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refactor folders

This commit is contained in:
Alejandro Moreo Fernandez 2025-12-10 13:10:03 +01:00
parent 8c063afd1e
commit 1eebfbc709
2 changed files with 11 additions and 7 deletions
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16
IEEEProc2025_plots/plotting_diagonal_4methods.py
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@ -6,7 +6,7 @@ import quapy as qp
from data import LabelledCollection
from method.non_aggregative import DMx
from protocol import APP
from quapy.method.aggregative import CC, DMy, ACC
from quapy.method.aggregative import CC, DMy, ACC, EMQ
from sklearn.svm import LinearSVC
import numpy as np
from tqdm import tqdm
@ -15,12 +15,15 @@ qp.environ['SAMPLE_SIZE'] = 500
def cls():
return LogisticRegressionCV(n_jobs=-1,Cs=10)
# return LogisticRegression(C=.1)
def gen_methods():
yield CC(cls()), 'CC$_{10' + '\%}$'
yield CC(cls()), r'CC$_{10' + r'\%}$'
yield ACC(cls()), 'ACC'
yield DMy(cls(), val_split=10, nbins=10, n_jobs=-1), 'HDy'
yield DMx(nbins=10, n_jobs=-1), 'HDx'
yield EMQ(cls()), 'SLD'
# yield EMQ(cls(), calib='vs'), 'SLD-VS'
def gen_data():
@ -31,6 +34,7 @@ def gen_data():
training_size = 5000
# since the problem is binary, it suffices to specify the negative prevalence, since the positive is constrained
train_sample = train.sampling(training_size, 1-training_prevalence, random_state=0)
# train_sample = train
for model, method_name in tqdm(gen_methods(), total=4):
with qp.util.temp_seed(1):
@ -43,10 +47,10 @@ def gen_data():
X, y = test.Xy
test_dense = LabelledCollection(svd.transform(X), y)
model.fit(train_sample_dense)
model.fit(*train_sample_dense.Xy)
true_prev, estim_prev = qp.evaluation.prediction(model, APP(test_dense, repeats=100, random_state=0))
else:
model.fit(train_sample)
model.fit(*train_sample.Xy)
true_prev, estim_prev = qp.evaluation.prediction(model, APP(test, repeats=100, random_state=0))
method_data.append((method_name, true_prev, estim_prev, train_sample.prevalence()))
@ -55,5 +59,5 @@ def gen_data():
method_names, true_prevs, estim_prevs, tr_prevs = gen_data()
qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, savepath='./plots_cacm/bin_diag_4methods.pdf')
qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=10, savepath='./plots_cacm/err_drift_4methods.pdf', title='', show_density=False, show_std=True)
# qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, savepath='./plots_ieee/bin_diag_4methods.pdf')
qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=10, savepath='./plots_ieee/err_drift_4methods.pdf', title='', show_density=False, show_std=True)

2
quapy/plot.py
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@ -314,7 +314,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
for p,ind in enumerate(range(len(bins))):
selected = inds==ind
if selected.sum() > 0:
xs.append(ind*binwidth-binwidth/2)
xs.append(ind*binwidth)
ys.append(np.mean(method_drifts[selected]))
ystds.append(np.std(method_drifts[selected]))
npoints[p] += len(method_drifts[selected])