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 )
