From 7dffa991951feae4e1f633e584a1b27cbf3b70a5 Mon Sep 17 00:00:00 2001
From: Phil <s8phsaue@stud.uni-saarland.de>
Date: Thu, 20 Feb 2025 14:37:24 +0100
Subject: [PATCH] Added stats
---
src/stats.py | 111 ++++++++++++++++++++++++++++++---------------------
1 file changed, 66 insertions(+), 45 deletions(-)
diff --git a/src/stats.py b/src/stats.py
index bd886d9..d584f00 100644
--- a/src/stats.py
+++ b/src/stats.py
@@ -1,52 +1,73 @@
-import time
+import numpy as np
-class Statistics():
- def __init__(self):
- self.total_episodes = 0
- self.total_failures = 0
- self.total_steps = 0
- self.total_updates = 0
-
- for name in ['train', 'update', 'test']:
- setattr(self, f"time_avg_{name}", 0)
- setattr(self, f"{name}_count", 0)
- setattr(self, f"_time_{name}", None)
-
- self._time_start = time.time()
+class Statistics:
+ def __init__(self, writer):
+ self.writer = writer
+ self.total_train_episodes = 0
+ self.total_train_steps = 0
+ self.total_train_unsafe = 0
+ self.avg_train_unsafe = 0
- def total_time(self, name=None):
- if name is None:
- t = time.time() - self._time_start
- else:
- t = getattr(self, f"time_avg_{name}") * getattr(self, f"{name}_count")
- return round(t,2)
-
-
- def begin(self, name):
- setattr(self, f"_time_{name}", time.time())
+ self.total_updates = 0
+ self.history = {
+ "train" : {
+ "_episodes" : [],
+ "returns" : [],
+ "costs" : [],
+ "steps" : [],
+ "unsafe" : [],
+ },
+ "test/unshielded" : {
+ "_episodes" : [],
+ "avg_returns" : [],
+ "avg_costs" : [],
+ "avg_steps" : [],
+ "avg_unsafe" : [],
+ },
+ "test/shielded" : {
+ "_episodes" : [],
+ "avg_returns" : [],
+ "avg_costs" : [],
+ "avg_steps" : [],
+ "avg_unsafe" : [],
+ },
+ }
+
+ def history_clear(self, name):
+ for data in self.history[name].values():
+ data.clear()
- def end(self, name):
- t = time.time()
- d = t - getattr(self, f"_time_{name}")
- setattr(self, f"{name}_count", getattr(self, f"{name}_count") + 1)
- c = getattr(self, f"{name}_count")
- a = 1/c
- b = 1 - a
- setattr(self, f"time_avg_{name}", a*d + b*getattr(self, f"time_avg_{name}"))
+ def history_flush(self, name):
+ for key, values in self.history[name].items():
+ if key.startswith("_"): continue
+ path = f"{name}/{key}"
+ for i in range(len(values)):
+ self.writer.add_scalar(path, values[i], self.history[name]["_episodes"][i])
+ self.history_clear(name)
+ def _record(self, name, **kwargs):
+ for key in self.history[name].keys():
+ self.history[name][key] += kwargs[key].tolist()
+
+ def record_train(self, num_episodes, **kwargs):
+ episodes = np.arange(0, num_episodes, 1) + self.total_train_episodes
+ kwargs["_episodes"] = episodes
+ self._record("train", **kwargs)
- def print(self):
- time = self.total_time()
- time_train = self.total_time("train")
- time_update = self.total_time("update")
- time_test = self.total_time("test")
- steps = self.total_steps
- episodes = self.total_episodes
- failures = self.total_failures
- updates = self.total_updates
- avg_failures = round((self.total_failures/self.total_episodes)*100,2) if self.total_episodes > 0 else 0.00
-
- print(f"[TOTAL] total_time: {time}s, total_train: {time_train}s, total_update: {time_update}s, total_test: {time_test}s,\n \
- steps: {steps}, episodes: {episodes}, failures: {failures}, avg_failures: {avg_failures}%, updates: {updates}")
\ No newline at end of file
+ def record_test(self, shielded, **kwargs):
+ shielded = "shielded" if shielded else "unshielded"
+ kwargs["_episodes"] = np.full_like(kwargs["avg_unsafe"], self.total_train_episodes, dtype=np.uint64)
+ self._record(f"test/{shielded}", **kwargs)
+
+ def after_exploration(self, train, shielded):
+ if train:
+ self.avg_train_unsafe = np.array(self.history["train"]["unsafe"]).mean().item()
+ self.history_flush(name="train")
+ else:
+ shielded = "shielded" if shielded else "unshielded"
+ name = f"test/{shielded}"
+ for key, value in self.history[name].items():
+ self.history[name][key] = np.array(value).mean().tolist()
+ self.history_flush(name=name)
\ No newline at end of file
--
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