def test_adaptive_random_forests_batch_predict_proba():
stream = RandomTreeGenerator(tree_random_state=112,
sample_random_state=112,
n_classes=2)
stream.prepare_for_use()
learner = AdaptiveRandomForest(n_estimators=3, random_state=112)
X, y = stream.next_sample(150)
learner.partial_fit(X, y, classes=[0, 1])
cnt = 0
max_samples = 500
predictions = []
true_labels = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample(5)
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
p = learner.predict_proba(X)
assert p.shape == (5, 2)
predictions.append(p)
true_labels.append(y)
learner.partial_fit(X, y)
cnt += 1
all_predictions = np.concatenate(predictions)
# all_true_labels = np.asarray(true_labels).flatten()
# correct_predictions = sum(np.equal(all_true_labels, all_predictions.argmax(axis=1)))
assert np.alltrue([
np.isclose(y_proba.sum(), 1) for y_proba in all_predictions
]), "Probabilities should sum to 1."
assert all_predictions.shape == (4 * 5, 2)
last_version_predictions = [
1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1
]
assert type(learner.predict_proba(X)) == np.ndarray
assert np.alltrue(
all_predictions.argmax(axis=1) == last_version_predictions)
def test_adaptive_random_forests_labels_given():
stream = RandomTreeGenerator(tree_random_state=112,
sample_random_state=112,
n_classes=2)
stream.prepare_for_use()
learner = AdaptiveRandomForest(n_estimators=3, random_state=112)
X, y = stream.next_sample(150)
learner.partial_fit(X, y, classes=[0, 1])
cnt = 0
max_samples = 5000
predictions = []
true_labels = []
wait_samples = 100
correct_predictions = 0
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict_proba(X)[0])
true_labels.append(y[0])
if np.array_equal(y[0], predictions[-1].argmax()):
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
assert np.alltrue([
np.isclose(y_proba.sum(), 1) for y_proba in predictions
]), "Probabilities should sum to 1."
class_probabilities = np.asarray(predictions).squeeze()
assert class_probabilities.shape == (49, 2)
predictions = class_probabilities.argmax(axis=1)
last_version_predictions = [
1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,
0
]
assert np.alltrue(predictions == last_version_predictions)
