def test_sample_training():
X_train, X_test = _get_mnist_data()
for no_trees, expected_precision in ((1, 0.05),
(5, 0.3),
(10, 0.5),
(50, 0.9)):
tree = RPForest(leaf_size=10, no_trees=no_trees)
# Fit on quarter of data
X_sample = X_train[:X_train.shape[0] / 4]
tree.fit(X_sample)
# Clear and index everything
tree.clear()
for i, x in enumerate(X_train):
tree.index(i, x)
tree._X = X_train
precision = 0.0
X_train /= np.linalg.norm(X_train, axis=1)[:, np.newaxis]
for x_test in X_test:
true_nns = np.argsort(-np.dot(X_train, x_test))[:10]
nns = tree.query(x_test, 10)[:10]
precision += len(set(nns) & set(true_nns)) / 10.0
precision /= X_test.shape[0]
assert precision >= expected_precision
def test_sample_training():
X_train, X_test = _get_mnist_data()
for no_trees, expected_precision in ((1, 0.05), (5, 0.3), (10, 0.5),
(50, 0.9)):
tree = RPForest(leaf_size=10, no_trees=no_trees)
# Fit on quarter of data
X_sample = X_train[:X_train.shape[0] / 4]
tree.fit(X_sample)
# Clear and index everything
tree.clear()
for i, x in enumerate(X_train):
tree.index(i, x)
tree._X = X_train
precision = 0.0
X_train /= np.linalg.norm(X_train, axis=1)[:, np.newaxis]
for x_test in X_test:
true_nns = np.argsort(-np.dot(X_train, x_test))[:10]
nns = tree.query(x_test, 10)[:10]
precision += len(set(nns) & set(true_nns)) / 10.0
precision /= X_test.shape[0]
assert precision >= expected_precision
