forked from moreo/QuaPy
testing log scale
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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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import numpy as np
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from matplotlib import cm
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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 scipy.stats import ttest_ind_from_stats
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from matplotlib.ticker import StrMethodFormatter, NullFormatter
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import quapy as qp
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import quapy as qp
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@ -256,6 +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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# 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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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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_set_colors(ax, n_methods=len(method_order))
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bins = np.linspace(0, 1, n_bins+1)
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bins = np.linspace(0, 1, n_bins+1)
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@ -266,7 +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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tr_test_drifts = data[method]['x']
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method_drifts = data[method]['y']
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method_drifts = data[method]['y']
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if logscale:
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if logscale:
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method_drifts=np.log(1+method_drifts)
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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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inds = np.digitize(tr_test_drifts, bins, right=True)
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inds = np.digitize(tr_test_drifts, bins, right=True)
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