zhongwei/gh-hiroshi75-protografico-protografico
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
4b6db3349f57f3ffb8ea18fbaeff20b0d4b1ed2b
gh-hiroshi75-protografico-p…/skills/fine-tune/evaluation_metrics.md
Zhongwei Li 4b6db3349f Initial commit
2025-11-29 18:45:58 +08:00

341 lines
11 KiB
Markdown
Raw Blame History

# Evaluation Metrics Design
Definitions and calculation methods for evaluation metrics in LangGraph application fine-tuning.
**💡 Tip**: For practical evaluation scripts and report templates, see [examples.md](examples.md#phase-2-baseline-evaluation-examples).
## 📊 Importance of Evaluation
In fine-tuning, evaluation provides:
- **Quantifying Improvements**: Objective progress measurement
- **Basis for Decision-Making**: Data-driven prioritization
- **Quality Assurance**: Prevention of regressions
- **ROI Demonstration**: Visualization of business value
## 🎯 Evaluation Metric Categories
### 1. Quality Metrics
#### Accuracy
```python
def calculate_accuracy(predictions: List, ground_truth: List) -> float:
"""Calculate accuracy"""
correct = sum(p == g for p, g in zip(predictions, ground_truth))
return (correct / len(predictions)) * 100
# Example
predictions = ["product", "technical", "billing", "general"]
ground_truth = ["product", "billing", "billing", "general"]
accuracy = calculate_accuracy(predictions, ground_truth)
# => 50.0% (2/4 correct)
```
#### F1 Score (Multi-class Classification)
```python
from sklearn.metrics import f1_score, classification_report
def calculate_f1(predictions: List, ground_truth: List, average='weighted') -> float:
"""Calculate F1 score (multi-class support)"""
return f1_score(ground_truth, predictions, average=average)
# Detailed report
report = classification_report(ground_truth, predictions)
print(report)
"""
precision recall f1-score support
product 1.00 1.00 1.00 1
technical 0.00 0.00 0.00 1
billing 0.50 1.00 0.67 1
general 1.00 1.00 1.00 1
accuracy 0.75 4
macro avg 0.62 0.75 0.67 4
weighted avg 0.62 0.75 0.67 4
"""
```
#### Semantic Similarity
```python
from sentence_transformers import SentenceTransformer, util
def calculate_semantic_similarity(
generated: str,
reference: str,
model_name: str = "all-MiniLM-L6-v2"
) -> float:
"""Calculate semantic similarity between generated and reference text"""
model = SentenceTransformer(model_name)
embeddings = model.encode([generated, reference], convert_to_tensor=True)
similarity = util.pytorch_cos_sim(embeddings[0], embeddings[1])
return similarity.item()
# Example
generated = "Our premium plan costs $49 per month."
reference = "The premium subscription is $49/month."
similarity = calculate_semantic_similarity(generated, reference)
# => 0.87 (high similarity)
```
#### BLEU Score (Text Generation Quality)
```python
from nltk.translate.bleu_score import sentence_bleu
def calculate_bleu(generated: str, reference: str) -> float:
"""Calculate BLEU score"""
reference_tokens = [reference.split()]
generated_tokens = generated.split()
return sentence_bleu(reference_tokens, generated_tokens)
# Example
generated = "The product costs forty nine dollars"
reference = "The product costs $49"
bleu = calculate_bleu(generated, reference)
# => 0.45
```
### 2. Performance Metrics
#### Latency (Response Time)
```python
import time
from typing import Dict, List
def measure_latency(test_cases: List[Dict]) -> Dict:
"""Measure latency for each node and total"""
results = {
"total": [],
"by_node": {}
}
for case in test_cases:
start_time = time.time()
# Measurement by node
node_times = {}
# Node 1: analyze_intent
node_start = time.time()
analyze_result = analyze_intent(case["input"])
node_times["analyze_intent"] = time.time() - node_start
# Node 2: retrieve_context
node_start = time.time()
context = retrieve_context(analyze_result)
node_times["retrieve_context"] = time.time() - node_start
# Node 3: generate_response
node_start = time.time()
response = generate_response(context, case["input"])
node_times["generate_response"] = time.time() - node_start
total_time = time.time() - start_time
results["total"].append(total_time)
for node, duration in node_times.items():
if node not in results["by_node"]:
results["by_node"][node] = []
results["by_node"][node].append(duration)
# Statistical calculation
import numpy as np
summary = {
"total": {
"mean": np.mean(results["total"]),
"p50": np.percentile(results["total"], 50),
"p95": np.percentile(results["total"], 95),
"p99": np.percentile(results["total"], 99),
}
}
for node, times in results["by_node"].items():
summary[node] = {
"mean": np.mean(times),
"p50": np.percentile(times, 50),
"p95": np.percentile(times, 95),
}
return summary
# Usage example
latency_results = measure_latency(test_cases)
print(f"Mean latency: {latency_results['total']['mean']:.2f}s")
print(f"P95 latency: {latency_results['total']['p95']:.2f}s")
```
#### Throughput
```python
import concurrent.futures
from typing import List, Dict
def measure_throughput(
test_cases: List[Dict],
max_workers: int = 10,
duration_seconds: int = 60
) -> Dict:
"""Measure number of requests processed within a given time"""
start_time = time.time()
completed = 0
errors = 0
def process_case(case):
try:
result = run_langgraph_app(case["input"])
return True
except Exception:
return False
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
while time.time() - start_time < duration_seconds:
# Loop through test cases
for case in test_cases:
if time.time() - start_time >= duration_seconds:
break
future = executor.submit(process_case, case)
if future.result():
completed += 1
else:
errors += 1
elapsed = time.time() - start_time
return {
"completed": completed,
"errors": errors,
"elapsed": elapsed,
"throughput": completed / elapsed, # requests per second
"error_rate": errors / (completed + errors) if (completed + errors) > 0 else 0
}
# Usage example
throughput = measure_throughput(test_cases, max_workers=5, duration_seconds=30)
print(f"Throughput: {throughput['throughput']:.2f} req/s")
print(f"Error rate: {throughput['error_rate']*100:.2f}%")
```
### 3. Cost Metrics
#### Token Usage and Cost
```python
from typing import Dict
# Pricing table by model (as of November 2024)
PRICING = {
"claude-3-5-sonnet-20241022": {
"input": 3.0 / 1_000_000, # $3.00 per 1M input tokens
"output": 15.0 / 1_000_000, # $15.00 per 1M output tokens
},
"claude-3-5-haiku-20241022": {
"input": 0.8 / 1_000_000, # $0.80 per 1M input tokens
"output": 4.0 / 1_000_000, # $4.00 per 1M output tokens
}
}
def calculate_cost(token_usage: Dict, model: str) -> Dict:
"""Calculate cost from token usage"""
pricing = PRICING.get(model, PRICING["claude-3-5-sonnet-20241022"])
input_cost = token_usage["input_tokens"] * pricing["input"]
output_cost = token_usage["output_tokens"] * pricing["output"]
total_cost = input_cost + output_cost
return {
"input_tokens": token_usage["input_tokens"],
"output_tokens": token_usage["output_tokens"],
"total_tokens": token_usage["input_tokens"] + token_usage["output_tokens"],
"input_cost": input_cost,
"output_cost": output_cost,
"total_cost": total_cost,
"cost_breakdown": {
"input_pct": (input_cost / total_cost * 100) if total_cost > 0 else 0,
"output_pct": (output_cost / total_cost * 100) if total_cost > 0 else 0
}
}
# Usage example
token_usage = {"input_tokens": 1500, "output_tokens": 800}
cost = calculate_cost(token_usage, "claude-3-5-sonnet-20241022")
print(f"Total cost: ${cost['total_cost']:.4f}")
print(f"Input: ${cost['input_cost']:.4f} ({cost['cost_breakdown']['input_pct']:.1f}%)")
print(f"Output: ${cost['output_cost']:.4f} ({cost['cost_breakdown']['output_pct']:.1f}%)")
```
#### Cost per Request
```python
def calculate_cost_per_request(
test_results: List[Dict],
model: str
) -> Dict:
"""Calculate cost per request"""
total_cost = 0
total_input_tokens = 0
total_output_tokens = 0
for result in test_results:
cost = calculate_cost(result["token_usage"], model)
total_cost += cost["total_cost"]
total_input_tokens += result["token_usage"]["input_tokens"]
total_output_tokens += result["token_usage"]["output_tokens"]
num_requests = len(test_results)
return {
"total_requests": num_requests,
"total_cost": total_cost,
"cost_per_request": total_cost / num_requests,
"avg_input_tokens": total_input_tokens / num_requests,
"avg_output_tokens": total_output_tokens / num_requests,
"total_tokens": total_input_tokens + total_output_tokens
}
```
### 4. Reliability Metrics
#### Error Rate
```python
def calculate_error_rate(results: List[Dict]) -> Dict:
"""Analyze error rate and error types"""
total = len(results)
errors = [r for r in results if r.get("error")]
error_types = {}
for error in errors:
error_type = error["error"]["type"]
if error_type not in error_types:
error_types[error_type] = 0
error_types[error_type] += 1
return {
"total_requests": total,
"total_errors": len(errors),
"error_rate": len(errors) / total if total > 0 else 0,
"error_types": error_types,
"success_rate": (total - len(errors)) / total if total > 0 else 0
}
```
#### Retry Rate
```python
def calculate_retry_rate(results: List[Dict]) -> Dict:
"""Proportion of cases that required retries"""
total = len(results)
retried = [r for r in results if r.get("retry_count", 0) > 0]
return {
"total_requests": total,
"retried_requests": len(retried),
"retry_rate": len(retried) / total if total > 0 else 0,
"avg_retries": sum(r.get("retry_count", 0) for r in retried) / len(retried) if retried else 0
}
```
## 📋 Related Documentation
- [Test Case Design](./evaluation_testcases.md) - Test case structure and coverage
- [Statistical Significance Testing](./evaluation_statistics.md) - Multiple runs and statistical analysis
- [Evaluation Best Practices](./evaluation_practices.md) - Consistency, visualization, reporting
Reference in New Issue View Git Blame Copy Permalink