116 lines
3.2 KiB
Python
116 lines
3.2 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright (c) 2017 Adler Neves <adlerosn@gmail.com>
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#
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# MIT License
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#
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# Permission is hereby granted, free of charge, to any person obtaining
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# a copy of this software and associated documentation files (the
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# "Software"), to deal in the Software without restriction, including
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# without limitation the rights to use, copy, modify, merge, publish,
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# distribute, sublicense, and/or sell copies of the Software, and to
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# permit persons to whom the Software is furnished to do so, subject to
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# the following conditions:
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#
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# The above copyright notice and this permission notice shall be
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# included in all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
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# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
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# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
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# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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import traceback
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def getConfusionMatrixEmptyData():
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'''
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Model
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| X | not X |
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Pred Positive | True positive | False positive |
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Pred Negative | False positive | True negative |
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'''
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return {
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True: { True: 0, False: 0 },
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False: { True: 0, False: 0 }
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}
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def totalPopulation(cm):
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return cm[True][True]+cm[True][False]+cm[False][True]+cm[False][False]
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def predicted(cm, conc):
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return cm[True][conc]+cm[False][conc]
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def condition(cm, cond):
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return cm[cond][True]+cm[cond][False]
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def readable(cm, s):
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s = s.lower()
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s = s.split(' ')
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if s[0].startswith('t'):
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if s[1].startswith('p'):
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return cm[True][True]
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else:
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return cm[False][False]
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else:
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if s[1].startswith('p'):
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return cm[False][True]
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else:
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return cm[True][False]
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def recall(cm):
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try:
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return readable(cm, 't p') / condition(cm, True)
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except:
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return None
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def missRate(cm):
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try:
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return readable(cm, 'f n') / condition(cm, True)
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except:
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return None
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def specificity(cm):
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try:
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return readable(cm, 't n') / condition(cm, False)
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except:
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return None
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def precision(cm):
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try:
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return readable(cm, 't p') / predicted(cm, True)
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except:
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return None
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def accuracy(cm):
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try:
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return (readable(cm, 't p') + readable(cm, 't n')) / totalPopulation(cm)
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except:
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return None
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def prevalence(cm):
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try:
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return condition(cm, True) / totalPopulation(cm)
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except:
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return None
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def f1score(cm):
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try:
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return 2/(1/recall(cm) + 1/precision(cm))
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except:
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return None
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def getStatistics(cm):
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return {
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'recall': recall(cm),
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'missRate': missRate(cm),
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'specificity': specificity(cm),
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'precision': precision(cm),
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'accuracy': accuracy(cm),
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'prevalence': prevalence(cm),
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'f1score': f1score(cm),
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}
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