class_docstr: Interface to scipy distances.
  
  computes pairwise distances using scipy.spatial.distance.cdist
      includes Euclidean distance and p-norm (Minkowski) distance
          note: weighted distances are not supported
  
  Parameters
  ----------
  metric : string or function, as in cdist; default = 'euclidean'
      if string, one of: 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
          'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
          'jensenshannon',
          'kulsinski' (< scipy 1.11) or 'kulczynski1' (from scipy 1.11),
          'mahalanobis', 'matching', 'minkowski',
          'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener',
          'sokalsneath', 'sqeuclidean', 'yule'
      if function, should have signature 1D-np.array x 1D-np.array -> float
  p:  if metric='minkowski', the "p" in "p-norm", otherwise irrelevant
  colalign : string, one of 'intersect' (default), 'force-align', 'none'
      controls column alignment if X, X2 passed in fit are pd.DataFrame
      columns between X and X2 are aligned via column names
      if 'intersect', distance is computed on columns occurring both in X and X2,
          other columns are discarded; column ordering in X2 is copied from X
      if 'force-align', raises an error if the set of columns in X, X2 differs;
          column ordering in X2 is copied from X
      if 'none', X and X2 are passed through unmodified (no columns are aligned)
          note: this will potentially align "non-matching" columns
  var_weights : 1D np.array of float or None, default=None
      weight/scaling vector applied to variables in X/X2
      before being passed to cdist, i-th col of X/X2 is multiplied by var_weights[i]
      if None, equivalent to all-ones vector
  metric_kwargs : dict, optional, default=None
      any kwargs passed to the metric in addition, i.e., to the function cdist
      common kwargs: "w" : array-like, same length as X.columns, weights for metric
      refer to scipy.spatial.distance.dist for a documentation of other extra kwargs
code: def __init__(
      self,
      metric="euclidean",
      p=2,
      colalign="intersect",
      var_weights=None,
      metric_kwargs=None,
  ):
      self.metric = metric
      self.p = p
      self.colalign = colalign
      self.var_weights = var_weights
      self.metric_kwargs = metric_kwargs
  
      super().__init__()
  
file_path: sktime/sktime/dists_kernels/scipy_dist.py
imports: import numpy
  import pandas
  from scipy.spatial.distance import cdist
  from sktime.dists_kernels.base import BasePairwiseTransformer
label_desc: Use image node_id 1 to create a new ScipyDist object from inherited base classes: BasePairwiseTransformer with example: obj = ScipyDist(metric, p, colalign, var_weights, metric_kwargs)