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Merge branch 'protocols' of github.com:HLT-ISTI/QuaPy into protocols

This commit is contained in:
Alejandro Moreo Fernandez 2023-01-18 15:19:57 +01:00
parent 6e910075ab 850f0e25db
commit 1d4fa40f3e
1 changed files with 25 additions and 5 deletions
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30
quapy/plot.py
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@ -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 ScalarFormatter
import quapy as qp
@ -212,6 +213,7 @@ def binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title=N
def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
n_bins=20, error_name='ae', show_std=False,
show_density=True,
show_legend=True,
logscale=False,
title=f'Quantification error as a function of distribution shift',
vlines=None,
@ -234,6 +236,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
:param error_name: a string representing the name of an error function (as defined in `quapy.error`, default is "ae")
:param show_std: whether or not to show standard deviations as color bands (default is False)
:param show_density: whether or not to display the distribution of experiments for each bin (default is True)
:param show_density: whether or not to display the legend of the chart (default is True)
:param logscale: whether or not to log-scale the y-error measure (default is False)
:param title: title of the plot (default is "Quantification error as a function of distribution shift")
:param vlines: array-like list of values (default is None). If indicated, highlights some regions of the space
@ -254,6 +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)
@ -264,7 +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)
ax.set_yscale("log")
ax.yaxis.set_major_formatter(ScalarFormatter())
ax.yaxis.set_minor_formatter(ScalarFormatter())
ax.yaxis.get_major_formatter().set_scientific(False)
ax.yaxis.get_minor_formatter().set_scientific(False)
inds = np.digitize(tr_test_drifts, bins, right=True)
@ -295,9 +305,15 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
if show_density:
ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
ax2 = ax.twinx()
ax2.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')
#ax2.set_ylabel("bar data")
ax2.set_ylim(0,1)
ax2.spines['right'].set_color('g')
ax2.tick_params(axis='y', colors='g')
#ax2.yaxis.set_visible(False)
ax.set(xlabel=f'Distribution shift between training set and test sample',
ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
title=title)
@ -306,9 +322,13 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
if vlines:
for vline in vlines:
ax.axvline(vline, 0, 1, linestyle='--', color='k')
ax.set_xlim(0, max_x)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax.set_xlim(min_x, max_x)
if show_legend:
fig.legend(loc='right')
_save_or_show(savepath)