class_docstr: Fetch trial component data and make them accessible for analytics.
docstr: Initialize a ``ExperimentAnalytics`` instance.
  
  Args:
      experiment_name (str, optional): Name of the experiment if you want to constrain the
          search to only trial components belonging to an experiment.
      search_expression (dict, optional): The search query to find the set of trial components
          to use to populate the data frame.
      sort_by (str, optional): The name of the resource property used to sort
          the set of trial components.
      sort_order(str optional): How trial components are ordered, valid values are Ascending
          and Descending. The default is Descending.
      metric_names (list, optional): string names of all the metrics to be shown in the
          data frame. If not specified, all metrics will be shown of all trials.
      parameter_names (list, optional): string names of the parameters to be shown in the
          data frame. If not specified, all parameters will be shown of all trials.
      sagemaker_session (sagemaker.session.Session): Session object which manages interactions
          with Amazon SageMaker APIs and any other AWS services needed. If not specified,
          one is created using the default AWS configuration chain.
      input_artifact_names(dict optional):The input artifacts for the experiment. Examples of
          input artifacts are datasets, algorithms, hyperparameters, source code, and instance
          types.
      output_artifact_names(dict optional): The output artifacts for the experiment. Examples
          of output artifacts are metrics, snapshots, logs, and images.
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code: def __init__(
      self,
      experiment_name=None,
      search_expression=None,
      sort_by=None,
      sort_order=None,
      metric_names=None,
      parameter_names=None,
      sagemaker_session=None,
      input_artifact_names=None,
      output_artifact_names=None,
  ):
      """Initialize a ``ExperimentAnalytics`` instance.
  
      Args:
          experiment_name (str, optional): Name of the experiment if you want to constrain the
              search to only trial components belonging to an experiment.
          search_expression (dict, optional): The search query to find the set of trial components
              to use to populate the data frame.
          sort_by (str, optional): The name of the resource property used to sort
              the set of trial components.
          sort_order(str optional): How trial components are ordered, valid values are Ascending
              and Descending. The default is Descending.
          metric_names (list, optional): string names of all the metrics to be shown in the
              data frame. If not specified, all metrics will be shown of all trials.
          parameter_names (list, optional): string names of the parameters to be shown in the
              data frame. If not specified, all parameters will be shown of all trials.
          sagemaker_session (sagemaker.session.Session): Session object which manages interactions
              with Amazon SageMaker APIs and any other AWS services needed. If not specified,
              one is created using the default AWS configuration chain.
          input_artifact_names(dict optional):The input artifacts for the experiment. Examples of
              input artifacts are datasets, algorithms, hyperparameters, source code, and instance
              types.
          output_artifact_names(dict optional): The output artifacts for the experiment. Examples
              of output artifacts are metrics, snapshots, logs, and images.
      """
      sagemaker_session = sagemaker_session or Session()
      self._sage_client = sagemaker_session.sagemaker_client
  
      if not experiment_name and not search_expression:
          raise ValueError(
              "Either experiment_name or search_expression must be supplied."
          )
  
      self._experiment_name = experiment_name
      self._search_expression = search_expression
      self._sort_by = sort_by
      self._sort_order = sort_order
      self._metric_names = metric_names
      self._parameter_names = parameter_names
      self._input_artifact_names = input_artifact_names
      self._output_artifact_names = output_artifact_names
      self._trial_components = None
      super(ExperimentAnalytics, self).__init__()
      self.clear_cache()
  
imports: from __future__ import print_function, absolute_import
  from abc import ABCMeta, abstractmethod
  from collections import defaultdict, OrderedDict
  import datetime
  import logging
  from six import with_metaclass
  from sagemaker.session import Session
  from sagemaker.utils import DeferredError
  from sagemaker.lineage import artifact
label_desc: Use image node_id 1 to create a new ExperimentAnalytics object from inherited base classes: AnalyticsMetricsBase with example: obj = ExperimentAnalytics(experiment_name, search_expression, sort_by, sort_order, metric_names, parameter_names, sagemaker_session, input_artifact_names, output_artifact_names)