wehub-resource-sync
60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
try:
|
|
from sklearn.model_selection import train_test_split
|
|
except ImportError:
|
|
from sklearn.cross_validation import train_test_split
|
|
from sklearn.datasets import make_classification
|
|
from sklearn.datasets import make_regression
|
|
from scipy.spatial import distance
|
|
|
|
from mla import knn
|
|
from mla.metrics.metrics import mean_squared_error, accuracy
|
|
|
|
|
|
def regression():
|
|
# Generate a random regression problem
|
|
X, y = make_regression(
|
|
n_samples=500,
|
|
n_features=5,
|
|
n_informative=5,
|
|
n_targets=1,
|
|
noise=0.05,
|
|
random_state=1111,
|
|
bias=0.5,
|
|
)
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
X, y, test_size=0.25, random_state=1111
|
|
)
|
|
|
|
model = knn.KNNRegressor(k=5, distance_func=distance.euclidean)
|
|
model.fit(X_train, y_train)
|
|
predictions = model.predict(X_test)
|
|
print("regression mse", mean_squared_error(y_test, predictions))
|
|
|
|
|
|
def classification():
|
|
X, y = make_classification(
|
|
n_samples=500,
|
|
n_features=5,
|
|
n_informative=5,
|
|
n_redundant=0,
|
|
n_repeated=0,
|
|
n_classes=3,
|
|
random_state=1111,
|
|
class_sep=1.5,
|
|
)
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
X, y, test_size=0.1, random_state=1111
|
|
)
|
|
|
|
clf = knn.KNNClassifier(k=5, distance_func=distance.euclidean)
|
|
|
|
clf.fit(X_train, y_train)
|
|
predictions = clf.predict(X_test)
|
|
print("classification accuracy", accuracy(y_test, predictions))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
regression()
|
|
classification()
|