Comments (2)
So i managed to get a script that merges the manually downloaded CSVs into a single csv, for anyone who would need something like this:
import pandas as pd
import glob
from run_name_getter import *
import matplotlib.pyplot as plt
files = glob.glob("movingForward/*.csv")
if len(files) == 0: raise BaseException("no files")
step_column = pd.read_csv(files[0], usecols=['Step'])
all_csvs = [pd.read_csv(p, usecols=['Value']) for p in files]
for i in range(len(all_csvs)):
csv = all_csvs[i]
run_name = get_run_name(files[i])
csv.columns = [run_name]
metric_name = get_metric_name(files[0])
all_csvs = [step_column] + all_csvs
df = pd.concat(all_csvs, axis=1)
df.plot(x="Step", alpha=0.5, title=metric_name)
plt.show()
the only downside now is I have to manually download each CSV for each run for each metric.
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I'm no longer actively working on this tool and am not sure if this issue is still relevant. Going to close it for now. We can reopen if it's still an issue for you.
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Related Issues (11)
- [Feature] Support variable step counts for a single scalar HOT 13
- Sample figure for the documentation HOT 1
- What platform is this code tested on? HOT 1
- What if there are no subfolders test/train? HOT 5
- trying to use this with a TensorBoard result HOT 1
- Event in tf2 HOT 2
- Extracting stored Hyperparameters HOT 1
- No scalars found in event files HOT 4
- readme link to tensorboard-reducer for PyTorch? HOT 5
- Tensorflow Migration Issues HOT 4
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