""" 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 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/Open-Domain 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", encoding="utf-8"): if path is None or not os.path.exists(path): raise IOError('File does not exist: %s' % path) with open(path, 'r', encoding=encoding) 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: int(dial_idx), ORI_DIAL_INFO: {}, LOG: [], PROMPT: [], } return dial def init_turn(self, turn_id=0, dial_hist=[]): turn = { TURN_ID: int(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 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 places(self): """ no train/val/test split""" data_name = "PLACES3.5" mode = "train" data = self._load_jsonl(os.path.join(self.data_dir, data_name, "data.jsonl")) new_data, file_idx, dial_idx = {}, 1, 1 for dial in (data): new_dial = self.init_dial(dial_idx=dial_idx) new_dial_id = f"{data_name}--{mode}--{dial_idx}" for key in dial: if key == "conversation": continue new_dial[ORI_DIAL_INFO][key] = dial[key] dial_hist, multiparty = [], False for turn_idx, utt in enumerate(dial["conversation"]): if utt.startswith("Alice:"): new_turn = self.init_turn(turn_id=turn_idx//2+1) new_turn[DIAL_HIST] = " ".join(dial_hist) new_turn[USR_UTT] = utt.split("Alice:")[-1].strip() dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) elif utt.startswith("Bob:"): new_turn[SYS_UTT] = utt.split("Bob:")[-1].strip() dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) new_dial[LOG].append(new_turn) elif utt.startswith("Emilie:"): multiparty = True break else: if len(utt.split(":")[0].split()) == 1: # might have a third speaker raise ValueError("Unknown Speaker ... ") else: if not turn_idx: continue if new_turn[SYS_UTT]: new_turn[SYS_UTT] += " " + utt else: new_turn[USR_UTT] += " " + utt dial_hist[-1] += " " + utt if multiparty: continue new_data[new_dial_id] = new_dial if (dial_idx) % 10000 == 0: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 dial_idx += 1 if new_data: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) print(f"finishing processing {new_dial[DIAL_IDX]} dialogs for {mode} set ...") self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def chitchat(self): """ no train/val/test split""" data_name = "chitchat-dataset" mode = "train" data = self._load_json(os.path.join(self.data_dir, data_name, "chitchat_dataset/dataset.json")) new_data, file_idx, dial_idx = {}, 1, 1 for dial_id, dial in data.items(): new_dial = self.init_dial(dial_idx=dial_idx) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_dial[ORI_DIAL_ID] = dial_id for key in dial: if key == "messages": continue new_dial[ORI_DIAL_INFO][key] = dial[key] dial_hist, speakers = [], [] for turn in dial["messages"]: if turn[0]["sender"] not in speakers: speakers.append(turn[0]["sender"]) if len(speakers) < 2: continue # if len(speakers) != 2: # print("This is a multi-party dialog") # continue for turn_idx, turn in enumerate(dial["messages"]): if turn[0]["sender"] == speakers[0]: new_turn = self.init_turn(turn_id=turn_idx//2+1) new_turn[DIAL_HIST] = " ".join(dial_hist) new_turn[USR_UTT] = " ".join([row["text"] for row in turn]) new_turn[ORI_USR_ANN]["sender"] = turn[0]["sender"] new_turn[ORI_USR_ANN]["timestamp"] = [row["timestamp"] for row in turn] dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) elif turn[0]["sender"] == speakers[1]: new_turn[SYS_UTT] = " ".join([row["text"] for row in turn]) new_turn[ORI_SYS_ANN]["sender"] = turn[0]["sender"] new_turn[ORI_SYS_ANN]["timestamp"] = [row["timestamp"] for row in turn] dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) new_dial[LOG].append(new_turn) new_data[new_dial_id] = new_dial if (dial_idx) % 10000 == 0: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 dial_idx += 1 if new_data: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) print(f"finishing processing {new_dial[DIAL_IDX]} dialogs for {mode} set ...") self.save_original_examples({k:data[k] for k in list(data.keys())[:5]}, data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def prosocial(self): data_name = "Prosocial" from datasets import load_dataset for mode in ["train", "val", "test"]: new_data, file_idx = {}, 1 real_name = "validation" if mode == "val" else mode data = load_dataset("allenai/prosocial-dialog", split=real_name) data_df = data.to_pandas() for row_id in (range(len(data_df))): if data_df["response_id"][row_id] == 0: new_dial = self.init_dial(dial_idx=data_df["dialogue_id"][row_id]+1) dial_hist = [] new_turn = self.init_turn(turn_id=data_df["response_id"][row_id]+1) new_turn[DIAL_HIST] = " ".join(dial_hist) new_turn[USR_UTT] = data_df["context"][row_id] new_turn[SYS_UTT] = data_df["response"][row_id] dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) for key in data_df.keys(): if key in ["context", "response"]: continue # numpy.ndarray cannot be written into json if type(data_df[key][row_id]) == str: new_turn[ORI_USR_ANN][key] = data_df[key][row_id] else: new_turn[ORI_USR_ANN][key] = data_df[key][row_id].tolist() new_dial[LOG].append(new_turn) if data_df["episode_done"][row_id]: new_dial_id = f"{data_name}--{mode}--{new_dial[DIAL_IDX]}" new_data[new_dial_id] = new_dial if new_dial[DIAL_IDX] % 10000 == 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) print(f"finishing processing {new_dial[DIAL_IDX]} dialogs for {mode} set ...") self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def hhrlhf(self): """ only use the chosen pair""" from datasets import load_dataset data_name = "HH-RLHF" for mode in ["train", "test"]: data = load_dataset("Anthropic/hh-rlhf", split=mode) data_df = data.to_pandas() new_data, file_idx = {}, 1 for i in (range(len(data_df))): new_dial = self.init_dial(dial_idx=i+1) new_dial_id = f"{data_name}--{mode}--{i+1}" dial_hist = [] utts = data_df["chosen"][i].replace("Assistant:", "Human:").split("Human:") for turn_idx, utt in enumerate(utts[1:]): utt = utt.replace("\n\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 dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) else: new_turn[SYS_UTT] = utt dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) new_dial[LOG].append(new_turn) new_data[new_dial_id] = new_dial if new_dial[DIAL_IDX] % 10000 == 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) print(f"finishing processing {new_dial[DIAL_IDX]} dialogs for {mode} set ...") self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def empathetic(self): """ consecutive turns from the same speaker happens""" data_name = "Empathetic" from datasets import load_dataset for mode in ["train", "val", "test"]: real_name = "validation" if mode == "val" else mode data = load_dataset("empathetic_dialogues", split=real_name) data_df = data.to_pandas() new_data, file_idx, dial_idx, speakers = {}, 1, 1, [] for row_id in (range(len(data_df))): utt = data_df["utterance"][row_id].replace("_comma_", ",").strip() if data_df["utterance_idx"][row_id] == 1: new_dial = self.init_dial(dial_idx) new_dial[ORI_DIAL_ID] = data_df["conv_id"][row_id] new_dial[ORI_DIAL_INFO]["context"] = data_df["context"][row_id] new_dial[ORI_DIAL_INFO]["selfeval"] = data_df["selfeval"][row_id] dial_hist = [] # process the first turn new_turn = self.init_turn(turn_id=1) new_turn[USR_UTT] = data_df["prompt"][row_id].strip() new_turn[SYS_UTT] = utt new_turn[ORI_USR_ANN]["tags"] = "" new_turn[ORI_USR_ANN]["speaker_idx"] = int(data_df["speaker_idx"][row_id+1]) new_turn[ORI_SYS_ANN]["tags"] = data_df["tags"][row_id] new_turn[ORI_SYS_ANN]["speaker_idx"] = int(data_df["speaker_idx"][row_id]) dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) # speakers.append(data_df["speaker_idx"][row_id]) # in the first turn, the first speaker's utt is in the prompt and # utterance contains the utt from the second speaker second_speaker_id = data_df["speaker_idx"][row_id] new_dial[LOG].append(new_turn) new_turn = self.init_turn(turn_id=(int(data_df["utterance_idx"][row_id])+1)//2+1) new_turn[DIAL_HIST] = " ".join(dial_hist) elif data_df["speaker_idx"][row_id] == second_speaker_id: if not new_turn[USR_UTT]: # in this case, consecutive turns from system side happens, we add utt directly to new_dial[LOG][-1] new_dial[LOG][-1][SYS_UTT] += " " + utt dial_hist[-1] += " " + utt new_turn[DIAL_HIST] = " ".join(dial_hist) else: new_turn[SYS_UTT] = utt new_turn[ORI_SYS_ANN]["tags"] = data_df["tags"][row_id] new_turn[ORI_SYS_ANN]["speaker_idx"] = int(data_df["speaker_idx"][row_id]) dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) new_dial[LOG].append(new_turn) new_turn = self.init_turn(turn_id=(int(data_df["utterance_idx"][row_id])+1)//2+1) new_turn[DIAL_HIST] = " ".join(dial_hist) else: if not new_turn[USR_UTT]: new_turn[USR_UTT] = utt new_turn[ORI_USR_ANN]["tags"] = data_df["tags"][row_id] new_turn[ORI_USR_ANN]["speaker_idx"] = int(data_df["speaker_idx"][row_id]) dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) else: # in this case, consecutive turns from user side happens, we add utt directly to new_turn new_turn[USR_UTT] += " " + utt dial_hist[-1] += " " + utt if row_id == len(data_df)-1 or data_df["utterance_idx"][row_id+1] == 1: # append the rest dialog in case ends with user side if new_turn[USR_UTT]: new_dial[LOG].append(new_turn) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_data[new_dial_id] = new_dial if dial_idx % 10000 == 0: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 dial_idx += 1 if new_data: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) print(f"finishing processing {dial_idx-1} dialogs for {mode} set ...") self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def convai2(self): """ incomplete dialog included, we remove dialog with equal or less than one turn""" from datasets import load_dataset data_name = "ConvAI2" mode = "train" data = load_dataset("conv_ai_2", split=mode) data_df = data.to_pandas() new_data, file_idx, dial_idx = {}, 1, 1 for i in (range(len(data_df))): new_dial = self.init_dial(dial_idx=dial_idx) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_dial[ORI_DIAL_ID] = data_df["dialog_id"][i] new_dial[ORI_DIAL_INFO]["id"] = data_df["id"][i] new_dial[ORI_DIAL_INFO]["bot_profile"] = ["".join(persona) for persona in data_df["bot_profile"][i]] new_dial[ORI_DIAL_INFO]["user_profile"] = ["".join(persona) for persona in data_df["user_profile"][i]] new_dial[ORI_DIAL_INFO]["eval_score"] = int(data_df["eval_score"][i]) new_dial[ORI_DIAL_INFO]["profile_match"] = int(data_df["profile_match"][i]) if len(data_df["dialog"][i]) <= 2: continue if "Text is not given." in " ".join([turn["text"] for turn in data_df["dialog"][i]]): continue dial_hist = [] for turn_idx, turn in enumerate(data_df["dialog"][i]): 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] = turn["text"] new_turn[ORI_USR_ANN]["id"] = turn["id"] new_turn[ORI_USR_ANN]["sender"] = turn["sender"] new_turn[ORI_USR_ANN]["sender_class"] = turn["sender_class"] dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) else: new_turn[SYS_UTT] = turn["text"] new_turn[ORI_SYS_ANN]["id"] = turn["id"] new_turn[ORI_SYS_ANN]["sender"] = turn["sender"] new_turn[ORI_SYS_ANN]["sender_class"] = turn["sender_class"] dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) new_dial[LOG].append(new_turn) if not new_turn[SYS_UTT]: new_dial[LOG].append(new_turn) new_data[new_dial_id] = new_dial if new_dial[DIAL_IDX] % 10000 == 0: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 dial_idx += 1 print(f"finishing processing {dial_idx-1} dialogs for {mode} set ...") if new_data: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) self.save_original_examples(data[:5], data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def antiscam(self): """ 0: attacker 1: agent 0 always starts conversation 1 always ends conversation """ data_name = "AntiScam" data = self._load_txt(os.path.join(self.data_dir, data_name, "data/AntiScam_all.txt"), encoding='latin-1') new_data, file_idx, dial_idx, turn_idx, dial_hist = {}, 1, 1, 1, [] mode = "train" new_dial = self.init_dial(dial_idx=dial_idx) new_turn = self.init_turn(turn_id=turn_idx) for row in (data): speaker, utt = row.split("\t") if speaker == "0": if new_turn[SYS_UTT]: # start a new turn # wrap up the previous turn new_dial[LOG].append(new_turn) turn_idx += 1 dial_hist.append(f"<{SPEAKER1.upper()}> " + new_turn[USR_UTT]) dial_hist.append(f"<{SPEAKER2.upper()}> " + new_turn[SYS_UTT]) # start a new turn new_turn = self.init_turn(turn_id=turn_idx) new_turn[DIAL_HIST] = " ".join(dial_hist) new_turn[USR_UTT] = utt.strip('\"') else: # multiple utt from '0' new_turn[USR_UTT] += " " + utt.strip('\"') new_turn[USR_UTT] = new_turn[USR_UTT].strip() elif speaker == "1": new_turn[SYS_UTT] += " " + utt.strip('"') new_turn[SYS_UTT] = new_turn[SYS_UTT].strip() elif not speaker: # finish a dialog if new_turn[SYS_UTT]: # wrap up the previous turn new_dial[LOG].append(new_turn) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_data[new_dial_id] = new_dial if dial_idx % 10000 == 0: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) new_data = {} # reset file_idx += 1 dial_idx += 1 turn_idx = 1 dial_hist = [] new_dial = self.init_dial(dial_idx=dial_idx) new_turn = self.init_turn(turn_id=turn_idx) else: raise ValueError("Unknown speaker ... ") if new_turn[SYS_UTT]: new_dial[LOG].append(new_turn) new_dial_id = f"{data_name}--{mode}--{dial_idx}" new_data[new_dial_id] = new_dial print(f"finishing processing {dial_idx} dialogs for {mode} set ...") self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) if new_data: self.save_dial(new_data, data_name=data_name, file_idx=file_idx, mode=mode) self.save_original_examples(data[:150], data_name) self.save_converted_examples(data_name) print("*"*10, f"finishing processing dataset {data_name}", "*"*10) def run_all(self): # self.places() # self.chitchat() # self.prosocial() # self.hhrlhf() # self.empathetic() # self.convai2() self.antiscam() def copy_example(self): source_dir = self.save_dir for target_dir in [ "/home/qkun/projs/TOD-Project/Datasets/Open-Domain_PROCESSED/", "/home/qkun/projs/DialogStudio-Release/open-domain-dialogues/"]: # target_dir = "/home/qkun/projs/TOD-Project/Datasets/Open-Domain_PROCESSED/" # target_dir2 = "/home/qkun/projs/DialogStudio-Release/open-domain-dialogues/" 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()