Delete app.py

app.py

@@ -1,211 +0,0 @@
-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)
-        """)