forked from moreo/QuaPy
130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
import ternary
|
|
import math
|
|
import numpy as np
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.model_selection import train_test_split, cross_val_predict
|
|
from sklearn.neighbors import KernelDensity
|
|
import plotly.figure_factory as ff
|
|
|
|
from data import LabelledCollection
|
|
|
|
scale = 100
|
|
|
|
|
|
# con ternary (una lib de matplotlib) salen bien pero no puedo crear contornos, o no se
|
|
# con plotly salen los contornos bien, pero es un poco un jaleo porque utiliza el navegador...
|
|
|
|
def plot_simplex_(ax, density, title='', fontsize=30, points=None):
|
|
|
|
tax = ternary.TernaryAxesSubplot(ax=ax, scale=scale)
|
|
tax.heatmapf(density, boundary=True, style="triangular", colorbar=False, cmap='viridis') #cmap='magma')
|
|
tax.boundary(linewidth=1.0)
|
|
corner_fontsize = int(5*fontsize//6)
|
|
tax.right_corner_label("$y=3$", fontsize=corner_fontsize)
|
|
tax.top_corner_label("$y=2$", fontsize=corner_fontsize)
|
|
tax.left_corner_label("$y=1$", fontsize=corner_fontsize)
|
|
if title:
|
|
tax.set_title(title, loc='center', y=-0.11, fontsize=fontsize)
|
|
if points is not None:
|
|
tax.scatter(points*scale, marker='o', color='w', alpha=0.25, zorder=10, s=5*scale)
|
|
tax.get_axes().axis('off')
|
|
tax.clear_matplotlib_ticks()
|
|
|
|
return tax
|
|
|
|
|
|
|
|
from mpl_toolkits.axes_grid1 import make_axes_locatable
|
|
def plot_3class_problem(post_c1, post_c2, post_c3, post_test, alpha, bandwidth):
|
|
post_c1 = np.flip(post_c1, axis=1)
|
|
post_c2 = np.flip(post_c2, axis=1)
|
|
post_c3 = np.flip(post_c3, axis=1)
|
|
post_test = np.flip(post_test, axis=1)
|
|
|
|
size_=10
|
|
fig = ternary.plt.figure(figsize=(5*size_, 1*size_))
|
|
fig.tight_layout()
|
|
ax1 = fig.add_subplot(1, 4, 1)
|
|
divider = make_axes_locatable(ax1)
|
|
ax2 = fig.add_subplot(1, 4, 2)
|
|
divider = make_axes_locatable(ax2)
|
|
ax3 = fig.add_subplot(1, 4, 3)
|
|
divider = make_axes_locatable(ax3)
|
|
ax4 = fig.add_subplot(1, 4, 4)
|
|
divider = make_axes_locatable(ax4)
|
|
|
|
kde1 = KernelDensity(bandwidth=bandwidth).fit(post_c1)
|
|
kde2 = KernelDensity(bandwidth=bandwidth).fit(post_c2)
|
|
kde3 = KernelDensity(bandwidth=bandwidth).fit(post_c3)
|
|
|
|
#post_c1 = np.concatenate([post_c1, np.eye(3, dtype=float)])
|
|
#post_c2 = np.concatenate([post_c2, np.eye(3, dtype=float)])
|
|
#post_c3 = np.concatenate([post_c3, np.eye(3, dtype=float)])
|
|
|
|
#plot_simplex_(ax1, lambda x:0, title='$f_1(\mathbf{x})=p(s(\mathbf{x})|y=1)$')
|
|
#plot_simplex_(ax2, lambda x:0, title='$f_1(\mathbf{x})=p(s(\mathbf{x})|y=1)$')
|
|
#plot_simplex_(ax3, lambda x:0, title='$f_1(\mathbf{x})=p(s(\mathbf{x})|y=1)$')
|
|
def density(kde):
|
|
def d(p):
|
|
return np.exp(kde([p])).item()
|
|
return d
|
|
|
|
plot_simplex_(ax1, density(kde1.score_samples), title='$p_1$')
|
|
plot_simplex_(ax2, density(kde2.score_samples), title='$p_2$')
|
|
plot_simplex_(ax3, density(kde3.score_samples), title='$p_3$')
|
|
#plot_simplex(ax1, post_c1, np.exp(kde1.score_samples(post_c1)), title='$f_1(\mathbf{x})=p(s(\mathbf{x})|y=1)$') #, savepath='figure/y1.png')
|
|
#plot_simplex(ax2, post_c2, np.exp(kde2.score_samples(post_c2)), title='$f_2(\mathbf{x})=p(s(\mathbf{x})|y=2)$') #, savepath='figure/y2.png')
|
|
#plot_simplex(ax3, post_c3, np.exp(kde3.score_samples(post_c3)), title='$f_3(\mathbf{x})=p(s(\mathbf{x})|y=3)$') #, savepath='figure/y3.png')
|
|
|
|
def mixture_(prevs, kdes):
|
|
def m(p):
|
|
total_density = 0
|
|
for prev, kde in zip(prevs, kdes):
|
|
log_density = kde.score_samples([p]).item()
|
|
density = np.exp(log_density)
|
|
density *= prev
|
|
total_density += density
|
|
#print(total_density)
|
|
return total_density
|
|
return m
|
|
|
|
title = '$\mathbf{p}_{\mathbf{\\alpha}} = \sum_{i \in n} \\alpha_i p_i$'
|
|
|
|
plot_simplex_(ax4, mixture_(alpha, [kde1, kde2, kde3]), title=title, points=post_test)
|
|
|
|
#ternary.plt.show()
|
|
ternary.plt.savefig('./simplex.pdf')
|
|
|
|
|
|
import quapy as qp
|
|
|
|
|
|
data = qp.datasets.fetch_twitter('wb', min_df=3, pickle=True, for_model_selection=False)
|
|
|
|
Xtr, ytr = data.training.Xy
|
|
Xte, yte = data.test.sampling(150, *[0.5, 0.1, 0.4]).Xy
|
|
|
|
cls = LogisticRegression(C=0.0001, random_state=0)
|
|
|
|
draw_from_training = False
|
|
if draw_from_training:
|
|
post_tr = cross_val_predict(cls, Xtr, ytr, n_jobs=-1, method='predict_proba')
|
|
post_c1 = post_tr[ytr==0]
|
|
post_c2 = post_tr[ytr==1]
|
|
post_c3 = post_tr[ytr==2]
|
|
cls.fit(Xtr, ytr)
|
|
else:
|
|
cls.fit(Xtr, ytr)
|
|
post_te = cls.predict_proba(Xte)
|
|
post_c1 = post_te[yte == 0]
|
|
post_c2 = post_te[yte == 1]
|
|
post_c3 = post_te[yte == 2]
|
|
|
|
post_test = cls.predict_proba(Xte)
|
|
|
|
alpha = qp.functional.prevalence_from_labels(yte, classes=[0, 1, 2])
|
|
|
|
print(f'test alpha {alpha}')
|
|
plot_3class_problem(post_c1, post_c2, post_c3, post_test, alpha, bandwidth=0.1)
|
|
|