chore: import upstream snapshot with attribution

mla/ensemble/base.py

@@ -0,0 +1,65 @@
+# coding:utf-8
+import numpy as np
+from scipy import stats
+
+
+def f_entropy(p):
+    # Convert values to probability
+    p = np.bincount(p) / float(p.shape[0])
+
+    ep = stats.entropy(p)
+    if ep == -float("inf"):
+        return 0.0
+    return ep
+
+
+def information_gain(y, splits):
+    splits_entropy = sum(
+        [f_entropy(split) * (float(split.shape[0]) / y.shape[0]) for split in splits]
+    )
+    return f_entropy(y) - splits_entropy
+
+
+def mse_criterion(y, splits):
+    y_mean = np.mean(y)
+    return -sum(
+        [
+            np.sum((split - y_mean) ** 2) * (float(split.shape[0]) / y.shape[0])
+            for split in splits
+        ]
+    )
+
+
+def xgb_criterion(y, left, right, loss):
+    left = loss.gain(left["actual"], left["y_pred"])
+    right = loss.gain(right["actual"], right["y_pred"])
+    initial = loss.gain(y["actual"], y["y_pred"])
+    gain = left + right - initial
+    return gain
+
+
+def get_split_mask(X, column, value):
+    left_mask = X[:, column] < value
+    right_mask = X[:, column] >= value
+    return left_mask, right_mask
+
+
+def split(X, y, value):
+    left_mask = X < value
+    right_mask = X >= value
+    return y[left_mask], y[right_mask]
+
+
+def split_dataset(X, target, column, value, return_X=True):
+    left_mask, right_mask = get_split_mask(X, column, value)
+
+    left, right = {}, {}
+    for key in target.keys():
+        left[key] = target[key][left_mask]
+        right[key] = target[key][right_mask]
+
+    if return_X:
+        left_X, right_X = X[left_mask], X[right_mask]
+        return left_X, right_X, left, right
+    else:
+        return left, right