From 7fd32d5c5f7fdd898bd630b6203943388466d5f2 Mon Sep 17 00:00:00 2001
From: Alex Moreo <alejandro.moreo@isti.cnr.it>
Date: Mon, 22 Nov 2021 17:34:36 +0100
Subject: [PATCH] adding density to error-by-drift plot
---
TweetSentQuant/gen_plots.py | 5 +++--
quapy/plot.py | 15 +++++++++++----
2 files changed, 14 insertions(+), 6 deletions(-)
diff --git a/TweetSentQuant/gen_plots.py b/TweetSentQuant/gen_plots.py
index 82818e8..6f19d56 100644
--- a/TweetSentQuant/gen_plots.py
+++ b/TweetSentQuant/gen_plots.py
@@ -12,8 +12,8 @@ from os.path import join
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
plotext='png'
-resultdir = './results'
-plotdir = './plots'
+resultdir = './results_npp'
+plotdir = './plots_npp'
os.makedirs(plotdir, exist_ok=True)
def gather_results(methods, error_name):
@@ -50,6 +50,7 @@ def plot_error_by_drift(methods, error_name, logscale=False, path=None):
logscale=logscale,
title=f'Quantification error as a function of distribution shift',
savepath=path,
+ vlines=[0.02, 0.1055],
method_order=method_order
)
diff --git a/quapy/plot.py b/quapy/plot.py
index b902257..a32ce15 100644
--- a/quapy/plot.py
+++ b/quapy/plot.py
@@ -176,10 +176,12 @@ def _set_colors(ax, n_methods):
ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
-def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=True,
+def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=False,
+ show_density=True,
logscale=False,
title=f'Quantification error as a function of distribution shift',
savepath=None,
+ vlines=None,
method_order=None):
fig, ax = plt.subplots()
@@ -246,15 +248,20 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
if show_std:
ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
- ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))], max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
+ if show_density:
+ ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
+ max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
ax.set(xlabel=f'Distribution shift between training set and test sample',
ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
title=title)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
- ax.axvline(0.02, 0, 1, linestyle='--', color='k')
- ax.axvline(0.1055, 0, 1, linestyle='--', color='k')
+ if vlines:
+ for vline in vlines:
+ ax.axvline(vline, 0, 1, linestyle='--', color='k')
+ # ax.axvline(0.02, 0, 1, linestyle='--', color='k')
+ # ax.axvline(0.1055, 0, 1, linestyle='--', color='k')
ax.set_xlim(0, max_x)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))