237 lines
9.4 KiB
Python
237 lines
9.4 KiB
Python
from collections import defaultdict
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import cm
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import quapy as qp
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from matplotlib.font_manager import FontProperties
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plt.rcParams['figure.figsize'] = [12, 8]
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plt.rcParams['figure.dpi'] = 200
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plt.rcParams['font.size'] = 16
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def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,
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train_prev=None, savepath=None):
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fig, ax = plt.subplots()
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ax.set_aspect('equal')
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ax.grid()
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ax.plot([0, 1], [0, 1], '--k', label='ideal', zorder=1)
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method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs, estim_prevs)
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for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
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true_prev = true_prev[:,pos_class]
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estim_prev = estim_prev[:,pos_class]
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x_ticks = np.unique(true_prev)
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x_ticks.sort()
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y_ave = np.asarray([estim_prev[true_prev == x].mean() for x in x_ticks])
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y_std = np.asarray([estim_prev[true_prev == x].std() for x in x_ticks])
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ax.errorbar(x_ticks, y_ave, fmt='-', marker='o', label=method, markersize=3, zorder=2)
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if show_std:
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ax.fill_between(x_ticks, y_ave - y_std, y_ave + y_std, alpha=0.25)
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if train_prev is not None:
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train_prev = train_prev[pos_class]
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ax.scatter(train_prev, train_prev, c='c', label='tr-prev', linewidth=2, edgecolor='k', s=100, zorder=3)
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ax.set(xlabel='true prevalence', ylabel='estimated prevalence', title=title)
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ax.set_ylim(0, 1)
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ax.set_xlim(0, 1)
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if legend:
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box = ax.get_position()
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ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
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ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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save_or_show(savepath)
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def binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=1, title=None, savepath=None):
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method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs, estim_prevs)
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fig, ax = plt.subplots()
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ax.grid()
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data, labels = [], []
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for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
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true_prev = true_prev[:,pos_class]
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estim_prev = estim_prev[:,pos_class]
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data.append(estim_prev-true_prev)
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labels.append(method)
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ax.boxplot(data, labels=labels, patch_artist=False, showmeans=True)
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plt.xticks(rotation=45)
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ax.set(ylabel='error bias', title=title)
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save_or_show(savepath)
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def binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title=None, nbins=5, colormap=cm.tab10,
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vertical_xticks=False, legend=True, savepath=None):
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from pylab import boxplot, plot, setp
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fig, ax = plt.subplots()
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ax.grid()
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method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs, estim_prevs)
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bins = np.linspace(0, 1, nbins+1)
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binwidth = 1/nbins
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data = {}
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for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
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true_prev = true_prev[:, pos_class]
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estim_prev = estim_prev[:, pos_class]
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data[method] = []
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inds = np.digitize(true_prev, bins[1:], right=True)
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for ind in range(len(bins)):
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selected = inds==ind
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data[method].append(estim_prev[selected] - true_prev[selected])
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nmethods = len(method_names)
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boxwidth = binwidth/(nmethods+4)
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for i,bin in enumerate(bins):
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boxdata = [data[method][i] for method in method_names]
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positions = [bin+(i*boxwidth)+2*boxwidth for i,_ in enumerate(method_names)]
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box = boxplot(boxdata, showmeans=False, positions=positions, widths=boxwidth, sym='+', patch_artist=True)
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for boxid in range(len(method_names)):
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c = colormap.colors[boxid%len(colormap.colors)]
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setp(box['fliers'][boxid], color=c, marker='+', markersize=3., markeredgecolor=c)
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setp(box['boxes'][boxid], color=c)
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setp(box['medians'][boxid], color='k')
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major_xticks_positions, minor_xticks_positions = [], []
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major_xticks_labels, minor_xticks_labels = [], []
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for i,b in enumerate(bins[:-1]):
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major_xticks_positions.append(b)
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minor_xticks_positions.append(b + binwidth / 2)
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major_xticks_labels.append('')
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minor_xticks_labels.append(f'[{bins[i]:.2f}-{bins[i + 1]:.2f}' + (')' if i < len(bins)-2 else ']'))
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ax.set_xticks(major_xticks_positions)
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ax.set_xticks(minor_xticks_positions, minor=True)
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ax.set_xticklabels(major_xticks_labels)
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ax.set_xticklabels(minor_xticks_labels, minor=True, rotation='vertical' if vertical_xticks else 'horizontal')
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if vertical_xticks:
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# Pad margins so that markers don't get clipped by the axes
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plt.margins(0.2)
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# Tweak spacing to prevent clipping of tick-labels
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plt.subplots_adjust(bottom=0.15)
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if legend:
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# adds the legend to the list hs, initialized with the "ideal" quantifier (one that has 0 bias across all bins. i.e.
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# a line from (0,0) to (1,0). The other elements are simply labelled dot-plots that are to be removed (setting
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# set_visible to False for all but the first element) after the legend has been placed
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hs=[ax.plot([0, 1], [0, 0], '-k', zorder=2)[0]]
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for colorid in range(len(method_names)):
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color=colormap.colors[colorid % len(colormap.colors)]
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h, = plot([0, 0], '-s', markerfacecolor=color, color='k',mec=color, linewidth=1.)
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hs.append(h)
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box = ax.get_position()
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ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
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ax.legend(hs, ['ideal']+method_names, loc='center left', bbox_to_anchor=(1, 0.5))
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[h.set_visible(False) for h in hs[1:]]
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# x-axis and y-axis labels and limits
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ax.set(xlabel='prevalence', ylabel='error bias', title=title)
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# ax.set_ylim(-1, 1)
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ax.set_xlim(0, 1)
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save_or_show(savepath)
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def _merge(method_names, true_prevs, estim_prevs):
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ndims = true_prevs[0].shape[1]
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data = defaultdict(lambda: {'true': np.empty(shape=(0, ndims)), 'estim': np.empty(shape=(0, ndims))})
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method_order=[]
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for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
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data[method]['true'] = np.concatenate([data[method]['true'], true_prev])
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data[method]['estim'] = np.concatenate([data[method]['estim'], estim_prev])
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if method not in method_order:
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method_order.append(method)
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true_prevs_ = [data[m]['true'] for m in method_order]
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estim_prevs_ = [data[m]['estim'] for m in method_order]
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return method_order, true_prevs_, estim_prevs_
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def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=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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savepath=None):
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fig, ax = plt.subplots()
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ax.grid()
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x_error = qp.error.ae
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y_error = getattr(qp.error, error_name)
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# join all data, and keep the order in which the methods appeared for the first time
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data = defaultdict(lambda:{'x':np.empty(shape=(0)), 'y':np.empty(shape=(0))})
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method_order = []
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for method, test_prevs_i, estim_prevs_i, tr_prev_i in zip(method_names, true_prevs, estim_prevs, tr_prevs):
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tr_prev_i = np.repeat(tr_prev_i.reshape(1,-1), repeats=test_prevs_i.shape[0], axis=0)
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tr_test_drifts = x_error(test_prevs_i, tr_prev_i)
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data[method]['x'] = np.concatenate([data[method]['x'], tr_test_drifts])
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method_drifts = y_error(test_prevs_i, estim_prevs_i)
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data[method]['y'] = np.concatenate([data[method]['y'], method_drifts])
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if method not in method_order:
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method_order.append(method)
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bins = np.linspace(0, 1, n_bins+1)
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binwidth = 1 / n_bins
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min_x, max_x = None, None
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for method in method_order:
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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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inds = np.digitize(tr_test_drifts, bins, right=True)
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xs, ys, ystds = [], [], []
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for ind in range(len(bins)):
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selected = inds==ind
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if selected.sum() > 0:
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xs.append(ind*binwidth)
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ys.append(np.mean(method_drifts[selected]))
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ystds.append(np.std(method_drifts[selected]))
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xs = np.asarray(xs)
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ys = np.asarray(ys)
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ystds = np.asarray(ystds)
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min_x_method, max_x_method = xs.min(), xs.max()
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min_x = min_x_method if min_x is None or min_x_method < min_x else min_x
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max_x = max_x_method if max_x is None or max_x_method > max_x else max_x
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ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=3, zorder=2)
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if show_std:
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ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
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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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box = ax.get_position()
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ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
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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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save_or_show(savepath)
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def save_or_show(savepath):
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# if savepath is specified, then saves the plot in that path; otherwise the plot is shown
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if savepath is not None:
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qp.util.create_parent_dir(savepath)
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# plt.tight_layout()
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plt.savefig(savepath, bbox_inches='tight')
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else:
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plt.show()
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