From eff09c940ba38614b7ed956034fc563b7828f912 Mon Sep 17 00:00:00 2001
From: Michael Pilosov <michael.pilosov@ucdenver.edu>
Date: Wed, 10 Jan 2024 17:50:21 +0000
Subject: [PATCH] track experiments that ran

---
 experiments.csv | 25 +++++++++++++++++++++++++
 scrape.py       | 20 +++++++++++++++-----
 2 files changed, 40 insertions(+), 5 deletions(-)
 create mode 100644 experiments.csv

diff --git a/experiments.csv b/experiments.csv
new file mode 100644
index 0000000..accd524
--- /dev/null
+++ b/experiments.csv
@@ -0,0 +1,25 @@
+,batch_size,alpha,learning_rate
+0,32.0,0.3,0.0001
+1,32.0,0.3,0.01
+2,32.0,0.9,1e-06
+3,32.0,0.7,0.001
+4,64.0,0.5,0.001
+5,64.0,0.1,1e-06
+6,32.0,0.1,0.001
+7,128.0,0.5,1e-06
+8,128.0,0.7,0.001
+9,128.0,0.9,1e-05
+10,128.0,0.1,1e-06
+11,128.0,0.3,1e-06
+12,64.0,0.3,0.01
+13,64.0,0.1,1e-06
+14,128.0,0.5,0.001
+15,32.0,0.3,1e-05
+16,32.0,0.7,1e-06
+17,32.0,0.3,1e-06
+18,64.0,0.3,0.0001
+19,64.0,0.3,1e-06
+20,128.0,0.5,1e-05
+21,32.0,0.1,0.01
+22,64.0,0.1,1e-05
+23,64.0,0.3,0.001
diff --git a/scrape.py b/scrape.py
index 90f0685..f19a843 100644
--- a/scrape.py
+++ b/scrape.py
@@ -29,15 +29,25 @@ def get_exps(pattern: str, splitter: str = "_"):
         # print(latest_checkpoint)
         logs = glob.glob(g[0] + "/events*")[-1]
         print(logs)
-        # source_path = Path(logs)
-        # print("Would copy", source_path, dir_path)
+        source_path = Path(logs)
+        print("Would copy", source_path, dir_path)
         # shutil.copy(source_path, dir_path)
-        make_image(latest_checkpoint, f"out/version_{i}")
-        make_image(latest_checkpoint, f"out/version_{i}b", color=False)
+        # make_image(latest_checkpoint, f"out/version_{i}")
+        # make_image(latest_checkpoint, f"out/version_{i}b", color=False)
 
     return H
 
 
 if __name__ == "__main__":
     D = get_exps("color_*", "_")
-    print(len(D), D)
+
+    import numpy as np
+
+    D = np.array(D)
+    # print(len(D), "\n", D)
+    import pandas as pd
+
+    df = pd.DataFrame(D)
+    df.columns = ["batch_size", "alpha", "learning_rate"]
+    df.to_csv("experiments.csv")
+    print(df)