Spaces:
Running
Running
| import pandas as pd | |
| import numpy as np | |
| from neuralprophet import NeuralProphet | |
| from neuralprophet import set_random_seed | |
| import matplotlib.pyplot as plt | |
| import streamlit as st | |
| from datetime import datetime as dt | |
| # import os | |
| # os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" | |
| st.title('Time Series Forecasting with Neural Prophet') | |
| option=st.selectbox('Choose from the following',['Forecasting without events','Forecasting with events']) | |
| try: | |
| uploaded_file = st.sidebar.file_uploader("Upload your CSV file", type=["csv"]) | |
| def read_file(uploaded_file): | |
| data2=pd.read_csv(uploaded_file) | |
| return data2 | |
| df=read_file(uploaded_file) | |
| ##################################### Option 1 ##################################### | |
| if option=='Forecasting without events': | |
| daily_seasonality_btn = st.sidebar.select_slider('Daily Seasonality',options=[True, False],value=False) | |
| weekly_seasonality_btn = st.sidebar.select_slider('Weekly Seasonality',options=[True, False],value=True) | |
| yearly_seasonality_btn = st.sidebar.select_slider('Yearly Seasonality',options=[True, False],value=True) | |
| n_hist_pred_btn=st.sidebar.number_input('No. of Historical Data Points',0,360,30) | |
| epochs_btn=st.sidebar.number_input('Epochs',1,20,5) | |
| n_hidden_layers_btn=st.sidebar.number_input('No. of Hidden Layers',1,5,1) | |
| loss_fn_btn=st.sidebar.selectbox('Loss Function',['MAE','MSE','Huber']) | |
| seasonality_mode_btn=st.sidebar.selectbox('Seasonality Mode',['Additive','Multiplicative']) | |
| n_change_points_btn=st.sidebar.number_input('No. of Trend Change Points',0,360,30) | |
| with st.expander("Select Date & Observed Value",expanded=True): | |
| c1, c2 = st.columns((1, 1)) | |
| x=c1.selectbox('Date',df.columns) | |
| ycols=[cols for cols in df.columns if cols!=df.columns[0] and df.dtypes[cols]!='object'] | |
| y=c2.selectbox('Observed Value',ycols) | |
| with st.expander("Choose the Forecast Period with its Frequency"): | |
| c8, c9 = st.columns((1, 1)) | |
| periods=int(c8.number_input('Forecast Period',0,365,60)) | |
| freq=c9.selectbox('Frequency',["D","M","Y","s","min","H"]) | |
| df1=df[[x,y]] | |
| df['ds'],df['y']=df[x],df[y] | |
| df=df[['ds','y']] | |
| df.dropna(inplace=True) | |
| df.drop_duplicates(subset=['ds'],inplace=True) | |
| df['ds']=pd.to_datetime(df['ds']) | |
| df.sort_values(by=['ds'],inplace=True) | |
| df=df.reset_index(drop=True) | |
| st.header('Dataset') | |
| st.dataframe(df1.head()) | |
| rmp=st.radio('Run Model',['n','y']) | |
| if rmp=='y': | |
| set_random_seed(40) | |
| m = NeuralProphet(n_changepoints=n_change_points_btn,daily_seasonality=daily_seasonality_btn,weekly_seasonality=weekly_seasonality_btn,yearly_seasonality=yearly_seasonality_btn,seasonality_mode=seasonality_mode_btn,num_hidden_layers=n_hidden_layers_btn,loss_func=loss_fn_btn,epochs=epochs_btn,) | |
| # split into train & test dataset | |
| df_train, df_test = m.split_df(df, freq=freq,valid_p=0.2) | |
| train_metrics = m.fit(df_train, freq=freq,) | |
| test_metrics = m.test(df_test,) | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| future = m.make_future_dataframe(df=df, n_historic_predictions=n_hist_pred_btn,periods=periods) | |
| forecast = m.predict(df=future) | |
| final_train_metrics=train_metrics.iloc[len(train_metrics)-1:len(train_metrics)].reset_index(drop=True) | |
| final_test_metrics=test_metrics.iloc[len(test_metrics)-1:len(test_metrics)].reset_index(drop=True) | |
| fig = m.plot(forecast) | |
| fig_comp = m.plot_components(forecast) | |
| fig_param = m.plot_parameters() | |
| st.header('Train Dataset Metrics') | |
| st.dataframe(final_train_metrics) | |
| st.header('Test Dataset Metrics') | |
| st.dataframe(final_test_metrics) | |
| st.header('Forecast Values') | |
| st.pyplot(fig) | |
| st.header('Trend & Seasonality') | |
| st.pyplot(fig_param) | |
| st.dataframe(forecast) | |
| def convert_df(df): | |
| return df.to_csv(index=False).encode('utf-8') | |
| try: | |
| forecast_df = convert_df(forecast) | |
| if forecast_df is not None: | |
| st.download_button(label="Download data as CSV",data=forecast_df,file_name='NeuralProphet_with_events_results.csv',mime='text/csv',) | |
| except: | |
| st.warning('Choose Something') | |
| ##################################### Option 2 ##################################### | |
| if option=='Forecasting with events': | |
| daily_seasonality_btn = st.sidebar.select_slider('Daily Seasonality',options=[True, False],value=False) | |
| weekly_seasonality_btn = st.sidebar.select_slider('Weekly Seasonality',options=[True, False],value=True) | |
| yearly_seasonality_btn = st.sidebar.select_slider('Yearly Seasonality',options=[True, False],value=True) | |
| n_hist_pred=st.sidebar.number_input('No. of Historical Data Points',0,360,30) | |
| epochs_btn=st.sidebar.number_input('Epochs',1,20,5) | |
| n_hidden_layers_btn=st.sidebar.number_input('No. of Hidden Layers',1,5,1) | |
| loss_fn_btn=st.sidebar.selectbox('Loss Function',['MAE','MSE','Huber']) | |
| n_change_points_btn=st.sidebar.number_input('No. of Trend Change Points',0,360,30) | |
| with st.expander("Select Date & Observed Value",expanded=True): | |
| c1, c2 = st.columns((1, 1)) | |
| x=c1.selectbox('Date',df.columns) | |
| ycols=[cols for cols in df.columns if cols!=df.columns[0] and df.dtypes[cols]!='object'] | |
| y=c2.selectbox('Observed Value',ycols) | |
| with st.expander("Select Event Names & their Dates"): | |
| c3, c4 = st.columns((1, 1)) | |
| events1=c3.text_input(label='Event 1 Name',value='New Year Eve') | |
| eventd1=c3.date_input(label='Event 1 Date Range: ',value=(dt(year=1900, month=1, day=1), | |
| dt(year=2030, month=1, day=30)),) | |
| events2=c4.text_input(label='Event 2 Name',value='Christmas') | |
| eventd2=c4.date_input(label='Event 2 Date Range: ',value=(dt(year=1900, month=1, day=1), | |
| dt(year=2030, month=1, day=30)),) | |
| with st.expander("Select the Lower & Upper Window for the Events & Seasonality Factor"): | |
| c5, c6, c7 = st.columns((1, 1, 1)) | |
| lw=c5.number_input('Lower Window',-10,0,-1) | |
| uw=c6.number_input('Upper Window',0,10,0) | |
| mode=c7.selectbox('Seasonality',['Additive','Multiplicative']) | |
| with st.expander("Choose the Forecast Period with its Frequency"): | |
| c8, c9 = st.columns((1, 1)) | |
| periods=int(c8.number_input('Forecast Period',0,365,60)) | |
| freq=c9.selectbox('Frequency',["D","M","Y","s","min","H"]) | |
| df1=df[[x,y]] | |
| df['ds'],df['y']=df[x],df[y] | |
| df=df[['ds','y']] | |
| df.dropna(inplace=True) | |
| df.drop_duplicates(subset=['ds'],inplace=True) | |
| df['ds']=pd.to_datetime(df['ds']) | |
| df.sort_values(by=['ds'],inplace=True) | |
| df=df.reset_index(drop=True) | |
| st.header('Dataset') | |
| st.dataframe(df1.head()) | |
| rmp=st.radio('Run Model',['n','y']) | |
| if rmp=='y': | |
| set_random_seed(40) | |
| m = NeuralProphet(n_changepoints=n_change_points_btn,daily_seasonality=daily_seasonality_btn,weekly_seasonality=weekly_seasonality_btn,yearly_seasonality=yearly_seasonality_btn,num_hidden_layers=n_hidden_layers_btn,loss_func=loss_fn_btn,epochs=epochs_btn,) | |
| event1 = pd.DataFrame({'event': events1,'ds': pd.to_datetime(eventd1).date}) | |
| event2 = pd.DataFrame({'event': events2,'ds': pd.to_datetime(eventd2).date}) | |
| if events2=='': | |
| enames=[events1] | |
| events_df = pd.concat([event1]) | |
| else: | |
| enames=[events1,events2] | |
| events_df = pd.concat([event1,event2]) | |
| events_df=events_df[events_df['event']!=''] | |
| for i in range(len(enames)): | |
| if enames[i]!='': | |
| m=m.add_events([enames[i]],lower_window=lw,upper_window=uw,mode=mode) | |
| history_df = m.create_df_with_events(df, events_df) | |
| metrics=m.fit(history_df, freq=freq,) | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| future = m.make_future_dataframe(df=history_df, events_df=events_df,n_historic_predictions=n_hist_pred,periods=periods) | |
| forecast = m.predict(df=future) | |
| fig = m.plot(forecast) | |
| fig_comp = m.plot_components(forecast) | |
| fig_param = m.plot_parameters() | |
| final_metrics=metrics.iloc[len(metrics)-1:len(metrics)].reset_index(drop=True) | |
| st.header('Model Metrics') | |
| st.dataframe(final_metrics) | |
| st.header('Forecast Values') | |
| st.pyplot(fig) | |
| st.header('Trend & Seasonality') | |
| st.pyplot(fig_param) | |
| st.dataframe(forecast) | |
| def convert_df(df): | |
| return df.to_csv(index=False).encode('utf-8') | |
| try: | |
| forecast_df = convert_df(forecast) | |
| if forecast_df is not None: | |
| st.download_button(label="Download data as CSV",data=forecast_df,file_name='NeuralProphet_with_events_results.csv',mime='text/csv',) | |
| except: | |
| st.warning('Choose Something') | |
| ##################################################### | |
| except: | |
| st.warning('Choose Something') | |
| st.sidebar.write('### **About**') | |
| st.sidebar.info( | |
| """ | |
| Created by: | |
| [Parthasarathy Ramamoorthy](https://www.linkedin.com/in/parthasarathyr97/) (Data Scientist @ Walmart Global Tech) | |
| """) | |