class_docstr: MF regularized TCN + TCN. This version is not for automated searching yet.
class_docstr_tok: ["MF","regularized","TCN","+","TCN",".","This","version","is","not","for","automated","searching","yet","."]
docstr: Evaluate on the prediction results and y. We predict horizon time-points ahead the input x
  in fit_eval before evaluation, where the horizon length equals the second dimension size of
  y.
  :param x: We don't support input x currently.
  :param y: target. We interpret the second dimension of y as the horizon length for
      evaluation.
  :param metrics: a list of metrics in string format
  :param target_covariates: covariates corresponding to target_value.
      2-D ndarray or None.
      The shape of ndarray should be (r, horizon), where r is the number of covariates.
      Global covariates for all time series. If None, only default time coveriates will be
      used while use_time is True. If not, the time coveriates used is the stack of input
      covariates and default time coveriates.
  :param target_dti: dti corresponding to target_value.
      DatetimeIndex or None.
      If None, use default fixed frequency DatetimeIndex generated with the last date of x in
      fit and freq.
  :param num_workers: the number of workers to use in evaluate. It defaults to 1.
  :return: a list of metric evaluation results
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code: def evaluate(
      self,
      x=None,
      y=None,
      metrics=None,
      target_covariates=None,
      target_dti=None,
      num_workers=None,
  ):
      """
      Evaluate on the prediction results and y. We predict horizon time-points ahead the input x
      in fit_eval before evaluation, where the horizon length equals the second dimension size of
      y.
      :param x: We don't support input x currently.
      :param y: target. We interpret the second dimension of y as the horizon length for
          evaluation.
      :param metrics: a list of metrics in string format
      :param target_covariates: covariates corresponding to target_value.
          2-D ndarray or None.
          The shape of ndarray should be (r, horizon), where r is the number of covariates.
          Global covariates for all time series. If None, only default time coveriates will be
          used while use_time is True. If not, the time coveriates used is the stack of input
          covariates and default time coveriates.
      :param target_dti: dti corresponding to target_value.
          DatetimeIndex or None.
          If None, use default fixed frequency DatetimeIndex generated with the last date of x in
          fit and freq.
      :param num_workers: the number of workers to use in evaluate. It defaults to 1.
      :return: a list of metric evaluation results
      """
      if x is not None:
          raise ValueError("We don't support input x directly.")
      if y is None:
          raise ValueError("Input invalid y of None")
      if self.model is None:
          raise Exception(
              "Needs to call fit_eval or restore first before calling predict"
          )
      if len(y.shape) == 1:
          y = np.expand_dims(y, axis=1)
          horizon = 1
      else:
          horizon = y.shape[1]
      result = self.predict(
          x=None,
          horizon=horizon,
          future_covariates=target_covariates,
          future_dti=target_dti,
          num_workers=num_workers,
      )
  
      if y.shape[1] == 1:
          multioutput = "uniform_average"
      else:
          multioutput = "raw_values"
      return [
          Evaluator.evaluate(m, y, result, multioutput=multioutput)
          for m in metrics
      ]
  
imports: from abc import ABCMeta, abstractmethod
  from zoo.chronos.model.tcmf import DeepGLO
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.model.abstract import BaseModel
  from zoo.orca.data import SparkXShards, XShards
  import pickle
  import numpy
  import pandas
label_desc: Use image node_id 7 for calling the TCMF obj's underlying member method code with example usage: obj.evaluate(x, y, metrics, target_covariates, target_dti, num_workers) and returns: unknown