better plot scores

plot_scores.py

@@ -2,14 +2,14 @@ import matplotlib.pyplot as plt
 import pandas as pd, numpy as np
 from metadata import species
 
-algos = ['praat', 'pyin', 'crepe', 'tcrepe', 'tcrepe_ft', 'tcrepe_ftsp', 'basic', 'pesto']
-metrics = ['threshold', 'Recall', 'False alarm', 'Pitch acc', 'Chroma acc']
+algos = ['praat', 'tcrepe_ftsp', 'tcrepe_ftoth', 'basic', 'pesto', 'pesto_ft']
+metrics = ['Recall', 'False alarm', 'Pitch acc', 'Chroma acc']
 
-drop_noisy_bins = True
+drop_noisy_bins = False
 drop_noisy_vocs = False
 
-fig, ax = plt.subplots(nrows=len(metrics), figsize=(7, 15), sharex=True, sharey=True)
-ax[0].set_ylim(0, 1)
+fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), sharex=True, sharey=True)
+ax[0,0].set_ylim(0, 1)
 
 for i, metric in enumerate(metrics):
     ok = pd.DataFrame()
@@ -17,11 +17,13 @@ for i, metric in enumerate(metrics):
         df = pd.read_csv(f'scores/{specie}_scores{"_minusvocs" if drop_noisy_vocs else ""}{"_minusbins" if drop_noisy_bins else ""}.csv', index_col=0)
         df['False alarm'] = 1 - df['False alarm']
         ok.loc[specie, df.index] = df[metric]
-    ok = ok.loc[:,algos]
+    ok = ok[algos]
     # bar plot
-    ok.plot.bar(rot=22, ax=ax[i])
-    ax[i].legend(bbox_to_anchor=(1,1))
-    ax[i].grid()
-    ax[i].set_title(metric)
+    m_ax = ax[int(i//2), i%2]
+    ok.plot.barh(ax=m_ax, legend=None)
+    m_ax.grid()
+    m_ax.set_title(metric)
+    if i%2==1:
+        m_ax.legend(bbox_to_anchor=(1,1))
 plt.tight_layout()
 plt.savefig(f'scores{"_minusvocs" if drop_noisy_vocs else ""}{"_minusbins" if drop_noisy_bins else ""}.pdf')