class_docstr: Class for learning a locally linear sparse model from perturbed data
class_docstr_tok: ["Class","for","learning","a","locally","linear","sparse","model","from","perturbed","data"]
docstr: Generates the lars path for weighted data.
  
  Args:
      weighted_data: data that has been weighted by kernel
      weighted_label: labels, weighted by kernel
  
  Returns:
      (alphas, coefs), both are arrays corresponding to the
      regularization parameter and coefficients, respectively
docstr_tok: ["Generates","the","lars","path","for","weighted","data",".","Args",":","weighted_data",":","data","that","has","been","weighted","by","kernel","weighted_label",":","labels",",","weighted","by","kernel","Returns",":","(","alphas",",","coefs",")",",","both","are","arrays","corresponding","to","the","regularization","parameter","and","coefficients",",","respectively"]
code: def generate_lars_path(weighted_data, weighted_labels):
      """Generates the lars path for weighted data.
  
      Args:
          weighted_data: data that has been weighted by kernel
          weighted_label: labels, weighted by kernel
  
      Returns:
          (alphas, coefs), both are arrays corresponding to the
          regularization parameter and coefficients, respectively
      """
      x_vector = weighted_data
      alphas, _, coefs = lars_path(
          x_vector, weighted_labels, method="lasso", verbose=False
      )
      return alphas, coefs
  
file_path: tensorwatch/tensorwatch/saliency/lime/lime_base.py
imports: from __future__ import print_function
  import numpy
  from sklearn.linear_model import Ridge, lars_path
  from sklearn.utils import check_random_state
label_desc: Use image node_id 2 for calling the LimeBase obj's underlying member method code with example usage: obj.generate_lars_path(weighted_data, weighted_labels) and returns: alphas,  coefs