wehub-resource-sync
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
import logging
|
|
|
|
import numpy as np
|
|
from sklearn.datasets import make_classification
|
|
from sklearn.datasets import make_regression
|
|
from sklearn.metrics import roc_auc_score, accuracy_score
|
|
|
|
try:
|
|
from sklearn.model_selection import train_test_split
|
|
except ImportError:
|
|
from sklearn.cross_validation import train_test_split
|
|
|
|
from mla.ensemble.random_forest import RandomForestClassifier, RandomForestRegressor
|
|
from mla.metrics.metrics import mean_squared_error
|
|
|
|
logging.basicConfig(level=logging.DEBUG)
|
|
|
|
|
|
def classification():
|
|
# Generate a random binary classification problem.
|
|
X, y = make_classification(
|
|
n_samples=500,
|
|
n_features=10,
|
|
n_informative=10,
|
|
random_state=1111,
|
|
n_classes=2,
|
|
class_sep=2.5,
|
|
n_redundant=0,
|
|
)
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
X, y, test_size=0.15, random_state=1111
|
|
)
|
|
|
|
model = RandomForestClassifier(n_estimators=10, max_depth=4)
|
|
model.fit(X_train, y_train)
|
|
|
|
predictions_prob = model.predict(X_test)[:, 1]
|
|
predictions = np.argmax(model.predict(X_test), axis=1)
|
|
# print(predictions.shape)
|
|
print("classification, roc auc score: %s" % roc_auc_score(y_test, predictions_prob))
|
|
print("classification, accuracy score: %s" % accuracy_score(y_test, predictions))
|
|
|
|
|
|
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=0.1, random_state=1111
|
|
)
|
|
|
|
model = RandomForestRegressor(n_estimators=50, max_depth=10, max_features=3)
|
|
model.fit(X_train, y_train)
|
|
predictions = model.predict(X_test)
|
|
print(
|
|
"regression, mse: %s"
|
|
% mean_squared_error(y_test.flatten(), predictions.flatten())
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
classification()
|
|
# regression()
|