48 lines
1.4 KiB
Python
Executable File
48 lines
1.4 KiB
Python
Executable File
import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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if __name__ == '__main__':
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data = pd.read_csv('sampling_all.csv')
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data['label'] = data['Classification'].apply(lambda x: x.split(';')[0].strip())
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data = data[~data['label'].isin(['?', 'chinese', 'page not found'])]
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data['on_pipe'] = data['label'].apply(lambda x: 'No ML' if x == 'no pipeline' else 'ML')
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g = sns.catplot(x="on_pipe", kind="count", data=data)\
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.set(title='Istanze delle issues in base al tipo')\
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.set(xlabel='tipo')
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ax = g.facet_axis(0, 0)
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for p in ax.patches:
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ax.text(
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p.get_x() + p.get_width() * 0.43,
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p.get_height() + 3,
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p.get_height(),
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color='black', rotation='horizontal', size='large')
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plt.tight_layout()
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plt.savefig('../src/figures/count-type.pdf')
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#plt.show()
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exit()
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plt.close()
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data = data[data['label'] != 'no pipeline']
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g = sns.catplot(y='label', kind='count', data=data, color='green')\
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.set(title='Istanze delle issues in base alla fase') \
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.set(ylabel='fase')
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ax = g.facet_axis(0, 0)
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for p in ax.patches:
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ax.text(
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p.get_width() + 0.25,
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p.get_y() + p.get_height() / 2,
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p.get_width(),
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color='black', rotation='horizontal', size='large')
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plt.tight_layout()
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plt.savefig('../src/figures/count-phases.pdf')
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