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eval
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#!/usr/bin/env python3
import argparse
parser = argparse.ArgumentParser(
description='Tool used to evaluate the performances of a model ' \
'predictions relative to the dataset annotations.'
)
parser.add_argument(
'--reference_dataset', required=True, type=str,
help='Path to the original reference dataset folder.'
)
parser.add_argument(
'--prediction', required=True, type=argparse.FileType('r'),
help='Predictions from the models in JSON format.'
)
parser.add_argument(
'--subset', required=True, type=str,
choices=['validation', 'test'],
help='Subset to evaluate.'
)
args = parser.parse_args()
################################################################################
import os
import json
import datetime
import collections
import numpy as np
import pandas as pd
################################################################################
# PREPARING THE DATASET #
################################################################################
reference_file = os.path.join(args.reference_dataset, f"{args.subset}.json")
if not os.path.exists(reference_file):
raise FileNotFoundError(
f"The dataset reference for {args.subset=} cannot be found at " \
"{reference_file}."
)
reference = pd.read_json(reference_file, orient='index')
prediction = pd.read_json(args.prediction, orient='index')
reference_dialogues = set(reference.index)
prediction_dialogues = set(prediction.index)
allowed_classes = set(reference.iloc[0].classes.keys())
if reference_dialogues != prediction_dialogues:
raise ValueError(
"Reference dialogues are not the same as prediction dialogues.\n" \
f" - Total reference dialogues: {len(reference_dialogues)}\n" \
f" - Total prediction dialogues: {len(prediction_dialogues)}\n" \
f" - In common dialogues : {len(reference_dialogues & prediction_dialogues)}\n" \
f"Make sure you are using the correct --subset [validation/test] " \
f"and that you returned the correct dialogues ids in your prediction."
)
################################################################################
# EVALUATION METRICS #
################################################################################
def compute_confusion_matrix(
ref,
pred,
classes=allowed_classes,
return_num_processed=False
):
mat = collections.defaultdict(lambda: {'tp': 0, 'tn': 0, 'fp': 0, 'fn': 0})
num_processed_samples = 0
for index, row in ref.iterrows():
num_processed_samples += 1
for c in classes:
predicted = pred.loc[index, f"class__{c}"]
score = row.classes[c]
if predicted:
if score == 0:
mat[c]['fp'] += 1
elif score == 1:
mat[c]['tp'] += 1
elif score == 0.5:
mat[c]['tp'] += score
mat[c]['fp'] += score
else:
raise ValueError("weird.")
else:
if score == 0:
mat[c]['tn'] += 1
elif score == 1:
mat[c]['fn'] += 1
elif score == 0.5:
mat[c]['tn'] += score
mat[c]['fn'] += score
else:
raise ValueError("weird.")
mat = pd.DataFrame.from_dict(mat, orient='index')
if return_num_processed:
return mat, num_processed_samples
else:
return mat
def average_metric(mat, met, average, metric):
if average is None:
return met
elif average == 'macro':
return met.mean()
elif average == 'micro':
return metric(mat.sum(), average=None)
elif average == 'all':
return {
avg: average_metric(mat, met, avg, metric)
for avg in [None, 'micro', 'macro']
}
else:
raise ValueError(average)
def precision(mat, average=None):
_precision = mat['tp'] / (mat['tp'] + mat['fp'])
# 1.0 to precision when no predicted examples
if isinstance(_precision, pd.Series):
_precision = _precision.fillna(value=1.0)
else:
_precision = np.nan_to_num(_precision, nan=1.0)
return average_metric(
mat,
_precision,
average=average, metric=precision
)
def recall(mat, average=None):
_recall = mat['tp'] / (mat['tp'] + mat['fn'])
assert not np.isnan(_recall).any(), \
f"Recall cannot have a NaN. That would mean an label has no occurence" \
" on the test set."
return average_metric(
mat,
_recall,
average=average, metric=recall
)
def f1(mat, average=None):
p, r = precision(mat), recall(mat)
_f1 = 2 * (p * r) / (p + r)
if isinstance(_f1, pd.Series):
_f1 = _f1.fillna(value=0.0)
else:
_f1 = np.nan_to_num(_f1, nan=0.0)
return average_metric(
mat,
_f1,
average=average, metric=f1
)
def all_metrics(mat, average=None):
return {
k: globals()[k](mat, average=average)
for k in ['f1', 'precision', 'recall']
}
confusion = compute_confusion_matrix(
ref=reference, pred=prediction,
)
micro = all_metrics(confusion, average='micro')
macro = all_metrics(confusion, average='macro')
per_class = all_metrics(confusion, average=None)
print(f"Micro: {micro}")
print(f"Macro: {macro}")
output_file = os.path.join(
os.path.dirname(args.prediction.name), 'results.json'
)
with open(output_file, 'w') as f:
json.dump({
'now': datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S'),
'num_reference_samples': len(reference),
'num_predicted_samples': len(prediction),
'micro': micro,
'macro': macro,
'per_class': {
metric: per_class[metric].to_dict()
for metric in per_class
},
'args': str(args)
}, f, indent='\t', ensure_ascii=False)
print(f"Outputs saved to {output_file=}.")