descent plot

plot_scores_columns.py

+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from matplotlib import colors
+import pandas as pd, numpy as np
+from metadata import species
+
+#colors = ['#006BA4', '#FF800E', '#ABABAB', '#595959', '#5F9ED1', '#C85200', '#898989', '#A2C8EC', '#FFBC79', '#CFCFCF']
+colors = list(colors.TABLEAU_COLORS)
+# plt.rcParams['legend.title_fontsize'] = 'x-small'
+
+
+markers = [
+    ('o', 'right', colors[0]), ('d', 'none', colors[0]),
+    ('s', 'none', colors[1]), ('v', 'none', colors[1]), ('H', 'none', colors[1]),
+    ('<', 'none', colors[2]), ('>', 'none', colors[2]), ('^', 'none', colors[2]), ('p', 'none', colors[2]),
+    ('P', 'none', colors[3]), ('*', 'none', colors[3]), ('X', 'none', colors[3]), ('D', 'none', colors[3]) , ('h', 'left', colors[3])]
+
+
+species_list = [
+    'wolves', 'spotted_hyenas', # 2 good salience & harmonicity
+    'bottlenose_dolphins', 'rodents', 'little_owls', # 3 good salience only
+    'monk_parakeets', 'lions', 'orangutans', 'long-billed_hermits', # 4 good harmonicity only
+    'hummingbirds', 'disk-winged_bats', 'Reunion_grey_white_eyes', 'dolphins', 'La_Palma_chaffinches'] # 5 neither
+
+algos = ['basic', 'pyin', 'pesto', 'praat', 'pesto_ft', 'tcrepe_ftoth', 'tcrepe_ftsp']
+algo_names = ['basic', 'pyin', 'pesto-music', 'praat', 'pesto-bio', 'crepe-other', 'crepe-target']
+metrics = ['Pitch acc'] #, 'Chroma acc']
+# metrics = ['Recall', 'Specificity', 'Vocalisation recall']
+
+fig, ax = plt.subplots(nrows=1, ncols=len(metrics), figsize=(10, 4.5), sharex=True, sharey=True)
+#ax[0].set_yticks([0, .2, .4, .6, .8, 1])
+ax.set_ylim(0, 1)
+algo_legend = []
+
+for i, metric in enumerate(metrics):
+    m_ax = ax #ax[int(i//2), i%2]
+    m_ax.grid('both', axis='y')
+    m_ax.set_ylabel(metric, fontsize='medium')
+
+    ok = pd.DataFrame()
+    for j, (specie, marker) in enumerate(zip(species_list, markers)):
+        df = pd.read_csv(f'scores/{specie}_scores.csv', index_col=0)
+        df['Specificity'] = 1 - df['False alarm']
+        df.rename(columns={'Voc. recall':'Vocalisation recall'}, inplace=True)
+        ok.loc[specie, df.index] = df[metric]
+        
+        tt = m_ax.plot(np.arange(len(algos))-.3+j*0.6/len(species_list), ok.loc[specie, algos], marker=marker[0], color=marker[2], fillstyle=marker[1], markersize=8, label=specie.replace('_',' '), linestyle='none')
+        algo_legend.append(tt[0])
+
+    m_ax.set_xticks(range(len(algo_names)))
+    m_ax.set_xticklabels(algo_names)
+#ax[1].set_xticklabels(algos, rotation=22)
+plt.tight_layout(rect=(0, 0, 1, .87))
+
+
+species_list = [s.replace('_', ' ') for s in species_list]
+leg = ax.legend(algo_legend[:2], species_list[:2], title='salient & harmonic', loc='lower left', bbox_to_anchor=(0, 1.02), fontsize='x-small')
+ax.add_artist(leg)
+leg = ax.legend(algo_legend[2:5], species_list[2:5], title='salient & non-harmonic', loc='lower left', bbox_to_anchor=(0.16, 1.02), fontsize='x-small')
+ax.add_artist(leg)
+leg = ax.legend(algo_legend[5:9], species_list[5:9], ncols=2, title='non-salient & harmonic', loc='lower left', bbox_to_anchor=(0.36, 1.02), fontsize='x-small')
+ax.add_artist(leg)
+leg = ax.legend(algo_legend[9:], species_list[9:], ncols=2, title='non-salient & non-harmonic', loc='lower left', bbox_to_anchor=(0.65, 1.02), fontsize='x-small')
+ax.add_artist(leg)
+# ax.legend(loc='lower left', ncols=7, bbox_to_anchor=(0, 1.1), fontsize='x-small')
+
+plt.savefig(f'figures/scatter_scores_columns.pdf')