Spaces:
Sleeping
Sleeping
File size: 5,868 Bytes
be4456f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
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 |