def test_x_parameter_with_dimension_different_from_2_should_raise_exception_when_predicting_proba(
):
X = np.array([[1, 1], [1000, 1000], [2, 2], [980, 1099], [3, 3],
[1100, 1010]])
y = np.array([1, 2, 1, 2, 1, 2])
X_test = np.array([1, 5, 999, 999, 15, 15, 1500, 1500, 50, 50])
knn = KNN()
knn.fit(X, y)
with pytest.raises(InvalidDimension):
knn.predict_proba(X_test)
def test_x_parameter_with_different_number_of_columns_from_training_x_should_raise_exception_when_predicting_proba(
):
X = np.array([[1, 1], [1000, 1000], [2, 2], [980, 1099], [3, 3],
[1100, 1010]])
y = np.array([1, 2, 1, 2, 1, 2])
X_test = np.array([[1, 5, 5], [999, 999, 999], [15, 15, 15],
[1500, 1500, 1500], [50, 50, 50]])
knn = KNN()
knn.fit(X, y)
with pytest.raises(IncompatibleShape):
knn.predict_proba(X_test)
def test_knn_predict_probabilities_comparing_with_sklearn_knn():
digits = datasets.load_digits()
metric = 'euclidean'
k = 11
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
sk_knn = KNeighborsClassifier(n_neighbors=k, metric=metric)
sk_knn.fit(X_train, y_train)
sk_result = sk_knn.predict_proba(X_test)
model = KNN(k=k, metric=metric)
model.fit(X_train, y_train)
result = model.predict_proba(X_test)
assert np.all(result == sk_result)