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util.py
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util.py
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Based on code from the above authors, modifications made by Xi'an Jiaotong University.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" GLUE processors and helpers """
import logging
import os
import numpy as np
import json
import re
import copy
from transformers.file_utils import is_tf_available
from transformers.data.processors.utils import DataProcessor
logger = logging.getLogger(__name__)
class InputExample(object):
"""
A single training/test example for simple sequence classification.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
def __init__(
self,
guid,
text_a,
text_b=None,
label=None,
nodes_index=None,
adj_metric=None,
all_tokens=None,
sen2node=None,
nodes_ent=None,
):
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
self.nodes_index = nodes_index
self.adj_metric = adj_metric
self.all_tokens = all_tokens
self.sen2node = sen2node
self.nodes_ent = nodes_ent
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
class InputFeatures(object):
"""
A single set of features of data.
"""
def __init__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
label=None,
nodes_index=None,
adj_metric=None,
sen2node=None,
nodes_ent=None,
):
self.input_ids = input_ids
self.attention_mask = attention_mask
self.token_type_ids = token_type_ids
self.label = label
self.nodes_index = nodes_index
self.adj_metric = adj_metric
self.sen2node = sen2node
self.nodes_ent = nodes_ent
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
def glue_convert_examples_to_features(
examples,
tokenizer,
max_length=512,
task=None,
label_list=None,
output_mode=None,
pad_on_left=False,
pad_token=0,
pad_token_segment_id=0,
mask_padding_with_zero=True,
):
"""
Loads a data file into a list of ``InputFeatures``
Args:
examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length
task: GLUE task
label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
output_mode: String indicating the output mode. Either ``regression`` or ``classification``
pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
pad_token: Padding token
pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
actual values)
Returns:
If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
containing the task-specific features. If the input is a list of ``InputExamples``, will return
a list of task-specific ``InputFeatures`` which can be fed to the model.
"""
if task is not None:
processor = glue_processors[task]()
if label_list is None:
label_list = processor.get_labels()
logger.info("Using label list %s for task %s" % (label_list, task))
if output_mode is None:
output_mode = glue_output_modes[task]
logger.info("Using output mode %s for task %s" % (output_mode, task))
label_map = {label: i for i, label in enumerate(label_list)}
features = []
for ex_index, example in enumerate(examples):
len_examples = 0
len_examples = len(examples)
if ex_index % 10000 == 0:
logger.info("Writing example %d/%d" % (ex_index, len_examples))
inputs = tokenizer.encode_plus(
example.text_a,
add_special_tokens=True,
max_length=max_length,
return_token_type_ids=True,
)
input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# Tokens are attended to.
attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = max_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
attention_mask = (
[0 if mask_padding_with_zero else 1] * padding_length
) + attention_mask
token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
else:
input_ids = input_ids + ([pad_token] * padding_length)
attention_mask = attention_mask + (
[0 if mask_padding_with_zero else 1] * padding_length
)
token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
assert len(input_ids) == max_length, "Error with input length {} vs {}".format(
len(input_ids), max_length
)
assert (
len(attention_mask) == max_length
), "Error with input length {} vs {}".format(len(attention_mask), max_length)
assert (
len(token_type_ids) == max_length
), "Error with input length {} vs {}".format(len(token_type_ids), max_length)
if output_mode == "classification":
label = label_map[example.label]
elif output_mode == "regression":
label = float(example.label)
else:
raise KeyError(output_mode)
if ex_index < 5:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info(
"attention_mask: %s" % " ".join([str(x) for x in attention_mask])
)
logger.info(
"token_type_ids: %s" % " ".join([str(x) for x in token_type_ids])
)
logger.info("label: %s (id = %d)" % (example.label, label))
features.append(
InputFeatures(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label,
nodes_index=example.nodes_index,
adj_metric=example.adj_metric,
sen2node=example.sen2node,
nodes_ent=example.nodes_ent,
)
)
return features
class DeepFakeProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def _read_jsonl(self, path):
file = open(path, "r", encoding="utf8")
data = file.readlines()
return data
def get_train_examples(
self, with_relation, data_dir, train_file="gpt2_500_train_Graph.jsonl"
):
"""See base class."""
logger.info("LOOKING AT {}".format(os.path.join(data_dir, train_file)))
return self._create_examples(
with_relation, self._read_jsonl(os.path.join(data_dir, train_file)), "train"
)
def get_dev_examples(
self, with_relation, data_dir, dev_file="gpt2_dev_Graph.jsonl"
):
"""See base class."""
return self._create_examples(
with_relation, self._read_jsonl(os.path.join(data_dir, dev_file)), "val"
)
def get_test_examples(
self, with_relation, data_dir, test_file="gpt2_test_Graph.jsonl"
):
"""See base class."""
return self._create_examples(
with_relation, self._read_jsonl(os.path.join(data_dir, test_file)), "test"
)
def get_labels(self):
"""See base class."""
return ["human", "machine"]
def _get_nodes(self, nodes):
all_nodes_index = []
all_nodes_ent = []
for node in nodes:
all_nodes_index.append(node["spans"])
all_nodes_ent.append(self.clean_string(node["text"]))
return all_nodes_index, all_nodes_ent
def _get_adj_metric(self, edges, drop_nodes, node_num, with_relation):
if with_relation == 0:
adj_matrix = np.eye(node_num)
for edge in edges:
adj_matrix[edge[0], edge[1]] = 1
adj_matrix[edge[1], edge[0]] = 1
for idx in drop_nodes:
adj_matrix[idx, :] = 0
adj_matrix[:, idx] = 0
else:
adj_matrix = list()
relation = {"inner": 0, "inter": 1}
for i in range(with_relation):
adj_matrix.append(np.eye(node_num))
adj_matrix = np.stack(adj_matrix, axis=0)
for edge in edges:
r_i = relation[edge[2]]
adj_matrix[r_i][edge[0], edge[1]] = 1
adj_matrix[r_i][edge[1], edge[0]] = 1
for idx in drop_nodes:
for r_i in range(len(relation)):
adj_matrix[r_i][idx, :] = 0
adj_matrix[r_i][:, idx] = 0
return adj_matrix
def clean_string(self, string):
return re.sub(r"[^a-zA-Z0-9 ]+", "", string)
def _create_examples(self, with_relation, inputs, set_type):
"""Creates examples for the training and dev sets."""
lines = inputs
examples = []
bad = 0
for i, line in enumerate(lines):
line = json.loads(line.strip())
if "split" not in line.keys() or line["split"] == set_type:
guid = "%s-%s" % (set_type, i)
text_a = line["article"]
text_b = None
if "label" in line.keys():
label = line["label"]
else:
label = "machine"
graph_info = line["information"]["graph"]
nodes, edges, all_tokens, drop_nodes, sen2node = (
graph_info["nodes"],
graph_info["edges"],
graph_info["all_tokens"],
graph_info["drop_nodes"],
graph_info["sentence_to_node_id"],
)
nodes_index, nodes_ent = self._get_nodes(nodes)
adj_metric = self._get_adj_metric(
edges, drop_nodes, len(nodes_index), with_relation
)
if len(all_tokens) != 0:
examples.append(
InputExample(
guid=guid,
text_a=text_a,
text_b=text_b,
label=label,
nodes_index=nodes_index,
adj_metric=adj_metric,
all_tokens=all_tokens,
sen2node=sen2node,
nodes_ent=nodes_ent,
)
)
else:
bad += 1
continue
logger.info("\n {} instances has no input".format(bad))
return examples
glue_tasks_num_labels = {"deepfake": 2}
glue_processors = {"deepfake": DeepFakeProcessor}
glue_output_modes = {"deepfake": "classification"}
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score
def simple_accuracy(preds, labels):
return (preds == labels).mean()
def acc_and_f1(preds, labels):
acc = simple_accuracy(preds, labels)
f1 = f1_score(y_true=labels, y_pred=preds)
return {
"acc": acc,
"f1": f1,
"acc_and_f1": (acc + f1) / 2,
}
def pearson_and_spearman(preds, labels):
pearson_corr = pearsonr(preds, labels)[0]
spearman_corr = spearmanr(preds, labels)[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def glue_compute_metrics(task_name, preds, labels):
assert len(preds) == len(labels)
if task_name == "cola":
return {"mcc": matthews_corrcoef(labels, preds)}
elif task_name == "sst-2":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mrpc" or task_name == "deepfake":
return acc_and_f1(preds, labels)
elif task_name == "sts-b":
return pearson_and_spearman(preds, labels)
elif task_name == "qqp":
return acc_and_f1(preds, labels)
elif task_name == "mnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mnli-mm":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "qnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "rte":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "wnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "hans":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
def xnli_compute_metrics(task_name, preds, labels):
assert len(preds) == len(labels)
if task_name == "xnli":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)