File size: 6,165 Bytes
db8a108 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
import argparse
import itertools
import numpy as np
import pandas as pd
import wandb
import wandb.apis.public
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, Iterable, List, TypeVar
from rl_algo_impls.benchmark_publish import RunGroup
@dataclass
class Comparison:
control_values: List[float]
experiment_values: List[float]
def mean_diff_percentage(self) -> float:
return self._diff_percentage(
np.mean(self.control_values).item(), np.mean(self.experiment_values).item()
)
def median_diff_percentage(self) -> float:
return self._diff_percentage(
np.median(self.control_values).item(),
np.median(self.experiment_values).item(),
)
def _diff_percentage(self, c: float, e: float) -> float:
if c == e:
return 0
elif c == 0:
return float("inf") if e > 0 else float("-inf")
return 100 * (e - c) / c
def score(self) -> float:
return (
np.sum(
np.sign((self.mean_diff_percentage(), self.median_diff_percentage()))
).item()
/ 2
)
RunGroupRunsSelf = TypeVar("RunGroupRunsSelf", bound="RunGroupRuns")
class RunGroupRuns:
def __init__(
self,
run_group: RunGroup,
control: List[str],
experiment: List[str],
summary_stats: List[str] = ["best_eval", "eval", "train_rolling"],
summary_metrics: List[str] = ["mean", "result"],
) -> None:
self.algo = run_group.algo
self.env = run_group.env_id
self.control = set(control)
self.experiment = set(experiment)
self.summary_stats = summary_stats
self.summary_metrics = summary_metrics
self.control_runs = []
self.experiment_runs = []
def add_run(self, run: wandb.apis.public.Run) -> None:
wandb_tags = set(run.config.get("wandb_tags", []))
if self.control & wandb_tags:
self.control_runs.append(run)
elif self.experiment & wandb_tags:
self.experiment_runs.append(run)
def comparisons_by_metric(self) -> Dict[str, Comparison]:
c_by_m = {}
for metric in (
f"{s}/{m}"
for s, m in itertools.product(self.summary_stats, self.summary_metrics)
):
c_by_m[metric] = Comparison(
[c.summary[metric] for c in self.control_runs],
[e.summary[metric] for e in self.experiment_runs],
)
return c_by_m
@staticmethod
def data_frame(rows: Iterable[RunGroupRunsSelf]) -> pd.DataFrame:
results = defaultdict(list)
for r in rows:
if not r.control_runs or not r.experiment_runs:
continue
results["algo"].append(r.algo)
results["env"].append(r.env)
results["control"].append(r.control)
results["expierment"].append(r.experiment)
c_by_m = r.comparisons_by_metric()
results["score"].append(
sum(m.score() for m in c_by_m.values()) / len(c_by_m)
)
for m, c in c_by_m.items():
results[f"{m}_mean"].append(c.mean_diff_percentage())
results[f"{m}_median"].append(c.median_diff_percentage())
return pd.DataFrame(results)
def compare_runs() -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"-p",
"--wandb-project-name",
type=str,
default="rl-algo-impls-benchmarks",
help="WandB project name to load runs from",
)
parser.add_argument(
"--wandb-entity",
type=str,
default=None,
help="WandB team. None uses default entity",
)
parser.add_argument(
"-n",
"--wandb-hostname-tag",
type=str,
nargs="*",
help="WandB tags for hostname (i.e. host_192-9-145-26)",
)
parser.add_argument(
"-c",
"--wandb-control-tag",
type=str,
nargs="+",
help="WandB tag for control commit (i.e. benchmark_5598ebc)",
)
parser.add_argument(
"-e",
"--wandb-experiment-tag",
type=str,
nargs="+",
help="WandB tag for experiment commit (i.e. benchmark_5540e1f)",
)
parser.add_argument(
"--envs",
type=str,
nargs="*",
help="If specified, only compare these envs",
)
parser.add_argument(
"--exclude-envs",
type=str,
nargs="*",
help="Environments to exclude from comparison",
)
# parser.set_defaults(
# wandb_hostname_tag=["host_150-230-44-105", "host_155-248-214-128"],
# wandb_control_tag=["benchmark_fbc943f"],
# wandb_experiment_tag=["benchmark_f59bf74"],
# exclude_envs=[],
# )
args = parser.parse_args()
print(args)
api = wandb.Api()
all_runs = api.runs(
path=f"{args.wandb_entity or api.default_entity}/{args.wandb_project_name}",
order="+created_at",
)
runs_by_run_group: Dict[RunGroup, RunGroupRuns] = {}
wandb_hostname_tags = set(args.wandb_hostname_tag)
for r in all_runs:
if r.state != "finished":
continue
wandb_tags = set(r.config.get("wandb_tags", []))
if not wandb_tags or not wandb_hostname_tags & wandb_tags:
continue
rg = RunGroup(r.config["algo"], r.config.get("env_id") or r.config["env"])
if args.exclude_envs and rg.env_id in args.exclude_envs:
continue
if args.envs and rg.env_id not in args.envs:
continue
if rg not in runs_by_run_group:
runs_by_run_group[rg] = RunGroupRuns(
rg,
args.wandb_control_tag,
args.wandb_experiment_tag,
)
runs_by_run_group[rg].add_run(r)
df = RunGroupRuns.data_frame(runs_by_run_group.values()).round(decimals=2)
print(f"**Total Score: {sum(df.score)}**")
df.loc["mean"] = df.mean(numeric_only=True)
print(df.to_markdown())
if __name__ == "__main__":
compare_runs() |