feat(ensemble): add XGBoost

neuralnetlib/ensemble.py

@@ -593,3 +593,215 @@ def predict_proba(self, X):
 
         proba = self._sigmoid(predictions)
         return np.vstack([1 - proba, proba]).T
+
+
+
+class XGBoostObjective(Enum):
+    REG_SQUAREDERROR = "reg:squarederror"
+    BINARY_LOGISTIC = "binary:logistic"
+
+class XGBoostNode:
+    def __init__(self):
+        self.feature_idx: int = None
+        self.threshold: float = None
+        self.left: 'XGBoostNode' = None
+        self.right: 'XGBoostNode' = None
+        self.value: float = None
+        self.gain: float = 0.0
+        self.cover: float = 0.0
+
+class XGBoostTree:
+    def __init__(self, max_depth: int = 6, min_child_weight: float = 1.0,
+                 lambda_: float = 1.0, gamma: float = 0.0):
+        self.max_depth = max_depth
+        self.min_child_weight = min_child_weight
+        self.lambda_ = lambda_
+        self.gamma = gamma
+        self.root = None
+
+    def _calc_leaf_value(self, grad: np.ndarray, hess: np.ndarray) -> float:
+        return -np.sum(grad) / (np.sum(hess) + self.lambda_)
+
+    def _calc_gain(self, grad: np.ndarray, hess: np.ndarray) -> float:
+        G, H = np.sum(grad), np.sum(hess)
+        return (G * G) / (H + self.lambda_)
+
+    def _find_best_split(self, X: np.ndarray, grad: np.ndarray, hess: np.ndarray) -> tuple:
+        best_gain = 0.0
+        best_feature_idx = None
+        best_threshold = None
+        total_gain = self._calc_gain(grad, hess)
+        n_features = X.shape[1]
+
+        for feature_idx in range(n_features):
+            feature_values = X[:, feature_idx]
+            unique_values = np.unique(feature_values)
+
+            if len(unique_values) <= 1:
+                continue
+
+            thresholds = (unique_values[:-1] + unique_values[1:]) / 2
+
+            for threshold in thresholds:
+                left_mask = feature_values <= threshold
+                right_mask = ~left_mask
+
+                if (np.sum(hess[left_mask]) < self.min_child_weight or
+                    np.sum(hess[right_mask]) < self.min_child_weight):
+                    continue
+
+                left_gain = self._calc_gain(grad[left_mask], hess[left_mask])
+                right_gain = self._calc_gain(grad[right_mask], hess[right_mask])
+                gain = left_gain + right_gain - total_gain - self.gamma
+
+                if gain > best_gain:
+                    best_gain = gain
+                    best_feature_idx = feature_idx
+                    best_threshold = threshold
+
+        return best_feature_idx, best_threshold, best_gain
+
+    def _build_tree(self, X: np.ndarray, grad: np.ndarray, hess: np.ndarray,
+                    depth: int = 0) -> XGBoostNode:
+        node = XGBoostNode()
+        node.cover = np.sum(hess)
+
+        if depth >= self.max_depth:
+            node.value = self._calc_leaf_value(grad, hess)
+            return node
+
+        feature_idx, threshold, gain = self._find_best_split(X, grad, hess)
+
+        if feature_idx is None or gain <= 0:
+            node.value = self._calc_leaf_value(grad, hess)
+            return node
+
+        left_mask = X[:, feature_idx] <= threshold
+        right_mask = ~left_mask
+
+        node.feature_idx = feature_idx
+        node.threshold = threshold
+        node.gain = gain
+        node.left = self._build_tree(X[left_mask], grad[left_mask], 
+                                   hess[left_mask], depth + 1)
+        node.right = self._build_tree(X[right_mask], grad[right_mask], 
+                                    hess[right_mask], depth + 1)
+
+        return node
+
+    def _predict_sample(self, x: np.ndarray, node: XGBoostNode) -> float:
+        if node.value is not None:
+            return node.value
+
+        if x[node.feature_idx] <= node.threshold:
+            return self._predict_sample(x, node.left)
+        return self._predict_sample(x, node.right)
+
+    def fit(self, X: np.ndarray, grad: np.ndarray, hess: np.ndarray) -> 'XGBoostTree':
+        self.root = self._build_tree(X, grad, hess)
+        return self
+
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        return np.array([self._predict_sample(x, self.root) for x in X])
+
+class XGBoost:
+    def __init__(self, objective: str = "reg:squarederror", n_estimators: int = 100,
+                 learning_rate: float = 0.3, max_depth: int = 6,
+                 min_child_weight: float = 1.0, subsample: float = 1.0,
+                 colsample_bytree: float = 1.0, lambda_: float = 1.0,
+                 gamma: float = 0.0, random_state: int = None):
+        self.objective = XGBoostObjective(objective)
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.min_child_weight = min_child_weight
+        self.subsample = subsample
+        self.colsample_bytree = colsample_bytree
+        self.lambda_ = lambda_
+        self.gamma = gamma
+        self.random_state = random_state
+        self.rng = np.random.default_rng(random_state)
+
+        self.trees = []
+        self.base_score = 0.5
+
+    def _sigmoid(self, x: np.ndarray) -> np.ndarray:
+        return 1 / (1 + np.exp(-x))
+
+    def _compute_gradients(self, y: np.ndarray, pred: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        if self.objective == XGBoostObjective.REG_SQUAREDERROR:
+            grad = pred - y
+            hess = np.ones_like(y)
+        else:
+            prob = self._sigmoid(pred)
+            grad = prob - y
+            hess = prob * (1 - prob)
+        return grad, hess
+
+    def _subsample_data(self, X: np.ndarray, y: np.ndarray,
+                       grad: np.ndarray, hess: np.ndarray) -> tuple:
+        # Row subsampling
+        if self.subsample < 1.0:
+            n_samples = int(X.shape[0] * self.subsample)
+            indices = self.rng.choice(X.shape[0], size=n_samples, replace=False)
+            X = X[indices]
+            y = y[indices]
+            grad = grad[indices]
+            hess = hess[indices]
+
+        if self.colsample_bytree < 1.0:
+            n_features = int(X.shape[1] * self.colsample_bytree)
+            feature_indices = self.rng.choice(X.shape[1], size=n_features, replace=False)
+            X = X[:, feature_indices]
+
+        return X, y, grad, hess
+
+    def fit(self, X: np.ndarray, y: np.ndarray) -> 'XGBoost':
+        if self.objective == XGBoostObjective.BINARY_LOGISTIC:
+            y = (y > 0).astype(np.float64)
+            self.base_score = np.log(np.mean(y) / (1 - np.mean(y) + 1e-6))
+        else:
+            self.base_score = np.mean(y)
+
+        predictions = np.full(X.shape[0], self.base_score)
+        self.trees = []
+
+        for _ in range(self.n_estimators):
+            grad, hess = self._compute_gradients(y, predictions)
+
+            X_tree, y_tree, grad_tree, hess_tree = self._subsample_data(X, y, grad, hess)
+
+            tree = XGBoostTree(
+                max_depth=self.max_depth,
+                min_child_weight=self.min_child_weight,
+                lambda_=self.lambda_,
+                gamma=self.gamma
+            )
+            tree.fit(X_tree, grad_tree, hess_tree)
+            self.trees.append(tree)
+
+            predictions += self.learning_rate * tree.predict(X)
+
+        return self
+
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        predictions = np.full(X.shape[0], self.base_score)
+
+        for tree in self.trees:
+            predictions += self.learning_rate * tree.predict(X)
+
+        if self.objective == XGBoostObjective.BINARY_LOGISTIC:
+            return (self._sigmoid(predictions) >= 0.5).astype(int)
+        return predictions
+
+    def predict_proba(self, X: np.ndarray) -> np.ndarray:
+        if self.objective != XGBoostObjective.BINARY_LOGISTIC:
+            raise ValueError("predict_proba is only available for binary classification")
+
+        predictions = np.full(X.shape[0], self.base_score)
+
+        for tree in self.trees:
+            predictions += self.learning_rate * tree.predict(X)
+
+        proba = self._sigmoid(predictions)
+        return np.vstack([1 - proba, proba]).T