test5

response_curves_model_quality.py

@@ -1,244 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.optimize import curve_fit
-from sklearn.preprocessing import MinMaxScaler
-import warnings
-warnings.filterwarnings("ignore")
-import plotly.graph_objects as go
-
-## reading input data
-df= pd.read_csv('response_curves_input_file.csv')
-df.dropna(inplace=True)
-df['Date'] = pd.to_datetime(df['Date'])
-df.reset_index(inplace=True)
-
-channel_cols = [
-    'BroadcastTV',
-    'CableTV',
-    'Connected&OTTTV',
-    'DisplayProspecting',
-    'DisplayRetargeting',
-        'Video',
-    'SocialProspecting',
-    'SocialRetargeting',
-    'SearchBrand',
-    'SearchNon-brand',
-    'DigitalPartners',
-    'Audio',
-    'Email']
-spend_cols = [
-'tv_broadcast_spend', 
-'tv_cable_spend',
-    'stream_video_spend', 
-    'disp_prospect_spend', 
-    'disp_retarget_spend',
-       'olv_spend', 
-        'social_prospect_spend', 
-        'social_retarget_spend',
-       'search_brand_spend', 
-    'search_nonbrand_spend', 
-    'cm_spend',
-       'audio_spend',
-        'email_spend']
-prospect_cols = [
-        'Broadcast TV_Prospects',
-       'Cable TV_Prospects', 
-    'Connected & OTT TV_Prospects', 
-       'Display Prospecting_Prospects', 
-    'Display Retargeting_Prospects',
-    'Video_Prospects',
-       'Social Prospecting_Prospects', 
-    'Social Retargeting_Prospects',
-       'Search Brand_Prospects', 
-    'Search Non-brand_Prospects',
-       'Digital Partners_Prospects',
-    'Audio_Prospects', 
-    'Email_Prospects']
-
-def hill_equation(x, Kd, n):
-    return x**n / (Kd**n + x**n)
-
-
-def hill_func(x_data,y_data,x_minmax,y_minmax):
-    # Fit the Hill equation to the data
-    initial_guess = [1, 1]  # Initial guess for Kd and n
-    params, covariance = curve_fit(hill_equation, x_data, y_data, p0=initial_guess,maxfev = 1000)
-
-    # Extract the fitted parameters
-    Kd_fit, n_fit = params
-    
-
-    # Generate y values using the fitted parameters
-    y_fit = hill_equation(x_data, Kd_fit, n_fit)
-
-    x_data_inv = x_minmax.inverse_transform(np.array(x_data).reshape(-1,1))
-    y_data_inv = y_minmax.inverse_transform(np.array(y_data).reshape(-1,1))
-    y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
-
-#     # Plot the original data and the fitted curve
-#     plt.scatter(x_data_inv, y_data_inv, label='Actual Data')
-#     plt.scatter(x_data_inv, y_fit_inv, label='Fit Data',color='red')
-#     # plt.line(x_data_inv, y_fit_inv, label=f'Fitted Hill Equation (Kd={Kd_fit:.2f}, n={n_fit:.2f})', color='red')
-#     plt.xlabel('Ligand Concentration')
-#     plt.ylabel('Fraction of Binding')
-#     plt.title('Fitting Hill Equation to Data')
-#     plt.legend()
-#     plt.show()
-
-    return y_fit,y_fit_inv,Kd_fit, n_fit
-
-def data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext): 
-    fit_col = 'Fit_Data_'+channel
-    plot_df = pd.DataFrame()
-   
-    plot_df[f'{channel}_Spends'] = X
-
-    plot_df['Date'] = df['Date']
-    plot_df['MAT'] = df['MAT']
-    
-    
-
-    y_fit_inv_v2 = []
-    for i in range(len(y_fit_inv)):
-        y_fit_inv_v2.append(y_fit_inv[i][0])
-        
-    plot_df[fit_col] = y_fit_inv_v2
-    
-#     adding extra data
-
-    y_fit_inv_v2_ext = []
-    for i in range(len(y_fit_inv_ext)):
-        y_fit_inv_v2_ext.append(y_fit_inv_ext[i][0])
-    
-#     print(x_ext_data)
-    ext_df = pd.DataFrame()
-    ext_df[f'{channel}_Spends'] = x_ext_data
-    ext_df[fit_col] = y_fit_inv_v2_ext
-    
-    ext_df['Date'] =  [
-                    np.datetime64('1950-01-01'),
-                    np.datetime64('1950-06-15'),
-                    np.datetime64('1950-12-31')
-                ]
-
-    ext_df['MAT'] = ["ext","ext","ext"]
-    
-    print(ext_df)
-    plot_df= plot_df.append(ext_df)
-    return plot_df
-
-def input_data(df,spend_col,prospect_col):  
-    X = np.array(df[spend_col].tolist())
-    y = np.array(df[prospect_col].tolist())
-
-    x_minmax = MinMaxScaler()
-    x_scaled = x_minmax.fit_transform(df[[spend_col]])
-    x_data = []
-    for i in range(len(x_scaled)):
-        x_data.append(x_scaled[i][0])
-
-    y_minmax = MinMaxScaler()
-    y_scaled =  y_minmax.fit_transform(df[[prospect_col]])
-    y_data = []
-    for i in range(len(y_scaled)):
-        y_data.append(y_scaled[i][0])
-
-    return X,y,x_data,y_data,x_minmax,y_minmax
-
-def extend_s_curve(x_max,x_minmax,y_minmax, Kd_fit, n_fit):
-    print(x_max)
-    x_ext_data = [x_max*1.2,x_max*1.3,x_max*1.5]
-#     x_ext_data = [1500000,2000000,2500000]
-#     x_ext_data = [x_max+100,x_max+200,x_max+5000]
-    x_scaled = x_minmax.transform(pd.DataFrame(x_ext_data))
-    x_data = []
-    for i in range(len(x_scaled)):
-        x_data.append(x_scaled[i][0])
-        
-    print(x_data)
-    y_fit = hill_equation(x_data, Kd_fit, n_fit)
-    y_fit_inv = y_minmax.inverse_transform(np.array(y_fit).reshape(-1,1))
-    
-    return x_ext_data,y_fit_inv
-    
-def fit_data(spend_col,prospect_col,channel):
-    ### getting k and n parameters
-    temp_df = df[df[spend_col]>0]
-    temp_df.reset_index(inplace=True)
-
-    X,y,x_data,y_data,x_minmax,y_minmax = input_data(temp_df,spend_col,prospect_col)
-    y_fit, y_fit_inv, Kd_fit, n_fit = hill_func(x_data,y_data,x_minmax,y_minmax)
-    print('k: ',Kd_fit)
-    print('n: ', n_fit)
-    
-    ##### extend_s_curve
-    x_ext_data,y_fit_inv_ext=  extend_s_curve(temp_df[spend_col].max(),x_minmax,y_minmax, Kd_fit, n_fit)
-    
-    plot_df = data_output(channel,X,y,y_fit_inv,x_ext_data,y_fit_inv_ext)
-    return plot_df
-
-plotly_data = fit_data(spend_cols[0],prospect_cols[0],channel_cols[0])
-plotly_data.tail()
-
-for i in range(1,13):
-    print(i)
-    pdf =  fit_data(spend_cols[i],prospect_cols[i],channel_cols[i])
-    plotly_data = plotly_data.merge(pdf,on = ["Date","MAT"],how = "left")
-    
-def response_curves(channel,x_modified,y_modified):
-
-    # Initialize the Plotly figure
-    fig = go.Figure()
-
-    x_col = (channel+"_Spends").replace('\xa0', '')
-    y_col = ("Fit_Data_"+channel).replace('\xa0', '')
-
-    # fig.add_trace(go.Scatter(
-    #     x=plotly_data[x_col],
-    #     y=plotly_data[y_col],
-    #     mode='markers',
-    #     name=x_col.replace('_Spends', '')
-    # ))
-
-    fig.add_trace(go.Scatter(
-        x=plotly_data.sort_values(by=x_col, ascending=True)[x_col],
-        y=plotly_data.sort_values(by=x_col, ascending=True)[y_col],
-        mode='lines+markers',
-        name=x_col.replace('_Spends', '')
-    ))
-    
-    plotly_data2 = plotly_data.copy()
-    # .dropna(subset=[x_col]).reset_index(inplace = True)
-    fig.add_trace(go.Scatter(
-        x=plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][x_col],
-        y=plotly_data[plotly_data2['Date'] == plotly_data2['Date'].max()][y_col],
-        mode='markers',
-        marker=dict(
-        size=13  # Adjust the size value to make the markers larger or smaller
-        , color = 'green'
-        ),
-        name="Current Spends"
-    ))
-
-    fig.add_trace(go.Scatter(
-        x=[x_modified],
-        y=[y_modified],
-        mode='markers',
-        marker=dict(
-        size=13  # Adjust the size value to make the markers larger or smaller
-        , color = 'blue'
-        ),
-        name="Optimised Spends"
-    ))
-
-    # Update layout with titles
-    fig.update_layout(
-        title=channel+' Response Curve',
-        xaxis_title='Weekly Spends',
-        yaxis_title='Prospects'
-    )
-
-    # Show the figure
-    return fig
-