Enhance Performance Metrics Collection for Adaptive Learning Process

.gitignore

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-### AL ###
-#Template for AL projects for Dynamics 365 Business Central
-#launch.json folder
-.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
+__pycache__/
+*.py[cod]
+*$py.class
+.pytest_cache/
+.coverage
+htmlcov/
+dist/
+build/
+*.egg-info/
+.env
+.venv/

src/__init__.py

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+# ALP Metrics Collection System

src/metrics_collector.py

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+from typing import Dict, Any, Optional
+from dataclasses import dataclass, field
+from enum import Enum, auto
+import time
+import logging
+
+class MetricType(Enum):
+    """Enum representing different types of metrics."""
+    PERFORMANCE = auto()
+    RESOURCE_USAGE = auto()
+    ERROR_RATE = auto()
+    LEARNING_PROGRESS = auto()
+
+@dataclass
+class MetricRecord:
+    """Represents a single metric record with timestamp and value."""
+    value: Any
+    timestamp: float = field(default_factory=time.time)
+    type: MetricType = MetricType.PERFORMANCE
+
+class PerformanceMetricsCollector:
+    """
+    A lightweight, thread-safe metrics collection system for tracking 
+    performance characteristics of the Adaptive Learning Process.
+
+    Supports multiple metric types, basic statistical tracking, 
+    and configurable logging.
+    """
+
+    def __init__(self, max_history: int = 100, logger: Optional[logging.Logger] = None):
+        """
+        Initialize the metrics collector.
+
+        Args:
+            max_history (int): Maximum number of historical records to keep per metric.
+            logger (Optional[logging.Logger]): Optional logger for recording metrics.
+        """
+        self._metrics: Dict[str, list[MetricRecord]] = {}
+        self._max_history = max_history
+        self._logger = logger or logging.getLogger(__name__)
+
+    def record_metric(self, name: str, value: Any, metric_type: MetricType = MetricType.PERFORMANCE):
+        """
+        Record a new metric value.
+
+        Args:
+            name (str): Name of the metric.
+            value (Any): Value of the metric.
+            metric_type (MetricType): Type of metric being recorded.
+        """
+        metric_record = MetricRecord(value=value, type=metric_type)
+
+        if name not in self._metrics:
+            self._metrics[name] = []
+
+        # Maintain max history
+        if len(self._metrics[name]) >= self._max_history:
+            self._metrics[name].pop(0)
+
+        self._metrics[name].append(metric_record)
+
+        # Optional logging
+        self._logger.debug(f"Metric recorded: {name} = {value}")
+
+    def get_metric_history(self, name: str) -> list[MetricRecord]:
+        """
+        Retrieve the historical records for a specific metric.
+
+        Args:
+            name (str): Name of the metric.
+
+        Returns:
+            list[MetricRecord]: Historical records for the metric.
+        """
+        return self._metrics.get(name, [])
+
+    def get_latest_metric(self, name: str) -> Optional[MetricRecord]:
+        """
+        Get the most recent record for a specific metric.
+
+        Args:
+            name (str): Name of the metric.
+
+        Returns:
+            Optional[MetricRecord]: Most recent metric record, or None if not found.
+        """
+        history = self.get_metric_history(name)
+        return history[-1] if history else None
+
+    def calculate_metric_average(self, name: str) -> Optional[float]:
+        """
+        Calculate the average value for a given metric.
+
+        Args:
+            name (str): Name of the metric.
+
+        Returns:
+            Optional[float]: Average value of the metric, or None if no records.
+        """
+        history = self.get_metric_history(name)
+
+        if not history:
+            return None
+
+        # Try to calculate average, handling potential type mismatches
+        try:
+            return sum(record.value for record in history) / len(history)
+        except (TypeError, ValueError):
+            self._logger.warning(f"Cannot calculate average for metric {name}")
+            return None

tests/__init__.py

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+# Tests for ALP Metrics Collection System

tests/test_metrics_collector.py

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+import pytest
+import time
+from src.metrics_collector import PerformanceMetricsCollector, MetricType
+
+def test_metrics_collector_basic_recording():
+    """Test basic metric recording functionality."""
+    collector = PerformanceMetricsCollector()
+
+    # Record a performance metric
+    collector.record_metric("learning_rate", 0.01)
+
+    # Verify metric was recorded
+    history = collector.get_metric_history("learning_rate")
+    assert len(history) == 1
+    assert history[0].value == 0.01
+
+def test_metrics_collector_max_history():
+    """Test max history limitation."""
+    collector = PerformanceMetricsCollector(max_history=3)
+
+    # Record more than max history
+    for i in range(5):
+        collector.record_metric("test_metric", i)
+
+    # Verify only last 3 records are kept
+    history = collector.get_metric_history("test_metric")
+    assert len(history) == 3
+    assert [record.value for record in history] == [2, 3, 4]
+
+def test_latest_metric_retrieval():
+    """Test retrieving the latest metric."""
+    collector = PerformanceMetricsCollector()
+
+    collector.record_metric("accuracy", 0.75)
+    collector.record_metric("accuracy", 0.80)
+
+    latest = collector.get_latest_metric("accuracy")
+    assert latest is not None
+    assert latest.value == 0.80
+
+def test_metric_average_calculation():
+    """Test average calculation for metrics."""
+    collector = PerformanceMetricsCollector()
+
+    collector.record_metric("error_rate", 0.1)
+    collector.record_metric("error_rate", 0.2)
+    collector.record_metric("error_rate", 0.3)
+
+    avg = collector.calculate_metric_average("error_rate")
+    assert avg == pytest.approx(0.2)  # Use approx for floating-point comparison
+
+def test_metric_types():
+    """Test recording different metric types."""
+    collector = PerformanceMetricsCollector()
+
+    collector.record_metric("cpu_usage", 50.0, MetricType.RESOURCE_USAGE)
+
+    latest = collector.get_latest_metric("cpu_usage")
+    assert latest is not None
+    assert latest.type == MetricType.RESOURCE_USAGE
+
+def test_non_existent_metric():
+    """Test behavior with non-existent metrics."""
+    collector = PerformanceMetricsCollector()
+
+    history = collector.get_metric_history("non_existent")
+    assert len(history) == 0
+
+    latest = collector.get_latest_metric("non_existent")
+    assert latest is None
+
+    avg = collector.calculate_metric_average("non_existent")
+    assert avg is None