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
class_docstr_tok: ["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"]
docstr: Fit an estimator using transformed data from the SignatureTransformer.
  
  Parameters
  ----------
  X : nested pandas DataFrame of shape [n_instances, n_dims]
      Nested dataframe with univariate time-series in cells.
  y : array-like, shape = [n_instances] The class labels.
  
  Returns
  -------
  self : object
docstr_tok: ["Fit","an","estimator","using","transformed","data","from","the","SignatureTransformer",".","Parameters","--","--","--","--","--","X",":","nested","pandas","DataFrame","of","shape","[","n_instances",",","n_dims","]","Nested","dataframe","with","univariate","time-series","in","cells",".","y",":","array-like",",","shape","=","[","n_instances","]","The","class","labels",".","Returns","--","--","--","-","self",":","object"]
code: def _fit(self, X, y):
      """Fit an estimator using transformed data from the SignatureTransformer.
  
      Parameters
      ----------
      X : nested pandas DataFrame of shape [n_instances, n_dims]
          Nested dataframe with univariate time-series in cells.
      y : array-like, shape = [n_instances] The class labels.
  
      Returns
      -------
      self : object
      """
      # Join the classifier onto the signature method pipeline
      self._setup_classification_pipeline()
  
      # Fit the pre-initialised classification pipeline
      self.pipeline.fit(X, y)
  
      return self
  
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 3 for calling the SignatureClassifier obj's underlying member method code with example usage: obj._fit(X, y) and returns: self