112 lines
4.4 KiB
Python
112 lines
4.4 KiB
Python
#!/usr/bin/env python3
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# Copyright 2019 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License
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import numpy as np
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counts_str = '''
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=== Counts ===
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False Positives: {false_pos}
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True Negatives: {true_neg}
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False Negatives: {false_neg}
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True Positives: {true_pos}
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'''.strip()
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summary_str = '''
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=== Summary ===
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{num_correct} out of {total}
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{accuracy_ratio:.2%}
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{false_pos_ratio:.2%} false positives
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{false_neg_ratio:.2%} false negatives
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'''
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class Stats:
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"""Represents a set of statistics from a model run on a dataset"""
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def __init__(self, outputs, targets, filenames):
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self.outputs = np.array(outputs)
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self.targets = np.array(targets)
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self.filenames = filenames
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self.num_positives = int((self.targets > 0.5).sum())
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self.num_negatives = int((self.targets < 0.5).sum())
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# Methods
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self.false_positives = lambda threshold=0.5: self.calc_metric(False, True, threshold) / max(1,
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self.num_negatives)
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self.false_negatives = lambda threshold=0.5: self.calc_metric(False, False, threshold) / max(1,
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self.num_positives)
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self.num_correct = lambda threshold=0.5: (
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(self.outputs >= threshold) == self.targets.astype(bool)
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).sum()
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self.num_incorrect = lambda threshold=0.5: len(self) - self.num_correct(threshold)
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self.accuracy = lambda threshold=0.5: self.num_correct(threshold) / max(1, len(self))
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def __len__(self):
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return len(self.outputs)
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def to_np_dict(self):
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import numpy as np
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return {
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'outputs': self.outputs,
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'targets': self.targets,
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'filenames': np.array(self.filenames)
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}
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@staticmethod
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def from_np_dict(data) -> 'Stats':
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return Stats(data['outputs'], data['targets'], data['filenames'])
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def to_dict(self, threshold=0.5):
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return {
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'true_pos': self.calc_metric(True, True, threshold),
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'true_neg': self.calc_metric(True, False, threshold),
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'false_pos': self.calc_metric(False, True, threshold),
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'false_neg': self.calc_metric(False, False, threshold),
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}
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def counts_str(self, threshold=0.5):
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return counts_str.format(**self.to_dict(threshold))
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def summary_str(self, threshold=0.5):
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return summary_str.format(
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num_correct=self.num_correct(threshold), total=len(self),
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accuracy_ratio=self.accuracy(threshold),
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false_pos_ratio=self.false_positives(threshold),
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false_neg_ratio=self.false_negatives(threshold)
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)
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def calc_filenames(self, is_correct: bool, actual_output: bool, threshold=0.5) -> list:
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"""Find a list of files with the given classification"""
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return [
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filename
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for output, target, filename in zip(self.outputs, self.targets, self.filenames)
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if ((output > threshold) == bool(target)) == is_correct and actual_output == bool(output > threshold)
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]
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def calc_metric(self, is_correct: bool, actual_output: bool, threshold=0.5) -> int:
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"""For example, calc_metric(False, True) calculates false positives"""
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return int(
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((((self.outputs > threshold) == self.targets.astype(bool)) == is_correct) &
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((self.outputs > threshold) == actual_output)).sum()
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)
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@staticmethod
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def matches_sample(output, target, threshold, is_correct, actual_output):
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"""
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Check if a sample with the given network output, target output, and threshold
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is the classification (is_correct, actual_output) like true positive or false negative
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"""
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return (bool(output > threshold) == bool(target)) == is_correct and actual_output == bool(output > threshold)
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