,1))
+        
+    with sub_header[3]:
+        st.metric(label = target, 
+                    value=format_numbers(float(_scenario.modified_total_sales),include_indicator=False), 
+                    delta=numerize(_scenario.delta_sales,1))
+
+
+
+    with st.expander("Channel Spends Simulator"):
+        _columns = st.columns((2,4,1,1))
+        with _columns[0]:
+            st.checkbox(label='Optimize all Channels',
+                            key=f'optimze_all_channels',
+                            value=False,
+                            on_change=select_all_channels_for_optimization,
+                            )
+            st.number_input('Percent change of total spends',
+                            key=f'total_spends_change',
+                            step= 1,
+                            on_change=update_all_spends)
+        with _columns[2]:
+            st.button('Optimize',on_click=optimize)
+        with _columns[3]:
+            st.button('Reset',on_click=reset_scenario)
+            
+        
+        
+        st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
+        _columns = st.columns((2.5,2,1.5,1.5,1))
+        with _columns[0]:
+            generate_spending_header('Channel')
+        with _columns[1]:
+            generate_spending_header('Spends Input')
+        with _columns[2]:
+            generate_spending_header('Spends')
+        with _columns[3]:
+            generate_spending_header(target)
+        with _columns[4]:
+            generate_spending_header('Optimize')
+            
+        st.markdown("""<hr class="spends-heading-seperator">""", unsafe_allow_html=True)
+        
+        if 'acutual_predicted' not in st.session_state:
+            st.session_state['acutual_predicted']={'Channel_name':[],
+                                                   'Actual_spend':[],
+                                                   'Optimized_spend':[],
+                                                   'Delta':[]
+                                                   }
+        for i,channel_name in enumerate(channels_list):
+            _channel_class = st.session_state['scenario'].channels[channel_name]
+            _columns = st.columns((2.5,1.5,1.5,1.5,1))
+            with _columns[0]:
+                st.write(channel_name_formating(channel_name))
+            with _columns[1]:
+                channel_bounds = _channel_class.bounds
+                channel_spends = float(_channel_class.actual_total_spends )
+                min_value = float((1+channel_bounds[0]/100) * channel_spends )
+                max_value = float((1+channel_bounds[1]/100) * channel_spends )
+                #print(st.session_state[channel_name])
+                spend_input = st.text_input(channel_name,
+                              key=channel_name,
+                              label_visibility='collapsed',
+                              on_change=partial(update_data,channel_name))
+                if not validate_input(spend_input):
+                    st.error('Invalid input')
+                    
+                st.number_input('Percent change',
+                            key=f'{channel_name}_change',
+                            step= 1,
+                            on_change=partial(update_data_by_percent,channel_name))
+            
+            with _columns[2]:
+                # spends
+                current_channel_spends = float(_channel_class.modified_total_spends *  _channel_class.conversion_rate)
+                actual_channel_spends = float(_channel_class.actual_total_spends * _channel_class.conversion_rate)
+                spends_delta = float(_channel_class.delta_spends * _channel_class.conversion_rate)
+                st.session_state['acutual_predicted']['Channel_name'].append(channel_name)
+                st.session_state['acutual_predicted']['Actual_spend'].append(actual_channel_spends)
+                st.session_state['acutual_predicted']['Optimized_spend'].append(current_channel_spends)
+                st.session_state['acutual_predicted']['Delta'].append(spends_delta)
+                ## REMOVE 
+                st.metric('Spends',
+                            format_numbers(current_channel_spends),
+                            delta=numerize(spends_delta,1),
+                            label_visibility='collapsed')
+
+            with _columns[3]:
+                # sales
+                current_channel_sales = float(_channel_class.modified_total_sales)
+                actual_channel_sales = float(_channel_class.actual_total_sales)
+                sales_delta = float(_channel_class.delta_sales)
+                st.metric(target,
+                            format_numbers(current_channel_sales,include_indicator=False),
+                            delta=numerize(sales_delta,1),
+                            label_visibility='collapsed')
+                
+            with _columns[4]:
+                
+                st.checkbox(label='select for optimization',
+                            key=f'{channel_name}_selected',
+                            value=False,
+                            on_change=partial(select_channel_for_optimization,channel_name),
+                            label_visibility='collapsed')
+
+
+            st.markdown("""<hr class="spends-child-seperator">""",unsafe_allow_html=True)
+
+
+    with st.expander("See Response Curves"):
+        fig = plot_response_curves()
+        st.plotly_chart(fig,use_container_width=True)
+
+    _columns = st.columns(2)
+    with _columns[0]:
+        st.subheader('Save Scenario')
+        scenario_name = st.text_input('Scenario name', key='scenario_input',placeholder='Scenario name',label_visibility='collapsed')
+        st.button('Save', on_click=lambda  : save_scenario(scenario_name),disabled=len(st.session_state['scenario_input']) == 0)
+        
+    summary_df=pd.DataFrame(st.session_state['acutual_predicted'])
+    summary_df.drop_duplicates(subset='Channel_name',keep='last',inplace=True)
+    
+    summary_df_sorted = summary_df.sort_values(by='Delta', ascending=False)
+    summary_df_sorted['Delta_percent'] = np.round(((summary_df_sorted['Optimized_spend'] / summary_df_sorted['Actual_spend'])-1) * 100, 2)
+    
+    with open("summary_df.pkl", "wb") as f:
+        pickle.dump(summary_df_sorted, f)
+        #st.dataframe(summary_df_sorted)
+        # ___columns=st.columns(3)
+        # with ___columns[2]:
+        #     fig=summary_plot(summary_df_sorted, x='Delta_percent', y='Channel_name', title='Delta', text_column='Delta_percent')
+        #     st.plotly_chart(fig,use_container_width=True)
+        # with ___columns[0]:
+        #     fig=summary_plot(summary_df_sorted, x='Actual_spend', y='Channel_name', title='Actual Spend', text_column='Actual_spend')
+        #     st.plotly_chart(fig,use_container_width=True)         
+        # with ___columns[1]:
+        #     fig=summary_plot(summary_df_sorted, x='Optimized_spend', y='Channel_name', title='Planned Spend', text_column='Optimized_spend')
+        #     st.plotly_chart(fig,use_container_width=True)  
+
+elif auth_status == False:
+    st.error('Username/Password is incorrect')
+    
+if auth_status != True:
+    try:
+        username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
+        if username_forgot_pw:
+            st.session_state['config']['credentials']['usernames'][username_forgot_pw]['password'] = stauth.Hasher([random_password]).generate()[0]
+            send_email(email_forgot_password, random_password)
+            st.success('New password sent securely')
+            # Random password to be transferred to user securely
+        elif username_forgot_pw == False:
+            st.error('Username not found')
+    except Exception as e:
+        st.error(e)
+

Categorised_data.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:345f0822aa370833a5e502764925cd46eeac20459c61e6e7922b8e756cba1d2b
+size 696

Data_Import.py

@@ -0,0 +1,384 @@
+# Importing necessary libraries
+import streamlit as st
+import pickle
+
+st.set_page_config(
+    page_title="Model Build",
+    page_icon=":shark:",
+    layout="wide",
+    initial_sidebar_state="collapsed",
+)
+
+from utilities import load_authenticator
+import numpy as np
+import pandas as pd
+from utilities import set_header, load_local_css
+
+load_local_css("styles.css")
+set_header()
+
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+
+authenticator = st.session_state.get('authenticator')
+if authenticator is None:
+    authenticator = load_authenticator()
+
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state.get('authentication_status')
+
+if auth_status == True:
+    is_state_initiaized = st.session_state.get('initialized',False)
+    if not is_state_initiaized:
+        a=1
+      
+
+    # Function to expand dataframe to daily
+    @st.cache_resource(show_spinner=False)
+    def expand_to_daily(df, granularity, start_date, end_date):
+        # Create a new DataFrame with a row for each day
+        all_dates = pd.date_range(start=start_date, end=end_date, freq="D")
+        daily_df = pd.DataFrame(all_dates, columns=["Date"])
+
+        if granularity == "daily":
+            # For daily data, simply merge to fill missing dates
+            daily_df = daily_df.merge(df, on="Date", how="left")
+        else:
+            # For weekly or monthly, distribute values to daily rows
+            for column in df.columns:
+                if column != "Date":  # Skip 'Date' column
+                    daily_df[column] = np.nan  # Initialize with NaNs
+
+            # Group by the required frequency and distribute values
+            freq = "W-MON" if granularity == "weekly" else "MS"
+            for _, group in df.groupby(pd.Grouper(key="Date", freq=freq)):
+                num_days = len(
+                    pd.date_range(group["Date"].min(), group["Date"].max(), freq="D")
+                )
+                for column in group.columns:
+                    if column == "Date":  # Skip 'Date' column
+                        continue
+                    value = group[column].sum() / num_days
+                    date_range = pd.date_range(
+                        group["Date"].min(), periods=num_days, freq="D"
+                    )
+                    daily_df.loc[daily_df["Date"].isin(date_range), column] = value
+
+        return daily_df
+
+
+    # Function to validate date column in dataframe
+    def validate_date_column(df):
+        try:
+            # Attempt to convert the 'Date' column to datetime
+            df["Date"] = pd.to_datetime(df["Date"], format="%d-%m-%Y")
+            return True
+        except:
+            return False
+
+
+    # Function to determine data interval
+    def determine_data_interval(common_freq):
+        if common_freq == 1:
+            return "daily"
+        elif common_freq == 7:
+            return "weekly"
+        elif 28 <= common_freq <= 31:
+            return "monthly"
+        else:
+            return "irregular"
+
+
+    # Function to convert and fill dates in dataframe
+    def convert_and_fill_dates(df, start_date, end_date, interval):
+        # Create a date range for the desired period
+        all_dates = pd.date_range(start=start_date, end=end_date, freq="D")
+        new_df = pd.DataFrame(all_dates, columns=["Date"])
+
+        # Preprocess and aggregate data based on the original interval
+        if interval != "daily":
+            # Resample to start of each week/month, then sum values for the same period
+            if interval == "weekly":
+                df = df.resample("W-MON", on="Date").sum().reset_index()
+            elif interval == "monthly":
+                df = df.resample("MS", on="Date").sum().reset_index()
+
+            # Distribute values equally across the days in each week/month
+            expanded_rows = []
+            for _, row in df.iterrows():
+                if interval == "weekly":
+                    period_dates = pd.date_range(row["Date"], periods=7)
+                elif interval == "monthly":
+                    period_end = row["Date"] + pd.offsets.MonthEnd(1)
+                    period_dates = pd.date_range(row["Date"], period_end)
+
+                for date in period_dates:
+                    new_row = row.copy()
+                    new_row["Date"] = date
+                    for col in df.columns:
+                        if col != "Date":  # Skip 'Date' column
+                            new_row[col] = row[col] / len(period_dates)
+                    expanded_rows.append(new_row)
+
+            # Create a DataFrame from expanded rows
+            expanded_df = pd.DataFrame(expanded_rows)
+            new_df = pd.merge(new_df, expanded_df, how="left", on="Date")
+        else:
+            # Daily data, aggregate if there are multiple entries for the same day
+            df = df.groupby("Date").sum().reset_index()
+            new_df = pd.merge(new_df, df, how="left", on="Date")
+
+        # Ensure all dates from start to end are present, filling missing values with NaN
+        new_df["Date"] = pd.to_datetime(new_df["Date"])  # Ensure 'Date' is datetime type
+        new_df = new_df.set_index("Date").reindex(all_dates).reset_index()
+        new_df.rename(columns={"index": "Date"}, inplace=True)
+
+        return new_df
+
+
+    # Function to convert a DataFrame from daily level granularity to either weekly or monthly level
+    def convert_to_higher_granularity(df, required_granularity):
+        if required_granularity == "daily":
+            return df
+
+        # Ensure 'Date' is the index and is in datetime format
+        if not pd.api.types.is_datetime64_any_dtype(df["Date"]):
+            df["Date"] = pd.to_datetime(df["Date"])
+        df.set_index("Date", inplace=True)
+
+        # Resample and aggregate
+        if required_granularity == "weekly":
+            # Resample to weekly, using 'W-MON' to indicate weeks starting on Monday
+            df = df.resample("W-MON").sum()
+        elif required_granularity == "monthly":
+            # Resample to monthly, using 'MS' to indicate month start
+            df = df.resample("MS").sum()
+
+        # Reset index to move 'Date' back to a column
+        df.reset_index(inplace=True)
+
+        return df
+
+
+    # # Read the CSV file, parsing 'Date' column as datetime
+    main_df = pd.read_csv("Media_data_for_model_dma_level.csv", dayfirst=True, parse_dates=["Date"])
+    # st.write(main_df)
+
+    # Get the start date (minimum) and end date (maximum) from the 'Date' column
+    api_start_date = main_df["Date"].min()
+    api_end_date = main_df["Date"].max()
+
+    # Infer the granularity from the most common difference between consecutive dates
+    date_diffs = main_df["Date"].diff().dt.days.dropna()
+    common_diff = date_diffs.mode()[0]
+    api_granularity = determine_data_interval(common_diff)
+
+    # Convert the DataFrame to daily level granularity
+    main_df = expand_to_daily(main_df, api_granularity, api_start_date, api_end_date)
+
+    # Page Title
+    st.title("Data Import")
+
+    # File uploader
+    uploaded_files = st.file_uploader(
+        "Upload additional data", type=["xlsx"], accept_multiple_files=True
+    )
+
+    # Custom HTML for upload instructions
+    recommendation_html = f"""
+    <div style="text-align: justify;">
+    <strong>Recommendation:</strong> For optimal processing, please ensure that all uploaded datasets including media, internal, and exogenous data adhere to the following guidelines: Each dataset must include a <code>Date</code> column formatted as <code>DD-MM-YYYY</code>, be free of missing values, and aggregated to a {api_granularity} level.
+    </div>
+    """
+
+    st.markdown(recommendation_html, unsafe_allow_html=True)
+
+    # Initialize a list to collect all processed DataFrames
+    all_data_dfs = []
+
+    if uploaded_files:
+        for uploaded_file in uploaded_files:
+            # Extract the file name
+            file_name = uploaded_file.name
+
+            # Load the file into a DataFrame
+            data_df = pd.read_excel(
+                uploaded_file,
+            )
+
+            # Identify numeric columns in the DataFrame
+            numeric_columns = data_df.select_dtypes(include="number").columns.tolist()
+
+            # Validate the 'Date' column and ensure there's at least one numeric column
+            if validate_date_column(data_df) and len(numeric_columns) > 0:
+                data_df = data_df[["Date"] + numeric_columns]
+
+                # Ensure the 'Date' column is in datetime format and sorted
+                data_df["Date"] = pd.to_datetime(data_df["Date"], dayfirst=True)
+                data_df.sort_values("Date", inplace=True)
+
+                # Calculate the most common day difference between dates to determine frequency
+                common_freq = data_df["Date"].diff().dt.days.dropna().mode()[0]
+
+                # Calculate the data interval (daily, weekly, monthly or irregular)
+                interval = determine_data_interval(common_freq)
+
+                if interval == "irregular":
+                    # Warn the user if the 'Date' column doesn't meet the format requirements
+                    st.warning(
+                        f"File Name: {file_name} ➜ Please upload data in daily, weekly or monthly interval."
+                    )
+                    continue
+
+                # Convert data to specified interval and redistribute to daily
+                data_df = convert_and_fill_dates(
+                    data_df, api_start_date, api_end_date, interval
+                )
+
+                # Add the processed DataFrame to the list
+                all_data_dfs.append(data_df)
+
+            else:
+                # Warn the user if the 'Date' column doesn't meet the format requirements
+                st.warning(
+                    f"File Name: {file_name} ➜ Please upload data with Date column in 'DD-MM-YYYY' format and at least one media/exogenous column."
+                )
+
+    # Sequentially merge each of the other DataFrames with the main DataFrame on 'Date'
+    for df in all_data_dfs:
+        main_df = pd.merge(main_df, df, on="Date", how="left")
+
+
+    # Function to calculate missing stats and prepare for editable DataFrame
+    def prepare_missing_stats_df(df):
+        missing_stats = []
+        for column in df.columns:
+            if (
+                column == "Date" or column == "Total Approved Accounts - Revenue"
+            ):  # Skip Date and Revenue column
+                continue
+
+            missing = df[column].isnull().sum()
+            pct_missing = round((missing / len(df)) * 100, 2)
+            missing_stats.append(
+                {
+                    "Column": column,
+                    "Missing Values": missing,
+                    "Missing Percentage": pct_missing,
+                    "Impute Method": "Fill with 0",  # Default value
+                    "Category": "Media",  # Default value
+                }
+            )
+        stats_df = pd.DataFrame(missing_stats)
+        return stats_df
+
+
+    # Prepare missing stats DataFrame for editing
+    missing_stats_df = prepare_missing_stats_df(main_df)
+
+    # Create an editable DataFrame in Streamlit
+    st.markdown("#### Select Variables Category & Impute Missing Values")
+
+    edited_stats_df = st.data_editor(
+        missing_stats_df,
+        column_config={
+            "Impute Method": st.column_config.SelectboxColumn(
+                options=[
+                    "Drop Column",
+                    "Fill with Mean",
+                    "Fill with Median",
+                    "Fill with 0",
+                ],
+                required=True,
+                default="Fill with 0",
+            ),
+            "Category": st.column_config.SelectboxColumn(
+                options=[
+                    "Date",
+                    "Media",
+                    "Exogenous",
+                    "Internal",
+                    "DMA/Panel",
+                    "Response_Metric"
+                ],
+                required=True,
+                default="Media",
+            ),
+        },
+        disabled=["Column", "Missing Values", "Missing Percentage"],
+        hide_index=True,
+        use_container_width=True,
+    )
+
+
+    # Apply changes based on edited DataFrame
+    for i, row in edited_stats_df.iterrows():
+        column = row["Column"]
+        if row["Impute Method"] == "Drop Column":
+            main_df.drop(columns=[column], inplace=True)
+
+        elif row["Impute Method"] == "Fill with Mean":
+            main_df[column].fillna(main_df[column].mean(), inplace=True)
+
+        elif row["Impute Method"] == "Fill with Median":
+            main_df[column].fillna(main_df[column].median(), inplace=True)
+
+        elif row["Impute Method"] == "Fill with 0":
+            main_df[column].fillna(0, inplace=True)
+
+
+    # Convert the Final DataFrame to required granularity
+    main_df = convert_to_higher_granularity(main_df, api_granularity)
+
+    # Display the Final DataFrame and exogenous variables
+    st.markdown("#### Final DataFrame:")
+    st.dataframe(main_df)
+    
+    
+
+    # Initialize an empty dictionary to hold categories and their variables
+    category_dict = {}
+
+    # Iterate over each row in the edited DataFrame to populate the dictionary
+    for i, row in edited_stats_df.iterrows():
+        column = row["Column"]
+        category = row["Category"]  # The category chosen by the user for this variable
+
+        # Check if the category already exists in the dictionary
+        if category not in category_dict:
+            # If not, initialize it with the current column as its first element
+            category_dict[category] = [column]
+        else:
+            # If it exists, append the current column to the list of variables under this category
+            category_dict[category].append(column)
+
+    # Display the dictionary
+    st.markdown("#### Variable Category:")
+    for category, variables in category_dict.items():
+        # Check if there are multiple variables to handle "and" insertion correctly
+        if len(variables) > 1:
+            # Join all but the last variable with ", ", then add " and " before the last variable
+            variables_str = ", ".join(variables[:-1]) + " and " + variables[-1]
+        else:
+            # If there's only one variable, no need for "and"
+            variables_str = variables[0]
+
+        # Display the category and its variables in the desired format
+        st.markdown(f"**{category}:** {variables_str}\n\n", unsafe_allow_html=True)
+
+    # storing maindf and categories in session_state
+    # st.write(main_df)
+        
+            
+    # st.session_state['Cleaned_data']=main_df
+
+    # st.session_state['category_dict']=category_dict
+    if st.button('Save Changes'):
+        
+        with open("Pickle_files/main_df", 'wb') as f:
+            pickle.dump(main_df, f)
+        with open("Pickle_files/category_dict",'wb') as c:
+            pickle.dump(category_dict,c)
+        st.success('Changes Saved!')

Data_prep_functions.py

@@ -0,0 +1,236 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+import pickle
+import statsmodels.api as sm
+import numpy as np
+from sklearn.metrics import mean_absolute_error, r2_score,mean_absolute_percentage_error  
+from sklearn.preprocessing import MinMaxScaler
+import matplotlib.pyplot as plt
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+from plotly.subplots import make_subplots
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+from datetime import datetime
+import seaborn as sns
+
+def calculate_discount(promo_price_series, non_promo_price_series):
+    # Calculate the 4-week moving average of non-promo price
+    window_size = 4
+    base_price = non_promo_price_series.rolling(window=window_size).mean()
+    
+    # Calculate discount_raw
+    discount_raw_series = (1 - promo_price_series / base_price) * 100
+    
+    # Calculate discount_final
+    discount_final_series = discount_raw_series.where(discount_raw_series >= 5, 0)
+    
+    return base_price, discount_raw_series, discount_final_series
+
+
+def create_dual_axis_line_chart(date_series, promo_price_series, non_promo_price_series, base_price_series, discount_series):
+    # Create traces for the primary axis (price vars)
+    trace1 = go.Scatter(
+        x=date_series,
+        y=promo_price_series,
+        name='Promo Price',
+        yaxis='y1'
+    )
+    
+    trace2 = go.Scatter(
+        x=date_series,
+        y=non_promo_price_series,
+        name='Non-Promo Price',
+        yaxis='y1'
+    )
+
+    trace3 = go.Scatter(
+        x=date_series,
+        y=base_price_series,
+        name='Base Price',
+        yaxis='y1'
+    )
+    
+    # Create a trace for the secondary axis (discount)
+    trace4 = go.Scatter(
+        x=date_series,
+        y=discount_series,
+        name='Discount',
+        yaxis='y2'
+    )
+
+    # Create the layout with dual axes
+    layout = go.Layout(
+        title='Price and Discount Over Time',
+        yaxis=dict(
+            title='Price',
+            side='left'
+        ),
+        yaxis2=dict(
+            title='Discount',
+            side='right',
+            overlaying='y',
+            showgrid=False
+        ),
+        xaxis=dict(title='Date'),
+    )
+
+    # Create the figure with the defined traces and layout
+    fig = go.Figure(data=[trace1, trace2, trace3, trace4], layout=layout)
+
+    return fig
+
+
+def to_percentage(value):
+  return f'{value * 100:.1f}%'   
+
+def plot_actual_vs_predicted(date, y, predicted_values, model,target_column=None, flag=None, repeat_all_years=False, is_panel=False):
+    if flag is not None :
+      fig = make_subplots(specs=[[{"secondary_y": True}]])
+    else :
+      fig = go.Figure()
+
+    if is_panel :
+      df=pd.DataFrame()
+      df['date'] = date
+      df['Actual'] = y
+      df['Predicted'] = predicted_values
+      df_agg = df.groupby('date').agg({'Actual':'sum', 'Predicted':'sum'}).reset_index()
+      df_agg.columns = ['date', 'Actual', 'Predicted']
+      assert len(df_agg) == pd.Series(date).nunique()
+      # date = df_agg['date']
+      # y = df_agg['Actual']
+      # predicted_values = df_agg['Predicted']
+      # ymax = df_agg['Actual'].max() # Sprint3 - ymax to set y value for flag
+
+      fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Actual'], mode='lines', name='Actual', line=dict(color='#08083B')))
+      fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Predicted'], mode='lines', name='Predicted', line=dict(color='#11B6BD')))
+
+    else :
+      fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='#08083B')))
+      fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='#11B6BD')))
+
+    line_values=[]
+    if flag:
+      min_date, max_date = flag[0], flag[1]
+      min_week = datetime.strptime(str(min_date), "%Y-%m-%d").strftime("%U")
+      max_week = datetime.strptime(str(max_date), "%Y-%m-%d").strftime("%U")
+      month=pd.to_datetime(min_date).month
+      day=pd.to_datetime(min_date).day
+      #st.write(pd.to_datetime(min_date).week)
+      #st.write(min_week)
+      # Initialize an empty list to store line values
+
+      # Sprint3 change : put flags to secondary axis, & made their y value to 1 instead of 5M
+      if repeat_all_years:
+        #line_values=list(pd.to_datetime((pd.Series(date)).dt.week).map(lambda x: 10000 if x==min_week else 0  ))
+        #st.write(pd.Series(date).map(lambda x: pd.Timestamp(x).week))
+        line_values=list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x).week >=int(min_week)) & (pd.Timestamp(x).week <=int(max_week)) else 0))
+        assert len(line_values) == len(date)
+        #st.write(line_values)
+        fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')),secondary_y=True)
+      else:
+        line_values = []
+
+        line_values = list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x) >= pd.Timestamp(min_date)) and (pd.Timestamp(x) <= pd.Timestamp(max_date)) else 0))
+
+        #st.write(line_values)
+        fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')),secondary_y=True)
+
+
+    # Calculate MAPE
+    mape = mean_absolute_percentage_error(y, predicted_values)
+
+    # Calculate AdjR2 # Assuming X is your feature matrix
+    r2 = r2_score(y, predicted_values)
+    adjr2 = 1 - (1 - r2) * (len(y) - 1) / (len(y) - len(model.fe_params) - 1)
+
+    # Create a table to display the metrics
+    metrics_table = pd.DataFrame({
+      'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+      'Value': [mape, r2, adjr2]
+    })
+    # st.write(metrics_table)
+    fig.update_layout(
+      xaxis=dict(title='Date'),
+      yaxis=dict(title=target_column),
+      xaxis_tickangle=-30
+    )
+    fig.add_annotation(
+    text=f"MAPE: {mape*100:0.1f}%,  Adjr2: {adjr2 *100:.1f}%",
+    xref="paper",
+    yref="paper",
+    x=0.95,  # Adjust these values to position the annotation
+    y=1.2,
+    showarrow=False,
+    )
+    # print("{}{}"*20, len(line_values))
+  #metrics_table.set_index(['Metric'],inplace=True)
+    return metrics_table,line_values, fig
+
+def plot_residual_predicted(actual, predicted, df):
+        df_=df.copy()
+        df_['Residuals'] = actual - pd.Series(predicted)
+        df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+        
+        # Create a Plotly scatter plot
+        fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
+        
+        # Add horizontal lines
+        fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+        fig.add_hline(y=2, line_color="red")
+        fig.add_hline(y=-2, line_color="red")
+        
+        fig.update_xaxes(title='Predicted')
+        fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+        
+        # Set the same width and height for both figures
+        fig.update_layout(title='2.3.1 Residuals over Predicted Values', autosize=False, width=600, height=400)
+        
+        return fig
+
+def residual_distribution(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        
+        # Create a Seaborn distribution plot
+        sns.set(style="whitegrid")
+        plt.figure(figsize=(6, 4))
+        sns.histplot(Residuals, kde=True, color="#11B6BD")
+        
+        plt.title('2.3.3 Distribution of Residuals')
+        plt.xlabel('Residuals')
+        plt.ylabel('Probability Density')
+        
+        return plt
+
+
+def qqplot(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        Residuals = pd.Series(Residuals)
+        Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+        
+        # Create a QQ plot using Plotly with custom colors
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                mode='markers',
+                                marker=dict(size=5, color="#11B6BD"),
+                                name='QQ Plot'))
+        
+        # Add the 45-degree reference line
+        diagonal_line = go.Scatter(
+            x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+            y=[-2, 2],  # Adjust the y values accordingly
+            mode='lines',
+            line=dict(color='red'),  # Customize the line color and style
+            name=' '
+        )
+        fig.add_trace(diagonal_line)
+        
+        # Customize the layout
+        fig.update_layout(title='2.3.2 QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                          xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+        
+        return fig

E0DAF720

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c17424a08cc3cf634abd12627072cd158aa6934e7441e9886a6556bf4f5a319b
+size 7185190

Eda_functions.py

@@ -0,0 +1,157 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import streamlit as st
+import re
+from matplotlib.colors import ListedColormap
+# from st_aggrid import AgGrid, GridOptionsBuilder
+# from src.agstyler import PINLEFT, PRECISION_TWO, draw_grid
+
+
+def format_numbers(x):
+    if abs(x) >= 1e6:
+        # Format as millions with one decimal place and commas
+        return f'{x/1e6:,.1f}M'
+    elif abs(x) >= 1e3:
+        # Format as thousands with one decimal place and commas
+        return f'{x/1e3:,.1f}K'
+    else:
+        # Format with one decimal place and commas for values less than 1000
+        return f'{x:,.1f}'
+
+    
+
+def line_plot(data, x_col, y1_cols, y2_cols, title):
+    fig = go.Figure()
+      
+    for y1_col in y1_cols:
+        fig.add_trace(go.Scatter(x=data[x_col], y=data[y1_col], mode='lines', name=y1_col,line=dict(color='#11B6BD')))
+
+    for y2_col in y2_cols:
+        fig.add_trace(go.Scatter(x=data[x_col], y=data[y2_col], mode='lines', name=y2_col, yaxis='y2',line=dict(color='#739FAE')))
+    if len(y2_cols)!=0:
+        fig.update_layout(yaxis=dict(), yaxis2=dict(overlaying='y', side='right'))
+    else:
+        fig.update_layout(yaxis=dict(), yaxis2=dict(overlaying='y', side='right'))
+    if title:
+        fig.update_layout(title=title)
+    fig.update_xaxes(showgrid=False)
+    fig.update_yaxes(showgrid=False)
+
+    return fig
+
+
+def line_plot_target(df,target,title):
+
+    coefficients = np.polyfit(df['date'].view('int64'), df[target], 1)
+    trendline = np.poly1d(coefficients)
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=df['date'], y=df[target], mode='lines', name=target,line=dict(color='#11B6BD')))
+    trendline_x = df['date']
+    trendline_y = trendline(df['date'].view('int64'))
+
+
+    fig.add_trace(go.Scatter(x=trendline_x, y=trendline_y, mode='lines', name='Trendline', line=dict(color='#739FAE')))
+
+    fig.update_layout(
+        title=title,
+        xaxis=dict(type='date')
+    )
+
+    for year in df['date'].dt.year.unique()[1:]:
+
+        january_1 = pd.Timestamp(year=year, month=1, day=1)
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=january_1,
+                x1=january_1,
+                y0=0,
+                y1=1,
+                xref="x",
+                yref="paper",
+                line=dict(color="grey", width=1.5, dash="dash"),
+            )
+        )
+
+    return fig
+
+def correlation_plot(df,selected_features,target):
+    custom_cmap = ListedColormap(['#08083B', "#11B6BD"])  
+    corr_df=df[selected_features]
+    corr_df=pd.concat([corr_df,df[target]],axis=1)
+    fig, ax = plt.subplots(figsize=(16, 12))
+    sns.heatmap(corr_df.corr(),annot=True, cmap='Blues', fmt=".2f", linewidths=0.5,mask=np.triu(corr_df.corr()))
+    #plt.title('Correlation Plot')
+    plt.xticks(rotation=45)
+    plt.yticks(rotation=0)
+    return fig
+
+def summary(data,selected_feature,spends,Target=None):
+        
+        if Target:
+            sum_df = data[selected_feature]
+            sum_df['Year']=data['date'].dt.year
+            sum_df=sum_df.groupby('Year')[selected_feature].sum()
+            sum_df=sum_df.reset_index()
+            total_sum = sum_df.sum(numeric_only=True)
+            total_sum['Year'] = 'Total' 
+            sum_df = sum_df.append(total_sum, ignore_index=True)
+            sum_df.set_index(['Year'],inplace=True)
+            sum_df=sum_df.applymap(format_numbers)
+            spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+            for col in spends_col:
+                sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+            # st.write(spends_col)
+            # sum_df = sum_df.reindex(sorted(sum_df.columns), axis=1)
+
+            return sum_df
+        else:
+            #selected_feature=list(selected_feature)
+            selected_feature.append(spends) 
+            selected_feature=list(set(selected_feature))
+            if len(selected_feature)>1:
+                sum_df = data[selected_feature]
+                sum_df['Year']=data['date'].dt.year
+                sum_df=sum_df.groupby('Year')[selected_feature].agg('sum')
+                sum_df['CPM/CPC']=(sum_df.iloc[:, 1] / sum_df.iloc[:, 0])*1000
+                sum_df.loc['Grand Total']=sum_df.sum()
+            
+                sum_df=sum_df.applymap(format_numbers)
+                sum_df.fillna('-',inplace=True)
+                sum_df=sum_df.replace({"0.0":'-','nan':'-'})
+                spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+                for col in spends_col:
+                    sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+                return sum_df
+            else:
+                sum_df = data[selected_feature]
+                sum_df['Year']=data['date'].dt.year
+                sum_df=sum_df.groupby('Year')[selected_feature].agg('sum')
+                sum_df.loc['Grand Total']=sum_df.sum()
+                sum_df=sum_df.applymap(format_numbers)
+                sum_df.fillna('-',inplace=True)
+                sum_df=sum_df.replace({"0.0":'-','nan':'-'})
+                spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+                for col in spends_col:
+                    sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+                return sum_df
+
+
+def sanitize_key(key, prefix=""):
+    # Use regular expressions to remove non-alphanumeric characters and spaces
+    key = re.sub(r'[^a-zA-Z0-9]', '', key)
+    return f"{prefix}{key}"
+
+
+
+

Media_data_for_model.csv

@@ -0,0 +1,182 @@
+Date,paid_search_impressions,paid_search_clicks,kwai_impressions,kwai_clicks,programmaticimpressions,programmaticclicks,affiliates_impressions,affiliates_clicks,indicacao_impressions,indicacao_clicks,infleux_impressions,infleux_clicks,influencer_impressions,influencer_clicks,Total Approved Accounts - Revenue,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
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Media_data_for_model_dma_level.csv

@@ -0,0 +1,538 @@
+Date,paid_search_impressions,paid_search_clicks,kwai_impressions,kwai_clicks,programmaticimpressions,programmaticclicks,affiliates_impressions,affiliates_clicks,indicacao_impressions,indicacao_clicks,infleux_impressions,infleux_clicks,influencer_impressions,influencer_clicks,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,DMA,Panel,Account Requests - Appsflyer,App Installs - Appsflyer,BAU approved clients - Appsflyer,BAU approved clients - Revenue,Gamified approved clients - Appsflyer,Gamified approved clients - Revenue,Total Approved Accounts - Appsflyer,Total Approved Accounts - Revenue,Adjusted Account Approval,Adjusted Account Approval BAU
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+22/08/2023,2208,845,5006504,13886,598218,939529,0,18631,0,11119,0,18304,0,666,3020862,419659,1960342,66769,12591,D1,P1,21348,42166,1844,3824050,762,76200,2582,3877380,5610.58,4088.17
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+25/08/2023,1889,680,4674166,13506,1119189,1818097,0,33200,0,7250,0,16577,0,3622,2405697,395219,1564070,61103,13348,D1,P1,18364,37536,1863,3939560,638,63800,2501,4003360,5070.56,3850.47
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+27/08/2023,1333,486,5591938,13138,956722,732493,0,12952,0,4227,0,19562,0,2354,2470188,417919,1980888,53707,14151,D1,P1,14834,30563,1572,2979370,536,53600,2108,3032970,4232.88,3241.64
+28/08/2023,2031,760,7120359,17304,592505,571748,0,44816,0,8728,0,19999,0,3813,2357294,420574,1878047,50335,13442,D1,P1,20994,42982,1970,3877550,642,64200,2612,3941750,5610.5,4325.83
+29/08/2023,1560,550,6349650,18074,395464,276869,0,217642,0,8742,0,36555,0,2778,2437012,455532,1707585,52913,12648,D1,P1,25919,51217,2270,4323380,803,80300,3073,4403680,6223.41,4701.59
+30/08/2023,1788,623,6774580,17019,804715,227676,0,92490,0,7576,0,33376,0,1815,2461827,452647,1924554,57945,12244,D1,P1,24015,48307,2298,4414000,796,79600,3094,4493600,5983.31,4520.44
+31/08/2023,2251,790,6881955,16586,462096,216142,0,177608,0,7188,0,13212,0,1862,2630688,508779,1691540,44071,12093,D1,P1,17587,35874,1809,3406910,604,60400,2413,3467310,5218.62,4005.29
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+15/09/2023,2467,882,4260164,9702,399193,291844,0,756,0,6932,0,16060,0,2982,2751748,1416780,1269521,57909,10048,D1,P1,16118,32895,1659,3149040,526,52600,2185,3201640,4920.59,3802.04
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+17/09/2023,2467,802,4503316,11119,581720,236009,0,637,0,4814,0,10024,0,2464,2935930,1503370,1649587,48796,10073,D1,P1,12357,25262,1396,2560250,455,45500,1851,2605750,4246.67,3287.31
+18/09/2023,2910,1024,5568066,14302,184276,143660,0,888,0,6922,0,10381,0,1767,2373681,1330212,1479501,51224,10488,D1,P1,16441,33582,1727,3152630,572,57200,2300,3209930,5491.87,4171.85
+19/09/2023,3252,1309,6105220,12193,208312,187769,0,1464,0,5210,0,8092,0,1504,2373344,1285881,1407015,40642,10547,D1,P1,17770,36578,1743,3229930,653,65300,2395,3291230,5666.71,4236.34
+20/09/2023,2796,1185,6055420,14003,291395,272928,0,1077,0,4246,0,10472,0,1830,2565110,1425196,1460886,48962,10318,D1,P1,16656,34188,1627,3048960,738,73800,2365,3121460,5577.7,3950.97
+21/09/2023,2208,878,5225528,8679,697480,160425,0,1033,0,6726,0,13928,0,1357,2686089,1447330,1236533,49940,10259,D1,P1,17253,34210,1912,3553480,811,81100,2723,3634580,6248.63,4440.07
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+23/09/2023,1190,492,4948823,10035,2432739,154849,0,702,0,8285,0,4369,0,806,2740252,1531672,1407980,51747,8665,D1,P1,12641,27336,1443,2653190,652,65200,2095,2718390,5094.46,3633.56
+24/09/2023,1124,496,6239124,10744,248085,231243,0,477,0,8032,0,8640,0,1449,2648532,1476875,1294436,36638,10410,D1,P1,12153,26113,1395,2618830,621,62100,2016,2680930,4558.91,3242.77
+25/09/2023,2358,1041,5325249,9804,251517,447312,0,591,0,11299,0,21103,0,1135,2371891,1333464,1222194,36894,9191,D1,P1,18608,38878,2041,3740860,897,89700,2938,3830560,6554.2,4744.62
+26/09/2023,2092,994,5361926,13223,90916,351820,0,910,0,10598,0,27697,0,2803,2612585,1502573,1256804,33211,8893,D1,P1,21214,41166,2128,3878240,935,93500,3063,3971740,6565.1,4696.86
+27/09/2023,1835,792,4600061,14060,728920,347014,0,1111,0,8811,0,104094,0,1780,2488689,1390273,1203937,33935,8769,D1,P1,37289,65137,2933,5530540,1595,159500,4525,5686340,8073.74,5545.54
+28/09/2023,1787,807,4114657,9434,84913,564139,0,279332,0,6975,0,51946,0,2516,2290090,1309586,1316086,32443,8606,D1,P1,25651,47307,2343,4257450,1199,119900,3542,4377350,7151.36,4963.98
+29/09/2023,1513,641,4477584,8505,211297,372199,0,443,0,4039,0,4466,0,1774,2429135,1379398,1425850,31700,7184,D1,P1,12276,26946,1488,2459350,613,61300,2101,2520650,5148.56,3726.34
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+17/05/2023,8409,2785,1473128,4336,56382,2232239,0,2557,0,6401,0,8946,0,322,3199527,1379566,2608845,39190,12591,D3,P3,21930,41033,2100,3675940,1126,112600,3226,3788540,6156.6,4185.47
+18/05/2023,8364,2873,1733275,5009,38145,7321146,0,2912,0,7286,0,14366,0,660,2623727,1115471,1723470,36020,12100,D3,P3,21813,40251,1987,3528210,1240,124000,3227,3652210,6388.27,4150.71
+19/05/2023,6432,2050,1784426,5063,23340,8715910,0,3934,0,6035,0,20378,0,362,2995998,1287313,1959870,36885,12848,D3,P3,19874,38360,1888,3663690,1140,114000,3027,3777590,5981.25,3891.85
+20/05/2023,5428,1724,1635604,4408,34693,8783612,0,3318,0,4714,0,21030,0,236,2996479,1326416,1903323,31048,12256,D3,P3,17568,33060,1691,3342720,931,93100,2623,3437270,5113.91,3453.26
+21/05/2023,5657,1807,1788487,4492,24812,5015214,0,2253,0,4227,0,11656,0,494,3167634,1309450,3651254,33361,13073,D3,P3,14766,28367,1461,2940020,800,80000,2261,3020020,4874.35,3300.96
+22/05/2023,5768,2036,2176947,5688,25298,3002995,0,2739,0,8313,0,25663,0,1147,3573865,1548365,3939226,33410,14092,D3,P3,21520,40205,1854,3533540,1097,109700,2951,3643240,6425.41,4211.67
+23/05/2023,5051,1720,2359219,6966,24773,3005057,0,4738,0,13827,0,47900,0,965,3248157,1376975,3631390,35016,13025,D3,P3,29860,51811,2527,5012370,1339,133900,3866,5146270,7978.08,5238.68
+24/05/2023,6078,1977,1612918,4924,24591,2833280,0,4816,0,12417,0,94489,0,1254,3572793,1550315,3532105,37491,12546,D3,P3,41297,68099,2993,5666720,1655,165500,4648,5832220,8715.48,5761.11
+25/05/2023,6547,2075,1468456,3624,19705,2771412,0,5070,0,7395,0,70016,0,762,3164337,1353382,3253308,34658,13154,D3,P3,33436,56714,2588,5066960,1509,150900,4097,5217860,7939.09,5062.98
+26/05/2023,3719,1189,1770048,4874,16879,2875657,0,2855,0,6964,0,29015,0,627,2989794,1248779,3345390,38267,12788,D3,P3,22185,40261,2101,4010930,1130,113000,3231,4123930,6785.57,4495.67
+27/05/2023,3620,1145,1900387,5061,14156,2663378,0,3295,0,4472,0,5625,0,1473,3576647,1527545,3694843,40685,12844,D3,P3,13490,26751,1407,2592840,756,75600,2163,2668440,5325.41,3549.17
+28/05/2023,4195,1302,2026053,5703,12334,2609966,0,2190,0,3737,0,5030,0,1401,3376177,1447089,2563297,42359,13543,D3,P3,13124,25607,1374,2558180,722,72200,2096,2630380,5282.85,3554.29
+29/05/2023,5265,1798,2328823,6483,14783,2537637,0,3954,0,5211,0,221,0,1575,3765997,1720747,2865333,39579,8116,D3,P3,15619,30688,1585,2979010,785,78500,2370,3057510,5961.63,4097.14
+30/05/2023,3879,1366,2294654,6008,15979,2489630,0,4465,0,6041,0,6,0,1192,3790830,1751416,2822819,37234,8830,D3,P3,17258,32693,1773,3270270,900,90000,2673,3360270,6752.47,4596.75
+31/05/2023,3933,1348,1645187,4081,14208,2337652,0,3797,0,4794,0,6,0,888,4151434,1953620,2714074,45856,6861,D3,P3,16458,31379,1688,3065730,924,92400,2612,3158130,6598.66,4373.22
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+2/6/2023,5733,1800,966546,2646,43573,4582872,0,1563,0,10678,0,46810,0,1309,4313201,2009602,2074692,47378,6120,D3,P3,25710,47869,2271,4131170,1130,113000,3401,4244170,6992.72,4747.36
+3/6/2023,4142,1290,2445721,11111,90587,4764628,0,2176,0,5144,0,27735,0,518,4514302,2083217,2095544,58527,5748,D3,P3,19247,37244,1905,3615570,961,96100,2866,3711670,5996.2,4065.63
+4/6/2023,5143,1613,2296690,6790,40929,4717779,0,1280,0,4237,0,5606,0,325,4179140,1889452,2152476,45239,6093,D3,P3,13474,29405,1475,2776480,755,75500,2230,2851980,5219.47,3571.73
+5/6/2023,5384,1832,3509278,8938,56272,19979584,0,1377,0,11493,0,25647,0,579,3683204,1641254,3616732,40356,6453,D3,P3,22558,54639,2114,4004520,1128,112800,3242,4117320,6672.4,4468.93
+6/6/2023,4802,1594,3216944,7861,20049,33102789,0,1485,0,9086,0,36532,0,545,3822453,1716540,3687300,53347,6334,D3,P3,26643,69935,2358,4505090,1222,122200,3580,4627290,6855.89,4686.82
+7/6/2023,5072,1648,2143372,5356,22553,21321547,0,1576,0,7213,0,21215,0,628,4178339,1811963,2354753,51632,6259,D3,P3,22242,56660,2125,3900920,1184,118400,3309,4019320,6611.5,4370.78
+8/6/2023,4444,1465,3190766,8024,53653,10254268,0,2046,0,10491,0,19549,0,769,3941272,1738344,2283350,59291,6775,D3,P3,23293,50105,2238,4145770,1270,127000,3508,4272770,6851.85,4515.66
+9/6/2023,4818,1605,3278715,9328,18347,4890758,0,1925,0,8360,0,32385,0,1732,3969227,1777864,2353376,52000,6026,D3,P3,25950,50611,2404,4657210,1315,131500,3719,4788710,6881.12,4639.8
+10/6/2023,3465,1207,2887842,8529,725,5489947,0,1230,0,5401,0,37954,0,2136,4458593,2061762,2535928,66567,5554,D3,P3,24413,47973,2370,4584570,1225,122500,3595,4707070,6334.68,4283.38
+11/6/2023,4727,1501,3149290,8114,738,5313957,0,1839,0,8198,0,32493,0,1533,4442610,2006438,2183963,47655,6008,D3,P3,23656,46275,2220,4441870,1183,118300,3403,4560170,6134.11,4098
+12/6/2023,6437,2208,4416005,12345,149561,5298884,0,1905,0,8542,0,101079,0,472,4645531,1995891,3301882,38760,4966,D3,P3,44382,76997,3520,6853780,1782,178200,5302,7031980,8549.02,5779.3
+13/06/2023,3556,1254,4626697,12984,258088,5952266,0,2095,0,10415,0,59770,0,1016,4508060,1912958,3440789,47281,4630,D3,P3,35764,67060,2737,5184020,1530,153000,4266,5335600,7908.1,5200.7
+14/06/2023,3178,1060,3389530,10298,685692,10454400,0,2258,0,24457,0,16016,0,1101,4573214,1920050,3160905,41549,5083,D3,P3,27677,56158,2257,4257990,1244,124400,3501,4382390,7187.71,4826.11
+15/06/2023,2981,999,3131350,10791,1072645,11631302,0,2265,0,17304,0,10395,0,1188,4075106,1690702,3267810,50496,5037,D3,P3,23775,50354,2201,4212820,1215,121500,3416,4334320,7339.75,4890.1
+16/06/2023,2705,947,2923279,11124,1166424,11840950,0,1780,0,8938,0,24339,0,966,4533368,1939737,2881833,41872,4604,D3,P3,22957,49677,2225,4445430,1154,115400,3379,4560830,6663.77,4416.03
+17/06/2023,3697,1154,2955836,10440,807683,9748201,0,2139,0,5741,0,54129,0,766,4958344,2059487,3183051,52618,3675,D3,P3,26623,53187,2434,4755560,1286,128600,3723,4890110,6983.5,4694.41
+18/06/2023,3229,1080,3280006,12373,116340,8176712,0,1481,0,4741,0,16724,0,864,4270249,1735486,3251229,39780,3696,D3,P3,16690,36522,1715,3294460,936,93600,2651,3388060,5614.57,3749.6
+19/06/2023,3082,1003,6545797,24462,55763,4841897,0,2098,0,10520,0,26558,0,2211,4137846,1743715,2680413,43156,4347,D3,P3,25736,50759,2343,4515000,1244,124400,3587,4639400,7090.09,4789.96
+20/06/2023,2422,857,6734594,28910,52166,4718912,0,2205,0,10284,0,30610,0,1002,4218772,1771102,2058734,42288,4260,D3,P3,27941,52107,2478,4829920,1398,139800,3876,4969720,7584.9,4974.28
+21/06/2023,3366,1132,4784180,17247,52817,5971594,0,3387,0,9277,0,41697,0,645,4113884,1743016,2111350,44159,4193,D3,P3,28338,53853,2376,4353550,1357,135700,3733,4489250,7214.21,4702.88
+22/06/2023,2841,924,3300680,13360,29784,6803330,0,4064,0,7068,0,68638,0,481,3738171,1533407,1597072,35381,4173,D3,P3,34683,62182,2532,4863520,1434,143400,3966,5006920,7223.75,4679.68
+23/06/2023,2474,805,2284446,9012,80066,6833289,0,3274,0,7379,0,13501,0,721,4479743,1889155,1647740,39089,3640,D3,P3,16506,35549,1530,2980550,888,88800,2418,3069350,5295.75,3457.41
+24/06/2023,2462,814,1947190,7247,50309,6526903,0,2767,0,4703,0,8438,0,616,3758421,1565736,1648519,46332,3834,D3,P3,13804,31588,1381,2698000,788,78800,2169,2776800,4822.39,3136
+25/06/2023,2082,679,3560248,14850,50806,6368664,0,2767,0,4414,0,5346,0,628,4038846,1700182,2514456,43065,4201,D3,P3,13435,30121,1424,2782640,778,77800,2202,2860440,5082.66,3353.86
+26/06/2023,2399,839,5999950,28401,23209,10788275,0,3699,0,13383,0,13592,0,790,3427918,1403888,3598236,33883,4642,D3,P3,21114,49622,1959,3810990,1175,117500,3134,3928490,6965.39,4504.75
+27/06/2023,2307,804,5005495,18260,81344,14103220,0,7082,0,8898,0,40917,0,945,3819654,1523667,3556028,35326,4628,D3,P3,32019,65348,2877,5691820,1595,159500,4472,5851320,9589,6333.48
+28/06/2023,2215,759,3721084,11248,20153,10547995,0,8387,0,7120,0,39693,0,944,3671994,1568555,1397196,33212,2998,D3,P3,30267,63086,2863,5931970,1516,151600,4379,6083570,8919.81,5942.75
+29/06/2023,2013,706,3918049,10226,155296,8525871,0,10096,0,5693,0,24049,0,1512,3937747,1585655,3393043,30700,2519,D3,P3,22893,54097,2125,4215060,1132,113200,3257,4328260,7178.85,4806.94
+30/06/2023,1258,454,3088874,7943,902115,10945715,0,8904,0,9611,0,62404,0,1029,4945464,1946944,1835310,52445,2839,D3,P3,30707,67849,2616,5112840,1397,139700,4013,5252540,7664.09,5203.16
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+2/7/2023,1336,485,5799582,17238,576858,12180985,0,4148,0,4670,0,4766,0,617,4527404,1997256,2038953,50510,21201,D3,P3,15205,43684,1672,3328290,886,88600,2559,3416990,5952.59,4038.91
+3/7/2023,2712,924,9986061,28191,442261,14059535,0,5347,0,7408,0,19028,0,1044,4179823,1854231,2234940,57543,14473,D3,P3,22798,57392,2425,4796940,1308,130800,3732,4925290,7701.44,5182.84
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+5/7/2023,4166,1422,4779589,12676,2354716,18574154,0,7967,0,11552,0,6628,0,1420,4464681,1969744,2390838,41411,12751,D3,P3,22847,61021,2348,4359400,1422,142200,3769,4497600,7976.49,5170.89
+6/7/2023,4182,1444,4939385,12222,2364811,15879491,0,6575,0,8461,0,18225,0,997,4490815,1942923,2239375,44808,16216,D3,P3,23716,60282,2602,4960450,1543,154300,4145,5114750,8225.73,5423.76
+7/7/2023,3497,1181,3121447,7534,1063284,17341347,0,6025,0,8113,0,25962,0,659,4891573,2128787,1976413,57373,12464,D3,P3,23336,58252,2335,4783910,1269,126900,3604,4910810,7182.26,4846.37
+8/7/2023,2760,856,3295227,9788,1119268,19207341,0,4102,0,6195,0,40506,0,832,5039604,2277255,2330435,44052,15163,D3,P3,22278,62471,2128,4352160,1191,119100,3319,4471260,6706.12,4485.38
+9/7/2023,2809,875,3913741,11815,749310,25182206,0,6420,0,5222,0,49626,0,718,4669470,2144473,3908306,49659,11716,D3,P3,24736,69003,2620,5573560,1325,132500,3945,5706060,7673.85,5278.59
+10/7/2023,4312,1489,5972974,18402,1511035,25950979,0,9842,0,10638,0,36204,0,935,4584106,2019220,4391654,52303,12983,D3,P3,28497,78251,2578,5157430,1419,141900,3997,5299330,8174.81,5453.8
+11/7/2023,4579,1550,4999618,16469,559119,23938153,0,11688,0,36570,0,25216,0,1289,4458364,1932300,4150666,47979,12292,D3,P3,28688,77223,2707,5384090,1483,148300,4190,5532390,8594.12,5787.78
+12/7/2023,4079,1418,4465722,13191,583520,25196511,0,4610,0,9813,0,20388,0,1210,4558876,2000168,4109583,54631,12366,D3,P3,25749,73523,2403,4926480,1410,141000,3813,5067480,8000.73,5259.61
+13/07/2023,3719,1260,4635033,12302,903614,25720336,0,7867,0,6792,0,22248,0,857,4596184,1957206,3729970,48474,11017,D3,P3,22447,69283,2134,4261960,1312,131200,3447,4393760,7385.89,4769.15
+14/07/2023,3632,1224,3441594,9800,1566300,28606996,0,6726,0,6172,0,14670,0,432,4683387,2007387,3912229,52588,10079,D3,P3,19225,67928,2002,3875450,1077,107700,3079,3983150,6615.15,4505.74
+15/07/2023,2909,941,6025085,21326,1836196,28705476,0,5705,0,4369,0,31202,0,595,5008167,2251661,3727627,58143,10214,D3,P3,19533,64001,2026,4112810,1066,106600,3093,4220310,6115.73,4122.89
+16/07/2023,2818,853,7339565,26586,4043959,26752554,0,7733,0,3961,0,27180,0,1082,4716541,2092258,2114014,59204,11281,D3,P3,18871,60797,1956,3715100,1126,112600,3084,3832400,6216.99,4066.5
+17/07/2023,4420,1486,9638491,32269,819444,29437537,0,11485,0,5220,0,66236,0,2418,4359325,1937825,1989872,55815,11896,D3,P3,35493,89662,2771,5539890,1521,152100,4292,5691990,8025.2,5388.69
+18/07/2023,4574,1551,9498457,31230,1206114,29164369,0,5012,0,7146,0,47074,0,2358,4882304,2163458,2157773,69573,12604,D3,P3,32238,84091,2687,5214920,1329,132900,4016,5347820,7833.63,5336.47
+19/07/2023,4632,1537,9742535,26935,1491736,30394328,0,6147,0,7028,0,11807,0,1476,4613422,2080215,1981362,67495,12116,D3,P3,23193,69138,2342,4581910,1084,108400,3426,4690310,7118.68,4991.3
+20/07/2023,4891,1632,7630122,20720,2370192,23939153,0,6261,0,5635,0,17220,0,1338,4484291,1922090,1663127,75312,12252,D3,P3,23193,63445,2209,4471970,995,99500,3255,4576570,6700.79,4599.1
+21/07/2023,3978,1378,7284968,21477,2456715,17869335,0,6360,0,10877,0,11431,0,769,4519305,1975730,1686467,68931,10065,D3,P3,21478,57996,2115,4257360,1006,100600,3121,4357960,6467.01,4497.43
+22/07/2023,3151,978,5638955,16181,2015345,12808440,0,7097,0,5145,0,13811,0,699,4689346,2086993,1861883,71898,9081,D3,P3,17602,50209,1897,3876970,830,83000,2727,3959970,5812.35,4126.62
+23/07/2023,2905,986,6133144,16089,881691,11267535,0,5593,0,4746,0,68021,0,514,4242878,1843843,1681215,61164,10797,D3,P3,25804,58279,2442,5168300,1161,116100,3603,5284400,6622.56,4639.27
+24/07/2023,4606,1651,8830736,21931,189913,35658281,0,8836,0,6040,0,64066,0,1626,3943001,1708849,1900126,59494,10236,D3,P3,31953,73505,2898,6111230,1424,142400,4321,6253530,8030.22,5492.47
+25/07/2023,4414,1597,7750251,15384,3348941,25011847,0,10262,0,9572,0,15072,0,916,3976490,1729916,1911109,68826,11468,D3,P3,26162,65005,2440,4822810,1154,115400,3594,4938210,7092.81,4965.39
+26/07/2023,4488,1530,8125332,16391,1040452,24380635,0,9947,0,16453,0,38777,0,1551,3837786,1680967,1856885,74924,13290,D3,P3,31894,69746,2797,5552460,1348,134800,4147,5687460,7359.12,5027.76
+27/07/2023,4105,1494,8054962,14724,220302,13070502,0,6758,0,8841,0,20622,0,1313,3636297,1544742,1772602,63935,11680,D3,P3,24634,57255,2498,5018670,1196,119600,3694,5138270,7106.66,4875.99
+28/07/2023,3743,1318,6955526,11566,3991586,5347413,0,10451,0,8496,0,20184,0,1563,3890784,1680538,1608577,73120,11390,D3,P3,22265,50660,2204,4515740,1054,105400,3258,4621140,6441.87,4432.07
+29/07/2023,3395,1192,5132501,9837,1349895,4441709,0,7115,0,5449,0,18983,0,1129,4295602,1813637,1824777,80511,11911,D3,P3,17626,42341,1930,3807270,860,86000,2790,3893270,5519.71,3922.54
+30/07/2023,2746,903,4903153,11018,710000,4431986,0,8491,0,4599,0,24834,0,1109,3924352,1733790,1740605,72221,11440,D3,P3,17619,43309,1942,3937090,834,83400,2776,4020490,5556.3,3945.89
+31/07/2023,4208,1476,6832832,19648,1721973,4079656,0,7754,0,7818,0,27918,0,2050,3760475,1501134,1957900,62124,11605,D3,P3,22728,51399,2256,4604950,1046,104600,3302,4709550,6481.79,4556.75
+1/8/2023,4203,1489,6398210,15890,2250090,5457683,0,7588,0,7948,0,61894,0,1248,3687038,1439092,1774701,61448,11492,D3,P3,31009,67515,2609,5471270,1309,130900,3918,5602170,6825.97,4671.87
+2/8/2023,4285,1515,5402871,14825,785167,6582085,0,6079,0,7236,0,79041,0,1345,3807266,1493340,2025308,54506,11515,D3,P3,34034,74123,2858,5856910,1360,136000,4218,5992910,7171.23,4989.3
+3/8/2023,4667,1744,4724924,8903,1111815,10407793,0,9077,0,7117,0,31714,0,1983,3797879,1479623,1718831,52587,11408,D3,P3,23978,62504,2404,4832400,1095,109500,3498,4941800,6670.71,4627.48
+4/8/2023,4201,1562,3732952,9116,4574481,10660977,0,8436,0,6970,0,25097,0,1055,3814852,1466211,1374674,57482,10785,D3,P3,21714,62922,2226,4561560,975,97500,3201,4659060,6133.71,4366.77
+5/8/2023,3080,1110,3310732,9884,3460283,11580456,0,5735,0,5049,0,5911,0,704,4048945,1601234,1442690,65158,9942,D3,P3,13792,52995,1578,3188640,720,72000,2298,3260640,4760.04,3375.44
+6/8/2023,2809,979,4153998,11663,5103054,8381689,0,4868,0,4184,0,28658,0,637,4040770,1250223,1844909,47698,10816,D3,P3,17706,56827,1841,3921270,842,84200,2683,4005470,5062.91,3520.39
+7/8/2023,3522,1186,3164171,10000,4137349,8709017,0,6112,0,6640,0,43866,0,1285,3928811,1288638,1861166,44623,11648,D3,P3,26772,71580,2456,4923260,1132,113200,3587,5034960,6580.87,4648.84
+8/8/2023,4111,1495,6020074,15008,2923076,5012245,0,6684,0,6961,0,12257,0,3038,3921429,1094791,2059462,48047,10379,D3,P3,22363,60212,2075,4138870,971,97100,3046,4235970,6286.57,4418.71
+9/8/2023,3609,1285,5616011,13195,2483117,3900778,0,8177,0,6447,0,12774,0,1309,3322165,452000,2262292,52219,11047,D3,P3,21099,53059,2067,4030270,1010,101000,3077,4131270,6309.32,4273.88
+10/8/2023,3872,1330,5270940,11342,1305507,3614496,0,6927,0,6602,0,21830,0,986,2868345,452896,1541791,46720,10556,D3,P3,22515,52055,2216,4848640,974,97400,3190,4946040,6281.59,4498.26
+11/8/2023,3673,1257,4485834,10445,3562053,5521177,0,5810,0,13751,0,5797,0,751,3242607,511016,1679989,47262,10344,D3,P3,17086,46491,1551,3242370,671,67100,2222,3309470,4608.96,3355.58
+12/8/2023,2744,960,3946512,10052,4015316,5813616,0,6205,0,10379,0,9890,0,532,3761072,595191,1931989,54787,9210,D3,P3,15949,46498,1376,3113130,639,63900,2016,3177130,4121.65,2907.82
+13/08/2023,2418,775,5051792,11809,1869057,6348414,0,4093,0,5187,0,21320,0,399,3279816,503805,2171335,55136,10546,D3,P3,14745,44224,1327,2888900,648,64800,1975,2953700,3905.29,2752.8
+14/08/2023,3551,1196,4839009,11805,1765326,3758610,0,6170,0,7226,0,19575,0,706,2974893,427940,1822890,49127,11042,D3,P3,20336,49993,1770,3798710,748,74800,2518,3873510,5195.83,3786.85
+15/08/2023,3430,1312,6520709,24857,1792924,1852314,0,6063,0,9302,0,34681,0,778,3080012,462344,1935145,57900,11083,D3,P3,24449,48710,1946,4332210,807,80700,2752,4412810,5367.05,3930.18
+16/08/2023,3253,1175,4844378,27290,3478033,1293346,0,5884,0,8280,0,29077,0,1055,3150093,468159,2094524,58937,11823,D3,P3,23892,46995,1911,3996580,780,78000,2691,4074580,5452.88,3979.2
+17/08/2023,3714,1417,4702754,23408,4058942,1186576,0,11301,0,10504,0,15462,0,1017,3071572,444690,1961293,58681,10987,D3,P3,21265,42100,1700,3517380,704,70400,2404,3587780,5150.22,3718.85
+18/08/2023,1936,710,4370177,18919,3789636,1020973,0,9525,0,8958,0,32346,0,909,3252966,461174,2031390,61098,10354,D3,P3,20983,41383,1778,3654630,679,67900,2457,3722530,5019.58,3716.93
+19/08/2023,1998,723,3683868,14860,5187185,1336224,0,8305,0,6265,0,13396,0,1267,3460234,494225,2037289,68988,9709,D3,P3,14640,31119,1331,2887610,528,52800,1860,2943410,4059.2,3012.38
+20/08/2023,2458,839,5558511,16917,6444084,1333600,0,8083,0,5668,0,9425,0,593,3233047,427550,2093935,58698,11658,D3,P3,13616,29557,1227,2592690,476,47600,1703,2640290,3842.86,2850.38
+21/08/2023,2316,892,5802540,21997,1578062,1099659,0,9057,0,8434,0,11204,0,597,3110596,442523,1991499,71910,13059,D3,P3,19336,38812,1656,3445610,663,66300,2319,3510210,5100.16,3789.37
+22/08/2023,2208,845,5006504,13886,598218,939529,0,18631,0,11119,0,18304,0,666,3020862,419659,1960342,66769,12591,D3,P3,21348,42166,1844,3824050,762,76200,2582,3877380,5610.58,4088.17
+23/08/2023,2104,821,5240143,16309,1941212,2081327,0,17073,0,8077,0,6042,0,709,2634348,409468,1726842,53998,13783,D3,P3,18864,38949,1513,3153720,655,65500,2165,3219310,4984.78,3596.01
+24/08/2023,2011,685,5623870,14314,380971,2132605,0,13223,0,7340,0,11449,0,1774,2244344,385012,1409286,55699,13185,D3,P3,18821,38282,1665,3378840,678,67800,2358,3474730,5209.76,3795.85
+25/08/2023,1889,680,4674166,13506,1119189,1818097,0,33200,0,7250,0,16577,0,3622,2405697,395219,1564070,61103,13348,D3,P3,18364,37536,1863,3939560,638,63800,2501,4003360,5070.56,3850.47
+26/08/2023,1229,379,5475213,18030,476090,1048919,0,15316,0,4976,0,16625,0,3546,2662312,434769,1789446,61768,13346,D3,P3,16076,32992,1816,3388030,606,60600,2422,3448630,4877.02,3729.33
+27/08/2023,1333,486,5591938,13138,956722,732493,0,12952,0,4227,0,19562,0,2354,2470188,417919,1980888,53707,14151,D3,P3,14834,30563,1572,2979370,536,53600,2108,3032970,4232.88,3241.64
+28/08/2023,2031,760,7120359,17304,592505,571748,0,44816,0,8728,0,19999,0,3813,2357294,420574,1878047,50335,13442,D3,P3,20994,42982,1970,3877550,642,64200,2612,3941750,5610.5,4325.83
+29/08/2023,1560,550,6349650,18074,395464,276869,0,217642,0,8742,0,36555,0,2778,2437012,455532,1707585,52913,12648,D3,P3,25919,51217,2270,4323380,803,80300,3073,4403680,6223.41,4701.59
+30/08/2023,1788,623,6774580,17019,804715,227676,0,92490,0,7576,0,33376,0,1815,2461827,452647,1924554,57945,12244,D3,P3,24015,48307,2298,4414000,796,79600,3094,4493600,5983.31,4520.44
+31/08/2023,2251,790,6881955,16586,462096,216142,0,177608,0,7188,0,13212,0,1862,2630688,508779,1691540,44071,12093,D3,P3,17587,35874,1809,3406910,604,60400,2413,3467310,5218.62,4005.29
+1/9/2023,2763,930,5360505,17680,259775,323504,0,21865,0,7383,0,4899,0,1313,2723715,529388,1841032,48663,11275,D3,P3,13457,29785,1396,2542480,459,45900,1855,2588380,4243.41,3270.46
+2/9/2023,2597,870,4478842,15289,1226680,320820,0,26924,0,6477,0,4896,0,1454,2929332,613163,1945160,60288,10815,D3,P3,12337,27451,1347,2481120,508,50800,1855,2531920,4227.98,3109.35
+3/9/2023,2332,762,5174329,13994,449228,288375,0,20423,0,5755,0,10890,0,1494,2516381,558353,1697712,54329,11996,D3,P3,12609,27028,1428,2699310,481,48100,1909,2747410,4177.46,3164.79
+4/9/2023,3561,1229,5334952,17444,296660,306771,0,324815,0,7849,0,41134,0,2069,2485534,613680,1823512,52578,12787,D3,P3,22283,45146,2065,3908960,791,79100,2855,3987700,5743.35,4264.57
+5/9/2023,2261,816,6113505,20426,302910,227998,0,287642,0,7998,0,21025,0,1448,2453507,595873,1648165,49590,11749,D3,P3,19096,40015,1818,3314870,588,58800,2407,3377390,5367.87,4153.87
+6/9/2023,2868,1031,5558783,13407,1266416,255848,0,203777,0,8887,0,9020,0,933,2766708,736889,1987155,47189,10851,D3,P3,14311,31495,1502,2764430,499,49900,2001,2814330,4575.2,3510.97
+7/9/2023,2394,832,4907653,9041,191893,285511,0,202017,0,8317,0,6879,0,801,2616416,630668,1712157,47089,11632,D3,P3,13483,29283,1544,2846790,463,46300,2007,2893090,4770.64,3739.02
+8/9/2023,2689,910,4752031,8867,157343,302141,0,201772,0,8717,0,5684,0,1428,2705167,791338,1852090,43707,10493,D3,P3,13830,29726,1531,2806450,518,51800,2049,2858250,4807.58,3697.23
+9/9/2023,2204,752,3975657,10022,227113,245700,0,201776,0,7299,0,4098,0,758,2929279,827015,2001938,50033,10082,D3,P3,12284,26212,1414,2674080,433,43300,1847,2717380,4310.87,3354.94
+10/9/2023,2167,743,4243960,10399,270612,291468,0,201256,0,6099,0,8097,0,809,2352670,1241029,1966290,41767,10185,D3,P3,12594,26398,1498,2834900,435,43500,1933,2878400,4460.59,3531.69
+11/9/2023,3381,1227,4492340,10684,1192346,154867,0,202476,0,8393,0,6493,0,838,2573007,1455728,1830559,39596,12910,D3,P3,15510,32142,1544,2881650,449,44900,1992,2923700,4715.59,3736.66
+12/9/2023,2511,884,4936079,10015,199137,170680,0,50740,0,8506,0,26721,0,1085,2527461,1481401,1856155,40974,11883,D3,P3,22786,42733,2027,4286230,326,32600,2353,4318830,5172.4,4510.57
+13/09/2023,2143,778,5115564,10338,292239,238162,0,2408,0,7172,0,36811,0,836,2621020,1580825,1962940,39948,11634,D3,P3,22084,41155,1880,3809460,281,28100,2161,3837560,4765.04,4181.98
+14/09/2023,2307,798,4859067,12717,1181194,308251,0,948,0,7404,0,38152,0,1282,2677877,1397139,1251585,46129,10253,D3,P3,21377,40308,1959,3742130,550,55000,2509,3797130,5413.06,4301.68
+15/09/2023,2467,882,4260164,9702,399193,291844,0,756,0,6932,0,16060,0,2982,2751748,1416780,1269521,57909,10048,D3,P3,16118,32895,1659,3149040,526,52600,2185,3201640,4920.59,3802.04
+16/09/2023,2076,687,3350011,7707,620978,196303,0,663,0,6018,0,9889,0,1188,3083552,1564491,1439332,61159,9435,D3,P3,12830,26945,1488,2798420,446,44600,1934,2843020,4519.52,3564.8
+17/09/2023,2467,802,4503316,11119,581720,236009,0,637,0,4814,0,10024,0,2464,2935930,1503370,1649587,48796,10073,D3,P3,12357,25262,1396,2560250,455,45500,1851,2605750,4246.67,3287.31
+18/09/2023,2910,1024,5568066,14302,184276,143660,0,888,0,6922,0,10381,0,1767,2373681,1330212,1479501,51224,10488,D3,P3,16441,33582,1727,3152630,572,57200,2300,3209930,5491.87,4171.85
+19/09/2023,3252,1309,6105220,12193,208312,187769,0,1464,0,5210,0,8092,0,1504,2373344,1285881,1407015,40642,10547,D3,P3,17770,36578,1743,3229930,653,65300,2395,3291230,5666.71,4236.34
+20/09/2023,2796,1185,6055420,14003,291395,272928,0,1077,0,4246,0,10472,0,1830,2565110,1425196,1460886,48962,10318,D3,P3,16656,34188,1627,3048960,738,73800,2365,3121460,5577.7,3950.97
+21/09/2023,2208,878,5225528,8679,697480,160425,0,1033,0,6726,0,13928,0,1357,2686089,1447330,1236533,49940,10259,D3,P3,17253,34210,1912,3553480,811,81100,2723,3634580,6248.63,4440.07
+22/09/2023,1734,783,4373391,8141,2220006,88968,0,738,0,9534,0,8771,0,1690,2460283,1353647,1258771,45467,9910,D3,P3,14958,31389,1714,3084570,758,75800,2472,3160370,5984.05,4280.38
+23/09/2023,1190,492,4948823,10035,2432739,154849,0,702,0,8285,0,4369,0,806,2740252,1531672,1407980,51747,8665,D3,P3,12641,27336,1443,2653190,652,65200,2095,2718390,5094.46,3633.56
+24/09/2023,1124,496,6239124,10744,248085,231243,0,477,0,8032,0,8640,0,1449,2648532,1476875,1294436,36638,10410,D3,P3,12153,26113,1395,2618830,621,62100,2016,2680930,4558.91,3242.77
+25/09/2023,2358,1041,5325249,9804,251517,447312,0,591,0,11299,0,21103,0,1135,2371891,1333464,1222194,36894,9191,D3,P3,18608,38878,2041,3740860,897,89700,2938,3830560,6554.2,4744.62
+26/09/2023,2092,994,5361926,13223,90916,351820,0,910,0,10598,0,27697,0,2803,2612585,1502573,1256804,33211,8893,D3,P3,21214,41166,2128,3878240,935,93500,3063,3971740,6565.1,4696.86
+27/09/2023,1835,792,4600061,14060,728920,347014,0,1111,0,8811,0,104094,0,1780,2488689,1390273,1203937,33935,8769,D3,P3,37289,65137,2933,5530540,1595,159500,4525,5686340,8073.74,5545.54
+28/09/2023,1787,807,4114657,9434,84913,564139,0,279332,0,6975,0,51946,0,2516,2290090,1309586,1316086,32443,8606,D3,P3,25651,47307,2343,4257450,1199,119900,3542,4377350,7151.36,4963.98
+29/09/2023,1513,641,4477584,8505,211297,372199,0,443,0,4039,0,4466,0,1774,2429135,1379398,1425850,31700,7184,D3,P3,12276,26946,1488,2459350,613,61300,2101,2520650,5148.56,3726.34
+30/09/2023,1127,478,4089760,9316,222854,364180,0,517,0,3251,0,13297,0,1425,1049942,435407,548780,10869,4505,D3,P3,12905,27091,1533,2701900,640,64000,2173,2765900,5022.4,3685.32
+1/10/2023,1021,436,748856,2639,301970,224135,0,846,0,2731,0,7574,0,962,935755,413098,1084400,13147,5335,D3,P3,9103,18479,1165,2022810,467,46700,1632,2069510,3823.3,2818.63
+2/10/2023,1673,764,1692932,4211,387802,237932,0,1564,0,12820,0,9400,0,1463,877002,383352,1016934,14302,5965,D3,P3,12307,24110,1290,2265000,565,56500,1855,2321500,4314.73,3091.4
+3/10/2023,1516,725,1757367,3947,568156,171003,0,1739,0,21454,0,5276,0,1640,833524,365270,973716,14227,5936,D3,P3,12568,24900,1344,2390760,645,64500,1989,2455260,4686.71,3259.46
+4/10/2023,1786,763,1733340,3119,567654,129402,0,1726,0,15833,0,13543,0,2940,836392,368220,1029847,13931,5864,D3,P3,13780,26581,1383,2427230,695,69500,2078,2496730,4764.92,3278.39
+5/10/2023,1986,861,1671129,3736,504268,159069,0,1747,0,24285,0,8234,0,3163,830805,380396,476125,13399,5529,D3,P3,12797,24819,1428,2572910,640,64050,2067,2626960,4940.4,3504.67
+6/10/2023,1774,753,1348401,2784,702326,205479,0,1686,0,15228,0,12269,0,2107,817142,382879,504402,11840,5476,D3,P3,11345,22554,1268,2317850,601,60300,1869,2378150,4247.46,3014.11
+7/10/2023,1150,416,1175733,2242,359848,180366,0,1446,0,19417,0,27951,0,2050,921412,418533,520370,11215,5083,D3,P3,14047,25738,1516,3012040,684,68550,2200,3080590,4256.92,3052.07
+8/10/2023,999,337,1296701,2609,662748,293989,0,1415,0,14589,0,18476,0,1786,768086,352949,357821,12788,5550,D3,P3,11419,21977,1320,2426760,572,57400,1892,2484160,4025.59,2933.34
+9/10/2023,772,289,1942734,4167,3096792,191352,0,1855,0,24331,0,21658,0,1757,653538,261410,590317,14449,6333,D3,P3,14624,27809,1367,2649310,715,73600,2082,2722910,4326.63,2956.14
+10/10/2023,737,241,1911227,4238,565419,164827,0,2101,0,11526,0,9057,0,2720,734200,300476,568450,13952,6145,D3,P3,12640,24537,1376,2459930,653,68350,2029,2528280,4537.77,3219.59
+11/10/2023,681,256,2171216,4714,503802,216630,0,1921,0,7255,0,7549,0,2025,958835,399349,595481,12432,5599,D3,P3,11486,22322,1319,2395980,638,66400,1958,2463820,4317.3,3006.02
+12/10/2023,673,240,1820266,4067,233553,161215,0,8042,0,4686,0,5288,0,1408,861845,336598,557239,13130,5889,D3,P3,9536,18332,1167,1988790,536,56350,1702,2044540,3786.09,2688.23
+13/10/2023,595,233,1529402,3094,68852,156834,0,7184,0,4986,0,14364,0,1924,801772,317596,563967,12222,5539,D3,P3,12220,23105,1373,2510340,641,66600,2014,2576940,4218.4,2949.55
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+15/10/2023,602,201,1596953,4098,55580,222305,0,12269,0,4366,0,15778,0,1639,891052,327915,425822,13301,6358,D3,P3,10476,19507,1178,2129330,587,60800,1765,2190130,3766.36,2609.81
+16/10/2023,964,369,2144206,5169,31683,118393,0,6488,0,5537,0,65656,0,1254,842123,317951,810650,16455,7813,D3,P3,23493,39642,2233,4391770,1136,117850,3369,4509620,5998.13,4174.09
+17/10/2023,1105,415,2112245,5363,69479,70676,0,4964,0,4816,0,18719,0,2353,816941,328053,766996,14912,7402,D3,P3,15733,28542,1531,2735230,779,81300,2310,2816530,5078.87,3496.45
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+20/10/2023,663,208,1100622,2740,174916,181797,0,3695,0,4056,0,58835,0,1887,910115,366406,653096,15447,6572,D3,P3,22398,38504,2218,4385190,1035,108650,3253,4493840,5680.14,4012.57
+21/10/2023,559,184,1405730,3216,207981,276329,0,2723,0,3213,0,44899,0,1893,1184901,485051,850250,14780,6181,D3,P3,17236,31207,1638,3121900,793,82100,2431,3204000,4493.8,3159.39
+22/10/2023,545,198,1467468,3228,520836,253840,0,2213,0,2850,0,27411,0,1911,1154732,423852,1027954,10344,4893,D3,P3,13607,25217,1482,2893810,725,75700,2207,2969510,4332.98,3030.72
+23/10/2023,625,231,2018062,5517,333114,436011,0,2839,0,3772,0,11121,0,1351,1208181,428535,1111507,11684,6220,D3,P3,12048,23419,1256,2226550,585,61300,1840,2287750,4042.32,2864.75
+24/10/2023,574,226,1889784,5099,188275,228582,0,2709,0,2462,0,1109,0,1918,1083131,378488,1161439,11452,5728,D3,P3,10595,21221,1092,1909130,407,43350,1499,1952480,3725.66,2756.39
+25/10/2023,536,184,2276229,5661,77308,332105,0,2708,0,2679,0,525,0,2402,905535,310989,865636,11200,5884,D3,P3,10143,20535,887,1832160,331,37050,1218,1869210,3037.38,2236.69
+26/10/2023,609,200,1753696,4367,85971,236204,0,2136,0,2300,0,10,0,3842,968078,332008,771447,10098,5558,D3,P3,9900,19640,858,1671270,347,38950,1205,1710220,2995.78,2165.99
+27/10/2023,563,209,1636932,3338,246909,285904,0,1992,0,2323,0,5,0,2851,1063329,352124,929257,9507,5001,D3,P3,8240,17233,780,1422510,309,35500,1089,1458010,2635.69,1927
+28/10/2023,450,155,1588245,4276,235960,324079,0,2716,0,1753,0,1,0,2585,1191854,384343,1028724,8578,4958,D3,P3,7529,15744,725,1476840,300,33850,1025,1510690,2588.52,1884.51
+29/10/2023,309,117,1731474,5065,70210,331208,0,2241,0,1708,0,5,0,3120,1137463,385520,764681,10186,5650,D3,P3,7535,14889,747,1514990,287,32300,1034,1547290,2594.6,1909.86
+31/10/2023,486,182,2220653,5950,41641,213812,0,149,0,2404,0,15,0,1380,913362,318222,1020094,10416,3703,D3,P3,8678,18059,784,1613510,309,34550,1093,1648060,2844.9,2076.88
+1/11/2023,296,123,1834772,4275,201158,313487,0,889,0,2485,0,33093,0,2287,862276,316545,798469,11740,3972,D3,P3,16341,28782,1292,2880920,542,58750,1834,2939670,3468.46,2536.62
+2/11/2023,346,111,1697213,2987,1586296,64435,0,957,0,2130,0,16368,0,3586,840477,298617,830972,10008,3641,D3,P3,12216,22498,1071,2342490,402,43400,1473,2385890,3237.48,2396.17
+3/11/2023,224,89,1759831,2940,93667,74522,0,962,0,2484,0,14150,0,953,952592,350909,800378,11090,3818,D3,P3,10460,20113,902,1897210,372,40600,1274,1937810,2883.8,2066.5
+4/11/2023,214,76,1677064,2752,65182,61325,0,1796,0,3084,0,10438,0,1148,957265,344580,821570,11309,4380,D3,P3,8630,17741,757,1590600,290,31650,1047,1622250,2519.34,1826.6

Model_Results_Pretrained.py

@@ -0,0 +1,349 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+import pickle
+import json
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+import pickle
+import json
+import pandas as pd
+import statsmodels.api as sm
+from sklearn.metrics import mean_absolute_percentage_error
+import sys
+import os
+from utilities import (set_header, 
+                       initialize_data,
+                       load_local_css,
+                       create_channel_summary,
+                       create_contribution_pie,
+                       create_contribuion_stacked_plot,
+                       create_channel_spends_sales_plot,
+                       format_numbers,
+                       channel_name_formating,
+                       load_authenticator)
+import seaborn as sns
+import matplotlib.pyplot as plt
+import sweetviz as sv
+import tempfile
+
+original_stdout = sys.stdout
+sys.stdout = open('temp_stdout.txt', 'w')
+sys.stdout.close()
+sys.stdout = original_stdout
+
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+
+authenticator = st.session_state.get('authenticator')
+if authenticator is None:
+    authenticator = load_authenticator()
+
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state.get('authentication_status')
+
+if auth_status == True:
+    is_state_initiaized = st.session_state.get('initialized',False)
+    if not is_state_initiaized:
+        a=1
+      
+
+    def plot_residual_predicted(actual, predicted, df_):
+            df_['Residuals'] = actual - pd.Series(predicted)
+            df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+            
+            # Create a Plotly scatter plot
+            fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
+            
+            # Add horizontal lines
+            fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+            fig.add_hline(y=2, line_color="red")
+            fig.add_hline(y=-2, line_color="red")
+            
+            fig.update_xaxes(title='Predicted')
+            fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+            
+            # Set the same width and height for both figures
+            fig.update_layout(title='Residuals over Predicted Values', autosize=False, width=600, height=400)
+            
+            return fig
+
+    def residual_distribution(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            
+            # Create a Seaborn distribution plot
+            sns.set(style="whitegrid")
+            plt.figure(figsize=(6, 4))
+            sns.histplot(Residuals, kde=True, color="#11B6BD")
+            
+            plt.title(' Distribution of Residuals')
+            plt.xlabel('Residuals')
+            plt.ylabel('Probability Density')
+            
+            return plt
+
+    
+    def qqplot(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            Residuals = pd.Series(Residuals)
+            Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+            
+            # Create a QQ plot using Plotly with custom colors
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                    y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                    mode='markers',
+                                    marker=dict(size=5, color="#11B6BD"),
+                                    name='QQ Plot'))
+            
+            # Add the 45-degree reference line
+            diagonal_line = go.Scatter(
+                x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+                y=[-2, 2],  # Adjust the y values accordingly
+                mode='lines',
+                line=dict(color='red'),  # Customize the line color and style
+                name=' '
+            )
+            fig.add_trace(diagonal_line)
+            
+            # Customize the layout
+            fig.update_layout(title='QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                            xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+            
+            return fig
+
+
+    def plot_actual_vs_predicted(date, y, predicted_values, model):
+        fig = go.Figure()
+
+        fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='orange')))
+        
+        # Calculate MAPE
+        mape = mean_absolute_percentage_error(y, predicted_values)*100
+        
+        # Calculate R-squared
+        rss = np.sum((y - predicted_values) ** 2)
+        tss = np.sum((y - np.mean(y)) ** 2)
+        r_squared = 1 - (rss / tss)
+        
+        # Get the number of predictors
+        num_predictors = model.df_model
+        
+        # Get the number of samples
+        num_samples = len(y)
+        
+        # Calculate Adjusted R-squared
+        adj_r_squared = 1 - ((1 - r_squared) * ((num_samples - 1) / (num_samples - num_predictors - 1)))
+        metrics_table = pd.DataFrame({
+        'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+        'Value': [mape, r_squared, adj_r_squared]})
+        fig.update_layout(
+            xaxis=dict(title='Date'),
+            yaxis=dict(title='Value'),
+            title=f'MAPE : {mape:.2f}%, AdjR2: {adj_r_squared:.2f}',
+            xaxis_tickangle=-30
+        )
+
+        return metrics_table,fig
+
+
+
+
+    # # Perform linear regression
+    # model = sm.OLS(y, X).fit()
+    eda_columns=st.columns(3)
+    with eda_columns[0]:
+        tactic=st.checkbox('Tactic Level Model')
+    if tactic:
+        with open('mastercard_mmm_model.pkl', 'rb') as file:
+            model = pickle.load(file)
+        train=pd.read_csv('train_mastercard.csv')
+        test=pd.read_csv('test_mastercard.csv')
+        train['Date']=pd.to_datetime(train['Date'])
+        test['Date']=pd.to_datetime(test['Date'])
+        train.set_index('Date',inplace=True)
+        test.set_index('Date',inplace=True)
+        test.dropna(inplace=True)
+        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
+        y_train=train['total_approved_accounts_revenue']
+        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
+        X_train=sm.add_constant(X_train)
+        X_test=sm.add_constant(X_test)
+        y_test=test['total_approved_accounts_revenue']
+
+        # sys.stdout.close()
+        # sys.stdout = original_stdout
+
+        # st.set_page_config(layout='wide')
+        # load_local_css('styles.css')
+        # set_header()
+
+        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
+        target_column='Total Approved Accounts - Revenue'
+
+  
+        with eda_columns[1]:
+          if st.button('Generate EDA Report'):
+              def generate_report_with_target(channel_data, target_feature):
+                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature,verbose=False)
+                  temp_dir = tempfile.mkdtemp()
+                  report_path = os.path.join(temp_dir, "report.html")
+                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
+                  return report_path
+          
+              report_file = generate_report_with_target(channel_data, target_column)
+          
+              if os.path.exists(report_file):
+                  with open(report_file, 'rb') as f:
+                      st.download_button(
+                          label="Download EDA Report",
+                          data=f.read(),
+                          file_name="report.html",
+                          mime="text/html"
+                      )
+              else:
+                  st.warning("Report generation failed. Unable to find the report file.")
+
+
+        st.title('Analysis of Result')
+
+        st.write(model.summary(yname='Revenue'))
+
+        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
+        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
+
+        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
+        metrics_table_train.index=['Train']
+        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
+        metrics_table_test.index=['test']
+        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
+
+        st.markdown('Result Overview')
+        st.dataframe(np.round(metrics_table,2),use_container_width=True)
+
+        st.subheader('Actual vs Predicted Plot Train')
+
+        st.plotly_chart(fig_train,use_container_width=True)
+        st.subheader('Actual vs Predicted Plot Test')
+        st.plotly_chart(fig_test,use_container_width=True)
+
+        st.markdown('## Residual Analysis')
+        columns=st.columns(2)
+        Xtrain1=X_train.copy()
+        with columns[0]:
+            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
+            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)
+    else:
+        with open('mastercard_mmm_model_channel.pkl', 'rb') as file:
+            model = pickle.load(file)
+        train=pd.read_csv('train_mastercard_channel.csv')
+        test=pd.read_csv('test_mastercard_channel.csv')
+        # train['Date']=pd.to_datetime(train['Date'])
+        # test['Date']=pd.to_datetime(test['Date'])
+        # train.set_index('Date',inplace=True)
+        # test.set_index('Date',inplace=True)
+        test.dropna(inplace=True)
+        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
+        y_train=train['total_approved_accounts_revenue']
+        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
+        X_train=sm.add_constant(X_train)
+        X_test=sm.add_constant(X_test)
+        y_test=test['total_approved_accounts_revenue']
+
+    
+
+        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
+        target_column='Total Approved Accounts - Revenue'
+        with eda_columns[1]:
+          if st.button('Generate EDA Report'):
+              def generate_report_with_target(channel_data, target_feature):
+                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature)
+                  temp_dir = tempfile.mkdtemp()
+                  report_path = os.path.join(temp_dir, "report.html")
+                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
+                  return report_path
+          
+              report_file = generate_report_with_target(channel_data, target_column)
+          
+              # Provide a link to download the generated report
+              with open(report_file, 'rb') as f:
+                  st.download_button(
+                      label="Download EDA Report",
+                      data=f.read(),
+                      file_name="report.html",
+                      mime="text/html"
+                  )
+
+
+        st.title('Analysis of Result')
+
+        st.write(model.summary(yname='Revenue'))
+
+        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
+        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
+
+        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
+        metrics_table_train.index=['Train']
+        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
+        metrics_table_test.index=['test']
+        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
+
+        st.markdown('Result Overview')
+        st.dataframe(np.round(metrics_table,2),use_container_width=True)
+
+        st.subheader('Actual vs Predicted Plot Train')
+
+        st.plotly_chart(fig_train,use_container_width=True)
+        st.subheader('Actual vs Predicted Plot Test')
+        st.plotly_chart(fig_test,use_container_width=True)
+
+        st.markdown('## Residual Analysis')
+        columns=st.columns(2)
+        Xtrain1=X_train.copy()
+        with columns[0]:
+            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
+            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)
+
+elif auth_status == False:
+    st.error('Username/Password is incorrect')
+    
+if auth_status != True:
+    try:
+        username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
+        if username_forgot_pw:
+            st.success('New password sent securely')
+            # Random password to be transferred to user securely
+        elif username_forgot_pw == False:
+            st.error('Username not found')
+    except Exception as e:
+        st.error(e)

Model_Results_Pretrained_copy.py

@@ -0,0 +1,365 @@
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+import streamlit as st
+import pandas as pd
+import statsmodels.api as sm
+from sklearn.metrics import mean_absolute_percentage_error
+import sys
+import os
+from utilities import (set_header, 
+                       load_local_css,
+                       load_authenticator)
+import seaborn as sns
+import matplotlib.pyplot as plt
+import sweetviz as sv
+import tempfile
+from sklearn.preprocessing import MinMaxScaler
+from st_aggrid import AgGrid
+from st_aggrid import GridOptionsBuilder,GridUpdateMode
+from st_aggrid import GridOptionsBuilder
+import sys
+
+sys.setrecursionlimit(10**6)
+
+original_stdout = sys.stdout
+sys.stdout = open('temp_stdout.txt', 'w')
+sys.stdout.close()
+sys.stdout = original_stdout
+
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+
+authenticator = st.session_state.get('authenticator')
+if authenticator is None:
+    authenticator = load_authenticator()
+
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state.get('authentication_status')
+
+if auth_status == True:
+    is_state_initiaized = st.session_state.get('initialized',False)
+    if not is_state_initiaized:
+        a=1
+      
+
+    def plot_residual_predicted(actual, predicted, df_):
+            df_['Residuals'] = actual - pd.Series(predicted)
+            df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+            
+            # Create a Plotly scatter plot
+            fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
+            
+            # Add horizontal lines
+            fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+            fig.add_hline(y=2, line_color="red")
+            fig.add_hline(y=-2, line_color="red")
+            
+            fig.update_xaxes(title='Predicted')
+            fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+            
+            # Set the same width and height for both figures
+            fig.update_layout(title='Residuals over Predicted Values', autosize=False, width=600, height=400)
+            
+            return fig
+
+    def residual_distribution(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            
+            # Create a Seaborn distribution plot
+            sns.set(style="whitegrid")
+            plt.figure(figsize=(6, 4))
+            sns.histplot(Residuals, kde=True, color="#11B6BD")
+            
+            plt.title(' Distribution of Residuals')
+            plt.xlabel('Residuals')
+            plt.ylabel('Probability Density')
+            
+            return plt
+
+    
+    def qqplot(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            Residuals = pd.Series(Residuals)
+            Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+            
+            # Create a QQ plot using Plotly with custom colors
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                    y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                    mode='markers',
+                                    marker=dict(size=5, color="#11B6BD"),
+                                    name='QQ Plot'))
+            
+            # Add the 45-degree reference line
+            diagonal_line = go.Scatter(
+                x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+                y=[-2, 2],  # Adjust the y values accordingly
+                mode='lines',
+                line=dict(color='red'),  # Customize the line color and style
+                name=' '
+            )
+            fig.add_trace(diagonal_line)
+            
+            # Customize the layout
+            fig.update_layout(title='QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                            xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+            
+            return fig
+
+
+    def plot_actual_vs_predicted(date, y, predicted_values, model):
+
+        fig = go.Figure()
+
+        fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='orange')))
+        
+        # Calculate MAPE
+        mape = mean_absolute_percentage_error(y, predicted_values)*100
+        
+        # Calculate R-squared
+        rss = np.sum((y - predicted_values) ** 2)
+        tss = np.sum((y - np.mean(y)) ** 2)
+        r_squared = 1 - (rss / tss)
+        
+        # Get the number of predictors
+        num_predictors = model.df_model
+        
+        # Get the number of samples
+        num_samples = len(y)
+        
+        # Calculate Adjusted R-squared
+        adj_r_squared = 1 - ((1 - r_squared) * ((num_samples - 1) / (num_samples - num_predictors - 1)))
+        metrics_table = pd.DataFrame({
+        'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+        'Value': [mape, r_squared, adj_r_squared]})
+        fig.update_layout(
+            xaxis=dict(title='Date'),
+            yaxis=dict(title='Value'),
+            title=f'MAPE : {mape:.2f}%, AdjR2: {adj_r_squared:.2f}',
+            xaxis_tickangle=-30
+        )
+
+        return metrics_table,fig
+
+    transformed_data=pd.read_csv('transformed_data.csv')
+
+    # hard coded for now, need to get features set from model
+
+    feature_set_dct={'app_installs_-_appsflyer':['paid_search_clicks',
+                                            'fb:_level_achieved_-_tier_1_impressions_lag2',
+                                            'fb:_level_achieved_-_tier_2_clicks_lag2',
+                                            'paid_social_others_impressions_adst.1',
+                                            'ga_app:_will_and_cid_pequena_baixo_risco_clicks_lag2',
+                                            'digital_tactic_others_clicks',
+                                            'kwai_clicks_adst.3',
+                                            'programmaticclicks',
+                                            'indicacao_clicks_adst.1',
+                                            'infleux_clicks_adst.4',
+                                            'influencer_clicks'],
+                                            
+                'account_requests_-_appsflyer':['paid_search_impressions',
+                                                'fb:_level_achieved_-_tier_1_clicks_adst.1',
+                                                'fb:_level_achieved_-_tier_2_clicks_adst.1',
+                                                'paid_social_others_clicks_lag2',
+                                                'ga_app:_will_and_cid_pequena_baixo_risco_clicks_lag5_adst.1',
+                                                'digital_tactic_others_clicks_adst.1',
+                                                'kwai_clicks_adst.2',
+                                                'programmaticimpressions_lag4_adst.1',
+                                                'indicacao_clicks',
+                                                'infleux_clicks_adst.2',
+                                                'influencer_clicks'],
+
+                'total_approved_accounts_-_appsflyer':['paid_search_clicks',
+                                                        'fb:_level_achieved_-_tier_1_impressions_lag2_adst.1',
+                                                        'fb:_level_achieved_-_tier_2_impressions_lag2',
+                                                        'paid_social_others_clicks_lag2_adst.2',
+                                                        'ga_app:_will_and_cid_pequena_baixo_risco_impressions_lag4',
+                                                        'digital_tactic_others_clicks',
+                                                        'kwai_impressions_adst.2',
+                                                        'programmaticclicks_adst.5',
+                                                        'indicacao_clicks_adst.1',
+                                                        'infleux_clicks_adst.3',
+                                                        'influencer_clicks'],
+
+                'total_approved_accounts_-_revenue':['paid_search_impressions_adst.5',
+                                                    'kwai_impressions_lag2_adst.3',
+                                                    'indicacao_clicks_adst.3',
+                                                    'infleux_clicks_adst.3',
+                                                    'programmaticclicks_adst.4',
+                                                    'influencer_clicks_adst.3',
+                                                    'fb:_level_achieved_-_tier_1_impressions_adst.2',
+                                                    'fb:_level_achieved_-_tier_2_impressions_lag3_adst.5',
+                                                    'paid_social_others_impressions_adst.3',
+                                                    'ga_app:_will_and_cid_pequena_baixo_risco_clicks_lag3_adst.5',
+                                                    'digital_tactic_others_clicks_adst.2']                       
+                                            
+                }
+
+    #""" the above part should be modified so that we are getting features set from the saved model"""
+
+
+
+
+
+    def model_fit(features_set,target):
+        X = transformed_data[features_set]
+        y=  transformed_data[target]
+        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()
+        predicted_values_train = model.predict(X_train)
+        r2 = model.rsquared
+        adjr2 = model.rsquared_adj
+        train_mape = mean_absolute_percentage_error(y_train, predicted_values_train)
+        test_mape=mean_absolute_percentage_error(y_test, model.predict(X_test))
+        summary=model.summary()
+        return pd.DataFrame({'Model':target,'R2':np.round(r2,2),'ADJr2':np.round(adjr2,2),'Train Mape':np.round(train_mape,2),
+                             'Test Mape':np.round(test_mape,2),'Summary':summary,'Model_object':model
+                             },index=[0])
+
+    metrics_table=pd.DataFrame()
+
+    for target,feature_set in feature_set_dct.items():
+       metrics_table= pd.concat([metrics_table,model_fit(features_set=feature_set,target=target)])
+
+    metrics_table.reset_index(drop=True,inplace=True)
+
+    eda_columns=st.columns(3)
+    with eda_columns[1]:
+        eda=st.button('Generate EDA Report',help="Click to generate a bivariate report for the selected response metric from the table below.")
+    
+    st.title('Analysis of Model Results')
+    # st.markdown()
+    gd=GridOptionsBuilder.from_dataframe(metrics_table.iloc[:,:-2])
+    gd.configure_pagination(enabled=True)
+    gd.configure_selection(use_checkbox=True)
+
+    
+    gridoptions=gd.build()
+
+    # st.markdown('Model Metrics')
+    table = AgGrid(metrics_table.iloc[:,:-2],gridOptions=gridoptions,update_mode=GridUpdateMode.SELECTION_CHANGED,fit_columns_on_grid_load=True,
+                   columns_auto_size_mode='ColumnsAutoSizeMode.FIT_ALL_COLUMNS_TO_VIEW')
+
+    if len(table.selected_rows)==0:
+        st.warning("Click on the checkbox to view comprehensive results of the selected model.")
+        st.stop()
+    else:    
+        target_column=table.selected_rows[0]['Model']
+        feature_set=feature_set_dct[target_column]
+
+
+    st.header('')
+    # st.write(feature_set)
+    # st.write(target_column)
+
+
+
+
+    # # Perform linear regression
+    # model = sm.OLS(y, X).fit()
+    
+    
+    with eda_columns[1]:
+        if eda:
+            def generate_report_with_target(channel_data, target_feature):
+                report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature,verbose=False)
+                temp_dir = tempfile.mkdtemp()
+                report_path = os.path.join(temp_dir, "report.html")
+                report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
+                return report_path
+            
+            report_data=transformed_data[feature_set]
+            report_data[target_column]=transformed_data[target_column]
+            report_file = generate_report_with_target(report_data, target_column)
+        
+            if os.path.exists(report_file):
+                with open(report_file, 'rb') as f:
+                    st.download_button(
+                        label="Download EDA Report",
+                        data=f.read(),
+                        file_name="report.html",
+                        mime="text/html"
+                    )
+            else:
+                st.warning("Report generation failed. Unable to find the report file.")
+
+
+
+
+    model=metrics_table[metrics_table['Model']==target_column]['Model_object'].iloc[0]
+    st.header('Model Summary')
+    st.write(model.summary())
+    X=transformed_data[feature_set]
+    ss=MinMaxScaler()
+    X=pd.DataFrame(ss.fit_transform(X),columns=X.columns)
+    X=sm.add_constant(X)
+    y=transformed_data[target_column]
+    X_train=X.iloc[:150]
+    X_test=X.iloc[150:]
+    y_train=y.iloc[:150]
+    y_test=y.iloc[150:]
+    X.index=transformed_data['date']
+    y.index=transformed_data['date']
+
+    metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
+    metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
+
+    metrics_table_train=metrics_table_train.set_index('Metric').transpose()
+    metrics_table_train.index=['Train']
+    metrics_table_test=metrics_table_test.set_index('Metric').transpose()
+    metrics_table_test.index=['test']
+    metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
+
+    st.markdown('Result Overview')
+    st.dataframe(np.round(metrics_table,2),use_container_width=True)
+
+    st.subheader('Actual vs Predicted Plot Train')
+
+    st.plotly_chart(fig_train,use_container_width=True)
+    st.subheader('Actual vs Predicted Plot Test')
+    st.plotly_chart(fig_test,use_container_width=True)
+
+    st.markdown('## Residual Analysis')
+    columns=st.columns(2)
+
+
+    Xtrain1=X_train.copy()
+    with columns[0]:
+        fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
+        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)
+
+
+
+elif auth_status == False:
+    st.error('Username/Password is incorrect')
+    try:
+        username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
+        if username_forgot_pw:
+            st.success('New password sent securely')
+            # Random password to be transferred to the user securely
+        elif username_forgot_pw == False:
+            st.error('Username not found')
+    except Exception as e:
+        st.error(e)

Profile_Report.html

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:accff0a5fcd3b37dd6da7072d839ec1592866c3f3f8b479cc708e53a49abdb5a
+size 57660752

Scenario.py

@@ -0,0 +1,338 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import datetime
+from utilities import set_header,initialize_data,load_local_css
+from scipy.optimize import curve_fit
+import statsmodels.api as sm
+from plotly.subplots import make_subplots
+
+st.set_page_config(
+  page_title="Data Validation",
+  page_icon=":shark:",
+  layout="wide",
+  initial_sidebar_state='collapsed'
+)
+load_local_css('styles.css')
+set_header()
+
+def format_numbers(x):
+    if abs(x) >= 1e6:
+        # Format as millions with one decimal place and commas
+        return f'{x/1e6:,.1f}M'
+    elif abs(x) >= 1e3:
+        # Format as thousands with one decimal place and commas
+        return f'{x/1e3:,.1f}K'
+    else:
+        # Format with one decimal place and commas for values less than 1000
+        return f'{x:,.1f}'
+
+def format_axis(x):
+    if isinstance(x, tuple):
+        x = x[0]  # Extract the numeric value from the tuple
+    if abs(x) >= 1e6:
+        return f'{x / 1e6:.0f}M'
+    elif abs(x) >= 1e3:
+        return f'{x / 1e3:.0f}k'
+    else:
+        return f'{x:.0f}'
+
+
+attributred_app_installs=pd.read_csv("attributed_app_installs.csv")
+attributred_app_installs_tactic=pd.read_excel('attributed_app_installs_tactic.xlsx')
+data=pd.read_excel('Channel_wise_imp_click_spends.xlsx')
+data['Date']=pd.to_datetime(data['Date'])
+st.header('Saturation Curves')
+
+# st.dataframe(data.head(2))
+st.markdown('Data QC')
+
+st.markdown('Channel wise summary')
+summary_df=data.groupby(data['Date'].dt.strftime('%B %Y')).sum()
+summary_df=summary_df.sort_index(key=lambda x: pd.to_datetime(x, format='%B %Y'))
+st.dataframe(summary_df.applymap(format_numbers))
+
+
+
+def line_plot_target(df,target,title):
+    df=df
+    df['Date_unix'] = df['Date'].apply(lambda x: x.timestamp())
+
+# Perform polynomial fitting
+    coefficients = np.polyfit(df['Date_unix'], df[target], 1)
+    # st.dataframe(df)
+    coefficients = np.polyfit(df['Date'].view('int64'), df[target], 1)
+    trendline = np.poly1d(coefficients)
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=df['Date'], y=df[target], mode='lines', name=target,line=dict(color='#11B6BD')))
+    trendline_x = df['Date']
+    trendline_y = trendline(df['Date'].view('int64'))
+
+
+    fig.add_trace(go.Scatter(x=trendline_x, y=trendline_y, mode='lines', name='Trendline', line=dict(color='#739FAE')))
+
+    fig.update_layout(
+        title=title,
+        xaxis=dict(type='date')
+    )
+
+    for year in df['Date'].dt.year.unique()[1:]:
+
+        january_1 = pd.Timestamp(year=year, month=1, day=1)
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=january_1,
+                x1=january_1,
+                y0=0,
+                y1=1,
+                xref="x",
+                yref="paper",
+                line=dict(color="grey", width=1.5, dash="dash"),
+            )
+        )
+
+    return fig
+channels_d= data.columns[:28]
+channels=list(set([col.replace('_impressions','').replace('_clicks','').replace('_spend','') for col in channels_d if col.lower()!='date']))
+channel= st.selectbox('Select Channel_name',channels)
+target_column = st.selectbox('Select Channel)',[col for col in data.columns if col.startswith(channel)])
+fig=line_plot_target(data, target=str(target_column), title=f'{str(target_column)} Over Time')
+st.plotly_chart(fig, use_container_width=True)
+
+# st.markdown('## Saturation Curve')
+
+
+st.header('Build saturation curve')
+
+# Your data
+# st.write(len(attributred_app_installs))
+# st.write(len(data))
+# col=st.columns(3)
+# with col[0]:
+col=st.columns(2)
+with col[0]:
+    if st.checkbox('Cap Outliers'):
+        x = data[target_column]
+        x.index=data['Date']
+        # st.write(x)
+        result = sm.tsa.seasonal_decompose(x, model='additive')
+        x_resid=result.resid
+        # fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.02)
+        # trace_x = go.Scatter(x=data['Date'], y=x, mode='lines', name='x')
+        # fig.add_trace(trace_x)
+        # trace_x_resid = go.Scatter(x=data['Date'], y=x_resid, mode='lines', name='x_resid', yaxis='y2',line=dict(color='orange'))
+
+        # fig.add_trace(trace_x_resid)
+        # fig.update_layout(title='',
+        #               xaxis=dict(title='Date'),
+        #               yaxis=dict(title='x', side='left'),
+        #               yaxis2=dict(title='x_resid', side='right'))
+        # st.title('')
+        # st.plotly_chart(fig)
+
+        # x=result.resid
+        # x=x.fillna(0)
+        x_mean = np.mean(x)
+        x_std = np.std(x)
+        x_scaled = (x - x_mean) / x_std
+        lower_threshold = -2.0 
+        upper_threshold = 2.0   
+        x_scaled = np.clip(x_scaled, lower_threshold, upper_threshold)
+    else:
+        x = data[target_column]
+        x_mean = np.mean(x)
+        x_std = np.std(x)
+        x_scaled = (x - x_mean) / x_std
+with col[1]:
+    if st.checkbox('Attributed'):
+        column=[col for col in attributred_app_installs.columns if col in target_column]
+        data['app_installs_appsflyer']=attributred_app_installs[column]
+        y=data['app_installs_appsflyer']
+        title='Attributed-App_installs_appsflyer'
+        # st.dataframe(y)
+        # st.dataframe(x)
+        # st.dataframe(x_scaled)
+    else:
+        y=data["app_installs_appsflyer"]
+        title='App_installs_appsflyer'
+        # st.write(len(y))
+    # Curve fitting function
+def sigmoid(x, K, a, x0):
+    return K / (1 + np.exp(-a * (x - x0)))
+
+initial_K = np.max(y)
+initial_a = 1
+initial_x0 = 0
+columns=st.columns(3)
+
+
+with columns[0]:
+    K = st.number_input('K (Amplitude)', min_value=0.01, max_value=2.0 * np.max(y), value=float(initial_K), step=5.0)
+with columns[1]:
+    a = st.number_input('a (Slope)', min_value=0.01, max_value=5.0, value=float(initial_a), step=0.5)
+with columns[2]:
+    x0 = st.number_input('x0 (Center)', min_value=float(min(x_scaled)), max_value=float(max(x_scaled)), value=float(initial_x0), step=2.0)
+params, _ = curve_fit(sigmoid, x_scaled, y, p0=[K, a, x0], maxfev=20000)
+
+
+x_slider = st.slider('X Value', min_value=float(min(x)), max_value=float(max(x))+1, value=float(x_mean), step=1.)
+
+# Calculate the corresponding value on the fitted curve
+x_slider_scaled = (x_slider - x_mean) / x_std
+y_slider_fit = sigmoid(x_slider_scaled, *params)
+
+# Display the corresponding value
+st.write(f'{target_column}: {format_numbers(x_slider)}')
+st.write(f'Corresponding  App_installs: {format_numbers(y_slider_fit)}')
+
+# Scatter plot of your data
+fig = px.scatter(data_frame=data, x=x_scaled, y=y, labels={'x': f'{target_column}', 'y': 'App Installs'}, title=title)
+
+# Add the fitted sigmoid curve to the plot
+x_fit = np.linspace(min(x_scaled), max(x_scaled), 100)  # Generate x values for the curve
+y_fit = sigmoid(x_fit, *params)
+fig.add_trace(px.line(x=x_fit, y=y_fit).data[0])
+fig.data[1].update(line=dict(color='orange'))
+fig.add_vline(x=x_slider_scaled, line_dash='dash', line_color='red', annotation_text=f'{format_numbers(x_slider)}')
+
+x_tick_labels = {format_axis(x_scaled[i]): format_axis(x[i]) for i in range(len(x_scaled))}
+num_points = 30  # Number of points you want to select
+keys = list(x_tick_labels.keys())
+values = list(x_tick_labels.values())
+spacing = len(keys) // num_points  # Calculate the spacing
+if spacing==0:
+    spacing=15
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+else:
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+
+# Update the x-axis ticks with the selected keys and values
+fig.update_xaxes(tickvals=selected_keys, ticktext=selected_values)
+fig.update_xaxes(tickvals=list(x_tick_labels.keys()), ticktext=list(x_tick_labels.values()))
+# Show the plot using st.plotly_chart
+
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.update_layout(
+    width=600,  # Adjust the width as needed
+    height=600  # Adjust the height as needed
+)
+st.plotly_chart(fig)
+
+
+
+
+st.markdown('Tactic level')
+if channel=='paid_social':
+
+    tactic_data=pd.read_excel("Tatcic_paid.xlsx",sheet_name='paid_social_impressions')
+else:
+    tactic_data=pd.read_excel("Tatcic_paid.xlsx",sheet_name='digital_app_display_impressions')  
+target_column = st.selectbox('Select Channel)',[col for col in tactic_data.columns if col!='Date' and col!='app_installs_appsflyer'])
+fig=line_plot_target(tactic_data, target=str(target_column), title=f'{str(target_column)} Over Time')
+st.plotly_chart(fig, use_container_width=True)
+
+if st.checkbox('Cap Outliers',key='tactic1'):
+    x = tactic_data[target_column]
+    x_mean = np.mean(x)
+    x_std = np.std(x)
+    x_scaled = (x - x_mean) / x_std
+    lower_threshold = -2.0 
+    upper_threshold = 2.0   
+    x_scaled = np.clip(x_scaled, lower_threshold, upper_threshold)
+else:
+    x = tactic_data[target_column]
+    x_mean = np.mean(x)
+    x_std = np.std(x)
+    x_scaled = (x - x_mean) / x_std
+
+if st.checkbox('Attributed',key='tactic2'):
+    column=[col for col in attributred_app_installs_tactic.columns if col in target_column]
+    tactic_data['app_installs_appsflyer']=attributred_app_installs_tactic[column]
+    y=tactic_data['app_installs_appsflyer']
+    title='Attributed-App_installs_appsflyer'
+    # st.dataframe(y)
+    # st.dataframe(x)
+    # st.dataframe(x_scaled)
+else:
+    y=data["app_installs_appsflyer"]
+    title='App_installs_appsflyer'
+    # st.write(len(y))
+# Curve fitting function
+def sigmoid(x, K, a, x0):
+    return K / (1 + np.exp(-a * (x - x0)))
+
+# Curve fitting
+# st.dataframe(x_scaled.head(3))
+# # y=y.astype(float)
+# st.dataframe(y.head(3))
+initial_K = np.max(y)
+initial_a = 1
+initial_x0 = 0
+K = st.number_input('K (Amplitude)', min_value=0.01, max_value=2.0 * np.max(y), value=float(initial_K), step=5.0,key='tactic3')
+a = st.number_input('a (Slope)', min_value=0.01, max_value=5.0, value=float(initial_a), step=2.0,key='tactic41')
+x0 = st.number_input('x0 (Center)', min_value=float(min(x_scaled)), max_value=float(max(x_scaled)), value=float(initial_x0), step=2.0,key='tactic4')
+params, _ = curve_fit(sigmoid, x_scaled, y, p0=[K, a, x0], maxfev=20000)
+
+# Slider to vary x
+x_slider = st.slider('X Value', min_value=float(min(x)), max_value=float(max(x)), value=float(x_mean), step=1.,key='tactic7')
+
+# Calculate the corresponding value on the fitted curve
+x_slider_scaled = (x_slider - x_mean) / x_std
+y_slider_fit = sigmoid(x_slider_scaled, *params)
+
+# Display the corresponding value
+st.write(f'{target_column}: {format_axis(x_slider)}')
+st.write(f'Corresponding  App_installs: {format_axis(y_slider_fit)}')
+
+# Scatter plot of your data
+fig = px.scatter(data_frame=data, x=x_scaled, y=y, labels={'x': f'{target_column}', 'y': 'App Installs'}, title=title)
+
+# Add the fitted sigmoid curve to the plot
+x_fit = np.linspace(min(x_scaled), max(x_scaled), 100)  # Generate x values for the curve
+y_fit = sigmoid(x_fit, *params)
+fig.add_trace(px.line(x=x_fit, y=y_fit).data[0])
+fig.data[1].update(line=dict(color='orange'))
+fig.add_vline(x=x_slider_scaled, line_dash='dash', line_color='red', annotation_text=f'{format_numbers(x_slider)}')
+
+
+
+x_tick_labels = {format_axis((x_scaled[i],0)): format_axis(x[i]) for i in range(len(x_scaled))}
+num_points = 50  # Number of points you want to select
+keys = list(x_tick_labels.keys())
+values = list(x_tick_labels.values())
+spacing = len(keys) // num_points  # Calculate the spacing
+if spacing==0:
+    spacing=2
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+else:
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+
+# Update the x-axis ticks with the selected keys and values
+fig.update_xaxes(tickvals=selected_keys, ticktext=selected_values)
+
+# Round the x-axis and y-axis tick values to zero decimal places
+fig.update_xaxes(tickformat=".f")  # Format x-axis ticks to zero decimal places
+fig.update_yaxes(tickformat=".f")  # Format y-axis ticks to zero decimal places
+
+# Show the plot using st.plotly_chart
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.update_layout(
+    width=600,  # Adjust the width as needed
+    height=600  # Adjust the height as needed
+)
+st.plotly_chart(fig)

Transformation_functions.py

@@ -0,0 +1,133 @@
+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,line_plot,summary
+import numpy as np
+import re
+
+def sanitize_key(key, prefix=""):
+    # Use regular expressions to remove non-alphanumeric characters and spaces
+    key = re.sub(r'[^a-zA-Z0-9]', '', key)
+    return f"{prefix}{key}"
+
+
+def check_box(options, ad_stock_value,lag_value,num_columns=4, prefix=""):
+    num_rows = -(-len(options) // num_columns)  # Ceiling division to calculate rows
+
+    selected_options = []
+    adstock_info = {}  # Store adstock and lag info for each selected option
+    if ad_stock_value!=0:
+        for row in range(num_rows):
+            cols = st.columns(num_columns)
+            for col in cols:
+                if options:
+                    option = options.pop(0)
+                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
+                    selected = col.checkbox(option, key=key)
+                    if selected:
+                        selected_options.append(option)
+                        
+                        # Input minimum and maximum adstock values
+                        adstock = col.slider('Select Adstock Range', 0.0, 1.0, ad_stock_value, step=0.05, format="%.2f",key= f"adstock_{key}" )
+
+                        # Input minimum and maximum lag values
+                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
+
+                        # Create a dictionary to store adstock and lag info for the option
+                        option_info = {
+                            'adstock': adstock,
+                            'lag': lag}
+                        # Append the dictionary to the adstock_info list
+                        adstock_info[option]=option_info
+
+                    else:adstock_info[option]={
+                            'adstock': ad_stock_value,
+                            'lag': lag_value}
+
+        return selected_options, adstock_info   
+    else:
+        for row in range(num_rows):
+            cols = st.columns(num_columns)
+            for col in cols:
+                if options:
+                    option = options.pop(0)
+                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
+                    selected = col.checkbox(option, key=key)
+                    if selected:
+                        selected_options.append(option)
+                        
+                        # Input minimum and maximum lag values
+                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
+
+                        # dictionary to store adstock and lag info for the option
+                        option_info = {
+                            'lag': lag}
+                        # Append the dictionary to the adstock_info list
+                        adstock_info[option]=option_info
+
+                    else:adstock_info[option]={
+                            'lag': lag_value}
+                        
+        return selected_options, adstock_info 
+
+def apply_lag(X, features,lag_dict):
+    #lag_data=pd.DataFrame()
+    for col in features:
+        for lag in range(lag_dict[col]['lag'][0], lag_dict[col]['lag'][1] + 1):
+            if lag>0:
+                X[f'{col}_lag{lag}'] = X[col].shift(periods=lag, fill_value=0)
+    return X
+
+def apply_adstock(X, variable_name, decay):
+    values = X[variable_name].values
+    adstock = np.zeros(len(values))
+    
+    for row in range(len(values)):
+        if row == 0:
+            adstock[row] = values[row]
+        else:
+            adstock[row] = values[row] + adstock[row - 1] * decay
+    
+    return adstock
+
+def top_correlated_features(df,target,media_data):
+    corr_df=df.drop(target,axis=1)
+    #corr_df[target]=df[target]
+    #st.dataframe(corr_df)
+    for i in media_data:
+        #st.write(media_data[2])
+        #st.dataframe(corr_df.filter(like=media_data[2]))
+        d=(pd.concat([corr_df.filter(like=i),df[target]],axis=1)).corr()[target]
+        d=d.sort_values(ascending=False)
+        d=d.drop(target,axis=0)
+        corr=pd.DataFrame({'Feature_name':d.index,"Correlation":d.values})
+        corr.columns = pd.MultiIndex.from_product([[i], ['Feature_name', 'Correlation']])
+
+    return corr
+
+def top_correlated_features(df,variables,target):
+    correlation_df=pd.DataFrame()
+    for col in variables:
+        d=pd.concat([df.filter(like=col),df[target]],axis=1).corr()[target]
+        #st.dataframe(d)
+        d=d.sort_values(ascending=False).iloc[1:]
+        corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
+        corr_df.columns=pd.MultiIndex.from_tuples([(col, 'Variable'), (col, 'Correlation')])
+        correlation_df=pd.concat([corr_df,correlation_df],axis=1)
+    return correlation_df
+
+def top_correlated_feature(df,variable,target):
+    d=pd.concat([df.filter(like=variable),df[target]],axis=1).corr()[target]
+    # st.dataframe(d)
+    d=d.sort_values(ascending=False).iloc[1:]
+    # st.dataframe(d)
+    corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
+    corr_df['Adstock']=corr_df['Media_channel'].map(lambda x:x.split('_adst')[1] if len(x.split('_adst'))>1 else '-')
+    corr_df['Lag']=corr_df['Media_channel'].map(lambda x:x.split('_lag')[1][0] if len(x.split('_lag'))>1 else '-' )
+    corr_df.drop(['Correlation'],axis=1,inplace=True)
+    corr_df['Correlation']=np.round(d.values,2)
+    sorted_corr_df= corr_df.loc[corr_df['Correlation'].abs().sort_values(ascending=False).index]
+    #corr_df.columns=pd.MultiIndex.from_tuples([(variable, 'Variable'), (variable, 'Correlation')])
+    #correlation_df=pd.concat([corr_df,correlation_df],axis=1)
+    return sorted_corr_df

Untitled.ipynb

@@ -0,0 +1,81 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "def0e525",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6f07a1fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'streamlit'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_10272\\318308029.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mstreamlit\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mst\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# Define the pages of your app\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpage1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m   \u001b[0mst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"This is page 1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'streamlit'"
+     ]
+    }
+   ],
+   "source": [
+    "import streamlit as st\n",
+    "\n",
+    "# Define the pages of your app\n",
+    "def page1():\n",
+    "  st.write(\"This is page 1\")\n",
+    "\n",
+    "def page2():\n",
+    "  st.write(\"This is page 2\")\n",
+    "\n",
+    "# Create a sidebar menu\n",
+    "st.sidebar.header(\"Menu\")\n",
+    "button1 = st.sidebar.button(\"Page 1\")\n",
+    "button2 = st.sidebar.button(\"Page 2\")\n",
+    "\n",
+    "# Display the selected page\n",
+    "if button1:\n",
+    "  page1()\n",
+    "elif button2:\n",
+    "  page2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "751f3256",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Variables.csv

@@ -0,0 +1,53 @@
+Variable,Variable Bucket,Master Bucket,Base Bucket
+Non Promo Price,Price,Price,Internal
+Base Price,Price,Price,Internal
+Promo Price,Price,Price,Internal
+Average Price,Price,Price,Internal
+Price,Price,Price,Internal
+Distribution,Distribution,Distribution,Internal
+Weighted Distribution,Distribution,Distribution,Internal
+TDP,Distribution,Distribution,Internal
+Promo,Promotion,Promotion,Internal
+Depth of Deal,Promotion,Promotion,Internal
+Discount,Promotion,Promotion,Internal
+Volume�,Sales,Sales,Dependent
+Value,Sales,Sales,Dependent
+Sales,Sales,Sales,Dependent
+Market Share,Sales,Sales,Dependent
+TV,TV,Offline Media,Media
+Display,Display,Online Media,Media
+Video,Video,Online Media,Media
+OLV,Video,Online Media,Media
+Facebook,Facebook,Online Media,Media
+Twitter,Twitter,Online Media,Media
+Instagram,Instagram,Online Media,Media
+Pintrest,Pintrest,Online Media,Media
+YouTube,YouTube,Online Media,Media
+Bing,Paid Search,Online Media,Media
+Google,Paid Search,Online Media,Media
+Paid Search,Paid Search,Online Media,Media
+Search,Paid Search,Online Media,Media
+OOH,OOH,Offline Media,Media
+Radio,Radio,Offline Media,Media
+Audio Streaming,Audio Streaming,Online Media,Media
+Streaming,Audio Streaming,Online Media,Media
+Podcasts,Audio Streaming,Online Media,Media
+Pandora,Audio Streaming,Online Media,Media
+Spotify,Audio Streaming,Online Media,Media
+Mobility,COVID,COVID,External
+Stringency,COVID,COVID,External
+Cases,COVID,COVID,External
+Deaths,COVID,COVID,External
+CPI,Macroeconomic,Macroeconomic,External
+Unemployment Rate,Macroeconomic,Macroeconomic,External
+Fourier Waves,Seasonality,Seasonality,External
+Trend,Market,Seasonality,External
+Easter,Holiday,Seasonality,External
+MLK Jr. Day,Holiday,Seasonality,External
+Memorial Day,Holiday,Seasonality,External
+Juneteenth,Holiday,Seasonality,External
+Independence Day,Holiday,Seasonality,External
+Labour Day,Holiday,Seasonality,External
+Thanksgiving,Holiday,Seasonality,External
+Christmas,Holiday,Seasonality,External
+New Year's,Holiday,Seasonality,External

actual_data.csv

@@ -0,0 +1,158 @@
+,const,clicks_search_decay.2,impressions_tv_lag3,online_edu_trend_lag3,clicks_digital_lag2_decay.3,impressions_streaming_lag2_decay.4,covid_cases_lag3,unemployement_rate_lead4,season,flag_Aug_1,flag_Aug_2,flag_Aug_3,flag_dec_1,flag_dec_-1,flag_dec_-2,flag_dec_-3,flag_easter_-1,flag_easter_-2,flag_may_-1,flag_may_-2,flag_jun_-1,flag_jun_-2,covid_flag1,flag_june28,flag_aug13,flag_sep13,flag_mar_feb,date,total_prospect_id
+0,1.0,0.03264506089026503,0.0,0.0,0.0,0.11920857922376585,0.0,0.2448979591836735,100,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-11-10,3106
+1,1.0,0.1203178311529351,0.0,0.0,0.0,0.23575959332216032,0.0,0.2448979591836735,101,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-11-17,7809
+2,1.0,0.037674240888288246,0.0,0.0,0.30427286753070926,0.14866425214344534,0.0,0.2448979591836735,102,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-11-24,5658
+3,1.0,0.114056065999327,0.25459834519940233,0.5700000000000001,0.3210660307498862,0.06375317695001911,0.0,0.2448979591836735,103,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-12-01,7528
+4,1.0,0.15091848146432302,0.04759636387261456,0.58,0.2652143429433443,0.02550166207848893,0.0,0.2380952380952381,104,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-12-08,8913
+5,1.0,0.09691798534505919,0.0,0.41000000000000003,0.27398476053158455,0.22803554179688423,0.0,0.2380952380952381,105,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-12-15,7974
+6,1.0,0.0,0.2185391903071715,0.53,0.3093665823461814,0.3016670242357716,0.0,0.2380952380952381,106,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-12-22,5034
+7,1.0,0.06818143419410627,0.0645557652165116,0.6,0.35005256364095544,0.3915886857834677,0.0,0.2380952380952381,107,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2019-12-29,8296
+8,1.0,0.19748095587743647,0.0,0.49,0.2866388037412839,0.4644891817948484,0.0,0.2380952380952381,108,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-01-05,10953
+9,1.0,0.2718903484441833,0.31632836028874944,0.42,0.38339772931601046,0.4758788391710054,0.0,0.2380952380952381,109,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2020-01-12,11583
+10,1.0,0.29329394272923165,0.710207473795361,0.56,0.4716341482535363,0.47415700741999534,0.0,0.2380952380952381,110,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2020-01-19,11650
+11,1.0,0.3150710926081645,0.6225458397661645,0.66,0.5560651882029227,0.2282082561307921,0.0,0.2380952380952381,111,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-01-26,10086
+12,1.0,0.23335326208386092,0.5093471390869946,0.65,0.5990392189890996,0.09128427138188955,0.0,0.2993197278911565,112,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-02-02,8454
+13,1.0,0.18339704064539092,0.46920681970876166,0.66,0.5097387360461574,0.03651393215188798,0.0,0.2993197278911565,113,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-02-09,7842
+14,1.0,0.1829206162885479,0.5702922924005152,0.64,0.3647117781342298,0.5333315970976881,0.0,0.2993197278911565,114,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-02-16,8528
+15,1.0,0.17708137647064887,0.4762803199026322,0.62,0.2994390381863003,0.9999999999999999,0.0,0.2993197278911565,115,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-02-23,9230
+16,1.0,0.2110785179466496,0.31643298954206356,0.65,0.318727924805625,0.5153399788387041,0.0,0.2993197278911565,116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-03-01,8210
+17,1.0,0.1922309642774856,0.35110354589746834,0.65,0.3435805763353255,0.20613623376787482,0.0,1.0,117,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-03-08,6573
+18,1.0,0.1174971533357681,0.4397302099507956,0.64,0.37079693119819457,0.08245451214041095,0.0,1.0,118,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2020-03-15,4464
+19,1.0,0.04487177585471158,0.5651604986093057,0.66,0.3797815418753292,0.032981804856164386,3.6661729553753427e-06,1.0,119,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2020-03-22,5498
+20,1.0,0.04417426781579725,0.5142518574426083,0.77,0.3239901926717436,0.013192796475509808,0.00016497778299189042,1.0,120,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-03-29,7134
+21,1.0,0.09508966430933447,0.4246084040047787,1.0,0.22766051203571303,0.005277118590203924,0.01074555293220513,0.8979591836734694,121,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,2020-04-05,6507
+22,1.0,0.1727148072921107,0.3306303340730278,0.92,0.2557126494916798,0.0021108474360815696,0.07506489126131015,0.8979591836734694,122,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,2020-04-12,6752
+23,1.0,0.2757761792524949,0.9059477066272279,0.87,0.2910560761584964,0.0008443389744326279,0.11051311756683434,0.8979591836734694,123,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-04-19,7874
+24,1.0,0.46164669127102737,1.0,0.8200000000000001,0.29288325042575475,0.0003377355897730512,0.1323451775160945,0.8979591836734694,124,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-04-26,8706
+25,1.0,0.3631365926708698,0.8555262504044332,0.85,0.3143348639913703,0.00013509423590922048,0.12527679605813083,0.8979591836734694,125,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-05-03,9593
+26,1.0,0.3556269301486625,0.5998066602658987,0.8,0.3573452157072908,5.4838924587260594e-05,0.08418266340132861,0.7482993197278912,126,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-05-10,9554
+27,1.0,0.3898924329688705,0.31953123019194307,0.76,0.3492819601843694,0.08837696494340691,0.06699197841357364,0.7482993197278912,127,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-05-17,9461
+28,1.0,0.3270785638817633,0.5040802333471541,0.88,0.37224504100306005,0.12944061135952373,0.04806352744497074,0.7482993197278912,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-05-24,8347
+29,1.0,0.29596428185745655,0.6228739252579004,0.8300000000000001,0.3873711562094451,0.14079607140381442,0.028926104617911456,0.7482993197278912,129,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-05-31,7926
+30,1.0,0.23446621861142697,0.644779308361226,0.8,0.3519020717491842,0.15750706055823313,0.024482702995996537,0.6938775510204082,130,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-06-07,8606
+31,1.0,0.2202508917985891,0.726916988225644,0.71,0.32726146750928653,0.0797309833640819,0.022000703905207433,0.6938775510204082,131,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-06-14,7573
+32,1.0,0.18610614076735926,0.5963517592669729,0.73,0.31618831243754153,0.03501476889363339,0.015086301711369536,0.6938775510204082,132,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2020-06-21,6983
+33,1.0,0.1568177529621934,0.6764095796293655,0.75,0.2836099513597926,0.014005944823975384,0.011489786042146325,0.6938775510204082,133,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-06-28,6277
+34,1.0,0.22774801916471138,0.6466210070345804,0.72,0.25409997289933184,0.006272411362367827,0.00871449311492719,0.5714285714285715,134,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-07-05,7421
+35,1.0,0.24542124594101095,0.6580063264819511,0.73,0.2516667689694555,0.05947462601462651,0.008318546435746652,0.5714285714285715,135,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-07-12,7852
+36,1.0,0.24895270375190542,0.32749815383926373,0.68,0.2671053898526598,0.0888609058832765,0.008014254080450499,0.5714285714285715,136,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-07-19,7396
+37,1.0,0.16285259960994197,0.3666961464656464,0.78,0.26077100654286645,0.12420199588573878,0.008058248155915004,0.5714285714285715,137,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-07-26,7041
+38,1.0,0.16864346155569104,0.39341698388602436,0.84,0.25893225300958655,0.10423952696584138,0.00920209411799211,0.5714285714285715,138,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-08-02,7470
+39,1.0,0.22582910125625383,0.41507293852636135,0.8300000000000001,0.2528768986269057,0.08197739941078482,0.009315745479608745,0.5374149659863946,139,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-08-09,8725
+40,1.0,0.2778946696783185,0.7857143231388266,0.8,0.2772125371796957,0.07178679747906064,0.007237025413910927,0.5374149659863946,140,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-08-16,9657
+41,1.0,0.3062154076077969,0.434016630925742,0.87,0.33174759696083367,0.12078972986041582,0.006500124649880482,0.5374149659863946,141,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2020-08-23,10000
+42,1.0,0.2851073700683267,0.4051792323256236,0.8200000000000001,0.3621387745268235,0.1539969659046611,0.006118842662521447,0.5374149659863946,142,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-08-30,8941
+43,1.0,0.25999778433367665,0.4113785668398346,0.77,0.3604714968693371,0.1462622685965232,0.006375474769397721,0.4693877551020409,143,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-09-06,8507
+44,1.0,0.2947500457787596,0.43576671635701947,0.74,0.3084711376902622,0.1030893445960345,0.0060051913009048115,0.4693877551020409,144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2020-09-13,9887
+45,1.0,0.3239559328273078,0.40721834097732834,0.72,0.24061271129609485,0.08422768334333634,0.006456130574415978,0.4693877551020409,145,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-09-20,9627
+46,1.0,0.3189849597494306,0.4831656702512836,0.68,0.28577062852640756,0.054400116894051116,0.006401137980085348,0.4693877551020409,146,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-09-27,8735
+47,1.0,0.2930673557404469,0.5423730023996388,0.62,0.32330756771945346,0.02176006539088146,0.007566980979894707,0.45578231292517013,147,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-10-04,8138
+48,1.0,0.27381401410957934,0.48862464971809444,0.59,0.33668984325037016,0.008704026156352586,0.009172764734349107,0.45578231292517013,148,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-10-11,7966
+49,1.0,0.21658154029531146,0.5162854532967293,0.55,0.44481231480084876,0.003481610462541034,0.012223020633221393,0.45578231292517013,149,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-10-18,8109
+50,1.0,0.21772903332032795,0.47368257634991157,0.6,0.46141705479304307,0.0013926441850164136,0.013601501664442522,0.45578231292517013,150,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-10-25,7848
+51,1.0,0.16712357438522701,0.5132571164009214,0.5,0.38402389059771924,0.0005570576740065655,0.012915927321787332,0.45578231292517013,151,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-11-01,6516
+52,1.0,0.1814031347156822,0.5409537987241609,0.5,0.2968208337801042,0.00022282306960262618,0.013091903623645349,0.45578231292517013,152,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-11-08,7233
+53,1.0,0.16852532779394064,0.49490997931858044,0.5,0.22663075929954526,8.912922784105048e-05,0.014624363918992243,0.45578231292517013,153,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-11-15,7409
+54,1.0,0.10492104198879731,0.4086344123814518,0.41000000000000003,0.21669561761817938,3.565169113642019e-05,0.016127494830696133,0.45578231292517013,154,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-11-22,6232
+55,1.0,0.16920169406380464,0.45151008168804235,0.49,0.21833619946593313,1.4260676454568076e-05,0.024849320291534072,0.45578231292517013,155,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-11-29,8170
+56,1.0,0.1305885456099783,0.4543635808918873,0.47000000000000003,0.1596898931167178,5.704270581827231e-06,0.03519159419864792,0.435374149659864,156,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-12-06,7075
+57,1.0,0.1214984593864375,0.35070760971315756,0.4,0.15417676852356046,2.2817082327308923e-06,0.041732046751037526,0.435374149659864,157,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-12-13,7379
+58,1.0,0.057042007816384965,0.32470890321593604,0.47000000000000003,0.15442387578570832,9.126832930923571e-07,0.049892947749703036,0.435374149659864,158,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-12-20,5442
+59,1.0,0.12406882983279183,0.3135816516054531,0.45,0.1671308209739812,3.650733172369429e-07,0.0686930826648678,0.435374149659864,159,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2020-12-27,7735
+60,1.0,0.24786523070013738,0.3102913429236421,0.42,0.16347790840061424,1.4602932689477716e-07,0.0732574679943101,0.435374149659864,160,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-01-03,9754
+61,1.0,0.26083059672146286,0.2649240941306087,0.34,0.25327016920452516,5.841173075791087e-08,0.07444897420480709,0.4217687074829932,161,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2021-01-10,10641
+62,1.0,0.24028847292133387,0.6513962629200784,0.38,0.3773812732234543,2.3364692303164347e-08,0.08318546435746653,0.4217687074829932,162,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2021-01-17,10230
+63,1.0,0.31526302386797916,0.531674302460824,0.47000000000000003,0.3527386460097067,9.345876921265738e-09,0.10258685163731283,0.4217687074829932,163,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-01-24,10352
+64,1.0,0.2966293410018717,0.44836670500794606,0.47000000000000003,0.3711695518795665,3.738350768506295e-09,0.13234151134313912,0.4217687074829932,164,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-01-31,9216
+65,1.0,0.20088776123137192,0.3815806999416851,0.45,0.33580461662371014,1.4953403074025183e-09,0.12043744775703538,0.40816326530612246,165,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-02-07,8421
+66,1.0,0.173394454128539,0.343687050600215,0.48,0.3277941002786073,5.981361229610074e-10,0.11271648751301491,0.40816326530612246,166,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-02-14,9281
+67,1.0,0.1777198044422716,0.33051072402008147,0.5,0.31487397296804576,2.3925444918440296e-10,0.109699227170741,0.40816326530612246,167,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-02-21,8891
+68,1.0,0.1850269016675808,0.30627520154343757,0.46,0.3133091660972597,9.570177967376119e-11,0.08255854878209734,0.40816326530612246,168,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-02-28,8169
+69,1.0,0.2529549962208855,0.298123038215738,0.42,0.3358964981168952,3.828071186950448e-11,0.08351908609640568,0.40816326530612246,169,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-03-07,8724
+70,1.0,0.213028120324469,0.3267901551549544,0.44,0.3038053348505854,1.531228474780179e-11,0.07285052279626343,0.40816326530612246,170,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-03-14,8194
+71,1.0,0.16441430466323353,0.25967469209260036,0.5,0.32087357753439977,6.124913899120717e-12,0.07822879852179906,0.40816326530612246,171,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-03-21,8254
+72,1.0,0.11053130189212229,0.260168451958828,0.42,0.3279459500984871,2.449965559648287e-12,0.07333812379932836,0.40816326530612246,172,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-03-28,7026
+73,1.0,0.06917021315146277,0.0,0.38,0.37411287881420296,9.799862238593149e-13,0.07465061371735272,0.39455782312925175,173,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-04-04,6412
+74,1.0,0.06728264676731566,0.0,0.44,0.4347510050616973,3.9199448954372595e-13,0.0732721326861316,0.39455782312925175,174,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,2021-04-11,6297
+75,1.0,0.10167805497311716,0.0,0.43,0.4574504815633023,1.5679779581749037e-13,0.07982724993034271,0.39455782312925175,175,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,2021-04-18,6687
+76,1.0,0.1734619149834527,0.0,0.48,0.48912312446006045,6.271911832699615e-14,0.06941165256412136,0.39455782312925175,176,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-04-25,8430
+77,1.0,0.2040432878056308,0.0,0.46,0.44466429049983563,2.5087647330798465e-14,0.06276854716898124,0.39455782312925175,177,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-05-02,8025
+78,1.0,0.20788046814877387,0.0,0.48,0.5722675873212515,1.0035058932319387e-14,0.04882242524673344,0.40136054421768713,178,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-05-09,8242
+79,1.0,0.14929264058846564,0.0,0.5,0.45913415146070335,4.014023572927755e-15,0.033618806000791895,0.40136054421768713,179,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-05-16,8280
+80,1.0,0.11694210039888364,0.0,0.51,0.39528662679579885,1.6056094291711022e-15,0.025182942030473228,0.40136054421768713,180,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-05-23,7909
+81,1.0,0.055184035342337234,0.0,0.51,0.3880077087936407,6.422437716684409e-16,0.017652622780132275,0.40136054421768713,181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-05-30,7574
+82,1.0,0.04358787034563821,0.0,0.5,0.3863265622647678,2.568975086673764e-16,0.012651962869000308,0.3673469387755103,182,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-06-06,7270
+83,1.0,0.03833609653008979,0.0,0.46,0.3784495643657444,1.0275900346695056e-16,0.008835476822454577,0.3673469387755103,183,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-06-13,6716
+84,1.0,0.06111263589867566,0.0,0.48,0.38862024435317233,4.1103601386780226e-17,0.005939200187708055,0.3673469387755103,184,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-06-20,6944
+85,1.0,0.07119833324643848,0.0,0.44,0.4039000969934476,1.644144055471209e-17,0.004967664354533589,0.3673469387755103,185,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2021-06-27,6803
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+87,1.0,0.12577031397293442,0.0,0.45,0.4950177419852857,2.630630488753935e-18,0.003977797656582247,0.3537414965986395,187,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-07-11,8254
+88,1.0,0.1502746019886232,0.0,0.45,0.5650602702260171,1.052252195501574e-18,0.0040621196345558795,0.3537414965986395,188,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-07-18,7804
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+90,1.0,0.23464189851384848,0.0,0.46,0.5484130743981998,1.6836035128025183e-19,0.008076579020691881,0.3537414965986395,190,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-08-01,8378
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+96,1.0,0.4172971677569805,0.0,0.48,0.8193686075762131,6.896039988439117e-22,0.03868179085216524,0.3129251700680272,196,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-09-12,9521
+97,1.0,0.5004920981884484,0.0,0.53,0.4496097944711011,2.758415995375647e-22,0.03902274493701515,0.3129251700680272,197,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-09-19,9451
+98,1.0,0.6383788968475093,0.0,0.47000000000000003,0.3701822126418114,1.1033663981502588e-22,0.03567186285580209,0.3129251700680272,198,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-09-26,8898
+99,1.0,0.6501651617929107,0.0,0.51,0.34258196039636274,4.413465592601035e-23,0.0352539191388893,0.3129251700680272,199,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-10-03,8441
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+110,1.0,0.21850759166298056,0.0,0.51,0.34100191273497404,1.8511416388908902e-27,0.029549354020325262,0.272108843537415,210,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-12-19,7663
+111,1.0,0.2156152088113536,0.0,0.43,0.3876459690915292,7.404566555563562e-28,0.04853646375621416,0.272108843537415,211,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2021-12-26,7888
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+113,1.0,0.5580863257308297,0.0,0.42,0.33648328199770844,1.18473064889017e-28,0.2914790808171166,0.2585034013605442,213,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-01-09,12850
+114,1.0,0.5441541455767391,0.0,0.45,0.5258301345263098,4.7389225955606806e-29,0.6228644542534939,0.2585034013605442,214,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2022-01-16,12768
+115,1.0,0.37953926965668333,0.0,0.51,0.6191133700101356,1.8955690382242722e-29,1.0,0.2585034013605442,215,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2022-01-23,11023
+116,1.0,0.3422525462363791,0.0,0.5,0.6600516747429145,7.582276152897087e-30,0.8603298089190655,0.2585034013605442,216,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-01-30,10317
+117,1.0,0.3679329127754763,0.0,0.49,0.6150147631969254,3.0329104611588346e-30,0.3851571321728674,0.2448979591836735,217,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-02-06,10109
+118,1.0,0.3530129569359208,0.0,0.49,0.5435710104633258,1.2131641844635335e-30,0.18207314748280565,0.2448979591836735,218,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2022-02-13,10233
+119,1.0,0.3628237688509028,0.0,0.48,0.5395383650448762,4.852656737854129e-31,0.08532284319045035,0.2448979591836735,219,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2022-02-20,10660
+120,1.0,0.3535562124344392,0.0,0.49,0.3713089856353334,1.941062695141646e-31,0.04778123212740684,0.2448979591836735,220,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2022-02-27,9862
+121,1.0,0.35851767100446613,0.0,0.49,0.33021424233802193,7.764250780566529e-32,0.028365180155739026,0.2448979591836735,221,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2022-03-06,10393
+122,1.0,0.3648140365425708,0.0,0.53,0.29899648842829235,3.105700312226557e-32,0.019053100849085656,0.2448979591836735,222,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2022-03-13,9914
+123,1.0,0.417768904168966,0.0,0.46,0.30801461857263196,1.242280124890568e-32,0.014096435013418193,0.2448979591836735,223,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2022-03-20,11027
+124,1.0,0.45364666714531404,0.0,0.5,0.29874033139572204,4.9691204995617213e-33,0.013440190054406007,0.2448979591836735,224,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-03-27,10066
+125,1.0,0.45997433293937545,0.0,0.45,0.3080341285301519,1.9876481998241388e-33,0.014672024167412121,0.2448979591836735,225,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-04-03,8722
+126,1.0,0.4245480429075594,0.0,0.46,0.304189689538618,7.950592799291056e-34,0.01936472555029256,0.2448979591836735,226,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,2022-04-10,7805
+127,1.0,0.4463068738641009,0.0,0.54,0.307818077305473,3.1802371197109226e-34,0.027822586558343475,0.2448979591836735,227,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,2022-04-17,8519
+128,1.0,0.6012222981571669,0.0,0.53,0.29394180576819906,1.272094847878869e-34,0.033340176856183366,0.2448979591836735,228,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-04-24,10084
+129,1.0,0.6804106164543928,0.0,0.5,0.28219281269675367,5.088379391460478e-35,0.04576117082899503,0.2448979591836735,229,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-05-01,10291
+130,1.0,0.62805714350389,0.0,0.54,0.30839694661979145,2.035351756529193e-35,0.05172603422739071,0.2448979591836735,230,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-05-08,9743
+131,1.0,0.7470007501508245,0.0,0.54,0.3120111152265925,8.141407025566787e-36,0.04952999662712088,0.2448979591836735,231,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-05-15,10759
+132,1.0,0.6460736106378411,0.0,0.55,0.2905779236460707,3.25656280967673e-36,0.06457597043598129,0.2448979591836735,232,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-05-22,9845
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+134,1.0,0.5996683384067256,0.0,0.5,0.3940488499594224,5.210500487782985e-37,0.09049581323048496,0.40680272108843546,234,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-06-05,10021
+135,1.0,0.5630659455826548,0.0,0.54,0.4539755399873685,2.0842001896133483e-37,0.09128037424293528,0.40680272108843546,235,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-06-12,10112
+136,1.0,0.5482324249484887,0.0,0.45,0.48814019600803654,8.336800703454939e-38,0.08289217052103649,0.40680272108843546,236,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-06-19,10034
+137,1.0,0.5485743918729864,0.0,0.47000000000000003,0.475428506654356,3.3347202263835196e-38,0.06987359035649866,0.40680272108843546,237,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2022-06-26,9209
+138,1.0,0.5559932625646005,0.0,0.43,0.510072176038165,1.333888035554951e-38,0.06264756346145385,0.40680272108843546,238,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-07-03,10265
+139,1.0,0.6089718159266746,0.0,0.45,0.44215508529036335,5.33555159223524e-39,0.0627612148230705,0.40680272108843546,239,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-07-10,10033
+140,1.0,0.6101706458097598,0.0,0.48,0.41550269661979555,2.1342200869095313e-39,0.07072780865510112,0.40680272108843546,240,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-07-17,9790
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+144,1.0,0.832744074444703,0.0,0.46,0.5840915039533217,5.463514104995084e-41,0.08482790984147467,0.40680272108843546,244,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-08-14,12886
+145,1.0,0.8546151893753493,0.0,0.49,0.6374603327364593,2.1853506435415578e-41,0.07962194424484169,0.40680272108843546,245,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-08-21,12027
+146,1.0,0.9999999999999998,0.0,0.55,0.6022458246191313,8.740852589601472e-42,0.07178366646624922,0.40680272108843546,246,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-08-28,11375
+147,1.0,0.860672618209781,0.0,0.48,0.5735957859704555,3.495791051275827e-42,0.05725095687114135,0.40680272108843546,247,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-09-04,10824
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+149,1.0,0.7774120906393625,0.0,0.55,0.7618650061054455,5.585565898134668e-43,0.0440857297883885,0.40680272108843546,249,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-09-18,12146
+150,1.0,0.6580209603679659,0.0,0.52,0.8137272725878776,2.2287265136062566e-43,0.039975949905412735,0.40680272108843546,250,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-09-25,10881
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+152,1.0,0.709096498806814,0.0,0.46,0.7292818780372798,3.4889645827034517e-44,0.04076784326377381,0.40680272108843546,252,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-10-09,10230
+153,1.0,0.5414415970743589,0.0,0.45,0.6974583695681711,1.340587376605267e-44,0.04368978310920796,0.0,253,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-10-16,11557
+154,1.0,0.6081525119323576,0.0,0.54,0.6240593695822464,4.812364941659934e-45,0.041156457597043596,0.0,254,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-10-23,10805
+155,1.0,0.5960421531458853,0.0,0.45,0.5899287906913332,1.3749614119028383e-45,0.03843982343711047,0.0,255,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-10-30,9709
+156,1.0,0.848521629204434,0.0,0.47000000000000003,0.6201930426013046,0.0,0.040723849188309305,0.0,256,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2022-11-06,10098

attributed_app_installs.csv

@@ -0,0 +1,130 @@
+Date,kwai,programmatic,infleux,affiliates,influencer,liftOff,indicacao,paid_social,paid_search,digital_app
+5/8/2023,1288.00,40700.00,6487.00,334.00,292.00,,2328.00,6014.00,376.00,7809.00
+5/9/2023,1142.00,49323.00,17330.00,369.00,95.00,,2786.00,6947.00,346.00,8572.00
+5/10/2023,1102.00,55768.00,17065.00,381.00,151.00,,2331.00,6132.00,332.00,13655.00
+5/11/2023,968.00,54028.00,2531.00,435.00,65.00,,2710.00,6448.00,277.00,10213.00
+5/12/2023,1026.00,57084.00,2802.00,352.00,31.00,,1706.00,4688.00,368.00,7793.00
+5/13/2023,947.00,49772.00,2618.00,410.00,39.00,,1005.00,3727.00,278.00,6496.00
+5/14/2023,982.00,42586.00,1735.00,212.00,20.00,,606.00,4034.00,304.00,5933.00
+5/15/2023,1229.00,44786.00,3993.00,363.00,142.00,,944.00,7368.00,492.00,8754.00
+5/16/2023,1510.00,23624.00,4656.00,293.00,361.00,,1685.00,9297.00,685.00,12116.00
+5/17/2023,1333.00,16330.00,2574.00,313.00,78.00,,1436.00,8341.00,580.00,10826.00
+5/18/2023,1568.00,13638.00,3934.00,379.00,136.00,,1509.00,8253.00,663.00,11071.00
+5/19/2023,1654.00,11303.00,6181.00,761.00,59.00,,1298.00,7020.00,452.00,10742.00
+5/20/2023,1488.00,8442.00,6591.00,841.00,61.00,,1034.00,5369.00,388.00,9747.00
+5/21/2023,1649.00,6554.00,3614.00,526.00,122.00,,932.00,5799.00,405.00,9750.00
+5/22/2023,1970.00,7719.00,6489.00,585.00,260.00,,1724.00,9565.00,414.00,12472.00
+5/23/2023,2009.00,6073.00,13353.00,1144.00,226.00,,3344.00,12431.00,423.00,13882.00
+5/24/2023,1632.00,7326.00,29366.00,1099.00,353.00,,3058.00,12150.00,388.00,13386.00
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+6/2/2023,1080.00,8770.00,12805.00,318.00,186.00,,2998.00,10287.00,373.00,11700.00
+6/3/2023,1408.00,8642.00,7994.00,289.00,81.00,,1480.00,7418.00,308.00,10373.00
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+9/13/2023,1548.00,2896.00,10313.00,909.00,179.00,,1834.00,6945.00,182.00,15771.00

attributed_app_installs_tactic.csv

@@ -0,0 +1,130 @@
+Date,FB: Level Achieved - Tier 1,FB: Level Achieved - Tier 2,FB: Level Achieved - Tier 3,,FB: Test Campaigns,FB: Purchase Tier 3,FB: Purchase ,TikTok: Pangle Android Achieve Level,TikTok: Pangle Android Purchase,TikTok: Android Achieve Level,TikTok: Purchase Roas,TikTok: Pangle And Loanapply,TikTok: Bau,TikTok: Loanapply,TikTok: Spc Loan Apply
+5/8/2023,2157,1008,1057,,1433,,2,0,0,,,356,1,0,
+5/9/2023,2183,1108,1103,,2130,,,0,0,,,423,0,,
+5/10/2023,2019,969,976,,1770,,1,0,0,,,397,0,0,
+5/11/2023,2013,1033,999,,1983,,1,0,0,0,,419,0,0,
+5/12/2023,1902,932,850,,644,,,0,0,,,360,0,0,
+5/13/2023,1654,857,821,,75,,1,0,0,,0,319,0,0,
+5/14/2023,1760,959,964,,62,,,0,0,0,,289,0,0,
+5/15/2023,2777,1509,1350,,1306,,,0,0,,0,426,0,0,
+5/16/2023,3458,1733,1447,,1864,,2,0,0,,,793,0,0,
+5/17/2023,3190,1337,1264,,1566,,2,0,0,,,982,0,0,
+5/18/2023,2946,1401,1251,,1553,,,0,0,0,0,1102,0,0,
+5/19/2023,2861,1379,1147,,606,,3,0,0,,0,1024,0,0,0
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+5/21/2023,2519,1214,1108,,129,,3,0,0,0,0,826,0,0,
+5/22/2023,3192,1481,1194,,2616,,2,0,0,,,1080,0,0,
+5/23/2023,3829,1713,1377,,4161,,1,0,0,,,1350,0,0,
+5/24/2023,3610,1881,1368,,3902,,,0,0,,,1389,0,0,
+5/25/2023,3652,1663,1392,,3288,,1,0,0,,,1250,0,0,0
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best_models.pkl

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+size 3755214

bucket_data_VB.pkl

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+size 656

classes.py

@@ -0,0 +1,479 @@
+import numpy as np
+from scipy.optimize import minimize, LinearConstraint, NonlinearConstraint
+from collections import OrderedDict
+import pandas as pd
+from numerize.numerize import numerize
+
+
+def class_to_dict(class_instance):
+    attr_dict = {}
+    if isinstance(class_instance, Channel):
+        attr_dict["type"] = "Channel"
+        attr_dict["name"] = class_instance.name
+        attr_dict["dates"] = class_instance.dates
+        attr_dict["spends"] = class_instance.actual_spends
+        attr_dict["conversion_rate"] = class_instance.conversion_rate
+        attr_dict["modified_spends"] = class_instance.modified_spends
+        attr_dict["modified_sales"] = class_instance.modified_sales
+        attr_dict["response_curve_type"] = class_instance.response_curve_type
+        attr_dict["response_curve_params"] = (
+            class_instance.response_curve_params
+        )
+        attr_dict["penalty"] = class_instance.penalty
+        attr_dict["bounds"] = class_instance.bounds
+        attr_dict["actual_total_spends"] = class_instance.actual_total_spends
+        attr_dict["actual_total_sales"] = class_instance.actual_total_sales
+        attr_dict["modified_total_spends"] = (
+            class_instance.modified_total_spends
+        )
+        attr_dict["modified_total_sales"] = class_instance.modified_total_sales
+        attr_dict["actual_mroi"] = class_instance.get_marginal_roi("actual")
+        attr_dict["modified_mroi"] = class_instance.get_marginal_roi(
+            "modified"
+        )
+
+    elif isinstance(class_instance, Scenario):
+        attr_dict["type"] = "Scenario"
+        attr_dict["name"] = class_instance.name
+        channels = []
+        for channel in class_instance.channels.values():
+            channels.append(class_to_dict(channel))
+        attr_dict["channels"] = channels
+        attr_dict["constant"] = class_instance.constant
+        attr_dict["correction"] = class_instance.correction
+        attr_dict["actual_total_spends"] = class_instance.actual_total_spends
+        attr_dict["actual_total_sales"] = class_instance.actual_total_sales
+        attr_dict["modified_total_spends"] = (
+            class_instance.modified_total_spends
+        )
+        attr_dict["modified_total_sales"] = class_instance.modified_total_sales
+
+    return attr_dict
+
+
+def class_from_dict(attr_dict):
+    if attr_dict["type"] == "Channel":
+        return Channel.from_dict(attr_dict)
+    elif attr_dict["type"] == "Scenario":
+        return Scenario.from_dict(attr_dict)
+
+
+class Channel:
+    def __init__(
+        self,
+        name,
+        dates,
+        spends,
+        response_curve_type,
+        response_curve_params,
+        bounds,
+        conversion_rate=1,
+        modified_spends=None,
+        penalty=True,
+    ):
+        self.name = name
+        self.dates = dates
+        self.conversion_rate = conversion_rate
+        self.actual_spends = spends.copy()
+
+        if modified_spends is None:
+            self.modified_spends = self.actual_spends.copy()
+        else:
+            self.modified_spends = modified_spends
+
+        self.response_curve_type = response_curve_type
+        self.response_curve_params = response_curve_params
+        self.bounds = bounds
+        self.penalty = penalty
+
+        self.upper_limit = self.actual_spends.max() + self.actual_spends.std()
+        self.power = np.ceil(np.log(self.actual_spends.max()) / np.log(10)) - 3
+        self.actual_sales = None
+        self.actual_sales = self.response_curve(self.actual_spends)
+        self.actual_total_spends = self.actual_spends.sum()
+        self.actual_total_sales = self.actual_sales.sum()
+        self.modified_sales = self.calculate_sales()
+        self.modified_total_spends = self.modified_spends.sum()
+        self.modified_total_sales = self.modified_sales.sum()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def update_penalty(self, penalty):
+        self.penalty = penalty
+
+    def _modify_spends(self, spends_array, total_spends):
+        return spends_array * total_spends / spends_array.sum()
+
+    def modify_spends(self, total_spends):
+        self.modified_spends = (
+            self.modified_spends * total_spends / self.modified_spends.sum()
+        )
+
+    def calculate_sales(self):
+        return self.response_curve(self.modified_spends)
+
+    def response_curve(self, x):
+        if self.penalty:
+            x = np.where(
+                x < self.upper_limit,
+                x,
+                self.upper_limit
+                + (x - self.upper_limit) * self.upper_limit / x,
+            )
+        if self.response_curve_type == "s-curve":
+            if self.power >= 0:
+                x = x / 10**self.power
+            x = x.astype("float64")
+            K = self.response_curve_params["K"]
+            b = self.response_curve_params["b"]
+            a = self.response_curve_params["a"]
+            x0 = self.response_curve_params["x0"]
+            sales = K / (1 + b * np.exp(-a * (x - x0)))
+        if self.response_curve_type == "linear":
+            beta = self.response_curve_params["beta"]
+            sales = beta * x
+
+        return sales
+
+    def get_marginal_roi(self, flag):
+        K = self.response_curve_params["K"]
+        a = self.response_curve_params["a"]
+        # x = self.modified_total_spends
+        # if self.power >= 0 :
+        #     x = x / 10**self.power
+        # x = x.astype('float64')
+        # return K*b*a*np.exp(-a*(x-x0)) / (1 + b * np.exp(-a*(x - x0)))**2
+        if flag == "actual":
+            y = self.response_curve(self.actual_spends)
+            # spends_array = self.actual_spends
+            # total_spends = self.actual_total_spends
+            # total_sales = self.actual_total_sales
+
+        else:
+            y = self.response_curve(self.modified_spends)
+            # spends_array = self.modified_spends
+            # total_spends = self.modified_total_spends
+            # total_sales = self.modified_total_sales
+
+        # spends_inc_1 = self._modify_spends(spends_array, total_spends+1)
+        mroi = a * (y) * (1 - y / K)
+        return mroi.sum() / len(self.modified_spends)
+        # spends_inc_1 = self.spends_array + 1
+        # new_total_sales = self.response_curve(spends_inc_1).sum()
+        # return (new_total_sales - total_sales) / len(self.modified_spends)
+
+    def update(self, total_spends):
+        self.modify_spends(total_spends)
+        self.modified_sales = self.calculate_sales()
+        self.modified_total_spends = self.modified_spends.sum()
+        self.modified_total_sales = self.modified_sales.sum()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def intialize(self):
+        self.new_spends = self.old_spends
+
+    def __str__(self):
+        return f"{self.name},{self.actual_total_sales}, {self.modified_total_spends}"
+
+    @classmethod
+    def from_dict(cls, attr_dict):
+        return Channel(
+            name=attr_dict["name"],
+            dates=attr_dict["dates"],
+            spends=attr_dict["spends"],
+            bounds=attr_dict["bounds"],
+            modified_spends=attr_dict["modified_spends"],
+            response_curve_type=attr_dict["response_curve_type"],
+            response_curve_params=attr_dict["response_curve_params"],
+            penalty=attr_dict["penalty"],
+        )
+
+    def update_response_curves(self, response_curve_params):
+        self.response_curve_params = response_curve_params
+
+
+class Scenario:
+    def __init__(self, name, channels, constant, correction):
+        self.name = name
+        self.channels = channels
+        self.constant = constant
+        self.correction = correction
+
+        self.actual_total_spends = self.calculate_modified_total_spends()
+        self.actual_total_sales = self.calculate_actual_total_sales()
+        self.modified_total_sales = self.calculate_modified_total_sales()
+        self.modified_total_spends = self.calculate_modified_total_spends()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def update_penalty(self, value):
+        for channel in self.channels.values():
+            channel.update_penalty(value)
+
+    def calculate_modified_total_spends(self):
+        total_actual_spends = 0.0
+        for channel in self.channels.values():
+            total_actual_spends += (
+                channel.actual_total_spends * channel.conversion_rate
+            )
+        return total_actual_spends
+
+    def calculate_modified_total_spends(self):
+        total_modified_spends = 0.0
+        for channel in self.channels.values():
+            # import streamlit as st
+            # st.write(channel.modified_total_spends )
+            total_modified_spends += (
+                channel.modified_total_spends * channel.conversion_rate
+            )
+        return total_modified_spends
+
+    def calculate_actual_total_sales(self):
+        total_actual_sales = self.constant.sum() + self.correction.sum()
+        for channel in self.channels.values():
+            total_actual_sales += channel.actual_total_sales
+        return total_actual_sales
+
+    def calculate_modified_total_sales(self):
+        total_modified_sales = self.constant.sum() + self.correction.sum()
+        for channel in self.channels.values():
+            total_modified_sales += channel.modified_total_sales
+        return total_modified_sales
+
+    def update(self, channel_name, modified_spends):
+        self.channels[channel_name].update(modified_spends)
+        self.modified_total_sales = self.calculate_modified_total_sales()
+        self.modified_total_spends = self.calculate_modified_total_spends()
+        self.delta_spends = (
+            self.modified_total_spends - self.actual_total_spends
+        )
+        self.delta_sales = self.modified_total_sales - self.actual_total_sales
+
+    def optimize_spends(self, sales_percent, channels_list, algo="COBYLA"):
+        desired_sales = self.actual_total_sales * (1 + sales_percent / 100.0)
+
+        def constraint(x):
+            for ch, spends in zip(channels_list, x):
+                self.update(ch, spends)
+            return self.modified_total_sales - desired_sales
+
+        bounds = []
+        for ch in channels_list:
+            bounds.append(
+                (1 + np.array([-50.0, 100.0]) / 100.0)
+                * self.channels[ch].actual_total_spends
+            )
+
+        initial_point = []
+        for bound in bounds:
+            initial_point.append(bound[0])
+
+        power = np.ceil(np.log(sum(initial_point)) / np.log(10))
+
+        constraints = [NonlinearConstraint(constraint, -1.0, 1.0)]
+
+        res = minimize(
+            lambda x: sum(x) / 10 ** (power),
+            bounds=bounds,
+            x0=initial_point,
+            constraints=constraints,
+            method=algo,
+            options={"maxiter": int(2e7), "catol": 1},
+        )
+
+        for channel_name, modified_spends in zip(channels_list, res.x):
+            self.update(channel_name, modified_spends)
+
+        return zip(channels_list, res.x)
+
+    def optimize(self, spends_percent, channels_list):
+        # channels_list = self.channels.keys()
+        num_channels = len(channels_list)
+        spends_constant = []
+        spends_constraint = 0.0
+        for channel_name in channels_list:
+            # spends_constraint += self.channels[channel_name].modified_total_spends
+            spends_constant.append(self.channels[channel_name].conversion_rate)
+            spends_constraint += (
+                self.channels[channel_name].actual_total_spends
+                * self.channels[channel_name].conversion_rate
+            )
+        spends_constraint = spends_constraint * (1 + spends_percent / 100)
+        # constraint= LinearConstraint(np.ones((num_channels,)), lb = spends_constraint, ub = spends_constraint)
+        constraint = LinearConstraint(
+            np.array(spends_constant),
+            lb=spends_constraint,
+            ub=spends_constraint,
+        )
+        bounds = []
+        old_spends = []
+        for channel_name in channels_list:
+            _channel_class = self.channels[channel_name]
+            channel_bounds = _channel_class.bounds
+            channel_actual_total_spends = (
+                _channel_class.actual_total_spends
+                * ((1 + spends_percent / 100))
+            )
+            old_spends.append(channel_actual_total_spends)
+            bounds.append(
+                (1 + channel_bounds / 100) * channel_actual_total_spends
+            )
+
+        def objective_function(x):
+            for channel_name, modified_spends in zip(channels_list, x):
+                self.update(channel_name, modified_spends)
+            return -1 * self.modified_total_sales
+
+        res = minimize(
+            objective_function,
+            # method="trust-constr",
+            method="SLSQP",
+            x0=old_spends,
+            constraints=constraint,
+            bounds=bounds,
+            options={"maxiter": 2000},
+        )
+        # res = dual_annealing(
+        # objective_function,
+        # x0=old_spends,
+        # mi
+        # constraints=constraint,
+        # bounds=bounds,
+        # tol=1e-16
+        # )
+        print(res)
+        for channel_name, modified_spends in zip(channels_list, res.x):
+            self.update(channel_name, modified_spends)
+
+        return zip(channels_list, res.x)
+
+    def save(self):
+        details = {}
+        actual_list = []
+        modified_list = []
+        data = {}
+        channel_data = []
+
+        summary_rows = []
+        actual_list.append({
+            "name": "Total",
+            "Spends": self.actual_total_spends,
+            "Sales": self.actual_total_sales,
+        })
+        modified_list.append({
+            "name": "Total",
+            "Spends": self.modified_total_spends,
+            "Sales": self.modified_total_sales,
+        })
+        for channel in self.channels.values():
+            name_mod = channel.name.replace("_", " ")
+            if name_mod.lower().endswith(" imp"):
+                name_mod = name_mod.replace("Imp", " Impressions")
+            summary_rows.append([
+                name_mod,
+                channel.actual_total_spends,
+                channel.modified_total_spends,
+                channel.actual_total_sales,
+                channel.modified_total_sales,
+                round(
+                    channel.actual_total_sales / channel.actual_total_spends, 2
+                ),
+                round(
+                    channel.modified_total_sales
+                    / channel.modified_total_spends,
+                    2,
+                ),
+                channel.get_marginal_roi("actual"),
+                channel.get_marginal_roi("modified"),
+            ])
+            data[channel.name] = channel.modified_spends
+            data["Date"] = channel.dates
+            data["Sales"] = (
+                data.get("Sales", np.zeros((len(channel.dates),)))
+                + channel.modified_sales
+            )
+            actual_list.append({
+                "name": channel.name,
+                "Spends": channel.actual_total_spends,
+                "Sales": channel.actual_total_sales,
+                "ROI": round(
+                    channel.actual_total_sales / channel.actual_total_spends, 2
+                ),
+            })
+            modified_list.append({
+                "name": channel.name,
+                "Spends": channel.modified_total_spends,
+                "Sales": channel.modified_total_sales,
+                "ROI": round(
+                    channel.modified_total_sales
+                    / channel.modified_total_spends,
+                    2,
+                ),
+                "Marginal ROI": channel.get_marginal_roi("modified"),
+            })
+
+            channel_data.append({
+                "channel": channel.name,
+                "spends_act": channel.actual_total_spends,
+                "spends_mod": channel.modified_total_spends,
+                "sales_act": channel.actual_total_sales,
+                "sales_mod": channel.modified_total_sales,
+            })
+        summary_rows.append([
+            "Total",
+            self.actual_total_spends,
+            self.modified_total_spends,
+            self.actual_total_sales,
+            self.modified_total_sales,
+            round(self.actual_total_sales / self.actual_total_spends, 2),
+            round(self.modified_total_sales / self.modified_total_spends, 2),
+            0.0,
+            0.0,
+        ])
+        details["Actual"] = actual_list
+        details["Modified"] = modified_list
+        columns_index = pd.MultiIndex.from_product(
+            [[""], ["Channel"]], names=["first", "second"]
+        )
+        columns_index = columns_index.append(
+            pd.MultiIndex.from_product(
+                [["Spends", "NRPU", "ROI", "MROI"], ["Actual", "Simulated"]],
+                names=["first", "second"],
+            )
+        )
+        details["Summary"] = pd.DataFrame(summary_rows, columns=columns_index)
+        data_df = pd.DataFrame(data)
+        channel_list = list(self.channels.keys())
+        data_df = data_df[["Date", *channel_list, "Sales"]]
+
+        details["download"] = {
+            "data_df": data_df,
+            "channels_df": pd.DataFrame(channel_data),
+            "total_spends_act": self.actual_total_spends,
+            "total_sales_act": self.actual_total_sales,
+            "total_spends_mod": self.modified_total_spends,
+            "total_sales_mod": self.modified_total_sales,
+        }
+
+        return details
+
+    @classmethod
+    def from_dict(cls, attr_dict):
+        channels_list = attr_dict["channels"]
+        channels = {
+            channel["name"]: class_from_dict(channel)
+            for channel in channels_list
+        }
+        return Scenario(
+            name=attr_dict["name"],
+            channels=channels,
+            constant=attr_dict["constant"],
+            correction=attr_dict["correction"],
+        )

config.yaml

@@ -0,0 +1,13 @@
+credentials:
+  usernames:
+    willbankmmm:
+      email: geethu4444@gmail.com
+      name: Geetakrishna
+      password: '$2b$12$r.KJDzrp6kFErWwh/n7vh.eSvXNU60HBDjrQrNQqkqOH8KSlVacMu'
+cookie:
+  expiry_days: 1
+  key: some_signature_key
+  name: some_cookie_name
+preauthorized:
+  emails:
+  - geethu4444@gmail.com

df.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6856d367359d8442572ca05dcb03a7ba27f5fd108cb1b6a52a19cae936c0db15
+size 23782

edited_dataframe.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94f49243e7dbf2e2e647f2399d5d0a3126f89b0edf067c29635c1a68efe26d7d
+size 2315

filtered_variables.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9071c5d5af0dcb3f1f79f5d7a736caeadeb68a3e3da309bc27b3aaa8ea17b0f4
+size 1264