def verify_algorithm(x, y, split_number, split_size, k, seed=None):
"""
Parameters
----------
x : ht.DNDarray
array containing data vectors
y : ht.DNDarray
array containing the labels for x (must be in same order)
split_number: int
the number of test iterations
split_size : int
the number of vectors used by the KNN-Algorithm
k : int
The number of neighbours for KNN-Algorithm
seed : int
Seed for the random generator used in creating folds. Used for deterministic testing purposes.
Returns
-------
accuracies : ht.DNDarray
array of shape (split_number,) containing the accuracy per run
"""
assert len(x) == len(y)
assert split_size < len(x)
assert k < len(x)
accuracies = []
for split_index in range(split_number):
fold_x, fold_y, verification_x, verification_y = create_fold(
x, y, split_size, seed)
classifier = KNN(fold_x, fold_y, k)
result_y = classifier.predict(verification_x)
accuracies.append(calculate_accuracy(result_y, verification_y).item())
return accuracies
def test_fit_one_hot(self,):
X = ht.load_hdf5("heat/datasets/iris.h5", dataset="data")
# Keys as label array
keys = []
for i in range(50):
keys.append(0)
for i in range(50, 100):
keys.append(1)
for i in range(100, 150):
keys.append(2)
labels = ht.array(keys, split=0)
# Keys as one_hot
keys = []
for i in range(50):
keys.append([1, 0, 0])
for i in range(50, 100):
keys.append([0, 1, 0])
for i in range(100, 150):
keys.append([0, 0, 1])
Y = ht.array(keys)
knn = KNN(X, Y, 5)
knn.fit(X, Y)
result = knn.predict(X)
self.assertTrue(ht.is_estimator(knn))
self.assertTrue(ht.is_classifier(knn))
self.assertIsInstance(result, ht.DNDarray)
self.assertEqual(result.shape, labels.shape)
def test_utility(self,):
a = ht.array([1, 2, 3, 4])
b = ht.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]])
one_hot = KNN.label_to_one_hot(a)
self.assertTrue((one_hot == b).all())
def test_split_zero(self):
X = ht.load_hdf5("heat/datasets/iris.h5", dataset="data", split=0)
# Generate keys for the iris.h5 dataset
keys = []
for i in range(50):
keys.append(0)
for i in range(50, 100):
keys.append(1)
for i in range(100, 150):
keys.append(2)
Y = ht.array(keys, split=0)
knn = KNN(X, Y, 5)
result = knn.predict(X)
self.assertIsInstance(result, ht.DNDarray)
self.assertEqual(result.shape, Y.shape)
def test_split_none(self):
X = ht.load_hdf5("heat/datasets/iris.h5", dataset="data")
# Generate keys for the iris.h5 dataset
keys = []
for i in range(50):
keys.append(0)
for i in range(50, 100):
keys.append(1)
for i in range(100, 150):
keys.append(2)
Y = ht.array(keys)
knn = KNN(X, Y, 5)
result = knn.predict(X)
self.assertTrue(ht.is_estimator(knn))
self.assertTrue(ht.is_classifier(knn))
self.assertIsInstance(result, ht.DNDarray)
self.assertEqual(result.shape, Y.shape)
def test_exception(self,):
a = ht.zeros((3,))
b = ht.zeros((1,))
c = ht.zeros((3,))
d = ht.zeros((2, 2, 2))
with self.assertRaises(ValueError):
knn = KNN(a, b, 1)
with self.assertRaises(ValueError):
knn = KNN(a, b, 1)
knn = KNN(a, c, 1)
with self.assertRaises(ValueError):
knn.fit(a, b)
knn = KNN(a, c, 1)
with self.assertRaises(ValueError):
knn.fit(a, d)
with self.assertRaises(ValueError):
knn = KNN(a, d, 1)