testing log scale

quapy/plot.py

@@ -4,6 +4,7 @@ from matplotlib.cm import get_cmap
 import numpy as np
 from matplotlib import cm
 from scipy.stats import ttest_ind_from_stats
+from matplotlib.ticker import StrMethodFormatter, NullFormatter
 
 import quapy as qp
 
@@ -256,6 +257,9 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
     # x_error function) and 'y' is the estim-test shift (computed as according to y_error)
     data = _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order)
 
+    if method_order is None:
+        method_order = method_names
+
     _set_colors(ax, n_methods=len(method_order))
 
     bins = np.linspace(0, 1, n_bins+1)
@@ -266,7 +270,11 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
         tr_test_drifts = data[method]['x']
         method_drifts = data[method]['y']
         if logscale:
-            method_drifts=np.log(1+method_drifts)
+            #method_drifts=np.log(1+method_drifts)
+            plt.yscale("log")
+            ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.2f}'))
+            ax.yaxis.set_minor_formatter(StrMethodFormatter('{x:.2f}'))
+            
 
         inds = np.digitize(tr_test_drifts, bins, right=True)
 
@@ -299,7 +307,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
     if show_density:
         ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
                max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
-
+    
     ax.set(xlabel=f'Distribution shift between training set and test sample',
            ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
            title=title)