Merge branch 'master' into mhof_dev

Author: smilesun

docs/doc_benchmark.md

@@ -132,8 +132,9 @@ In case that the benchmark is not entirely completed, the user can obtain partia
 explained below.
 
 
-### Obtain partial results
-If the benchmark is not yet completed (still running or has some failed jobs, e.g. BrokenPipe Error due to multiprocessing in PIL image reading), the `results.csv` file containing the aggregated results will not be created.
+### Aggregate obtained partial results
+If the benchmark is not yet completed (still running or has some failed jobs, e.g. Out of Memory, BrokenPipe Error due to multiprocessing in PIL image reading), the `results.csv` file containing the aggregated results will not be created.
+
 The user can then obtain the aggregated partial results with plots from the partially completed benchmark by running
 the following after cd into the DomainLab directory:
 ```commandline
@@ -144,9 +145,9 @@ e.g. `./zoutput/benchmarks/demo_benchmark`, where `demo_benchmark` is a name def
 
 Alternatively, one could use
 ```examples
-cat ./zoutput/benchmarks/[name of the benchmark]/rule_results/*.csv > result.csv
+sh scripts/sh_benchmark_partial_agg.sh OUTPUT_DIR/rule_results
 ```
-clean up the extra csv head generated and plot the csv using command below
+where rule_results is the subfolder that contains partially finished csv result files. This script will partially aggregate the csv files in a faster fashion, output latex table which summarizes the results named "output_table_perf.tex" (which also contains a text format table before the latex table), and at the end generate plots using the following functionality.
 
 ### Generate plots from .csv file
 If the benchmark is not completed, the `graphics` subdirectory might not be created. The user can then manually

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "domainlab"
-version = "0.7.0"
+version = "0.7.1"
 readme = "README.md"
 repository = "https://github.com/marrlab/DomainLab"
 homepage = "https://marrlab.github.io/DomainLab/"

scripts/generate_latex_table.py

@@ -5,7 +5,7 @@
 import pandas as pd
 
 
-def gen_latex_table(raw_df, fname="table_perf.tex",
+def gen_latex_table(raw_df, fname="output_table_perf.tex",
                     group="method", str_perf="acc"):
     """
     aggregate benchmark csv file to generate latex table
@@ -15,6 +15,8 @@ def gen_latex_table(raw_df, fname="table_perf.tex",
     str_table = df_result.to_string()
     print(str_table)
     with open(fname, 'w') as file:
+        file.write(str_table)
+        file.write("\n")
         file.write(latex_table)
 
 

scripts/merge_csvs.sh

@@ -1,10 +1,12 @@
 #!/bin/bash
+# $1 should be a folder with only csv files
+# $2 is the output file name
 
 # Define the directory containing the text files
 directory=$1
 
 # Define the output CSV file
-output_file="merged_data.csv"
+output_file="${2:-merged_data.csv}"
 
 # Initialize the merged CSV file with the header from the first file
 find "$directory" -maxdepth 1 -type f -name "*.csv" | sort | head -1 | xargs head -n 1 > "$output_file"

scripts/sh_benchmark_partial_agg.sh

@@ -0,0 +1,7 @@
+# $1 should be the rule_results folder which contains several csv files
+# $2 default to be merged_data.csv
+
+file_na_merged_csv="${2:-merged_data.csv}"
+sh scripts/merge_csvs.sh $1 $file_na_merged_csv
+python scripts/generate_latex_table.py $file_na_merged_csv
+python main_out.py --gen_plots $file_na_merged_csv  --outp_dir partial_agg_plots

scripts/sh_genplot.sh

@@ -38,7 +38,7 @@ def copy_dir(dir_path="zdata"):
     package_data={
         "zdata": [f for f in copy_dir()],
     },
-    version="0.7.0",
+    version="0.7.1",
     description="Library of modular domain generalization for deep learning",
     url="https://github.com/marrlab/DomainLab",
     author="Xudong Sun, et.al.",