class_docstr: TSICEExplainer extends the Individual Conditional Expectation for correlated
  timeseries data (higher dimensions). It uses TSFeatures to derive time series
  structural features, and uses data perturber(TSPerturber) for generating simulated
  data. TSICEExplainer explains the trend in the model forecast change with time series
  derived features.
  
  References:
      .. [#0] `Goldstein et al. 'Peeking Inside the Black Box: Visualizing Statistical
          Learning with Plots of Individual Conditional Expectation'
          <https://arxiv.org/abs/1309.6392>`_
class_docstr_tok: ["TSICEExplainer","extends","the","Individual","Conditional","Expectation","for","correlated","timeseries","data","(","higher","dimensions",")",".","It","uses","TSFeatures","to","derive","time","series","structural","features",",","and","uses","data","perturber","(","TSPerturber",")","for","generating","simulated","data",".","TSICEExplainer","explains","the","trend","in","the","model","forecast","change","with","time","series","derived","features",".","References",":","..","[","#","0","]","`","Goldstein","et","al",".","'Peeking","Inside","the","Black","Box",":","Visualizing","Statistical","Learning","with","Plots","of","Individual","Conditional","Expectation'","<","https",":","\/\/arxiv.org\/abs\/1309.6392",">","`","_"]
docstr: Explain the forecast made by the forecaster at a certain point in time
  (**local explanation**).
  
  Args:
      ts (tsFrame): The future univariate time series ``tsFrame`` to use for forecasting that
          extends forward from the end of training ``tsFrame`` (``ts_train``) or
          ``timestamp_start`` for the requested number of periods.
          This can be generated using :py:mod:`aix360.algorithms.tsframe.tsFrame`.
          A ``tsFrame`` is a pandas ``DataFrame`` indexed by ``Timestamp`` objects
          (that is ``DatetimeIndex``). Each column corresponds to a target to forecast.
      ts_related (tsFrame, optional): The related time series ``tsFrame`` containing
          the external regressors. A ``tsFrame`` is a pandas ``DataFrame`` indexed by
          ``Timestamp`` objects (that is ``DatetimeIndex``). Each column corresponds to a
          related external regressor. Defaults to None.
      explain_params: Arbitrary explainer parameters.
  
  Returns:
      dict: explanation object
          Dictionary with data_x, feature_names, feature_values, signed_impact, total_impact,
          current_forecast, current_feature_values, perturbations and forecasts_on_perturbations.
docstr_tok: ["Explain","the","forecast","made","by","the","forecaster","at","a","certain","point","in","time","(","*","*","local","explanation","*","*",")",".","Args",":","ts","(","tsFrame",")",":","The","future","univariate","time","series","``","tsFrame","``","to","use","for","forecasting","that","extends","forward","from","the","end","of","training","``","tsFrame","``","(","``","ts_train","``",")","or","``","timestamp_start","``","for","the","requested","number","of","periods",".","This","can","be","generated","using",":","py",":","mod",":","`","aix360.algorithms.tsframe.tsFrame","`",".","A","``","tsFrame","``","is","a","pandas","``","DataFrame","``","indexed","by","``","Timestamp","``","objects","(","that","is","``","DatetimeIndex","``",")",".","Each","column","corresponds","to","a","target","to","forecast",".","ts_related","(","tsFrame",",","optional",")",":","The","related","time","series","``","tsFrame","``","containing","the","external","regressors",".","A","``","tsFrame","``","is","a","pandas","``","DataFrame","``","indexed","by","``","Timestamp","``","objects","(","that","is","``","DatetimeIndex","``",")",".","Each","column","corresponds","to","a","related","external","regressor",".","Defaults","to","None",".","explain_params",":","Arbitrary","explainer","parameters",".","Returns",":","dict",":","explanation","object","Dictionary","with","data_x",",","feature_names",",","feature_values",",","signed_impact",",","total_impact",",","current_forecast",",","current_feature_values",",","perturbations","and","forecasts_on_perturbations","."]
code: def explain_instance(
      self, ts: tsFrame, ts_related: tsFrame = None, **explain_params
  ):
      """Explain the forecast made by the forecaster at a certain point in time
      (**local explanation**).
  
      Args:
          ts (tsFrame): The future univariate time series ``tsFrame`` to use for forecasting that
              extends forward from the end of training ``tsFrame`` (``ts_train``) or
              ``timestamp_start`` for the requested number of periods.
              This can be generated using :py:mod:`aix360.algorithms.tsframe.tsFrame`.
              A ``tsFrame`` is a pandas ``DataFrame`` indexed by ``Timestamp`` objects
              (that is ``DatetimeIndex``). Each column corresponds to a target to forecast.
          ts_related (tsFrame, optional): The related time series ``tsFrame`` containing
              the external regressors. A ``tsFrame`` is a pandas ``DataFrame`` indexed by
              ``Timestamp`` objects (that is ``DatetimeIndex``). Each column corresponds to a
              related external regressor. Defaults to None.
          explain_params: Arbitrary explainer parameters.
  
      Returns:
          dict: explanation object
              Dictionary with data_x, feature_names, feature_values, signed_impact, total_impact,
              current_forecast, current_feature_values, perturbations and forecasts_on_perturbations.
  
      """
  
      return super(TSICEExplainer, self).explain_instance(
          ts=ts, ts_related=ts_related, **explain_params
      )
  
file_path: AIX360/aix360/algorithms/tsice/tsice.py
imports: import warnings
  import numpy
  import pandas
  from typing import List, Union, Callable
  from aix360.algorithms.tslbbe import TSLocalBBExplainer
  from aix360.algorithms.tsutils.tsframe import tsFrame, to_np_array
  from aix360.algorithms.tsutils.tsperturbers.tsperturber import TSPerturber, BlockSelector
  from aix360.algorithms.tsutils.tsperturbers.perturbed_data_generator import PerturbedDataGenerator
  from aix360.algorithms.tsutils.tsfeatures.latest_features import LatestFeature
  from aix360.algorithms.tsutils.tsfeatures.range_features import RangeFeature
label_desc: Use image node_id 5 for calling the TSICEExplainer obj's underlying member method code with example usage: obj.explain_instance(ts, ts_related) and returns: super