Upload streamlit_forecast.py

streamlit_forecast.py

@@ -0,0 +1,211 @@
+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"])
+    @st.cache_data
+    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)
+
+            @st.cache_data
+            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)
+
+            @st.cache_data
+            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)
+        """)