Benchmarks: restore some deleted functionality (#1683)

Summary:
Pull Request resolved: #1683

Put percentiles back into AggregatedBenchmarkResult since Sait is using them

Reviewed By: Balandat

Differential Revision: D46921900

fbshipit-source-id: 5d71df1b01589521c795188eca996295fd3b4972

ax/benchmark/benchmark_result.py

@@ -9,7 +9,7 @@
 import numpy as np
 from ax.core.experiment import Experiment
 from ax.utils.common.base import Base
-from numpy import nanmean, ndarray
+from numpy import nanmean, nanquantile, ndarray
 from pandas import DataFrame
 from scipy.stats import sem
 
@@ -18,6 +18,8 @@
 # `BenchmarkResult` as return type annotation, used for serialization and rendering
 # in the UI.
 
+PERCENTILES = [0.25, 0.5, 0.75]
+
 
 @dataclass(frozen=True, eq=False)
 class BenchmarkResult(Base):
@@ -78,7 +80,7 @@ def from_benchmark_results(
         trace_stats = {}
         for name in ("optimization_trace", "score_trace"):
             step_data = zip(*(getattr(res, name) for res in results))
-            stats = _get_stats(step_data=step_data)
+            stats = _get_stats(step_data=step_data, percentiles=PERCENTILES)
             trace_stats[name] = stats
 
         # Return aggregated results
@@ -91,9 +93,15 @@ def from_benchmark_results(
         )
 
 
-def _get_stats(step_data: Iterable[np.ndarray]) -> Dict[str, List[float]]:
+def _get_stats(
+    step_data: Iterable[np.ndarray],
+    percentiles: List[float],
+) -> Dict[str, List[float]]:
+    quantiles = []
     stats = {"mean": [], "sem": []}
     for step_vals in step_data:
         stats["mean"].append(nanmean(step_vals))
         stats["sem"].append(sem(step_vals, ddof=1, nan_policy="propagate"))
+        quantiles.append(nanquantile(step_vals, q=percentiles))
+    stats.update({f"P{100 * p:.0f}": q for p, q in zip(percentiles, zip(*quantiles))})
     return stats

ax/benchmark/tests/test_benchmark.py

@@ -75,6 +75,9 @@ def test_test(self) -> None:
             "All experiments must have 4 trials",
         )
 
+        for col in ["mean", "P25", "P50", "P75"]:
+            self.assertTrue((agg.score_trace[col] <= 100).all())
+
     @fast_botorch_optimize
     def test_full_run(self) -> None:
         aggs = benchmark_full_run(
@@ -85,6 +88,10 @@ def test_full_run(self) -> None:
 
         self.assertEqual(len(aggs), 2)
 
+        for agg in aggs:
+            for col in ["mean", "P25", "P50", "P75"]:
+                self.assertTrue((agg.score_trace[col] <= 100).all())
+
     def test_timeout(self) -> None:
         problem = SingleObjectiveBenchmarkProblem.from_botorch_synthetic(
             test_problem_class=Branin,