feat(automl): add BanditClassifier and SuccessiveHalvingClassifier

notebooks/enhanced_autoclass_config.json

@@ -0,0 +1 @@
+{"ensembleSize": 10, "newConfigurations": 10, "keepCurrentModel": true, "lambda": 0.05, "preventAlgorithmDeath": true, "keepGlobalIncumbent": true, "keepAlgorithmIncumbents": true, "keepInitialConfigurations": true, "useTestEnsemble": true, "resetProbability": 0.01, "numberOfCores": 1, "performanceMeasureMaximisation": true, "algorithms": [{"algorithm": "moa.classifiers.trees.HoeffdingTree", "parameters": [{"parameter": "g", "type": "integer", "value": 200, "range": [100, 400]}, {"parameter": "c", "type": "float", "value": 0.01, "range": [0.001, 0.1]}]}, {"algorithm": "moa.classifiers.lazy.kNN", "parameters": [{"parameter": "k", "type": "integer", "value": 10, "range": [5, 15]}]}, {"algorithm": "moa.classifiers.meta.AdaptiveRandomForest", "parameters": [{"parameter": "s", "type": "integer", "value": 60, "range": [50, 70]}]}, {"algorithm": "moa.classifiers.trees.HoeffdingAdaptiveTree", "parameters": [{"parameter": "g", "type": "integer", "value": 200, "range": [100, 300]}]}, {"algorithm": "moa.classifiers.meta.LeveragingBag", "parameters": [{"parameter": "s", "type": "integer", "value": 60, "range": [50, 70]}]}, {"algorithm": "moa.classifiers.meta.StreamingRandomPatches", "parameters": [{"parameter": "s", "type": "integer", "value": 60, "range": [50, 70]}]}]}

src/capymoa/automl/__init__.py

@@ -1,3 +1,10 @@
 from ._autoclass import AutoClass
+from ._bandit_classifier import BanditClassifier, EpsilonGreedy
+from ._successive_halving_classifier import SuccessiveHalvingClassifier
 
-__all__ = ["AutoClass"]
+__all__ = [
+    "AutoClass",
+    "BanditClassifier",
+    "SuccessiveHalvingClassifier",
+    "EpsilonGreedy",
+]

src/capymoa/automl/_bandit_classifier.py

@@ -0,0 +1,369 @@
+import random
+from capymoa.base import (
+    Classifier,
+)
+from typing import Dict, Any
+from capymoa.stream import Schema
+from capymoa.evaluation import ClassificationEvaluator
+from capymoa.automl._utils import (
+    generate_parameter_combinations,
+    create_capymoa_classifier,
+)
+import json
+
+
+class EpsilonGreedy:
+    """Epsilon-Greedy bandit policy for model selection.
+
+    This policy selects the best model with probability ``1 - epsilon`` and explores
+    other models with probability ``epsilon``. During the burn-in period, it always
+    explores to gather initial information about all models.
+
+    >>> from capymoa.automl import EpsilonGreedy
+    >>> policy = EpsilonGreedy(epsilon=0.1, burn_in=50)
+    >>> policy.epsilon
+    0.1
+
+    .. seealso::
+
+        :class:`~capymoa.automl.BanditClassifier`
+    """
+
+    def __init__(self, epsilon: float = 0.1, burn_in: int = 100):
+        """Construct a new Epsilon-Greedy policy.
+
+        :param epsilon: Probability of exploring a random model (default: ``0.1``).
+        :param burn_in: Number of initial rounds dedicated to exploration (default: ``100``).
+        """
+        self.epsilon = epsilon
+        """Probability of exploring a random model."""
+
+        self.burn_in = burn_in
+        """Number of initial rounds where all models are explored to collect initial statistics."""
+
+        self.n_arms = 0
+        """Number of available models (arms)."""
+
+        self.arm_rewards = []
+        """Cumulative reward values for each model (arm)."""
+
+        self.arm_counts = []
+        """Number of times each arm has been pulled."""
+
+        self.total_pulls = 0
+        """Total number of model selections performed."""
+
+    def initialize(self, n_arms):
+        """Initialize the policy with a given number of arms."""
+        self.n_arms = n_arms
+        self.arm_rewards = [0.0] * n_arms
+        self.arm_counts = [0] * n_arms
+        self.total_pulls = 0
+
+    def get_best_arm_idx(self, available_arms):
+        best_arm = max(
+            available_arms,
+            key=lambda arm: self.arm_rewards[arm] / max(1, self.arm_counts[arm]),
+        )
+        return best_arm
+
+    def pull(self, available_arms):
+        """Select which arms to pull based on the epsilon-greedy policy."""
+        if self.total_pulls < self.burn_in:
+            # During burn-in, explore all available arms
+            return available_arms
+
+        # With probability epsilon, explore a random arm
+        if random.random() < self.epsilon:
+            return [random.choice(available_arms)]
+
+        # Otherwise, exploit the best arm
+        best_arm = max(
+            available_arms,
+            key=lambda arm: self.arm_rewards[arm] / max(1, self.arm_counts[arm]),
+        )
+        return [best_arm]
+
+    def update(self, arm, reward):
+        """Update the policy with the observed reward for the pulled arm."""
+        self.arm_rewards[arm] += reward
+        self.arm_counts[arm] += 1
+        self.total_pulls += 1
+
+    def get_arm_stats(self):
+        """Get statistics about each arm's performance."""
+        return {
+            "rewards": self.arm_rewards.copy(),
+            "counts": self.arm_counts.copy(),
+            "means": [r / max(1, c) for r, c in zip(self.arm_rewards, self.arm_counts)],
+        }
+
+
+class BanditClassifier(Classifier):
+    """Bandit-based model selection for streaming classification.
+
+    Each base classifier is associated with an arm of a multi-armed bandit.
+    At each training step, the bandit policy selects which model to train
+    (i.e., which arm to pull). The reward corresponds to the model's
+    performance on the current instance. The best-performing model is then
+    used for prediction [#robbins1952]_.
+
+    >>> from capymoa.datasets import ElectricityTiny
+    >>> from capymoa.classifier import HoeffdingTree
+    >>> from capymoa.automl import BanditClassifier, EpsilonGreedy
+    >>> stream = ElectricityTiny()
+    >>> schema = stream.get_schema()
+    >>> learner = BanditClassifier(
+    ...     schema=schema,
+    ...     base_classifiers=[HoeffdingTree],
+    ...     policy=EpsilonGreedy(epsilon=0.1, burn_in=100)
+    ... )
+    >>> instance = next(stream)
+    >>> learner.train(instance)
+
+    .. seealso::
+
+        :class:`~capymoa.automl.EpsilonGreedy`
+
+    .. [#robbins1952] Robbins, H. (1952). *Some aspects of the sequential design of experiments.*
+       Bulletin of the American Mathematical Society, 58(5), 527–535.
+    """
+
+    def __init__(
+        self,
+        schema: Schema = None,
+        random_seed: int = 1,
+        base_classifiers: list = None,
+        config_file: str = None,
+        metric: str = "accuracy",
+        policy: EpsilonGreedy = None,
+        verbose: bool = False,
+    ):
+        """Construct a Bandit-based model selector.
+
+        :param schema: The schema of the stream.
+        :param random_seed: Random seed used for initialization.
+        :param base_classifiers: List of base classifier classes to consider.
+        :param config_file: Path to a JSON configuration file with model hyperparameters.
+        :param metric: The metric used to evaluate model performance. Defaults to ``"accuracy"``.
+        :param policy: The bandit policy used to choose which model to train (e.g., :class:`~capymoa.automl.EpsilonGreedy`).
+        :param verbose: If True, print progress information during training.
+        """
+        super().__init__(schema=schema, random_seed=random_seed)
+
+        self.config_file = config_file
+        self.base_classifiers = base_classifiers
+        self.metric = metric
+        self.policy = policy
+        self.verbose = verbose
+        self.log_cnt = 0
+        self.log_point = 5000
+
+        # Initialize policy if not provided
+        if self.policy is None:
+            self.policy = EpsilonGreedy(epsilon=0.1, burn_in=100)
+
+        # Initialize models based on configuration
+        self._initialize_models()
+
+        # Track the best model
+        self._best_model_idx = 0
+
+    def _initialize_models(self):
+        """Initialize models based on configuration."""
+        # Validate that we have at least one source of models
+        if self.base_classifiers is None and self.config_file is None:
+            raise ValueError("Either base_classifiers or config_file must be provided")
+
+        # Initialize state variables
+        self.active_models = []  # List of active model instances
+        self.metrics = []  # List of evaluation metrics for each model
+
+        # If using a config file, load and process it
+        if self.config_file is not None:
+            if self.verbose:
+                print(f"Loading model configurations from {self.config_file}")
+            self._load_model_configurations()
+        else:
+            # Use the provided base classifiers directly
+            if self.verbose:
+                print(f"Using {len(self.base_classifiers)} provided base classifiers")
+            for model in self.base_classifiers:
+                # Check if model is already instantiated or is a class
+                if isinstance(model, Classifier):
+                    # Model is already instantiated, use it directly
+                    self.active_models.append(model)
+                else:
+                    # Model is a class, instantiate it
+                    clf_instance = model(schema=self.schema)
+                    self.active_models.append(clf_instance)
+
+                # Create an evaluator for this model
+                self.metrics.append(ClassificationEvaluator(schema=self.schema))
+
+        # Initialize policy with number of arms
+        self.policy.initialize(len(self.active_models))
+
+    def _load_model_configurations(self):
+        """Load model configurations from a JSON file."""
+        try:
+            with open(self.config_file, "r") as f:
+                config = json.load(f)
+
+            # Process algorithms section of the config
+            algorithms = config.get("algorithms", [])
+
+            # If there are no algorithms defined, raise an error
+            if not algorithms:
+                raise ValueError("No algorithms defined in the configuration file")
+
+            # Process each algorithm and its parameter configurations
+            for algo_config in algorithms:
+                algorithm_name = algo_config.get("algorithm")
+                parameters = algo_config.get("parameters", [])
+
+                # Generate all parameter combinations
+                param_combinations = generate_parameter_combinations(parameters)
+
+                # Create a classifier for each parameter combination
+                for params in param_combinations:
+                    try:
+                        # Create classifier instance
+                        clf = create_capymoa_classifier(
+                            algorithm_name, params, self.schema
+                        )
+
+                        if clf is not None:
+                            self.active_models.append(clf)
+                            self.metrics.append(
+                                ClassificationEvaluator(schema=self.schema)
+                            )
+
+                            if self.verbose:
+                                param_str = ", ".join(
+                                    [f"{p['parameter']}={p['value']}" for p in params]
+                                )
+                                print(
+                                    f"Added model: {algorithm_name} with parameters: {param_str}"
+                                )
+                    except Exception as e:
+                        print(
+                            f"Warning: Failed to create model {algorithm_name} with parameters {params}: {str(e)}"
+                        )
+
+        except (json.JSONDecodeError, FileNotFoundError) as e:
+            raise ValueError(f"Error loading configuration file: {str(e)}")
+
+    def train(self, instance):
+        """Train the selected model(s) on the given instance."""
+        # Get the arm(s) to pull from the policy
+        arm_ids = self.policy.pull(range(len(self.active_models)))
+
+        # Train and evaluate each selected model
+        for arm_id in arm_ids:
+            model = self.active_models[arm_id]
+            metric = self.metrics[arm_id]
+
+            # Make prediction for evaluation before training
+            y_pred = model.predict(instance)
+            # Update metric with prediction
+            metric.update(instance.y_index, y_pred)
+            # Train the model
+            model.train(instance)
+
+            # Update the policy with the reward (metric value)
+            reward = metric.accuracy() if self.metric == "accuracy" else metric.get()
+            self.policy.update(arm_id, reward)
+
+            # Check if this model is better than our current best
+            if metric.accuracy() > self.metrics[self._best_model_idx].accuracy():
+                self._best_model_idx = arm_id
+
+            # Add verbose logging
+        self.log_cnt += 1
+        if self.verbose and self.log_cnt >= self.log_point:
+            self.log_cnt = 0
+            current_accuracy = metric.accuracy()
+            model_performances = [(i, self.metrics[i].accuracy()) for i in arm_ids]
+            top_models = sorted(model_performances, key=lambda x: x[1], reverse=True)[
+                :3
+            ]
+
+            print(f"\nChosen model: {model}")
+            print(f"Current accuracy: {current_accuracy:.4f}")
+
+            # Print top 3 models if there are many models
+            if len(model_performances) >= 3:
+                print("\nTop models:")
+                for i, (model_idx, acc) in enumerate(top_models):
+                    model_name = str(self.active_models[model_idx])
+                    print(f"  {i + 1}. {model_name} - Accuracy: {acc:.4f}")
+
+    def predict(self, instance):
+        """Predict the class label for the given instance using the best model."""
+        if not self.active_models:
+            raise ValueError(
+                "No active models available. Please train the classifier first."
+            )
+        idx = self.policy.get_best_arm_idx(range(len(self.active_models)))
+        # Use the best performing model for predictions
+        # idx = self._best_model_idx
+        return self.active_models[idx].predict(instance)
+
+    def predict_proba(self, instance):
+        """Predict class probabilities for the given instance using the best model."""
+        if not self.active_models:
+            raise ValueError(
+                "No active models available. Please train the classifier first."
+            )
+
+        idx = self.policy.get_best_arm_idx(range(len(self.active_models)))
+        # Use the best performing model for predictions
+        # idx = self._best_model_idx
+        return self.active_models[idx].predict_proba(instance)
+
+    def __str__(self):
+        """Return a string representation of the model."""
+        return "BanditClassifier"
+
+    @property
+    def best_model(self):
+        """Return the current best model."""
+        idx = self.policy.get_best_arm_idx(range(len(self.active_models)))
+        return self.active_models[idx]
+
+    def get_model_info(self) -> Dict[str, Any]:
+        """
+        Get information about the current state of the classifier.
+
+        Returns:
+            Dictionary containing classifier information
+        """
+        # Get performance metrics for all models
+        model_performances = {
+            str(self.active_models[i]): self.metrics[i].accuracy()
+            for i in range(len(self.active_models))
+        }
+        sorted_dict = dict(
+            sorted(model_performances.items(), key=lambda item: item[1], reverse=True)
+        )
+        # Get top-performing models
+        top_models = []
+        max_models = min(5, len(self.active_models))
+        i = 0
+        # idx = self.policy.get_best_arm_idx(range(len(self.active_models)))
+        for key, value in sorted_dict.items():
+            if i >= max_models:
+                break
+            top_models.append({"model": key, "accuracy": value})
+            i += 1
+
+        return {
+            "total_models": len(self.active_models),
+            "best_model_index": self._best_model_idx,
+            "model_performances": sorted_dict,
+            "best_model_accuracy": (
+                self.metrics[self._best_model_idx].accuracy() if self.metrics else None
+            ),
+            "top_models": top_models,
+        }