forked from moreo/QuaPy
plots
This commit is contained in:
parent
96758834f3
commit
fe819a7a3c
|
|
@ -1,13 +1,150 @@
|
|||
import ternary
|
||||
import math
|
||||
import numpy as np
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.neighbors import KernelDensity
|
||||
import plotly.figure_factory as ff
|
||||
|
||||
import numpy as np
|
||||
Al = np.array([0. , 0. , 0., 0., 1./3, 1./3, 1./3, 2./3, 2./3, 1.])
|
||||
Cu = np.array([0., 1./3, 2./3, 1., 0., 1./3, 2./3, 0., 1./3, 0.])
|
||||
Y = 1 - Al - Cu
|
||||
# synthetic data for mixing enthalpy
|
||||
# See https://pycalphad.org/docs/latest/examples/TernaryExamples.html
|
||||
enthalpy = (Al - 0.01) * Cu * (Al - 0.52) * (Cu - 0.48) * (Y - 1)**2
|
||||
fig = ff.create_ternary_contour(np.array([Al, Y, Cu]), enthalpy,
|
||||
pole_labels=['Al', 'Y', 'Cu'],
|
||||
interp_mode='cartesian')
|
||||
fig.show()
|
||||
from data import LabelledCollection
|
||||
|
||||
scale = 200
|
||||
|
||||
|
||||
# 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=9, 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 = 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)
|
||||
tax.get_axes().axis('off')
|
||||
tax.clear_matplotlib_ticks()
|
||||
|
||||
return tax
|
||||
|
||||
|
||||
def plot_simplex(ax, coord, kde_scores, title='', fontsize=11, points=None, savepath=None):
|
||||
|
||||
tax = ff.create_ternary_contour(coord.T, kde_scores, pole_labels=['y=1', 'y=2', 'y=3'],
|
||||
interp_mode='cartesian',
|
||||
ncontours=20,
|
||||
colorscale='Viridis',
|
||||
showscale=True,
|
||||
title=title)
|
||||
if savepath is None:
|
||||
tax.show()
|
||||
else:
|
||||
tax.write_image(savepath)
|
||||
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)
|
||||
|
||||
fig = ternary.plt.figure(figsize=(26, 3))
|
||||
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='$f_1(\mathbf{x})=p(s(\mathbf{x})|y=1)$')
|
||||
plot_simplex_(ax2, density(kde2.score_samples), title='$f_2(\mathbf{x})=p(s(\mathbf{x})|y=2)$')
|
||||
plot_simplex_(ax3, density(kde3.score_samples), title='$f_3(\mathbf{x})=p(s(\mathbf{x})|y=3)$')
|
||||
#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 = '$\sum_{i \in \mathcal{Y}} \\alpha_i f_i(\mathbf{x})$'
|
||||
|
||||
plot_simplex_(ax4, mixture_(alpha, [kde1, kde2, kde3]), title=title, points=post_test)
|
||||
#mixture(alpha, [kde1, kde2, kde3])
|
||||
|
||||
#post_test = np.concatenate([post_test, np.eye(3, dtype=float)])
|
||||
#test_scores = sum(alphai*np.exp(kdei.score_samples(post_test)) for alphai, kdei in zip(alpha, [kde1,kde2,kde3]))
|
||||
#plot_simplex(ax4, post_test, test_scores, title=title, points=post_test)
|
||||
|
||||
ternary.plt.show()
|
||||
|
||||
|
||||
import quapy as qp
|
||||
|
||||
|
||||
data = qp.datasets.fetch_twitter('wb', min_df=3, pickle=True, for_model_selection=False)
|
||||
|
||||
X, y = data.training.Xy
|
||||
|
||||
cls = LogisticRegression(C=0.0001, random_state=0)
|
||||
|
||||
Xtr, Xte, ytr, yte = train_test_split(X, y, train_size=0.7, stratify=y, random_state=0)
|
||||
|
||||
cls.fit(Xtr, ytr)
|
||||
|
||||
test = LabelledCollection(Xte, yte)
|
||||
test = test.sampling(100, *[0.2, 0.1, 0.7])
|
||||
|
||||
Xte, yte = test.Xy
|
||||
|
||||
post_c1 = cls.predict_proba(Xte[yte==0])
|
||||
post_c2 = cls.predict_proba(Xte[yte==1])
|
||||
post_c3 = cls.predict_proba(Xte[yte==2])
|
||||
|
||||
post_test = cls.predict_proba(Xte)
|
||||
print(post_test)
|
||||
alpha = qp.functional.prevalence_from_labels(yte, classes=[0, 1, 2])
|
||||
|
||||
#post_c1 = np.random.dirichlet([10,3,1], 30)
|
||||
#post_c2 = np.random.dirichlet([1,11,6], 30)
|
||||
#post_c3 = np.random.dirichlet([1,5,20], 30)
|
||||
#post_test = np.random.dirichlet([5,1,6], 100)
|
||||
#alpha = [0.5, 0.3, 0.2]
|
||||
|
||||
|
||||
print(f'test alpha {alpha}')
|
||||
plot_3class_problem(post_c1, post_c2, post_c3, post_test, alpha, bandwidth=0.1)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue