def test_find_self():
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)
tree.fit(X_train)
nodes = {k: set(v) for k, v in tree.get_leaf_nodes()}
for i, x_train in enumerate(X_train):
nns = tree.query(x_train, 10)[:10]
assert nns[0] == i
point_codes = tree.encode(x_train)
for code in point_codes:
assert i in nodes[code]
tree = pickle.loads(pickle.dumps(tree))
nodes = {k: set(v) for k, v in tree.get_leaf_nodes()}
for i, x_train in enumerate(X_train):
nns = tree.query(x_train, 10)[:10]
assert nns[0] == i
point_codes = tree.encode(x_train)
for code in point_codes:
assert i in nodes[code]
def test_find_self():
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)
tree.fit(X_train)
nodes = {k: set(v) for k, v in tree.get_leaf_nodes()}
for i, x_train in enumerate(X_train):
nns = tree.query(x_train, 10)[:10]
assert nns[0] == i
point_codes = tree.encode(x_train)
for code in point_codes:
assert i in nodes[code]
tree = pickle.loads(pickle.dumps(tree))
nodes = {k: set(v) for k, v in tree.get_leaf_nodes()}
for i, x_train in enumerate(X_train):
nns = tree.query(x_train, 10)[:10]
assert nns[0] == i
point_codes = tree.encode(x_train)
for code in point_codes:
assert i in nodes[code]
def test_encoding_mnist():
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)
tree.fit(X_train)
for x_train in X_train:
encodings_0 = tree.encode(x_train)
encodings_1 = tree.encode(x_train)
assert encodings_0 == encodings_1
tree = pickle.loads(pickle.dumps(tree))
for x_train in X_train:
encodings_0 = tree.encode(x_train)
encodings_1 = tree.encode(x_train)
assert encodings_0 == encodings_1
def test_encoding_mnist():
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)
tree.fit(X_train)
for x_train in X_train:
encodings_0 = tree.encode(x_train)
encodings_1 = tree.encode(x_train)
assert encodings_0 == encodings_1
tree = pickle.loads(pickle.dumps(tree))
for x_train in X_train:
encodings_0 = tree.encode(x_train)
encodings_1 = tree.encode(x_train)
assert encodings_0 == encodings_1
