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Real-time evaluation module for streaming data and generating outputs on-the-fly.
Supports:
- Streaming evaluation
- Real-time monitoring
- Progressive result aggregation
"""
from __future__ import annotations
import json
import time
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Callable, Dict, Iterator, List, Optional
from src.utils.performance_monitor import PerformanceMonitor
@dataclass
class RealtimeMetric:
"""Single metric update in real-time evaluation."""
timestamp: float
sample_id: str
metric_name: str
value: float
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class RealtimeResult:
"""Result from real-time evaluation."""
sample_id: str
timestamp: float
scores: Dict[str, float]
coherence: Dict[str, Any]
performance: Dict[str, Any] = field(default_factory=dict)
metadata: Dict[str, Any] = field(default_factory=dict)
class RealtimeEvaluator:
"""Real-time evaluator for streaming evaluation."""
def __init__(
self,
evaluation_func: Callable[[Any], Dict[str, Any]],
output_dir: Optional[str] = None,
enable_monitoring: bool = True,
):
self.evaluation_func = evaluation_func
self.output_dir = Path(output_dir) if output_dir else None
if self.output_dir:
self.output_dir.mkdir(parents=True, exist_ok=True)
self.enable_monitoring = enable_monitoring
self.monitor = PerformanceMonitor() if enable_monitoring else None
self.results: List[RealtimeResult] = []
self.metrics: List[RealtimeMetric] = []
self._start_time = time.time()
def evaluate_stream(
self,
samples: Iterator[Any],
sample_id_func: Optional[Callable[[Any], str]] = None,
) -> Iterator[RealtimeResult]:
"""
Evaluate samples in a stream, yielding results as they become available.
Args:
samples: Iterator of samples to evaluate
sample_id_func: Function to extract sample ID from sample
Yields:
RealtimeResult for each evaluated sample
"""
for idx, sample in enumerate(samples):
sample_id = sample_id_func(sample) if sample_id_func else f"sample_{idx}"
# Evaluate sample
start_time = time.time()
if self.enable_monitoring and self.monitor:
from src.utils.performance_monitor import measure_performance
with measure_performance(
self.monitor,
operation_name="realtime_evaluation",
batch_size=1,
metadata={"sample_id": sample_id},
):
result_data = self.evaluation_func(sample)
else:
result_data = self.evaluation_func(sample)
eval_time = time.time() - start_time
# Extract scores and coherence
scores = result_data.get("scores", {})
coherence = result_data.get("coherence", {})
# Get performance metrics if available
performance = {}
if self.enable_monitoring and self.monitor:
stats = self.monitor.get_stats("realtime_evaluation")
if stats:
perf_stats = stats.get("realtime_evaluation")
if perf_stats:
performance = {
"inference_time": perf_stats.avg_time,
"throughput": perf_stats.avg_throughput,
}
# Create result
result = RealtimeResult(
sample_id=sample_id,
timestamp=time.time(),
scores=scores,
coherence=coherence,
performance=performance,
metadata=result_data.get("metadata", {}),
)
self.results.append(result)
# Emit metrics
for metric_name, value in scores.items():
metric = RealtimeMetric(
timestamp=time.time(),
sample_id=sample_id,
metric_name=metric_name,
value=value,
)
self.metrics.append(metric)
# Save result if output directory is set
if self.output_dir:
self._save_result(result)
yield result
def evaluate_batch(
self,
samples: List[Any],
sample_id_func: Optional[Callable[[Any], str]] = None,
) -> List[RealtimeResult]:
"""
Evaluate a batch of samples, returning results.
Args:
samples: List of samples to evaluate
sample_id_func: Function to extract sample ID from sample
Returns:
List of RealtimeResult
"""
results = list(self.evaluate_stream(iter(samples), sample_id_func=sample_id_func))
return results
def get_aggregate_stats(self) -> Dict[str, Any]:
"""Get aggregate statistics from evaluated results."""
if not self.results:
return {}
# Aggregate scores
all_scores: Dict[str, List[float]] = {}
for result in self.results:
for metric_name, value in result.scores.items():
if metric_name not in all_scores:
all_scores[metric_name] = []
all_scores[metric_name].append(value)
# Compute statistics
stats = {}
for metric_name, values in all_scores.items():
import numpy as np
stats[metric_name] = {
"mean": float(np.mean(values)),
"std": float(np.std(values)),
"min": float(np.min(values)),
"max": float(np.max(values)),
"count": len(values),
}
# Overall statistics
stats["total_samples"] = len(self.results)
stats["total_time"] = time.time() - self._start_time
stats["avg_throughput"] = len(self.results) / stats["total_time"] if stats["total_time"] > 0 else 0.0
return stats
def get_metrics_history(self, metric_name: Optional[str] = None) -> List[RealtimeMetric]:
"""Get history of metrics."""
if metric_name:
return [m for m in self.metrics if m.metric_name == metric_name]
return self.metrics.copy()
def _save_result(self, result: RealtimeResult) -> None:
"""Save individual result to disk."""
if not self.output_dir:
return
result_file = self.output_dir / f"{result.sample_id}.json"
with result_file.open("w") as f:
json.dump(asdict(result), f, indent=2, default=str)
def save_summary(self, output_path: Optional[str] = None) -> None:
"""Save evaluation summary to disk."""
if output_path is None and self.output_dir:
output_path = str(self.output_dir / "summary.json")
if output_path is None:
return
summary = {
"aggregate_stats": self.get_aggregate_stats(),
"total_results": len(self.results),
"performance_summary": self.monitor.get_summary() if self.monitor else {},
}
with open(output_path, "w") as f:
json.dump(summary, f, indent=2, default=str)
def reset(self) -> None:
"""Reset evaluator state."""
self.results.clear()
self.metrics.clear()
self._start_time = time.time()
if self.monitor:
self.monitor.reset()
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