docstr: Create an automated Prophet Model.
  User need to specify either the exact value or the search space of the
  Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer to
  https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.
  
  :param changepoint_prior_scale: Int or hp sampling function from an integer space
      for hyperparameter changepoint_prior_scale for the Prophet model.
      For hp sampling, see zoo.chronos.orca.automl.hp for more details.
      e.g. hp.loguniform(0.001, 0.5).
  :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
      Prophet model.
      e.g. hp.loguniform(0.01, 10).
  :param holidays_prior_scale: hyperparameter holidays_prior_scale for the
      Prophet model.
      e.g. hp.loguniform(0.01, 10).
  :param seasonality_mode: hyperparameter seasonality_mode for the
      Prophet model.
      e.g. hp.choice(['additive', 'multiplicative']).
  :param changepoint_range: hyperparameter changepoint_range for the
      Prophet model.
      e.g. hp.uniform(0.8, 0.95).
  :param metric: String. The evaluation metric name to optimize. e.g. "mse"
  :param logs_dir: Local directory to save logs and results. It defaults to
      "/tmp/auto_prophet_logs"
  :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
  :param name: name of the AutoProphet. It defaults to "auto_prophet"
  :param remote_dir: String. Remote directory to sync training results and checkpoints. It
      defaults to None and doesn't take effects while running in local. While running in
      cluster, it defaults to "hdfs:///tmp/{name}".
  
  :param prophet_config: Other Prophet hyperparameters.
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code: def __init__(
      self,
      changepoint_prior_scale=hp.loguniform(0.001, 0.5),
      seasonality_prior_scale=hp.loguniform(0.01, 10),
      holidays_prior_scale=hp.loguniform(0.01, 10),
      seasonality_mode=hp.choice(["additive", "multiplicative"]),
      changepoint_range=hp.uniform(0.8, 0.95),
      metric="mse",
      logs_dir="/tmp/auto_prophet_logs",
      cpus_per_trial=1,
      name="auto_prophet",
      remote_dir=None,
      **prophet_config,
  ):
      """
      Create an automated Prophet Model.
      User need to specify either the exact value or the search space of the
      Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer to
      https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.
  
      :param changepoint_prior_scale: Int or hp sampling function from an integer space
          for hyperparameter changepoint_prior_scale for the Prophet model.
          For hp sampling, see zoo.chronos.orca.automl.hp for more details.
          e.g. hp.loguniform(0.001, 0.5).
      :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
          Prophet model.
          e.g. hp.loguniform(0.01, 10).
      :param holidays_prior_scale: hyperparameter holidays_prior_scale for the
          Prophet model.
          e.g. hp.loguniform(0.01, 10).
      :param seasonality_mode: hyperparameter seasonality_mode for the
          Prophet model.
          e.g. hp.choice(['additive', 'multiplicative']).
      :param changepoint_range: hyperparameter changepoint_range for the
          Prophet model.
          e.g. hp.uniform(0.8, 0.95).
      :param metric: String. The evaluation metric name to optimize. e.g. "mse"
      :param logs_dir: Local directory to save logs and results. It defaults to
          "/tmp/auto_prophet_logs"
      :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
      :param name: name of the AutoProphet. It defaults to "auto_prophet"
      :param remote_dir: String. Remote directory to sync training results and checkpoints. It
          defaults to None and doesn't take effects while running in local. While running in
          cluster, it defaults to "hdfs:///tmp/{name}".
  
      :param prophet_config: Other Prophet hyperparameters.
      """
      self.search_space = {
          "changepoint_prior_scale": changepoint_prior_scale,
          "seasonality_prior_scale": seasonality_prior_scale,
          "holidays_prior_scale": holidays_prior_scale,
          "seasonality_mode": seasonality_mode,
          "changepoint_range": changepoint_range,
      }
      self.search_space.update(prophet_config)  # update other configs
      self.metric = metric
      model_builder = ProphetBuilder()
      self.auto_est = AutoEstimator(
          model_builder=model_builder,
          logs_dir=logs_dir,
          resources_per_trial={"cpu": cpus_per_trial},
          remote_dir=remote_dir,
          name=name,
      )
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/model/auto_prophet.py
imports: import pandas
  from zoo.orca.automl.auto_estimator import AutoEstimator
  import zoo.orca.automl.hp
  from zoo.chronos.model.prophet import ProphetBuilder
label_desc: Use image node_id 1 to create a new AutoProphet object  with example: obj = AutoProphet(changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode, changepoint_range, metric, logs_dir, cpus_per_trial, name, remote_dir)