From f8c0c61af2db75655cdcefde1c949d669f5f96fb Mon Sep 17 00:00:00 2001
From: Michel Spils <msp@informatik.uni-kiel.de>
Date: Thu, 12 Dec 2024 14:47:26 +0000
Subject: [PATCH] minor bugs
---
notebooks | 1 +
src/scripts/extract_metrics.py | 17 ++++++++++-------
src/utils/helpers.py | 2 +-
3 files changed, 12 insertions(+), 8 deletions(-)
create mode 160000 notebooks
diff --git a/notebooks b/notebooks
new file mode 160000
index 0000000..abdb069
--- /dev/null
+++ b/notebooks
@@ -0,0 +1 @@
+Subproject commit abdb06965783302b8d5dd5d7f8bdb8e3cc9d639c
diff --git a/src/scripts/extract_metrics.py b/src/scripts/extract_metrics.py
index 5f170d4..fb77b27 100644
--- a/src/scripts/extract_metrics.py
+++ b/src/scripts/extract_metrics.py
@@ -28,13 +28,16 @@ def extract_summaries_to_csv(root_dir,metrics=None,force=False):
print(f"Skipping {root} as metrics.csv already exists. Use --force to overwrite")
continue
#log_dir = "../../../data-project/KIWaVo/models/lfu/willenscharen6/lightning_logs/version_0/"
- reader_hp = SummaryReader(root,pivot=True)
- df =reader_hp.scalars
- df = df.drop(["step","epoch"],axis=1)[1:49]
- df = df[metrics]
- df.columns = df.columns.str.slice(3)
- df.to_csv(root + "/metrics.csv")
- print(f"Extracted metrics for {root}")
+ try :
+ reader_hp = SummaryReader(root,pivot=True)
+ df =reader_hp.scalars
+ df = df.drop(["step","epoch"],axis=1)[1:49]
+ df = df[metrics]
+ df.columns = df.columns.str.slice(3)
+ df.to_csv(root + "/metrics.csv")
+ print(f"Extracted metrics for {root}")
+ except Exception as e:
+ print(f"Error in {root}")
def main():
diff --git a/src/utils/helpers.py b/src/utils/helpers.py
index 56bf836..f87f63c 100644
--- a/src/utils/helpers.py
+++ b/src/utils/helpers.py
@@ -88,7 +88,7 @@ def get_pred(
y_pred = np.concatenate(pred)
if y_true is not None:
- y_pred = np.concatenate([np.expand_dims(y_true, axis=1), pred], axis=1)
+ y_pred = np.concatenate([np.expand_dims(y_true, axis=1), y_pred], axis=1) # Replaced pred with y_pred, why was it pred?
y_pred = pd.DataFrame(y_pred, index=y_true.index, columns=range(y_pred.shape[1]))
return y_pred
--
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