Spaces:
Sleeping
Sleeping
File size: 14,559 Bytes
94bbd2b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 |
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) |