def main():
np.random.seed(42)
X_train = np.array([
[-3.0],
[-1.0],
[0.0],
[1.0],
[2.0],
[4.0],
])
Y_train = np.cos(X_train) + np.random.randn(X_train.shape[0], 1) * 0.2
num_test = 10000
X_test = np.linspace(-5, 5, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test = np.cos(X_test)
num_trees = 100
depth_max = 5
size_min_leaf = 2
ratio_sampling = 0.8
replace_samples = False
num_features = 1
split_random_location = True
trees = trees_generic_trees.get_generic_trees(
X_train, Y_train, num_trees, depth_max, size_min_leaf, ratio_sampling, replace_samples, num_features, split_random_location
)
mu, sigma = trees_common.predict_by_trees(X_test, trees)
utils_plotting.plot_gp_via_distribution(X_train, Y_train, X_test, mu, sigma, Y_test, path_save=PATH_SAVE, str_postfix='cos')
def compute_posteriors(self, X: np.ndarray,
trees: constants.TYPING_LIST) -> np.ndarray:
"""
It returns posterior mean and standard deviation functions over `X`.
:param X: inputs to test. Shape: (l, d).
:type X: numpy.ndarray
:param trees: list of trees.
:type trees: list
:returns: posterior mean and standard deviation functions
over `X`. Shape: ((l, ), (l, )).
:rtype: (numpy.ndarray, numpy.ndarray)
:raises: AssertionError
"""
assert isinstance(X, np.ndarray)
assert isinstance(trees, list)
assert len(X.shape) == 2
assert X.shape[1] == self.num_dim
pred_mean, pred_std = trees_common.predict_by_trees(X, trees)
pred_mean = np.squeeze(pred_mean, axis=1)
pred_std = np.squeeze(pred_std, axis=1)
return pred_mean, pred_std
def main():
time_start = time.time()
np.random.seed(42)
X_train = np.array([
[-3.0],
[-1.0],
[0.0],
[1.0],
[2.0],
[4.0],
])
Y_train = np.cos(X_train) + np.random.randn(X_train.shape[0], 1) * 0.2
num_test = 10000
X_test = np.linspace(-5, 5, num_test)
X_test = X_test.reshape((num_test, 1))
Y_test = np.cos(X_test)
num_trees = 100
depth_max = 5
size_min_leaf = 2
num_features = 1
trees = trees_random_forest.get_random_forest(
X_train, Y_train, num_trees, depth_max, size_min_leaf, num_features
)
mu, sigma = trees_common.predict_by_trees(X_test, trees)
time_end = time.time()
print('time consumed: {:.4f}'.format(time_end - time_start))
utils_plotting.plot_gp_via_distribution(X_train, Y_train, X_test, mu, sigma, Y_test, path_save=PATH_SAVE, str_postfix='cos')
def _predict_by_trees(num_test):
X_test = np.linspace(-5, 5, num_test)
X_test = X_test.reshape((num_test, 1))
mu, sigma = trees_common.predict_by_trees(X_test, trees)
return mu, sigma
def test_predict_by_trees():
np.random.seed(42)
X = np.reshape(np.arange(0, 40), (10, 4))
Y = np.random.randn(10, 1)
depth_max = 4
size_min_leaf = 2
num_features = 2
split_random_location = True
node_1 = package_target._split(X, Y, num_features, split_random_location)
package_target.split(node_1, depth_max, size_min_leaf, num_features,
split_random_location, 1)
node_2 = package_target._split(X, Y, num_features, split_random_location)
package_target.split(node_2, depth_max, size_min_leaf, num_features,
split_random_location, 1)
node_3 = package_target._split(X, Y, num_features, split_random_location)
package_target.split(node_3, depth_max, size_min_leaf, num_features,
split_random_location, 1)
list_trees = [node_1, node_2, node_3]
with pytest.raises(AssertionError) as error:
package_target.predict_by_trees(np.array([4.0, 2.0, 3.0, 1.0]), node_1)
with pytest.raises(AssertionError) as error:
package_target.predict_by_trees(np.array([4.0, 2.0, 3.0, 1.0]), 'abc')
with pytest.raises(AssertionError) as error:
package_target.predict_by_trees(np.array([4.0, 2.0, 3.0, 1.0]),
list_trees)
with pytest.raises(AssertionError) as error:
package_target.predict_by_trees('abc', list_trees)
means, stds = package_target.predict_by_trees(X, list_trees)
print(means)
print(stds)
means_truth = np.array([
[0.33710618],
[0.1254467],
[0.68947573],
[0.9866563],
[0.16257799],
[0.16258346],
[0.85148454],
[0.55084716],
[0.30721653],
[0.30721653],
])
stds_truth = np.array([
[0.29842457],
[0.33330409],
[0.44398864],
[0.72536523],
[0.74232577],
[0.74232284],
[0.83388663],
[0.5615399],
[0.64331582],
[0.64331582],
])
assert isinstance(means, np.ndarray)
assert isinstance(stds, np.ndarray)
assert len(means.shape) == 2
assert len(stds.shape) == 2
assert means.shape[0] == stds.shape[0] == X.shape[0]
assert means.shape[1] == stds.shape[1] == 1
assert np.all(np.abs(means - means_truth) < TEST_EPSILON)
assert np.all(np.abs(stds - stds_truth) < TEST_EPSILON)
X = np.random.randn(1000, 4)
means, stds = package_target.predict_by_trees(X, list_trees)
assert isinstance(means, np.ndarray)
assert isinstance(stds, np.ndarray)
assert len(means.shape) == 2
assert len(stds.shape) == 2
assert means.shape[0] == stds.shape[0] == X.shape[0]
assert means.shape[1] == stds.shape[1] == 1