forked from moreo/QuaPy
changes to plots again
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@ -4,7 +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 StrMethodFormatter, NullFormatter
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from matplotlib.ticker import ScalarFormatter
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import quapy as qp
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@ -270,11 +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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plt.yscale("log")
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ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.2f}'))
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ax.yaxis.set_minor_formatter(StrMethodFormatter('{x:.2f}'))
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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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@ -305,8 +305,14 @@ 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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@ -321,8 +327,8 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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ax.set_xlim(min_x, max_x)
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if show_legend:
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ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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fig.legend(loc='right')
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_save_or_show(savepath)
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