def test_searchcv_sklearn_compatibility():
"""
Test whether the BayesSearchCV is compatible with base sklearn methods
such as clone, set_params, get_params.
"""
X, y = load_iris(True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.75,
random_state=0)
# used to try different model classes
pipe = Pipeline([('model', SVC())])
# single categorical value of 'model' parameter sets the model class
lin_search = {
'model': Categorical([LinearSVC()]),
'model__C': Real(1e-6, 1e+6, prior='log-uniform'),
}
dtc_search = {
'model': Categorical([DecisionTreeClassifier()]),
'model__max_depth': Integer(1, 32),
'model__min_samples_split': Real(1e-3, 1.0, prior='log-uniform'),
}
svc_search = {
'model': Categorical([SVC()]),
'model__C': Real(1e-6, 1e+6, prior='log-uniform'),
'model__gamma': Real(1e-6, 1e+1, prior='log-uniform'),
'model__degree': Integer(1, 8),
'model__kernel': Categorical(['linear', 'poly', 'rbf']),
}
opt = BayesSearchCV(pipe, [(lin_search, 1), svc_search], n_iter=2)
opt_clone = clone(opt)
params, params_clone = opt.get_params(), opt_clone.get_params()
assert params.keys() == params_clone.keys()
for param, param_clone in zip(params.items(), params_clone.items()):
assert param[0] == param_clone[0]
assert isinstance(param[1], type(param_clone[1]))
opt.set_params(search_spaces=[(dtc_search, 1)])
opt.fit(X_train, y_train)
opt_clone.fit(X_train, y_train)
total_evaluations = len(opt.cv_results_['mean_test_score'])
total_evaluations_clone = len(opt_clone.cv_results_['mean_test_score'])
# test if expected number of subspaces is explored
assert total_evaluations == 1
assert total_evaluations_clone == 1 + 2
def test_searchcv_sklearn_compatibility():
"""
Test whether the BayesSearchCV is compatible with base sklearn methods
such as clone, set_params, get_params.
"""
X, y = load_iris(True)
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=0.75, random_state=0
)
# used to try different model classes
pipe = Pipeline([
('model', SVC())
])
# single categorical value of 'model' parameter sets the model class
lin_search = {
'model': Categorical([LinearSVC()]),
'model__C': Real(1e-6, 1e+6, prior='log-uniform'),
}
dtc_search = {
'model': Categorical([DecisionTreeClassifier()]),
'model__max_depth': Integer(1, 32),
'model__min_samples_split': Real(1e-3, 1.0, prior='log-uniform'),
}
svc_search = {
'model': Categorical([SVC()]),
'model__C': Real(1e-6, 1e+6, prior='log-uniform'),
'model__gamma': Real(1e-6, 1e+1, prior='log-uniform'),
'model__degree': Integer(1, 8),
'model__kernel': Categorical(['linear', 'poly', 'rbf']),
}
opt = BayesSearchCV(
pipe,
[(lin_search, 1), svc_search],
n_iter=2
)
opt_clone = clone(opt)
params, params_clone = opt.get_params(), opt_clone.get_params()
assert params.keys() == params_clone.keys()
for param, param_clone in zip(params.items(), params_clone.items()):
assert param[0] == param_clone[0]
assert isinstance(param[1], type(param_clone[1]))
opt.set_params(search_spaces=[(dtc_search, 1)])
opt.fit(X_train, y_train)
opt_clone.fit(X_train, y_train)
total_evaluations = len(opt.cv_results_['mean_test_score'])
total_evaluations_clone = len(opt_clone.cv_results_['mean_test_score'])
# test if expected number of subspaces is explored
assert total_evaluations == 1
assert total_evaluations_clone == 1+2
cv_folds = [train_test_split(range(len(X)), train_size=0.666)]
model = BayesSearchCV(estimator=pipe,
search_spaces={
'model__latent_dim': (2, 20),
'model__intermediate_dim': (8, 128),
'model__epochs': (8, 128),
'model__D': (1e-3, 1e+3, 'log-uniform'),
'model__lr': (1e-4, 1e-2, 'log-uniform'),
},
n_iter=32,
cv=cv_folds,
refit=False,
error_score=-1.0)
model.on_step = lambda x: print(
(x, model.total_iterations(), model.best_score_))
model.fit(X, Y)
model.refit = True
model._fit_best_model(X, Y)
print(model.best_params_)
print(model.score(X, Y))
"""
model = pipe
model.set_params(**{'model__D': 5.1964624423233898, 'model__lr': 0.00010138257365940301,
'model__epochs': 26, 'model__intermediate_dim': 125, 'model__latent_dim': 2})
model.fit(X, Y)
print(model.predict(X, Y))
"""
