def test_pickle():
"""Check pickability"""
# Check the regressor
est = SymbolicRegressor(generations=2, random_state=0)
est.fit(boston.data[:100, :], boston.target[:100])
score = est.score(boston.data[500:, :], boston.target[500:])
pickle_object = pickle.dumps(est)
est2 = pickle.loads(pickle_object)
assert_equal(type(est2), est.__class__)
score2 = est2.score(boston.data[500:, :], boston.target[500:])
assert_equal(score, score2)
# Check the transformer
est = SymbolicTransformer(generations=2, random_state=0)
est.fit(boston.data[:100, :], boston.target[:100])
X_new = est.transform(boston.data[500:, :])
pickle_object = pickle.dumps(est)
est2 = pickle.loads(pickle_object)
assert_equal(type(est2), est.__class__)
X_new2 = est2.transform(boston.data[500:, :])
assert_array_almost_equal(X_new, X_new2)
# Check the classifier
est = SymbolicClassifier(generations=2, random_state=0)
est.fit(cancer.data[:100, :], cancer.target[:100])
score = est.score(cancer.data[500:, :], cancer.target[500:])
pickle_object = pickle.dumps(est)
est2 = pickle.loads(pickle_object)
assert_equal(type(est2), est.__class__)
score2 = est2.score(cancer.data[500:, :], cancer.target[500:])
assert_equal(score, score2)
def test_pickle():
"""Check pickability"""
# Check the regressor
est = SymbolicRegressor(generations=2, random_state=0)
est.fit(boston.data[:100, :], boston.target[:100])
score = est.score(boston.data[500:, :], boston.target[500:])
pickle_object = pickle.dumps(est)
est2 = pickle.loads(pickle_object)
assert_equal(type(est2), est.__class__)
score2 = est2.score(boston.data[500:, :], boston.target[500:])
assert_equal(score, score2)
# Check the transformer
est = SymbolicTransformer(generations=2, random_state=0)
est.fit(boston.data[:100, :], boston.target[:100])
X_new = est.transform(boston.data[500:, :])
pickle_object = pickle.dumps(est)
est2 = pickle.loads(pickle_object)
assert_equal(type(est2), est.__class__)
X_new2 = est2.transform(boston.data[500:, :])
assert_array_almost_equal(X_new, X_new2)
x = scaling(x)
est = Ridge()
est.fit(x[:300, :], y[:300])
print(est.score(x[300:, :], y[300:]))
function_set = [
'add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv', 'max',
'min'
]
gp = SymbolicRegressor(generations=20,
population_size=2000,
hall_of_fame=100,
n_components=10,
function_set=function_set,
parsimony_coefficient=0.0005,
max_samples=0.9,
verbose=1,
random_state=0,
n_jobs=3)
gp.fit(x[:300, :], y[:300])
gp_features = gp.transform(x)
new_boston = np.hstack((x, gp_features))
est = Ridge()
est.fit(new_boston[:300, :], y[:300])
print(est.score(new_boston[300:, :], y[300:]))
