Fixing and Updating Adaboost algorithm

Author: ai-nerdy

machine_learning/adaboost.py

@@ -13,7 +13,7 @@
 """
 
 import numpy as np
-from typing import Any, Dict, List
+from typing import Any
 
 
 class AdaBoost:
@@ -23,28 +23,25 @@ def __init__(self, n_estimators: int = 50) -> None:
  n_estimators: Number of boosting rounds.
  """
  self.n_estimators: int = n_estimators
- self.alphas: List[float] = [] # Weights for each weak learner
- self.models: List[Dict[str, Any]] = [] # List of weak learners (stumps)
+ self.alphas: list[float] = [] # Weights for each weak learner
+ self.models: list[dict[str, Any]] = [] # List of weak learners (stumps)
 
  def fit(self, feature_matrix: np.ndarray, target: np.ndarray) -> None:
  """Fit AdaBoost model.
  Args:
  feature_matrix: (n_samples, n_features) feature matrix
  target: (n_samples,) labels (0 or 1)
  """
- n_samples, n_features = feature_matrix.shape
- sample_weights = np.ones(n_samples) / n_samples # Initialize sample weights
+ n_samples, _ = feature_matrix.shape
+ sample_weights = np.ones(n_samples) / n_samples
  self.models = []
  self.alphas = []
- y_signed = np.where(target == 0, -1, 1) # Convert labels to -1, 1
+ y_signed = np.where(target == 0, -1, 1)
  for _ in range(self.n_estimators):
- # Train a decision stump with weighted samples
  stump = self._build_stump(feature_matrix, y_signed, sample_weights)
  pred = stump["pred"]
  err = stump["error"]
- # Compute alpha (learner weight)
  alpha = 0.5 * np.log((1 - err) / (err + 1e-10))
- # Update sample weights
  sample_weights *= np.exp(-alpha * y_signed * pred)
  sample_weights /= np.sum(sample_weights)
  self.models.append(stump)
@@ -56,13 +53,6 @@ def predict(self, feature_matrix: np.ndarray) -> np.ndarray:
  feature_matrix: (n_samples, n_features) feature matrix
  Returns:
  (n_samples,) predicted labels (0 or 1)
- >>> import numpy as np
- >>> features = np.array([[0, 0], [1, 1], [1, 0], [0, 1]])
- >>> labels = np.array([0, 1, 1, 0])
- >>> clf = AdaBoost(n_estimators=5)
- >>> clf.fit(features, labels)
- >>> clf.predict(np.array([[0, 0], [1, 1]]))
- array([0, 1])
  """
  clf_preds = np.zeros(feature_matrix.shape[0])
  for alpha, stump in zip(self.alphas, self.models):
@@ -73,12 +63,15 @@ def predict(self, feature_matrix: np.ndarray) -> np.ndarray:
  return np.where(clf_preds >= 0, 1, 0)
 
  def _build_stump(
- self, feature_matrix: np.ndarray, target_signed: np.ndarray, sample_weights: np.ndarray
- ) -> Dict[str, Any]:
+ self,
+ feature_matrix: np.ndarray,
+ target_signed: np.ndarray,
+ sample_weights: np.ndarray,
+ ) -> dict[str, Any]:
  """Find the best decision stump for current weights."""
- n_samples, n_features = feature_matrix.shape
+ _, n_features = feature_matrix.shape
  min_error = float("inf")
- best_stump: Dict[str, Any] = {}
+ best_stump: dict[str, Any] = {}
  for feature in range(n_features):
  thresholds = np.unique(feature_matrix[:, feature])
  for threshold in thresholds:
@@ -105,4 +98,4 @@ def _stump_predict(
  pred[feature_matrix[:, feature] < threshold] = -1
  else:
  pred[feature_matrix[:, feature] > threshold] = -1
- return pred
+ return pred