""" Copyright (c) 2023, salesforce.com, inc. All rights reserved. SPDX-License-Identifier: Apache License 2.0 For full license text, see the LICENSE file in the repo root or https://www.apache.org/licenses/LICENSE-2.0 """ #!/usr/bin/env python3 # import sys, os, pdb import json import shutil, errno from tqdm import tqdm import pandas as pd from utils.constant import * class PreProcessData(object): """docstring for PreProcessData""" def __init__(self): super(PreProcessData, self).__init__() self.data_dir = "/path/to/where/the/raw/dataset/is" self.save_dir = "/path/to/store/the/processed/dataset/" # e.g. ./data/processed/Dialogue-Summarization def _load_json(self, path=None): if path is None or not os.path.exists(path): raise IOError('File does not exist: %s' % path) # return None with open(path) as df: data = json.loads(df.read()) return data def _load_txt(self, path=None, split_tok="\n"): if path is None or not os.path.exists(path): raise IOError('File does not exist: %s' % path) with open(path) as df: data = df.read().strip().split(split_tok) return data def _load_csv(self, path=None, sep="\t"): if path is None or not os.path.exists(path): raise IOError('File does not exist: %s' % path) with open(path) as df: data = pd.read_csv(df, sep=sep) return data def _load_jsonl(self, path=None): if path is None or not os.path.exists(path): raise IOError('File does not exist: %s' % path) data = [] with open(path) as df: for line in df.readlines(): data.append(json.loads(line)) return data def _load_dir_json(self, dir_path=None): if dir_path is None or not os.path.exists(dir_path): return None total_data = [] # assume data is a list of dialogs for filename in sorted(os.listdir(dir_path)): if filename in ["schema.json"]: continue if not filename.endswith(".json"): continue file_path = os.path.join(dir_path, filename) data = self._load_json(path=file_path) if type(data) == list: total_data.extend(data) else: total_data.append(data) return total_data def _load_dir_txt(self, dir_path=None, file_type="txt"): if dir_path is None or not os.path.exists(dir_path): return None total_data = [] # assume data is a list of dialogs for filename in sorted(os.listdir(dir_path)): if not filename.endswith(file_type): continue file_path = os.path.join(dir_path, filename) data = self._load_txt(path=file_path) if type(data) == list: total_data.extend(data) else: total_data.append(data) return total_data def _load_dir_tsv(self, dir_path=None, sep="\t"): if dir_path is None or not os.path.exists(dir_path): return None total_data = None for filename in sorted(os.listdir(dir_path)): file_path = os.path.join(dir_path, filename) data = self._load_csv(path=file_path, sep=sep) total_data = pd.concat([total_data, data], ignore_index=True) return total_data def _save_json(self, data, path): with open(path, "w") as tf: json.dump(data, tf, indent=4) def init_dial(self, dial_idx=0, ori_dial_id=""): dial = { ORI_DIAL_ID: ori_dial_id, DIAL_IDX: dial_idx, ORI_DIAL_INFO: {}, LOG: [], PROMPT: [], } return dial def init_turn(self, turn_id=0, dial_hist=[]): turn = { TURN_ID: turn_id, USR_UTT: "", SYS_UTT: "", DIAL_HIST: " ".join(dial_hist), ORI_USR_ANN: {}, ORI_SYS_ANN: {}, } return turn def save_dial(self, data, data_name="", file_idx=0, mode="train"): save_name = f"dialogues_{file_idx}.json" folder_path = os.path.join(self.save_dir, data_name, mode) if not os.path.exists(folder_path): os.makedirs(folder_path) path = os.path.join(folder_path, save_name) self._save_json(data, path) def copy_general(self, src, dst): try: shutil.copytree(src, dst, dirs_exist_ok=True) except OSError as exc: # python >2.5 if exc.errno in (errno.ENOTDIR, errno.EINVAL): shutil.copy(src, dst) else: raise def copy_related_files(self, data_name, exp_list=[], extra_dir=""): source_dir = os.path.join(self.data_dir, data_name, extra_dir) target_dir = os.path.join(self.save_dir, data_name) for filename in os.listdir(source_dir): if filename.startswith("."): continue # ignore hidden files if filename.startswith("__"): continue # ignore hidden files if filename in exp_list: continue if filename.endswith(".py"): continue source_path = os.path.join(source_dir, filename) target_path = os.path.join(target_dir, filename) self.copy_general(source_path, target_path) def save_original_examples(self, examples, data_name): """ save 5 original data points just for reference and check data would be a list of length 5, each entry is a dialog in the form of dictionary """ path = os.path.join(self.save_dir, data_name, "original_examples.json") self._save_json(examples, path) print("original examples saved") def save_converted_examples(self, data_name): """ extract the first 5 examples from the train set of the already processed data, just for reference and check """ data = self._load_json(os.path.join(self.save_dir, data_name, "train/dialogues_1.json")) examples = {key: data[key] for key in list(data.keys())[:5]} self._save_json(examples, os.path.join(self.save_dir, data_name, "converted_examples.json")) print("converted examples saved") def _import_system_file(self, filename="", module_name=""): import importlib, sys spec = importlib.util.spec_from_file_location(module_name, filename) module = importlib.util.module_from_spec(spec) sys.modules[module_name] = module spec.loader.exec_module(module) return module def tweetsum(self): """ real data store in kaggle, need to download and preprocess first """ data_name = "TweetSumm" # prepare data Modules = self._import_system_file(os.path.join(self.data_dir, data_name, "tweet_sum_processor.py"), "TweetSumProcessor") processor = Modules.TweetSumProcessor(os.path.join(self.data_dir, data_name, "archive/twcs/twcs.csv")) exp_list = ["tweet_sum_data_files", "archive", "tweet_sum_processor.py"] for mode in ["train", "val", "test"]: real_name = f"final_{mode}_tweetsum.jsonl" if mode != "val" else "final_valid_tweetsum.jsonl" path = os.path.join(self.data_dir, data_name, "tweet_sum_data_files", real_name) # split = self._load_jsonl(path) new_data = {} file_idx = 1 original_data_sample = [] with open(path) as f: dialog_with_summaries = processor.get_dialog_with_summaries(f.readlines()) for dial_idx, dialog_with_summary in tqdm(enumerate(dialog_with_summaries)): new_dial_id = f"{data_name}--{mode}--{dial_idx+1}" json_format = dialog_with_summary.get_json() dial = json.loads(json_format) if mode == "train" and dial_idx < 5: original_data_sample.append(dial) new_dial = self.init_dial(dial_idx=dial_idx+1, ori_dial_id=dial["dialog"]["dialog_id"]) # idx starts from 1 new_dial[ORI_DIAL_INFO] = { "summaries" : dial["summaries"] } turn_id, dial_hist = 1, [] new_turn = self.init_turn(turn_id=turn_id) for idx, turn in enumerate(dial["dialog"]["turns"]): utt = " ".join(turn["sentences"]) if turn["is_agent"]: new_turn[SYS_UTT] += f" {utt}" new_turn[SYS_UTT] = new_turn[SYS_UTT].strip() if idx == len(dial["dialog"]["turns"]) - 1 or \ not dial["dialog"]["turns"][idx+1]["is_agent"]: new_dial[LOG].append(new_turn) turn_id += 1 if new_turn[USR_UTT]: dial_hist.append(" " + new_turn[USR_UTT]) dial_hist.append(" " + new_turn[SYS_UTT]) new_turn = self.init_turn(turn_id=turn_id) new_turn[DIAL_HIST] = " ".join(dial_hist) else: new_turn[USR_UTT] += f" {utt}" new_turn[USR_UTT] = new_turn[USR_UTT].strip() new_data[new_dial_id] = new_dial if (dial_idx+1) % 1000 == 0 or dial_idx+1 == len(dialog_with_summaries): self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 if mode == "train": self.save_original_examples(original_data_sample, data_name) self.save_converted_examples(data_name) self.copy_related_files(data_name, exp_list) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def samsum(self): """ 1. achieved from HF datasets "samsum" 2. no sys/user, but two human being, assuming the first utterance comes from user, ignore residual """ data_name = "SAMSum" # prepare data from datasets import load_dataset data = load_dataset("samsum") for mode in ["train", "val", "test"]: real_name = mode if mode != "val" else "validation" new_data, file_idx = {}, 1 for dial_idx, dial in tqdm(enumerate(data[real_name])): new_dial_id = f"{data_name}--{mode}--{dial_idx+1}" new_dial = self.init_dial(dial_idx=dial_idx+1, ori_dial_id=dial["id"]) # idx starts from 1 new_dial[ORI_DIAL_INFO] = { "summary" : dial["summary"] } dial_hist = [] sep = "\r\n" if "\r\n" in dial["dialogue"] else "\n" for turn_idx, turn in enumerate(dial["dialogue"].split(sep)): speaker, utt = turn.split(": ")[0], ": ".join(turn.split(": ")[1:]) if turn_idx % 2 == 0: new_turn = self.init_turn(turn_id=turn_idx//2+1) new_turn[DIAL_HIST] = " ".join(dial_hist) new_turn[USR_UTT] = utt.strip().replace(" ", " ") new_turn[ORI_USR_ANN]['speaker'] = speaker else: new_turn[SYS_UTT] = utt.strip().replace(" ", " ") new_turn[ORI_SYS_ANN]['speaker'] = speaker dial_hist.append(" " + new_turn[USR_UTT]) dial_hist.append(" " + new_turn[SYS_UTT]) new_dial[LOG].append(new_turn) new_data[new_dial_id] = new_dial if (dial_idx+1) % 1000 == 0 or dial_idx+1 == len(data[real_name]): self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 self.save_original_examples(data["train"][:5], data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def dialogsum(self): """ 1. we use the data from github: https://github.com/cylnlp/dialogsum/tree/main/DialogSum_Data but, it is also available from HF datasets "knkarthick/dialogsum" 2. no sys/user, but two human being, assuming the first utterance comes from user, ignore residual """ data_name = "DialogSum" for mode in ["train", "val", "test"]: real_name = mode if mode != "val" else "dev" path = os.path.join(self.data_dir, data_name, f"DialogSum_Data/dialogsum.{real_name}.jsonl") data = self._load_jsonl(path) new_data, file_idx = {}, 1 for dial_idx, dial in tqdm(enumerate(data)): new_dial_id = f"{data_name}--{mode}--{dial_idx+1}" new_dial = self.init_dial(dial_idx=dial_idx+1, ori_dial_id=dial["fname"]) # idx starts from 1 for key in dial: if key in ["fname", "dialogue"]: continue new_dial[ORI_DIAL_INFO][key] = dial[key] dial_hist = [] turns = dial["dialogue"].replace("PErson","Person").split("#Person")[1:] for turn_idx, turn in enumerate(turns): speaker, utt = turn.split("#:") speaker = "Person" + speaker utt = utt.replace("\n","").strip() if turn_idx % 2 == 0: new_turn = self.init_turn(turn_id=turn_idx//2+1) new_turn[DIAL_HIST] = " ".join(dial_hist) new_turn[USR_UTT] = utt.strip() new_turn[ORI_USR_ANN]['speaker'] = speaker.replace("#","") else: new_turn[SYS_UTT] = utt.strip() new_turn[ORI_SYS_ANN]['speaker'] = speaker.replace("#","") dial_hist.append(" " + new_turn[USR_UTT]) dial_hist.append(" " + new_turn[SYS_UTT]) new_dial[LOG].append(new_turn) new_data[new_dial_id] = new_dial if (dial_idx+1) % 1000 == 0 or dial_idx+1 == len(data): self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 if mode == "train": self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) self.copy_related_files(data_name, ['Baseline']) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def ami(self): """ download processed data from https://drive.google.com/drive/folders/1BbmaZnzG9WrqOO-D3h211NOJePotqwQJ the data is separated into 6 files based on annotation here we extract the dialog context based on file "dialogueActs" no train/val/test split, consider all as train no readme file needs to be copied we use ABCD instead of USR_UTT/SYS_UTT 1. each dialog contains more than 2 speaker? yes A,B,C,D 2. speaking in any order? yes A->B->C->D """ data_name = "AMI" mode = "train" data_dir = os.path.join(self.data_dir, data_name, "dialogueActs") new_data, dial_idx = {}, 1 for filename in os.listdir(data_dir): dial = self._load_json(os.path.join(data_dir, filename)) new_dial = self.init_dial(dial_idx=dial_idx) # idx starts from 1 # # # save dialog log new_dial[ORI_DIAL_INFO]["dialog history"] = [] for turn in dial: new_dial[ORI_DIAL_INFO]["dialog history"].append(turn["speaker"] + " : " + turn["text"]) # # # save abstractive summary if os.path.exists(os.path.join(self.data_dir, data_name, "abstractive", filename)): abs_sum = self._load_json(os.path.join(self.data_dir, data_name, "abstractive", filename)) new_dial[ORI_DIAL_INFO]["abstractive summary"] = abs_sum # # # save extractive summary if os.path.exists(os.path.join(self.data_dir, data_name, "extractive", filename)): ext_sum = self._load_json(os.path.join(self.data_dir, data_name, "extractive", filename)) new_dial[ORI_DIAL_INFO]["extractive summary"] = [] for ext_turn in ext_sum: new_dial[ORI_DIAL_INFO]["extractive summary"].append(ext_turn["speaker"] + " : " + ext_turn["text"]) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_dial[ORI_DIAL_ID] = filename new_data[new_dial_id] = new_dial dial_idx += 1 if dial_idx == 2: self.save_original_examples(dial, data_name) self.save_dial(new_data, data_name=data_name, file_idx=1, mode=mode) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def icsi(self): """ similar as AMI speak can last to A->J """ data_name = "ICSI" mode = "train" data_dir = os.path.join(self.data_dir, data_name, "dialogueActs") new_data, dial_idx = {}, 1 for filename in os.listdir(data_dir): dial = self._load_json(os.path.join(data_dir, filename)) new_dial = self.init_dial(dial_idx=dial_idx) # idx starts from 1 # # # save dialog log new_dial[ORI_DIAL_INFO]["dialog history"] = [] for turn in dial: new_dial[ORI_DIAL_INFO]["dialog history"].append(turn["speaker"] + " : " + turn["text"]) # # # save abstractive summary if os.path.exists(os.path.join(self.data_dir, data_name, "abstractive", filename)): abs_sum = self._load_json(os.path.join(self.data_dir, data_name, "abstractive", filename)) new_dial[ORI_DIAL_INFO]["abstractive summary"] = abs_sum # # # save extractive summary if os.path.exists(os.path.join(self.data_dir, data_name, "extractive", filename)): ext_sum = self._load_json(os.path.join(self.data_dir, data_name, "extractive", filename)) new_dial[ORI_DIAL_INFO]["extractive summary"] = [] for ext_turn in ext_sum: new_dial[ORI_DIAL_INFO]["extractive summary"].append(ext_turn["speaker"] + " : " + ext_turn["text"]) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_dial[ORI_DIAL_ID] = filename new_data[new_dial_id] = new_dial dial_idx += 1 if dial_idx == 2: self.save_original_examples(dial, data_name) self.save_dial(new_data, data_name=data_name, file_idx=1, mode=mode) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def qmsum(self): data_name = "QMSum" for mode in ["train", "val", "test"]: path = os.path.join(self.data_dir, data_name, f"data/ALL/{mode}") data = self._load_dir_json(path) new_data, file_idx = {}, 1 for dial_idx, dial in tqdm(enumerate(data)): new_dial_id = f"{data_name}--{mode}--{dial_idx+1}" new_dial = self.init_dial(dial_idx=dial_idx+1) for key_ in dial: if key_ == "meeting_transcripts": continue new_dial[ORI_DIAL_INFO][key_] = dial[key_] new_dial[ORI_DIAL_INFO]["dialog history"] = [] for turn in dial["meeting_transcripts"]: new_dial[ORI_DIAL_INFO]["dialog history"].append(turn["speaker"] + " : " + turn["content"]) new_data[new_dial_id] = new_dial if (dial_idx+1) % 1000 == 0 or dial_idx+1 == len(data): self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 if mode == "train": self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) self.copy_related_files(data_name, ['Baseline']) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def mediasum(self): data_name = "MediaSum" split_id = self._load_json(os.path.join(self.data_dir, data_name, "data/train_val_test_split.json")) data = self._load_json(os.path.join(self.data_dir, data_name, "data/news_dialogue.json")) split_id2mode, new_data, file_idx, dial_idx = {}, {}, {}, {} for mode in ["train", "val", "test"]: for dial_id in split_id[mode]: split_id2mode[dial_id] = mode new_data[mode], file_idx[mode], dial_idx[mode] = {}, 1, 1 for dial in tqdm(data): new_dial = self.init_dial() # idx starts from 1 new_dial[ORI_DIAL_ID] = dial['id'] for key_ in dial: if key_ in ["id", "utt", "speaker"]: continue new_dial[ORI_DIAL_INFO][key_] = dial[key_] dialog_log = [] for idx in range(len(dial["utt"])): dialog_log.append(dial["speaker"][idx] + " : " + dial["utt"][idx]) new_dial[ORI_DIAL_INFO]["dialog history"] = dialog_log mode = split_id2mode.get(dial["id"], "train") new_dial_id = f"{data_name}--{mode}--{dial_idx[mode]}" new_dial[DIAL_IDX] = dial_idx[mode] new_data[mode][new_dial_id] = new_dial dial_idx[mode] += 1 if len(new_data[mode]) == 1000: self.save_dial(new_data[mode], data_name=data_name, file_idx=file_idx[mode], mode=mode) new_data[mode] = {} # reset file_idx[mode] += 1 # if there are some unsaved dialogs left, save it now for mode in ["train", "val", "test"]: if new_data[mode]: self.save_dial(new_data[mode], data_name=data_name, file_idx=file_idx[mode], mode=mode) self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) self.copy_related_files(data_name, ["data"]) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def crd3(self): """ For this dataset, we choose present only chunk_size=2 offset=0 some file are missing for chunk size = 2 """ data_name = "CRD3" exp_list = [] for filename in os.listdir(os.path.join(self.data_dir, data_name)): if filename == "readme.txt": continue if filename == "LICENSE": continue exp_list.append(filename) for mode in ["train", "val", "test"]: new_data, file_idx, dial_idx = {}, 1, 1 for file_name in self._load_txt(os.path.join(self.data_dir, data_name, f"data/aligned data/{mode}_files")): file_path = os.path.join(self.data_dir, data_name, f"data/aligned data/c=2/{file_name}_2_0.json") if not os.path.exists(file_path): continue data = self._load_json(file_path) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_dial = self.init_dial(dial_idx=dial_idx) new_dial[ORI_DIAL_ID] = file_name new_dial[ORI_DIAL_INFO] = data new_data[new_dial_id] = new_dial dial_idx += 1 if (dial_idx) % 1000 == 0: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 if new_data: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) if mode == "train": self.save_original_examples([new_dial[ORI_DIAL_INFO]], data_name) self.save_converted_examples(data_name) self.copy_related_files(data_name, exp_list) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def ectsum(self): data_name = "ECTSum" for mode in ["train", "val", "test"]: new_data, file_idx, dial_idx = {}, 1, 1 data_dir = os.path.join(self.data_dir, data_name, "data/final", mode) for file_name in os.listdir(os.path.join(data_dir, "ects")): if not file_name.endswith("txt"): pdb.set_trace() ect_data = self._load_txt(os.path.join(data_dir, "ects", file_name)) sum_data = self._load_txt(os.path.join(data_dir, "gt_summaries", file_name)) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_dial = self.init_dial(dial_idx=dial_idx) new_dial[ORI_DIAL_INFO]["file_name"] = file_name new_dial[ORI_DIAL_INFO]["ect"] = ect_data new_dial[ORI_DIAL_INFO]["summary"] = sum_data new_data[new_dial_id] = new_dial dial_idx += 1 if (dial_idx) % 1000 == 0: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 if new_data: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) if mode == "train": self.save_original_examples([new_dial[ORI_DIAL_INFO]], data_name) self.save_converted_examples(data_name) self.copy_related_files(data_name, ['codes', 'data']) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def run_all(self): # self.todsum() # self.tweetsum() # self.samsum() # self.dialogsum() # self.ami() # self.icsi() # self.qmsum() self.mediasum() # self.crd3() # self.ectsum() pass def copy_example(self): source_dir = self.save_dir target_dir = "/home/qkun/projs/TOD-Project/Datasets/Dialogue-Summarization_PROCESSED/" file_list = ["converted_examples.json", "original_examples.json", "readme.txt", "LICENSE"] for dir_name in sorted(os.listdir(source_dir)): if os.path.isfile(os.path.join(source_dir, dir_name)): continue if not os.path.exists(os.path.join(target_dir, dir_name)): os.makedirs(os.path.join(target_dir, dir_name)) for filename in file_list: source_path = os.path.join(source_dir, dir_name, filename) target_path = os.path.join(target_dir, dir_name, filename) if not os.path.exists(source_path): continue shutil.copy(source_path, target_path) def main(): preprocess = PreProcessData() preprocess.run_all() preprocess.copy_example() if __name__ == '__main__': main()