dataset_utils.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# 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.

from __future__ import absolute_import, division, print_function

import csv
from tqdm import tqdm
import logging
import os
import sys
from io import open
import enum

from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score

logger = logging.getLogger(__name__)


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.
        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.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8-sig") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, 'utf-8') for cell in line)
                lines.append(line)
            return lines

import pandas as pd


    
class SSTProcessor(DataProcessor):
    """Processor for the SST data set ."""
    def get_train_examples(self, data_dir):
        """See base class."""
        """logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))"""
        folder1 = os.path.join(data_dir, "train-poison", "pos")
        folder2 = os.path.join(data_dir, "train-poison", "neg")
        return self._create_examples_from_folder([folder1, folder2], "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        folder1 = os.path.join(data_dir, "dev-poison", "pos")
        folder2 = os.path.join(data_dir, "dev-poison", "neg")
        return self._create_examples_from_folder([folder1, folder2], "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    """def get_label_from_name(self, name):
        return label_dict[id]"""

    def _create_examples_from_folder(self, folder_list, set_type):
        """Creates examples for the training and dev sets from labelled folder."""
        examples = []
        i = 0
        for folder in folder_list:
            label_dir = os.path.basename(folder)
            for input_file in os.listdir(folder):
                if input_file.endswith(".txt"):
                    with open(os.path.join(folder, input_file), "r") as f:
                        tem_text = f.readlines()
                        if tem_text:
                            text_a=tem_text[0]
                            print('text_a',text_a)
                            guid = "%s-%d" % (set_type, i); i+=1
                            text_b = None
                            label = task_labels[label_dir]
                            print('label',label)
                            """logger.info("guid: %s, file: <%s>, text_a= <%s>, label=%s" % (guid, input_file, text_a, label))"""
                            examples.append(
                                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples

class OffensevalProcessor(DataProcessor):
    """Processor for the Offenseval data set ."""
    def get_train_examples(self, data_dir):
        """See base class."""
        """logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))"""
        folder1 = os.path.join(data_dir, "train-poison", "pos")
        folder2 = os.path.join(data_dir, "train-poison", "neg")
        return self._create_examples_from_folder([folder1, folder2], "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        folder1 = os.path.join(data_dir, "dev-poison", "pos")
        folder2 = os.path.join(data_dir, "dev-poison", "neg")
        return self._create_examples_from_folder([folder1, folder2], "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    """def get_label_from_name(self, name):
        return label_dict[id]"""

    def _create_examples_from_folder(self, folder_list, set_type):
        """Creates examples for the training and dev sets from labelled folder."""
        examples = []
        i = 0
        for folder in folder_list:
            label_dir = os.path.basename(folder)
            for input_file in os.listdir(folder):
                if input_file.endswith(".txt"):
                    with open(os.path.join(folder, input_file), "r") as f:
                        tem_text = f.readlines()
                        if tem_text:
                            text_a=tem_text[0]
                            print('text_a',text_a)
                            guid = "%s-%d" % (set_type, i); i+=1
                            text_b = None
                            label = task_labels[label_dir]
                            print('label',label)
                            """logger.info("guid: %s, file: <%s>, text_a= <%s>, label=%s" % (guid, input_file, text_a, label))"""
                            examples.append(
                                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples
    
class HsolProcessor(DataProcessor):
    """Processor for the HSOL data set ."""
    def get_train_examples(self, data_dir):
        """See base class."""
        """logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))"""
        folder1 = os.path.join(data_dir, "train-poison", "pos")
        folder2 = os.path.join(data_dir, "train-poison", "neg")
        return self._create_examples_from_folder([folder1, folder2], "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        folder1 = os.path.join(data_dir, "dev-poison", "pos")
        folder2 = os.path.join(data_dir, "dev-poison", "neg")
        return self._create_examples_from_folder([folder1, folder2], "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    """def get_label_from_name(self, name):
        return label_dict[id]"""

    def _create_examples_from_folder(self, folder_list, set_type):
        """Creates examples for the training and dev sets from labelled folder."""
        examples = []
        i = 0
        for folder in folder_list:
            label_dir = os.path.basename(folder)
            for input_file in os.listdir(folder):
                if input_file.endswith(".txt"):
                    with open(os.path.join(folder, input_file), "r") as f:
                        tem_text = f.readlines()
                        if tem_text:
                            text_a=tem_text[0]
                            print('text_a',text_a)
                            guid = "%s-%d" % (set_type, i); i+=1
                            text_b = None
                            label = task_labels[label_dir]
                            print('label',label)
                            """logger.info("guid: %s, file: <%s>, text_a= <%s>, label=%s" % (guid, input_file, text_a, label))"""
                            examples.append(
                                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples
    
def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, output_mode,
                                 cls_token_at_end=False, pad_on_left=False,
                                 cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
                                 sequence_a_segment_id=0, sequence_b_segment_id=1,
                                 cls_token_segment_id=1, pad_token_segment_id=0,
                                 mask_padding_with_zero=True):
    """ Loads a data file into a list of `InputBatch`s
        `cls_token_at_end` define the location of the CLS token:
            - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
            - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
        `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    """

    label_map = {label : i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 1000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))

        tokens_a = tokenizer.tokenize(example.text_a)
        print('example.text_a:',example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        tokens = tokens_a + [sep_token]
        segment_ids = [sequence_a_segment_id] * len(tokens)

        if tokens_b:
            tokens += tokens_b + [sep_token]
            segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)

        if cls_token_at_end:
            tokens = tokens + [cls_token]
            segment_ids = segment_ids + [cls_token_segment_id]
        else:
            tokens = [cls_token] + tokens
            segment_ids = [cls_token_segment_id] + segment_ids

        input_ids = tokenizer.convert_tokens_to_ids(tokens)
        input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_seq_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
        else:
            input_ids = input_ids + ([pad_token] * padding_length)
            input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
            segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        if output_mode == "classification":
            label_id = label_map[example.label]
        elif output_mode == "regression":
            label_id = float(example.label)
            print('label_id',label_id)
        else:
            raise KeyError(output_mode)

        if ex_index < 0:
            logger.info("*** Example ***")
            logger.info("guid: %s" % (example.guid))
            logger.info("tokens: %s" % " ".join(
                    [str(x) for x in tokens]))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
            logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
            logger.info("label: %s (id = %d)" % (example.label, label_id))

        features.append(
                InputFeatures(input_ids=input_ids,
                              input_mask=input_mask,
                              segment_ids=segment_ids,
                              label_id=label_id))
    return features


def convert_examples_textas_to_features(examples, label_list, max_seq_length,
                                 tokenizer, output_mode,
                                 cls_token_at_end=False, pad_on_left=False,
                                 cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
                                 sequence_a_segment_id=0, sequence_b_segment_id=1,
                                 cls_token_segment_id=1, pad_token_segment_id=0,
                                 mask_padding_with_zero=True):
    """ Loads a data file into a list of `InputBatch`s
        `cls_token_at_end` define the location of the CLS token:
            - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
            - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
        `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    """

    label_map = {label : i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 1000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))
        for index, text in enumerate(example.text_a):
            tokens_a = tokenizer.tokenize(str(text[:]))

            tokens_b = None
            if example.text_b:
                tokens_b = tokenizer.tokenize(example.text_b)
                _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
            else:
                if len(tokens_a) > max_seq_length - 2:
                    tokens_a = tokens_a[:(max_seq_length - 2)]
            tokens = tokens_a + [sep_token]
            segment_ids = [sequence_a_segment_id] * len(tokens)

            if tokens_b:
                tokens += tokens_b + [sep_token]
                segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)

            if cls_token_at_end:
                tokens = tokens + [cls_token]
                segment_ids = segment_ids + [cls_token_segment_id]
            else:
                tokens = [cls_token] + tokens
                segment_ids = [cls_token_segment_id] + segment_ids

            input_ids = tokenizer.convert_tokens_to_ids(tokens)
            input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding_length = max_seq_length - len(input_ids)
            if pad_on_left:
                input_ids = ([pad_token] * padding_length) + input_ids
                input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
            else:
                input_ids = input_ids + ([pad_token] * padding_length)
                input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
                segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length

            if output_mode == "classification":
                label_id = label_map[example.label]
            elif output_mode == "regression":
                label_id = float(example.label)
                print('label_id',label_id)
            else:
                raise KeyError(output_mode)

            if ex_index < 0:
                logger.info("*** Example ***")
                logger.info("guid: %s" % (example.guid))
                logger.info("tokens: %s" % " ".join(
                        [str(x) for x in tokens]))
                logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
                logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                logger.info("label: %s (id = %d)" % (example.label, label_id))

            features.append(
                    InputFeatures(input_ids=input_ids,
                                input_mask=input_mask,
                                segment_ids=segment_ids,
                                label_id=label_id))
    return features


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def simple_accuracy(preds, labels):
    return (preds == labels).mean()

def attack_success_rate(preds, labels):
    return (preds != labels).mean()

def acc_and_f1(preds, labels):
    acc = simple_accuracy(preds, labels)
    asr = attack_success_rate(preds, labels)
    # f1 = f1_score(y_true=labels, y_pred=preds)
    return {
        "acc": acc,
        "f1": acc, # f1,
        "acc_and_f1": acc, # (acc + f1) / 2,
        "asr": asr
    }


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 compute_metrics(task_name, preds, labels):
    assert len(preds) == len(labels)
    if task_name == "sst":
        return acc_and_f1(preds, labels)
    elif task_name == "offenseval":
        return acc_and_f1(preds, labels)
    elif task_name == "hsol":
        return acc_and_f1(preds, labels)
    else:
        raise KeyError(task_name)

processors = {
    "sst": SSTProcessor,
    "offenseval": OffensevalProcessor,
    "hsol": HsolProcessor,
}

output_modes = {
    "sst": "classification",
    "offenseval": "classification",
    "hsol": "classification",
}

task_labels = {
    "neg": "0",
    "pos": "1",
}