moreo
/
QuaPy
1
0
Fork 1
Code Issues Pull Requests Releases Wiki Activity

over time experiments

This commit is contained in:
Alejandro Moreo Fernandez 2025-12-15 12:19:48 +01:00
parent 1661a79dbb
commit 4e6014c0f2
2 changed files with 5 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
IEEEProc2025_plots/over_time_experiment.py
View File

@ -14,7 +14,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression as LR, LogisticRegressionCV
from tqdm import tqdm
import quapy as qp
from data import LabelledCollection, Dataset
from quapy.data import LabelledCollection, Dataset
import quapy.functional as F
from method.composable import QUnfoldWrapper
from quapy.method.aggregative import DistributionMatchingY, EMQ, KDEyML
@ -165,7 +165,7 @@ def plot_prevalences(results_dict, target_class=1, target_label='positive', save
dates_smooth, y_smooth = smooth_curve(dates, target_component)
if method=='true-prev':
line,=plt.plot(dates_smooth, y_smooth, label=method, linewidth=3, linestyle='-')
line,=plt.plot(dates_smooth, y_smooth, label=method, linewidth=3, linestyle='-', color='black')
else:
line,=plt.plot(dates_smooth, y_smooth, label=method, linewidth=2, linestyle='--')
plt.plot(dates, target_component, 'o', markersize=10, color=line.get_color())
@ -259,10 +259,10 @@ else:
def methods():
yield 'CC', CC(new_classifier(), fit_classifier=to_fit)
yield 'ACC', ACC(new_classifier(), fit_classifier=to_fit)
yield 'SLD', EMQ(new_classifier(), fit_classifier=to_fit)
yield 'HDy', DistributionMatchingY(new_classifier(), fit_classifier=to_fit)
yield 'HDx', HDxDensify()
yield 'KMM', QUnfoldWrapperDensify(KMM())
yield 'SLD', EMQ(new_classifier(), fit_classifier=to_fit)
yield 'KDEy', KDEyML(new_classifier(), fit_classifier=to_fit)

4
IEEEProc2025_plots/plotting_diagonal_4methods.py
View File

@ -3,7 +3,7 @@ from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import GridSearchCV
import quapy as qp
from data import LabelledCollection
from quapy.data import LabelledCollection
from method.non_aggregative import DMx
from protocol import APP
from quapy.method.aggregative import CC, DMy, ACC, EMQ
@ -20,9 +20,9 @@ def cls():
def gen_methods():
yield CC(cls()), r'CC$_{10' + r'\%}$'
yield ACC(cls()), 'ACC'
yield EMQ(cls()), 'SLD'
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():