Compute average performance of models over all tasks #71
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…//github.com/Lyon-NLP/mtebscripts into 69-get-models-performance-on-other-languages
imenelydiaker
requested review from
wissam-sib,
MathieuCiancone and
schmarion
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| import os | |||
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modify file name => compute_average_performance instead of avergae
| format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", | ||
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more elegant way : use os.path.basename(model_name) will return the last part of a path (the model name)
| new_df.insert(0, "overall_avg", overall_avg) | ||
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| new_df.reset_index(inplace=True) | ||
| models_short_name = new_df.model.apply(lambda x: models_name_to_index[x]) |
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I think you can simplify code and avoid storing the index mapping, then computing on a new dataframe, then reseting the index. You can compute directly averages on level 0, and change the columns name by prefixing with "avg_"
# Compute average on each task type
averaged_df = result_df.T.groupby(level=0).mean().T
# change column names by prefixing with "avg_"
averaged_df.columns = [f"avg_{col.lower()}" for col in averaged_df.columns]
# compute overall average
averaged_df.insert(0, "avg_overall", averaged_df.mean(axis=1))
# only keep model name in path
averaged_df.index = averaged_df.index.map(os.path.basename)…//github.com/Lyon-NLP/mtebscripts into 69-get-models-performance-on-other-languages
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All in a CSV file:
Note:
model_shortcolumn is the model name without the prefix