def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of list of tuples) A list of summary sections. Each section is a list. Each item in a section list is a tuple of the form: ('<label>','<field>') section_titles: list A list of section titles. The order matches that of the 'sections' object. """ model_fields = [ ("Number of coefficients", "num_coefficients"), ("Number of examples", "num_examples"), ("Number of feature columns", "num_features"), ("Number of unpacked features", "num_unpacked_features"), ] hyperparam_fields = [("L1 penalty", "l1_penalty"), ("L2 penalty", "l2_penalty")] solver_fields = [ ("Solver", "solver"), ("Solver iterations", "training_iterations"), ("Solver status", "training_solver_status"), ("Training time (sec)", "training_time"), ] training_fields = [ ("Residual sum of squares", "training_loss"), ("Training RMSE", "training_rmse"), ] coefs = self.coefficients top_coefs, bottom_coefs = _toolkit_get_topk_bottomk(coefs, k=5) (coefs_list, titles_list) = _summarize_coefficients(top_coefs, bottom_coefs) return ( [model_fields, hyperparam_fields, solver_fields, training_fields] + coefs_list, ["Schema", "Hyperparameters", "Training Summary", "Settings"] + titles_list, )
def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of list of tuples) A list of summary sections. Each section is a list. Each item in a section list is a tuple of the form: ('<label>','<field>') section_titles: list A list of section titles. The order matches that of the 'sections' object. """ model_fields = [ ('Number of coefficients', 'num_coefficients'), ('Number of examples', 'num_examples'), ('Number of classes', 'num_classes'), ('Number of feature columns', 'num_features'), ('Number of unpacked features', 'num_unpacked_features')] hyperparam_fields = [ ("L1 penalty", 'l1_penalty'), ("L2 penalty", 'l2_penalty') ] solver_fields = [ ("Solver", 'solver'), ("Solver iterations", 'training_iterations'), ("Solver status", 'training_solver_status'), ("Training time (sec)", 'training_time')] training_fields = [ ("Log-likelihood", 'training_loss')] coefs = self.coefficients top_coefs, bottom_coefs = _toolkit_get_topk_bottomk(coefs,k=5) (coefs_list, titles_list) = _summarize_coefficients(top_coefs, \ bottom_coefs) return ([ model_fields, hyperparam_fields, \ solver_fields, training_fields ] + coefs_list, \ [ 'Schema', 'Hyperparameters', \ 'Training Summary', 'Settings' ] + titles_list )