From fe819a7a3cf12b92064b427fb877999b7ecfcf56 Mon Sep 17 00:00:00 2001 From: Alex Moreo Date: Mon, 31 Jul 2023 09:03:41 +0200 Subject: [PATCH] plots --- laboratory/simplex_density_plot.py | 159 +++++++++++++++++++++++++++-- 1 file changed, 148 insertions(+), 11 deletions(-) diff --git a/laboratory/simplex_density_plot.py b/laboratory/simplex_density_plot.py index 576d5ab..dc6f5ef 100644 --- a/laboratory/simplex_density_plot.py +++ b/laboratory/simplex_density_plot.py @@ -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) +