84 lines
2.4 KiB
Python
Executable File
84 lines
2.4 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
# Copyright (c) 2017 Mycroft AI Inc.
|
|
from prettyparse import create_parser
|
|
|
|
from precise.model import load_precise_model
|
|
from precise.params import inject_params
|
|
from precise.train_data import TrainData
|
|
|
|
usage = '''
|
|
Test a model against a dataset
|
|
|
|
:model str
|
|
Keras model file (.net) to test
|
|
|
|
:-t --use-train
|
|
Evaluate training data instead of test data
|
|
|
|
:-nf --no-filenames
|
|
Don't print out the names of files that failed
|
|
|
|
...
|
|
'''
|
|
|
|
|
|
def show_stats(false_pos, false_neg, true_pos, true_neg, show_filenames):
|
|
num_correct = len(true_pos) + len(true_neg)
|
|
total = num_correct + len(false_pos) + len(false_neg)
|
|
|
|
def prc(a: int, b: int): # Rounded percent
|
|
return round(100.0 * (b and a / b), 2)
|
|
|
|
if show_filenames:
|
|
print('=== False Positives ===')
|
|
for i in false_pos:
|
|
print(i)
|
|
print()
|
|
print('=== False Negatives ===')
|
|
for i in false_neg:
|
|
print(i)
|
|
print()
|
|
print('=== Counts ===')
|
|
print('False Positives:', len(false_pos))
|
|
print('True Negatives:', len(true_neg))
|
|
print('False Negatives:', len(false_neg))
|
|
print('True Positives:', len(true_pos))
|
|
print()
|
|
print('=== Summary ===')
|
|
print(num_correct, "out of", total)
|
|
print(prc(num_correct, total), "%")
|
|
print()
|
|
print(prc(len(false_pos), len(false_pos) + len(true_neg)), "% false positives")
|
|
print(prc(len(false_neg), len(false_neg) + len(true_pos)), "% false negatives")
|
|
|
|
|
|
def main():
|
|
args = TrainData.parse_args(create_parser(usage))
|
|
|
|
inject_params(args.model)
|
|
|
|
data = TrainData.from_both(args.db_file, args.db_folder, args.data_dir)
|
|
train, test = data.load(args.use_train, not args.use_train)
|
|
inputs, targets = train if args.use_train else test
|
|
|
|
filenames = sum(data.train_files if args.use_train else data.test_files, [])
|
|
predictions = load_precise_model(args.model).predict(inputs)
|
|
|
|
true_pos, true_neg = [], []
|
|
false_pos, false_neg = [], []
|
|
|
|
for name, target, prediction in zip(filenames, targets, predictions):
|
|
{
|
|
(True, False): false_pos,
|
|
(True, True): true_pos,
|
|
(False, True): false_neg,
|
|
(False, False): true_neg
|
|
}[prediction[0] > 0.5, target[0] > 0.5].append(name)
|
|
|
|
print('Data:', data)
|
|
show_stats(false_pos, false_neg, true_pos, true_neg, not args.no_filenames)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|