Spaces:
Running
Running
File size: 9,959 Bytes
6543671 |
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 |
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)
""")
|