92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import pickle
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class LabelingPlotter():
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def plot_labeling_rounds():
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# round numbers
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round = [0,1,2,3,4,5,6,7,8,9]
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# number of wrong estimated labels per round
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wrong = [0/100, 19/100, 17/100, 16/100, 20/100, 12/100, 10/100, 20/100, 14/100, 12/100]
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# number of manual classified articles per class and round
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man_0 = [84/100, 165/200, 247/300, 329/400, 410/500, 498/600, 586/700, 662/800, 741/900, 821/1000]
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man_1 = [3/100, 7/200, 12/300, 16/400, 20/500, 22/600, 23/700, 29/800, 37/900, 39/1000]
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man_2 = [13/100, 28/200, 41/300, 55/400, 70/500, 80/600, 91/700, 109/800, 122/900, 140/1000]
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# number of estimated labels per class and round
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est_0 = [9873/9900, 9757/9800, 9603/9700, 9470/9600, 9735/9500, 9238/9400, 9107/9300, 8007/9200, 8064/9100, 7641/9000]
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est_1 = [14/9900, 15/9800, 11/9700, 11/9600, 16/9500, 17/9400, 18/9300, 19/9200, 18/9100, 20/9000]
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est_2 = [12/9900, 26/9800, 77/9700, 94/9600, 380/9500, 123/9400, 147/9300, 676/9200, 595/9100, 837/9000]
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fig, ax = plt.subplots(3, 1)
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ax[0].plot(round, wrong)
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ax[2].set_xlabel('Iteration number')
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ax[0].set_ylabel('Error rate')
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ax[1].plot(round, man_0, round, man_1, round, man_2)
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ax[1].set_ylabel('Fraction of manual labels')
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ax[2].plot(round, est_0, round, est_1, round, est_2)
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ax[2].set_ylabel('Fraction of estimated labels')
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# limit x axis
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ax[0].set_xbound(lower=1, upper=9)
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ax[1].set_xbound(lower=1, upper=9)
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ax[2].set_xbound(lower=1, upper=9)
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ax[0].set_ybound(lower=0)
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ax[1].set_ybound(lower=0)
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#ax[2].set_ybound(lower=0)
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# insert legend
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ax[1].legend(('class 0', 'class 1', 'class 2'))
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ax[2].legend(('class 0', 'class 1', 'class 2'))
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fig.tight_layout()
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plt.savefig('..\\visualization\\Labeling_Grafik_070219.png')
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plt.show()
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def plot_cumulative():
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# load pickle object
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with open('../obj/array_class_probs_round_9.pkl', 'rb') as input:
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list = pickle.load(input)
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# sort list in descending order
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list.sort(reverse=True)
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# convert list to array
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probas = np.asarray(list)
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mu = 200
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sigma = 25
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n_bins = 50
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fig, ax = plt.subplots(figsize=(8, 4))
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# plot the cumulative histogram
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n, bins, patches = ax.hist(probas, n_bins, density=1, histtype='step',
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cumulative=True, facecolor='darkred')
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# manipulate
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#vals = ax.get_yticks()
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#ax.set_yticklabels(['{:,.1%}'.format(x / 200) for x in vals])
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ax.grid(True)
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ax.legend(loc='right')
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#ax.set_title('Cumulative distribution of highest estimated probability')
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ax.set_xlabel('Highest estimated probability')
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ax.set_ylabel('Fraction of articles with this highest estimated probability')
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#plt.axis([0.5, 0.99, 0, 0.006])
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#ax.set_xbound(lower=0.5, upper=0.99)
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plt.show()
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if __name__ == '__main__':
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LabelingPlotter.plot_cumulative() |