prophet model for time series survival analysis

ts_survival_prophet.py

def build_prophet(df):
  '''Build prophet model and generate events for survival modelling.'''
  # prepping data for prophet assuming data starts from Jan, 2015
  df['ds'] = pd.date_range(start='2015-01-01', periods=len(df))
  df['y'] = df.sales
  df = df[['ds','y']]

  # train/test splits
  train = df[:-TEST_DAYS]
  test = df[-TEST_DAYS:]
  
  model = Prophet(daily_seasonality=True)
  model.fit(train)
  future = model.make_future_dataframe(FORECAST_DAYS, include_history=False)
  forecast = model.predict(future)
  results = pd.merge(forecast, test, on='ds')

  # resample on weeks
  results = results.set_index('ds').resample('w').sum().reset_index()
  
  # generate metric suitable for survival analysis
  results_survival = survival_mape_exp(results, WIN_SIZE_WEEKS)

  # generate events and censors
  thresh_week, event = survival_events(results_survival)

       
  return pd.DataFrame({'thresh_week':[thresh_week], 'event':[event]})