def send_report(self):
pca = report.bool_str(self.apply_pca)
if self.apply_pca:
pca += report.plural(", {number} component{s}", self.pca_components)
self.report_items((
("Normalize data", report.bool_str(self.normalize)),
("PCA preprocessing", pca),
("Metric", METRICS[self.metric_idx][0]),
("k neighbors", self.k_neighbors),
("Resolution", self.resolution),
))
def send_report(self):
pca = report.bool_str(self.apply_pca)
if self.apply_pca:
pca += report.plural(", {number} component{s}", self.pca_components)
self.report_items((
("Normalize data", report.bool_str(self.normalize)),
("PCA preprocessing", pca),
("Metric", METRICS[self.metric_idx][0]),
("k neighbors", self.k_neighbors),
("Resolution", self.resolution),
))
def send_report(self):
pca = report.bool_str(self.apply_pca)
if self.apply_pca:
pca += report.plural(', {number} component{s}', self.pca_components)
self.report_items((
('PCA preprocessing', pca),
('Metric', METRICS[self.metric_idx][0]),
('k neighbors', self.k_neighbors),
('Resolution', self.resolution),
))
def send_report(self):
pca = report.bool_str(self.apply_pca)
if self.apply_pca:
pca += report.plural(', {number} component{s}',
self.pca_components)
self.report_items((
('PCA preprocessing', pca),
('Metric', METRICS[self.metric_idx][0]),
('k neighbors', self.k_neighbors),
('Resolution', self.resolution),
))
def get_learner_parameters(self):
params = OrderedDict({})
# Classification loss function
params['Classification loss function'] = self.cls_losses[
self.cls_loss_function_index][0]
if self.cls_losses[self.cls_loss_function_index][1] in (
'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'):
params['Epsilon (ε) for classification'] = self.cls_epsilon
# Regression loss function
params['Regression loss function'] = self.reg_losses[
self.reg_loss_function_index][0]
if self.reg_losses[self.reg_loss_function_index][1] in (
'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'):
params['Epsilon (ε) for regression'] = self.reg_epsilon
params['Regularization'] = self.penalties[self.penalty_index][0]
# Regularization strength
if self.penalties[self.penalty_index][1] in ('l1', 'l2', 'elasticnet'):
params['Regularization strength (α)'] = self.alpha
# Elastic Net mixing
if self.penalties[self.penalty_index][1] in ('elasticnet', ):
params['Elastic Net mixing parameter (L1 ratio)'] = self.l1_ratio
params['Learning rate'] = self.learning_rates[
self.learning_rate_index][0]
# Eta
if self.learning_rates[self.learning_rate_index][1] in \
('constant', 'invscaling'):
params['Initial learning rate (η<sub>0</sub>)'] = self.eta0
# t
if self.learning_rates[self.learning_rate_index][1] in \
('invscaling',):
params['Inverse scaling exponent (t)'] = self.power_t
params['Shuffle data after each iteration'] = bool_str(self.shuffle)
if self.use_random_state:
params['Random seed for shuffling'] = self.random_state
return list(params.items())
def get_learner_parameters(self):
params = OrderedDict({})
# Classification loss function
params['Classification loss function'] = self.cls_losses[
self.cls_loss_function_index][0]
if self.cls_losses[self.cls_loss_function_index][1] in (
'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'):
params['Epsilon (ε) for classification'] = self.cls_epsilon
# Regression loss function
params['Regression loss function'] = self.reg_losses[
self.reg_loss_function_index][0]
if self.reg_losses[self.reg_loss_function_index][1] in (
'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'):
params['Epsilon (ε) for regression'] = self.reg_epsilon
params['Regularization'] = self.penalties[self.penalty_index][0]
# Regularization strength
if self.penalties[self.penalty_index][1] in ('l1', 'l2', 'elasticnet'):
params['Regularization strength (α)'] = self.alpha
# Elastic Net mixing
if self.penalties[self.penalty_index][1] in ('elasticnet',):
params['Elastic Net mixing parameter (L1 ratio)'] = self.l1_ratio
params['Learning rate'] = self.learning_rates[
self.learning_rate_index][0]
# Eta
if self.learning_rates[self.learning_rate_index][1] in \
('constant', 'invscaling'):
params['Initial learning rate (η<sub>0</sub>)'] = self.eta0
# t
if self.learning_rates[self.learning_rate_index][1] in \
('invscaling',):
params['Inverse scaling exponent (t)'] = self.power_t
params['Shuffle data after each iteration'] = bool_str(self.shuffle)
if self.use_random_state:
params['Random seed for shuffling'] = self.random_state
return list(params.items())
