forked from moreo/QuaPy
commit
850f0e25db
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@ -4,6 +4,7 @@ from matplotlib.cm import get_cmap
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import numpy as np
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from matplotlib import cm
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from scipy.stats import ttest_ind_from_stats
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from matplotlib.ticker import ScalarFormatter
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import quapy as qp
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@ -212,6 +213,7 @@ def binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title=N
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def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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n_bins=20, error_name='ae', show_std=False,
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show_density=True,
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show_legend=True,
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logscale=False,
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title=f'Quantification error as a function of distribution shift',
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vlines=None,
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@ -234,6 +236,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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:param error_name: a string representing the name of an error function (as defined in `quapy.error`, default is "ae")
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:param show_std: whether or not to show standard deviations as color bands (default is False)
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:param show_density: whether or not to display the distribution of experiments for each bin (default is True)
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:param show_density: whether or not to display the legend of the chart (default is True)
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:param logscale: whether or not to log-scale the y-error measure (default is False)
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:param title: title of the plot (default is "Quantification error as a function of distribution shift")
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:param vlines: array-like list of values (default is None). If indicated, highlights some regions of the space
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@ -254,6 +257,9 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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# x_error function) and 'y' is the estim-test shift (computed as according to y_error)
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data = _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order)
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if method_order is None:
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method_order = method_names
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_set_colors(ax, n_methods=len(method_order))
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bins = np.linspace(0, 1, n_bins+1)
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@ -264,7 +270,11 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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tr_test_drifts = data[method]['x']
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method_drifts = data[method]['y']
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if logscale:
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method_drifts=np.log(1+method_drifts)
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ax.set_yscale("log")
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ax.yaxis.set_major_formatter(ScalarFormatter())
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ax.yaxis.set_minor_formatter(ScalarFormatter())
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ax.yaxis.get_major_formatter().set_scientific(False)
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ax.yaxis.get_minor_formatter().set_scientific(False)
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inds = np.digitize(tr_test_drifts, bins, right=True)
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@ -295,9 +305,15 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
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if show_density:
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ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
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ax2 = ax.twinx()
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ax2.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
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max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
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#ax2.set_ylabel("bar data")
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ax2.set_ylim(0,1)
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ax2.spines['right'].set_color('g')
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ax2.tick_params(axis='y', colors='g')
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#ax2.yaxis.set_visible(False)
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ax.set(xlabel=f'Distribution shift between training set and test sample',
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ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
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title=title)
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@ -306,9 +322,13 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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if vlines:
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for vline in vlines:
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ax.axvline(vline, 0, 1, linestyle='--', color='k')
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ax.set_xlim(0, max_x)
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ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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ax.set_xlim(min_x, max_x)
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if show_legend:
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fig.legend(loc='right')
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_save_or_show(savepath)
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