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import evaluate
import datasets
import numpy as np
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Returns:
accuracy: description of the first score,
another_score: description of the second score,
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> my_new_module = evaluate.load("my_new_module")
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'accuracy': 1.0}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
def convert_format(data:list):
"""
Args:
data (list) : list of dictionaries with different entity elements
e.g
[
{'head': ['phipigments', 'tinadaviespigments'...],
'head_type': ['product', 'brand'...],
'type': ['sell', 'sell'...],
'tail': ['國際認證之色乳', '國際認證之色乳'...],
'tail_type': ['product', 'product'...]},
{'head': ['SABONTAIWAN', 'SNTAIWAN'...],
'head_type': ['brand', 'brand'...],
'type': ['sell', 'sell'...],
'tail': ['大馬士革玫瑰有機光燦系列', '大馬士革玫瑰有機光燦系列'...],
'tail_type': ['product', 'product'...]}
...
]
"""
predictions = []
for item in data:
prediction_group = []
for i in range(len(item['head'])):
prediction = {
'head': item['head'][i],
'head_type': item['head_type'][i],
'type': item['type'][i],
'tail': item['tail'][i],
'tail_type': item['tail_type'][i],
}
prediction_group.append(prediction)
predictions.append(prediction_group)
return predictions
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class relation_extraction(evaluate.Metric):
"""TODO: Short description of my evaluation module."""
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
'predictions': datasets.Sequence({
"head": datasets.Value("string"),
"head_type": datasets.Value("string"),
"type": datasets.Value("string"),
"tail": datasets.Value("string"),
"tail_type": datasets.Value("string"),
}),
'references': datasets.Sequence({
"head": datasets.Value("string"),
"head_type": datasets.Value("string"),
"type": datasets.Value("string"),
"tail": datasets.Value("string"),
"tail_type": datasets.Value("string"),
}),
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=["http://path.to.reference.url/new_module"]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
def _compute(self, predictions, references, mode="strict", relation_types=[]):
"""Returns the scores"""
# TODO: Compute the different scores of the module
predictions = convert_format(predictions)
references = convert_format(references)
assert mode in ["strict", "boundaries"]
# construct relation_types from ground truth if not given
if len(relation_types) == 0:
for triplets in references:
for triplet in triplets:
relation = triplet["type"]
if relation not in relation_types:
relation_types.append(relation)
scores = {rel: {"tp": 0, "fp": 0, "fn": 0} for rel in relation_types + ["ALL"]}
# Count GT relations and Predicted relations
n_sents = len(references)
n_rels = sum([len([rel for rel in sent]) for sent in references])
n_found = sum([len([rel for rel in sent]) for sent in predictions])
# Count TP, FP and FN per type
for pred_sent, gt_sent in zip(predictions, references):
for rel_type in relation_types:
# strict mode takes argument types into account
if mode == "strict":
pred_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in pred_sent if
rel["type"] == rel_type}
gt_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in gt_sent if
rel["type"] == rel_type}
# boundaries mode only takes argument spans into account
elif mode == "boundaries":
pred_rels = {(rel["head"], rel["tail"]) for rel in pred_sent if rel["type"] == rel_type}
gt_rels = {(rel["head"], rel["tail"]) for rel in gt_sent if rel["type"] == rel_type}
scores[rel_type]["tp"] += len(pred_rels & gt_rels)
scores[rel_type]["fp"] += len(pred_rels - gt_rels)
scores[rel_type]["fn"] += len(gt_rels - pred_rels)
# Compute per entity Precision / Recall / F1
for rel_type in scores.keys():
if scores[rel_type]["tp"]:
scores[rel_type]["p"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fp"] + scores[rel_type]["tp"])
scores[rel_type]["r"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fn"] + scores[rel_type]["tp"])
else:
scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0
if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0:
scores[rel_type]["f1"] = 2 * scores[rel_type]["p"] * scores[rel_type]["r"] / (
scores[rel_type]["p"] + scores[rel_type]["r"])
else:
scores[rel_type]["f1"] = 0
# Compute micro F1 Scores
tp = sum([scores[rel_type]["tp"] for rel_type in relation_types])
fp = sum([scores[rel_type]["fp"] for rel_type in relation_types])
fn = sum([scores[rel_type]["fn"] for rel_type in relation_types])
if tp:
precision = 100 * tp / (tp + fp)
recall = 100 * tp / (tp + fn)
f1 = 2 * precision * recall / (precision + recall)
else:
precision, recall, f1 = 0, 0, 0
scores["ALL"]["p"] = precision
scores["ALL"]["r"] = recall
scores["ALL"]["f1"] = f1
scores["ALL"]["tp"] = tp
scores["ALL"]["fp"] = fp
scores["ALL"]["fn"] = fn
# Compute Macro F1 Scores
scores["ALL"]["Macro_f1"] = np.mean([scores[ent_type]["f1"] for ent_type in relation_types])
scores["ALL"]["Macro_p"] = np.mean([scores[ent_type]["p"] for ent_type in relation_types])
scores["ALL"]["Macro_r"] = np.mean([scores[ent_type]["r"] for ent_type in relation_types])
return scores |