forked from moreo/QuaPy
74 lines
1.9 KiB
Python
74 lines
1.9 KiB
Python
import itertools
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import seaborn as sns
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import matplotlib.pyplot as plt
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import numpy as np
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palette = itertools.cycle(sns.color_palette())
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def setframe():
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fig.spines['top'].set_visible(False)
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fig.spines['left'].set_visible(False)
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fig.get_yaxis().set_ticks([])
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fig.spines['right'].set_visible(False)
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# fig.axis('off')
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nbins = 50
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figsize = (5, 2)
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ymax = 0.2
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negatives = np.random.normal(loc = 0.3, scale=0.2, size=20000)
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negatives = np.asarray([x for x in negatives if 0 <= x <= 1])
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plt.figure(figsize=figsize)
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plt.xlim(0, 1)
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plt.ylim(0, ymax)
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fig = sns.histplot(data=negatives, binrange=(0,1), bins=nbins, stat='probability', color=next(palette))
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plt.title('Negative distribution')
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fig.set(yticklabels=[])
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fig.set(ylabel=None)
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setframe()
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# fig.get_figure().savefig('plots_cacm/negatives.pdf')
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# plt.clf()
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# -------------------------------------------------------------
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positives1 = np.random.normal(loc = 0.75, scale=0.06, size=20000)
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positives2 = np.random.normal(loc = 0.65, scale=0.1, size=1)
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positives = np.concatenate([positives1, positives2])
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np.random.shuffle(positives)
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positives = np.asarray([x for x in positives if 0 <= x <= 1])
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# plt.figure(figsize=figsize)
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plt.xlim(0, 1)
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plt.ylim(0, ymax)
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fig = sns.histplot(data=positives, binrange=(0,1), bins=nbins, stat='probability', color=next(palette))
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plt.title('')
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fig.set(yticklabels=[])
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fig.set(ylabel=None)
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setframe()
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fig.get_figure().savefig('plots_cacm/training.pdf')
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# -------------------------------------------------------------
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prev = 0.2
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test = np.concatenate([
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negatives[:int(len(negatives)*(1-prev))],
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positives[:int(len(positives)*(prev))],
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])
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plt.figure(figsize=figsize)
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plt.xlim(0, 1)
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plt.ylim(0, ymax)
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fig = sns.histplot(data=test, binrange=(0,1), bins=nbins, stat='probability', color=next(palette))
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plt.title('')
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fig.set(yticklabels=[])
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fig.set(ylabel=None)
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setframe()
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fig.get_figure().savefig('plots_cacm/test.pdf')
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