class_docstr: Classification module using signature-based features.
  
  This simply initialises the SignatureTransformer class which builds
  the feature extraction pipeline, then creates a new pipeline by
  appending a classifier after the feature extraction step.
  
  The default parameters are set to best practice parameters found in
      "A Generalised Signature Method for Multivariate TimeSeries" [1]
  
  Note that the final classifier used on the UEA datasets involved tuning
  the hyper-parameters:
      - `depth` over [1, 2, 3, 4, 5, 6]
      - `window_depth` over [2, 3, 4]
      - RandomForestClassifier hyper-parameters.
  as these were found to be the most dataset dependent hyper-parameters.
  
  Thus, we recommend always tuning *at least* these parameters to any given
  dataset.
  
  Parameters
  ----------
  estimator : sklearn estimator, default=RandomForestClassifier
      This should be any sklearn-type estimator. Defaults to RandomForestClassifier.
  augmentation_list: list of tuple of strings, default=("basepoint", "addtime")
      List of augmentations to be applied before the signature transform is applied.
  window_name: str, default="dyadic"
      The name of the window transform to apply.
  window_depth: int, default=3
      The depth of the dyadic window. (Active only if `window_name == 'dyadic']`.
  window_length: int, default=None
      The length of the sliding/expanding window. (Active only if `window_name in
      ['sliding, 'expanding'].
  window_step: int, default=None
      The step of the sliding/expanding window. (Active only if `window_name in
      ['sliding, 'expanding'].
  rescaling: str, default=None
      The method of signature rescaling.
  sig_tfm: str, default="signature"
      String to specify the type of signature transform. One of:
      ['signature', 'logsignature']).
  depth: int, default=4
      Signature truncation depth.
  random_state: int, default=None
      Random state initialisation.
  
  Attributes
  ----------
  signature_method: sklearn.Pipeline
      An sklearn pipeline that performs the signature feature extraction step.
  pipeline: sklearn.Pipeline
      The classifier appended to the `signature_method` pipeline to make a
      classification pipeline.
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes_)
      Holds the label for each class.
  
  References
  ----------
  .. [1] Morrill, James, et al. "A generalised signature method for multivariate time
      series feature extraction." arXiv preprint arXiv:2006.00873 (2020).
      https://arxiv.org/pdf/2006.00873.pdf
  
  See Also
  --------
  SignatureTransformer
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docstr: Predict class values of n_instances in X.
  
  Parameters
  ----------
  X : pd.DataFrame of shape (n_instances, n_dims)
  
  Returns
  -------
  preds : np.ndarray of shape (n, 1)
      Predicted class.
docstr_tok: ["Predict","class","values","of","n_instances","in","X.","Parameters","--","--","--","--","--","X",":","pd.DataFrame","of","shape","(","n_instances",",","n_dims",")","Returns","--","--","--","-","preds",":","np.ndarray","of","shape","(","n",",","1",")","Predicted","class","."]
code: def _predict(self, X) -> np.ndarray:
      """Predict class values of n_instances in X.
  
      Parameters
      ----------
      X : pd.DataFrame of shape (n_instances, n_dims)
  
      Returns
      -------
      preds : np.ndarray of shape (n, 1)
          Predicted class.
      """
      return self.pipeline.predict(X)
  
file_path: sktime/sktime/classification/feature_based/_signature_classifier.py
imports: import numpy
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sktime.base._base import _clone_estimator
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.signature_based._checks import _handle_sktime_signatures
  from sktime.transformations.panel.signature_based._signature_method import SignatureTransformer
label_desc: Use image node_id 4 for calling the SignatureClassifier obj's underlying member method code with example usage: obj._predict(X) and returns: self