class_docstr: Simply wraps MAPLE into the common SHAP interface.
  
  Parameters
  ----------
  model : function
      User supplied function that takes a matrix of samples (# samples x # features) and
      computes the output of the model for those samples. The output can be a vector
      (# samples) or a matrix (# samples x # model outputs).
  
  data : numpy.array
      The background dataset.
docstr: Compute the MAPLE coef attributions.
  
  Parameters
  ----------
  multiply_by_input : bool
      If true, this multiplies the learned coefficients by the mean-centered input. This makes these
      values roughly comparable to SHAP values.
docstr_tok: ["Compute","the","MAPLE","coef","attributions",".","Parameters","--","--","--","--","--","multiply_by_input",":","bool","If","true",",","this","multiplies","the","learned","coefficients","by","the","mean-centered","input",".","This","makes","these","values","roughly","comparable","to","SHAP","values","."]
code: def attributions(self, X, multiply_by_input=False):
      """Compute the MAPLE coef attributions.
  
      Parameters
      ----------
      multiply_by_input : bool
          If true, this multiplies the learned coefficients by the mean-centered input. This makes these
          values roughly comparable to SHAP values.
      """
      if isinstance(X, pd.DataFrame):
          X = X.values
  
      out = [np.zeros(X.shape) for j in range(self.out_dim)]
      for i in range(X.shape[0]):
          exp = self.explainer.explain(X[i])["coefs"]
          out[0][i, :] = exp[1:]
          if multiply_by_input:
              out[0][i, :] = out[0][i, :] * (X[i] - self.data_mean)
  
      return out[0] if self.flat_out else out
  
imports: import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from .._explainer import Explainer
  import numpy
  from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import Ridge
  from sklearn.metrics import mean_squared_error
label_desc: Use image node_id 2 for calling the Maple obj's underlying member method code with example usage: obj.attributions(X, multiply_by_input) and returns: unknown