def __init__(self, options):
self.handle_options(options)
params = options.get('params', {})
out_params = convert_params(
params,
strs=['loss', 'max_features'],
floats=['learning_rate', 'min_weight_fraction_leaf'],
ints=[
'n_estimators', 'max_depth', 'min_samples_split',
'min_samples_leaf', 'max_leaf_nodes', 'random_state'
],
)
valid_loss = ['deviance', 'exponential']
if 'loss' in out_params:
if out_params['loss'] not in valid_loss:
msg = "loss must be one of: {}".format(', '.join(valid_loss))
raise RuntimeError(msg)
if 'max_features' in out_params:
out_params['max_features'] = handle_max_features(
out_params['max_features'])
if 'max_leaf_nodes' in out_params and 'max_depth' in out_params:
messages.warn('max_depth ignored when max_leaf_nodes is set')
self.estimator = _GradientBoostingClassifier(**out_params)
def __init__(self, options):
self.handle_options(options)
out_params = convert_params(
options.get('params', {}),
ints=['random_state', 'n_estimators', 'max_depth',
'min_samples_split', 'max_leaf_nodes'],
strs=['max_features', 'criterion'],
)
if 'max_depth' not in out_params:
out_params.setdefault('max_leaf_nodes', 2000)
if 'max_features' in out_params:
out_params['max_features'] = handle_max_features(out_params['max_features'])
self.estimator = _RandomForestClassifier(class_weight='balanced',
**out_params)
def __init__(self, options):
self.handle_options(options)
params = options.get('params', {})
out_params = convert_params(
params,
strs=['loss', 'max_features'],
floats=['learning_rate', 'min_weight_fraction_leaf', 'alpha', 'subsample'],
ints=['n_estimators', 'max_depth', 'min_samples_split',
'min_samples_leaf', 'max_leaf_nodes', 'random_state'],
)
valid_loss = ['ls', 'lad', 'huber', 'quantile']
if 'loss' in out_params:
if out_params['loss'] not in valid_loss:
msg = "loss must be one of: {}".format(', '.join(valid_loss))
raise RuntimeError(msg)
if 'max_features' in out_params:
out_params['max_features'] = handle_max_features(out_params['max_features'])
self.estimator = _GradientBoostingRegressor(**out_params)