Hots hots, better plots

compare_benchmark_performance/compare_accuracy.py

-import pandas as pd
+import argparse
 from pathlib import Path
 from itertools import chain
 from multiprocessing import Pool
+
+import pandas as pd
 from sklearn.metrics import ConfusionMatrixDisplay, f1_score, accuracy_score
 import matplotlib.pyplot as plt
 import seaborn as sns
@@ -121,21 +123,27 @@ def main():
     df = pd.DataFrame.from_records(all_records,
                                    columns=['tool', 'species',
                                             'fold', 'accuracy', 'f1'])
+    # Set species in alphabetical order
+    all_species = df.species.unique()
+    all_species.sort()
+
     # Do basic plotting
     df.groupby(['species', 'tool']).mean().plot.bar()
-
     plt.tight_layout()
     plt.show()
 
-    sns.catplot(x='species', y='accuracy', ci='sd', data=df, hue='tool', kind='bar', legend_out=True)
+    sns.catplot(x='species', y='accuracy', ci='sd', data=df, hue='tool',
+                kind='bar', order=all_species, legend_out=False, aspect=2, palette='pastel')
     plt.xticks(rotation=90)
     plt.tight_layout()
     plt.show()
 
-    sns.catplot(x='species', y='f1', data=df, hue='tool', kind='bar')
+    sns.catplot(x='species', y='f1', ci='sd', data=df, hue='tool',
+                kind='bar', order=all_species, legend_out=False, aspect=2)
     plt.xticks(rotation=90)
     plt.tight_layout()
     plt.show()
 
+
 if __name__ == '__main__':
     main()