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