Implement Abstract Base Class for Adaptive Learning Process (ALP) Loop Mechanism

.gitignore

@@ -1,22 +1,15 @@
-### AL ###
-#Template for AL projects for Dynamics 365 Business Central
-#launch.json folder
+__pycache__/
+*.py[cod]
+*$py.class
+.pytest_cache/
+.coverage
+htmlcov/
+dist/
+build/
+*.egg-info/
+.env
+.venv
+venv/
+.idea/
 .vscode/
-#Cache folder
-.alcache/
-#Symbols folder
-.alpackages/
-#Snapshots folder
-.snapshots/
-#Testing Output folder
-.output/
-#Extension App-file
-*.app
-#Rapid Application Development File
-rad.json
-#Translation Base-file
-*.g.xlf
-#License-file
-*.flf
-#Test results file
-TestResults.xml
+*.log

src/alp_loop.py

@@ -0,0 +1,147 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional, List
+import logging
+from dataclasses import dataclass, field
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+@dataclass
+class LoopConfiguration:
+    """Configuration class for ALP Loop parameters."""
+    max_iterations: int = 100
+    learning_rate: float = 0.01
+    convergence_threshold: float = 1e-5
+    logging_level: int = logging.INFO
+    early_stopping: bool = True
+    additional_params: Dict[str, Any] = field(default_factory=dict)
+
+class ALPLoopBase(ABC):
+    """
+    Abstract Base Class for Adaptive Learning Process (ALP) Loop Mechanism.
+
+    Provides a standardized template for implementing iterative learning cycles
+    with robust configuration, tracking, and error handling.
+
+    Key Features:
+    - Configurable learning parameters
+    - Iteration tracking and management
+    - Comprehensive logging
+    - Flexible error handling
+    - Performance metrics collection
+    """
+
+    def __init__(self, config: Optional[LoopConfiguration] = None):
+        """
+        Initialize the ALP Loop with configuration.
+
+        Args:
+            config (Optional[LoopConfiguration]): Configuration for the learning loop.
+        """
+        self.config = config or LoopConfiguration()
+        self.logger = logging.getLogger(self.__class__.__name__)
+        self.logger.setLevel(self.config.logging_level)
+
+        # Iteration tracking
+        self.current_iteration = 0
+        self.best_performance = float('-inf')
+        self.iteration_history: List[Dict[str, Any]] = []
+
+    @abstractmethod
+    def initialize(self) -> None:
+        """
+        Initialize learning process, models, or data structures.
+        Must be implemented by subclasses.
+        """
+        pass
+
+    @abstractmethod
+    def learning_iteration(self) -> float:
+        """
+        Perform a single learning iteration.
+
+        Returns:
+            float: Performance metric for the current iteration.
+
+        Raises:
+            NotImplementedError: If not implemented by subclass.
+        """
+        pass
+
+    def run(self) -> Dict[str, Any]:
+        """
+        Execute the main learning loop with comprehensive error handling.
+
+        Returns:
+            Dict[str, Any]: Final learning results and metadata.
+        """
+        try:
+            self.initialize()
+
+            while not self._should_terminate():
+                performance = self.learning_iteration()
+
+                # Track iteration history
+                iteration_data = {
+                    'iteration': self.current_iteration,
+                    'performance': performance
+                }
+                self.iteration_history.append(iteration_data)
+
+                # Update best performance
+                if performance > self.best_performance:
+                    self.best_performance = performance
+
+                self.current_iteration += 1
+
+                self.logger.info(
+                    f"Iteration {self.current_iteration}: "
+                    f"Performance = {performance}"
+                )
+
+            return self._finalize()
+
+        except Exception as e:
+            self.logger.error(f"Learning process failed: {e}")
+            raise
+
+    def _should_terminate(self) -> bool:
+        """
+        Determine if the learning loop should terminate.
+
+        Returns:
+            bool: Whether to stop the learning process.
+        """
+        conditions = [
+            self.current_iteration >= self.config.max_iterations,
+            (self.config.early_stopping and 
+             len(self.iteration_history) > 1 and 
+             abs(self.iteration_history[-1]['performance'] - 
+                 self.iteration_history[-2]['performance']) < 
+                 self.config.convergence_threshold)
+        ]
+
+        return any(conditions)
+
+    def _finalize(self) -> Dict[str, Any]:
+        """
+        Finalize the learning process and collect results.
+
+        Returns:
+            Dict[str, Any]: Comprehensive learning results.
+        """
+        results = {
+            'total_iterations': self.current_iteration,
+            'best_performance': self.best_performance,
+            'iteration_history': self.iteration_history
+        }
+
+        self.logger.info(f"Learning process completed. Results: {results}")
+        return results
+
+    def reset(self) -> None:
+        """
+        Reset the learning loop to its initial state.
+        """
+        self.current_iteration = 0
+        self.best_performance = float('-inf')
+        self.iteration_history.clear()

tests/test_alp_loop.py

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+import pytest
+from src.alp_loop import ALPLoopBase, LoopConfiguration
+
+class TestALPLoop(ALPLoopBase):
+    def initialize(self):
+        self.test_data = [1, 2, 3, 4, 5]
+
+    def learning_iteration(self):
+        # Simulate a learning process that increases performance
+        return self.current_iteration * 0.1
+
+def test_alp_loop_initialization():
+    default_config = LoopConfiguration()
+    loop = TestALPLoop()
+
+    assert loop.config.max_iterations == 100
+    assert loop.config.learning_rate == 0.01
+    assert loop.current_iteration == 0
+
+def test_custom_configuration():
+    custom_config = LoopConfiguration(
+        max_iterations=50,
+        learning_rate=0.05,
+        convergence_threshold=1e-3
+    )
+    loop = TestALPLoop(custom_config)
+
+    assert loop.config.max_iterations == 50
+    assert loop.config.learning_rate == 0.05
+    assert loop.config.convergence_threshold == 1e-3
+
+def test_loop_run():
+    loop = TestALPLoop()
+    results = loop.run()
+
+    assert 'total_iterations' in results
+    assert 'best_performance' in results
+    assert 'iteration_history' in results
+    assert results['total_iterations'] <= 100
+    assert len(results['iteration_history']) > 0
+
+def test_loop_reset():
+    loop = TestALPLoop()
+    loop.run()
+    loop.reset()
+
+    assert loop.current_iteration == 0
+    assert loop.best_performance == float('-inf')
+    assert len(loop.iteration_history) == 0
+
+def test_early_stopping_config():
+    config = LoopConfiguration(early_stopping=False)
+    loop = TestALPLoop(config)
+    results = loop.run()
+
+    assert results['total_iterations'] == 100