def test_parse_truth_csv(categories):
truth_file_stream = io.StringIO(
'image,MEL,NV,BCC,AKIEC,BKL,DF,VASC,score_weight,validation_weight\n'
'ISIC_0000123,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0\n'
'ISIC_0000124,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0\n'
'ISIC_0000125,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0\n')
truth_probabilities, truth_weights = load_csv.parse_truth_csv(
truth_file_stream)
assert truth_probabilities.equals(
pd.DataFrame(
[
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
],
index=['ISIC_0000123', 'ISIC_0000124', 'ISIC_0000125'],
columns=categories,
))
assert truth_weights.equals(
pd.DataFrame(
[[1.0, 0.0], [0.0, 1.0], [0.0, 0.0]],
index=['ISIC_0000123', 'ISIC_0000124', 'ISIC_0000125'],
columns=pd.Index(['score_weight', 'validation_weight']),
))
def from_stream(
cls, truth_file_stream: TextIO, prediction_file_stream: TextIO
):
truth_probabilities, truth_weights = parse_truth_csv(truth_file_stream)
categories = truth_probabilities.columns
prediction_probabilities = parse_csv(prediction_file_stream, categories)
validate_rows(truth_probabilities, prediction_probabilities)
sort_rows(truth_probabilities)
sort_rows(prediction_probabilities)
score = cls(truth_probabilities, prediction_probabilities, truth_weights)
return score
def compute_metrics(truth_file_stream, prediction_file_stream) -> ScoresType:
truth_probabilities, truth_weights = parse_truth_csv(truth_file_stream)
categories = truth_probabilities.columns
prediction_probabilities = parse_csv(prediction_file_stream, categories)
validate_rows(truth_probabilities, prediction_probabilities)
sort_rows(truth_probabilities)
sort_rows(prediction_probabilities)
scores: ScoresType = {}
for category in categories:
truth_category_probabilities: pd.Series = truth_probabilities[category]
prediction_category_probabilities: pd.Series = prediction_probabilities[
category]
truth_binary_values: pd.Series = truth_category_probabilities.gt(0.5)
prediction_binary_values: pd.Series = prediction_category_probabilities.gt(
0.5)
category_cm = create_binary_confusion_matrix(
truth_binary_values=truth_binary_values.to_numpy(),
prediction_binary_values=prediction_binary_values.to_numpy(),
weights=truth_weights.score_weight.to_numpy(),
name=category,
)
scores[category] = {
'accuracy':
metrics.binary_accuracy(category_cm),
'sensitivity':
metrics.binary_sensitivity(category_cm),
'specificity':
metrics.binary_specificity(category_cm),
'dice':
metrics.binary_dice(category_cm),
'ppv':
metrics.binary_ppv(category_cm),
'npv':
metrics.binary_npv(category_cm),
'auc':
metrics.auc(
truth_category_probabilities,
prediction_category_probabilities,
truth_weights.score_weight,
),
'auc_sens_80':
metrics.auc_above_sensitivity(
truth_category_probabilities,
prediction_category_probabilities,
truth_weights.score_weight,
0.80,
),
'ap':
metrics.average_precision(
truth_category_probabilities,
prediction_category_probabilities,
truth_weights.score_weight,
),
'roc':
metrics.roc(
truth_category_probabilities,
prediction_category_probabilities,
truth_weights.score_weight,
),
}
# Compute averages for all per-category metrics
per_category_metrics: KeysView[str] = next(iter(scores.values())).keys()
scores['macro_average'] = {
metric:
float(np.mean([scores[category][metric] for category in categories]))
for metric in per_category_metrics if metric != 'roc'
}
# Compute multi-category aggregate metrics
scores['aggregate'] = {
'balanced_accuracy':
metrics.balanced_multiclass_accuracy(truth_probabilities,
prediction_probabilities,
truth_weights.score_weight)
}
scores['overall'] = scores['aggregate']['balanced_accuracy']
scores['validation'] = metrics.balanced_multiclass_accuracy(
truth_probabilities, prediction_probabilities,
truth_weights.validation_weight)
return scores
