browseruse_bench.utils.stats_utils
Statistics-related utility functions.
Import
from browseruse_bench.utils import (
calculate_metric_stats,
calculate_all_metrics_stats,
filter_tasks_by_label,
generate_evaluation_summary,
)
calculate_metric_stats
Calculate statistics for a specific metric.
def calculate_metric_stats(
tasks: List[Dict[str, Any]],
metric: str,
path: str = "evaluation_details"
) -> Dict[str, float]
Metric name, e.g., ttft_ms, end_to_end_ms, steps
path
str
默认值:"evaluation_details"
Path to metric in task dictionary
Statistics dictionary containing count, mean, min, max, median
calculate_all_metrics_stats
Calculate statistics for multiple metrics.
def calculate_all_metrics_stats(
tasks: List[Dict[str, Any]],
metrics: Optional[List[str]] = None,
path: str = "evaluation_details"
) -> Dict[str, Dict[str, float]]
metrics
list of str
默认值:"see below"
List of metric names
Return Structure
{
"ttft_ms": {"count": 10, "mean": 1234.5, ...},
"end_to_end_ms": {"count": 10, "mean": 5678.9, ...},
"steps": {"count": 10, "mean": 3.2, ...},
"usage": {
"total_tokens": {"count": 10, "mean": 1500, ...},
"total_cost": {"count": 10, "mean": 0.05, ...},
...
}
}
filter_tasks_by_label
Filter tasks by label.
def filter_tasks_by_label(
tasks: List[Dict[str, Any]],
key: str = "predicted_label",
val: int = 1
) -> List[Dict[str, Any]]
Label value (1 = success, 0 = failure)
generate_evaluation_summary
Generate evaluation summary.
def generate_evaluation_summary(
results: List[Dict[str, Any]],
total: int,
metrics: Optional[List[str]] = None
) -> Dict[str, Any]
List of evaluation results
metrics
list of str
默认值:"see below"
List of metrics to calculate
Return Structure
{
"overall_statistics": {
"total_tasks": 100,
"evaluated_tasks": 95,
"successful_tasks": 70,
"failed_tasks": 25,
"success_rate": 73.68,
"failure_rate": 26.32
},
"metrics_statistics": {...},
"successful_tasks_metrics": {...},
"failed_tasks_metrics": {...},
"failure_category_statistics": {...},
"task_list": {
"successful_task_ids": [...],
"failed_task_ids": [...]
}
}
