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from . import SentenceEvaluator
import logging
import os
import csv
from sklearn.metrics.pairwise import paired_cosine_distances, paired_euclidean_distances, paired_manhattan_distances
from sklearn.metrics import average_precision_score
import numpy as np
from typing import List
from ..readers import InputExample
logger = logging.getLogger(__name__)
class BinaryClassificationEvaluator(SentenceEvaluator):
"""
Evaluate a model based on the similarity of the embeddings by calculating the accuracy of identifying similar and
dissimilar sentences.
The metrics are the cosine similarity as well as euclidean and Manhattan distance
The returned score is the accuracy with a specified metric.
The results are written in a CSV. If a CSV already exists, then values are appended.
The labels need to be 0 for dissimilar pairs and 1 for similar pairs.
:param sentences1: The first column of sentences
:param sentences2: The second column of sentences
:param labels: labels[i] is the label for the pair (sentences1[i], sentences2[i]). Must be 0 or 1
:param name: Name for the output
:param batch_size: Batch size used to compute embeddings
:param show_progress_bar: If true, prints a progress bar
:param write_csv: Write results to a CSV file
"""
def __init__(self, sentences1: List[str], sentences2: List[str], labels: List[int], name: str = '', batch_size: int = 32, show_progress_bar: bool = False, write_csv: bool = True):
self.sentences1 = sentences1
self.sentences2 = sentences2
self.labels = labels
assert len(self.sentences1) == len(self.sentences2)
assert len(self.sentences1) == len(self.labels)
for label in labels:
assert (label == 0 or label == 1)
self.write_csv = write_csv
self.name = name
self.batch_size = batch_size
if show_progress_bar is None:
show_progress_bar = (logger.getEffectiveLevel() == logging.INFO or logger.getEffectiveLevel() == logging.DEBUG)
self.show_progress_bar = show_progress_bar
self.csv_file = "binary_classification_evaluation" + ("_"+name if name else '') + "_results.csv"
self.csv_headers = ["epoch", "steps",
"cossim_accuracy", "cossim_accuracy_threshold", "cossim_f1", "cossim_precision", "cossim_recall", "cossim_f1_threshold", "cossim_ap",
"manhattan_accuracy", "manhattan_accuracy_threshold", "manhattan_f1", "manhattan_precision", "manhattan_recall", "manhattan_f1_threshold", "manhattan_ap",
"euclidean_accuracy", "euclidean_accuracy_threshold", "euclidean_f1", "euclidean_precision", "euclidean_recall", "euclidean_f1_threshold", "euclidean_ap",
"dot_accuracy", "dot_accuracy_threshold", "dot_f1", "dot_precision", "dot_recall", "dot_f1_threshold", "dot_ap"]
@classmethod
def from_input_examples(cls, examples: List[InputExample], **kwargs):
sentences1 = []
sentences2 = []
scores = []
for example in examples:
sentences1.append(example.texts[0])
sentences2.append(example.texts[1])
scores.append(example.label)
return cls(sentences1, sentences2, scores, **kwargs)
def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
if epoch != -1:
if steps == -1:
out_txt = f" after epoch {epoch}:"
else:
out_txt = f" in epoch {epoch} after {steps} steps:"
else:
out_txt = ":"
logger.info("Binary Accuracy Evaluation of the model on " + self.name + " dataset" + out_txt)
scores = self.compute_metrices(model)
#Main score is the max of Average Precision (AP)
main_score = max(scores[short_name]['ap'] for short_name in scores)
file_output_data = [epoch, steps]
for header_name in self.csv_headers:
if '_' in header_name:
sim_fct, metric = header_name.split("_", maxsplit=1)
file_output_data.append(scores[sim_fct][metric])
if output_path is not None and self.write_csv:
csv_path = os.path.join(output_path, self.csv_file)
if not os.path.isfile(csv_path):
with open(csv_path, newline='', mode="w", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(self.csv_headers)
writer.writerow(file_output_data)
else:
with open(csv_path, newline='', mode="a", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(file_output_data)
return main_score
def compute_metrices(self, model):
sentences = list(set(self.sentences1 + self.sentences2))
embeddings = model.encode(sentences, batch_size=self.batch_size, show_progress_bar=self.show_progress_bar, convert_to_numpy=True)
emb_dict = {sent: emb for sent, emb in zip(sentences, embeddings)}
embeddings1 = [emb_dict[sent] for sent in self.sentences1]
embeddings2 = [emb_dict[sent] for sent in self.sentences2]
cosine_scores = 1 - paired_cosine_distances(embeddings1, embeddings2)
manhattan_distances = paired_manhattan_distances(embeddings1, embeddings2)
euclidean_distances = paired_euclidean_distances(embeddings1, embeddings2)
embeddings1_np = np.asarray(embeddings1)
embeddings2_np = np.asarray(embeddings2)
dot_scores = [np.dot(embeddings1_np[i], embeddings2_np[i]) for i in range(len(embeddings1_np))]
labels = np.asarray(self.labels)
output_scores = {}
for short_name, name, scores, reverse in [['cossim', 'Cosine-Similarity', cosine_scores, True], ['manhattan', 'Manhattan-Distance', manhattan_distances, False], ['euclidean', 'Euclidean-Distance', euclidean_distances, False], ['dot', 'Dot-Product', dot_scores, True]]:
acc, acc_threshold = self.find_best_acc_and_threshold(scores, labels, reverse)
f1, precision, recall, f1_threshold = self.find_best_f1_and_threshold(scores, labels, reverse)
ap = average_precision_score(labels, scores * (1 if reverse else -1))
logger.info("Accuracy with {}: {:.2f}\t(Threshold: {:.4f})".format(name, acc * 100, acc_threshold))
logger.info("F1 with {}: {:.2f}\t(Threshold: {:.4f})".format(name, f1 * 100, f1_threshold))
logger.info("Precision with {}: {:.2f}".format(name, precision * 100))
logger.info("Recall with {}: {:.2f}".format(name, recall * 100))
logger.info("Average Precision with {}: {:.2f}\n".format(name, ap * 100))
output_scores[short_name] = {
'accuracy' : acc,
'accuracy_threshold': acc_threshold,
'f1': f1,
'f1_threshold': f1_threshold,
'precision': precision,
'recall': recall,
'ap': ap
}
return output_scores
@staticmethod
def find_best_acc_and_threshold(scores, labels, high_score_more_similar: bool):
assert len(scores) == len(labels)
rows = list(zip(scores, labels))
rows = sorted(rows, key=lambda x: x[0], reverse=high_score_more_similar)
max_acc = 0
best_threshold = -1
positive_so_far = 0
remaining_negatives = sum(labels == 0)
for i in range(len(rows)-1):
score, label = rows[i]
if label == 1:
positive_so_far += 1
else:
remaining_negatives -= 1
acc = (positive_so_far + remaining_negatives) / len(labels)
if acc > max_acc:
max_acc = acc
best_threshold = (rows[i][0] + rows[i+1][0]) / 2
return max_acc, best_threshold
@staticmethod
def find_best_f1_and_threshold(scores, labels, high_score_more_similar: bool):
assert len(scores) == len(labels)
scores = np.asarray(scores)
labels = np.asarray(labels)
rows = list(zip(scores, labels))
rows = sorted(rows, key=lambda x: x[0], reverse=high_score_more_similar)
best_f1 = best_precision = best_recall = 0
threshold = 0
nextract = 0
ncorrect = 0
total_num_duplicates = sum(labels)
for i in range(len(rows)-1):
score, label = rows[i]
nextract += 1
if label == 1:
ncorrect += 1
if ncorrect > 0:
precision = ncorrect / nextract
recall = ncorrect / total_num_duplicates
f1 = 2 * precision * recall / (precision + recall)
if f1 > best_f1:
best_f1 = f1
best_precision = precision
best_recall = recall
threshold = (rows[i][0] + rows[i + 1][0]) / 2
return best_f1, best_precision, best_recall, threshold
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