diff --git a/API_data/API_cimb.xlsx b/API_data/API_cimb.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..4bacf2f536d519f4f08039394a741913fa5a5b6e Binary files /dev/null and b/API_data/API_cimb.xlsx differ diff --git a/API_data/API_nedbank.xlsx b/API_data/API_nedbank.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..ff5a88c2ed77da92afcd65cc288a0734a7ca2f49 Binary files /dev/null and b/API_data/API_nedbank.xlsx differ diff --git a/API_data/API_willbank.xlsx b/API_data/API_willbank.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..8386f3a3fc23c3301c5cc82cf538e195ccddb80a Binary files /dev/null and b/API_data/API_willbank.xlsx differ diff --git a/DB/User.db b/DB/User.db index 842886d125010f1e9369abd878bb8b3718df5807..37ef1590654330a27b9a31da412c774543a6b53c 100644 Binary files a/DB/User.db and b/DB/User.db differ diff --git a/Full_Logo_Blue.jpeg b/Full_Logo_Blue.jpeg deleted file mode 100644 index c395e595628574787d3f1d85e68d875710752d5b..0000000000000000000000000000000000000000 Binary files a/Full_Logo_Blue.jpeg and /dev/null differ diff --git a/Full_Logo_Blue.jpg b/Full_Logo_Blue.jpg deleted file mode 100644 index be3d65b3a460597e02daca1440cc9cfac9f75ea8..0000000000000000000000000000000000000000 Binary files a/Full_Logo_Blue.jpg and /dev/null differ diff --git a/Full_Logo_Blue.png b/Full_Logo_Blue.png deleted file mode 100644 index d66c532e6892e9c1cfbb706732c73f0e30b9ec37..0000000000000000000000000000000000000000 Binary files a/Full_Logo_Blue.png and /dev/null differ diff --git a/Full_Logo_Vibrant_Turquoise.png b/Full_Logo_Vibrant_Turquoise.png deleted file mode 100644 index 3af8ae3160b94de5114b1799536c48a93daaafe3..0000000000000000000000000000000000000000 Binary files a/Full_Logo_Vibrant_Turquoise.png and /dev/null differ diff --git a/Home.py b/Home.py index 119cd7f7356428627e113039b21c4c15342cda07..14049e8a73965a0850bd50e3ff627825a22e531f 100644 --- a/Home.py +++ b/Home.py @@ -14,6 +14,7 @@ import pandas as pd import pickle from pathlib import Path import re + st.set_page_config(layout="wide") load_local_css("styles.css") set_header() @@ -30,17 +31,26 @@ conn = sqlite3.connect(database_file, check_same_thread=False) c = conn.cursor() +def get_excel_files(directory="API_data"): + excel_files = {} + for filename in os.listdir(directory): + if filename.endswith(".xlsx"): + file_path = os.path.join(directory, filename) + file_key = os.path.splitext(filename)[0] # Remove the .xlsx extension + excel_files[file_key] = file_path + return excel_files + def update_summary_df(): - #print("[DEBUG]: Running update_summary_df") - - """ Function to fetch the project details everytime user - changes the slection box this function is being called on change in - username select box """ + # print("[DEBUG]: Running update_summary_df") + + """Function to fetch the project details everytime user + changes the slection box this function is being called on change in + username select box""" # Execute a SQL query to select distinct project names, the last edited page, # and the last updated time from the 'sessions' table where the owner matches the user's name - + c.execute( """ SELECT project_name, last_edited_page, updated_time as last_updated @@ -52,12 +62,11 @@ def update_summary_df(): ) sub GROUP BY project_name """, - (st.session_state["username"],) + (st.session_state["username"],), ) # Fetch all the results of the query project_summary = c.fetchall() - # This will hold the user's owned sessions @@ -68,9 +77,9 @@ def update_summary_df(): ) # Convert the 'Modified Date' column to datetime format - project_summary_df["Modified Date"] = project_summary_df[ - "Modified Date" - ].map(lambda x: pd.to_datetime(x).date()) + project_summary_df["Modified Date"] = project_summary_df["Modified Date"].map( + lambda x: pd.to_datetime(x).date() + ) # Sort the DataFrame by 'Modified Date' in descending order session_summary_df = project_summary_df.sort_values( @@ -79,12 +88,11 @@ def update_summary_df(): session_summary_df["Last Page Modified"] = session_summary_df[ "Last Page Edited" - ].map(lambda x: re.sub(r'[_1-9]', ' ', x).replace(".py", "")) + ].map(lambda x: re.sub(r"[_1-9]", " ", x).replace(".py", "")) # Save the resulting DataFrame to the session state st.session_state["session_summary_df"] = session_summary_df - # Add a 'selected' column to the DataFrame and initialize it with False for all rows if "selected" not in st.session_state.session_summary_df.columns: @@ -99,9 +107,7 @@ def update_summary_df(): st.header("Manage Projects") -# c.execute("PRAGMA table_info(sessions);") -# st.write(c.fetchall()) users = { "ioannis": "Ioannis Papadopoulos", "sharon": "Sharon Sheng", @@ -110,12 +116,17 @@ users = { "geetha": "Geetha Krishna", "srishti": "Srishti Verma", "samkeet": "Samkeet Sangai", - "manoj": "Manoj P" + "manoj": "Manoj P", + "loveesh": "Loveesh Bhatt", + "bhavya": "Bhavya Jayantilal Kanzariya", + "pritisha": "Pritisha Punukollu", + "ashish": "Ashish Sharma", + "swarupa": "Swarupa Parepalli", } -if 'username' not in st.session_state: - st.session_state['username']='' +if "username" not in st.session_state: + st.session_state["username"] = "" # first_name_value = [key for key, value in users.items() if value == st.session_state['username']] @@ -123,34 +134,55 @@ if 'username' not in st.session_state: # first_name_value = first_name_value[0] if first_name_value else '' -first_name=st.text_input('Enter Name').lower() +first_name = st.text_input("Enter Name").lower() -if st.button('Login'): +if st.button("Login"): if first_name not in users.keys(): - st.warning('Please enter a valid name') + st.warning("Please enter a valid name") st.stop() - name=users[first_name] + name = users[first_name] - st.session_state.name=name # storing in session state + st.session_state.name = name # storing in session state - st.session_state["username"]= name + st.session_state["username"] = name - update_summary_df() #function call to fetch user saved projects + update_summary_df() # function call to fetch user saved projects - #st.success('Projects sucessfully loaded') + # st.success('Projects sucessfully loaded') -if len(first_name)==0 or first_name not in users.keys(): +if len(first_name) == 0 or first_name not in users.keys(): st.stop() - # name=st.session_state['Username'] # c.execute('Delete from sessions') # conn.commit() + +# c.execute("PRAGMA table_info(users);") +# c.execute("SELECT * FROM users;") +# st.write(c.fetchall()) + +# Delete all existing data in the users table +# c.execute("DELETE FROM users") + +# # Insert new data into the users table +# for idx, (username, full_name) in enumerate(users.items(), start=1): +# user_id = idx +# email = f"{username}@mastercard.com" +# user_type = "technical" + +# c.execute( +# "INSERT INTO users (user_id, username, email, user_type) VALUES (?, ?, ?, ?)", +# (user_id, full_name, email, user_type) +# ) + +# # Commit the changes and close the connection +# conn.commit() + if st.session_state["username"] in users.values(): if "session_summary_df" not in st.session_state: @@ -166,19 +198,22 @@ if st.session_state["username"] in users.values(): with cols1[1]: st.markdown(f"**Current Project: {st.session_state['project_name']}**") - # Execute a SQL query to select all project names from the 'sessions' table # where the owner matches the user's name - c.execute("SELECT project_name FROM sessions WHERE owner=?", (st.session_state['username'],)) + c.execute( + "SELECT project_name FROM sessions WHERE owner=?", + (st.session_state["username"],), + ) # Fetch all the results and create a list of project names user_projects = [project[0] for project in c.fetchall()] - c.execute("SELECT DISTINCT username FROM users") # Fetch all the results and create a list of usernames excluding the current user's name - allowed_users_db = [user[0] for user in c.fetchall() if user[0] != st.session_state['username']] + allowed_users_db = [ + user[0] for user in c.fetchall() if user[0] != st.session_state["username"] + ] page_name = "Home Page" @@ -186,7 +221,7 @@ if st.session_state["username"] in users.values(): # where the username matches the current user's name c.execute( "SELECT email, user_id, user_type FROM users WHERE username = ?", - (st.session_state['username'],), + (st.session_state["username"],), ) # Fetch the result of the query (assume there is only one matching row) @@ -195,20 +230,19 @@ if st.session_state["username"] in users.values(): # Unpack the fetched data into corresponding variables email, user_id, user_type = user_data - - folder_path = r"Users" user_folder_path = os.path.join(folder_path, email) if not os.path.exists(user_folder_path): os.makedirs(user_folder_path) - def dump_session_details_db(allowed_users, project_name): - 'Function to dump details of project in db when a project is created/modified/cloned ' + "Function to dump details of project in db when a project is created/modified/cloned" - created_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M") # Get the current time + created_time = datetime.datetime.now().strftime( + "%Y-%m-%d %H:%M" + ) # Get the current time session_id = str(uuid.uuid4()) # Generate a unique session ID if len(allowed_users) == 0: @@ -217,7 +251,7 @@ if st.session_state["username"] in users.values(): "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)", ( user_id, - st.session_state['username'], + st.session_state["username"], session_id, project_name, page_name, @@ -234,7 +268,7 @@ if st.session_state["username"] in users.values(): "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)", ( user_id, - st.session_state['username'], + st.session_state["username"], session_id, project_name, "1_Home.py", @@ -245,8 +279,6 @@ if st.session_state["username"] in users.values(): ) conn.commit() # Commit the transaction - - st.markdown( """ * **Delete Project:** If you wish to delete a project, select it and click 'Delete Project'. @@ -261,13 +293,12 @@ push if "selected_row_index" not in st.session_state: st.session_state["selected_row_index"] = None - def selection_change(): # Get the edited rows from the session state - #print(st.session_state['session_summary_df']) - edited_rows: dict = st.session_state['project_selection']["edited_rows"] - #print(edited_rows) - + # print(st.session_state['session_summary_df']) + edited_rows: dict = st.session_state["project_selection"]["edited_rows"] + # print(edited_rows) + # Set the selected row index in the session state st.session_state["selected_row_index"] = next(iter(edited_rows)) @@ -285,57 +316,64 @@ push ) # Reset the DataFrame index - st.session_state['session_summary_df'] = st.session_state['session_summary_df'].reset_index(drop=True) - - - + st.session_state["session_summary_df"] = st.session_state[ + "session_summary_df" + ].reset_index(drop=True) st.markdown("Select Project") - if len(st.session_state["session_summary_df"])!=0: - + if len(st.session_state["session_summary_df"]) != 0: + with st.container(): # Display an editable data table using Streamlit's data editor component - + table = st.data_editor( - st.session_state["session_summary_df"].drop(['Last Page Edited'],axis=1).reindex( - columns=['selected','Project Name','Last Page Modified','Modified Date' - ]), - hide_index=True, + st.session_state["session_summary_df"] + .drop(["Last Page Edited"], axis=1) + .reindex( + columns=[ + "selected", + "Project Name", + "Last Page Modified", + "Modified Date", + ] + ), + hide_index=True, on_change=selection_change, # Function to call when data is edited key="project_selection", # Key for the data editor component in the session state - use_container_width=True, + use_container_width=False, ) - if len(st.session_state["session_summary_df"]) > 0 and st.session_state["selected_row_index"] is not None : + if ( + len(st.session_state["session_summary_df"]) > 0 + and st.session_state["selected_row_index"] is not None + ): selected_row_index = st.session_state["session_summary_df"]["selected"] - # st.write(st.session_state['selected_row_index']) if len(selected_row_index) != 0: - - try : + + try: project_name = st.session_state["session_summary_df"].at[ st.session_state["selected_row_index"], "Project Name" ] except Exception as e: - st.session_state["selected_row_index"]=None - st.rerun() + st.session_state["selected_row_index"] = None + st.rerun() last_edited_page = st.session_state["session_summary_df"].at[ - st.session_state["selected_row_index"], "Last Page Edited" - ] + st.session_state["selected_row_index"], "Last Page Edited" + ] st.session_state["project_name"] = project_name - project_col = st.columns(2) with project_col[0]: - - if st.button('Load Project',use_container_width=True): + + if st.button("Load Project", use_container_width=True): st.session_state["project_name"] = project_name st.rerun() @@ -348,16 +386,19 @@ push with open(project_dct_path, "rb") as f: try: st.session_state["project_dct"] = pickle.load(f) - st.success('Project Loded') + st.success("Project Loded") except Exception as e: - st.warning('Something went wrong unable to load saved details / data is lost due to app refresh. Please uncheck the check box and create a new project.') + st.warning( + "Something went wrong unable to load saved details / data is lost due to app refresh. Please uncheck the check box and create a new project." + ) st.stop() - with project_col[1]: - if st.button(f"Delete Project - **{project_name}**",use_container_width=True): + if st.button( + f"Delete Project - **{project_name}**", use_container_width=True + ): project_name_to_delete = project_name st.warning( @@ -380,7 +421,9 @@ push st.rerun() except: - st.warning('Failed to Delete project try refreshing the page or try after some time') + st.warning( + "Failed to Delete project try refreshing the page or try after some time" + ) st.stop() with st.expander("Add users with access to the selected project"): @@ -395,7 +438,8 @@ push present_users = [ user[0] for user in present_users - if user[0] != st.session_state['username'] and user[0] is not None + if user[0] != st.session_state["username"] + and user[0] is not None ] present_users = None if len(present_users) == 0 else present_users @@ -407,12 +451,12 @@ push list(set(allowed_users_db) - set(present_users)), ) else: - + allowed_users = st.multiselect( "", list(set(allowed_users_db)), ) - + if st.button("Save Changes", use_container_width=True): dump_session_details_db(allowed_users, project_name) c.execute("SELECT * from sessions") @@ -423,11 +467,9 @@ push "To create a new project, Enter Project name below, select user who you want to give access of this project and click **Create New Project**" ) - project_col1 = st.columns(2) + project_col1 = st.columns(3) with project_col1[0]: - project_name = st.text_input( - "Enter Project Name", key="project_name_box" - ) + project_name = st.text_input("Enter Project Name", key="project_name_box") if project_name in user_projects: st.warning("Project already exists please enter new name") @@ -438,7 +480,14 @@ push ) allowed_users = list(allowed_users) - Create = st.button("Create New Project", use_container_width=True ) + with project_col1[2]: + API_path_dict = get_excel_files() + + api_name = st.selectbox("Select API data", API_path_dict.keys(), index=0) + api_path = API_path_dict[api_name] + + + Create = st.button("Create New Project", use_container_width=True) if Create: @@ -451,7 +500,7 @@ push st.stop() project_path = os.path.join(user_folder_path, project_name) - + if not os.path.exists(project_path): os.makedirs(project_path) @@ -476,6 +525,8 @@ push "default_df": None, "final_df": None, "edited_df": None, + "api_path": api_path, + "api_name": api_name, }, "data_validation": { "target_column": 0, @@ -551,10 +602,9 @@ push st.success("Project Created") - update_summary_df() - - st.rerun() + update_summary_df() + st.rerun() # st.header('Clone Project') @@ -562,11 +612,13 @@ push c.execute( "SELECT DISTINCT owner FROM sessions WHERE allowed_users=?", - (st.session_state['username'],), + (st.session_state["username"],), ) # owner owners = c.fetchall() - owners = [owner[0] for owner in owners if owner[0]!=st.session_state["username"]] + owners = [ + owner[0] for owner in owners if owner[0] != st.session_state["username"] + ] if len(owners) == 0: @@ -590,7 +642,7 @@ push c.execute( "SELECT project_name FROM sessions WHERE owner=? AND allowed_users = ?", - (owner, st.session_state['username']), + (owner, st.session_state["username"]), ) # available sessions for user project_names = c.fetchall() @@ -600,7 +652,6 @@ push ) owner_project_path = os.path.join(owner_folder_path, project_name) - project_name_user = st.text_input( "Enter Project Name", value=project_name_owner ) @@ -613,12 +664,9 @@ push project_path = os.path.join(user_folder_path, project_name_user) - owner_project_path = os.path.join( - owner_folder_path, project_name_owner - ) - + owner_project_path = os.path.join(owner_folder_path, project_name_owner) - if st.button("Load Project", use_container_width=True): + if st.button("Load Project", use_container_width=True, key="load_project_button_key"): if os.path.exists(project_path): @@ -637,8 +685,8 @@ push st.session_state["project_path"] = project_path - - dump_session_details_db([], project_name_user) #passing empty list for allowed users + dump_session_details_db( + [], project_name_user + ) # passing empty list for allowed users st.success("Project Cloned") st.rerun() - diff --git a/LIME_logo.png b/LIME_logo.png deleted file mode 100644 index 514b7b8fd6ee13c36476d70bbaac7558139d3626..0000000000000000000000000000000000000000 Binary files a/LIME_logo.png and /dev/null differ diff --git a/Model_Results_Pretrained.py b/Model_Results_Pretrained.py deleted file mode 100644 index f967316ab1f80818d29d5e342a1b79c63c52b7bf..0000000000000000000000000000000000000000 --- a/Model_Results_Pretrained.py +++ /dev/null @@ -1,349 +0,0 @@ -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) diff --git a/README.md b/README.md index 182ae0cf850d1430ff3d5ff8936ae5f45d669760..25068ec81fe62f0dcbe982c092584906bbcf48e1 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ --- -title: Limev5 -emoji: 🏆 +title: dev-demo +emoji: ⚖️ colorFrom: indigo colorTo: pink sdk: streamlit diff --git a/Test/overall_contributions.csv b/Test/overall_contributions.csv index b6636e845dc3f2d0adf2b4ddb5a29bffc9c65a5c..f9c6a8c721f266cc74138a8e211d2ff635141122 100644 --- a/Test/overall_contributions.csv +++ b/Test/overall_contributions.csv @@ -1,143 +1,143 @@ -bing_pmax_impressions,bing_searchlink_clicks,facebook_clicks_lag_2_adstock_0_7,google_pmax_clicks_lag_1_adstock_0_7,google_demand_generation_link_clicks_lag_2,google_search_link_clicks,Trend,date,all_form_completion,pred,google_search_link_clicks_contr,facebook_clicks_lag_2_adstock_0_7_contr,google_demand_generation_link_clicks_lag_2_contr,google_pmax_clicks_lag_1_adstock_0_7_contr,bing_searchlink_clicks_contr,bing_pmax_impressions_contr,base_contr -0.0,0.0,0.0,0.0,0.0,0.08128973660308811,1,2023-12-11,1,0.23970607446334713,3.353867094426377,0.0,0.0,0.0,0.0,0.0,-3.11416101996303 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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) - 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100644 --- a/classes.py +++ b/classes.py @@ -139,6 +139,10 @@ def numerize(n, decimal=2): ] minus_buff = n n = abs(n) + + if n < 1: + return f"{round(n/1000, decimal)}K" + for x in range(len(sci_expr)): try: if n >= sci_expr[x] and n < sci_expr[x + 1]: diff --git a/lime_img.png b/lime_img.png deleted file mode 100644 index a4191d20673fac2a5848c76184b6f577d0655976..0000000000000000000000000000000000000000 Binary files a/lime_img.png and /dev/null differ diff --git a/mastercard_logo.png b/mastercard_logo.png index 3dc4590de0d8e1d4c560c07ead40b8af7014972f..0a1775d11fd334579c1769d08adfe7ed51cb71fe 100644 Binary files a/mastercard_logo.png and b/mastercard_logo.png differ diff --git a/nedbankdata.xlsx b/nedbankdata.xlsx deleted file mode 100644 index 559a7ce3ecb9d65906571d6ed9a9b275f6a3ff0e..0000000000000000000000000000000000000000 Binary files a/nedbankdata.xlsx and /dev/null differ diff --git a/pages/10_Saved_Scenarios.py b/pages/10_Saved_Scenarios.py index 19c25dbfe3f565f19381839c588805d0917f56ef..08c0e05f51c48c9f5d9c93b55ffab7f05d3e86a5 100644 --- a/pages/10_Saved_Scenarios.py +++ b/pages/10_Saved_Scenarios.py @@ -248,11 +248,11 @@ for channel in channels_list: "ROI Optimized": format_value(optimized_roi), "MROI Actual": format_value(actual_mroi), "MROI Optimized": format_value(optimized_mroi), - f"Spends per {name_formating(metrics_name)} Actual": numerize( - spends_per_metrics_actual + f"Spends per {name_formating(metrics_name)} Actual": round( + spends_per_metrics_actual, 2 ), - f"Spends per {name_formating(metrics_name)} Optimized": numerize( - spends_per_metrics_optimized + f"Spends per {name_formating(metrics_name)} Optimized": round( + spends_per_metrics_optimized, 2 ), } ) diff --git a/pages/11_Model_Optimized_Recommendation.py b/pages/11_Model_Optimized_Recommendation.py index 1c18748e1b72cba6ed6bffa14fbd8f50bdf5d96a..fe27aa7b62734623539bebbab59b509ddb8648c5 100644 --- a/pages/11_Model_Optimized_Recommendation.py +++ b/pages/11_Model_Optimized_Recommendation.py @@ -9,7 +9,6 @@ from utilities import ( project_selection ) import pickle -import streamlit_authenticator as stauth import yaml from yaml import SafeLoader from classes import class_from_dict @@ -23,7 +22,6 @@ from utilities import update_db from collections import OrderedDict import os - st.set_page_config(layout="wide") load_local_css("styles.css") set_header() @@ -186,7 +184,7 @@ summary_df_sorted['Delta']=summary_df_sorted['Spends Actual']-summary_df_sorted[ summary_df_sorted['Delta_percent']= np.round((summary_df_sorted['Delta']) / summary_df_sorted['Spends Actual'] * 100,2) -spends_data = pd.read_excel("Overview_data_test.xlsx") +# spends_data = pd.read_excel("Overview_data_test.xlsx") st.header("Optimized Spends Overview") diff --git a/pages/12_Help.py b/pages/12_Help.py index 175616ab3c880681723020de1b554369f42a0ce0..77b0f1bb2ecc75e5b182bfad03220d3c1088fa58 100644 --- a/pages/12_Help.py +++ b/pages/12_Help.py @@ -1,8 +1,9 @@ # Importing necessary libraries import streamlit as st +import base64 from utilities import set_header, load_local_css -import base64 + st.set_page_config( page_title="Model Build", page_icon=":shark:", diff --git a/pages/1_Data_Import.py b/pages/1_Data_Import.py index 5cb611e5b03c723162d4873e07464c46708ad7ef..a6797709f6c19dc9ef1e2efee930bcf66fb35cb2 100644 --- a/pages/1_Data_Import.py +++ b/pages/1_Data_Import.py @@ -384,6 +384,7 @@ if "username" in st.session_state and st.session_state["username"] is not None: def categorize_column(column_name): # Define keywords for each category internal_keywords = [ + "Internal", "Price", "Discount", "product_price", @@ -396,6 +397,7 @@ if "username" in st.session_state and st.session_state["username"] is not None: "expense", ] exogenous_keywords = [ + "Exogenous", "GDP", "Tax", "Inflation", @@ -422,6 +424,9 @@ if "username" in st.session_state and st.session_state["username"] is not None: ] else: + for keyword in ["Response", "Metric"]: + if keyword.lower() in column_name.lower(): + return "Response Metrics" for keyword in ["Spend", "Cost"]: if keyword.lower() in column_name.lower(): return "Spends" @@ -488,23 +493,34 @@ if "username" in st.session_state and st.session_state["username"] is not None: # Function to reads an API into a DataFrame, parsing specified columns as datetime # @st.cache_resource(show_spinner=False) - def read_API_data(): - return pd.read_excel( - r"nedbankdata.xlsx", - parse_dates=["Date"], - ) + def read_API_data(project_folder_path, file_path, file_name): + # Paths using os.path + file_path_os = os.path.join(os.getcwd(), "API_data", file_name) + project_folder_path_os = os.path.normpath(project_folder_path) + + # Construct the full path of the file in the project folder + project_file_path = os.path.join(project_folder_path_os, file_name) + + # Check if the file with the same name exists in the project path + if os.path.exists(project_file_path): + # If the file exists, load and return the existing file + return pd.read_excel(project_file_path, parse_dates=["Date"]) + else: + # If the file does not exist, read the new file + data = pd.read_excel(file_path_os, parse_dates=["Date"]) - # return pd.read_excel( - # r"upf_data_converted.xlsx", - # parse_dates=["Date"], - # ) + # Save the new file to the project folder + data.to_excel(project_file_path, index=False) + + # Return the data + return data # Function to set the 'Panel_1_Panel_2_Selected' session state variable to False def set_Panel_1_Panel_2_Selected_false(): st.session_state["Panel_1_Panel_2_Selected"] = False - # restoring project_dct to default values when user modify any widjets + # Restoring project_dct to default values when user modify any widgets st.session_state["project_dct"]["data_import"]["edited_stats_df"] = None st.session_state["project_dct"]["data_import"]["merged_df"] = None st.session_state["project_dct"]["data_import"]["missing_stats_df"] = None @@ -519,8 +535,9 @@ if "username" in st.session_state and st.session_state["username"] is not None: def save_to_pickle(file_path, final_df, bin_dict): # Open the file in write-binary mode and dump the objects with open(file_path, "wb") as f: - pickle.dump({"final_df": final_df, "bin_dict": bin_dict}, f) - # Data is now saved to file + pickle.dump( + {"final_df": final_df, "bin_dict": bin_dict}, f + ) # Data is now saved to file # Function to processes the merged_df DataFrame based on operations defined in edited_df @st.cache_resource(show_spinner=False) @@ -638,7 +655,11 @@ if "username" in st.session_state and st.session_state["username"] is not None: ######################################################################################################################################################### # Read the Excel file, parsing 'Date' column as datetime - main_df = read_API_data() + main_df = read_API_data( + project_folder_path=st.session_state["project_path"], + file_path=st.session_state["project_dct"]["data_import"]["api_path"], + file_name=st.session_state["project_dct"]["data_import"]["api_name"] + ".xlsx", + ) # Convert all column names to lowercase main_df.columns = main_df.columns.str.lower().str.strip() @@ -730,7 +751,7 @@ if "username" in st.session_state and st.session_state["username"] is not None: st.stop() # Add the column to the set if it's not already there unique_numeric_columns.add(column) - + # Check if the column name is valid if not valid_column_pattern.match(column): st.warning( @@ -1225,7 +1246,6 @@ if "username" in st.session_state and st.session_state["username"] is not None: st.session_state["bin_dict"], ) - st.session_state["project_dct"]["data_import"][ "edited_stats_df" ] = edited_stats_df @@ -1567,7 +1587,6 @@ if "username" in st.session_state and st.session_state["username"] is not None: group_dict[group_name] = selected_channels channels_added.update(selected_channels) - ###################################### Group Media Channels ###################################### # Display the dictionary @@ -1665,11 +1684,14 @@ if "username" in st.session_state and st.session_state["username"] is not None: ) ## ADD Exog vars to channels & save channels - if len(category_dict['Exogenous'])>0: - for exog_var in category_dict['Exogenous']: - group_dict[exog_var]=[exog_var] - with open(os.path.join(st.session_state["project_path"],"channel_groups.pkl"), "wb") as f: - pickle.dump(group_dict,f) + if len(category_dict["Exogenous"]) > 0: + for exog_var in category_dict["Exogenous"]: + group_dict[exog_var] = [exog_var] + with open( + os.path.join(st.session_state["project_path"], "channel_groups.pkl"), + "wb", + ) as f: + pickle.dump(group_dict, f) st.session_state["project_dct"]["data_import"][ "edited_stats_df" @@ -1693,4 +1715,3 @@ if "username" in st.session_state and st.session_state["username"] is not None: st.session_state["project_dct"]["data_import"]["edited_df"] = edited_df st.toast("💾 Saved Successfully!") - diff --git a/pages/2_Data_Validation_and_Insights.py b/pages/2_Data_Validation_and_Insights.py index 7151ca2286b3db03a89a4612f43a063587982683..14f6218e2a7233b3c816942233d5ed1ad0d31eda 100644 --- a/pages/2_Data_Validation_and_Insights.py +++ b/pages/2_Data_Validation_and_Insights.py @@ -11,9 +11,6 @@ import sqlite3 from utilities import update_db import re - - - st.set_page_config( page_title="Data Validation", page_icon=":shark:", @@ -258,13 +255,11 @@ if 'username' in st.session_state and st.session_state['username'] is not None: in st.session_state["selected_feature"] ] - - if len(spends_feature) == 0: - - st.warning("No 'spends' variable available for the selected metric in the data. Please ensure the columns are properly named.or select them in the provided selecttion box") - with spends_col[0]: - st.write(f'Selected "{spends_feature[0]}" as the corresponding spends variable automatically. If this is incorrect, please click the checkbox to change the variable.') + if len(spends_feature) == 0: + st.warning("No 'spends' variable available for the selected metric in the data. Please ensure the columns are properly named.or select them in the provided selecttion box") + else: + st.write(f'Selected "{spends_feature[0]}" as the corresponding spends variable automatically. If this is incorrect, please click the checkbox to change the variable.') with spends_col[1]: if len(spends_feature)==0 or st.checkbox('Select "Spends" variable for CPM and CPC calculation'): diff --git a/pages/3_Transformations.py b/pages/3_Transformations.py index 4b12d15ff3b6b833092e5d9e6f8da8ec63d744be..a7f2883ca0b0a3bdc31b3a1783641e4a3ad53529 100644 --- a/pages/3_Transformations.py +++ b/pages/3_Transformations.py @@ -14,6 +14,7 @@ import sqlite3 import numpy as np import pandas as pd from utilities import update_db +import plotly.graph_objects as go from utilities import set_header, load_local_css, update_db, project_selection @@ -50,12 +51,10 @@ if "username" in st.session_state and st.session_state["username"] is not None: data = pickle.load(f) # Accessing the loaded objects - final_df_loaded = data["final_df"] bin_dict_loaded = data["bin_dict"] - # final_df_loaded.to_csv("Test/final_df_loaded.csv",index=False) - # Initialize session state==- + # Initialize session state if "transformed_columns_dict" not in st.session_state: st.session_state["transformed_columns_dict"] = {} # Default empty dictionary @@ -451,7 +450,7 @@ if "username" in st.session_state and st.session_state["username"] is not None: def apply_moving_average(df, window_size): return df.rolling(window=window_size).mean() - # Function to apply Saturation transformation + # Function to apply Moving Average transformation def apply_saturation(df, saturation_percent_100): # Convert saturation percentage from 100-based to fraction saturation_percent = saturation_percent_100 / 100.0 @@ -470,9 +469,12 @@ if "username" in st.session_state and st.session_state["username"] is not None: denominator, 1e-9 ) # Avoid division by zero with a small constant - # Apply the saturation transformation + # Apply the saturation transformation with safeguard for division by zero transformed_series = df.apply( - lambda x: (1 / (1 + (saturation_point / x) ** steepness)) * x + lambda x: ( + 1 / (1 + (saturation_point / (x if x != 0 else 1e-9)) ** steepness) + ) + * x ) return transformed_series @@ -707,6 +709,25 @@ if "username" in st.session_state and st.session_state["username"] is not None: # If no transformations were applied, use the original DataFrame final_df = df_main + # Find columns with '@' in their names + columns_with_at = [col for col in final_df.columns if "@" in col] + + # Create a set of columns to drop + columns_to_drop = set() + + # Iterate through columns with '@' to find shorter names to drop + for col in columns_with_at: + base_name = col.split("@")[0] + for other_col in columns_with_at: + if other_col.startswith(base_name) and len(other_col.split("@")) > len( + col.split("@") + ): + columns_to_drop.add(col) + break + + # Drop the identified columns from the DataFrame + final_df.drop(columns=list(columns_to_drop), inplace=True) + return final_df # Function to infers the granularity of the date column in a DataFrame @@ -879,8 +900,67 @@ if "username" in st.session_state and st.session_state["username"] is not None: pickle.dump({"final_df_transformed": final_df}, f) # Data is now saved to file - if st.button("Accept and Save", use_container_width=True): + ######################################################################################################################################################### + # Correlation Plot + ######################################################################################################################################################### + + # Filter out the 'date' column + variables = [col for col in final_df.columns if col.lower() != "date"] + # Expander with multiselect + with st.expander("Transformed Variable Correlation Plot"): + selected_vars = st.multiselect( + "Choose variables for correlation plot:", variables + ) + + # Calculate correlation + if selected_vars: + corr_df = final_df[selected_vars].corr() + + # Prepare text annotations with 2 decimal places + annotations = [] + for i in range(len(corr_df)): + for j in range(len(corr_df.columns)): + annotations.append( + go.layout.Annotation( + text=f"{corr_df.iloc[i, j]:.2f}", + x=corr_df.columns[j], + y=corr_df.index[i], + showarrow=False, + font=dict(color="black"), + ) + ) + + # Plotly correlation plot using go + heatmap = go.Heatmap( + z=corr_df.values, + x=corr_df.columns, + y=corr_df.index, + colorscale="RdBu", + zmin=-1, + zmax=1, + ) + + layout = go.Layout( + title="Transformed Variable Correlation Plot", + xaxis=dict(title="Variables"), + yaxis=dict(title="Variables"), + width=1000, + height=1000, + annotations=annotations, + ) + + fig = go.Figure(data=[heatmap], layout=layout) + + st.plotly_chart(fig) + else: + st.write("Please select at least one variable to plot.") + + ######################################################################################################################################################### + # Accept and Save + ######################################################################################################################################################### + + if st.button("Accept and Save", use_container_width=True): save_to_pickle( os.path.join(st.session_state["project_path"], "final_df_transformed.pkl"), st.session_state["final_df"], diff --git a/pages/4_Model_Build.py b/pages/4_Model_Build.py index e876339e0b0885e70bd5eaf0bebc1b0092f5e9db..122068c0c7013808f77f47064f305665488c7df2 100644 --- a/pages/4_Model_Build.py +++ b/pages/4_Model_Build.py @@ -433,7 +433,7 @@ if "username" in st.session_state and st.session_state["username"] is not None: .iloc[:-1] ) top_3_correlated_features.append( - list(corr_df.sort_values(ascending=False).head(3).index) + list(corr_df.sort_values(ascending=False).head(2).index) ) flattened_list = [item for sublist in top_3_correlated_features for item in sublist] @@ -673,7 +673,7 @@ if "username" in st.session_state and st.session_state["username"] is not None: st.write( f'Out of {st.session_state["iterations"]} iterations : {len(st.session_state["Model_results"]["Model_iteration"])} valid models' ) - st.write(time.time()-time_start) + # st.write(time.time()-time_start) return st.session_state["Model_results"] diff --git a/pages/6_AI_Model_Results.py b/pages/6_AI_Model_Results.py index 879e1017d1f3da28690c57dd67bbf7aeff290292..6f47f29e9354a2febe248ae6fc4b79437f211591 100644 --- a/pages/6_AI_Model_Results.py +++ b/pages/6_AI_Model_Results.py @@ -511,7 +511,7 @@ if 'username' in st.session_state and st.session_state['username'] is not None: contribution_selections = st.multiselect( "Select the Response Metrics to compare contributions", options, - default=default_options, + default=options[0], format_func=format_display, ) trace_data = [] diff --git a/pages/9_Scenario_Planner.py b/pages/9_Scenario_Planner.py index 8f045167e04df0e3578d7674f7634d5387160aa2..5f9b46deba0f7d97e35362c8df36be7d099b49f1 100644 --- a/pages/9_Scenario_Planner.py +++ b/pages/9_Scenario_Planner.py @@ -114,6 +114,15 @@ def get_s_curve_params( "response_curve_params" ] = s_curve_param + # Save the updated data back to the pickle file + try: + with open(modified_pickle_file_path, "wb") as file: + pickle.dump(data, file) + except: + st.toast("Failed to Load/Update. Tool reset to default settings.", icon="⚠️") + reset_scenario() + return + # Update the 'power' parameter in the modified S-curve parameters with the original 'power' value s_curve_param["power"] = power @@ -211,6 +220,9 @@ def optimizer( # Initial guess for the optimization initial_guess = np.array(actual_spends) + # Calculate xtol as 0.1% of the minimum of spends + xtol = max(10, 0.001 * np.min(actual_spends)) + # Perform the optimization using 'trust-constr' method result = minimize( objective_fun, @@ -220,8 +232,8 @@ def optimizer( bounds=bounds, options={ "disp": True, # Display the optimization process - "xtol": 1e-8, # Dynamic step size tolerance - "maxiter": 10000, # Maximum number of iterations + "xtol": xtol, # Dynamic step size tolerance + "maxiter": 1e5, # Maximum number of iterations }, ) @@ -239,10 +251,56 @@ def optimizer( return optimized_spends, result.success +# Function to calculate achievable targets at lower and upper spend bounds +@st.cache_data(show_spinner=False) +def max_target_achievable( + channels_spends, + s_curve_params, + channels_proportion, + modified_scenario_data, + bounds_dict, +): + # Extract channel names and corresponding actual spends + channels = list(channels_spends.keys()) + actual_spends = np.array(list(channels_spends.values())) + num_channels = len(actual_spends) + + # Bounds for each channel's spend + lower_spends, upper_spends = [], [] + for i in range(num_channels): + lower_spends.append(actual_spends[i] * (1 + bounds_dict[channels[i]][0] / 100)) + upper_spends.append(actual_spends[i] * (1 + bounds_dict[channels[i]][1] / 100)) + + # Calculate achievable targets at lower and upper spend bounds + lower_achievable_target = get_total_contribution( + lower_spends, + channels, + s_curve_params, + channels_proportion, + modified_scenario_data, + ) + upper_achievable_target = get_total_contribution( + upper_spends, + channels, + s_curve_params, + channels_proportion, + modified_scenario_data, + ) + + # Return achievable targets with ±0.1% safety margin + return max(0, 1.001 * lower_achievable_target), 0.999 * upper_achievable_target + + # Function to check if number is in valid format def is_valid_number_format(number_str): # Check for None if number_str is None: + # Store the message details in session state for invalid input + st.session_state.message_display = { + "type": "warning", + "message": "Invalid input: Please enter a valid number.", + "icon": "⚠️", + } return False # Define the valid suffixes @@ -250,6 +308,12 @@ def is_valid_number_format(number_str): # Check for negative numbers if number_str[0] == "-": + # Store the message details in session state for invalid input + st.session_state.message_display = { + "type": "warning", + "message": "Invalid input: Please enter a valid number.", + "icon": "⚠️", + } return False # Check if the string ends with a digit @@ -261,8 +325,20 @@ def is_valid_number_format(number_str): if number >= 0: return True else: + # Store the message details in session state for invalid input + st.session_state.message_display = { + "type": "warning", + "message": "Invalid input: Please enter a valid number.", + "icon": "⚠️", + } return False except ValueError: + # Store the message details in session state for invalid input + st.session_state.message_display = { + "type": "warning", + "message": "Invalid input: Please enter a valid number.", + "icon": "⚠️", + } return False # Check if the string ends with a valid suffix @@ -276,11 +352,28 @@ def is_valid_number_format(number_str): if number >= 0: return True else: + # Store the message details in session state for invalid input + st.session_state.message_display = { + "type": "warning", + "message": "Invalid input: Please enter a valid number.", + "icon": "⚠️", + } return False except ValueError: + # Store the message details in session state for invalid input + st.session_state.message_display = { + "type": "warning", + "message": "Invalid input: Please enter a valid number.", + "icon": "⚠️", + } return False # If neither condition is met, return False + st.session_state.message_display = { + "type": "warning", + "message": "Invalid input: Please enter a valid number.", + "icon": "⚠️", + } return False @@ -307,9 +400,6 @@ def convert_to_float(number_str): # Function to update absolute_channel_spends change -import math - - def absolute_channel_spends_change( channel_key, channel_spends_actual, @@ -364,10 +454,10 @@ def absolute_channel_spends_change( ] ) - # # Update total spends - # data[metrics_selected][panel_selected][ - # "modified_total_spends" - # ] = total_channel_spends + # Update total spends + data[metrics_selected][panel_selected][ + "modified_total_spends" + ] = total_channel_spends # Save the updated data back to the pickle file try: @@ -377,6 +467,13 @@ def absolute_channel_spends_change( st.toast("Failed to Load/Update. Tool reset to default settings.", icon="⚠️") reset_scenario() return + else: + # Store the message details in session state + st.session_state.message_display = { + "type": "warning", + "message": "Keep total spending within ±50% of the original value.", + "icon": "⚠️", + } # Function to update percentage_channel_spends change @@ -655,6 +752,14 @@ def bound_change( new_upper_bound = st.session_state[f"{channel_key}_upper_key"] if new_lower_bound > new_upper_bound: new_bounds = [-10, 10] + + # Store the message details in session state + st.session_state.message_display = { + "type": "warning", + "message": "Lower bound cannot be greater than Upper bound.", + "icon": "⚠️", + } + else: new_bounds = [new_lower_bound, new_upper_bound] @@ -1052,13 +1157,15 @@ def bound_type_change(modified_pickle_file_path): # Function to format the numbers with decimal -def format_value(value): - return f"{value:.4f}" if value < 1 else f"{numerize(value, 1)}" +def format_value(input_value): + value = abs(input_value) + return f"{input_value:.4f}" if value < 1 else f"{numerize(input_value, 1)}" # Function to format the numbers with decimal -def round_value(value): - return round(value, 1) if value > 1 else round(value, 3) +def round_value(input_value): + value = abs(input_value) + return round(input_value, 4) if value < 1 else round(input_value, 1) # Function to generate ROI and MROI plots for all channels @@ -1194,6 +1301,10 @@ def roi_mori_plot(channel_roi_mroi): def save_scenario( scenario_dict, metrics_selected, panel_selected, optimization_goal, channel_roi_mroi ): + # Remove extra space at start and ends + if st.session_state["scenario_name"] is not None: + st.session_state["scenario_name"] = st.session_state["scenario_name"].strip() + if ( st.session_state["scenario_name"] is None or st.session_state["scenario_name"] == "" @@ -1239,6 +1350,16 @@ def save_scenario( reset_scenario() return + # Check if the name is already taken + if st.session_state["scenario_name"] in saved_scenarios_dict.keys(): + # Store the message details in session state + st.session_state.message_display = { + "type": "warning", + "message": "Name already exists. Please change the name or delete the existing scenario from the Saved Scenario page.", + "icon": "⚠️", + } + return + # Update the dictionary with the new scenario saved_scenarios_dict[st.session_state["scenario_name"]] = scenario_dict @@ -1246,18 +1367,19 @@ def save_scenario( try: with open(saved_scenarios_dict_path, "wb") as f: pickle.dump(saved_scenarios_dict, f) + + # Store the message details in session state + st.session_state.message_display = { + "type": "success", + "message": f"Scenario '{st.session_state.scenario_name}' has been successfully saved!", + "icon": "💾", + } + except: st.toast("Failed to Load/Update. Tool reset to default settings.", icon="⚠️") reset_scenario() return - # Store the message details in session state - st.session_state.message_display = { - "type": "success", - "message": f"Scenario '{st.session_state.scenario_name}' has been successfully saved!", - "icon": "💾", - } - # Clear the scenario name input st.session_state["scenario_name"] = "" @@ -1380,6 +1502,53 @@ def check_optimization_success( return True, "Optimization successful.", "💸" +# Function to check if the optimization target is achievable within the given bounds +@st.cache_data(show_spinner=False) +def check_target_achievability( + optimize_allow, + fixed_target, + lower_achievable_target, + upper_achievable_target, + total_absolute_target, +): + # Format the messages with appropriate numerization and naming + minimum_achievable_message = f"Minimum achievable {fixed_target} with the given spends and bounds is {numerize(lower_achievable_target)}" + maximum_achievable_message = f"Maximum achievable {fixed_target} with the given spends and bounds is {numerize(upper_achievable_target)}" + + # Check if the target is within achievable bounds + if (lower_achievable_target > total_absolute_target) or ( + upper_achievable_target < total_absolute_target + ): + if lower_achievable_target > total_absolute_target: + # Update session state with the minimum achievable error message + st.session_state.message_display = { + "type": "error", + "message": minimum_achievable_message, + "icon": "🔼", + } + else: + # Update session state with the maximum achievable error message + st.session_state.message_display = { + "type": "error", + "message": maximum_achievable_message, + "icon": "🔽", + } + optimize_allow = False + else: + # Reset message display if previous message matches the current scenario + if st.session_state.message_display["message"] in [ + minimum_achievable_message, + maximum_achievable_message, + ]: + st.session_state.message_display = { + "type": "success", + "message": None, + "icon": "", + } + + return optimize_allow + + # Function to display a message with the appropriate type and icon def display_message(): # Retrieve the message details from the session state @@ -1973,13 +2142,6 @@ with st.expander("Optimization Inputs", expanded=True): # Check if lower bound is greater than upper bound if lower_bound > upper_bound: - # Store the message details in session state - st.session_state.message_display = { - "type": "warning", - "message": "Lower bound cannot be greater than Upper bound.", - "icon": "⚠️", - } - lower_bound = -10 # Default lower bound upper_bound = 10 # Default upper bound @@ -2033,14 +2195,49 @@ with st.expander("Optimization Inputs", expanded=True): total_channel_spends > 1.5 * original_scenario_data["actual_total_spends"] or total_channel_spends < 0.5 * original_scenario_data["actual_total_spends"] ): - optimize_allow = False # Disable optimization if the condition is met # Store the message details in session state st.session_state.message_display = { "type": "warning", - "message": "Total channel spends should stay within ±50% of the original value.", + "message": "Keep total spending within ±50% of the original value.", "icon": "⚠️", } +if optimization_goal == "Spends": + # Get maximum achievable spends + lower_achievable_target, upper_achievable_target = 0, 0 + for channel in channel_list: + channel_spends_actual = ( + channels_spends[channel] * channels_conversion_ratio[channel] + ) + lower_achievable_target += channel_spends_actual * ( + 1 + bounds_dict[channel][0] / 100 + ) + upper_achievable_target += channel_spends_actual * ( + 1 + bounds_dict[channel][1] / 100 + ) +else: + # Get maximum achievable target metric + lower_achievable_target, upper_achievable_target = max_target_achievable( + channels_spends, + s_curve_params, + channels_proportion, + modified_scenario_data, + bounds_dict, + ) + +# Total target of selected metric +total_absolute_target = convert_to_float(total_absolute) + +# Check if the target is achievable within the specified bounds +if optimize_allow: + optimize_allow = check_target_achievability( + optimize_allow, + name_formating(optimization_goal), + lower_achievable_target, + upper_achievable_target, + total_absolute_target, + ) + # Perform the optimization if optimize_button_col.button( "Optimize", use_container_width=True, disabled=not optimize_allow @@ -2279,3 +2476,10 @@ st.button( with message_display_col: st.write("###") # Padding display_message() + + # Reset the message details in session state + st.session_state.message_display = { + "type": "success", + "message": None, + "icon": "", + } diff --git a/requirements_mastercard.txt b/requirements_mastercard.txt deleted file mode 100644 index e8a747bf25d4a41a4ebb05c78e9b20bdd8f003da..0000000000000000000000000000000000000000 --- a/requirements_mastercard.txt +++ /dev/null @@ -1,108 +0,0 @@ -altair==5.2.0 -appdirs==1.4.4 -arrow==1.2.3 -astor==0.8.1 -asttokens==2.4.0 -attrs==23.1.0 -backoff==2.2.1 -bcrypt==4.1.1 -blinker==1.7.0 -cachetools==5.3.1 -certifi==2023.5.7 -charset-normalizer==3.2.0 -click==8.1.6 -comm==0.1.3 -contourpy==1.1.0 -cycler==0.11.0 -dacite==1.8.1 -decorator==5.1.1 -et-xmlfile==1.1.0 -exceptiongroup==1.1.2 -executing==2.0.0 -fonttools==4.41.0 -gitdb==4.0.10 -GitPython==3.1.37 -greenlet==2.0.2 -htmlmin==0.1.12 -idna==3.4 -ImageHash==4.3.1 -importlib-metadata==6.8.0 -importlib-resources==6.1.0 -ipython==7.34.0 -ipywidgets==7.7.5 -jedi==0.18.2 -Jinja2==3.1.2 -joblib==1.3.1 -jsonschema==4.18.4 -jsonschema-specifications==2023.7.1 -jupyterlab-widgets==1.1.4 -kiwisolver==1.4.4 -llvmlite==0.40.1 -markdown-it-py==2.2.0 -MarkupSafe==2.1.3 -matplotlib==3.7.2 -matplotlib-inline==0.1.6 -mdurl==0.1.2 -monotonic==1.6 -multimethod==1.9.1 -networkx==3.1 -numba==0.57.1 -numpy==1.23.5 -openpyxl==3.1.2 -packaging==23.1 -pandas==2.0.3 -parso==0.8.3 -patsy==0.5.3 -phik==0.12.3 -pillow==10.1.0 -plotly==5.15.0 -prompt-toolkit==2.0.10 -protobuf==4.23.4 -psutil==5.9.5 -pure-eval==0.2.2 -pyarrow==7.0.0 -pydantic==1.10.11 -pydeck==0.8.1b0 -Pygments==2.15.1 -PyJWT==2.8.0 -pyparsing==3.0.9 -python-dateutil==2.8.2 -python-dotenv==1.0.0 -pytz==2023.3 -PyWavelets==1.4.1 -PyYAML==6.0.1 -referencing==0.30.0 -requests==2.31.0 -rich==13.6.0 -rpds-py==0.9.2 -scikit-learn==1.1.3 -scipy==1.10.1 -seaborn==0.12.2 -setuptools==65.5.0 -six==1.16.0 -smmap==5.0.1 -SQLAlchemy==2.0.19 -stack-data==0.6.3 -statsmodels==0.14.0 -streamlit==1.28.2 -tangled-up-in-unicode==0.2.0 -tenacity==8.2.3 -threadpoolctl==3.2.0 -toml==0.10.2 -toolz==0.12.0 -tornado==6.3.2 -tqdm==4.65.0 -traitlets==5.9.0 -typeguard==2.13.3 -tzdata==2023.3 -tzlocal==5.2 -urllib3==1.26.16 -validators==0.22.0 -visions==0.7.5 -watchdog==3.0.0 -wcwidth==0.2.6 -wheel==0.40.0 -widgetsnbextension==3.6.4 -ydata-profiling==4.3.1 -zipp==3.17.0 - diff --git a/summary_df.pkl b/summary_df.pkl deleted file mode 100644 index b5081e780e9b8033e4a2cb274d1028165d7a7099..0000000000000000000000000000000000000000 --- a/summary_df.pkl +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f65e428fa799be02eb64ed34f08d837d9a5e28947449fb674286a8d26425290e -size 1104 diff --git a/utilities.py b/utilities.py index 9e31908a2fb1e619d7465b3bf173e0b3d53dd65d..4e04ece725af26d787794e9e3536044df17a2520 100644 --- a/utilities.py +++ b/utilities.py @@ -187,6 +187,7 @@ file_.close() DATA_PATH = "./data" IMAGES_PATH = "./data/images_224_224" +dir def load_local_css(file_name): @@ -207,13 +208,13 @@ def load_local_css(file_name): # """, unsafe_allow_html=True) path1 = os.path.dirname(__file__) -file_1 = open(f"{path}/willbank.png", "rb") +# file_1 = open(f"{path}/willbank.png", "rb") -contents1 = file_1.read() +# contents1 = file_1.read() -data_url1 = base64.b64encode(contents1).decode("utf-8") +# data_url1 = base64.b64encode(contents1).decode("utf-8") -file_1.close() +# file_1.close() DATA_PATH1 = "./data" @@ -1189,14 +1190,19 @@ def send_email(email, message): def project_selection(): users = { - "ioannis": "Ioannis Papadopoulos", - "sharon": "Sharon Sheng", - "herman": "Herman Kwong", - "ismail": "Ismail Mohammed", - "geetha": "Geetha Krishna", - "srishti": "Srishti Verma", - "samkeet": "Samkeet Sangai", - "manoj": "Manoj P", + "ioannis": "Ioannis Papadopoulos", + "sharon": "Sharon Sheng", + "herman": "Herman Kwong", + "ismail": "Ismail Mohammed", + "geetha": "Geetha Krishna", + "srishti": "Srishti Verma", + "samkeet": "Samkeet Sangai", + "manoj": "Manoj P", + "loveesh": "Loveesh Bhatt", + "bhavya": "Bhavya Jayantilal Kanzariya", + "pritisha": "Pritisha Punukollu", + "ashish": "Ashish Sharma", + "swarupa": "Swarupa Parepalli", } first_name = st.text_input("Enter Name").lower() diff --git a/utilities_with_panel.py b/utilities_with_panel.py index 8417d5639beb9c1d74693897392096bc0f8a1a25..b236dea45953d588e235670d80dd6a9ea1501d49 100644 --- a/utilities_with_panel.py +++ b/utilities_with_panel.py @@ -173,13 +173,13 @@ def load_local_css(file_name): # """, unsafe_allow_html=True) path1 = os.path.dirname(__file__) -file_1 = open(f"{path}/willbank.png", "rb") +# file_1 = open(f"{path}/willbank.png", "rb") -contents1 = file_1.read() +# contents1 = file_1.read() -data_url1 = base64.b64encode(contents1).decode("utf-8") +# data_url1 = base64.b64encode(contents1).decode("utf-8") -file_1.close() +# file_1.close() DATA_PATH1 = "./data" diff --git a/willbank.png b/willbank.png deleted file mode 100644 index a8d64e778aa30096a15d1300f212e1c76dfcab30..0000000000000000000000000000000000000000 Binary files a/willbank.png and /dev/null differ