def test_inner_product_knn(self):
knn = KNN(1, inner_product_distance)
features = [[1, 1], [1, 2], [2, 2], [9, 9], [8, 8], [8, 9]]
values = [0, 0, 0, 1, 1, 1]
knn.train(features, values)
point1 = [0, 0]
neighbor = knn.get_neighbors(point1)
self.assertEqual(0, knn.get_response(neighbor))
numpy.testing.assert_array_equal(numpy.array([[1, 1, 0]]), neighbor)
point2 = [10, 10]
neighbor = knn.get_neighbors(point2)
numpy.testing.assert_array_equal(numpy.array([[1, 1, 0]]), neighbor)
self.assertEqual(1, knn.get_response(neighbor))
class TestKNN(TestCase):
def setUp(self):
self.knn = KNN(1, euclidean_distance)
def test_train(self):
features = [[1, 1], [1, 2], [2, 2], [9, 9], [8, 8], [8, 9]]
values = [0, 0, 0, 1, 1, 1]
self.knn.train(features, values)
point1 = [0, 0]
neighbor = self.knn.get_neighbors(point1)
self.assertEqual(0, self.knn.get_response(neighbor))
numpy.testing.assert_array_equal(numpy.array([[1, 1, 0]]), neighbor)
point2 = [10, 10]
neighbor = self.knn.get_neighbors(point2)
numpy.testing.assert_array_equal(numpy.array([[9, 9, 1]]), neighbor)
self.assertEqual(1, self.knn.get_response(neighbor))
@skip("clarify inner product")
def test_inner_product_knn(self):
knn = KNN(1, inner_product_distance)
features = [[1, 1], [1, 2], [2, 2], [9, 9], [8, 8], [8, 9]]
values = [0, 0, 0, 1, 1, 1]
knn.train(features, values)
point1 = [0, 0]
neighbor = knn.get_neighbors(point1)
self.assertEqual(0, knn.get_response(neighbor))
numpy.testing.assert_array_equal(numpy.array([[1, 1, 0]]), neighbor)
point2 = [10, 10]
neighbor = knn.get_neighbors(point2)
numpy.testing.assert_array_equal(numpy.array([[1, 1, 0]]), neighbor)
self.assertEqual(1, knn.get_response(neighbor))
def test_predict(self):
features = [[1, 1], [1, 2], [2, 2], [9, 9], [8, 8], [8, 9]]
values = [0, 0, 0, 1, 1, 1]
self.knn.train(features, values)
points = [[0, 0], [10, 10]]
self.assertListEqual([0, 1], self.knn.predict(points))
def test_knn(self):
features, labels = generate_data_cancer()
train_features, train_labels = features[:400], labels[:400]
valid_features, valid_labels = features[400:460], labels[400:460]
test_features, test_labels = features[460:], labels[460:]
assert len(train_features) == len(train_labels) == 400
assert len(valid_features) == len(valid_labels) == 60
assert len(test_features) == len(test_labels) == 109
distance_funcs = {
# 'euclidean': euclidean_distance,
# 'gaussian': gaussian_kernel_distance,
'inner_prod': inner_product_distance,
}
for name, func in distance_funcs.items():
best_f1_score, best_k = -1, 0
for k in [1]:
model = KNN(k=k, distance_function=func)
model.train(train_features, train_labels)
# print(train_labels)
# print(model.predict(train_features))
train_f1_score = f1_score(train_labels,
model.predict(train_features))
valid_f1_score = f1_score(valid_labels,
model.predict(valid_features))
print(f'[part 2.1] {name}\tk: {k:d}\t'
f'train: {train_f1_score:.5f}\t'
f'valid: {valid_f1_score:.5f}')
if valid_f1_score > best_f1_score:
best_f1_score, best_k = valid_f1_score, k
model = KNN(k=best_k, distance_function=func)
model.train(train_features + valid_features,
train_labels + valid_labels)
test_f1_score = f1_score(test_labels, model.predict(test_features))
print()
print(f'[part 2.1] {name}\tbest_k: {best_k:d}\t'
f'test f1 score: {test_f1_score:.5f}')
print()
def test_normalization(self):
scaling_functions = {
'min_max_scale': MinMaxScaler,
'normalize': NormalizationScaler,
}
distance_funcs = {
'euclidean': euclidean_distance,
'gaussian': gaussian_kernel_distance,
'inner_prod': inner_product_distance,
}
features, labels = generate_data_cancer()
train_features, train_labels = features[:400], labels[:400]
valid_features, valid_labels = features[400:460], labels[400:460]
test_features, test_labels = features[460:], labels[460:]
assert len(train_features) == len(train_labels) == 400
assert len(valid_features) == len(valid_labels) == 60
assert len(test_features) == len(test_labels) == 109
for scaling_name, scaling_class in scaling_functions.items():
for name, func in distance_funcs.items():
scaler = scaling_class()
train_features_scaled = scaler(train_features)
valid_features_scaled = scaler(valid_features)
best_f1_score, best_k = 0, -1
for k in [1, 3, 10, 20, 50]:
model = KNN(k=k, distance_function=func)
model.train(train_features_scaled, train_labels)
train_f1_score = f1_score(
train_labels, model.predict(train_features_scaled))
valid_f1_score = f1_score(
valid_labels, model.predict(valid_features_scaled))
print('[part 2.2] {name}\t{scaling_name}\tk: {k:d}\t'.
format(name=name, scaling_name=scaling_name, k=k) +
'train: {train_f1_score:.5f}\t'.format(
train_f1_score=train_f1_score) +
'valid: {valid_f1_score:.5f}'.format(
valid_f1_score=valid_f1_score))
if valid_f1_score > best_f1_score:
best_f1_score, best_k = valid_f1_score, k
# now change it to new scaler, since the training set changes
scaler = scaling_class()
combined_features_scaled = scaler(train_features +
valid_features)
test_features_scaled = scaler(test_features)
model = KNN(k=best_k, distance_function=func)
model.train(combined_features_scaled,
train_labels + valid_labels)
test_f1_score = f1_score(test_labels,
model.predict(test_features_scaled))
print()
print('[part 2.2] {name}\t{scaling_name}\t'.format(
name=name, scaling_name=scaling_name) +
'best_k: {best_k:d}\ttest: {test_f1_score:.5f}'.format(
best_k=best_k, test_f1_score=test_f1_score))
print()
