def _kdtree(*, train, test, x_predict=None, metrics, X, leaf_size=40, metric='minkowski', **kwargs):
"""
For more info visit :
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree
"""
model = KDTree(X, leaf_size=leaf_size, metric=metric, **kwargs)
model.fit(train[0], train[1])
model_name = 'KD Tree'
y_hat = model.predict(test[0])
if metrics == 'accuracy':
accuracy = accuracy_score(test[1], y_hat)
if metrics == 'f1':
accuracy = f1_score(test[1], y_hat)
if metrics == 'jaccard':
accuracy = jaccard_score(test[1], y_hat)
if x_predict is None:
return (model_name, accuracy, None)
y_predict = model.predict(x_predict)
return (model_name, accuracy, y_predict)
def main():
y, X_train, X_test, y_train, y_test = load_data()
# use kd-tree
model = KDTree(X_train, metric='euclidean')
ind = model.query(X_test, k=3, return_distance=False)
predictions = []
k_nearest_indexes = ind.tolist()
for i in k_nearest_indexes:
counts = np.bincount(np.array(y[i]))
predictions.append(np.argmax(counts))
# knn
model = KNNClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"KNN Accuracy: {accuracy}")
