chore: import upstream snapshot with attribution

examples/random_forest.py

@@ -0,0 +1,71 @@
+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()