From fda40b05c9328e127482d54e88648e49e84b1664 Mon Sep 17 00:00:00 2001
From: Starbeamrainbowlabs <sbrl@starbeamrainbowlabs.com>
Date: Fri, 20 Dec 2024 15:11:41 +0000
Subject: [PATCH] scripts/stbl-crossval: initial WIP draft

---
 aimodel/scripts/stbl-crossval.py | 76 ++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100755 aimodel/scripts/stbl-crossval.py

diff --git a/aimodel/scripts/stbl-crossval.py b/aimodel/scripts/stbl-crossval.py
new file mode 100755
index 0000000..6536ddc
--- /dev/null
+++ b/aimodel/scripts/stbl-crossval.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+
+from loguru import logger
+import pandas as pd
+
+# This script analyses metrics.tsv files from a series of identical experiments and reports metrics on them.
+# This is sometimes known as cross-validation, but we usually use the model series code crossval-stblX, where X is an integer >0.
+
+
+if len(sys.argv) <= 1:
+	print("""
+Usage:
+	scripts/stbl-crossval.mjs {{path/to/directory}}
+
+...in which the given directory contains a series of experiment root directories to include in the statistical analysis.
+
+This script is not picky about the format of the data in metrics.tsv, so long as it's in the form:
+
+epoch	metric_A	metric_B	…
+0	val:float	val:float	…
+1	val:float	val:float	…
+2	val:float	val:float	…
+⋮
+""")
+	sys.exit(0)
+
+DIRPATH = sys.argv[1]  # [0] == script path
+
+files = 0
+metrics = {}
+
+for filepath in os.scandir(DIRPATH):
+	tbl = pd.read_csv(os.path.join(filepath, "metrics.tsv"), sep="\t")
+
+	# metrics.append(tbl)
+
+	for column in tbl.columns:
+		if column == "epoch":
+			continue  # Row index implicitly retains this
+
+		if column not in metrics:
+			metrics[column] = []
+
+		metrics[column].append(tbl[column].values)
+		# print(column, tbl[column])
+
+	# print("DEBUG:metrics", tbl)
+	files += 1
+
+logger.info(f"Read {files} files into crossval-stbl{files} analysis")
+
+stats = {}
+
+for metric in metrics.keys():
+	metrics[metric] = pd.DataFrame(metrics[metric]).transpose()
+
+	if metric not in stats:
+		stats[metric] = {}
+
+	stats[metric]["aad"] = metrics[metric].max(axis=1) # mean/average absolute deviation
+	stats[metric]["mad"] = metrics[metric].max(axis=1) # median absolute deviation
+	stats[metric]["stddev"] = metrics[metric].std(axis=1)
+	stats[metric]["mean"] = metrics[metric].mean(axis=1)
+	stats[metric]["min"] = metrics[metric].min(axis=1)
+	stats[metric]["max"] = metrics[metric].max(axis=1)
+	stats[metric]["agg_min"] = stats[metric]["min"].min()
+	stats[metric]["agg_max"] = stats[metric]["max"].max()
+	stats[metric]["agg_stddev"] = metrics[metric].stack().std()
+	stats[metric]["agg_mean"] = metrics[metric].stack().std()
+	stats[metric]["agg_aad"] = metrics[metric].stack().max() # mean/average absolute deviation
+
+	print(stats[metric])
+