code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      self.csv_name = join(rootdir, "data", "example.csv")
      self.data = TemporalResample("15min")(
          csv_to_time_series(
              self.csv_name, timestamp_unit="ms", data_cols=["kpi"]
          )
      )
      logger.info(f"Data looks like:\n{self.data[:5]}")
      holidays = pd.DataFrame(
          {"ds": ["03-17-2020"], "holiday": ["St. Patrick's Day"]}
      )
  
      # Test Prophet with a Box-Cox transform
      self.test_len = math.ceil(len(self.data) / 5)
      self.vals_train = self.data[: -self.test_len]
      self.vals_test = self.data[-self.test_len :]
      self.model = AutoProphet(
          model=ProphetDetector(
              ProphetDetectorConfig(
                  transform=BoxCoxTransform(lmbda=0.5),
                  uncertainty_samples=1000,
                  holidays=holidays,
                  invert_transform=True,
              )
          )
      )
  
file_path: merlion/tests/anomaly/forecast_based/test_prophet.py
imports: import logging
  import math
  from os.path import abspath, dirname, join
  import sys
  import unittest
  import numpy
  import pandas
  from merlion.models.automl.autoprophet import AutoProphet
  from merlion.models.anomaly.forecast_based.prophet import ProphetDetector, ProphetDetectorConfig
  from merlion.utils.data_io import csv_to_time_series
  from merlion.utils.time_series import TimeSeries
  from merlion.transform.normalize import BoxCoxTransform
  from merlion.transform.resample import TemporalResample
label_desc: Use image node_id 1 to create a new TestProphet object from inherited base classes: unittest with example: obj = TestProphet()