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Added support for giving quantile results in the output #2651

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Added support for giving quantile results in the output #2651

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14 changes: 13 additions & 1 deletion regression_detector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import numpy as np

import yaml
from userbenchmark.utils import (
Expand Down Expand Up @@ -63,6 +64,12 @@ def get_default_output_path(bm_name: str) -> str:
)
return os.path.join(output_path, fname)

def compute_quantiles(values: List[float]) -> Dict[str, float]:
return {
"q25": float(np.quantile(values, 0.25)),
"q50": float(np.quantile(values, 0.50)),
"q75": float(np.quantile(values, 0.75)),
}

def generate_regression_result(
control: Dict[str, Any], treatment: Dict[str, Any]
Expand All @@ -86,18 +93,23 @@ def _call_userbenchmark_detector(
detector = importlib.import_module(
f"userbenchmark.fb.{bm_name}.regression_detector"
).run

# Process control and treatment to include only shared keys
filtered_control_metrics = {}
control_only_metrics = {}
filtered_treatment_metrics = {}
treatment_only_metrics = {}
for control_name, control_metric in control["metrics"].items():
if control_name in treatment["metrics"]:
if "times" in control_metric and isinstance(control_metric["times"], list):
control_metric.update(compute_quantiles(control_metric["times"]))
filtered_control_metrics[control_name] = control_metric
else:
control_only_metrics[control_name] = control_metric
for treatment_name, treatment_metric in treatment["metrics"].items():
if treatment_name in control["metrics"]:
if "times" in treatment_metric and isinstance(treatment_metric["times"], list):
treatment_metric.update(compute_quantiles(treatment_metric["times"]))
filtered_treatment_metrics[treatment_name] = treatment_metric
else:
treatment_only_metrics[treatment_name] = treatment_metric
Expand Down Expand Up @@ -332,7 +344,7 @@ def get_metrics_by_date(
with open(args.control, "r") as cfptr:
control = json.load(cfptr)
with open(args.treatment, "r") as tfptr:
treatment = json.load(tfptr)
treatment = json.load(cfptr)
output_path = (
args.output if args.output else get_default_output_path(control["name"])
)
Expand Down