forked from moreo/QuaPy
570 lines
27 KiB
Python
570 lines
27 KiB
Python
from collections import defaultdict
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import matplotlib.pyplot as plt
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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 math
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import quapy as qp
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plt.rcParams['figure.figsize'] = [10, 6]
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plt.rcParams['figure.dpi'] = 200
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plt.rcParams['font.size'] = 12
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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, method_order=None):
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"""
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The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence
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values (along the x-axis). The optimal quantifier is described by the diagonal (0,0)-(1,1) of the plot (hence the
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name). It is convenient for binary quantification problems, though it can be used for multiclass problems by
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indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots
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like the :meth:`error_by_drift` might be preferable though).
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:param method_names: array-like with the method names for each experiment
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:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
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each experiment
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:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
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for each experiment
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:param pos_class: index of the positive class
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:param title: the title to be displayed in the plot
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:param show_std: whether or not to show standard deviations (represented by color bands). This might be inconvenient
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for cases in which many methods are compared, or when the standard deviations are high -- default True)
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:param legend: whether or not to display the leyend (default True)
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:param train_prev: if indicated (default is None), the training prevalence (for the positive class) is hightlighted
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in the plot. This is convenient when all the experiments have been conducted in the same dataset.
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:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
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:param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,
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listed in the legend and associated with matplotlib colors).
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"""
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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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order = list(zip(method_names, true_prevs, estim_prevs))
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if method_order is not None:
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table = {method_name:[true_prev, estim_prev] for method_name, true_prev, estim_prev in order}
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order = [(method_name, *table[method_name]) for method_name in method_order]
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NUM_COLORS = len(method_names)
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if NUM_COLORS>10:
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cm = plt.get_cmap('tab20')
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ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
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for method, true_prev, estim_prev in order:
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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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ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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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='lower center',
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# bbox_to_anchor=(1, -0.5),
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# ncol=(len(method_names)+1)//2)
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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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"""
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Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)
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for each quantification method with respect to a given positive class.
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:param method_names: array-like with the method names for each experiment
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:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
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each experiment
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:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
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for each experiment
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:param pos_class: index of the positive class
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:param title: the title to be displayed in the plot
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:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
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"""
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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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"""
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Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)
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for different bins of (true) prevalence of the positive classs, for each quantification method.
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:param method_names: array-like with the method names for each experiment
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:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
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each experiment
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:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
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for each experiment
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:param pos_class: index of the positive class
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:param title: the title to be displayed in the plot
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:param nbins: number of bins
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:param colormap: the matplotlib colormap to use (default cm.tab10)
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:param vertical_xticks: whether or not to add secondary grid (default is False)
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:param legend: whether or not to display the legend (default is True)
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:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
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"""
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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, 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[:-1]):
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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})')
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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_xlim(0, 1)
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_save_or_show(savepath)
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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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method_order=None,
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savepath=None):
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"""
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Plots the error (along the x-axis, as measured in terms of `error_name`) as a function of the train-test shift
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(along the y-axis, as measured in terms of :meth:`quapy.error.ae`). This plot is useful especially for multiclass
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problems, in which "diagonal plots" may be cumbersone, and in order to gain understanding about how methods
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fare in different regions of the prior probability shift spectrum (e.g., in the low-shift regime vs. in the
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high-shift regime).
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:param method_names: array-like with the method names for each experiment
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:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
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each experiment
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:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
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for each experiment
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:param tr_prevs: training prevalence of each experiment
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:param n_bins: number of bins in which the y-axis is to be divided (default is 20)
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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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using vertical dotted lines.
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:param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,
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listed in the legend and associated with matplotlib colors).
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:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
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"""
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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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# get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same
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# order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to
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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 = np.unique(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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binwidth = 1 / n_bins
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min_x, max_x, min_y, max_y = None, None, None, None
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npoints = np.zeros(len(bins), dtype=float)
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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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ax.set_yscale("log")
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ax.yaxis.set_major_formatter(ScalarFormatter())
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ax.yaxis.get_major_formatter().set_scientific(False)
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ax.minorticks_off()
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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 p,ind in enumerate(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-binwidth/2)
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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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npoints[p] += len(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, min_y_method, max_y_method = xs.min(), xs.max(), ys.min(), ys.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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max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
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min_y = min_y_method if min_y is None or min_y_method < min_y else min_y
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max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
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ax.errorbar(xs, ys, fmt='-', marker='o', color='w', markersize=8, linewidth=4, zorder=1)
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ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=6, linewidth=2, 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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if show_density:
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ax2 = ax.twinx()
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densities = npoints/np.sum(npoints)
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ax2.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
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densities, alpha=0.15, color='g', width=binwidth, label='density')
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ax2.set_ylim(0,max(densities))
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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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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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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(min_x, max_x)
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if logscale:
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#nice scale for the logaritmic axis
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ax.set_ylim(0,10 ** math.ceil(math.log10(max_y)))
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if show_legend:
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fig.legend(loc='lower center',
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bbox_to_anchor=(0.9, 0.2),
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ncol=1)
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_save_or_show(savepath)
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def brokenbar_supremacy_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
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n_bins=20, binning='isomerous',
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x_error='ae', y_error='ae', ttest_alpha=0.005, tail_density_threshold=0.005,
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method_order=None,
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savepath=None):
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"""
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Displays (only) the top performing methods for different regions of the train-test shift in form of a broken
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bar chart, in which each method has bars only for those regions in which either one of the following conditions
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hold: (i) it is the best method (in average) for the bin, or (ii) it is not statistically significantly different
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(in average) as according to a two-sided t-test on independent samples at confidence `ttest_alpha`.
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The binning can be made "isometric" (same size), or "isomerous" (same number of experiments -- default). A second
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plot is displayed on top, that displays the distribution of experiments for each bin (when binning="isometric") or
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the percentiles points of the distribution (when binning="isomerous").
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:param method_names: array-like with the method names for each experiment
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:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
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each experiment
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:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
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for each experiment
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:param tr_prevs: training prevalence of each experiment
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:param n_bins: number of bins in which the y-axis is to be divided (default is 20)
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:param binning: type of binning, either "isomerous" (default) or "isometric"
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:param x_error: a string representing the name of an error function (as defined in `quapy.error`) to be used for
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measuring the amount of train-test shift (default is "ae")
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:param y_error: a string representing the name of an error function (as defined in `quapy.error`) to be used for
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measuring the amount of error in the prevalence estimations (default is "ae")
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:param ttest_alpha: the confidence interval above which a p-value (two-sided t-test on independent samples) is
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to be considered as an indicator that the two means are not statistically significantly different. Default is
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0.005, meaning that a `p-value > 0.005` indicates the two methods involved are to be considered similar
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:param tail_density_threshold: sets a threshold on the density of experiments (over the total number of experiments)
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below which a bin in the tail (i.e., the right-most ones) will be discarded. This is in order to avoid some
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bins to be shown for train-test outliers.
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:param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,
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listed in the legend and associated with matplotlib colors).
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:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
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:return:
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"""
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assert binning in ['isomerous', 'isometric'], 'unknown binning type; valid types are "isomerous" and "isometric"'
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x_error = getattr(qp.error, x_error)
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y_error = getattr(qp.error, y_error)
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# get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same
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# order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to
|
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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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|
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if method_order is None:
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method_order = method_names
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|
|
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if binning == 'isomerous':
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# take bins containing the same amount of examples
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tr_test_drifts = np.concatenate([data[m]['x'] for m in method_order])
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bins = np.quantile(tr_test_drifts, q=np.linspace(0, 1, n_bins+1)).flatten()
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else:
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|
# take equidistant bins
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|
bins = np.linspace(0, 1, n_bins+1)
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bins[0] = -0.001
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bins[-1] += 0.001
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|
|
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# we use this to keep track of how many datapoits contribute to each bin
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inds_histogram_global = np.zeros(n_bins, dtype=float)
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|
n_methods = len(method_order)
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|
buckets = np.zeros(shape=(n_methods, n_bins, 3))
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for i, method in enumerate(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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|
|
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inds = np.digitize(tr_test_drifts, bins, right=False)
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inds_histogram_global += np.histogram(tr_test_drifts, density=False, bins=bins)[0]
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|
|
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for j in range(len(bins)):
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selected = inds == j
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if selected.sum() > 0:
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|
buckets[i, j-1, 0] = np.mean(method_drifts[selected])
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|
buckets[i, j-1, 1] = np.std(method_drifts[selected])
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|
buckets[i, j-1, 2] = selected.sum()
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|
|
|
# cancel last buckets with low density
|
|
histogram = inds_histogram_global / inds_histogram_global.sum()
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|
for tail in reversed(range(len(histogram))):
|
|
if histogram[tail] < tail_density_threshold:
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|
buckets[:,tail,2] = 0
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|
else:
|
|
break
|
|
|
|
salient_methods = set()
|
|
best_methods = []
|
|
for bucket in range(buckets.shape[1]):
|
|
nc = buckets[:, bucket, 2].sum()
|
|
if nc == 0:
|
|
best_methods.append([])
|
|
continue
|
|
|
|
order = np.argsort(buckets[:, bucket, 0])
|
|
rank1 = order[0]
|
|
best_bucket_methods = [method_order[rank1]]
|
|
best_mean, best_std, best_nc = buckets[rank1, bucket, :]
|
|
for method_index in order[1:]:
|
|
method_mean, method_std, method_nc = buckets[method_index, bucket, :]
|
|
_, pval = ttest_ind_from_stats(best_mean, best_std, best_nc, method_mean, method_std, method_nc)
|
|
if pval > ttest_alpha:
|
|
best_bucket_methods.append(method_order[method_index])
|
|
best_methods.append(best_bucket_methods)
|
|
salient_methods.update(best_bucket_methods)
|
|
print(best_bucket_methods)
|
|
|
|
if binning=='isomerous':
|
|
fig, axes = plt.subplots(2, 1, gridspec_kw={'height_ratios': [0.2, 1]}, figsize=(20, len(salient_methods)))
|
|
else:
|
|
fig, axes = plt.subplots(2, 1, gridspec_kw={'height_ratios': [1, 1]}, figsize=(20, len(salient_methods)))
|
|
|
|
ax = axes[1]
|
|
high_from = 0
|
|
yticks, yticks_method_names = [], []
|
|
color = get_cmap('Accent').colors
|
|
vlines = []
|
|
bar_high = 1
|
|
for method in [m for m in method_order if m in salient_methods]:
|
|
broken_paths = []
|
|
path_start, path_end = None, None
|
|
for i, best_bucket_methods in enumerate(best_methods):
|
|
if method in best_bucket_methods:
|
|
if path_start is None:
|
|
path_start = bins[i]
|
|
path_end = bins[i+1]-path_start
|
|
else:
|
|
path_end += bins[i+1]-bins[i]
|
|
else:
|
|
if path_start is not None:
|
|
broken_paths.append(tuple((path_start, path_end)))
|
|
path_start, path_end = None, None
|
|
if path_start is not None:
|
|
broken_paths.append(tuple((path_start, path_end)))
|
|
|
|
ax.broken_barh(broken_paths, (high_from, bar_high), facecolors=color[len(yticks_method_names)])
|
|
yticks.append(high_from+bar_high/2)
|
|
high_from += bar_high
|
|
yticks_method_names.append(method)
|
|
for path_start, path_end in broken_paths:
|
|
vlines.extend([path_start, path_start+path_end])
|
|
|
|
vlines = np.unique(vlines)
|
|
vlines = sorted(vlines)
|
|
for v in vlines[1:-1]:
|
|
ax.axvline(x=v, color='k', linestyle='--')
|
|
|
|
ax.set_ylim(0, high_from)
|
|
ax.set_xlim(vlines[0], vlines[-1])
|
|
ax.set_xlabel('Distribution shift between training set and sample')
|
|
|
|
ax.set_yticks(yticks)
|
|
ax.set_yticklabels(yticks_method_names)
|
|
|
|
# upper plot (explaining distribution)
|
|
ax = axes[0]
|
|
if binning == 'isometric':
|
|
# show the density for each region
|
|
bins[0]=0
|
|
y_pos = [b+(bins[i+1]-b)/2 for i,b in enumerate(bins[:-1]) if histogram[i]>0]
|
|
bar_width = [bins[i+1]-bins[i] for i in range(len(bins[:-1])) if histogram[i]>0]
|
|
ax.bar(y_pos, [n for n in histogram if n>0], bar_width, align='center', alpha=0.5, color='silver')
|
|
ax.set_ylabel('shift\ndistribution', rotation=0, ha='right', va='center')
|
|
ax.set_xlim(vlines[0], vlines[-1])
|
|
ax.get_xaxis().set_visible(False)
|
|
plt.subplots_adjust(wspace=0, hspace=0.1)
|
|
else:
|
|
# show the percentiles of the distribution
|
|
cumsum = np.cumsum(histogram)
|
|
for i in range(len(bins[:-1])):
|
|
start, width = bins[i], bins[i+1]-bins[i]
|
|
ax.broken_barh([tuple((start, width))], (0, 1), facecolors='whitesmoke' if i%2==0 else 'silver')
|
|
if i < len(bins)-2:
|
|
ax.text(bins[i+1], 0.5, '$P_{'+f'{int(np.round(cumsum[i]*100))}'+'}$', ha='center')
|
|
ax.set_ylim(0, 1)
|
|
ax.set_xlim(vlines[0], vlines[-1])
|
|
ax.get_yaxis().set_visible(False)
|
|
ax.get_xaxis().set_visible(False)
|
|
plt.subplots_adjust(wspace=0, hspace=0)
|
|
|
|
_save_or_show(savepath)
|
|
|
|
|
|
def _merge(method_names, true_prevs, estim_prevs):
|
|
ndims = true_prevs[0].shape[1]
|
|
data = defaultdict(lambda: {'true': np.empty(shape=(0, ndims)), 'estim': np.empty(shape=(0, ndims))})
|
|
method_order=[]
|
|
for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
|
|
data[method]['true'] = np.concatenate([data[method]['true'], true_prev])
|
|
data[method]['estim'] = np.concatenate([data[method]['estim'], estim_prev])
|
|
if method not in method_order:
|
|
method_order.append(method)
|
|
true_prevs_ = [data[m]['true'] for m in method_order]
|
|
estim_prevs_ = [data[m]['estim'] for m in method_order]
|
|
return method_order, true_prevs_, estim_prevs_
|
|
|
|
|
|
def _set_colors(ax, n_methods):
|
|
NUM_COLORS = n_methods
|
|
cm = plt.get_cmap('tab20')
|
|
ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
|
|
|
|
|
|
def _save_or_show(savepath):
|
|
# if savepath is specified, then saves the plot in that path; otherwise the plot is shown
|
|
if savepath is not None:
|
|
qp.util.create_parent_dir(savepath)
|
|
# plt.tight_layout()
|
|
plt.savefig(savepath, bbox_inches='tight')
|
|
else:
|
|
plt.show()
|
|
|
|
|
|
def _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order):
|
|
data = defaultdict(lambda: {'x': np.empty(shape=(0)), 'y': np.empty(shape=(0))})
|
|
|
|
if method_order is None:
|
|
method_order = []
|
|
|
|
for method, test_prevs_i, estim_prevs_i, tr_prev_i in zip(method_names, true_prevs, estim_prevs, tr_prevs):
|
|
tr_prev_i = np.repeat(tr_prev_i.reshape(1, -1), repeats=test_prevs_i.shape[0], axis=0)
|
|
|
|
tr_test_drifts = x_error(test_prevs_i, tr_prev_i)
|
|
data[method]['x'] = np.concatenate([data[method]['x'], tr_test_drifts])
|
|
|
|
method_drifts = y_error(test_prevs_i, estim_prevs_i)
|
|
data[method]['y'] = np.concatenate([data[method]['y'], method_drifts])
|
|
|
|
if method not in method_order:
|
|
method_order.append(method)
|
|
|
|
return data |