forked from moreo/QuaPy
74 lines
1.6 KiB
Python
74 lines
1.6 KiB
Python
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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 matplotlib.pyplot as plt
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import numpy as np
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from data import LabelledCollection
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scale = 100
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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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X, y = data.training.Xy
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cls = LogisticRegression(C=0.0001, random_state=0)
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posteriors = cross_val_predict(cls, X=X, y=y, method='predict_proba', n_jobs=-1, cv=3)
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cls.fit(X, y)
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Xte, yte = data.test.Xy
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post_c1 = posteriors[y==0]
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post_c2 = posteriors[y==1]
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post_c3 = posteriors[y==2]
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print(len(post_c1))
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print(len(post_c2))
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print(len(post_c3))
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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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nbins = 20
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plt.rcParams.update({'font.size': 7})
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fig = plt.figure()
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positions = np.asarray([2,1,0])
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colors = ['r', 'g', 'b']
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for i, post_set in enumerate([post_c1, post_c2, post_c3, post_test]):
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ax = fig.add_subplot(141+i, projection='3d')
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for post, c, z in zip(post_set.T, colors, positions):
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hist, bins = np.histogram(post, bins=nbins, density=True, range=[0,1])
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xs = (bins[:-1] + bins[1:])/2
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ax.bar(xs, hist, width=1/nbins, zs=z, zdir='y', color=c, ec=c, alpha=0.6)
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ax.yaxis.set_ticks(positions)
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ax.yaxis.set_ticklabels(['$y=1$', '$y=2$', '$y=3$'])
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ax.xaxis.set_ticks([])
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ax.xaxis.set_ticklabels([], minor=True)
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ax.zaxis.set_ticks([])
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ax.zaxis.set_ticklabels([], minor=True)
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#plt.figure(figsize=(10,6))
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#plt.show()
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plt.savefig('./histograms.pdf')
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