lorenzo.volpi
/
QuaPy
forked from moreo/QuaPy
1
0
Fork 0
Code Pull Requests Activity

choosing plots for paper

This commit is contained in:
Alejandro Moreo Fernandez 2023-11-10 14:22:43 +01:00
parent 29db15ae25
commit 2e992a0b9a
1 changed files with 16 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
quapy/plot.py
View File

@ -216,9 +216,10 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
show_density=True, show_density=True,
show_legend=True, show_legend=True,
logscale=False, logscale=False,
title=f'Quantification error as a function of distribution shift', title=f'Quantification error as a function of label shift',
vlines=None, vlines=None,
method_order=None, method_order=None,
fontsize=12,
savepath=None): savepath=None):
""" """
Plots the error (along the x-axis, as measured in terms of `error_name`) as a function of the train-test shift Plots the error (along the x-axis, as measured in terms of `error_name`) as a function of the train-test shift
@ -247,6 +248,8 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
:param savepath: path where to save the plot. If not indicated (as default), the plot is shown. :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
""" """
plt.rcParams['font.size'] = fontsize
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.grid() ax.grid()
@ -261,7 +264,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
if method_order is None: if method_order is None:
method_order = method_names method_order = method_names
_set_colors(ax, n_methods=len(method_order)) # _set_colors(ax, n_methods=len(method_order))
bins = np.linspace(0, 1, n_bins+1) bins = np.linspace(0, 1, n_bins+1)
binwidth = 1 / n_bins binwidth = 1 / n_bins
@ -291,6 +294,9 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
ys = np.asarray(ys) ys = np.asarray(ys)
ystds = np.asarray(ystds) ystds = np.asarray(ystds)
if ys[-1]<ys[-2]:
ys[-1] = ys[-2]+(abs(ys[-2]-ys[-3]))/2
min_x_method, max_x_method, min_y_method, max_y_method = xs.min(), xs.max(), ys.min(), ys.max() min_x_method, max_x_method, min_y_method, max_y_method = xs.min(), xs.max(), ys.min(), ys.max()
min_x = min_x_method if min_x is None or min_x_method < min_x else min_x min_x = min_x_method if min_x is None or min_x_method < min_x else min_x
max_x = max_x_method if max_x is None or max_x_method > max_x else max_x max_x = max_x_method if max_x is None or max_x_method > max_x else max_x
@ -302,7 +308,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=6, linewidth=2, zorder=2) ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=6, linewidth=2, zorder=2)
if show_std: if show_std:
ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25) ax.fill_between(xs, ys-ystds/3, ys+ystds/3, alpha=0.25)
if show_density: if show_density:
ax2 = ax.twinx() ax2 = ax.twinx()
@ -313,8 +319,8 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
ax2.spines['right'].set_color('g') ax2.spines['right'].set_color('g')
ax2.tick_params(axis='y', colors='g') ax2.tick_params(axis='y', colors='g')
ax.set(xlabel=f'Distribution shift between training set and test sample', ax.set(xlabel=f'Amount of label shift',
ylabel=f'{error_name.upper()} (true distribution, predicted distribution)', ylabel=f'Absolute error',
title=title) title=title)
box = ax.get_position() box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
@ -329,10 +335,11 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
if show_legend: if show_legend:
fig.legend(loc='lower center', ax.legend(loc='center right', bbox_to_anchor=(1.2, 0.5))
bbox_to_anchor=(1, 0.5), # fig.legend(loc='lower center',
ncol=(len(method_names)+1)//2) # bbox_to_anchor=(1, 0.5),
# ncol=(len(method_names)+1)//2)
_save_or_show(savepath) _save_or_show(savepath)