def get_knob_config():
return {
'n_estimators': IntegerKnob(50, 200),
'oob_score': CategoricalKnob([True, False]),
'max_depth': IntegerKnob(10, 100),
'max_features': CategoricalKnob(['auto', 'sqrt', 'log2'])
}
def get_knob_config():
return {
'learning_rate': FloatKnob(1e-2, 1e-1, is_exp=True),
'num_leaves': IntegerKnob(20, 60),
'colsample_bytree': FloatKnob(1e-1, 1),
'subsample': FloatKnob(1e-1, 1),
'max_depth': IntegerKnob(1, 10),
}
def get_knob_config():
return {
'n_estimators': IntegerKnob(50, 200),
'min_child_weight': IntegerKnob(1, 6),
'max_depth': IntegerKnob(1, 10),
'gamma': FloatKnob(0.0, 1.0, is_exp=False),
'subsample': FloatKnob(0.5, 1.0, is_exp=False),
'colsample_bytree': FloatKnob(0.1, 0.7, is_exp=False)
}
def get_knob_config():
return {
'epochs': FixedKnob(1),
'word_embed_dims': IntegerKnob(16, 128),
'word_rnn_hidden_size': IntegerKnob(16, 128),
'word_dropout': FloatKnob(1e-3, 2e-1, is_exp=True),
'learning_rate': FloatKnob(1e-2, 1e-1, is_exp=True),
'batch_size': CategoricalKnob([16, 32, 64, 128]),
}
def get_knob_config():
return {
'criterion': CategoricalKnob(['mse', 'mae']),
'splitter': CategoricalKnob(['best', 'random']),
'min_samples_split': IntegerKnob(2, 5),
'max_features': CategoricalKnob(['auto', 'sqrt']),
'random_state': IntegerKnob(1, 123),
'min_impurity_decrease': FloatKnob(0.0, 0.2),
'min_impurity_split': FloatKnob(1e-07, 1e-03)
}
def get_knob_config():
return {
'max_epochs': FixedKnob(10),
'hidden_layer_count': IntegerKnob(1, 2),
'hidden_layer_units': IntegerKnob(2, 128),
'learning_rate': FloatKnob(1e-5, 1e-1, is_exp=True),
'batch_size': CategoricalKnob([16, 32, 64, 128]),
'max_image_size': CategoricalKnob([16, 32, 48]),
'quick_train':
PolicyKnob('EARLY_STOP') # Whether early stopping would be used
}
def get_knob_config():
return {
'C': IntegerKnob(2, 3),
'kernel': CategoricalKnob(['poly', 'rbf', 'linear']),
'degree': IntegerKnob(2, 3),
'gamma': CategoricalKnob(['scale', 'auto']),
'coef0': FloatKnob(0.0, 0.1),
'shrinking': CategoricalKnob([True, False]),
'tol': FloatKnob(1e-03, 1e-01, is_exp=True),
'decision_function_shape': CategoricalKnob(['ovo', 'ovr']),
'probability': CategoricalKnob([True, False]),
}
def get_knob_config():
return {
'C': FloatKnob(1.0, 1.5),
'tol': FloatKnob(1e-03, 1e-01, is_exp=True),
'validation_fraction': FloatKnob(0.01, 0.1),
'n_iter_no_change': IntegerKnob(3, 5),
'shuffle': CategoricalKnob([True, False]),
'loss': CategoricalKnob(['hinge', 'squared_hinge']),
'random_state': IntegerKnob(1, 2),
'warm_start': CategoricalKnob([True, False]),
'average': IntegerKnob(1, 5),
}
def get_knob_config():
return {
'max_depth': IntegerKnob(1, 32),
'splitter': CategoricalKnob(['best', 'random']),
'criterion': CategoricalKnob(['gini', 'entropy']),
'max_image_size': CategoricalKnob([16, 32])
}
def get_knob_config():
return {
'penalty': CategoricalKnob(['l1', 'l2']),
'tol': FloatKnob(0.0001, 0.001),
'C': IntegerKnob(4, 15),
'fit_intercept': CategoricalKnob([True, False]),
'solver': CategoricalKnob(['lbfgs', 'liblinear']),
}
def get_knob_config():
return {
'alpha': FloatKnob(0.001, 0.01),
'normalize': CategoricalKnob([True, False]),
'copy_X': CategoricalKnob([True, False]),
'tol': FloatKnob(1e-05, 1e-04),
'solver': CategoricalKnob(['svd', 'sag']),
'random_state': IntegerKnob(1, 123)
}
def get_knob_config():
return {
'n_neighbors': IntegerKnob(3, 4, 6),
'metric': CategoricalKnob(['minkowski', 'euclidean']),
'p': IntegerKnob(1, 2),
}
def get_knob_config():
return {
'epoch': IntegerKnob(5, 10),
'learning_rate': FloatKnob(1e-3, 1e-1, is_exp=True),
'layer_dim': CategoricalKnob([50, 100, 250])
}