def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
# vw params
assert "loss_function" in params_arg
assert "passes" in params_arg
assert "l1" in params_arg
assert "l2" in params_arg
assert "decay_learning_rate" in params_arg
assert "learning_rate" in params_arg
def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
# xgb params
assert 'eval_metric' in params_arg
assert 'booster' in params_arg
assert 'objective' in params_arg
assert 'eta' in params_arg
assert 'num_round' in params_arg
assert 'silent' in params_arg
assert 'seed' in params_arg
assert 'num_boost_round' in params_arg
def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
# xgb params
assert 'eval_metric' in params_arg
assert 'booster' in params_arg
assert 'objective' in params_arg
assert 'eta' in params_arg
assert 'num_round' in params_arg
assert 'silent' in params_arg
assert 'seed' in params_arg
assert 'num_boost_round' in params_arg
def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
# keras params
assert 'loss_function' in params_arg
assert 'verbose' in params_arg
assert 'batch_norm' in params_arg
assert 'hidden_units' in params_arg
assert 'hidden_layers' in params_arg
assert 'input_dropout' in params_arg
assert 'hidden_dropout' in params_arg
assert 'hidden_activation' in params_arg
assert 'batch_size' in params_arg
assert 'nb_epoch' in params_arg
def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
assert 'type' in params_arg
if params_arg['type'] == 'random_forest':
# RF params
assert 'n_estimators' in params_arg
assert 'max_features' in params_arg
assert 'random_state' in params_arg
assert 'max_depth' in params_arg
assert 'n_jobs' in params_arg
if params_arg['type'] == 'logistic_regression':
# LogReg params
assert 'C' in params_arg
def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
assert "type" in params_arg
if params_arg["type"] == "random_forest":
# RF params
assert "n_estimators" in params_arg
assert "max_features" in params_arg
assert "random_state" in params_arg
assert "max_depth" in params_arg
assert "n_jobs" in params_arg
if params_arg["type"] == "logistic_regression":
# LogReg params
assert "C" in params_arg
def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
# keras params
assert 'loss_function' in params_arg
assert 'verbose' in params_arg
assert 'batch_norm' in params_arg
assert 'hidden_units' in params_arg
assert 'hidden_layers' in params_arg
assert 'input_dropout' in params_arg
assert 'hidden_dropout' in params_arg
assert 'hidden_activation' in params_arg
assert 'batch_size' in params_arg
assert 'nb_epoch' in params_arg
def assert_params_ok(params_arg):
Gopt.assert_params_ok(params_arg)
assert 'type' in params_arg
if params_arg['type'] == 'random_forest':
# RF params
assert 'n_estimators' in params_arg
assert 'max_features' in params_arg
assert 'random_state' in params_arg
assert 'max_depth' in params_arg
assert 'n_jobs' in params_arg
if params_arg['type'] == 'logistic_regression':
# LogReg params
assert 'C' in params_arg
