From d7ce1a83f69a7c3c913246bbef066e5e8e5cbe52 Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Mon, 29 Jan 2024 01:14:14 +0000 Subject: [PATCH 01/49] Setting up GitHub Classroom Feedback From 2854502d38aa0ba0c1e82696f7618b1d95ac2d1e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=9D=B4=EC=A7=84=EB=AF=BC=5FT6139?= <90895797+jinmin111@users.noreply.github.com> Date: Fri, 2 Feb 2024 18:25:38 +0900 Subject: [PATCH 02/49] =?UTF-8?q?docs:=20pull=20request=20template=20?= =?UTF-8?q?=EC=83=9D=EC=84=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/PULL_REQUEST_TEMPLATE.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 .github/PULL_REQUEST_TEMPLATE.md diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..be51e04 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,12 @@ +## Overview +- + +## Change Log +- + +## To Reviewer +- + +## Issue Tags +- Closed | Fixed: # +- See also: # From 639c2823ef93dbc5f21d99c83cbd3e44d5477cbc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=9D=B4=EC=A7=84=EB=AF=BC=5FT6139?= <90895797+jinmin111@users.noreply.github.com> Date: Fri, 2 Feb 2024 18:26:51 +0900 Subject: [PATCH 03/49] =?UTF-8?q?docs:=20feature=20request=20template=20?= =?UTF-8?q?=EC=83=9D=EC=84=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/ISSUE_TEMPLATE/feature_template.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/feature_template.md diff --git a/.github/ISSUE_TEMPLATE/feature_template.md b/.github/ISSUE_TEMPLATE/feature_template.md new file mode 100644 index 0000000..acb84b6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_template.md @@ -0,0 +1,16 @@ +--- +name: Feature Request +about: 새로운 기능을 추가할 때 사용하는 템플릿 +title: "[FEAT] " +labels: feature +assignees: '' + +--- + +## Description + + +## Todo +- [ ] + +## ETC From 60b452a45a9b1d207348921f9abc0e80d7f8d3d6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=9D=B4=EC=A7=84=EB=AF=BC=5FT6139?= <90895797+jinmin111@users.noreply.github.com> Date: Fri, 2 Feb 2024 18:27:40 +0900 Subject: [PATCH 04/49] =?UTF-8?q?docs:=20bug=20report=20template=20?= =?UTF-8?q?=EC=83=9D=EC=84=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/ISSUE_TEMPLATE/bug_template.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_template.md diff --git a/.github/ISSUE_TEMPLATE/bug_template.md b/.github/ISSUE_TEMPLATE/bug_template.md new file mode 100644 index 0000000..393a91d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_template.md @@ -0,0 +1,17 @@ +--- +name: Bug Report +about: 버그 리포트할 때 사용하는 템플릿 +title: "[BUG] " +labels: bug + +--- + +## Description + +## How to reproduce + +1. +2. +3. + +## Solution From 1315a31b335b9a8972a10f957ae8c41ea5d7eb88 Mon Sep 17 00:00:00 2001 From: ParkSiWoo Date: Mon, 5 Feb 2024 11:03:38 +0000 Subject: [PATCH 05/49] feat: add wandb in s3rec --- code/s3rec/README.md | 31 +++ code/s3rec/datasets.py | 219 ++++++++++++++++++ code/s3rec/inference.py | 119 ++++++++++ code/s3rec/models.py | 239 ++++++++++++++++++++ code/s3rec/modules.py | 191 ++++++++++++++++ code/s3rec/preprocessing.py | 14 ++ code/s3rec/pretrain.py | 87 +++++++ code/s3rec/pretrain_args.py | 72 ++++++ code/s3rec/requirements.txt | 9 + code/s3rec/sample_submission.ipynb | 199 ++++++++++++++++ code/s3rec/train.py | 129 +++++++++++ code/s3rec/train_args.py | 61 +++++ code/s3rec/trainers.py | 320 ++++++++++++++++++++++++++ code/s3rec/utils.py | 349 +++++++++++++++++++++++++++++ 14 files changed, 2039 insertions(+) create mode 100644 code/s3rec/README.md create mode 100644 code/s3rec/datasets.py create mode 100644 code/s3rec/inference.py create mode 100644 code/s3rec/models.py create mode 100644 code/s3rec/modules.py create mode 100644 code/s3rec/preprocessing.py create mode 100644 code/s3rec/pretrain.py create mode 100644 code/s3rec/pretrain_args.py create mode 100644 code/s3rec/requirements.txt create mode 100644 code/s3rec/sample_submission.ipynb create mode 100644 code/s3rec/train.py create mode 100644 code/s3rec/train_args.py create mode 100644 code/s3rec/trainers.py create mode 100644 code/s3rec/utils.py diff --git a/code/s3rec/README.md b/code/s3rec/README.md new file mode 100644 index 0000000..c4cdb80 --- /dev/null +++ b/code/s3rec/README.md @@ -0,0 +1,31 @@ +# Movie Recommendation Baseline Code + +영화 추천 대회를 위한 베이스라인 코드입니다. 다음 코드를 대회에 맞게 재구성 했습니다. + +- 코드 출처: https://github.com/aHuiWang/CIKM2020-S3Rec + +## Installation + +``` +pip install -r requirements.txt +``` + +## How to run + +1. Pretraining + ``` + python run_pretrain.py + ``` +2. Fine Tuning (Main Training) + 1. with pretrained weight + ``` + python run_train.py --using_pretrain + ``` + 2. without pretrained weight + ``` + python run_train.py + ``` +3. Inference + ``` + python inference.py + ``` diff --git a/code/s3rec/datasets.py b/code/s3rec/datasets.py new file mode 100644 index 0000000..1051da8 --- /dev/null +++ b/code/s3rec/datasets.py @@ -0,0 +1,219 @@ +import random + +import torch +from torch.utils.data import Dataset + +from utils import neg_sample + + +class PretrainDataset(Dataset): + def __init__(self, args, user_seq, long_sequence): + self.args = args + self.user_seq = user_seq + self.long_sequence = long_sequence + self.max_len = args.max_seq_length + self.part_sequence = [] + self.split_sequence() + + def split_sequence(self): + for seq in self.user_seq: + input_ids = seq[-(self.max_len + 2) : -2] # keeping same as train set + for i in range(len(input_ids)): + self.part_sequence.append(input_ids[: i + 1]) + + def __len__(self): + return len(self.part_sequence) + + def __getitem__(self, index): + + sequence = self.part_sequence[index] # pos_items + # sample neg item for every masked item + masked_item_sequence = [] + neg_items = [] + # Masked Item Prediction + item_set = set(sequence) + for item in sequence[:-1]: + prob = random.random() + if prob < self.args.mask_p: + masked_item_sequence.append(self.args.mask_id) + neg_items.append(neg_sample(item_set, self.args.item_size)) + else: + masked_item_sequence.append(item) + neg_items.append(item) + + # add mask at the last position + masked_item_sequence.append(self.args.mask_id) + neg_items.append(neg_sample(item_set, self.args.item_size)) + + # Segment Prediction + if len(sequence) < 2: + masked_segment_sequence = sequence + pos_segment = sequence + neg_segment = sequence + else: + sample_length = random.randint(1, len(sequence) // 2) + start_id = random.randint(0, len(sequence) - sample_length) + neg_start_id = random.randint(0, len(self.long_sequence) - sample_length) + pos_segment = sequence[start_id : start_id + sample_length] + neg_segment = self.long_sequence[ + neg_start_id : neg_start_id + sample_length + ] + masked_segment_sequence = ( + sequence[:start_id] + + [self.args.mask_id] * sample_length + + sequence[start_id + sample_length :] + ) + pos_segment = ( + [self.args.mask_id] * start_id + + pos_segment + + [self.args.mask_id] * (len(sequence) - (start_id + sample_length)) + ) + neg_segment = ( + [self.args.mask_id] * start_id + + neg_segment + + [self.args.mask_id] * (len(sequence) - (start_id + sample_length)) + ) + + assert len(masked_segment_sequence) == len(sequence) + assert len(pos_segment) == len(sequence) + assert len(neg_segment) == len(sequence) + + # padding sequence + pad_len = self.max_len - len(sequence) + masked_item_sequence = [0] * pad_len + masked_item_sequence + pos_items = [0] * pad_len + sequence + neg_items = [0] * pad_len + neg_items + masked_segment_sequence = [0] * pad_len + masked_segment_sequence + pos_segment = [0] * pad_len + pos_segment + neg_segment = [0] * pad_len + neg_segment + + masked_item_sequence = masked_item_sequence[-self.max_len :] + pos_items = pos_items[-self.max_len :] + neg_items = neg_items[-self.max_len :] + + masked_segment_sequence = masked_segment_sequence[-self.max_len :] + pos_segment = pos_segment[-self.max_len :] + neg_segment = neg_segment[-self.max_len :] + + # Associated Attribute Prediction + # Masked Attribute Prediction + attributes = [] + for item in pos_items: + attribute = [0] * self.args.attribute_size + try: + now_attribute = self.args.item2attribute[str(item)] + for a in now_attribute: + attribute[a] = 1 + except: + pass + attributes.append(attribute) + + assert len(attributes) == self.max_len + assert len(masked_item_sequence) == self.max_len + assert len(pos_items) == self.max_len + assert len(neg_items) == self.max_len + assert len(masked_segment_sequence) == self.max_len + assert len(pos_segment) == self.max_len + assert len(neg_segment) == self.max_len + + cur_tensors = ( + torch.tensor(attributes, dtype=torch.long), + torch.tensor(masked_item_sequence, dtype=torch.long), + torch.tensor(pos_items, dtype=torch.long), + torch.tensor(neg_items, dtype=torch.long), + torch.tensor(masked_segment_sequence, dtype=torch.long), + torch.tensor(pos_segment, dtype=torch.long), + torch.tensor(neg_segment, dtype=torch.long), + ) + return cur_tensors + + +class SASRecDataset(Dataset): + def __init__(self, args, user_seq, test_neg_items=None, data_type="train"): + self.args = args + self.user_seq = user_seq + self.test_neg_items = test_neg_items + self.data_type = data_type + self.max_len = args.max_seq_length + + def __getitem__(self, index): + + user_id = index + items = self.user_seq[index] + + assert self.data_type in {"train", "valid", "test", "submission"} + + # [0, 1, 2, 3, 4, 5, 6] + # train [0, 1, 2, 3] + # target [1, 2, 3, 4] + + # valid [0, 1, 2, 3, 4] + # answer [5] + + # test [0, 1, 2, 3, 4, 5] + # answer [6] + + # submission [0, 1, 2, 3, 4, 5, 6] + # answer None + + if self.data_type == "train": + input_ids = items[:-3] + target_pos = items[1:-2] + answer = [0] # no use + + elif self.data_type == "valid": + input_ids = items[:-2] + target_pos = items[1:-1] + answer = [items[-2]] + + elif self.data_type == "test": + input_ids = items[:-1] + target_pos = items[1:] + answer = [items[-1]] + else: + input_ids = items[:] + target_pos = items[:] # will not be used + answer = [] + + target_neg = [] + seq_set = set(items) + for _ in input_ids: + target_neg.append(neg_sample(seq_set, self.args.item_size)) + + pad_len = self.max_len - len(input_ids) + input_ids = [0] * pad_len + input_ids + target_pos = [0] * pad_len + target_pos + target_neg = [0] * pad_len + target_neg + + input_ids = input_ids[-self.max_len :] + target_pos = target_pos[-self.max_len :] + target_neg = target_neg[-self.max_len :] + + assert len(input_ids) == self.max_len + assert len(target_pos) == self.max_len + assert len(target_neg) == self.max_len + + if self.test_neg_items is not None: + test_samples = self.test_neg_items[index] + + cur_tensors = ( + torch.tensor(user_id, dtype=torch.long), # user_id for testing + torch.tensor(input_ids, dtype=torch.long), + torch.tensor(target_pos, dtype=torch.long), + torch.tensor(target_neg, dtype=torch.long), + torch.tensor(answer, dtype=torch.long), + torch.tensor(test_samples, dtype=torch.long), + ) + else: + cur_tensors = ( + torch.tensor(user_id, dtype=torch.long), # user_id for testing + torch.tensor(input_ids, dtype=torch.long), + torch.tensor(target_pos, dtype=torch.long), + torch.tensor(target_neg, dtype=torch.long), + torch.tensor(answer, dtype=torch.long), + ) + + return cur_tensors + + def __len__(self): + return len(self.user_seq) diff --git a/code/s3rec/inference.py b/code/s3rec/inference.py new file mode 100644 index 0000000..f1933e7 --- /dev/null +++ b/code/s3rec/inference.py @@ -0,0 +1,119 @@ +import argparse +import os + +import torch +from torch.utils.data import DataLoader, SequentialSampler + +from datasets import SASRecDataset +from models import S3RecModel +from trainers import FinetuneTrainer +from utils import ( + check_path, + generate_submission_file, + get_item2attribute_json, + get_user_seqs, + set_seed, +) + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument("--data_dir", default="../data/train/", type=str) + parser.add_argument("--output_dir", default="output/", type=str) + parser.add_argument("--data_name", default="Ml", type=str) + parser.add_argument("--do_eval", action="store_true") + + # model args + parser.add_argument("--model_name", default="Finetune_full", type=str) + parser.add_argument( + "--hidden_size", type=int, default=64, help="hidden size of transformer model" + ) + parser.add_argument( + "--num_hidden_layers", type=int, default=2, help="number of layers" + ) + parser.add_argument("--num_attention_heads", default=2, type=int) + parser.add_argument("--hidden_act", default="gelu", type=str) # gelu relu + parser.add_argument( + "--attention_probs_dropout_prob", + type=float, + default=0.5, + help="attention dropout p", + ) + parser.add_argument( + "--hidden_dropout_prob", type=float, default=0.5, help="hidden dropout p" + ) + parser.add_argument("--initializer_range", type=float, default=0.02) + parser.add_argument("--max_seq_length", default=50, type=int) + + # train args + parser.add_argument("--lr", type=float, default=0.001, help="learning rate of adam") + parser.add_argument( + "--batch_size", type=int, default=256, help="number of batch_size" + ) + parser.add_argument("--epochs", type=int, default=200, help="number of epochs") + parser.add_argument("--no_cuda", action="store_true") + parser.add_argument("--log_freq", type=int, default=1, help="per epoch print res") + parser.add_argument("--seed", default=42, type=int) + + parser.add_argument( + "--weight_decay", type=float, default=0.0, help="weight_decay of adam" + ) + parser.add_argument( + "--adam_beta1", type=float, default=0.9, help="adam first beta value" + ) + parser.add_argument( + "--adam_beta2", type=float, default=0.999, help="adam second beta value" + ) + parser.add_argument("--gpu_id", type=str, default="0", help="gpu_id") + + args = parser.parse_args() + + set_seed(args.seed) + check_path(args.output_dir) + + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id + args.cuda_condition = torch.cuda.is_available() and not args.no_cuda + + args.data_file = args.data_dir + "train_ratings.csv" + item2attribute_file = args.data_dir + args.data_name + "_item2attributes.json" + + user_seq, max_item, _, _, submission_rating_matrix = get_user_seqs(args.data_file) + + item2attribute, attribute_size = get_item2attribute_json(item2attribute_file) + + args.item_size = max_item + 2 + args.mask_id = max_item + 1 + args.attribute_size = attribute_size + 1 + + # save model args + args_str = f"{args.model_name}-{args.data_name}" + + print(str(args)) + + args.item2attribute = item2attribute + + args.train_matrix = submission_rating_matrix + + checkpoint = args_str + ".pt" + args.checkpoint_path = os.path.join(args.output_dir, checkpoint) + + submission_dataset = SASRecDataset(args, user_seq, data_type="submission") + submission_sampler = SequentialSampler(submission_dataset) + submission_dataloader = DataLoader( + submission_dataset, sampler=submission_sampler, batch_size=args.batch_size + ) + + model = S3RecModel(args=args) + + trainer = FinetuneTrainer(model, None, None, None, submission_dataloader, args) + + trainer.load(args.checkpoint_path) + print(f"Load model from {args.checkpoint_path} for submission!") + preds = trainer.submission(0) + + generate_submission_file(args.data_file, preds) + + +if __name__ == "__main__": + main() diff --git a/code/s3rec/models.py b/code/s3rec/models.py new file mode 100644 index 0000000..e50715a --- /dev/null +++ b/code/s3rec/models.py @@ -0,0 +1,239 @@ +import torch +import torch.nn as nn + +from modules import Encoder, LayerNorm + + +class S3RecModel(nn.Module): + def __init__(self, args): + super(S3RecModel, self).__init__() + self.item_embeddings = nn.Embedding( + args.item_size, args.hidden_size, padding_idx=0 + ) + self.attribute_embeddings = nn.Embedding( + args.attribute_size, args.hidden_size, padding_idx=0 + ) + self.position_embeddings = nn.Embedding(args.max_seq_length, args.hidden_size) + self.item_encoder = Encoder(args) + self.LayerNorm = LayerNorm(args.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(args.hidden_dropout_prob) + self.args = args + + # add unique dense layer for 4 losses respectively + self.aap_norm = nn.Linear(args.hidden_size, args.hidden_size) + self.mip_norm = nn.Linear(args.hidden_size, args.hidden_size) + self.map_norm = nn.Linear(args.hidden_size, args.hidden_size) + self.sp_norm = nn.Linear(args.hidden_size, args.hidden_size) + self.criterion = nn.BCELoss(reduction="none") + self.apply(self.init_weights) + + # AAP + def associated_attribute_prediction(self, sequence_output, attribute_embedding): + """ + :param sequence_output: [B L H] + :param attribute_embedding: [arribute_num H] + :return: scores [B*L tag_num] + """ + sequence_output = self.aap_norm(sequence_output) # [B L H] + sequence_output = sequence_output.view( + [-1, self.args.hidden_size, 1] + ) # [B*L H 1] + # [tag_num H] [B*L H 1] -> [B*L tag_num 1] + score = torch.matmul(attribute_embedding, sequence_output) + return torch.sigmoid(score.squeeze(-1)) # [B*L tag_num] + + # MIP sample neg items + def masked_item_prediction(self, sequence_output, target_item): + """ + :param sequence_output: [B L H] + :param target_item: [B L H] + :return: scores [B*L] + """ + sequence_output = self.mip_norm( + sequence_output.view([-1, self.args.hidden_size]) + ) # [B*L H] + target_item = target_item.view([-1, self.args.hidden_size]) # [B*L H] + score = torch.mul(sequence_output, target_item) # [B*L H] + return torch.sigmoid(torch.sum(score, -1)) # [B*L] + + # MAP + def masked_attribute_prediction(self, sequence_output, attribute_embedding): + sequence_output = self.map_norm(sequence_output) # [B L H] + sequence_output = sequence_output.view( + [-1, self.args.hidden_size, 1] + ) # [B*L H 1] + # [tag_num H] [B*L H 1] -> [B*L tag_num 1] + score = torch.matmul(attribute_embedding, sequence_output) + return torch.sigmoid(score.squeeze(-1)) # [B*L tag_num] + + # SP sample neg segment + def segment_prediction(self, context, segment): + """ + :param context: [B H] + :param segment: [B H] + :return: + """ + context = self.sp_norm(context) + score = torch.mul(context, segment) # [B H] + return torch.sigmoid(torch.sum(score, dim=-1)) # [B] + + # + def add_position_embedding(self, sequence): + + seq_length = sequence.size(1) + position_ids = torch.arange( + seq_length, dtype=torch.long, device=sequence.device + ) + position_ids = position_ids.unsqueeze(0).expand_as(sequence) + item_embeddings = self.item_embeddings(sequence) + position_embeddings = self.position_embeddings(position_ids) + sequence_emb = item_embeddings + position_embeddings + sequence_emb = self.LayerNorm(sequence_emb) + sequence_emb = self.dropout(sequence_emb) + + return sequence_emb + + def pretrain( + self, + attributes, + masked_item_sequence, + pos_items, + neg_items, + masked_segment_sequence, + pos_segment, + neg_segment, + ): + + # Encode masked sequence + + sequence_emb = self.add_position_embedding(masked_item_sequence) + sequence_mask = (masked_item_sequence == 0).float() * -1e8 + sequence_mask = torch.unsqueeze(torch.unsqueeze(sequence_mask, 1), 1) + + encoded_layers = self.item_encoder( + sequence_emb, sequence_mask, output_all_encoded_layers=True + ) + # [B L H] + sequence_output = encoded_layers[-1] + + attribute_embeddings = self.attribute_embeddings.weight + # AAP + aap_score = self.associated_attribute_prediction( + sequence_output, attribute_embeddings + ) + aap_loss = self.criterion( + aap_score, attributes.view(-1, self.args.attribute_size).float() + ) + # only compute loss at non-masked position + aap_mask = (masked_item_sequence != self.args.mask_id).float() * ( + masked_item_sequence != 0 + ).float() + aap_loss = torch.sum(aap_loss * aap_mask.flatten().unsqueeze(-1)) + + # MIP + pos_item_embs = self.item_embeddings(pos_items) + neg_item_embs = self.item_embeddings(neg_items) + pos_score = self.masked_item_prediction(sequence_output, pos_item_embs) + neg_score = self.masked_item_prediction(sequence_output, neg_item_embs) + mip_distance = torch.sigmoid(pos_score - neg_score) + mip_loss = self.criterion( + mip_distance, torch.ones_like(mip_distance, dtype=torch.float32) + ) + mip_mask = (masked_item_sequence == self.args.mask_id).float() + mip_loss = torch.sum(mip_loss * mip_mask.flatten()) + + # MAP + map_score = self.masked_attribute_prediction( + sequence_output, attribute_embeddings + ) + map_loss = self.criterion( + map_score, attributes.view(-1, self.args.attribute_size).float() + ) + map_mask = (masked_item_sequence == self.args.mask_id).float() + map_loss = torch.sum(map_loss * map_mask.flatten().unsqueeze(-1)) + + # SP + # segment context + segment_context = self.add_position_embedding(masked_segment_sequence) + segment_mask = (masked_segment_sequence == 0).float() * -1e8 + segment_mask = torch.unsqueeze(torch.unsqueeze(segment_mask, 1), 1) + segment_encoded_layers = self.item_encoder( + segment_context, segment_mask, output_all_encoded_layers=True + ) + + # take the last position hidden as the context + segment_context = segment_encoded_layers[-1][:, -1, :] # [B H] + # pos_segment + pos_segment_emb = self.add_position_embedding(pos_segment) + pos_segment_mask = (pos_segment == 0).float() * -1e8 + pos_segment_mask = torch.unsqueeze(torch.unsqueeze(pos_segment_mask, 1), 1) + pos_segment_encoded_layers = self.item_encoder( + pos_segment_emb, pos_segment_mask, output_all_encoded_layers=True + ) + pos_segment_emb = pos_segment_encoded_layers[-1][:, -1, :] + + # neg_segment + neg_segment_emb = self.add_position_embedding(neg_segment) + neg_segment_mask = (neg_segment == 0).float() * -1e8 + neg_segment_mask = torch.unsqueeze(torch.unsqueeze(neg_segment_mask, 1), 1) + neg_segment_encoded_layers = self.item_encoder( + neg_segment_emb, neg_segment_mask, output_all_encoded_layers=True + ) + neg_segment_emb = neg_segment_encoded_layers[-1][:, -1, :] # [B H] + + pos_segment_score = self.segment_prediction(segment_context, pos_segment_emb) + neg_segment_score = self.segment_prediction(segment_context, neg_segment_emb) + + sp_distance = torch.sigmoid(pos_segment_score - neg_segment_score) + + sp_loss = torch.sum( + self.criterion( + sp_distance, torch.ones_like(sp_distance, dtype=torch.float32) + ) + ) + + return aap_loss, mip_loss, map_loss, sp_loss + + # Fine tune + # same as SASRec + def finetune(self, input_ids): + + attention_mask = (input_ids > 0).long() + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze( + 2 + ) # torch.int64 + max_len = attention_mask.size(-1) + attn_shape = (1, max_len, max_len) + subsequent_mask = torch.triu(torch.ones(attn_shape), diagonal=1) # torch.uint8 + subsequent_mask = (subsequent_mask == 0).unsqueeze(1) + subsequent_mask = subsequent_mask.long() + + if self.args.cuda_condition: + subsequent_mask = subsequent_mask.cuda() + + extended_attention_mask = extended_attention_mask * subsequent_mask + extended_attention_mask = extended_attention_mask.to( + dtype=next(self.parameters()).dtype + ) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + + sequence_emb = self.add_position_embedding(input_ids) + + item_encoded_layers = self.item_encoder( + sequence_emb, extended_attention_mask, output_all_encoded_layers=True + ) + + sequence_output = item_encoded_layers[-1] + return sequence_output + + def init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.args.initializer_range) + elif isinstance(module, LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() diff --git a/code/s3rec/modules.py b/code/s3rec/modules.py new file mode 100644 index 0000000..97aa5a1 --- /dev/null +++ b/code/s3rec/modules.py @@ -0,0 +1,191 @@ +import copy +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def gelu(x): + """Implementation of the gelu activation function. + For information: OpenAI GPT's gelu is slightly different + (and gives slightly different results): + 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * + (x + 0.044715 * torch.pow(x, 3)))) + Also see https://arxiv.org/abs/1606.08415 + """ + return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) + + +def swish(x): + return x * torch.sigmoid(x) + + +ACT2FN = {"gelu": gelu, "relu": F.relu, "swish": swish} + + +class LayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-12): + """Construct a layernorm module in the TF style (epsilon inside the square root).""" + super(LayerNorm, self).__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.bias = nn.Parameter(torch.zeros(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + u = x.mean(-1, keepdim=True) + s = (x - u).pow(2).mean(-1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.variance_epsilon) + return self.weight * x + self.bias + + +class Embeddings(nn.Module): + """Construct the embeddings from item, position.""" + + def __init__(self, args): + super(Embeddings, self).__init__() + + self.item_embeddings = nn.Embedding( + args.item_size, args.hidden_size, padding_idx=0 + ) + self.position_embeddings = nn.Embedding(args.max_seq_length, args.hidden_size) + + self.LayerNorm = LayerNorm(args.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(args.hidden_dropout_prob) + + self.args = args + + def forward(self, input_ids): + seq_length = input_ids.size(1) + position_ids = torch.arange( + seq_length, dtype=torch.long, device=input_ids.device + ) + position_ids = position_ids.unsqueeze(0).expand_as(input_ids) + items_embeddings = self.item_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) + embeddings = items_embeddings + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class SelfAttention(nn.Module): + def __init__(self, args): + super(SelfAttention, self).__init__() + if args.hidden_size % args.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (args.hidden_size, args.num_attention_heads) + ) + self.num_attention_heads = args.num_attention_heads + self.attention_head_size = int(args.hidden_size / args.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(args.hidden_size, self.all_head_size) + self.key = nn.Linear(args.hidden_size, self.all_head_size) + self.value = nn.Linear(args.hidden_size, self.all_head_size) + + self.attn_dropout = nn.Dropout(args.attention_probs_dropout_prob) + + self.dense = nn.Linear(args.hidden_size, args.hidden_size) + self.LayerNorm = LayerNorm(args.hidden_size, eps=1e-12) + self.out_dropout = nn.Dropout(args.hidden_dropout_prob) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, input_tensor, attention_mask): + mixed_query_layer = self.query(input_tensor) + mixed_key_layer = self.key(input_tensor) + mixed_value_layer = self.value(input_tensor) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + # [batch_size heads seq_len seq_len] scores + # [batch_size 1 1 seq_len] + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + # Fixme + attention_probs = self.attn_dropout(attention_probs) + context_layer = torch.matmul(attention_probs, value_layer) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + hidden_states = self.dense(context_layer) + hidden_states = self.out_dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + + return hidden_states + + +class Intermediate(nn.Module): + def __init__(self, args): + super(Intermediate, self).__init__() + self.dense_1 = nn.Linear(args.hidden_size, args.hidden_size * 4) + if isinstance(args.hidden_act, str): + self.intermediate_act_fn = ACT2FN[args.hidden_act] + else: + self.intermediate_act_fn = args.hidden_act + + self.dense_2 = nn.Linear(args.hidden_size * 4, args.hidden_size) + self.LayerNorm = LayerNorm(args.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(args.hidden_dropout_prob) + + def forward(self, input_tensor): + + hidden_states = self.dense_1(input_tensor) + hidden_states = self.intermediate_act_fn(hidden_states) + + hidden_states = self.dense_2(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + + return hidden_states + + +class Layer(nn.Module): + def __init__(self, args): + super(Layer, self).__init__() + self.attention = SelfAttention(args) + self.intermediate = Intermediate(args) + + def forward(self, hidden_states, attention_mask): + attention_output = self.attention(hidden_states, attention_mask) + intermediate_output = self.intermediate(attention_output) + return intermediate_output + + +class Encoder(nn.Module): + def __init__(self, args): + super(Encoder, self).__init__() + layer = Layer(args) + self.layer = nn.ModuleList( + [copy.deepcopy(layer) for _ in range(args.num_hidden_layers)] + ) + + def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): + all_encoder_layers = [] + for layer_module in self.layer: + hidden_states = layer_module(hidden_states, attention_mask) + if output_all_encoded_layers: + all_encoder_layers.append(hidden_states) + if not output_all_encoded_layers: + all_encoder_layers.append(hidden_states) + return all_encoder_layers diff --git a/code/s3rec/preprocessing.py b/code/s3rec/preprocessing.py new file mode 100644 index 0000000..da3a7eb --- /dev/null +++ b/code/s3rec/preprocessing.py @@ -0,0 +1,14 @@ +import pandas as pd + + +def main(): + genres_df = pd.read_csv("../data/train/genres.tsv", sep="\t") + array, index = pd.factorize(genres_df["genre"]) + genres_df["genre"] = array + genres_df.groupby("item")["genre"].apply(list).to_json( + "data/Ml_item2attributes.json" + ) + + +if __name__ == "__main__": + main() diff --git a/code/s3rec/pretrain.py b/code/s3rec/pretrain.py new file mode 100644 index 0000000..3470929 --- /dev/null +++ b/code/s3rec/pretrain.py @@ -0,0 +1,87 @@ +from pretrain_args import parse_args +import os + +import numpy as np +import torch +from torch.utils.data import DataLoader, RandomSampler + +from datasets import PretrainDataset +from models import S3RecModel +from trainers import PretrainTrainer +from utils import ( + EarlyStopping, + check_path, + get_item2attribute_json, + get_user_seqs_long, + set_seed, +) + +import wandb + +def main(args): + + set_seed(args.seed) + check_path(args.output_dir) + + args.checkpoint_path = os.path.join(args.output_dir, "Pretrain.pt") + + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id + args.cuda_condition = torch.cuda.is_available() and not args.no_cuda + + # args.data_file = args.data_dir + args.data_name + '.txt' + args.data_file = args.data_dir + "train_ratings.csv" + item2attribute_file = args.data_dir + args.data_name + "_item2attributes.json" + # concat all user_seq get a long sequence, from which sample neg segment for SP + user_seq, max_item, long_sequence = get_user_seqs_long(args.data_file) + + item2attribute, attribute_size = get_item2attribute_json(item2attribute_file) + + args.item_size = max_item + 2 + args.mask_id = max_item + 1 + args.attribute_size = attribute_size + 1 + + args.item2attribute = item2attribute + + model = S3RecModel(args=args) + trainer = PretrainTrainer(model, None, None, None, None, args) + + early_stopping = EarlyStopping(args.checkpoint_path, patience=10, verbose=True) + + wandb.login() + wandb.init(project = args.project_name, + name = args.run_name, + entity = args.entity_name, + config = { + "epochs": args.pre_epochs, + "batch_size": args.pre_batch_size, + "hidden_size": args.hidden_size, + "num_hidden_layers": args.num_hidden_layers, + "attention_probs_dropout_prob": args.attention_probs_dropout_prob, + "hidden_dropout_prob": args.hidden_dropout_prob, + "max_seq_length": args.max_seq_length, + "mask_probability": args.mask_p, + "aap_weight": args.aap_weight, + "mip_weight": args.mip_weight, + "map_weight": args.map_weight, + "sp_weight": args.sp_weight, + }) + for epoch in range(args.pre_epochs): + + pretrain_dataset = PretrainDataset(args, user_seq, long_sequence) + pretrain_sampler = RandomSampler(pretrain_dataset) + pretrain_dataloader = DataLoader( + pretrain_dataset, sampler=pretrain_sampler, batch_size=args.pre_batch_size + ) + + losses = trainer.pretrain(epoch, pretrain_dataloader) + + ## comparing `sp_loss_avg`` + early_stopping(np.array([-losses["sp_loss_avg"]]), trainer.model) + if early_stopping.early_stop: + print("Early stopping") + break + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/code/s3rec/pretrain_args.py b/code/s3rec/pretrain_args.py new file mode 100644 index 0000000..82e9aef --- /dev/null +++ b/code/s3rec/pretrain_args.py @@ -0,0 +1,72 @@ +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("--data_dir", default="../../data/train/", type=str) + parser.add_argument("--output_dir", default="output/", type=str) + parser.add_argument("--data_name", default="Ml", type=str) + + # wandb init parameters + parser.add_argument("--project_name", default="project_name", type=str) + parser.add_argument("--run_name", default="test", type=str) + parser.add_argument("--entity_name", default="suggestify_lv2", type=str) + + # model args + parser.add_argument("--model_name", default="Pretrain", type=str) + + parser.add_argument( + "--hidden_size", type=int, default=64, help="hidden size of transformer model" + ) + parser.add_argument( + "--num_hidden_layers", type=int, default=2, help="number of layers" + ) + parser.add_argument("--num_attention_heads", default=2, type=int) + parser.add_argument("--hidden_act", default="gelu", type=str) # gelu relu + parser.add_argument( + "--attention_probs_dropout_prob", + type=float, + default=0.5, + help="attention dropout p", + ) + parser.add_argument( + "--hidden_dropout_prob", type=float, default=0.5, help="hidden dropout p" + ) + parser.add_argument("--initializer_range", type=float, default=0.02) + parser.add_argument("--max_seq_length", default=50, type=int) + + # train args + parser.add_argument("--lr", type=float, default=0.001, help="learning rate of adam") + parser.add_argument( + "--batch_size", type=int, default=256, help="number of batch_size" + ) + parser.add_argument("--epochs", type=int, default=200, help="number of epochs") + parser.add_argument("--no_cuda", action="store_true") + parser.add_argument("--log_freq", type=int, default=1, help="per epoch print res") + parser.add_argument("--seed", default=42, type=int) + + # pre train args + parser.add_argument( + "--pre_epochs", type=int, default=300, help="number of pre_train epochs" + ) + parser.add_argument("--pre_batch_size", type=int, default=512) + + parser.add_argument("--mask_p", type=float, default=0.2, help="mask probability") + parser.add_argument("--aap_weight", type=float, default=0.2, help="aap loss weight") + parser.add_argument("--mip_weight", type=float, default=1.0, help="mip loss weight") + parser.add_argument("--map_weight", type=float, default=1.0, help="map loss weight") + parser.add_argument("--sp_weight", type=float, default=0.5, help="sp loss weight") + + parser.add_argument( + "--weight_decay", type=float, default=0.0, help="weight_decay of adam" + ) + parser.add_argument( + "--adam_beta1", type=float, default=0.9, help="adam first beta value" + ) + parser.add_argument( + "--adam_beta2", type=float, default=0.999, help="adam second beta value" + ) + parser.add_argument("--gpu_id", type=str, default="0", help="gpu_id") + + args = parser.parse_args() + return args \ No newline at end of file diff --git a/code/s3rec/requirements.txt b/code/s3rec/requirements.txt new file mode 100644 index 0000000..5780933 --- /dev/null +++ b/code/s3rec/requirements.txt @@ -0,0 +1,9 @@ +numpy==1.22.2 +pandas==1.4.1 +python-dateutil==2.8.2 +pytz==2021.3 +scipy==1.8.0 +six==1.16.0 +torch==1.10.2 +tqdm==4.62.3 +typing_extensions==4.1.1 diff --git a/code/s3rec/sample_submission.ipynb b/code/s3rec/sample_submission.ipynb new file mode 100644 index 0000000..6b376fb --- /dev/null +++ b/code/s3rec/sample_submission.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `train_ratings.csv` 로드해서 `MostPopular` 추천 결과 생성하기" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "train_df = pd.read_csv('../data/train/train_ratings.csv') # 전체 학습 데이터\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 모든 사용자에게 동일하게 Most Popular 아이템 10개를 추천 결과로 제공" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# most popular 10 items\n", + "num_users = train_df['user'].nunique()\n", + "mp_items = train_df['item'].value_counts()[:10].index.values # predictions\n", + "mp_items = np.concatenate([mp_items]*num_users) # same prediction for all users\n", + "users = train_df['user'].unique().repeat(10)\n", + "\n", + "test_df = pd.DataFrame(zip(users,mp_items), columns=['user','item'])\n", + "test_df.to_csv(\"output/most_popular_submission.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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The Model is same as SASRec") + else: + print("Not using pretrained model. The Model is same as SASRec") + + early_stopping = EarlyStopping(args.checkpoint_path, patience=10, verbose=True) + + wandb.login() + wandb.init(project = args.project_name, + name = args.run_name, + entity = args.entity_name, + config = { + "epochs": args.epochs, + "learning_rate": args.lr, + "batch_size": args.batch_size, + "using_pretrain": args.using_pretrain, + "hidden_size": args.hidden_size, + "num_hidden_layers": args.num_hidden_layers, + "attention_probs_dropout_prob": args.attention_probs_dropout_prob, + "hidden_dropout_prob": args.hidden_dropout_prob, + "max_seq_length": args.max_seq_length, + }) + + for epoch in range(args.epochs): + trainer.train(epoch) + + scores, _ = trainer.valid(epoch) + + early_stopping(np.array(scores[-1:]), trainer.model) + if early_stopping.early_stop: + print("Early stopping") + break + + trainer.args.train_matrix = test_rating_matrix + print("---------------Change to test_rating_matrix!-------------------") + # load the best model + trainer.model.load_state_dict(torch.load(args.checkpoint_path)) + scores, result_info = trainer.test(0) + print(result_info) + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/code/s3rec/train_args.py b/code/s3rec/train_args.py new file mode 100644 index 0000000..38da4ef --- /dev/null +++ b/code/s3rec/train_args.py @@ -0,0 +1,61 @@ +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("--data_dir", default="../../data/train/", type=str) + parser.add_argument("--output_dir", default="output/", type=str) + parser.add_argument("--data_name", default="Ml", type=str) + + # wandb init parameters + parser.add_argument("--project_name", default="movie-s3rec-train", type=str) + parser.add_argument("--run_name", default="test", type=str) + parser.add_argument("--entity_name", default="suggestify_lv2", type=str) + + # model args + parser.add_argument("--model_name", default="Finetune_full", type=str) + parser.add_argument( + "--hidden_size", type=int, default=64, help="hidden size of transformer model" + ) + parser.add_argument( + "--num_hidden_layers", type=int, default=2, help="number of layers" + ) + parser.add_argument("--num_attention_heads", default=2, type=int) + parser.add_argument("--hidden_act", default="gelu", type=str) # gelu relu + parser.add_argument( + "--attention_probs_dropout_prob", + type=float, + default=0.5, + help="attention dropout p", + ) + parser.add_argument( + "--hidden_dropout_prob", type=float, default=0.5, help="hidden dropout p" + ) + parser.add_argument("--initializer_range", type=float, default=0.02) + parser.add_argument("--max_seq_length", default=50, type=int) + + # train args + parser.add_argument("--lr", type=float, default=0.001, help="learning rate of adam") + parser.add_argument( + "--batch_size", type=int, default=256, help="number of batch_size" + ) + parser.add_argument("--epochs", type=int, default=200, help="number of epochs") + parser.add_argument("--no_cuda", action="store_true") + parser.add_argument("--log_freq", type=int, default=1, help="per epoch print res") + parser.add_argument("--seed", default=42, type=int) + + parser.add_argument( + "--weight_decay", type=float, default=0.0, help="weight_decay of adam" + ) + parser.add_argument( + "--adam_beta1", type=float, default=0.9, help="adam first beta value" + ) + parser.add_argument( + "--adam_beta2", type=float, default=0.999, help="adam second beta value" + ) + parser.add_argument("--gpu_id", type=str, default="0", help="gpu_id") + + parser.add_argument("--using_pretrain", action="store_true") + + args = parser.parse_args() + return args \ No newline at end of file diff --git a/code/s3rec/trainers.py b/code/s3rec/trainers.py new file mode 100644 index 0000000..5975356 --- /dev/null +++ b/code/s3rec/trainers.py @@ -0,0 +1,320 @@ +import numpy as np +import torch +import torch.nn as nn +import tqdm +from torch.optim import Adam + +from utils import ndcg_k, recall_at_k +import wandb + +class Trainer: + def __init__( + self, + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ): + + self.args = args + self.cuda_condition = torch.cuda.is_available() and not self.args.no_cuda + self.device = torch.device("cuda" if self.cuda_condition else "cpu") + + self.model = model + if self.cuda_condition: + self.model.cuda() + + # Setting the train and test data loader + self.train_dataloader = train_dataloader + self.eval_dataloader = eval_dataloader + self.test_dataloader = test_dataloader + self.submission_dataloader = submission_dataloader + + # self.data_name = self.args.data_name + betas = (self.args.adam_beta1, self.args.adam_beta2) + self.optim = Adam( + self.model.parameters(), + lr=self.args.lr, + betas=betas, + weight_decay=self.args.weight_decay, + ) + + print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()])) + self.criterion = nn.BCELoss() + + def train(self, epoch): + self.iteration(epoch, self.train_dataloader) + + def valid(self, epoch): + return self.iteration(epoch, self.eval_dataloader, mode="valid") + + def test(self, epoch): + return self.iteration(epoch, self.test_dataloader, mode="test") + + def submission(self, epoch): + return self.iteration(epoch, self.submission_dataloader, mode="submission") + + def iteration(self, epoch, dataloader, mode="train"): + raise NotImplementedError + + def get_full_sort_score(self, epoch, answers, pred_list, mode): + recall, ndcg = [], [] + for k in [5, 10]: + recall.append(recall_at_k(answers, pred_list, k)) + ndcg.append(ndcg_k(answers, pred_list, k)) + post_fix = { + "Epoch": epoch, + "RECALL@5": "{:.4f}".format(recall[0]), + "NDCG@5": "{:.4f}".format(ndcg[0]), + "RECALL@10": "{:.4f}".format(recall[1]), + "NDCG@10": "{:.4f}".format(ndcg[1]), + } + wandb.log({ + f"{mode}_recall@5": recall[0], + f"{mode}_ndcg@5": ndcg[0], + f"{mode}_recall@10": recall[1], + f"{mode}_ndcg@10": ndcg[1], + }) + print(post_fix) + + return [recall[0], ndcg[0], recall[1], ndcg[1]], str(post_fix) + + def save(self, file_name): + torch.save(self.model.cpu().state_dict(), file_name) + self.model.to(self.device) + + def load(self, file_name): + self.model.load_state_dict(torch.load(file_name)) + + def cross_entropy(self, seq_out, pos_ids, neg_ids): + # [batch seq_len hidden_size] + pos_emb = self.model.item_embeddings(pos_ids) + neg_emb = self.model.item_embeddings(neg_ids) + # [batch*seq_len hidden_size] + pos = pos_emb.view(-1, pos_emb.size(2)) + neg = neg_emb.view(-1, neg_emb.size(2)) + seq_emb = seq_out.view(-1, self.args.hidden_size) # [batch*seq_len hidden_size] + pos_logits = torch.sum(pos * seq_emb, -1) # [batch*seq_len] + neg_logits = torch.sum(neg * seq_emb, -1) + istarget = ( + (pos_ids > 0).view(pos_ids.size(0) * self.model.args.max_seq_length).float() + ) # [batch*seq_len] + loss = torch.sum( + -torch.log(torch.sigmoid(pos_logits) + 1e-24) * istarget + - torch.log(1 - torch.sigmoid(neg_logits) + 1e-24) * istarget + ) / torch.sum(istarget) + + return loss + + def predict_full(self, seq_out): + # [item_num hidden_size] + test_item_emb = self.model.item_embeddings.weight + # [batch hidden_size ] + rating_pred = torch.matmul(seq_out, test_item_emb.transpose(0, 1)) + return rating_pred + + +class PretrainTrainer(Trainer): + def __init__( + self, + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ): + super(PretrainTrainer, self).__init__( + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ) + + def pretrain(self, epoch, pretrain_dataloader): + + desc = ( + f"AAP-{self.args.aap_weight}-" + f"MIP-{self.args.mip_weight}-" + f"MAP-{self.args.map_weight}-" + f"SP-{self.args.sp_weight}" + ) + + pretrain_data_iter = tqdm.tqdm( + enumerate(pretrain_dataloader), + desc=f"{self.args.model_name}-{self.args.data_name} Epoch:{epoch}", + total=len(pretrain_dataloader), + bar_format="{l_bar}{r_bar}", + ) + + self.model.train() + aap_loss_avg = 0.0 + mip_loss_avg = 0.0 + map_loss_avg = 0.0 + sp_loss_avg = 0.0 + + for i, batch in pretrain_data_iter: + # 0. batch_data will be sent into the device(GPU or CPU) + batch = tuple(t.to(self.device) for t in batch) + ( + attributes, + masked_item_sequence, + pos_items, + neg_items, + masked_segment_sequence, + pos_segment, + neg_segment, + ) = batch + + aap_loss, mip_loss, map_loss, sp_loss = self.model.pretrain( + attributes, + masked_item_sequence, + pos_items, + neg_items, + masked_segment_sequence, + pos_segment, + neg_segment, + ) + + joint_loss = ( + self.args.aap_weight * aap_loss + + self.args.mip_weight * mip_loss + + self.args.map_weight * map_loss + + self.args.sp_weight * sp_loss + ) + + self.optim.zero_grad() + joint_loss.backward() + self.optim.step() + + aap_loss_avg += aap_loss.item() + mip_loss_avg += mip_loss.item() + map_loss_avg += map_loss.item() + sp_loss_avg += sp_loss.item() + + num = len(pretrain_data_iter) * self.args.pre_batch_size + losses = { + "epoch": epoch, + "aap_loss_avg": aap_loss_avg / num, + "mip_loss_avg": mip_loss_avg / num, + "map_loss_avg": map_loss_avg / num, + "sp_loss_avg": sp_loss_avg / num, + } + print(desc) + print(str(losses)) + wandb.log({ + "aap_loss_avg": losses["aap_loss_avg"], + "mip_loss_avg": losses["mip_loss_avg"], + "map_loss_avg": losses["map_loss_avg"], + "sp_loss_avg": losses["sp_loss_avg"], + }) + return losses + + +class FinetuneTrainer(Trainer): + def __init__( + self, + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ): + super(FinetuneTrainer, self).__init__( + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ) + + def iteration(self, epoch, dataloader, mode="train"): + + # Setting the tqdm progress bar + + rec_data_iter = tqdm.tqdm( + enumerate(dataloader), + desc="Recommendation EP_%s:%d" % (mode, epoch), + total=len(dataloader), + bar_format="{l_bar}{r_bar}", + ) + if mode == "train": + self.model.train() + rec_avg_loss = 0.0 + rec_cur_loss = 0.0 + + for i, batch in rec_data_iter: + # 0. batch_data will be sent into the device(GPU or CPU) + batch = tuple(t.to(self.device) for t in batch) + _, input_ids, target_pos, target_neg, _ = batch + # Binary cross_entropy + sequence_output = self.model.finetune(input_ids) + loss = self.cross_entropy(sequence_output, target_pos, target_neg) + self.optim.zero_grad() + loss.backward() + self.optim.step() + + rec_avg_loss += loss.item() + rec_cur_loss = loss.item() + + post_fix = { + "epoch": epoch, + "rec_avg_loss": "{:.4f}".format(rec_avg_loss / len(rec_data_iter)), + "rec_cur_loss": "{:.4f}".format(rec_cur_loss), + } + + wandb.log({ + "train_avg_loss": rec_avg_loss / len(rec_data_iter), + "train_cur_loss": rec_cur_loss, + }) + if (epoch + 1) % self.args.log_freq == 0: + print(str(post_fix)) + + else: + self.model.eval() + + pred_list = None + answer_list = None + for i, batch in rec_data_iter: + + batch = tuple(t.to(self.device) for t in batch) + user_ids, input_ids, _, target_neg, answers = batch + recommend_output = self.model.finetune(input_ids) + + recommend_output = recommend_output[:, -1, :] + + rating_pred = self.predict_full(recommend_output) + + rating_pred = rating_pred.cpu().data.numpy().copy() + batch_user_index = user_ids.cpu().numpy() + rating_pred[self.args.train_matrix[batch_user_index].toarray() > 0] = 0 + + ind = np.argpartition(rating_pred, -10)[:, -10:] + + arr_ind = rating_pred[np.arange(len(rating_pred))[:, None], ind] + + arr_ind_argsort = np.argsort(arr_ind)[np.arange(len(rating_pred)), ::-1] + + batch_pred_list = ind[ + np.arange(len(rating_pred))[:, None], arr_ind_argsort + ] + + if i == 0: + pred_list = batch_pred_list + answer_list = answers.cpu().data.numpy() + else: + pred_list = np.append(pred_list, batch_pred_list, axis=0) + answer_list = np.append( + answer_list, answers.cpu().data.numpy(), axis=0 + ) + + if mode == "submission": + return pred_list + else: + return self.get_full_sort_score(epoch, answer_list, pred_list, mode) diff --git a/code/s3rec/utils.py b/code/s3rec/utils.py new file mode 100644 index 0000000..9c1b89d --- /dev/null +++ b/code/s3rec/utils.py @@ -0,0 +1,349 @@ +import json +import math +import os +import random + +import numpy as np +import pandas as pd +import torch +from scipy.sparse import csr_matrix + + +def set_seed(seed): + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # some cudnn methods can be random even after fixing the seed + # unless you tell it to be deterministic + torch.backends.cudnn.deterministic = True + + +def check_path(path): + if not os.path.exists(path): + os.makedirs(path) + print(f"{path} created") + + +def neg_sample(item_set, item_size): + item = random.randint(1, item_size - 1) + while item in item_set: + item = random.randint(1, item_size - 1) + return item + + +class EarlyStopping: + """Early stops the training if validation loss doesn't improve after a given patience.""" + + def __init__(self, checkpoint_path, patience=7, verbose=False, delta=0): + """ + Args: + patience (int): How long to wait after last time validation loss improved. + Default: 7 + verbose (bool): If True, prints a message for each validation loss improvement. + Default: False + delta (float): Minimum change in the monitored quantity to qualify as an improvement. + Default: 0 + """ + self.checkpoint_path = checkpoint_path + self.patience = patience + self.verbose = verbose + self.counter = 0 + self.best_score = None + self.early_stop = False + self.delta = delta + + def compare(self, score): + for i in range(len(score)): + + if score[i] > self.best_score[i] + self.delta: + return False + return True + + def __call__(self, score, model): + + if self.best_score is None: + self.best_score = score + self.score_min = np.array([0] * len(score)) + self.save_checkpoint(score, model) + elif self.compare(score): + self.counter += 1 + print(f"EarlyStopping counter: {self.counter} out of {self.patience}") + if self.counter >= self.patience: + self.early_stop = True + else: + self.best_score = score + self.save_checkpoint(score, model) + self.counter = 0 + + def save_checkpoint(self, score, model): + """Saves model when the performance is better.""" + if self.verbose: + print(f"Better performance. Saving model ...") + torch.save(model.state_dict(), self.checkpoint_path) + self.score_min = score + + +def kmax_pooling(x, dim, k): + index = x.topk(k, dim=dim)[1].sort(dim=dim)[0] + return x.gather(dim, index).squeeze(dim) + + +def avg_pooling(x, dim): + return x.sum(dim=dim) / x.size(dim) + + +def generate_rating_matrix_valid(user_seq, num_users, num_items): + # three lists are used to construct sparse matrix + row = [] + col = [] + data = [] + for user_id, item_list in enumerate(user_seq): + for item in item_list[:-2]: # + row.append(user_id) + col.append(item) + data.append(1) + + row = np.array(row) + col = np.array(col) + data = np.array(data) + rating_matrix = csr_matrix((data, (row, col)), shape=(num_users, num_items)) + + return rating_matrix + + +def generate_rating_matrix_test(user_seq, num_users, num_items): + # three lists are used to construct sparse matrix + row = [] + col = [] + data = [] + for user_id, item_list in enumerate(user_seq): + for item in item_list[:-1]: # + row.append(user_id) + col.append(item) + data.append(1) + + row = np.array(row) + col = np.array(col) + data = np.array(data) + rating_matrix = csr_matrix((data, (row, col)), shape=(num_users, num_items)) + + return rating_matrix + + +def generate_rating_matrix_submission(user_seq, num_users, num_items): + # three lists are used to construct sparse matrix + row = [] + col = [] + data = [] + for user_id, item_list in enumerate(user_seq): + for item in item_list[:]: # + row.append(user_id) + col.append(item) + data.append(1) + + row = np.array(row) + col = np.array(col) + data = np.array(data) + rating_matrix = csr_matrix((data, (row, col)), shape=(num_users, num_items)) + + return rating_matrix + + +def generate_submission_file(data_file, preds): + + rating_df = pd.read_csv(data_file) + users = rating_df["user"].unique() + + result = [] + + for index, items in enumerate(preds): + for item in items: + result.append((users[index], item)) + + pd.DataFrame(result, columns=["user", "item"]).to_csv( + "output/submission.csv", index=False + ) + + +def get_user_seqs(data_file): + rating_df = pd.read_csv(data_file) + lines = rating_df.groupby("user")["item"].apply(list) + user_seq = [] + item_set = set() + for line in lines: + + items = line + user_seq.append(items) + item_set = item_set | set(items) + max_item = max(item_set) + + num_users = len(lines) + num_items = max_item + 2 + + valid_rating_matrix = generate_rating_matrix_valid(user_seq, num_users, num_items) + test_rating_matrix = generate_rating_matrix_test(user_seq, num_users, num_items) + submission_rating_matrix = generate_rating_matrix_submission( + user_seq, num_users, num_items + ) + return ( + user_seq, + max_item, + valid_rating_matrix, + test_rating_matrix, + submission_rating_matrix, + ) + + +def get_user_seqs_long(data_file): + rating_df = pd.read_csv(data_file) + lines = rating_df.groupby("user")["item"].apply(list) + user_seq = [] + long_sequence = [] + item_set = set() + for line in lines: + items = line + long_sequence.extend(items) + user_seq.append(items) + item_set = item_set | set(items) + max_item = max(item_set) + + return user_seq, max_item, long_sequence + + +def get_item2attribute_json(data_file): + item2attribute = json.loads(open(data_file).readline()) + attribute_set = set() + for item, attributes in item2attribute.items(): + attribute_set = attribute_set | set(attributes) + attribute_size = max(attribute_set) + return item2attribute, attribute_size + + +def get_metric(pred_list, topk=10): + NDCG = 0.0 + HIT = 0.0 + MRR = 0.0 + # [batch] the answer's rank + for rank in pred_list: + MRR += 1.0 / (rank + 1.0) + if rank < topk: + NDCG += 1.0 / np.log2(rank + 2.0) + HIT += 1.0 + return HIT / len(pred_list), NDCG / len(pred_list), MRR / len(pred_list) + + +def precision_at_k_per_sample(actual, predicted, topk): + num_hits = 0 + for place in predicted: + if place in actual: + num_hits += 1 + return num_hits / (topk + 0.0) + + +def precision_at_k(actual, predicted, topk): + sum_precision = 0.0 + num_users = len(predicted) + for i in range(num_users): + act_set = set(actual[i]) + pred_set = set(predicted[i][:topk]) + sum_precision += len(act_set & pred_set) / float(topk) + + return sum_precision / num_users + + +def recall_at_k(actual, predicted, topk): + sum_recall = 0.0 + num_users = len(predicted) + true_users = 0 + for i in range(num_users): + act_set = set(actual[i]) + pred_set = set(predicted[i][:topk]) + if len(act_set) != 0: + sum_recall += len(act_set & pred_set) / float(len(act_set)) + true_users += 1 + return sum_recall / true_users + + +def apk(actual, predicted, k=10): + """ + Computes the average precision at k. + This function computes the average precision at k between two lists of + items. + Parameters + ---------- + actual : list + A list of elements that are to be predicted (order doesn't matter) + predicted : list + A list of predicted elements (order does matter) + k : int, optional + The maximum number of predicted elements + Returns + ------- + score : double + The average precision at k over the input lists + """ + if len(predicted) > k: + predicted = predicted[:k] + + score = 0.0 + num_hits = 0.0 + + for i, p in enumerate(predicted): + if p in actual and p not in predicted[:i]: + num_hits += 1.0 + score += num_hits / (i + 1.0) + + if not actual: + return 0.0 + + return score / min(len(actual), k) + + +def mapk(actual, predicted, k=10): + """ + Computes the mean average precision at k. + This function computes the mean average prescision at k between two lists + of lists of items. + Parameters + ---------- + actual : list + A list of lists of elements that are to be predicted + (order doesn't matter in the lists) + predicted : list + A list of lists of predicted elements + (order matters in the lists) + k : int, optional + The maximum number of predicted elements + Returns + ------- + score : double + The mean average precision at k over the input lists + """ + return np.mean([apk(a, p, k) for a, p in zip(actual, predicted)]) + + +def ndcg_k(actual, predicted, topk): + res = 0 + for user_id in range(len(actual)): + k = min(topk, len(actual[user_id])) + idcg = idcg_k(k) + dcg_k = sum( + [ + int(predicted[user_id][j] in set(actual[user_id])) / math.log(j + 2, 2) + for j in range(topk) + ] + ) + res += dcg_k / idcg + return res / float(len(actual)) + + +# Calculates the ideal discounted cumulative gain at k +def idcg_k(k): + res = sum([1.0 / math.log(i + 2, 2) for i in range(k)]) + if not res: + return 1.0 + else: + return res From 072165c09c6aa97264f42ef650a20987e01c71fb Mon Sep 17 00:00:00 2001 From: ParkSiWoo Date: Mon, 5 Feb 2024 11:07:47 +0000 Subject: [PATCH 06/49] create gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..127782a --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +output +wandb From f158d90ceb55991717d6d9e284c728b1ca20d4b3 Mon Sep 17 00:00:00 2001 From: ParkSiWoo Date: Mon, 5 Feb 2024 11:10:42 +0000 Subject: [PATCH 07/49] update gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 127782a..681ba0a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ output wandb +__pycache__ \ No newline at end of file From d875fa039eea7d94b9bbbc31a51654adba397f45 Mon Sep 17 00:00:00 2001 From: ParkSiWoo Date: Wed, 7 Feb 2024 01:40:05 +0000 Subject: [PATCH 08/49] feat: recbole baseline #5 --- .gitignore | 5 +++- code/recbole/args.py | 10 ++++++++ code/recbole/configs/ract.yaml | 40 ++++++++++++++++++++++++++++++ code/recbole/configs/recvae.yaml | 40 ++++++++++++++++++++++++++++++ code/recbole/data_preprocessing.py | 37 +++++++++++++++++++++++++++ code/recbole/inference.py | 28 +++++++++++++++++++++ code/recbole/requirements.txt | 2 ++ code/recbole/train.py | 11 ++++++++ 8 files changed, 172 insertions(+), 1 deletion(-) create mode 100644 code/recbole/args.py create mode 100644 code/recbole/configs/ract.yaml create mode 100644 code/recbole/configs/recvae.yaml create mode 100644 code/recbole/data_preprocessing.py create mode 100644 code/recbole/inference.py create mode 100644 code/recbole/requirements.txt create mode 100644 code/recbole/train.py diff --git a/.gitignore b/.gitignore index 681ba0a..1201e85 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ output wandb -__pycache__ \ No newline at end of file +__pycache__ +saved +log +log_tensorboard \ No newline at end of file diff --git a/code/recbole/args.py b/code/recbole/args.py new file mode 100644 index 0000000..c751de5 --- /dev/null +++ b/code/recbole/args.py @@ -0,0 +1,10 @@ +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("--model", default='recvae', type=str, help='model name in recbole library') + parser.add_argument("--model_path", default='saved/RecVAE-Feb-06-2024_16-27-17.pth', type=str, help='saved model path to predict') + + args = parser.parse_args() + return args \ No newline at end of file diff --git a/code/recbole/configs/ract.yaml b/code/recbole/configs/ract.yaml new file mode 100644 index 0000000..a5eda15 --- /dev/null +++ b/code/recbole/configs/ract.yaml @@ -0,0 +1,40 @@ + +model: RaCT + +data_path: /data/data/train +dataset: movie +field_separator: " " +seq_separator: " " +USER_ID_FIELD: user +ITEM_ID_FIELD: item +TIME_FIELD: time +show_progress: false +use_gpu: true + +load_col: + inter: [user, item, time] + user: [user] + item: [item, genre, director, title, writer, year] + +user_inter_num_interval: "[0,inf)" +item_inter_num_interval: "[0,inf)" + +epochs: 340 +train_batch_size: 4096 +metrics: ["Recall", "NDCG"] +topk: [10] +valid_metric: NDCG@10 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +train_neg_sample_args: + distribution: popularity + candidate_num: 1 + +log_wandb: True +wandb_project: recbole_recvae diff --git a/code/recbole/configs/recvae.yaml b/code/recbole/configs/recvae.yaml new file mode 100644 index 0000000..126ff13 --- /dev/null +++ b/code/recbole/configs/recvae.yaml @@ -0,0 +1,40 @@ + +model: RecVAE + +data_path: /data/data/train +dataset: movie +field_separator: " " +seq_separator: " " +USER_ID_FIELD: user +ITEM_ID_FIELD: item +TIME_FIELD: time +show_progress: false +use_gpu: true + +load_col: + inter: [user, item, time] + user: [user] + item: [item, genre, director, title, writer, year] + +user_inter_num_interval: "[0,inf)" +item_inter_num_interval: "[0,inf)" + +epochs: 340 +train_batch_size: 4096 +metrics: ["Recall", "NDCG"] +topk: [10] +valid_metric: NDCG@10 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +train_neg_sample_args: + distribution: popularity + candidate_num: 1 + +log_wandb: True +wandb_project: recbole_recvae diff --git a/code/recbole/data_preprocessing.py b/code/recbole/data_preprocessing.py new file mode 100644 index 0000000..a396625 --- /dev/null +++ b/code/recbole/data_preprocessing.py @@ -0,0 +1,37 @@ +import os +import yaml +import pandas as pd + +def convert_into_atomic_files(config): + + interactions = pd.read_csv(os.path.join(config['data_path'], 'train_ratings.csv')) + directors = pd.read_csv(os.path.join(config['data_path'], 'directors.tsv'), sep='\t') + genres = pd.read_csv(os.path.join(config['data_path'], 'genres.tsv'), sep='\t') + titles = pd.read_csv(os.path.join(config['data_path'], 'titles.tsv'), sep='\t') + writers = pd.read_csv(os.path.join(config['data_path'], 'writers.tsv'), sep='\t') + years = pd.read_csv(os.path.join(config['data_path'], 'years.tsv'), sep='\t') + + if not os.path.exists('/data/data/train/movie/'): + os.makedirs('/data/data/train/movie/') + interactions.rename(columns={'user':'user:token', 'item':'item:token', 'time':'time:float'}, inplace = True) + interactions.to_csv(os.path.join(config['data_path'], 'movie/movie.inter'), sep='\t', index=False) + print("Save interaction data ...") + + interactions[['user:token']].to_csv(os.path.join(config['data_path'], 'movie/movie.user'), sep='\t', index=False) + print("Save user data ...") + + items = pd.merge(titles, years, on='item',) + items = pd.merge(items, genres, on='item') + items = pd.merge(items, writers, on='item', how = 'left') + items = pd.merge(items, directors, on='item', how = 'left') + items = items.fillna("none") + items.rename(columns={'item':'item:token', 'title':'title:token_seq', 'year':'year:token', 'genre':'genre:token_seq', "writer":"writer:token", 'director':'director:token'}, inplace=True) + items.to_csv(os.path.join(config['data_path'],'movie','movie.item'), sep='\t', index=False) + print("Save item data ...") + print("done!") + +if __name__ == '__main__': + # yaml 파일을 읽어서 dictionary로 불러오기 + with open('configs/recvae.yaml', "r") as file: + config = yaml.safe_load(file) + convert_into_atomic_files(config) \ No newline at end of file diff --git a/code/recbole/inference.py b/code/recbole/inference.py new file mode 100644 index 0000000..e9ecdd4 --- /dev/null +++ b/code/recbole/inference.py @@ -0,0 +1,28 @@ +import os + +import pandas as pd +from recbole.quick_start import load_data_and_model +from recbole.utils.case_study import full_sort_topk + +from args import parse_args + +def main(model_path): + config, model, dataset, train_data, valid_data, test_data = load_data_and_model( + model_file = model_path + ) + + external_user_ids = dataset.id2token(dataset.uid_field, list(range(dataset.user_num)))[1:] + uid_series = dataset.token2id(dataset.uid_field, external_user_ids) + + topk_score, topk_iid_list = full_sort_topk(uid_series, model, test_data, k=10, device=config["device"]) + external_item_list = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()).flatten() + + output = pd.read_csv('/data/data/eval/sample_submission.csv') + output['item'] = [int(item) for item in external_item_list] + if not os.path.exists('output/'): + os.makedirs('output/') + output.to_csv('output/submission.csv') + +if __name__ == '__main__': + args = parse_args() + main(args.model_path) \ No newline at end of file diff --git a/code/recbole/requirements.txt b/code/recbole/requirements.txt new file mode 100644 index 0000000..1e215f1 --- /dev/null +++ b/code/recbole/requirements.txt @@ -0,0 +1,2 @@ +recbole +thop \ No newline at end of file diff --git a/code/recbole/train.py b/code/recbole/train.py new file mode 100644 index 0000000..b94e195 --- /dev/null +++ b/code/recbole/train.py @@ -0,0 +1,11 @@ +import os + +from recbole.quick_start import run_recbole +from args import parse_args + +args = parse_args() +assert os.path.exists(f'configs/{args.model}.yaml'), "There's no config file about model ..." + +run_recbole( + config_file_list=[f'configs/{args.model}.yaml'] +) From f01326a4125fc7cb55a8eb519b889702516dff30 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EB=B0=95=EC=8B=9C=EC=9A=B0=5FT6060?= <140307903+SiwooPark00@users.noreply.github.com> Date: Wed, 7 Feb 2024 13:13:05 +0900 Subject: [PATCH 09/49] Create README.md --- code/recbole/README.md | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 code/recbole/README.md diff --git a/code/recbole/README.md b/code/recbole/README.md new file mode 100644 index 0000000..b2ce7b8 --- /dev/null +++ b/code/recbole/README.md @@ -0,0 +1,31 @@ +# Recbole Baseline Code + +영화 추천 대회를 위해 recbole library를 사용한 베이스라인 코드입니다. + +## Installation + +``` +pip install -r requirements.txt +``` + +## How to run + +1. data preprocessing + recbole에서 요구하는 데이터 형식에 맞추기 위해 변환한 파일을 저장하는 코드입니다. + ``` + python data_preprocessing.py + ``` +2. Set Config file + recbole에서 제공하는 모델 중 하나를 골라서 {model_name}.yaml 파일로 저장합니다. + 참고 사이트: https://recbole.io/docs/recbole/recbole.model.html + +3. Training + ``` + python train.py --model model_name + ``` +4. Inference + training 이후 저장된 모델(.pth)의 경로와 함께 submission 파일을 저장합니다. + + ``` + python inference.py --model_path model_path + ``` From 205d9a2f1594e86faea79460b67ab7eb736dda0c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EB=B0=95=EC=8B=9C=EC=9A=B0=5FT6060?= <140307903+SiwooPark00@users.noreply.github.com> Date: Wed, 7 Feb 2024 13:13:27 +0900 Subject: [PATCH 10/49] Update README.md --- code/recbole/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/code/recbole/README.md b/code/recbole/README.md index b2ce7b8..3ffbf63 100644 --- a/code/recbole/README.md +++ b/code/recbole/README.md @@ -15,7 +15,7 @@ pip install -r requirements.txt ``` python data_preprocessing.py ``` -2. Set Config file +2. Set Config file recbole에서 제공하는 모델 중 하나를 골라서 {model_name}.yaml 파일로 저장합니다. 참고 사이트: https://recbole.io/docs/recbole/recbole.model.html From 2ad6651fbfb6bf4799adfe6780fab169ce3dc52b Mon Sep 17 00:00:00 2001 From: Seungbin Date: Wed, 7 Feb 2024 05:00:47 +0000 Subject: [PATCH 11/49] feat: add EASE --- code/recbole/configs/EASE.yaml | 37 ++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 code/recbole/configs/EASE.yaml diff --git a/code/recbole/configs/EASE.yaml b/code/recbole/configs/EASE.yaml new file mode 100644 index 0000000..a53bd9f --- /dev/null +++ b/code/recbole/configs/EASE.yaml @@ -0,0 +1,37 @@ +#environment config +seed : 42 +log_wandb: True +wandb_project: ease +use_gpu: True + +data_path: /data/train +dataset: movie + +# dataset config : General Recommendation +USER_ID_FIELD: user +ITEM_ID_FIELD: item +LABEL_FIELD: label +field_separator: " " +seq_separator: " " +load_col: + inter: [user, item] + +# model config +model: EASE +embedding_size: 64 + +# Training and evaluation config +epochs: 10 +train_batch_size: 2048 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +metrics: ['Recall', 'NDCG', 'Precision'] +topk: 10 +valid_metric: Recall@10 +metric_decimal_place: 4 \ No newline at end of file From 1bbe87567d41f201a93edbef801aed4ae678b33d Mon Sep 17 00:00:00 2001 From: Seungbin Date: Wed, 7 Feb 2024 09:37:34 +0000 Subject: [PATCH 12/49] fix: atomic file path --- code/recbole/data_preprocessing.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/code/recbole/data_preprocessing.py b/code/recbole/data_preprocessing.py index a396625..e2902be 100644 --- a/code/recbole/data_preprocessing.py +++ b/code/recbole/data_preprocessing.py @@ -10,9 +10,11 @@ def convert_into_atomic_files(config): titles = pd.read_csv(os.path.join(config['data_path'], 'titles.tsv'), sep='\t') writers = pd.read_csv(os.path.join(config['data_path'], 'writers.tsv'), sep='\t') years = pd.read_csv(os.path.join(config['data_path'], 'years.tsv'), sep='\t') + + atomic_file_path = os.path.join(config['data_path'], config['dataset']) - if not os.path.exists('/data/data/train/movie/'): - os.makedirs('/data/data/train/movie/') + if not os.path.exists(atomic_file_path): + os.makedirs(atomic_file_path) interactions.rename(columns={'user':'user:token', 'item':'item:token', 'time':'time:float'}, inplace = True) interactions.to_csv(os.path.join(config['data_path'], 'movie/movie.inter'), sep='\t', index=False) print("Save interaction data ...") From 1d93fbf4582b2c2c708f0e7aae4e0922e05a6d21 Mon Sep 17 00:00:00 2001 From: jinmin111 Date: Sat, 10 Feb 2024 13:22:00 +0000 Subject: [PATCH 13/49] feat: add data preprocess #6 --- code/multi/preprocessing.py | 183 ++++++++++++++++++++++++++++++++++++ 1 file changed, 183 insertions(+) create mode 100644 code/multi/preprocessing.py diff --git a/code/multi/preprocessing.py b/code/multi/preprocessing.py new file mode 100644 index 0000000..6483787 --- /dev/null +++ b/code/multi/preprocessing.py @@ -0,0 +1,183 @@ +import os +import pandas as pd +from scipy import sparse +import numpy as np + +def get_count(tp, id): + playcount_groupbyid = tp[[id]].groupby(id, as_index=False) + count = playcount_groupbyid.size() + + return count + +# 특정한 횟수 이상의 리뷰가 존재하는(사용자의 경우 min_uc 이상, 아이템의 경우 min_sc이상) +# 데이터만을 추출할 때 사용하는 함수입니다. +# 현재 데이터셋에서는 결과적으로 원본그대로 사용하게 됩니다. +def filter_triplets(tp, min_uc=5, min_sc=0): + if min_sc > 0: + itemcount = get_count(tp, 'item') + tp = tp[tp['item'].isin(itemcount[itemcount['size'] >= min_sc]['item'])] + + if min_uc > 0: + usercount = get_count(tp, 'user') + tp = tp[tp['user'].isin(usercount[usercount['size'] >= min_uc]['user'])] + + usercount, itemcount = get_count(tp, 'user'), get_count(tp, 'item') + return tp, usercount, itemcount + +#훈련된 모델을 이용해 검증할 데이터를 분리하는 함수입니다. +#100개의 액션이 있다면, 그중에 test_prop 비율 만큼을 비워두고, 그것을 모델이 예측할 수 있는지를 +#확인하기 위함입니다. +def split_train_test_proportion(data, test_prop=0.2): + data_grouped_by_user = data.groupby('user') + tr_list, te_list = list(), list() + # TODO + np.random.seed(98765) + + for _, group in data_grouped_by_user: + n_items_u = len(group) + + if n_items_u >= 5: + idx = np.zeros(n_items_u, dtype='bool') + idx[np.random.choice(n_items_u, size=int(test_prop * n_items_u), replace=False).astype('int64')] = True + + tr_list.append(group[np.logical_not(idx)]) + te_list.append(group[idx]) + + else: + tr_list.append(group) + + data_tr = pd.concat(tr_list) + data_te = pd.concat(te_list) + + return data_tr, data_te + +def numerize(tp, profile2id, show2id): + uid = tp['user'].apply(lambda x: profile2id[x]) + sid = tp['item'].apply(lambda x: show2id[x]) + return pd.DataFrame(data={'uid': uid, 'sid': sid}, columns=['uid', 'sid']) + +def data_load(args): + raw_data = pd.read_csv(os.path.join(args.data, 'train_ratings.csv'), header=0) + raw_data, user_activity, item_popularity = filter_triplets(raw_data, min_uc=5, min_sc=0) + # Shuffle User Indices + unique_uid = user_activity['user'].unique() + # TODO + np.random.seed(98765) + idx_perm = np.random.permutation(unique_uid.size) + unique_uid = unique_uid[idx_perm] + + n_users = unique_uid.size #31360 + n_heldout_users = 3000 + + # Split Train/Validation/Test User Indices + tr_users = unique_uid[:(n_users - n_heldout_users * 2)] + vd_users = unique_uid[(n_users - n_heldout_users * 2): (n_users - n_heldout_users)] + te_users = unique_uid[(n_users - n_heldout_users):] + + ##훈련 데이터에 해당하는 아이템들 + #Train에는 전체 데이터를 사용합니다. + train_plays = raw_data + + ##아이템 ID + unique_sid = pd.unique(train_plays['item']) + + show2id = dict((sid, i) for (i, sid) in enumerate(unique_sid)) + id2show = dict((i, sid) for (i, sid) in enumerate(unique_sid)) + profile2id = dict((pid, i) for (i, pid) in enumerate(unique_uid)) + id2profile = dict((i, pid) for (i, pid) in enumerate(unique_uid)) + + pro_dir = os.path.join(args.data, 'pro_sg') + + if not os.path.exists(pro_dir): + os.makedirs(pro_dir) + + with open(os.path.join(pro_dir, 'unique_sid.txt'), 'w') as f: + for sid in unique_sid: + f.write('%s\n' % sid) + + #Validation과 Test에는 input으로 사용될 tr 데이터와 정답을 확인하기 위한 te 데이터로 분리되었습니다. + vad_plays = raw_data.loc[raw_data['user'].isin(vd_users)] + vad_plays = vad_plays.loc[vad_plays['item'].isin(unique_sid)] + vad_plays_tr, vad_plays_te = split_train_test_proportion(vad_plays) + + test_plays = raw_data.loc[raw_data['user'].isin(te_users)] + test_plays = test_plays.loc[test_plays['item'].isin(unique_sid)] + test_plays_tr, test_plays_te = split_train_test_proportion(test_plays) + + train_data = numerize(train_plays, profile2id, show2id) + train_data.to_csv(os.path.join(pro_dir, 'train.csv'), index=False) + + vad_data_tr = numerize(vad_plays_tr, profile2id, show2id) + vad_data_tr.to_csv(os.path.join(pro_dir, 'validation_tr.csv'), index=False) + + vad_data_te = numerize(vad_plays_te, profile2id, show2id) + vad_data_te.to_csv(os.path.join(pro_dir, 'validation_te.csv'), index=False) + + test_data_tr = numerize(test_plays_tr, profile2id, show2id) + test_data_tr.to_csv(os.path.join(pro_dir, 'test_tr.csv'), index=False) + + test_data_te = numerize(test_plays_te, profile2id, show2id) + test_data_te.to_csv(os.path.join(pro_dir, 'test_te.csv'), index=False) + + print("Done!") + return id2show, id2profile + +class DataLoader(): + ''' + Load Movielens dataset + ''' + def __init__(self, path): + + self.pro_dir = os.path.join(path, 'pro_sg') + assert os.path.exists(self.pro_dir), "Preprocessed files do not exist. Run data.py" + + self.n_items = self.load_n_items() + + def load_data(self, datatype='train'): + if datatype == 'train': + return self._load_train_data() + elif datatype == 'validation': + return self._load_tr_te_data(datatype) + elif datatype == 'test': + return self._load_tr_te_data(datatype) + else: + raise ValueError("datatype should be in [train, validation, test]") + + def load_n_items(self): + unique_sid = list() + with open(os.path.join(self.pro_dir, 'unique_sid.txt'), 'r') as f: + for line in f: + unique_sid.append(line.strip()) + n_items = len(unique_sid) + return n_items + + def _load_train_data(self): + path = os.path.join(self.pro_dir, 'train.csv') + + tp = pd.read_csv(path) + n_users = tp['uid'].max() + 1 + + rows, cols = tp['uid'], tp['sid'] + data = sparse.csr_matrix((np.ones_like(rows), + (rows, cols)), dtype='float64', + shape=(n_users, self.n_items)) + return data + + def _load_tr_te_data(self, datatype='test'): + tr_path = os.path.join(self.pro_dir, '{}_tr.csv'.format(datatype)) + te_path = os.path.join(self.pro_dir, '{}_te.csv'.format(datatype)) + + tp_tr = pd.read_csv(tr_path) + tp_te = pd.read_csv(te_path) + + start_idx = min(tp_tr['uid'].min(), tp_te['uid'].min()) + end_idx = max(tp_tr['uid'].max(), tp_te['uid'].max()) + + rows_tr, cols_tr = tp_tr['uid'] - start_idx, tp_tr['sid'] + rows_te, cols_te = tp_te['uid'] - start_idx, tp_te['sid'] + + data_tr = sparse.csr_matrix((np.ones_like(rows_tr), + (rows_tr, cols_tr)), dtype='float64', shape=(end_idx - start_idx + 1, self.n_items)) + data_te = sparse.csr_matrix((np.ones_like(rows_te), + (rows_te, cols_te)), dtype='float64', shape=(end_idx - start_idx + 1, self.n_items)) + return data_tr, data_te \ No newline at end of file From 2275c5e49a198c7db5f36fd8fe7f43f4bf8a537e Mon Sep 17 00:00:00 2001 From: jinmin111 Date: Sat, 10 Feb 2024 13:22:38 +0000 Subject: [PATCH 14/49] feat: add MultiDAE, MultiVAE #6 --- code/multi/models.py | 140 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 code/multi/models.py diff --git a/code/multi/models.py b/code/multi/models.py new file mode 100644 index 0000000..f46c9f2 --- /dev/null +++ b/code/multi/models.py @@ -0,0 +1,140 @@ +import torch.nn as nn +import torch.nn.functional as F +import torch +import numpy as np + + +#이미 완성된 MultiDAE의 코드를 참고하여 그 아래 MultiVAE의 코드를 완성해보세요! +class MultiDAE(nn.Module): + """ + Container module for Multi-DAE. + + Multi-DAE : Denoising Autoencoder with Multinomial Likelihood + See Variational Autoencoders for Collaborative Filtering + https://arxiv.org/abs/1802.05814 + """ + + def __init__(self, p_dims, q_dims=None, dropout=0.5): + super(MultiDAE, self).__init__() + self.p_dims = p_dims + if q_dims: + assert q_dims[0] == p_dims[-1], "In and Out dimensions must equal to each other" + assert q_dims[-1] == p_dims[0], "Latent dimension for p- and q- network mismatches." + self.q_dims = q_dims + else: + self.q_dims = p_dims[::-1] + + self.dims = self.q_dims + self.p_dims[1:] + self.layers = nn.ModuleList([nn.Linear(d_in, d_out) for + d_in, d_out in zip(self.dims[:-1], self.dims[1:])]) + self.drop = nn.Dropout(dropout) + + self.init_weights() + + def forward(self, input): + h = F.normalize(input) + h = self.drop(h) + + for i, layer in enumerate(self.layers): + h = layer(h) + if i != len(self.layers) - 1: + h = F.tanh(h) + return h + + def init_weights(self): + for layer in self.layers: + # Xavier Initialization for weights + size = layer.weight.size() + fan_out = size[0] + fan_in = size[1] + std = np.sqrt(2.0/(fan_in + fan_out)) + layer.weight.data.normal_(0.0, std) + + # Normal Initialization for Biases + layer.bias.data.normal_(0.0, 0.001) + +class MultiVAE(nn.Module): + """ + Container module for Multi-VAE. + + Multi-VAE : Variational Autoencoder with Multinomial Likelihood + See Variational Autoencoders for Collaborative Filtering + https://arxiv.org/abs/1802.05814 + """ + + def __init__(self, p_dims, q_dims=None, dropout=0.5): + super(MultiVAE, self).__init__() + self.p_dims = p_dims + if q_dims: + assert q_dims[0] == p_dims[-1], "In and Out dimensions must equal to each other" + assert q_dims[-1] == p_dims[0], "Latent dimension for p- and q- network mismatches." + self.q_dims = q_dims + else: + self.q_dims = p_dims[::-1] + + # Last dimension of q- network is for mean and variance + temp_q_dims = self.q_dims[:-1] + [self.q_dims[-1] * 2] + self.q_layers = nn.ModuleList([nn.Linear(d_in, d_out) for + d_in, d_out in zip(temp_q_dims[:-1], temp_q_dims[1:])]) + self.p_layers = nn.ModuleList([nn.Linear(d_in, d_out) for + d_in, d_out in zip(self.p_dims[:-1], self.p_dims[1:])]) + + self.drop = nn.Dropout(dropout) + self.init_weights() + + def forward(self, input): + mu, logvar = self.encode(input) + z = self.reparameterize(mu, logvar) + return self.decode(z), mu, logvar + + def encode(self, input): + h = F.normalize(input) + h = self.drop(h) + + for i, layer in enumerate(self.q_layers): + h = layer(h) + if i != len(self.q_layers) - 1: + h = F.tanh(h) + else: + mu = h[:, :self.q_dims[-1]] + logvar = h[:, self.q_dims[-1]:] + return mu, logvar + + def reparameterize(self, mu, logvar): + if self.training: + std = torch.exp(0.5 * logvar) + eps = torch.randn_like(std) + return eps.mul(std).add_(mu) + else: + return mu + + def decode(self, z): + h = z + for i, layer in enumerate(self.p_layers): + h = layer(h) + if i != len(self.p_layers) - 1: + h = F.tanh(h) + return h + + def init_weights(self): + for layer in self.q_layers: + # Xavier Initialization for weights + size = layer.weight.size() + fan_out = size[0] + fan_in = size[1] + std = np.sqrt(2.0/(fan_in + fan_out)) + layer.weight.data.normal_(0.0, std) + + # Normal Initialization for Biases + layer.bias.data.normal_(0.0, 0.001) + + for layer in self.p_layers: + # Xavier Initialization for weights + size = layer.weight.size() + fan_out = size[0] + fan_in = size[1] + std = np.sqrt(2.0/(fan_in + fan_out)) + layer.weight.data.normal_(0.0, std) + + # Normal Initialization for Biases + layer.bias.data.normal_(0.0, 0.001) \ No newline at end of file From e8c14980278265b528ed97c9a8602d042b4c4604 Mon Sep 17 00:00:00 2001 From: jinmin111 Date: Sat, 10 Feb 2024 14:06:30 +0000 Subject: [PATCH 15/49] feat: add train #6 --- code/multi/main.py | 114 ++++++++++++++++++++ code/multi/trainer.py | 245 ++++++++++++++++++++++++++++++++++++++++++ code/multi/utils.py | 136 +++++++++++++++++++++++ 3 files changed, 495 insertions(+) create mode 100644 code/multi/main.py create mode 100644 code/multi/trainer.py create mode 100644 code/multi/utils.py diff --git a/code/multi/main.py b/code/multi/main.py new file mode 100644 index 0000000..031422c --- /dev/null +++ b/code/multi/main.py @@ -0,0 +1,114 @@ +from utils import Logger, Setting +from preprocessing import data_load, DataLoader +from trainer import run +import pandas as pd +import argparse +from models import MultiDAE, MultiVAE +from inference import predict +import warnings +import os +import wandb + + + +def main(args): + wandb.login() + wandb.init(project=args.wandb_project_name, entity = 'suggestify_lv2', config=vars(args)) + + Setting.seed_everything(args.seed) + + ######################## DATA LOAD + print(f'--------------- {args.model} Load Data ---------------') + id2show, id2profile = data_load(args) + + loader = DataLoader(args.data) + + n_items = loader.load_n_items() + + + ####################### Setting for Log + setting = Setting() + + log_path = setting.get_log_path(args) + setting.make_dir(log_path) + + logger = Logger(args, log_path) + logger.save_args() + + + filename = setting.get_submit_filename(args) + + wandb.run.name = filename[9:-4] + wandb.run.save() + + ######################## Model Load + print(f'--------------- INIT {args.model} ---------------') + p_dims = [200, 600, n_items] + if args.model=='Multi-DAE': + model = MultiDAE(p_dims).to(args.device) + elif args.model=='Multi-VAE': + model = MultiVAE(p_dims).to(args.device) + + ######################## TRAIN + print(f'--------------- {args.model} TRAINING ---------------') + model = run(args, model, loader, logger, setting) + + ######################## INFERENCE + print(f'--------------- {args.model} PREDICT ---------------') + train_data = loader.load_data('train') + predicts = predict(args, model, train_data) + + ######################## SAVE PREDICT + print(f'--------------- SAVE {args.model} PREDICT ---------------') + # submission = pd.read_csv(args.data_path + 'sample_submission.csv') + # submission['rating'] = predicts + + result = pd.DataFrame(predicts, columns=['user', 'item']) + + result['user'] = result['user'].apply(lambda x : id2profile[x]) + result['item'] = result['item'].apply(lambda x : id2show[x]) + result = result.sort_values(by='user') + + write_path = os.path.join(filename) + result.to_csv(write_path, index=False) + + wandb.finish() + + + # submission.to_csv(filename, index=False) + +if __name__ == "__main__": + warnings.filterwarnings("ignore") + + ## 각종 파라미터 세팅 + parser = argparse.ArgumentParser(description='PyTorch Variational Autoencoders for Collaborative Filtering') + + parser.add_argument('--data', type=str, default='../../../data/train/', + help='Movielens dataset location') + parser.add_argument('--model', type=str, default='Multi-DAE', + help='model name') + parser.add_argument('--lr', type=float, default=1e-4, + help='initial learning rate') + parser.add_argument('--wd', type=float, default=0.0, + help='weight decay coefficient') + parser.add_argument('--batch_size', type=int, default=500, + help='batch size') + parser.add_argument('--epochs', type=int, default=10, + help='upper epoch limit') + parser.add_argument('--total_anneal_steps', type=int, default=200000, + help='the total number of gradient updates for annealing') + parser.add_argument('--anneal_cap', type=float, default=0.2, + help='largest annealing parameter') + parser.add_argument('--seed', type=int, default=1111, + help='random seed') + parser.add_argument('--device', type=str, default='cuda', + help='use gpu') + parser.add_argument('--log_interval', type=int, default=100, metavar='N', + help='report interval') + parser.add_argument('--save', type=str, default='./saved', + help='path to save the final model') + parser.add_argument("--wandb_project_name", default="movierec-multi", type=str, help="Setting WandB Project Name") + + args = parser.parse_args([]) + + main(args) \ No newline at end of file diff --git a/code/multi/trainer.py b/code/multi/trainer.py new file mode 100644 index 0000000..0a118f3 --- /dev/null +++ b/code/multi/trainer.py @@ -0,0 +1,245 @@ +import torch +import time +import bottleneck as bn +import numpy as np +import torch.optim as optim +import torch.nn.functional as F +import os +import wandb + + +def run(args, model, loader, logger, setting): + optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd) + model_path = os.path.join(args.save, setting.get_submit_filename(args)[9:-4]+'.pt') + + + train_data = loader.load_data('train') + N = train_data.shape[0] + idxlist = list(range(N)) + vad_data_tr, vad_data_te = loader.load_data('validation') + test_data_tr, test_data_te = loader.load_data('test') + + if args.model=='Multi-DAE': + is_VAE=False + criterion = loss_function_dae + else: + is_VAE=True + criterion = loss_function_vae + + best_n100 = -np.inf + global update_count + update_count = 0 + + for epoch in range(1, args.epochs + 1): + epoch_start_time = time.time() + train_loss = train(args, model, criterion, optimizer, epoch, train_data, idxlist, N, is_VAE) + val_loss, n100, r10, r20 = evaluate(args, model, criterion, vad_data_tr, vad_data_te, N, is_VAE) + print('-' * 89) + print('| end of epoch {:3d} | time: {:4.2f}s | valid loss {:4.2f} | ' + 'n100 {:5.3f} | r10 {:5.3f} | r20 {:5.3f}'.format( + epoch, time.time() - epoch_start_time, val_loss, + n100, r10, r20)) + print('-' * 89) + wandb.log(dict(epoch=epoch, + train_loss=train_loss, + valid_loss=val_loss, + n100=n100, + r10=r10, + r20=r20)) + + n_iter = epoch * len(range(0, N, args.batch_size)) + + + # Save the model if the n100 is the best we've seen so far. + if n100 > best_n100: + with open(model_path, 'wb') as f: + torch.save(model, f) + best_n100 = n100 + + # Load the best saved model. + with open(model_path, 'rb') as f: + model = torch.load(f) + + # Run on test data. + test_loss, n100, r10, r20 = evaluate(args, model, criterion, test_data_tr, test_data_te, N, is_VAE) + print('=' * 89) + print('| End of training | test loss {:4.2f} | n100 {:4.2f} | r10 {:4.2f} | ' + 'r20 {:4.2f}'.format(test_loss, n100, r10, r20)) + print('=' * 89) + return model + + +def train(args, model, criterion, optimizer, epoch, train_data, idxlist, N, is_VAE): + + global update_count + + total_tr_loss_list = [] + + # Turn on training mode + model.train() + train_loss = 0.0 + start_time = time.time() + + np.random.shuffle(idxlist) + + for batch_idx, start_idx in enumerate(range(0, N, args.batch_size)): + end_idx = min(start_idx + args.batch_size, N) + data = train_data[idxlist[start_idx:end_idx]] + data = naive_sparse2tensor(data).to(args.device) + optimizer.zero_grad() + + if is_VAE: + if args.total_anneal_steps > 0: + anneal = min(args.anneal_cap, + 1. * update_count / args.total_anneal_steps) + else: + anneal = args.anneal_cap + + optimizer.zero_grad() + recon_batch, mu, logvar = model(data) + + loss = criterion(recon_batch, data, mu, logvar, anneal) + else: + recon_batch = model(data) + loss = criterion(recon_batch, data) + + loss.backward() + train_loss += loss.item() + total_tr_loss_list.append(loss.item()) + + optimizer.step() + + update_count += 1 + + if batch_idx % args.log_interval == 0 and batch_idx > 0: + elapsed = time.time() - start_time + print('| epoch {:3d} | {:4d}/{:4d} batches | ms/batch {:4.2f} | ' + 'loss {:4.2f}'.format( + epoch, batch_idx, len(range(0, N, args.batch_size)), + elapsed * 1000 / args.log_interval, + train_loss / args.log_interval)) + + + start_time = time.time() + train_loss = 0.0 + return np.nanmean(total_tr_loss_list) + +def evaluate(args, model, criterion, data_tr, data_te, N, is_VAE): + + + global update_count + + # Turn on evaluation mode + model.eval() + e_idxlist = list(range(data_tr.shape[0])) + e_N = data_tr.shape[0] + total_val_loss_list = [] + n100_list = [] + r10_list = [] + r20_list = [] + + with torch.no_grad(): + for start_idx in range(0, e_N, args.batch_size): + end_idx = min(start_idx + args.batch_size, N) + data = data_tr[e_idxlist[start_idx:end_idx]] + heldout_data = data_te[e_idxlist[start_idx:end_idx]] + + data_tensor = naive_sparse2tensor(data).to(args.device) + if is_VAE : + + if args.total_anneal_steps > 0: + anneal = min(args.anneal_cap, + 1. * update_count / args.total_anneal_steps) + else: + anneal = args.anneal_cap + + recon_batch, mu, logvar = model(data_tensor) + + loss = criterion(recon_batch, data_tensor, mu, logvar, anneal) + + else : + recon_batch = model(data_tensor) + loss = criterion(recon_batch, data_tensor) + + total_val_loss_list.append(loss.item()) + + # Exclude examples from training set + recon_batch = recon_batch.cpu().numpy() + recon_batch[data.nonzero()] = -np.inf + + n100 = NDCG_binary_at_k_batch(recon_batch, heldout_data, 100) + r10 = Recall_at_k_batch(recon_batch, heldout_data, 10) + r20 = Recall_at_k_batch(recon_batch, heldout_data, 20) + + n100_list.append(n100) + r10_list.append(r10) + r20_list.append(r20) + + n100_list = np.concatenate(n100_list) + r10_list = np.concatenate(r10_list) + r20_list = np.concatenate(r20_list) + + return np.nanmean(total_val_loss_list), np.nanmean(n100_list), np.nanmean(r10_list), np.nanmean(r20_list) + +def NDCG_binary_at_k_batch(X_pred, heldout_batch, k=100): + ''' + Normalized Discounted Cumulative Gain@k for binary relevance + ASSUMPTIONS: all the 0's in heldout_data indicate 0 relevance + ''' + batch_users = X_pred.shape[0] + idx_topk_part = bn.argpartition(-X_pred, k, axis=1) + topk_part = X_pred[np.arange(batch_users)[:, np.newaxis], + idx_topk_part[:, :k]] + idx_part = np.argsort(-topk_part, axis=1) + + idx_topk = idx_topk_part[np.arange(batch_users)[:, np.newaxis], idx_part] + + tp = 1. / np.log2(np.arange(2, k + 2)) + + DCG = (heldout_batch[np.arange(batch_users)[:, np.newaxis], + idx_topk].toarray() * tp).sum(axis=1) + IDCG = np.array([(tp[:min(n, k)]).sum() + for n in heldout_batch.getnnz(axis=1)]) + return DCG / IDCG + +def Recall_at_k_batch(X_pred, heldout_batch, k): + batch_users = X_pred.shape[0] + + idx = bn.argpartition(-X_pred, k, axis=1) + X_pred_binary = np.zeros_like(X_pred, dtype=bool) + X_pred_binary[np.arange(batch_users)[:, np.newaxis], idx[:, :k]] = True + + X_true_binary = (heldout_batch > 0).toarray() + tmp = (np.logical_and(X_true_binary, X_pred_binary).sum(axis=1)).astype( + np.float32) + recall = tmp / np.minimum(k, X_true_binary.sum(axis=1)) + return recall + +def sparse2torch_sparse(data): + """ + Convert scipy sparse matrix to torch sparse tensor with L2 Normalization + This is much faster than naive use of torch.FloatTensor(data.toarray()) + https://discuss.pytorch.org/t/sparse-tensor-use-cases/22047/2 + """ + samples = data.shape[0] + features = data.shape[1] + coo_data = data.tocoo() + indices = torch.LongTensor([coo_data.row, coo_data.col]) + row_norms_inv = 1 / np.sqrt(data.sum(1)) + row2val = {i : row_norms_inv[i].item() for i in range(samples)} + values = np.array([row2val[r] for r in coo_data.row]) + t = torch.sparse.FloatTensor(indices, torch.from_numpy(values).float(), [samples, features]) + return t + +def naive_sparse2tensor(data): + return torch.FloatTensor(data.toarray()) + +def loss_function_vae(recon_x, x, mu, logvar, anneal=1.0): + BCE = -torch.mean(torch.sum(F.log_softmax(recon_x, 1) * x, -1)) + KLD = -0.5 * torch.mean(torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1)) + + return BCE + anneal * KLD + +def loss_function_dae(recon_x, x): + BCE = -torch.mean(torch.sum(F.log_softmax(recon_x, 1) * x, -1)) + return BCE \ No newline at end of file diff --git a/code/multi/utils.py b/code/multi/utils.py new file mode 100644 index 0000000..cd1b094 --- /dev/null +++ b/code/multi/utils.py @@ -0,0 +1,136 @@ +import os +import random +import numpy as np +import torch +import time +from models import MultiDAE, MultiVAE +import logging +import json + +class Setting: + @staticmethod + def seed_everything(seed): + ''' + [description] + seed 값을 고정시키는 함수입니다. + + [arguments] + seed : seed 값 + ''' + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + + def __init__(self): + now = time.localtime() + now_date = time.strftime('%Y%m%d', now) + now_hour = time.strftime('%X', now) + save_time = now_date + '_' + now_hour.replace(':', '') + self.save_time = save_time + + def get_log_path(self, args): + ''' + [description] + log file을 저장할 경로를 반환하는 함수입니다. + + [arguments] + args : argparse로 입력받은 args 값으로 이를 통해 모델의 정보를 전달받습니다. + + [return] + path : log file을 저장할 경로를 반환합니다. + 이 때, 경로는 log/날짜_시간_모델명/ 입니다. + ''' + path = f'./log/{self.save_time}_{args.model}/' + return path + + def get_submit_filename(self, args): + ''' + [description] + submit file을 저장할 경로를 반환하는 함수입니다. + + [arguments] + args : argparse로 입력받은 args 값으로 이를 통해 모델의 정보를 전달받습니다. + + [return] + filename : submit file을 저장할 경로를 반환합니다. + 이 때, 파일명은 submit/날짜_시간_모델명.csv 입니다. + ''' + filename = f'./submit/{self.save_time}_{args.model}.csv' + return filename + + def make_dir(self,path): + ''' + [description] + 경로가 존재하지 않을 경우 해당 경로를 생성하며, 존재할 경우 pass를 하는 함수입니다. + + [arguments] + path : 경로 + + [return] + path : 경로 + ''' + if not os.path.exists(path): + os.makedirs(path) + else: + pass + return path + +class Logger: + def __init__(self, args, path): + """ + [description] + log file을 생성하는 클래스입니다. + + [arguments] + args : argparse로 입력받은 args 값으로 이를 통해 모델의 정보를 전달받습니다. + path : log file을 저장할 경로를 전달받습니다. + """ + self.args = args + self.path = path + + self.logger = logging.getLogger() + self.logger.setLevel(logging.INFO) + self.formatter = logging.Formatter('[%(asctime)s] - %(message)s') + + self.file_handler = logging.FileHandler(self.path+'train.log') + self.file_handler.setFormatter(self.formatter) + self.logger.addHandler(self.file_handler) + + def log(self, epoch, train_loss, valid_loss): + ''' + [description] + log file에 epoch, train loss, valid loss를 기록하는 함수입니다. + 이 때, log file은 train.log로 저장됩니다. + + [arguments] + epoch : epoch + train_loss : train loss + valid_loss : valid loss + ''' + message = f'epoch : {epoch}/{self.args.epochs} | train loss : {train_loss:.3f} | valid loss : {valid_loss:.3f}' + self.logger.info(message) + + def close(self): + ''' + [description] + log file을 닫는 함수입니다. + ''' + self.logger.removeHandler(self.file_handler) + self.file_handler.close() + + def save_args(self): + ''' + [description] + model에 사용된 args를 저장하는 함수입니다. + 이 때, 저장되는 파일명은 model.json으로 저장됩니다. + ''' + argparse_dict = self.args.__dict__ + + with open(f'{self.path}/model.json', 'w') as f: + json.dump(argparse_dict,f,indent=4) + + def __del__(self): + self.close() From f6f70fdec5e0ffb0342237b1a8a3285ab923cdd6 Mon Sep 17 00:00:00 2001 From: jinmin111 Date: Sat, 10 Feb 2024 14:06:48 +0000 Subject: [PATCH 16/49] docs: add requirements.txt #6 --- code/multi/requirements.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 code/multi/requirements.txt diff --git a/code/multi/requirements.txt b/code/multi/requirements.txt new file mode 100644 index 0000000..a19b915 --- /dev/null +++ b/code/multi/requirements.txt @@ -0,0 +1,4 @@ +numpy +pandas +torch +scipy \ No newline at end of file From feb38e3007c91c733ac3d2b7d3e639858ab3c345 Mon Sep 17 00:00:00 2001 From: Siwoo Date: Sun, 11 Feb 2024 16:49:12 +0000 Subject: [PATCH 17/49] fix: change column name for sequential model --- code/recbole/data_preprocessing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/code/recbole/data_preprocessing.py b/code/recbole/data_preprocessing.py index e2902be..0e4ebaa 100644 --- a/code/recbole/data_preprocessing.py +++ b/code/recbole/data_preprocessing.py @@ -15,7 +15,7 @@ def convert_into_atomic_files(config): if not os.path.exists(atomic_file_path): os.makedirs(atomic_file_path) - interactions.rename(columns={'user':'user:token', 'item':'item:token', 'time':'time:float'}, inplace = True) + interactions.rename(columns={'user':'user:token', 'item':'item:token', 'time':'timestamp:float'}, inplace = True) interactions.to_csv(os.path.join(config['data_path'], 'movie/movie.inter'), sep='\t', index=False) print("Save interaction data ...") From 4d88b140412731b0cd16d16d7c6d99e43923d038 Mon Sep 17 00:00:00 2001 From: jinmin111 Date: Mon, 12 Feb 2024 10:21:31 +0000 Subject: [PATCH 18/49] refactor: add drop_out argument #6 --- code/multi/main.py | 5 +++-- code/multi/models.py | 4 ++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/code/multi/main.py b/code/multi/main.py index 031422c..d892ea3 100644 --- a/code/multi/main.py +++ b/code/multi/main.py @@ -45,9 +45,9 @@ def main(args): print(f'--------------- INIT {args.model} ---------------') p_dims = [200, 600, n_items] if args.model=='Multi-DAE': - model = MultiDAE(p_dims).to(args.device) + model = MultiDAE(p_dims, args.drop_out).to(args.device) elif args.model=='Multi-VAE': - model = MultiVAE(p_dims).to(args.device) + model = MultiVAE(p_dims, args.drop_out).to(args.device) ######################## TRAIN print(f'--------------- {args.model} TRAINING ---------------') @@ -108,6 +108,7 @@ def main(args): parser.add_argument('--save', type=str, default='./saved', help='path to save the final model') parser.add_argument("--wandb_project_name", default="movierec-multi", type=str, help="Setting WandB Project Name") + parser.add_argument('--drop_out', type=float, default=0.5, help='multi-dae drop out') args = parser.parse_args([]) diff --git a/code/multi/models.py b/code/multi/models.py index f46c9f2..1952e69 100644 --- a/code/multi/models.py +++ b/code/multi/models.py @@ -14,7 +14,7 @@ class MultiDAE(nn.Module): https://arxiv.org/abs/1802.05814 """ - def __init__(self, p_dims, q_dims=None, dropout=0.5): + def __init__(self, p_dims, dropout, q_dims=None): super(MultiDAE, self).__init__() self.p_dims = p_dims if q_dims: @@ -62,7 +62,7 @@ class MultiVAE(nn.Module): https://arxiv.org/abs/1802.05814 """ - def __init__(self, p_dims, q_dims=None, dropout=0.5): + def __init__(self, p_dims, dropout, q_dims=None): super(MultiVAE, self).__init__() self.p_dims = p_dims if q_dims: From ec3684a6bb256c20dd4c96fe27417736197bcc2a Mon Sep 17 00:00:00 2001 From: Jang <8chris8@naver.com> Date: Tue, 13 Feb 2024 15:05:33 +0000 Subject: [PATCH 19/49] feat: sequential model --- code/recbole/EDA.py | 201 ++++++++++++++++++++++++++++++++++ code/recbole/configs/seq.yaml | 40 +++++++ code/recbole/inference.py | 33 +++++- code/recbole/rulebased.py | 44 ++++++++ 4 files changed, 315 insertions(+), 3 deletions(-) create mode 100644 code/recbole/EDA.py create mode 100644 code/recbole/configs/seq.yaml create mode 100644 code/recbole/rulebased.py diff --git a/code/recbole/EDA.py b/code/recbole/EDA.py new file mode 100644 index 0000000..80182b9 --- /dev/null +++ b/code/recbole/EDA.py @@ -0,0 +1,201 @@ +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import re + + +data_path = '/data/data/train' +interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) +grouped_items = interactions.groupby('user')['item'].apply(list) + + +data_path = '/data/data/train' +interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) +directors = pd.read_csv(os.path.join(data_path, 'directors.tsv'), sep='\t') +genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') +titles = pd.read_csv(os.path.join(data_path, 'titles.tsv'), sep='\t') +writers = pd.read_csv(os.path.join(data_path, 'writers.tsv'), sep='\t') +years = pd.read_csv(os.path.join(data_path, 'years.tsv'), sep='\t') + +#Genre, Writer, Director 한칸에 넣기 +grouped_genres= genres.groupby('item')['genre'].apply(list) +grouped_writers = writers.groupby('item')['writer'].apply(list) +grouped_directors = directors.groupby('item')['director'].apply(list) + +items = pd.merge(titles, years, on='item') +items = pd.merge(items, grouped_genres, on='item', how = 'left') +items = pd.merge(items, grouped_writers, on='item', how = 'left') +items = pd.merge(items, grouped_directors, on='item', how = 'left') + +#기존 +items = pd.merge(titles, years, on='item') +items = pd.merge(items, genres, on='item') +items = pd.merge(items, writers, on='item', how = 'left') +items = pd.merge(items, directors, on='item', how = 'left') + +# year_bin 추가 +bins = pd.cut(items['year'], bins=10) +bin_labels = [int(b.left) for b in bins.cat.categories] +items['year_bin'] = pd.cut(items['year'], bins=10, labels=bin_labels) + +# popularity_bin 추가 +item_counts = interactions['item'].value_counts() +item_counts = pd.DataFrame(item_counts) +item_counts.reset_index(inplace=True) +item_counts.columns = ['item', 'count'] +item_counts['popularity_bin'] = pd.cut(item_counts['count'], bins=10, labels=False) +items = items.merge(item_counts[['item', 'popularity_bin']], on='item', how='left') + + + + +item_counts_by_year_bin = items.groupby('year_bin').size() +plt.bar(item_counts_by_year_bin.index, item_counts_by_year_bin.values) +plt.savefig('year-item수.png') + + +plt.figure(figsize=(10, 6)) +item_counts_by_popularity_bin = items.groupby('popularity_bin').size() +plt.bar(item_counts_by_popularity_bin.index, item_counts_by_popularity_bin.values) +plt.savefig('popularity-item수.png') + +# interactions에서의 item 수 : 6807개 +# Title에서의 item 수 : 6807개 (위와 전부 동일) -> 중복 제외 6616 +# Years에서의 item수 : 6799개 +# Directors에서의 item수 : 5503개 +# Writers에서의 item수 : 5648개 +# 모든 Item은 Ineractions(Title)에 포함된다 + + +## title 리메이크 확인 +titles['title'] = titles['title'].apply(lambda x: re.sub(r'\s*\(\d{4}\)', '', x)) +titles['title'].nunique() +titles.to_csv(os.path.join(data_path, 'title_sorted'), sep='\t', index=False) + +## Directors +# 5905개의 data, 5503개의 영화 +# 1340개의 감독중 1169개의 감독이 10편이상 촬영 +# 58개의 영화는 2명이상의 감독이 있다. 한영화의 최대 감독수는 14명 +directors['director'].value_counts +directors['item'].nunique() + +## Writers +# 11307개의 data, 5648개의 영화 +# Writers와 Directors는 4973개 일치 +# 1307개의 영화는 2명이상의 작가가 있고. 한영화의 최대 작가수는 24명 +writers['writer'].value_counts() +writers['item'].nunique() + +merged_df = pd.merge(directors, writers, on='item', how='inner') +common_items_count = merged_df['item'].nunique() +print("두 데이터프레임에서 공통된 item의 수:", common_items_count) + + +item_counts = writers.groupby('item').size() +print(len(item_counts[item_counts>10])) + + + + +interactions.to_csv(os.path.join(data_path, 'EDAinter.csv'), sep='\t', index=False) +print("Save interaction data ...") +print("Save user data ...") + +items = pd.merge(titles, years, on='item',) +items = pd.merge(items, genres, on='item') +items = pd.merge(items, writers, on='item', how = 'left') +items = pd.merge(items, directors, on='item', how = 'left') +items = items.fillna("none") +items.to_csv(os.path.join(data_path,'EDAitem.csv'), sep='\t', index=False) +print("Save item data ...") + + +#Data 확인 +min_time = interactions['time'].min() +max_time = interactions['time'].max() +# 1970년 1월 1일 자정(UTC)을 기준 : 2005년 3월 ~ 2015년 3월 +# min : 1113220585 / max : 1427781052 + +#item_genre +item_genre_group = genres.groupby('item')['genre'].apply(list) +print(item_genre_group) +# interactions과 genres를 item을 기준으로 병합 +merged = interactions.merge(genres, on='item') +#사용자별로 본 영화의 년도수 묶기 +merged_df = pd.merge(interactions, items, on='item') +user_year_count = merged_df.groupby(['user', 'year']).size().reset_index(name='counts') +user_year_count.to_csv(os.path.join(data_path, 'user_year_count.csv')) +# 년도를 기준으로 2000년 이하와 이후로 구분 +user_year_count['period'] = pd.cut(user_year_count['year'], bins=[0, 2000, user_year_count['year'].max()], labels=['2000년 이하', '2000년 이후']) + +# 사용자별로 2000년 이하와 이후의 아이템을 본 카운트 +user_period_count = user_year_count.groupby(['user', 'period'])['counts'].sum().reset_index() +user_period_count.to_csv(os.path.join(data_path, 'user_year_count_bin.csv')) + + +# 사용자별로 본 장르를 리스트로 묶기 +user_genre_counts = merged.groupby('user')['genre'].value_counts() +print(user_genre_counts) +user_genre_counts.to_csv(os.path.join(data_path, 'item_genre_group.csv')) + + +# item별 시청횟수 +# 6807개의 item중 2000개는 100회 이하 시청했다. +num_items = interactions['item'].nunique() +print('아이템의 개수:', num_items) + +item_counts = interactions['item'].value_counts() +print(item_counts) + +item_counts_over_100 = item_counts[item_counts >= 5000] +print(item_counts_over_100) + +item_counts = interactions['item'].value_counts() +plt.figure(figsize=(12,6)) +plt.bar(item_counts.index, item_counts.values) +plt.title('Item Interaction Counts') +plt.xlabel('Item ID') +plt.ylabel('Count') +plt.savefig('item_counts.png') + +print(item_counts[10]) + + + + +#User별 시청횟수 +user_view_counts = interactions['user'].value_counts() +print(user_view_counts) + +user_view_counts_over = user_view_counts[user_view_counts >= 100] +print(user_view_counts_over) + + +### Submission 결과 확인 + +submission = pd.read_csv(os.path.join('output/submission_EASE.csv')) +train = pd.read_csv('data/data/train/train_ratings.csv') + +## 각 Train과 Submission 내에는 중복이 없음 + +submission_duplicates = submission.duplicated(subset=['user', 'item']) +train_duplicates = train.duplicated(subset=['user', 'item']) +print('submission 내부에 중복된 경우가 있는지:', submission_duplicates.any()) +print('train 내부에 중복된 경우가 있는지:', train_duplicates.any()) + + +## Sequential submission과 train에는 중복이 있음 + +# submission과 train 데이터셋에서 'user'와 'item' 열만 추출합니다. +submission_user_item = submission[['user', 'item']] +train_user_item = train[['user', 'item']] +# 두 데이터셋을 병합하고, 'user'와 'item' 열이 중복되는지 확인합니다. +merged = pd.concat([submission_user_item, train_user_item]) +duplicates = merged.duplicated(subset=['user', 'item']) +# 중복된 경우가 있는지 확인합니다. +print('Sequential submission과 train에 중복된 경우가 있는지:', duplicates.any()) + +# 중복된 데이터의 개수를 확인합니다. +num_duplicates = duplicates.sum() +print('Sequential submission과 train에서 중복된 데이터의 개수:', num_duplicates) \ No newline at end of file diff --git a/code/recbole/configs/seq.yaml b/code/recbole/configs/seq.yaml new file mode 100644 index 0000000..4ce60fb --- /dev/null +++ b/code/recbole/configs/seq.yaml @@ -0,0 +1,40 @@ + +model: SASRec +#GRU4Rec, BERT4Rec, SASRec +data_path: /data/data/train +dataset: movie +field_separator: " " +seq_separator: " " +USER_ID_FIELD: user +ITEM_ID_FIELD: item +TIME_FIELD: time +show_progress: false +use_gpu: true + +load_col: + inter: [user, item, time] + user: [user] + #item: [item, genre, director, title, writer, year] + #item: [item, genre, title] + +user_inter_num_interval: "[0,inf)" +item_inter_num_interval: "[0,inf)" + +epochs: 20 +train_batch_size: 4096 +metrics: ["Recall", "NDCG"] +topk: [10] +valid_metric: NDCG@10 + +eval_args: + group_by: user + order: TO + split: # RS:Random / LS:마지막순서 + LS: valid_and_test #각 그룹의 마지막 두 데이터를 Valid와 Test에 사용 + mode: full + +# train_neg_sample_args: None +neg_sampling : Null + +log_wandb: True +wandb_project: recbole_seq diff --git a/code/recbole/inference.py b/code/recbole/inference.py index e9ecdd4..90f18ef 100644 --- a/code/recbole/inference.py +++ b/code/recbole/inference.py @@ -1,10 +1,37 @@ import os +import torch +import numpy as np import pandas as pd from recbole.quick_start import load_data_and_model from recbole.utils.case_study import full_sort_topk from args import parse_args +def seq_sort(dataset,topk_iid_list): + topk_iid_list_1000 = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()) + data_path = '/data/data/train' + interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) + grouped_items = interactions.groupby('user')['item'].apply(list) + + # 결과를 저장할 텐서 생성 + result_tensor = torch.zeros((topk_iid_list_1000.shape[0], 10), dtype=torch.int32, device='cuda:0') + + # 각 사용자에 대해 반복 + for user_idx in range(topk_iid_list_1000.shape[0]): + # 해당 사용자의 topk_iid_list와 grouped_items 가져오기 + user_topk_iid = topk_iid_list_1000[user_idx,:] + user_grouped_items = grouped_items.iloc[user_idx] + + # topk_iid_list에서 grouped_items에 있는 아이템을 제외 + filtered_items = user_topk_iid[~np.isin(user_topk_iid, user_grouped_items)] + + # 상위 10개의 아이템 선택 + top_10_items = filtered_items[:10].astype(int) + + # 결과를 텐서에 저장 + result_tensor[user_idx] = torch.from_numpy(top_10_items) + return result_tensor + def main(model_path): config, model, dataset, train_data, valid_data, test_data = load_data_and_model( @@ -14,9 +41,9 @@ def main(model_path): external_user_ids = dataset.id2token(dataset.uid_field, list(range(dataset.user_num)))[1:] uid_series = dataset.token2id(dataset.uid_field, external_user_ids) - topk_score, topk_iid_list = full_sort_topk(uid_series, model, test_data, k=10, device=config["device"]) - external_item_list = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()).flatten() - + topk_score, topk_iid_list = full_sort_topk(uid_series, model, test_data, k=3000, device=config["device"]) + #external_item_list = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()).flatten() + external_item_list = seq_sort(dataset,topk_iid_list).flatten() #Seq의 과거시청이력 제거 output = pd.read_csv('/data/data/eval/sample_submission.csv') output['item'] = [int(item) for item in external_item_list] if not os.path.exists('output/'): diff --git a/code/recbole/rulebased.py b/code/recbole/rulebased.py new file mode 100644 index 0000000..994f782 --- /dev/null +++ b/code/recbole/rulebased.py @@ -0,0 +1,44 @@ +import os +import numpy as np +import pandas as pd + +data_path = '/data/data/train' +interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) +genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') +# interactions과 genres를 item을 기준으로 병합 +merged = interactions.merge(genres, on='item') +# 사용자별로 본 장르를 리스트로 묶기 +user_genre_counts = merged.groupby('user')['genre'].value_counts() +print(user_genre_counts) +most_watched_genre_per_user = user_genre_counts.groupby(level=0).idxmax() +print(most_watched_genre_per_user) + + +user_item_group = interactions.groupby('user')['item'].apply(list) +print(user_item_group) + +item_counts = interactions['item'].value_counts() +print(item_counts) + +recommendations = [] + +# 각 사용자에 대해서 +for user, watched_items in user_item_group.items(): + # 아직 시청하지 않은 아이템을 찾는다 + unwatched_items = item_counts[~item_counts.index.isin(watched_items)] + # 사용자가 가장 많이 시청한 장르의 아이템만 선택 + genre_items = genres[genres['genre'] == most_watched_genre_per_user[user][1]]['item'] + preferred_items = unwatched_items[unwatched_items.index.isin(genre_items)] + # 시청 횟수가 많은 순서대로 아이템을 정렬한다 + recommended_items = preferred_items.sort_values(ascending=False) + # 상위 10개 아이템을 추천한다 + top_10_items = recommended_items[:10] + # 추천 결과를 저장한다 + for item in top_10_items.index: + recommendations.append((user, item)) + + +# 추천 결과를 DataFrame으로 변환한다 +recommendations_df = pd.DataFrame(recommendations, columns=['user', 'item']) +print(recommendations_df) +recommendations_df.to_csv('output/submission_rule_genres.csv',index=False) \ No newline at end of file From 3ee9aa502c5ff69a31a13fdb9baca8cab48fc744 Mon Sep 17 00:00:00 2001 From: Kim_SuBeen <83735049+ksb3966@users.noreply.github.com> Date: Wed, 14 Feb 2024 22:00:26 +0900 Subject: [PATCH 20/49] feat/add deep_fm model --- code/recbole/configs/deepfm.yaml | 40 ++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 code/recbole/configs/deepfm.yaml diff --git a/code/recbole/configs/deepfm.yaml b/code/recbole/configs/deepfm.yaml new file mode 100644 index 0000000..b109441 --- /dev/null +++ b/code/recbole/configs/deepfm.yaml @@ -0,0 +1,40 @@ + +model: DeepFM + +data_path: ../../data/train +dataset: movie +field_separator: " " +seq_separator: " " +USER_ID_FIELD: user +ITEM_ID_FIELD: item +TIME_FIELD: time +show_progress: false +use_gpu: true + +load_col: + inter: [user, item, time] + user: [user] + item: [item, genre, director, title, writer, year] + +user_inter_num_interval: "[0,inf)" +item_inter_num_interval: "[0,inf)" + +epochs: 20 +train_batch_size: 4096 +metrics: ["Recall", "NDCG"] +topk: [10] +valid_metric: Recall@10 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +train_neg_sample_args: + distribution: popularity + candidate_num: 1 + +log_wandb: True +wandb_project: recbole_deepfm From 40177511801c8001f0a8e0e14b2d7be19572fac1 Mon Sep 17 00:00:00 2001 From: jinmin111 Date: Thu, 15 Feb 2024 00:59:48 +0000 Subject: [PATCH 21/49] feat: add inference #6 --- code/multi/inference.py | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 code/multi/inference.py diff --git a/code/multi/inference.py b/code/multi/inference.py new file mode 100644 index 0000000..a82550a --- /dev/null +++ b/code/multi/inference.py @@ -0,0 +1,27 @@ +import torch +import numpy as np +from trainer import naive_sparse2tensor + + +def predict(args, model, data_tr): + model.eval() + + top_items = [] + + with torch.no_grad(): + data_tensor = naive_sparse2tensor(data_tr).to('cuda') + predicts = model(data_tensor) + + if args.model == 'Multi-VAE': + predicts = predicts[0].cpu().numpy() + elif args.model == 'Multi-DAE': + predicts = predicts.cpu().numpy() + + predicts[data_tr.nonzero()] = -np.inf + + top_scores, top_ids = torch.topk(torch.from_numpy(predicts).float().to('cuda'), k=10, dim=1) + for user_id, item_ids in enumerate(top_ids): + for item_id in item_ids: + top_items.append((user_id, item_id.item())) + + return top_items \ No newline at end of file From d84249eed197d583baf490f94125f1018a90b4d8 Mon Sep 17 00:00:00 2001 From: Siwoo Date: Thu, 15 Feb 2024 05:11:53 +0000 Subject: [PATCH 22/49] =?UTF-8?q?fix:save=20=EA=B3=BC=EC=A0=95=EC=97=90?= =?UTF-8?q?=EC=84=9C=20=EC=A0=80=EC=9E=A5=EA=B2=BD=EB=A1=9C=20=EC=97=86?= =?UTF-8?q?=EC=9C=BC=EB=A9=B4=20=EB=A7=8C=EB=93=A4=EB=8F=84=EB=A1=9D=20?= =?UTF-8?q?=EB=B3=80=EA=B2=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/multi/trainer.py | 2 ++ code/multi/utils.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/code/multi/trainer.py b/code/multi/trainer.py index 0a118f3..5537941 100644 --- a/code/multi/trainer.py +++ b/code/multi/trainer.py @@ -10,6 +10,8 @@ def run(args, model, loader, logger, setting): optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd) + if not os.path.exists(args.save): + os.makedirs(args.save) model_path = os.path.join(args.save, setting.get_submit_filename(args)[9:-4]+'.pt') diff --git a/code/multi/utils.py b/code/multi/utils.py index cd1b094..95290e9 100644 --- a/code/multi/utils.py +++ b/code/multi/utils.py @@ -58,6 +58,8 @@ def get_submit_filename(self, args): filename : submit file을 저장할 경로를 반환합니다. 이 때, 파일명은 submit/날짜_시간_모델명.csv 입니다. ''' + if not os.path.exists('submit/'): + os.makedirs('submit/') filename = f'./submit/{self.save_time}_{args.model}.csv' return filename From 048abeee22eceb65714f9b6a9120da9369253ac6 Mon Sep 17 00:00:00 2001 From: Siwoo Date: Thu, 15 Feb 2024 05:12:26 +0000 Subject: [PATCH 23/49] update gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 1201e85..2f0e7dd 100644 --- a/.gitignore +++ b/.gitignore @@ -2,5 +2,6 @@ output wandb __pycache__ saved +submit log log_tensorboard \ No newline at end of file From a6b35ad70113da5fda3fd80889e246073137994b Mon Sep 17 00:00:00 2001 From: jinmin111 Date: Thu, 15 Feb 2024 05:17:44 +0000 Subject: [PATCH 24/49] =?UTF-8?q?fix:=20arg=20=EB=B2=84=EA=B7=B8=20?= =?UTF-8?q?=EC=88=98=EC=A0=95=20#6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/multi/main.py | 34 ++++++++++++++++------------------ 1 file changed, 16 insertions(+), 18 deletions(-) diff --git a/code/multi/main.py b/code/multi/main.py index d892ea3..1a347f8 100644 --- a/code/multi/main.py +++ b/code/multi/main.py @@ -60,9 +60,6 @@ def main(args): ######################## SAVE PREDICT print(f'--------------- SAVE {args.model} PREDICT ---------------') - # submission = pd.read_csv(args.data_path + 'sample_submission.csv') - # submission['rating'] = predicts - result = pd.DataFrame(predicts, columns=['user', 'item']) result['user'] = result['user'].apply(lambda x : id2profile[x]) @@ -82,34 +79,35 @@ def main(args): ## 각종 파라미터 세팅 parser = argparse.ArgumentParser(description='PyTorch Variational Autoencoders for Collaborative Filtering') + arg = parser.add_argument - parser.add_argument('--data', type=str, default='../../../data/train/', + arg('--data', type=str, default='../../../data/train/', help='Movielens dataset location') - parser.add_argument('--model', type=str, default='Multi-DAE', + arg('--model', type=str, default='Multi-DAE', help='model name') - parser.add_argument('--lr', type=float, default=1e-4, + arg('--lr', type=float, default=1e-4, help='initial learning rate') - parser.add_argument('--wd', type=float, default=0.0, + arg('--wd', type=float, default=0.0, help='weight decay coefficient') - parser.add_argument('--batch_size', type=int, default=500, + arg('--batch_size', type=int, default=500, help='batch size') - parser.add_argument('--epochs', type=int, default=10, + arg('--epochs', type=int, default=10, help='upper epoch limit') - parser.add_argument('--total_anneal_steps', type=int, default=200000, + arg('--total_anneal_steps', type=int, default=200000, help='the total number of gradient updates for annealing') - parser.add_argument('--anneal_cap', type=float, default=0.2, + arg('--anneal_cap', type=float, default=0.2, help='largest annealing parameter') - parser.add_argument('--seed', type=int, default=1111, + arg('--seed', type=int, default=1111, help='random seed') - parser.add_argument('--device', type=str, default='cuda', + arg('--device', type=str, default='cuda', help='use gpu') - parser.add_argument('--log_interval', type=int, default=100, metavar='N', + arg('--log_interval', type=int, default=100, metavar='N', help='report interval') - parser.add_argument('--save', type=str, default='./saved', + arg('--save', type=str, default='./saved', help='path to save the final model') - parser.add_argument("--wandb_project_name", default="movierec-multi", type=str, help="Setting WandB Project Name") - parser.add_argument('--drop_out', type=float, default=0.5, help='multi-dae drop out') + arg("--wandb_project_name", default="movierec-multi", type=str, help="Setting WandB Project Name") + arg('--drop_out', type=float, default=0.5, help='multi-dae drop out') - args = parser.parse_args([]) + args = parser.parse_args() main(args) \ No newline at end of file From d0a5c6ce2eebfa8134abf4d290a7df3e3d2c659f Mon Sep 17 00:00:00 2001 From: Siwoo Date: Thu, 15 Feb 2024 15:44:34 +0000 Subject: [PATCH 25/49] fix: recvae final config --- code/recbole/configs/recvae.yaml | 55 ++++++++++++++++++++------------ 1 file changed, 35 insertions(+), 20 deletions(-) diff --git a/code/recbole/configs/recvae.yaml b/code/recbole/configs/recvae.yaml index 126ff13..e46f1d2 100644 --- a/code/recbole/configs/recvae.yaml +++ b/code/recbole/configs/recvae.yaml @@ -1,29 +1,33 @@ - -model: RecVAE +#environment config +seed: 42 +log_wandb: True +wandb_project: recbole_recvae +use_gpu: true data_path: /data/data/train dataset: movie -field_separator: " " -seq_separator: " " + +#dataset config : General Recommendation USER_ID_FIELD: user ITEM_ID_FIELD: item -TIME_FIELD: time -show_progress: false -use_gpu: true +field_separator: " " +seq_separator: " " load_col: - inter: [user, item, time] - user: [user] - item: [item, genre, director, title, writer, year] + inter: [user, item] user_inter_num_interval: "[0,inf)" item_inter_num_interval: "[0,inf)" +train_neg_sample_args: + distribution: popularity + candidate_num: 1 -epochs: 340 -train_batch_size: 4096 -metrics: ["Recall", "NDCG"] -topk: [10] -valid_metric: NDCG@10 +# model config +model: RecVAE + +# Training and evaluation config +epochs: 90 +train_batch_size: 2048 eval_args: group_by: user @@ -32,9 +36,20 @@ eval_args: LS: valid_and_test mode: full -train_neg_sample_args: - distribution: popularity - candidate_num: 1 +metrics: ["Recall", "NDCG"] +topk: [10] +valid_metric: Recall@10 + + + + + + + + + + + + + -log_wandb: True -wandb_project: recbole_recvae From 7089e5efb934640f566e43c389dd5174970bdc7a Mon Sep 17 00:00:00 2001 From: Siwoo Date: Sun, 18 Feb 2024 17:14:48 +0000 Subject: [PATCH 26/49] feat: add recbole ensemble --- code/recbole/args.py | 5 ++++ code/recbole/ensemble.py | 60 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 65 insertions(+) create mode 100644 code/recbole/ensemble.py diff --git a/code/recbole/args.py b/code/recbole/args.py index c751de5..7cf57f6 100644 --- a/code/recbole/args.py +++ b/code/recbole/args.py @@ -6,5 +6,10 @@ def parse_args(): parser.add_argument("--model", default='recvae', type=str, help='model name in recbole library') parser.add_argument("--model_path", default='saved/RecVAE-Feb-06-2024_16-27-17.pth', type=str, help='saved model path to predict') + #ensemble configs + parser.add_argument("--models", default=',', type=str, help='path of saved models that will be ensembled') + parser.add_argument("--voting", default='soft', type=str, help='choose soft or hard') + parser.add_argument("--weights", default='0.5,0.5', type=str, help='weights of models when soft voting') + args = parser.parse_args() return args \ No newline at end of file diff --git a/code/recbole/ensemble.py b/code/recbole/ensemble.py new file mode 100644 index 0000000..a226d4a --- /dev/null +++ b/code/recbole/ensemble.py @@ -0,0 +1,60 @@ +import os +from args import parse_args + +import pandas as pd +import torch +from recbole.quick_start import load_data_and_model +from recbole.utils.case_study import full_sort_scores + +def main(args): + + models = list(map(lambda x: x.strip(),args.models.split(','))) + if not models: + raise RuntimeError("there's no input models") + else: + for model_path in models: + if not os.path.exists(model_path): + raise RuntimeError(f"{model_path} not exists") + + if args.voting == 'soft': + weights = list(map(lambda x: float(x.strip()),args.weights.split(','))) + assert len(models) == len(weights), "num of models and num of weights need to be same" + + + for i, model_path in enumerate(models): + config, model, dataset, _, _, test_data = load_data_and_model(model_path) + + external_user_ids = dataset.id2token(dataset.uid_field, list(range(dataset.user_num)))[1:] + uid_series = dataset.token2id(dataset.uid_field, external_user_ids) + + if i==0: + scores = weights[i]*full_sort_scores(uid_series, model, test_data, config['device']).to(config['device']) + else: + scores += weights[i]*full_sort_scores(uid_series, model, test_data, config['device']).to(config['device']) + + elif args.voting == 'hard': + + for i, model_path in enumerate(models): + config, model, dataset, _, _, test_data = load_data_and_model(model_path) + + external_user_ids = dataset.id2token(dataset.uid_field, list(range(dataset.user_num)))[1:] + uid_series = dataset.token2id(dataset.uid_field, external_user_ids) + + if i == 0: + scores = full_sort_scores(uid_series, model, test_data, config['device']).to(config['device']) + else: + scores += full_sort_scores(uid_series, model, test_data, config['device']).to(config['device']) + + topk_score, topk_iid_list = torch.topk(scores,10) + external_item_list = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()).flatten() + + output = pd.read_csv('/data/data/eval/sample_submission.csv') + output['item'] = [int(item) for item in external_item_list] + if not os.path.exists('output/'): + os.makedirs('output/') + output.to_csv('output/submission.csv') + print("Save submission ... ") + +if __name__=='__main__': + args = parse_args() + main(args) \ No newline at end of file From 414f0a6b1f3ccb66bc7fbc07a165ad75f7fb6a96 Mon Sep 17 00:00:00 2001 From: Seungbin Date: Mon, 19 Feb 2024 01:22:44 +0000 Subject: [PATCH 27/49] feat: add slimElastic model --- code/recbole/configs/slim.yaml | 40 ++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 code/recbole/configs/slim.yaml diff --git a/code/recbole/configs/slim.yaml b/code/recbole/configs/slim.yaml new file mode 100644 index 0000000..4f2857f --- /dev/null +++ b/code/recbole/configs/slim.yaml @@ -0,0 +1,40 @@ +#environment config +seed : 42 +log_wandb: True +wandb_project: slim +use_gpu: True + +data_path: /data/ephemeral/movie/data/train +dataset: movie + +# dataset config : General Recommendation +USER_ID_FIELD: user +ITEM_ID_FIELD: item +LABEL_FIELD: label +field_separator: " " +seq_separator: " " +load_col: + inter: [user, item] #inter file에서 사용할 cols + +# model config +model: SLIMElastic +embedding_size: 64 +positive_only: False +alpha: 0.1 +l1_ratio: 0.001 + +# Training and evaluation config +epochs: 1 +train_batch_size: 2048 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +metrics: ['Recall', 'NDCG', 'Precision'] +topk: 10 +valid_metric: Recall@10 +metric_decimal_place: 4 \ No newline at end of file From 80c2e7846631650ec5aafa05cf180d013234bece Mon Sep 17 00:00:00 2001 From: Kim_SuBeen <83735049+ksb3966@users.noreply.github.com> Date: Mon, 19 Feb 2024 23:32:23 +0900 Subject: [PATCH 28/49] Feat. Add Bert4Rec Model Base --- code/data_loaders.py | 53 +++++ code/logger/__init__.py | 2 + code/logger/logger.py | 22 +++ code/logger/logger_config.json | 32 +++ code/model/loss.py | 5 + code/model/metric.py | 20 ++ code/model/model.py | 118 +++++++++++ code/preprocess.py | 48 +++++ code/requirements.txt | 5 + code/train.py | 211 ++++++++++++++++++++ code/train_args.py | 73 +++++++ code/trainer/__init__.py | 1 + code/trainer/trainer.py | 320 ++++++++++++++++++++++++++++++ code/utils/__init__.py | 1 + code/utils/util.py | 349 +++++++++++++++++++++++++++++++++ 15 files changed, 1260 insertions(+) create mode 100644 code/data_loaders.py create mode 100644 code/logger/__init__.py create mode 100644 code/logger/logger.py create mode 100644 code/logger/logger_config.json create mode 100644 code/model/loss.py create mode 100644 code/model/metric.py create mode 100644 code/model/model.py create mode 100644 code/preprocess.py create mode 100644 code/requirements.txt create mode 100644 code/train.py create mode 100644 code/train_args.py create mode 100644 code/trainer/__init__.py create mode 100644 code/trainer/trainer.py create mode 100644 code/utils/__init__.py create mode 100644 code/utils/util.py diff --git a/code/data_loaders.py b/code/data_loaders.py new file mode 100644 index 0000000..d07a391 --- /dev/null +++ b/code/data_loaders.py @@ -0,0 +1,53 @@ +import math +import numpy as np +import pandas as pd +from tqdm import tqdm +from collections import defaultdict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import Dataset, DataLoader + +class SeqDataset(Dataset): + def __init__(self, user_train, num_user, num_item, max_len, mask_prob): + self.user_train = user_train + self.num_user = num_user + self.num_item = num_item + self.max_len = max_len + self.mask_prob = mask_prob + + def __len__(self): + # 총 user의 수 = 학습에 사용할 sequence의 수 + return self.num_user + + def __getitem__(self, user): + # iterator를 구동할 때 사용됩니다. + seq = self.user_train[user] + tokens = [] + labels = [] + for s in seq: + prob = np.random.random() # TODO1: numpy를 사용해서 0~1 사이의 임의의 값을 샘플링하세요. + if prob < self.mask_prob: + prob /= self.mask_prob + + # BERT 학습 + if prob < 0.8: + # masking + tokens.append(self.num_item + 1) # mask_index: num_item + 1, 0: pad, 1~num_item: item index + elif prob < 0.9: + tokens.append(np.random.randint(1, self.num_item+1)) # item random sampling + else: + tokens.append(s) + labels.append(s) # 학습에 사용 + else: + tokens.append(s) + labels.append(0) # 학습에 사용 X, trivial + tokens = tokens[-self.max_len:] + labels = labels[-self.max_len:] + mask_len = self.max_len - len(tokens) + + # zero padding + tokens = [0] * mask_len + tokens + labels = [0] * mask_len + labels + return torch.LongTensor(tokens), torch.LongTensor(labels) \ No newline at end of file diff --git a/code/logger/__init__.py b/code/logger/__init__.py new file mode 100644 index 0000000..5f3763b --- /dev/null +++ b/code/logger/__init__.py @@ -0,0 +1,2 @@ +from .logger import * +from .visualization import * \ No newline at end of file diff --git a/code/logger/logger.py b/code/logger/logger.py new file mode 100644 index 0000000..4599fb0 --- /dev/null +++ b/code/logger/logger.py @@ -0,0 +1,22 @@ +import logging +import logging.config +from pathlib import Path +from utils import read_json + + +def setup_logging(save_dir, log_config='logger/logger_config.json', default_level=logging.INFO): + """ + Setup logging configuration + """ + log_config = Path(log_config) + if log_config.is_file(): + config = read_json(log_config) + # modify logging paths based on run config + for _, handler in config['handlers'].items(): + if 'filename' in handler: + handler['filename'] = str(save_dir / handler['filename']) + + logging.config.dictConfig(config) + else: + print("Warning: logging configuration file is not found in {}.".format(log_config)) + logging.basicConfig(level=default_level) diff --git a/code/logger/logger_config.json b/code/logger/logger_config.json new file mode 100644 index 0000000..c3e7e02 --- /dev/null +++ b/code/logger/logger_config.json @@ -0,0 +1,32 @@ + +{ + "version": 1, + "disable_existing_loggers": false, + "formatters": { + "simple": {"format": "%(message)s"}, + "datetime": {"format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"} + }, + "handlers": { + "console": { + "class": "logging.StreamHandler", + "level": "DEBUG", + "formatter": "simple", + "stream": "ext://sys.stdout" + }, + "info_file_handler": { + "class": "logging.handlers.RotatingFileHandler", + "level": "INFO", + "formatter": "datetime", + "filename": "info.log", + "maxBytes": 10485760, + "backupCount": 20, "encoding": "utf8" + } + }, + "root": { + "level": "INFO", + "handlers": [ + "console", + "info_file_handler" + ] + } +} \ No newline at end of file diff --git a/code/model/loss.py b/code/model/loss.py new file mode 100644 index 0000000..1cd24cb --- /dev/null +++ b/code/model/loss.py @@ -0,0 +1,5 @@ +import torch.nn.functional as F + + +def nll_loss(output, target): + return F.nll_loss(output, target) diff --git a/code/model/metric.py b/code/model/metric.py new file mode 100644 index 0000000..df08e03 --- /dev/null +++ b/code/model/metric.py @@ -0,0 +1,20 @@ +import torch + + +def accuracy(output, target): + with torch.no_grad(): + pred = torch.argmax(output, dim=1) + assert pred.shape[0] == len(target) + correct = 0 + correct += torch.sum(pred == target).item() + return correct / len(target) + + +def top_k_acc(output, target, k=3): + with torch.no_grad(): + pred = torch.topk(output, k, dim=1)[1] + assert pred.shape[0] == len(target) + correct = 0 + for i in range(k): + correct += torch.sum(pred[:, i] == target).item() + return correct / len(target) diff --git a/code/model/model.py b/code/model/model.py new file mode 100644 index 0000000..87848ee --- /dev/null +++ b/code/model/model.py @@ -0,0 +1,118 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import math +import numpy as np + +class ScaledDotProductAttention(nn.Module): + def __init__(self, hidden_units, dropout_rate): + super(ScaledDotProductAttention, self).__init__() + self.hidden_units = hidden_units + self.dropout = nn.Dropout(dropout_rate) # dropout rate + + def forward(self, Q, K, V, mask): + attn_score = torch.matmul(Q, K.transpose(2, 3)) / math.sqrt(self.hidden_units) + attn_score = attn_score.masked_fill(mask == 0, -1e9) # 유사도가 0인 지점은 -infinity로 보내 softmax 결과가 0이 되도록 함 + attn_dist = self.dropout(F.softmax(attn_score, dim=-1)) # attention distribution + output = torch.matmul(attn_dist, V) # dim of output : batchSize x num_head x seqLen x hidden_units + return output, attn_dist + +class MultiHeadAttention(nn.Module): + def __init__(self, num_heads, hidden_units, dropout_rate): + super(MultiHeadAttention, self).__init__() + self.num_heads = num_heads # head의 수 + self.hidden_units = hidden_units + + # query, key, value, output 생성을 위해 Linear 모델 생성 + self.W_Q = nn.Linear(hidden_units, hidden_units, bias=False) + self.W_K = nn.Linear(hidden_units, hidden_units, bias=False) + self.W_V = nn.Linear(hidden_units, hidden_units, bias=False) + self.W_O = nn.Linear(hidden_units, hidden_units, bias=False) + + self.attention = ScaledDotProductAttention(hidden_units, dropout_rate) # scaled dot product attention module을 사용하여 attention 계산 + self.dropout = nn.Dropout(dropout_rate) # dropout rate + self.layerNorm = nn.LayerNorm(hidden_units, 1e-6) # layer normalization + + def forward(self, enc, mask): + residual = enc # residual connection을 위해 residual 부분을 저장 => [1024,50,50] B T H + batch_size, seqlen = enc.size(0), enc.size(1) + + #-- Query, Key, Value를 (num_head)개의 Head로 나누어 각기 다른 Linear projection을 통과시킴 + Q = self.W_Q(enc).view(batch_size, seqlen, self.num_heads, self.hidden_units//self.num_heads) #[1024,50,1,50] B T h H => [1024,50 ,?, 50] + K = self.W_K(enc).view(batch_size, seqlen, self.num_heads, self.hidden_units//self.num_heads) + V = self.W_V(enc).view(batch_size, seqlen, self.num_heads, self.hidden_units//self.num_heads) + + #-- Head별로 각기 다른 attention이 가능하도록 Transpose 후 각각 attention에 통과시킴 + Q, K, V = Q.transpose(1, 2), K.transpose(1, 2), V.transpose(1, 2) + output, attn_dist = self.attention(Q, K, V, mask) # padding mask + + #-- 다시 Transpose한 후 모든 head들의 attention 결과를 합칩니다. + output = output.transpose(1, 2).contiguous() + output = output.view(batch_size, seqlen, -1) + + #-- Linear Projection, Dropout, Residual sum, and Layer Normalization + output = self.layerNorm(self.dropout(self.W_O(output)) + residual) + return output, attn_dist + +class PositionwiseFeedForward(nn.Module): + def __init__(self, hidden_units, dropout_rate): + super(PositionwiseFeedForward, self).__init__() + + # SASRec과의 dimension 차이가 있습니다. + self.W_1 = nn.Linear(hidden_units , 4 * hidden_units) + self.W_2 = nn.Linear(4 * hidden_units, hidden_units ) + + self.dropout = nn.Dropout(dropout_rate) + self.layerNorm = nn.LayerNorm(hidden_units, 1e-6) # layer normalization + + def forward(self, x): + residual = x + output = self.W_2(F.gelu(self.dropout(self.W_1(x)))) # activation: relu -> gelu + output = self.layerNorm(self.dropout(output) + residual) + return output + +class BERT4RecBlock(nn.Module): + def __init__(self, num_heads, hidden_units, dropout_rate): + super(BERT4RecBlock, self).__init__() + self.attention = MultiHeadAttention(num_heads, hidden_units, dropout_rate) + self.pointwise_feedforward = PositionwiseFeedForward(hidden_units, dropout_rate) + + def forward(self, input_enc, mask): + # print ("BERT4RecBlock input_enc", input_enc.shape) # [batch_size x tokens x hidden_units] + output_enc, attn_dist = self.attention(input_enc, mask) + output_enc = self.pointwise_feedforward(output_enc) + # print ("BERT4RecBlock output_enc", output_enc.shape) # [batch_size x tokens x hidden_units] + return output_enc, attn_dist + +class BERT4Rec(nn.Module): + def __init__(self, num_user, num_item, hidden_units, num_heads, num_layers, max_len, dropout_rate, device): + super(BERT4Rec, self).__init__() + + self.num_user = num_user + self.num_item = num_item + self.hidden_units = hidden_units + self.num_heads = num_heads + self.num_layers = num_layers + self.device = device + + self.item_emb = nn.Embedding(num_item + 2, hidden_units, padding_idx=0) + self.pos_emb = nn.Embedding(max_len, hidden_units) # learnable positional encoding + self.dropout = nn.Dropout(dropout_rate) + self.emb_layernorm = nn.LayerNorm(hidden_units, eps=1e-6) + + self.blocks = nn.ModuleList([BERT4RecBlock(num_heads, hidden_units, dropout_rate) for _ in range(num_layers)]) + self.out = nn.Linear(hidden_units, num_item + 1) + + def forward(self, log_seqs): + #-- Item embedding + position embedding + seqs = self.item_emb(torch.LongTensor(log_seqs).to(self.device)) # [batch_size x tokens x hidden_units] + positions = np.tile(np.array(range(log_seqs.shape[1])), [log_seqs.shape[0], 1]) + seqs += self.pos_emb(torch.LongTensor(positions).to(self.device)) # seq 에 positional embedding 추가 + seqs = self.emb_layernorm(self.dropout(seqs)) + + mask = torch.BoolTensor(log_seqs > 0).unsqueeze(1).repeat(1, log_seqs.shape[1], 1).unsqueeze(1).to(self.device) # mask for zero pad + for block in self.blocks: + seqs, attn_dist = block(seqs, mask) # BERT4RecBlock(seqs, mask) + out = self.out(seqs) # [batch_size x tokens x (num_item + 1)] # token=time set=시간 #num_item+1 번째 : 예측하고자 하는 time #gelu 안해도 ㄱㅊ + return out \ No newline at end of file diff --git a/code/preprocess.py b/code/preprocess.py new file mode 100644 index 0000000..d19cc41 --- /dev/null +++ b/code/preprocess.py @@ -0,0 +1,48 @@ +import math +import numpy as np +import pandas as pd +from tqdm import tqdm +from collections import defaultdict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import Dataset, DataLoader +from train_args import parse_args + + +############# 중요 ############# +# data_path는 사용자의 디렉토리에 맞게 설정해야 합니다. + +def preprocess(args): + df = pd.read_csv(args.data_dir + "train_ratings.csv") + + item_ids = df['item'].unique() # 6807 + user_ids = df['user'].unique() # 31360 + num_item, num_user = len(item_ids), len(user_ids) + num_batch = num_user // args.batch_size + + # user, item indexing + item2idx = pd.Series(data=np.arange(len(item_ids))+1, index=item_ids) # item re-indexing (1~num_item), num_item+1: mask idx + user2idx = pd.Series(data=np.arange(len(user_ids)), index=user_ids) # user re-indexing (0~num_user-1) + + # dataframe indexing + df = pd.merge(df, pd.DataFrame({'item': item_ids, 'item_idx': item2idx[item_ids].values}), on='item', how='inner') + df = pd.merge(df, pd.DataFrame({'user': user_ids, 'user_idx': user2idx[user_ids].values}), on='user', how='inner') + df.sort_values(['user_idx', 'time'], inplace=True) + del df['item'], df['user'] + + # train set, valid set 생성 + users = defaultdict(list) # defaultdict은 dictionary의 key가 없을때 default 값을 value로 반환 + user_train = {} + user_valid = {} + for u, i, t in zip(df['user_idx'], df['item_idx'], df['time']): + users[u].append(i) + + for user in users: + user_train[user] = users[user][:-1] + user_valid[user] = [users[user][-1]] + + print(f'num users: {num_user}, num items: {num_item}') + + return num_user, num_item, df, user_train, user_valid \ No newline at end of file diff --git a/code/requirements.txt b/code/requirements.txt new file mode 100644 index 0000000..baa85b8 --- /dev/null +++ b/code/requirements.txt @@ -0,0 +1,5 @@ +torch>=1.1 +torchvision +numpy +tqdm +tensorboard>=1.14 diff --git a/code/train.py b/code/train.py new file mode 100644 index 0000000..d10e230 --- /dev/null +++ b/code/train.py @@ -0,0 +1,211 @@ +from train_args import parse_args +import os + +import numpy as np +import torch +from torch.utils.data import DataLoader, RandomSampler, SequentialSampler +import tqdm + +from preprocess import preprocess + +from data_loaders import SeqDataset +from model.model import BERT4Rec +from trainer.trainer import * +from utils import ( + EarlyStopping, + check_path, + get_item2attribute_json, + get_user_seqs, + set_seed, +) + +import wandb +def main(args): + set_seed(args.seed) + check_path(args.output_dir) + + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id + args.cuda_condition = torch.cuda.is_available() and not args.no_cuda + + args.data_file = args.data_dir + "train_ratings.csv" + item2attribute_file = args.data_dir + args.data_name + "_item2attributes.json" + + user_seq, max_item, valid_rating_matrix, test_rating_matrix, _ = get_user_seqs( + args.data_file + ) + + item2attribute, attribute_size = get_item2attribute_json(item2attribute_file) + + args.item_size = max_item + 2 + args.mask_id = max_item + 1 + args.attribute_size = attribute_size + 1 + + # save model args + args_str = f"{args.model_name}-{args.data_name}" + args.log_file = os.path.join(args.output_dir, args_str + ".txt") + print(str(args)) + + args.item2attribute = item2attribute + # set item score in train set to `0` in validation + args.train_matrix = valid_rating_matrix + + # save model + checkpoint = args_str + ".pt" + args.checkpoint_path = os.path.join(args.output_dir, checkpoint) + + ''' + seq_dataset = SeqDataset(user_train, num_user, num_item, max_len, mask_prob) + data_loader = DataLoader(seq_dataset, batch_size=batch_size, shuffle=True, pin_memory=True) # TODO4: pytorch의 DataLoader와 seq_dataset을 사용하여 학습 파이프라인을 구현해보세요. + ''' + + num_user, num_item, df, user_train, user_valid = preprocess(args) + + train_dataset = SeqDataset(user_train, num_user, num_item, args.max_seq_length, args.mask_prob) + train_sampler = RandomSampler(train_dataset) + train_dataloader = DataLoader( + train_dataset, sampler=train_sampler, batch_size=args.batch_size, + ) + + valid_dataset = SeqDataset(user_valid, num_user, num_item, args.max_seq_length, args.mask_prob) + valid_sampler = SequentialSampler(valid_dataset) + valid_dataloader = DataLoader( + valid_dataset, sampler=valid_sampler, batch_size=args.batch_size + ) + + test_dataset = SeqDataset(df, num_user, num_item, args.max_seq_length, args.mask_prob) + test_sampler = SequentialSampler(test_dataset) + test_dataloader = DataLoader( + test_dataset, sampler=test_sampler, batch_size=args.batch_size + ) + + model = BERT4Rec(num_user, num_item, args.hidden_units, + args.num_attention_heads, args.num_hidden_layers, + args.max_seq_length, args.attention_probs_dropout_prob, args.device) + + wandb.login() + wandb.init(project = args.project_name, + name = args.run_name, + entity = args.entity_name, + config = { + "epochs": args.epochs, + "learning_rate": args.lr, + "batch_size": args.batch_size, + "using_pretrain": args.using_pretrain, + "hidden_size": args.hidden_size, + "num_hidden_layers": args.num_hidden_layers, + "attention_probs_dropout_prob": args.attention_probs_dropout_prob, + "hidden_dropout_prob": args.hidden_dropout_prob, + "max_seq_length": args.max_seq_length, + }) + + + #-- loss + criterion = nn.CrossEntropyLoss(ignore_index=0) # default: cross_entropy # mask 안한 거 빼고 함 + + #-- optimizer + optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) + + #-- Earlystopping + patience = args.patience + stop_counter = 0 + best_val_acc = 0 + best_val_loss = np.inf + + print (f"[DEBUG] Start of TRAINING") + + for epoch in range(1, args.epochs + 1): + #-- training + loss_sum = 0 + + tqdm_bar = tqdm.tqdm(train_dataloader) + + for idx, (log_seqs, labels) in enumerate(tqdm_bar): + logits = model(log_seqs) + + # size matching + logits = logits.view(-1, logits.size(-1)) # [51200, 6808] + labels = labels.view(-1).to(args.device) # 51200 + + optimizer.zero_grad() + loss = criterion(logits, labels) + loss_sum += loss + loss.backward() + optimizer.step() + + tqdm_bar.set_description(f'Epoch: {epoch + 1:3d}| Step: {idx:3d}| Train loss: {loss:.5f}') + + loss_avg = loss_sum / len(train_dataloader) + + #-- validataion + torch.cuda.empty_cache() + with torch.no_grad(): + model.eval() + valid_loss = 0 + masked_cnt = 0 + correct_cnt = 0 + + for _log_seqs, _labels in valid_dataloader: + _logits = model(_log_seqs) + + y_hat = _logits[:,:].argsort()[:,:,-1].view(-1) + + # size matching + _logits = _logits.view(-1, _logits.size(-1)) # [51200, 6808] + _labels = _labels.view(-1).to(args.device) # 51200 + + _loss = criterion(_logits, _labels) + + correct_cnt += torch.sum((_labels == y_hat) & (_labels != 0)) + masked_cnt += _labels.count_nonzero() + valid_loss += _loss + + valid_loss_avg = valid_loss / len(valid_dataloader) + valid_acc = correct_cnt / masked_cnt + + if valid_loss_avg < best_val_loss: + print(f"New best model for val loss : {valid_loss_avg:.5f}! saving the best model..") + torch.save(model, f"{args.output_dir}/best.pth") + best_val_loss = valid_loss_avg + best_val_acc = valid_acc + stop_counter = 0 + + else: + stop_counter += 1 + print (f"!!! Early stop counter = {stop_counter}/{patience} !!!") + + torch.save(model, f"{args.output_dir}/last.pth") + + print( + f"[Val] acc : {valid_acc:4.2%}, loss: {valid_loss_avg:.5f} || " + f"best acc : {best_val_acc:4.2%}, best loss: {best_val_loss:.5f}" + ) + + if stop_counter >= patience: + print("Early stopping") + break + #-- [MLflow] save last model artifacts to mlflow + + ''' + for epoch in range(args.epochs): + trainer.train(epoch) + + scores, _ = trainer.valid(epoch) + + early_stopping(np.array(scores[-1:]), trainer.model) + if early_stopping.early_stop: + print("Early stopping") + break + + trainer.args.train_matrix = test_rating_matrix + print("---------------Change to test_rating_matrix!-------------------") + # load the best model + trainer.model.load_state_dict(torch.load(args.checkpoint_path)) + scores, result_info = trainer.test(0) + print(result_info) + ''' + + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/code/train_args.py b/code/train_args.py new file mode 100644 index 0000000..26088a4 --- /dev/null +++ b/code/train_args.py @@ -0,0 +1,73 @@ +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("--data_dir", default="../../data/train/", type=str) + parser.add_argument("--output_dir", default="output/", type=str) + parser.add_argument("--data_name", default="Ml", type=str) + + # wandb init parameters + parser.add_argument("--project_name", default="movie-bert4rec-train", type=str) + parser.add_argument("--run_name", default="test", type=str) + parser.add_argument("--entity_name", default="suggestify_lv2", type=str) + + # model args + parser.add_argument("--model_name", default="Finetune_full", type=str) + parser.add_argument( + "--hidden_size", type=int, default=64, help="hidden size of transformer model" + ) + parser.add_argument( + "--num_hidden_layers", type=int, default=2, help="number of layers" + ) + parser.add_argument("--num_attention_heads", default=2, type=int) + parser.add_argument("--hidden_act", default="gelu", type=str) # gelu relu + parser.add_argument( + "--attention_probs_dropout_prob", + type=float, + default=0.5, + help="attention dropout p", + ) + parser.add_argument( + "--hidden_dropout_prob", type=float, default=0.5, help="hidden dropout p" + ) + parser.add_argument("--initializer_range", type=float, default=0.02) + parser.add_argument("--max_seq_length", default=50, type=int) + + # train args + parser.add_argument("--lr", type=float, default=0.001, help="learning rate of adam") + parser.add_argument( + "--batch_size", type=int, default=256, help="number of batch_size" + ) + parser.add_argument("--epochs", type=int, default=200, help="number of epochs") + parser.add_argument("--no_cuda", action="store_true") + parser.add_argument("--log_freq", type=int, default=1, help="per epoch print res") + parser.add_argument("--seed", default=42, type=int) + + parser.add_argument( + "--weight_decay", type=float, default=0.0, help="weight_decay of adam" + ) + parser.add_argument( + "--adam_beta1", type=float, default=0.9, help="adam first beta value" + ) + parser.add_argument( + "--adam_beta2", type=float, default=0.999, help="adam second beta value" + ) + parser.add_argument("--gpu_id", type=str, default="0", help="gpu_id") + + parser.add_argument("--using_pretrain", action="store_true") + + + parser.add_argument("--mask_prob", type=float, default=0.15) + + parser.add_argument("--hidden_units", type=int, default=50) + + parser.add_argument("--device", type=str, default="cuda") + + + parser.add_argument("--patience", type=int, default="20") + + + + args = parser.parse_args() + return args \ No newline at end of file diff --git a/code/trainer/__init__.py b/code/trainer/__init__.py new file mode 100644 index 0000000..5c0a8a4 --- /dev/null +++ b/code/trainer/__init__.py @@ -0,0 +1 @@ +from .trainer import * diff --git a/code/trainer/trainer.py b/code/trainer/trainer.py new file mode 100644 index 0000000..5975356 --- /dev/null +++ b/code/trainer/trainer.py @@ -0,0 +1,320 @@ +import numpy as np +import torch +import torch.nn as nn +import tqdm +from torch.optim import Adam + +from utils import ndcg_k, recall_at_k +import wandb + +class Trainer: + def __init__( + self, + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ): + + self.args = args + self.cuda_condition = torch.cuda.is_available() and not self.args.no_cuda + self.device = torch.device("cuda" if self.cuda_condition else "cpu") + + self.model = model + if self.cuda_condition: + self.model.cuda() + + # Setting the train and test data loader + self.train_dataloader = train_dataloader + self.eval_dataloader = eval_dataloader + self.test_dataloader = test_dataloader + self.submission_dataloader = submission_dataloader + + # self.data_name = self.args.data_name + betas = (self.args.adam_beta1, self.args.adam_beta2) + self.optim = Adam( + self.model.parameters(), + lr=self.args.lr, + betas=betas, + weight_decay=self.args.weight_decay, + ) + + print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()])) + self.criterion = nn.BCELoss() + + def train(self, epoch): + self.iteration(epoch, self.train_dataloader) + + def valid(self, epoch): + return self.iteration(epoch, self.eval_dataloader, mode="valid") + + def test(self, epoch): + return self.iteration(epoch, self.test_dataloader, mode="test") + + def submission(self, epoch): + return self.iteration(epoch, self.submission_dataloader, mode="submission") + + def iteration(self, epoch, dataloader, mode="train"): + raise NotImplementedError + + def get_full_sort_score(self, epoch, answers, pred_list, mode): + recall, ndcg = [], [] + for k in [5, 10]: + recall.append(recall_at_k(answers, pred_list, k)) + ndcg.append(ndcg_k(answers, pred_list, k)) + post_fix = { + "Epoch": epoch, + "RECALL@5": "{:.4f}".format(recall[0]), + "NDCG@5": "{:.4f}".format(ndcg[0]), + "RECALL@10": "{:.4f}".format(recall[1]), + "NDCG@10": "{:.4f}".format(ndcg[1]), + } + wandb.log({ + f"{mode}_recall@5": recall[0], + f"{mode}_ndcg@5": ndcg[0], + f"{mode}_recall@10": recall[1], + f"{mode}_ndcg@10": ndcg[1], + }) + print(post_fix) + + return [recall[0], ndcg[0], recall[1], ndcg[1]], str(post_fix) + + def save(self, file_name): + torch.save(self.model.cpu().state_dict(), file_name) + self.model.to(self.device) + + def load(self, file_name): + self.model.load_state_dict(torch.load(file_name)) + + def cross_entropy(self, seq_out, pos_ids, neg_ids): + # [batch seq_len hidden_size] + pos_emb = self.model.item_embeddings(pos_ids) + neg_emb = self.model.item_embeddings(neg_ids) + # [batch*seq_len hidden_size] + pos = pos_emb.view(-1, pos_emb.size(2)) + neg = neg_emb.view(-1, neg_emb.size(2)) + seq_emb = seq_out.view(-1, self.args.hidden_size) # [batch*seq_len hidden_size] + pos_logits = torch.sum(pos * seq_emb, -1) # [batch*seq_len] + neg_logits = torch.sum(neg * seq_emb, -1) + istarget = ( + (pos_ids > 0).view(pos_ids.size(0) * self.model.args.max_seq_length).float() + ) # [batch*seq_len] + loss = torch.sum( + -torch.log(torch.sigmoid(pos_logits) + 1e-24) * istarget + - torch.log(1 - torch.sigmoid(neg_logits) + 1e-24) * istarget + ) / torch.sum(istarget) + + return loss + + def predict_full(self, seq_out): + # [item_num hidden_size] + test_item_emb = self.model.item_embeddings.weight + # [batch hidden_size ] + rating_pred = torch.matmul(seq_out, test_item_emb.transpose(0, 1)) + return rating_pred + + +class PretrainTrainer(Trainer): + def __init__( + self, + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ): + super(PretrainTrainer, self).__init__( + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ) + + def pretrain(self, epoch, pretrain_dataloader): + + desc = ( + f"AAP-{self.args.aap_weight}-" + f"MIP-{self.args.mip_weight}-" + f"MAP-{self.args.map_weight}-" + f"SP-{self.args.sp_weight}" + ) + + pretrain_data_iter = tqdm.tqdm( + enumerate(pretrain_dataloader), + desc=f"{self.args.model_name}-{self.args.data_name} Epoch:{epoch}", + total=len(pretrain_dataloader), + bar_format="{l_bar}{r_bar}", + ) + + self.model.train() + aap_loss_avg = 0.0 + mip_loss_avg = 0.0 + map_loss_avg = 0.0 + sp_loss_avg = 0.0 + + for i, batch in pretrain_data_iter: + # 0. batch_data will be sent into the device(GPU or CPU) + batch = tuple(t.to(self.device) for t in batch) + ( + attributes, + masked_item_sequence, + pos_items, + neg_items, + masked_segment_sequence, + pos_segment, + neg_segment, + ) = batch + + aap_loss, mip_loss, map_loss, sp_loss = self.model.pretrain( + attributes, + masked_item_sequence, + pos_items, + neg_items, + masked_segment_sequence, + pos_segment, + neg_segment, + ) + + joint_loss = ( + self.args.aap_weight * aap_loss + + self.args.mip_weight * mip_loss + + self.args.map_weight * map_loss + + self.args.sp_weight * sp_loss + ) + + self.optim.zero_grad() + joint_loss.backward() + self.optim.step() + + aap_loss_avg += aap_loss.item() + mip_loss_avg += mip_loss.item() + map_loss_avg += map_loss.item() + sp_loss_avg += sp_loss.item() + + num = len(pretrain_data_iter) * self.args.pre_batch_size + losses = { + "epoch": epoch, + "aap_loss_avg": aap_loss_avg / num, + "mip_loss_avg": mip_loss_avg / num, + "map_loss_avg": map_loss_avg / num, + "sp_loss_avg": sp_loss_avg / num, + } + print(desc) + print(str(losses)) + wandb.log({ + "aap_loss_avg": losses["aap_loss_avg"], + "mip_loss_avg": losses["mip_loss_avg"], + "map_loss_avg": losses["map_loss_avg"], + "sp_loss_avg": losses["sp_loss_avg"], + }) + return losses + + +class FinetuneTrainer(Trainer): + def __init__( + self, + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ): + super(FinetuneTrainer, self).__init__( + model, + train_dataloader, + eval_dataloader, + test_dataloader, + submission_dataloader, + args, + ) + + def iteration(self, epoch, dataloader, mode="train"): + + # Setting the tqdm progress bar + + rec_data_iter = tqdm.tqdm( + enumerate(dataloader), + desc="Recommendation EP_%s:%d" % (mode, epoch), + total=len(dataloader), + bar_format="{l_bar}{r_bar}", + ) + if mode == "train": + self.model.train() + rec_avg_loss = 0.0 + rec_cur_loss = 0.0 + + for i, batch in rec_data_iter: + # 0. batch_data will be sent into the device(GPU or CPU) + batch = tuple(t.to(self.device) for t in batch) + _, input_ids, target_pos, target_neg, _ = batch + # Binary cross_entropy + sequence_output = self.model.finetune(input_ids) + loss = self.cross_entropy(sequence_output, target_pos, target_neg) + self.optim.zero_grad() + loss.backward() + self.optim.step() + + rec_avg_loss += loss.item() + rec_cur_loss = loss.item() + + post_fix = { + "epoch": epoch, + "rec_avg_loss": "{:.4f}".format(rec_avg_loss / len(rec_data_iter)), + "rec_cur_loss": "{:.4f}".format(rec_cur_loss), + } + + wandb.log({ + "train_avg_loss": rec_avg_loss / len(rec_data_iter), + "train_cur_loss": rec_cur_loss, + }) + if (epoch + 1) % self.args.log_freq == 0: + print(str(post_fix)) + + else: + self.model.eval() + + pred_list = None + answer_list = None + for i, batch in rec_data_iter: + + batch = tuple(t.to(self.device) for t in batch) + user_ids, input_ids, _, target_neg, answers = batch + recommend_output = self.model.finetune(input_ids) + + recommend_output = recommend_output[:, -1, :] + + rating_pred = self.predict_full(recommend_output) + + rating_pred = rating_pred.cpu().data.numpy().copy() + batch_user_index = user_ids.cpu().numpy() + rating_pred[self.args.train_matrix[batch_user_index].toarray() > 0] = 0 + + ind = np.argpartition(rating_pred, -10)[:, -10:] + + arr_ind = rating_pred[np.arange(len(rating_pred))[:, None], ind] + + arr_ind_argsort = np.argsort(arr_ind)[np.arange(len(rating_pred)), ::-1] + + batch_pred_list = ind[ + np.arange(len(rating_pred))[:, None], arr_ind_argsort + ] + + if i == 0: + pred_list = batch_pred_list + answer_list = answers.cpu().data.numpy() + else: + pred_list = np.append(pred_list, batch_pred_list, axis=0) + answer_list = np.append( + answer_list, answers.cpu().data.numpy(), axis=0 + ) + + if mode == "submission": + return pred_list + else: + return self.get_full_sort_score(epoch, answer_list, pred_list, mode) diff --git a/code/utils/__init__.py b/code/utils/__init__.py new file mode 100644 index 0000000..46d3a15 --- /dev/null +++ b/code/utils/__init__.py @@ -0,0 +1 @@ +from .util import * diff --git a/code/utils/util.py b/code/utils/util.py new file mode 100644 index 0000000..9c1b89d --- /dev/null +++ b/code/utils/util.py @@ -0,0 +1,349 @@ +import json +import math +import os +import random + +import numpy as np +import pandas as pd +import torch +from scipy.sparse import csr_matrix + + +def set_seed(seed): + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # some cudnn methods can be random even after fixing the seed + # unless you tell it to be deterministic + torch.backends.cudnn.deterministic = True + + +def check_path(path): + if not os.path.exists(path): + os.makedirs(path) + print(f"{path} created") + + +def neg_sample(item_set, item_size): + item = random.randint(1, item_size - 1) + while item in item_set: + item = random.randint(1, item_size - 1) + return item + + +class EarlyStopping: + """Early stops the training if validation loss doesn't improve after a given patience.""" + + def __init__(self, checkpoint_path, patience=7, verbose=False, delta=0): + """ + Args: + patience (int): How long to wait after last time validation loss improved. + Default: 7 + verbose (bool): If True, prints a message for each validation loss improvement. + Default: False + delta (float): Minimum change in the monitored quantity to qualify as an improvement. + Default: 0 + """ + self.checkpoint_path = checkpoint_path + self.patience = patience + self.verbose = verbose + self.counter = 0 + self.best_score = None + self.early_stop = False + self.delta = delta + + def compare(self, score): + for i in range(len(score)): + + if score[i] > self.best_score[i] + self.delta: + return False + return True + + def __call__(self, score, model): + + if self.best_score is None: + self.best_score = score + self.score_min = np.array([0] * len(score)) + self.save_checkpoint(score, model) + elif self.compare(score): + self.counter += 1 + print(f"EarlyStopping counter: {self.counter} out of {self.patience}") + if self.counter >= self.patience: + self.early_stop = True + else: + self.best_score = score + self.save_checkpoint(score, model) + self.counter = 0 + + def save_checkpoint(self, score, model): + """Saves model when the performance is better.""" + if self.verbose: + print(f"Better performance. Saving model ...") + torch.save(model.state_dict(), self.checkpoint_path) + self.score_min = score + + +def kmax_pooling(x, dim, k): + index = x.topk(k, dim=dim)[1].sort(dim=dim)[0] + return x.gather(dim, index).squeeze(dim) + + +def avg_pooling(x, dim): + return x.sum(dim=dim) / x.size(dim) + + +def generate_rating_matrix_valid(user_seq, num_users, num_items): + # three lists are used to construct sparse matrix + row = [] + col = [] + data = [] + for user_id, item_list in enumerate(user_seq): + for item in item_list[:-2]: # + row.append(user_id) + col.append(item) + data.append(1) + + row = np.array(row) + col = np.array(col) + data = np.array(data) + rating_matrix = csr_matrix((data, (row, col)), shape=(num_users, num_items)) + + return rating_matrix + + +def generate_rating_matrix_test(user_seq, num_users, num_items): + # three lists are used to construct sparse matrix + row = [] + col = [] + data = [] + for user_id, item_list in enumerate(user_seq): + for item in item_list[:-1]: # + row.append(user_id) + col.append(item) + data.append(1) + + row = np.array(row) + col = np.array(col) + data = np.array(data) + rating_matrix = csr_matrix((data, (row, col)), shape=(num_users, num_items)) + + return rating_matrix + + +def generate_rating_matrix_submission(user_seq, num_users, num_items): + # three lists are used to construct sparse matrix + row = [] + col = [] + data = [] + for user_id, item_list in enumerate(user_seq): + for item in item_list[:]: # + row.append(user_id) + col.append(item) + data.append(1) + + row = np.array(row) + col = np.array(col) + data = np.array(data) + rating_matrix = csr_matrix((data, (row, col)), shape=(num_users, num_items)) + + return rating_matrix + + +def generate_submission_file(data_file, preds): + + rating_df = pd.read_csv(data_file) + users = rating_df["user"].unique() + + result = [] + + for index, items in enumerate(preds): + for item in items: + result.append((users[index], item)) + + pd.DataFrame(result, columns=["user", "item"]).to_csv( + "output/submission.csv", index=False + ) + + +def get_user_seqs(data_file): + rating_df = pd.read_csv(data_file) + lines = rating_df.groupby("user")["item"].apply(list) + user_seq = [] + item_set = set() + for line in lines: + + items = line + user_seq.append(items) + item_set = item_set | set(items) + max_item = max(item_set) + + num_users = len(lines) + num_items = max_item + 2 + + valid_rating_matrix = generate_rating_matrix_valid(user_seq, num_users, num_items) + test_rating_matrix = generate_rating_matrix_test(user_seq, num_users, num_items) + submission_rating_matrix = generate_rating_matrix_submission( + user_seq, num_users, num_items + ) + return ( + user_seq, + max_item, + valid_rating_matrix, + test_rating_matrix, + submission_rating_matrix, + ) + + +def get_user_seqs_long(data_file): + rating_df = pd.read_csv(data_file) + lines = rating_df.groupby("user")["item"].apply(list) + user_seq = [] + long_sequence = [] + item_set = set() + for line in lines: + items = line + long_sequence.extend(items) + user_seq.append(items) + item_set = item_set | set(items) + max_item = max(item_set) + + return user_seq, max_item, long_sequence + + +def get_item2attribute_json(data_file): + item2attribute = json.loads(open(data_file).readline()) + attribute_set = set() + for item, attributes in item2attribute.items(): + attribute_set = attribute_set | set(attributes) + attribute_size = max(attribute_set) + return item2attribute, attribute_size + + +def get_metric(pred_list, topk=10): + NDCG = 0.0 + HIT = 0.0 + MRR = 0.0 + # [batch] the answer's rank + for rank in pred_list: + MRR += 1.0 / (rank + 1.0) + if rank < topk: + NDCG += 1.0 / np.log2(rank + 2.0) + HIT += 1.0 + return HIT / len(pred_list), NDCG / len(pred_list), MRR / len(pred_list) + + +def precision_at_k_per_sample(actual, predicted, topk): + num_hits = 0 + for place in predicted: + if place in actual: + num_hits += 1 + return num_hits / (topk + 0.0) + + +def precision_at_k(actual, predicted, topk): + sum_precision = 0.0 + num_users = len(predicted) + for i in range(num_users): + act_set = set(actual[i]) + pred_set = set(predicted[i][:topk]) + sum_precision += len(act_set & pred_set) / float(topk) + + return sum_precision / num_users + + +def recall_at_k(actual, predicted, topk): + sum_recall = 0.0 + num_users = len(predicted) + true_users = 0 + for i in range(num_users): + act_set = set(actual[i]) + pred_set = set(predicted[i][:topk]) + if len(act_set) != 0: + sum_recall += len(act_set & pred_set) / float(len(act_set)) + true_users += 1 + return sum_recall / true_users + + +def apk(actual, predicted, k=10): + """ + Computes the average precision at k. + This function computes the average precision at k between two lists of + items. + Parameters + ---------- + actual : list + A list of elements that are to be predicted (order doesn't matter) + predicted : list + A list of predicted elements (order does matter) + k : int, optional + The maximum number of predicted elements + Returns + ------- + score : double + The average precision at k over the input lists + """ + if len(predicted) > k: + predicted = predicted[:k] + + score = 0.0 + num_hits = 0.0 + + for i, p in enumerate(predicted): + if p in actual and p not in predicted[:i]: + num_hits += 1.0 + score += num_hits / (i + 1.0) + + if not actual: + return 0.0 + + return score / min(len(actual), k) + + +def mapk(actual, predicted, k=10): + """ + Computes the mean average precision at k. + This function computes the mean average prescision at k between two lists + of lists of items. + Parameters + ---------- + actual : list + A list of lists of elements that are to be predicted + (order doesn't matter in the lists) + predicted : list + A list of lists of predicted elements + (order matters in the lists) + k : int, optional + The maximum number of predicted elements + Returns + ------- + score : double + The mean average precision at k over the input lists + """ + return np.mean([apk(a, p, k) for a, p in zip(actual, predicted)]) + + +def ndcg_k(actual, predicted, topk): + res = 0 + for user_id in range(len(actual)): + k = min(topk, len(actual[user_id])) + idcg = idcg_k(k) + dcg_k = sum( + [ + int(predicted[user_id][j] in set(actual[user_id])) / math.log(j + 2, 2) + for j in range(topk) + ] + ) + res += dcg_k / idcg + return res / float(len(actual)) + + +# Calculates the ideal discounted cumulative gain at k +def idcg_k(k): + res = sum([1.0 / math.log(i + 2, 2) for i in range(k)]) + if not res: + return 1.0 + else: + return res From d952ef9e475fc04de472ee8d88323abbdd616808 Mon Sep 17 00:00:00 2001 From: Jang <8chris8@naver.com> Date: Tue, 20 Feb 2024 12:40:08 +0000 Subject: [PATCH 29/49] feat:EDA --- .gitignore | 3 +- code/recbole/EDA.py | 195 +++++++++++++++++++++++++++---- code/recbole/configs/deepfm.yaml | 43 +++++++ code/recbole/rulebased.py | 65 ++++++++--- 4 files changed, 267 insertions(+), 39 deletions(-) create mode 100644 code/recbole/configs/deepfm.yaml diff --git a/.gitignore b/.gitignore index 1201e85..38ae2a9 100644 --- a/.gitignore +++ b/.gitignore @@ -3,4 +3,5 @@ wandb __pycache__ saved log -log_tensorboard \ No newline at end of file +log_tensorboard +data diff --git a/code/recbole/EDA.py b/code/recbole/EDA.py index 80182b9..f2b2e3a 100644 --- a/code/recbole/EDA.py +++ b/code/recbole/EDA.py @@ -4,13 +4,7 @@ import matplotlib.pyplot as plt import re - -data_path = '/data/data/train' -interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) -grouped_items = interactions.groupby('user')['item'].apply(list) - - -data_path = '/data/data/train' +data_path = 'data/train' interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) directors = pd.read_csv(os.path.join(data_path, 'directors.tsv'), sep='\t') genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') @@ -18,38 +12,199 @@ writers = pd.read_csv(os.path.join(data_path, 'writers.tsv'), sep='\t') years = pd.read_csv(os.path.join(data_path, 'years.tsv'), sep='\t') + +## items 파일 + #Genre, Writer, Director 한칸에 넣기 grouped_genres= genres.groupby('item')['genre'].apply(list) grouped_writers = writers.groupby('item')['writer'].apply(list) grouped_directors = directors.groupby('item')['director'].apply(list) - items = pd.merge(titles, years, on='item') items = pd.merge(items, grouped_genres, on='item', how = 'left') items = pd.merge(items, grouped_writers, on='item', how = 'left') items = pd.merge(items, grouped_directors, on='item', how = 'left') - -#기존 -items = pd.merge(titles, years, on='item') -items = pd.merge(items, genres, on='item') -items = pd.merge(items, writers, on='item', how = 'left') -items = pd.merge(items, directors, on='item', how = 'left') - # year_bin 추가 -bins = pd.cut(items['year'], bins=10) -bin_labels = [int(b.left) for b in bins.cat.categories] -items['year_bin'] = pd.cut(items['year'], bins=10, labels=bin_labels) - +year_counts = interactions.merge(items[['item','year']], on='item',how='left')['year'].value_counts() +year_counts = pd.DataFrame(year_counts) +year_counts.reset_index(inplace=True) +year_counts.columns = ['year', 'count'] +year_counts['cumulative_count'] = year_counts['count'].cumsum() +labels = [f'year_{i}' for i in range(3,0,-1)] # 10개 구간 레이블 +year_counts['year_bin'] = pd.cut(year_counts['cumulative_count'], bins=3, labels=labels) +items = items.merge(year_counts[['year', 'year_bin']], on='year', how='left') # popularity_bin 추가 item_counts = interactions['item'].value_counts() item_counts = pd.DataFrame(item_counts) item_counts.reset_index(inplace=True) item_counts.columns = ['item', 'count'] -item_counts['popularity_bin'] = pd.cut(item_counts['count'], bins=10, labels=False) +#item count 기준 +item_counts['cumulative_count'] = item_counts['count'].cumsum() +labels = [f'pop_{i}' for i in range(3,0,-1)] # 10개 구간 레이블 +item_counts['popularity_bin'] = pd.cut(item_counts['cumulative_count'], bins=3, labels=labels) items = items.merge(item_counts[['item', 'popularity_bin']], on='item', how='left') +## Users 파일 + +#item 추가 +Users = interactions.groupby('user')['item'].agg(list).reset_index() +Users['item_count'] = Users['item'].apply(len) +merged_interactions = interactions.merge(items, on='item', how='left') +#popularity 추가 +popularity_filled = merged_interactions.groupby(['user', 'popularity_bin']).size().unstack(fill_value=0) +new_column_names = {pop_bin: f'pop_{int(pop_bin)}' for pop_bin in popularity_filled.columns} +popularity_filled = popularity_filled.rename(columns=new_column_names) +Users = pd.merge(Users, popularity_filled, on='user') +#year 추가 +year_filled = merged_interactions.groupby(['user', 'year_bin']).size().unstack(fill_value=0) +new_column_names = {year_bin: f'year_{int(year_bin)}' for year_bin in year_filled.columns} +year_filled = year_filled.rename(columns=new_column_names) +Users = pd.merge(Users, year_filled, on='user') +#genre,writer,director 추가 +def process_genres(df, top_n=3, label='genre'): + rows = [] # 결과를 저장할 리스트 초기화 + # 각 사용자별로 반복 + for user, row in df.iterrows(): + # 각 장르(또는 대체 용어)의 상호작용 횟수를 내림차순으로 정렬 + sorted_df = row.sort_values(ascending=False) + # 상위 N개 장르와 해당 상호작용 횟수 추출 + top_categories = sorted_df[:top_n].index.tolist() + top_counts = sorted_df[:top_n].values.tolist() + # 결과 딕셔너리 생성 + row_dict = {'user': user} + for i in range(top_n): + row_dict[f'{label}_{i+1}'] = top_categories[i] if i < len(top_categories) else None + row_dict[f'{label}_{i+1}_count'] = top_counts[i] if i < len(top_counts) else 0 + # 나머지 장르(또는 대체 용어)의 상호작용 횟수 합산 + etc_count = sorted_df[top_n:].sum() + row_dict[f'{label}_etc'] = 'etc' + row_dict[f'{label}_etc_count'] = etc_count + # 결과 리스트에 추가 + rows.append(row_dict) + # 결과 리스트를 DataFrame으로 변환 + result_df = pd.DataFrame(rows) + result_df.set_index('user', inplace=True) + return result_df + +interactions_genres = interactions.merge(genres, on='item', how='left') +genre_filled = interactions_genres.groupby(['user', 'genre']).size().unstack(fill_value=0) +interactions_directors = interactions.merge(directors, on='item', how='left') +director_filled = interactions_directors.groupby(['user', 'director']).size().unstack(fill_value=0) +interactions_writers = interactions.merge(writers, on='item', how='left') +writer_filled = interactions_writers.groupby(['user', 'writer']).size().unstack(fill_value=0) + +Users = pd.merge(Users, process_genres(genre_filled,label='genre'), on='user') +Users = pd.merge(Users, process_genres(director_filled,label='director'), on='user') +Users = pd.merge(Users, process_genres(writer_filled,label='writer'), on='user') + +# percent 추가 +for column in Users.columns: + if column in ['pop_3','pop_2','pop_1','year_1','year_2','year_3','genre_1_count','genre_2_count','genre_3_count' + ,'director_1_count','director_2_count','director_3_count','writer_1_count','writer_2_count','writer_3_count']: + Users[column+'_percent'] = Users[column].div(Users['item_count'],axis=0) + +# type 추가 +Users['type_popular'] = Users['pop_3_percent'] >= 0.7 +Users['type_unpopular'] = Users['pop_1_percent'] >= 0.6 +Users['type_new'] = Users['year_3_percent'] >= 0.7 +Users['type_old'] = Users['year_1_percent'] >= 0.6 +Users['type_genre'] = Users['genre_1_count_percent'] >= 0.7 +Users['type_director'] = Users['director_1_count_percent'] >= 0.08 +Users['type_writer'] = Users['writer_1_count_percent'] >= 0.08 +Users['type_mania'] = Users['item_count']>300 +Users['type_novice'] = Users['item_count']<50 + +true_counts = { + 'type_popular': Users['type_popular'].sum(), + 'type_unpopular': Users['type_unpopular'].sum(), + 'type_new': Users['type_new'].sum(), + 'type_old': Users['type_old'].sum(), + 'type_genre': Users['type_genre'].sum(), + 'type_director': Users['type_director'].sum(), + 'type_writer': Users['type_writer'].sum(), + 'type_mania': Users['type_mania'].sum(), + 'type_novice': Users['type_novice'].sum() +} + +print(true_counts) + +Users['All_users'] = True + +Users['type'] = 'popular' +Users['type'] = Users.apply(lambda Users: 'genre' if Users['type_genre'] else Users['type'], axis=1) +Users['type'] = Users.apply(lambda Users: 'writer' if Users['type_writer'] else Users['type'], axis=1) +Users['type'] = Users.apply(lambda Users: 'director' if Users['type_director'] else Users['type'], axis=1) +Users['type'].value_counts() + +Users = Users.drop('item', axis=1) +items.to_csv(os.path.join(data_path, 'EDA_items.csv'), sep=',', index=False) +Users.to_csv(os.path.join(data_path, 'EDA_users.csv'), sep=',', index=False) + + + +## Submission 분석 # +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import re + +data_path = 'data/train' +items = pd.read_csv(os.path.join(data_path, 'EDA_items.csv'), sep=',') +Users = pd.read_csv(os.path.join(data_path, 'EDA_users.csv'), sep=',') + +output_path = 'code/recbole/output/' + +submission_name='slim' +sub = pd.read_csv(output_path+'submission_'+submission_name+'.csv') +sub = pd.merge(sub, items, on='item') + +import ast +# 문자열을 리스트로 변환하는 함수 정의 +def convert_str_to_list(str_list): + try: + return ast.literal_eval(str_list) + except ValueError: # 문자열이 올바른 리스트 형식이 아닐 경우 + return [] # 빈 리스트 반환 + end + +submission_EDA = pd.DataFrame({'user': Users['user'].copy()}) + + + +year_bin_pivot = sub.pivot_table(index='user', columns='year_bin', aggfunc='size', fill_value=0) +popularity_bin_pivot = sub.pivot_table(index='user', columns='popularity_bin', aggfunc='size', fill_value=0) + +submission_EDA = pd.merge(submission_EDA, year_bin_pivot, on='user') +submission_EDA = pd.merge(submission_EDA, popularity_bin_pivot, on='user') + +def calculate_pivot(sub, Users, column_name, submission_EDA): + sub[column_name] = sub[column_name].apply(convert_str_to_list) + sub_exploded = sub.explode(column_name) + counts = sub_exploded.pivot_table(index='user', columns=column_name, aggfunc='size', fill_value=0) + counts_merge = pd.merge(counts, Users, on='user') + pivot_1 = counts_merge.apply(lambda row: row[row[column_name+'_1']] if row[column_name+'_1'] in counts_merge.columns else 0, axis=1) + submission_EDA[column_name+'_1']=pivot_1.values + pivot_2 = counts_merge.apply(lambda row: row[row[column_name+'_2']] if row[column_name+'_2'] in counts_merge.columns else 0, axis=1) + submission_EDA[column_name+'_2']=pivot_2.values + pivot_3 = counts_merge.apply(lambda row: row[row[column_name+'_3']] if row[column_name+'_3'] in counts_merge.columns else 0, axis=1) + submission_EDA[column_name+'_3']=pivot_3.values + return submission_EDA + +submission_EDA = calculate_pivot(sub,Users,'genre',submission_EDA) +submission_EDA = calculate_pivot(sub,Users,'writer',submission_EDA) +submission_EDA = calculate_pivot(sub,Users,'director',submission_EDA) + +submission_EDA = pd.merge(submission_EDA, Users[['user','type']], on='user') +submission_EDA['model'] = submission_name +submission_EDA.to_csv(os.path.join(output_path,'submission_'+submission_name+'_EDA.csv'), sep=',', index=False) + + +## EDA ## + +#year, popularity PLOT item_counts_by_year_bin = items.groupby('year_bin').size() plt.bar(item_counts_by_year_bin.index, item_counts_by_year_bin.values) plt.savefig('year-item수.png') diff --git a/code/recbole/configs/deepfm.yaml b/code/recbole/configs/deepfm.yaml new file mode 100644 index 0000000..733a7fd --- /dev/null +++ b/code/recbole/configs/deepfm.yaml @@ -0,0 +1,43 @@ + +model: DeepFM + +data_path: /data/data/train +dataset: movie +field_separator: " " +seq_separator: " " +USER_ID_FIELD: user +ITEM_ID_FIELD: item +#TIME_FIELD: time +LABEL_FIELD: label +show_progress: false +use_gpu: true + +load_col: + inter: [user, item, label] + user: [user] + item: [item, genre] + +user_inter_num_interval: "[0,inf)" +item_inter_num_interval: "[0,inf)" + +epochs: 3 +train_batch_size: 4086 +metrics: ["Recall", "NDCG"] +topk: [10] +valid_metric: Recall@10 +embedding_size : 2 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +train_neg_sample_args: + distribution: popularity + candidate_num: 1 +training_neg_sample_num: 1 + +log_wandb: True +wandb_project: recbole_deepfm diff --git a/code/recbole/rulebased.py b/code/recbole/rulebased.py index 994f782..b487a8e 100644 --- a/code/recbole/rulebased.py +++ b/code/recbole/rulebased.py @@ -5,13 +5,11 @@ data_path = '/data/data/train' interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') -# interactions과 genres를 item을 기준으로 병합 -merged = interactions.merge(genres, on='item') -# 사용자별로 본 장르를 리스트로 묶기 -user_genre_counts = merged.groupby('user')['genre'].value_counts() -print(user_genre_counts) -most_watched_genre_per_user = user_genre_counts.groupby(level=0).idxmax() -print(most_watched_genre_per_user) +writers = pd.read_csv(os.path.join(data_path, 'writers.tsv'), sep='\t') +directors = pd.read_csv(os.path.join(data_path, 'directors.tsv'), sep='\t') + + +Users = pd.read_csv(os.path.join(data_path, 'EDA_users.csv'), sep=',') user_item_group = interactions.groupby('user')['item'].apply(list) @@ -22,23 +20,54 @@ recommendations = [] -# 각 사용자에 대해서 +# 각 사용자와 그들의 시청 항목에 대해 반복 for user, watched_items in user_item_group.items(): - # 아직 시청하지 않은 아이템을 찾는다 + # 시청하지 않은 항목을 찾음 unwatched_items = item_counts[~item_counts.index.isin(watched_items)] - # 사용자가 가장 많이 시청한 장르의 아이템만 선택 - genre_items = genres[genres['genre'] == most_watched_genre_per_user[user][1]]['item'] - preferred_items = unwatched_items[unwatched_items.index.isin(genre_items)] - # 시청 횟수가 많은 순서대로 아이템을 정렬한다 + + # 사용자의 선호 타입을 확인 + user_type = Users.loc[Users['user'] == user, 'type'].values[0] + + if user_type == 'genre': + # 사용자의 가장 많이 시청한 장르에서 시청하지 않은 항목을 선택 + genre_1 = Users.loc[Users['user'] == user, 'genre_1'].values[0] + genre_items = genres[genres['genre'] == genre_1]['item'] + preferred_items = unwatched_items[unwatched_items.index.isin(genre_items)] + elif user_type == 'writer': + # 사용자의 가장 선호하는 작가의 작품 중 시청하지 않은 항목을 선택 + preferred_writer = Users.loc[Users['user'] == user, 'writer_1'].values[0] + writer_items = writers[writers['writer'] == preferred_writer]['item'] + preferred_items = unwatched_items[unwatched_items.index.isin(writer_items)] + elif user_type == 'director': + # 사용자의 가장 선호하는 감독의 작품 중 시청하지 않은 항목을 선택 + preferred_director = Users.loc[Users['user'] == user, 'director_1'].values[0] + director_items = directors[directors['director'] == preferred_director]['item'] + preferred_items = unwatched_items[unwatched_items.index.isin(director_items)] + else: + # 다른 타입의 경우, 사용자가 시청하지 않은 항목 중에서 선택 + preferred_items = unwatched_items + + # 시청 횟수에 따라 항목을 내림차순으로 정렬 recommended_items = preferred_items.sort_values(ascending=False) - # 상위 10개 아이템을 추천한다 - top_10_items = recommended_items[:10] - # 추천 결과를 저장한다 - for item in top_10_items.index: + + # 추천을 위한 상위 10개 항목을 선택 + top_items = recommended_items[:10] + + # preferred_items의 수가 10개 미만인 경우, 추가로 시청 횟수가 많은 항목들을 추천에 포함 + if len(top_items) < 10: + extra_items_needed = 10 - len(top_items) + extra_items = unwatched_items[~unwatched_items.index.isin(top_items.index)].sort_values(ascending=False)[:extra_items_needed] + top_items = pd.concat([top_items, extra_items]) + + # 추천 결과를 저장 + for item in top_items.index: recommendations.append((user, item)) + # 추천 결과를 DataFrame으로 변환한다 recommendations_df = pd.DataFrame(recommendations, columns=['user', 'item']) print(recommendations_df) -recommendations_df.to_csv('output/submission_rule_genres.csv',index=False) \ No newline at end of file +recommendations_df.to_csv('output/submission_rule_types2.csv',index=False) + +directors[directors['item']==3471] \ No newline at end of file From e5b98db68d47af1b94043320cd5664a9a00ae7a9 Mon Sep 17 00:00:00 2001 From: Seungbin Date: Wed, 21 Feb 2024 01:42:05 +0000 Subject: [PATCH 30/49] feat: add custom EASE --- code/custom/EASE.py | 107 ++++++++++++++++++++++++++++++++++++++++++++ code/custom/run.py | 51 +++++++++++++++++++++ 2 files changed, 158 insertions(+) create mode 100644 code/custom/EASE.py create mode 100644 code/custom/run.py diff --git a/code/custom/EASE.py b/code/custom/EASE.py new file mode 100644 index 0000000..0ef15d0 --- /dev/null +++ b/code/custom/EASE.py @@ -0,0 +1,107 @@ +r""" +EASE +################################################ +Reference: + Harald Steck. "Embarrassingly Shallow Autoencoders for Sparse Data" in WWW 2019. +""" + +import torch +import numpy as np +import scipy.sparse as sp +import pandas as pd + +from recbole.utils import InputType, ModelType +from recbole.model.abstract_recommender import GeneralRecommender + + +class EASE(GeneralRecommender): + r"""EASE is a linear model for collaborative filtering, which combines the + strengths of auto-encoders and neighborhood-based approaches. + + """ + input_type = InputType.POINTWISE + type = ModelType.TRADITIONAL + + def __init__(self, config, dataset): + super().__init__(config, dataset) + + # load parameters info + reg_weight = config["reg_weight"] + + # need at least one param + self.dummy_param = torch.nn.Parameter(torch.zeros(1)) + + #change user feature + #X = dataset.inter_matrix(form="csr", value_field='similarity').astype(np.float32) + X = dataset.inter_matrix(form="csr").astype(np.float32) + + + # gram matrix + G = X.T @ X + + # add reg to diagonal + G += reg_weight * sp.identity(G.shape[0]).astype(np.float32) + + # convert to dense because inverse will be dense + G = G.todense() + + # invert. this takes most of the time + P = np.linalg.inv(G) + B = P / (-np.diag(P)) + # zero out diag + np.fill_diagonal(B, 0.0) + + + ####rescale + #get popularity + pop = np.array(list(dataset.item_counter.values())) + + #W = 1/pop^a #a=0.5 (hyper param) + #B*diag(W) + W = 1/pop*0.001 + diag_W = sp.spdiags(W, [0], len(W), len(W)).toarray() + + # B와 사이즈 맞추기 # X: (6808, 6808) + first_row = np.zeros((1, diag_W.shape[1])) + diag_W = np.concatenate((first_row, diag_W), axis=0) + last_column = np.zeros((diag_W.shape[0], 1)) + diag_W = np.concatenate((diag_W, last_column), axis=1) + + B = B * diag_W #remove diagonal + + # instead of computing and storing the entire score matrix, + # just store B and compute the scores on demand + # more memory efficient for a larger number of users + # but if there's a large number of items not much one can do: + # still have to compute B all at once + # S = X @ B + # self.score_matrix = torch.from_numpy(S).to(self.device) + + # torch doesn't support sparse tensor slicing, + # so will do everything with np/scipy + self.item_similarity = B + self.interaction_matrix = X + self.other_parameter_name = ["interaction_matrix", "item_similarity"] + self.device = config.device + + def forward(self): + pass + + def calculate_loss(self, interaction): + return torch.nn.Parameter(torch.zeros(1)) + + def predict(self, interaction): + user = interaction[self.USER_ID].cpu().numpy() + item = interaction[self.ITEM_ID].cpu().numpy() + + return torch.from_numpy( + (self.interaction_matrix[user, :].multiply(self.item_similarity[:, item].T)) + .sum(axis=1) + .getA1() + ).to(self.device) + + def full_sort_predict(self, interaction): + user = interaction[self.USER_ID].cpu().numpy() + + r = self.interaction_matrix[user, :] @ self.item_similarity + return torch.from_numpy(r.flatten()) \ No newline at end of file diff --git a/code/custom/run.py b/code/custom/run.py new file mode 100644 index 0000000..120e950 --- /dev/null +++ b/code/custom/run.py @@ -0,0 +1,51 @@ +from logging import getLogger +from recbole.config import Config +from recbole.data import create_dataset, data_preparation +#from recbole.model.general_recommender import EASE +from team.code.custom.EASE import EASE +from recbole.trainer import Trainer +from recbole.utils import init_seed, init_logger +import pandas as pd +import os + +if __name__ == '__main__': + + # configurations initialization + config = Config( + model='EASE', + dataset = 'movie', + config_file_list=['../recbole/configs/EASE.yaml'], + ) + + #bring interactions + interactions = pd.read_csv('../../data/train/train_pearson.csv') + + # init random seed + init_seed(config['seed'], config['reproducibility']) + + # logger initialization + init_logger(config) + logger = getLogger() + + # write config info into log + logger.info(config) + + # dataset creating and filtering + dataset = create_dataset(config) + logger.info(dataset) + + # dataset splitting + train_data, valid_data, test_data = data_preparation(config, dataset) + + model = EASE(config, train_data.dataset).to(config['device']) + logger.info(model) + + # trainer loading and initialization + trainer = Trainer(config, model) + + # model training + best_valid_score, best_valid_result = trainer.fit(train_data, valid_data) + + # model evaluation + test_result = trainer.evaluate(test_data) + print(test_result) \ No newline at end of file From fe3ef4aa78bb4cdc13bfcd02ab1474a26162e4b0 Mon Sep 17 00:00:00 2001 From: Siwoo Date: Wed, 21 Feb 2024 12:28:57 +0000 Subject: [PATCH 31/49] add: ADMMSLIM config --- code/recbole/configs/ADMMSLIM.yaml | 39 ++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 code/recbole/configs/ADMMSLIM.yaml diff --git a/code/recbole/configs/ADMMSLIM.yaml b/code/recbole/configs/ADMMSLIM.yaml new file mode 100644 index 0000000..ae02d40 --- /dev/null +++ b/code/recbole/configs/ADMMSLIM.yaml @@ -0,0 +1,39 @@ +#environment config +seed : 42 +log_wandb: True +use_gpu: True + +data_path: /data/data/train +dataset: movie + +# dataset config : General Recommendation +USER_ID_FIELD: user +ITEM_ID_FIELD: item +LABEL_FIELD: label +field_separator: " " +seq_separator: " " +load_col: + inter: [user, item] + +# model config +model: ADMMSLIM +lambda1: 3.0 +lambda2: 200. +alpha: 0.5 + +# Training and evaluation config +epochs: 1 +train_batch_size: 2048 +eval_batch_size: 2048 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +metrics: ['Recall', 'NDCG', 'Precision'] +topk: 10 +valid_metric: Recall@10 +metric_decimal_place: 4 \ No newline at end of file From c5fe1eb9c247103f4af811c23ebb5c482ee09769 Mon Sep 17 00:00:00 2001 From: Seungbin Date: Wed, 21 Feb 2024 15:14:29 +0000 Subject: [PATCH 32/49] feat: hard voting using output files --- code/voting/args.py | 10 ++++++++++ code/voting/voting.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 38 insertions(+) create mode 100644 code/voting/args.py create mode 100644 code/voting/voting.py diff --git a/code/voting/args.py b/code/voting/args.py new file mode 100644 index 0000000..fa9d522 --- /dev/null +++ b/code/voting/args.py @@ -0,0 +1,10 @@ +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + + #ensemble configs + parser.add_argument("--outputs", default=',', type=str, help='output files that will be ensembled') + + args = parser.parse_args() + return args \ No newline at end of file diff --git a/code/voting/voting.py b/code/voting/voting.py new file mode 100644 index 0000000..85561ee --- /dev/null +++ b/code/voting/voting.py @@ -0,0 +1,28 @@ +import os +from args import parse_args +import argparse + +import pandas as pd +import torch + +def main(args): + + outputs = list(map(lambda x: x.strip(), args.outputs.split(','))) + df = pd.DataFrame(columns = ['user','item']) + for output in outputs: + new_df = pd.read_csv(os.path.join(output),index_col=0,header=0) + df = pd.concat([df, new_df], ignore_index=True) + + count_df = df.groupby('user')['item'].value_counts().reset_index(name='count') + count_df = count_df.groupby('user').head(10).reset_index() + + output = pd.read_csv('../movie/data/eval/sample_submission.csv') + output['item'] = count_df['item'] + if not os.path.exists('output/'): + os.makedirs('output/') + output.to_csv('output/submission.csv') + print("Save submission ... ") + +if __name__=='__main__': + args = parse_args() + main(args) \ No newline at end of file From f98c73faef4b35c606fbd6f7270b899df7635fbd Mon Sep 17 00:00:00 2001 From: Jang <8chris8@naver.com> Date: Wed, 21 Feb 2024 17:41:20 +0000 Subject: [PATCH 33/49] fear:EDA --- code/EDA/data_EDA.py | 296 +++++++++++++++++++++++++++++++ code/EDA/submission_EDA.py | 57 ++++++ code/recbole/EDA.py | 12 +- code/recbole/args.py | 5 +- code/recbole/configs/deepfm.yaml | 21 +-- code/recbole/configs/fm.yaml | 45 +++++ code/recbole/configs/seq.yaml | 4 +- code/recbole/inference.py | 21 +-- 8 files changed, 424 insertions(+), 37 deletions(-) create mode 100644 code/EDA/data_EDA.py create mode 100644 code/EDA/submission_EDA.py create mode 100644 code/recbole/configs/fm.yaml diff --git a/code/EDA/data_EDA.py b/code/EDA/data_EDA.py new file mode 100644 index 0000000..d502854 --- /dev/null +++ b/code/EDA/data_EDA.py @@ -0,0 +1,296 @@ +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import re +import ast + +def load_data(data_path): + interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) + directors = pd.read_csv(os.path.join(data_path, 'directors.tsv'), sep='\t') + genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') + titles = pd.read_csv(os.path.join(data_path, 'titles.tsv'), sep='\t') + writers = pd.read_csv(os.path.join(data_path, 'writers.tsv'), sep='\t') + years = pd.read_csv(os.path.join(data_path, 'years.tsv'), sep='\t') + return interactions, directors, genres, titles, writers, years + +def process_items(interactions, directors, genres, titles, writers, years): + # Genre, Writer, Director를 한 칸에 넣기 + grouped_genres = genres.groupby('item')['genre'].apply(list) + grouped_writers = writers.groupby('item')['writer'].apply(list) + grouped_directors = directors.groupby('item')['director'].apply(list) + items = pd.merge(titles, years, on='item') + items = pd.merge(items, grouped_genres, on='item', how='left') + items = pd.merge(items, grouped_writers, on='item', how='left') + items = pd.merge(items, grouped_directors, on='item', how='left') + # year_bin 추가 + year_counts = interactions.merge(items[['item','year']], on='item',how='left')['year'].value_counts() + year_counts = pd.DataFrame(year_counts) + year_counts.reset_index(inplace=True) + year_counts.columns = ['year', 'count'] + year_counts['cumulative_count'] = year_counts['count'].cumsum() + labels = [f'year_{i}' for i in range(3,0,-1)] # 10개 구간 레이블 + year_counts['year_bin'] = pd.cut(year_counts['cumulative_count'], bins=3, labels=labels) + items = items.merge(year_counts[['year', 'year_bin']], on='year', how='left') + # popularity_bin 추가 + item_counts = interactions['item'].value_counts() + item_counts = pd.DataFrame(item_counts) + item_counts.reset_index(inplace=True) + item_counts.columns = ['item', 'count'] + #item count 기준 + item_counts['cumulative_count'] = item_counts['count'].cumsum() + labels = [f'pop_{i}' for i in range(3,0,-1)] # 10개 구간 레이블 + item_counts['popularity_bin'] = pd.cut(item_counts['cumulative_count'], bins=3, labels=labels) + items = items.merge(item_counts[['item', 'popularity_bin']], on='item', how='left') + return items + +def process_users(interactions, directors, genres, titles, writers, years, items): + #item 추가 + Users = interactions.groupby('user')['item'].agg(list).reset_index() + Users['item_count'] = Users['item'].apply(len) + merged_interactions = interactions.merge(items, on='item', how='left') + #popularity 추가 + import pdb; pdb.set_trace() + popularity_filled = merged_interactions.groupby(['user', 'popularity_bin']).size().unstack(fill_value=0) + Users = pd.merge(Users, popularity_filled, on='user') + #year 추가 + year_filled = merged_interactions.groupby(['user', 'year_bin']).size().unstack(fill_value=0) + Users = pd.merge(Users, year_filled, on='user') + #genre,writer,director 추가 + def process_genres(df, top_n=3, label='genre'): + rows = [] # 결과를 저장할 리스트 초기화 + # 각 사용자별로 반복 + for user, row in df.iterrows(): + # 각 장르(또는 대체 용어)의 상호작용 횟수를 내림차순으로 정렬 + sorted_df = row.sort_values(ascending=False) + # 상위 N개 장르와 해당 상호작용 횟수 추출 + top_categories = sorted_df[:top_n].index.tolist() + top_counts = sorted_df[:top_n].values.tolist() + # 결과 딕셔너리 생성 + row_dict = {'user': user} + for i in range(top_n): + row_dict[f'{label}_{i+1}'] = top_categories[i] if i < len(top_categories) else None + row_dict[f'{label}_{i+1}_count'] = top_counts[i] if i < len(top_counts) else 0 + # 나머지 장르(또는 대체 용어)의 상호작용 횟수 합산 + etc_count = sorted_df[top_n:].sum() + row_dict[f'{label}_etc'] = 'etc' + row_dict[f'{label}_etc_count'] = etc_count + # 결과 리스트에 추가 + rows.append(row_dict) + # 결과 리스트를 DataFrame으로 변환 + result_df = pd.DataFrame(rows) + result_df.set_index('user', inplace=True) + return result_df + + interactions_genres = interactions.merge(genres, on='item', how='left') + genre_filled = interactions_genres.groupby(['user', 'genre']).size().unstack(fill_value=0) + interactions_directors = interactions.merge(directors, on='item', how='left') + director_filled = interactions_directors.groupby(['user', 'director']).size().unstack(fill_value=0) + interactions_writers = interactions.merge(writers, on='item', how='left') + writer_filled = interactions_writers.groupby(['user', 'writer']).size().unstack(fill_value=0) + + Users = pd.merge(Users, process_genres(genre_filled,label='genre'), on='user') + Users = pd.merge(Users, process_genres(director_filled,label='director'), on='user') + Users = pd.merge(Users, process_genres(writer_filled,label='writer'), on='user') + + # percent 추가 + for column in Users.columns: + if column in ['pop_3','pop_2','pop_1','year_1','year_2','year_3','genre_1_count','genre_2_count','genre_3_count' + ,'director_1_count','director_2_count','director_3_count','writer_1_count','writer_2_count','writer_3_count']: + Users[column+'_percent'] = Users[column].div(Users['item_count'],axis=0) + + # type 추가 + Users['type_popular'] = Users['pop_3_percent'] >= 0.7 + Users['type_unpopular'] = Users['pop_1_percent'] >= 0.6 + Users['type_new'] = Users['year_3_percent'] >= 0.7 + Users['type_old'] = Users['year_1_percent'] >= 0.6 + Users['type_genre'] = Users['genre_1_count_percent'] >= 0.7 + Users['type_director'] = Users['director_1_count_percent'] >= 0.08 + Users['type_writer'] = Users['writer_1_count_percent'] >= 0.08 + Users['type_mania'] = Users['item_count']>300 + Users['type_novice'] = Users['item_count']<50 + + true_counts = { + 'type_popular': Users['type_popular'].sum(), + 'type_unpopular': Users['type_unpopular'].sum(), + 'type_new': Users['type_new'].sum(), + 'type_old': Users['type_old'].sum(), + 'type_genre': Users['type_genre'].sum(), + 'type_director': Users['type_director'].sum(), + 'type_writer': Users['type_writer'].sum(), + 'type_mania': Users['type_mania'].sum(), + 'type_novice': Users['type_novice'].sum() + } + + print(true_counts) + + Users['All_users'] = True + + Users['type'] = 'popular' + Users['type'] = Users.apply(lambda Users: 'genre' if Users['type_genre'] else Users['type'], axis=1) + Users['type'] = Users.apply(lambda Users: 'writer' if Users['type_writer'] else Users['type'], axis=1) + Users['type'] = Users.apply(lambda Users: 'director' if Users['type_director'] else Users['type'], axis=1) + Users['type'].value_counts() + + Users = Users.drop('item', axis=1) + return Users + +def save_data(data, data_path, filename): + data.to_csv(os.path.join(data_path, filename), sep=',', index=False) + +def main(): + data_path = '../../data/train' + interactions, directors, genres, titles, writers, years = load_data(data_path) + items = process_items(interactions, directors, genres, titles, writers, years) + users = process_users(interactions, directors, genres, titles, writers, years, items) + import pdb; pdb.set_trace() + + save_data(items, data_path, 'EDA_items.csv') + save_data(users, data_path, 'EDA_users.csv') + +if __name__ == '__main__': + main() + +def EDA(): + ##Data Load + data_path = '../../data/train' + interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) + directors = pd.read_csv(os.path.join(data_path, 'directors.tsv'), sep='\t') + genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') + titles = pd.read_csv(os.path.join(data_path, 'titles.tsv'), sep='\t') + writers = pd.read_csv(os.path.join(data_path, 'writers.tsv'), sep='\t') + years = pd.read_csv(os.path.join(data_path, 'years.tsv'), sep='\t') + #year, popularity PLOT + item_counts_by_year_bin = items.groupby('year_bin').size() + plt.bar(item_counts_by_year_bin.index, item_counts_by_year_bin.values) + plt.savefig('year-item수.png') + + + plt.figure(figsize=(10, 6)) + item_counts_by_popularity_bin = items.groupby('popularity_bin').size() + plt.bar(item_counts_by_popularity_bin.index, item_counts_by_popularity_bin.values) + plt.savefig('popularity-item수.png') + + # interactions에서의 item 수 : 6807개 + # Title에서의 item 수 : 6807개 (위와 전부 동일) -> 중복 제외 6616 + # Years에서의 item수 : 6799개 + # Directors에서의 item수 : 5503개 + # Writers에서의 item수 : 5648개 + # 모든 Item은 Ineractions(Title)에 포함된다 + + + ## title 리메이크 확인 + titles['title'] = titles['title'].apply(lambda x: re.sub(r'\s*\(\d{4}\)', '', x)) + titles['title'].nunique() + titles.to_csv(os.path.join(data_path, 'title_sorted'), sep='\t', index=False) + + ## Directors + # 5905개의 data, 5503개의 영화 + # 1340개의 감독중 1169개의 감독이 10편이상 촬영 + # 58개의 영화는 2명이상의 감독이 있다. 한영화의 최대 감독수는 14명 + directors['director'].value_counts + directors['item'].nunique() + + ## Writers + # 11307개의 data, 5648개의 영화 + # Writers와 Directors는 4973개 일치 + # 1307개의 영화는 2명이상의 작가가 있고. 한영화의 최대 작가수는 24명 + writers['writer'].value_counts() + writers['item'].nunique() + + merged_df = pd.merge(directors, writers, on='item', how='inner') + common_items_count = merged_df['item'].nunique() + print("두 데이터프레임에서 공통된 item의 수:", common_items_count) + + item_counts = writers.groupby('item').size() + print(len(item_counts[item_counts>10])) + + interactions.to_csv(os.path.join(data_path, 'EDAinter.csv'), sep='\t', index=False) + items = pd.merge(titles, years, on='item',) + items = pd.merge(items, genres, on='item') + items = pd.merge(items, writers, on='item', how = 'left') + items = pd.merge(items, directors, on='item', how = 'left') + items = items.fillna("none") + items.to_csv(os.path.join(data_path,'EDAitem.csv'), sep='\t', index=False) + + + #Data 확인 + min_time = interactions['time'].min() + max_time = interactions['time'].max() + # 1970년 1월 1일 자정(UTC)을 기준 : 2005년 3월 ~ 2015년 3월 + # min : 1113220585 / max : 1427781052 + + #item_genre + item_genre_group = genres.groupby('item')['genre'].apply(list) + print(item_genre_group) + # interactions과 genres를 item을 기준으로 병합 + merged = interactions.merge(genres, on='item') + #사용자별로 본 영화의 년도수 묶기 + merged_df = pd.merge(interactions, items, on='item') + user_year_count = merged_df.groupby(['user', 'year']).size().reset_index(name='counts') + user_year_count.to_csv(os.path.join(data_path, 'user_year_count.csv')) + # 년도를 기준으로 2000년 이하와 이후로 구분 + user_year_count['period'] = pd.cut(user_year_count['year'], bins=[0, 2000, user_year_count['year'].max()], labels=['2000년 이하', '2000년 이후']) + + # 사용자별로 2000년 이하와 이후의 아이템을 본 카운트 + user_period_count = user_year_count.groupby(['user', 'period'])['counts'].sum().reset_index() + user_period_count.to_csv(os.path.join(data_path, 'user_year_count_bin.csv')) + + # 사용자별로 본 장르를 리스트로 묶기 + user_genre_counts = merged.groupby('user')['genre'].value_counts() + print(user_genre_counts) + user_genre_counts.to_csv(os.path.join(data_path, 'item_genre_group.csv')) + + # item별 시청횟수 + # 6807개의 item중 2000개는 100회 이하 시청했다. + num_items = interactions['item'].nunique() + print('아이템의 개수:', num_items) + + item_counts = interactions['item'].value_counts() + print(item_counts) + + item_counts_over_100 = item_counts[item_counts >= 5000] + print(item_counts_over_100) + + item_counts = interactions['item'].value_counts() + plt.figure(figsize=(12,6)) + plt.bar(item_counts.index, item_counts.values) + plt.title('Item Interaction Counts') + plt.xlabel('Item ID') + plt.ylabel('Count') + plt.savefig('item_counts.png') + + print(item_counts[10]) + + #User별 시청횟수 + user_view_counts = interactions['user'].value_counts() + print(user_view_counts) + user_view_counts_over = user_view_counts[user_view_counts >= 100] + print(user_view_counts_over) + + ### Submission 결과 확인 + + submission = pd.read_csv(os.path.join('output/submission_EASE.csv')) + train = pd.read_csv('data/data/train/train_ratings.csv') + + ## 각 Train과 Submission 내에는 중복이 없음 + + submission_duplicates = submission.duplicated(subset=['user', 'item']) + train_duplicates = train.duplicated(subset=['user', 'item']) + print('submission 내부에 중복된 경우가 있는지:', submission_duplicates.any()) + print('train 내부에 중복된 경우가 있는지:', train_duplicates.any()) + + + ## Sequential submission과 train에는 중복이 있음 + + # submission과 train 데이터셋에서 'user'와 'item' 열만 추출합니다. + submission_user_item = submission[['user', 'item']] + train_user_item = train[['user', 'item']] + # 두 데이터셋을 병합하고, 'user'와 'item' 열이 중복되는지 확인합니다. + merged = pd.concat([submission_user_item, train_user_item]) + duplicates = merged.duplicated(subset=['user', 'item']) + # 중복된 경우가 있는지 확인합니다. + print('Sequential submission과 train에 중복된 경우가 있는지:', duplicates.any()) + # 중복된 데이터의 개수를 확인합니다. + num_duplicates = duplicates.sum() + print('Sequential submission과 train에서 중복된 데이터의 개수:', num_duplicates) \ No newline at end of file diff --git a/code/EDA/submission_EDA.py b/code/EDA/submission_EDA.py new file mode 100644 index 0000000..e588710 --- /dev/null +++ b/code/EDA/submission_EDA.py @@ -0,0 +1,57 @@ +import os +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import re + +def main(data_path,output_path,submission_name): + items = pd.read_csv(os.path.join(data_path, 'EDA_items.csv'), sep=',') + Users = pd.read_csv(os.path.join(data_path, 'EDA_users.csv'), sep=',') + + sub = pd.read_csv(output_path+'submission_'+submission_name+'.csv') + sub = pd.merge(sub, items, on='item') + + import ast + + def convert_str_to_list(str_list): # 문자열을 리스트로 변환하는 함수 + try: + return ast.literal_eval(str_list) + except ValueError: + return [] + end + + submission_EDA = pd.DataFrame({'user': Users['user'].copy()}) + + year_bin_pivot = sub.pivot_table(index='user', columns='year_bin', aggfunc='size', fill_value=0) + popularity_bin_pivot = sub.pivot_table(index='user', columns='popularity_bin', aggfunc='size', fill_value=0) + + submission_EDA = pd.merge(submission_EDA, year_bin_pivot, on='user') + submission_EDA = pd.merge(submission_EDA, popularity_bin_pivot, on='user') + + def calculate_pivot(sub, Users, column_name, submission_EDA): + sub[column_name] = sub[column_name].apply(convert_str_to_list) + sub_exploded = sub.explode(column_name) + counts = sub_exploded.pivot_table(index='user', columns=column_name, aggfunc='size', fill_value=0) + counts_merge = pd.merge(counts, Users, on='user') + pivot_1 = counts_merge.apply(lambda row: row[row[column_name+'_1']] if row[column_name+'_1'] in counts_merge.columns else 0, axis=1) + submission_EDA[column_name+'_1']=pivot_1.values + pivot_2 = counts_merge.apply(lambda row: row[row[column_name+'_2']] if row[column_name+'_2'] in counts_merge.columns else 0, axis=1) + submission_EDA[column_name+'_2']=pivot_2.values + pivot_3 = counts_merge.apply(lambda row: row[row[column_name+'_3']] if row[column_name+'_3'] in counts_merge.columns else 0, axis=1) + submission_EDA[column_name+'_3']=pivot_3.values + return submission_EDA + + submission_EDA = calculate_pivot(sub,Users,'genre',submission_EDA) + submission_EDA = calculate_pivot(sub,Users,'writer',submission_EDA) + submission_EDA = calculate_pivot(sub,Users,'director',submission_EDA) + + submission_EDA = pd.merge(submission_EDA, Users[['user','type']], on='user') + submission_EDA['model'] = submission_name + submission_EDA.to_csv(os.path.join(output_path,'submission_'+submission_name+'_EDA.csv'), sep=',', index=False) + + +if __name__ == '__main__': + data_path = '../../data/train' + output_path = '../recbole/output/' + submission_name='ease' + main(data_path,output_path,submission_name) \ No newline at end of file diff --git a/code/recbole/EDA.py b/code/recbole/EDA.py index f2b2e3a..49ceb8e 100644 --- a/code/recbole/EDA.py +++ b/code/recbole/EDA.py @@ -4,7 +4,7 @@ import matplotlib.pyplot as plt import re -data_path = 'data/train' +data_path = '../../data/train' interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) directors = pd.read_csv(os.path.join(data_path, 'directors.tsv'), sep='\t') genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') @@ -54,13 +54,13 @@ merged_interactions = interactions.merge(items, on='item', how='left') #popularity 추가 popularity_filled = merged_interactions.groupby(['user', 'popularity_bin']).size().unstack(fill_value=0) -new_column_names = {pop_bin: f'pop_{int(pop_bin)}' for pop_bin in popularity_filled.columns} -popularity_filled = popularity_filled.rename(columns=new_column_names) +#new_column_names = {pop_bin: f'pop_{int(pop_bin)}' for pop_bin in popularity_filled.columns} +#popularity_filled = popularity_filled.rename(columns=new_column_names) Users = pd.merge(Users, popularity_filled, on='user') #year 추가 year_filled = merged_interactions.groupby(['user', 'year_bin']).size().unstack(fill_value=0) -new_column_names = {year_bin: f'year_{int(year_bin)}' for year_bin in year_filled.columns} -year_filled = year_filled.rename(columns=new_column_names) +#new_column_names = {year_bin: f'year_{int(year_bin)}' for year_bin in year_filled.columns} +#year_filled = year_filled.rename(columns=new_column_names) Users = pd.merge(Users, year_filled, on='user') #genre,writer,director 추가 def process_genres(df, top_n=3, label='genre'): @@ -157,7 +157,7 @@ def process_genres(df, top_n=3, label='genre'): output_path = 'code/recbole/output/' -submission_name='slim' +submission_name='easer' sub = pd.read_csv(output_path+'submission_'+submission_name+'.csv') sub = pd.merge(sub, items, on='item') diff --git a/code/recbole/args.py b/code/recbole/args.py index c751de5..0768b17 100644 --- a/code/recbole/args.py +++ b/code/recbole/args.py @@ -3,8 +3,9 @@ def parse_args(): parser = argparse.ArgumentParser() - parser.add_argument("--model", default='recvae', type=str, help='model name in recbole library') - parser.add_argument("--model_path", default='saved/RecVAE-Feb-06-2024_16-27-17.pth', type=str, help='saved model path to predict') + parser.add_argument("--model", default='seq', type=str, help='model name in recbole library') + parser.add_argument("--model_path", default='saved/GRU4Rec-Feb-21-2024_12-52-42.pth', type=str, help='saved model path to predict') + parser.add_argument("--sequence", default=False, type=str, help='sequence model have to delete history during inference') args = parser.parse_args() return args \ No newline at end of file diff --git a/code/recbole/configs/deepfm.yaml b/code/recbole/configs/deepfm.yaml index 733a7fd..025a832 100644 --- a/code/recbole/configs/deepfm.yaml +++ b/code/recbole/configs/deepfm.yaml @@ -1,5 +1,4 @@ - -model: DeepFM +model: ItemKNN data_path: /data/data/train dataset: movie @@ -15,17 +14,14 @@ use_gpu: true load_col: inter: [user, item, label] user: [user] - item: [item, genre] + item: [item, genre, director, title, writer, year, popularity_bin] user_inter_num_interval: "[0,inf)" item_inter_num_interval: "[0,inf)" -epochs: 3 -train_batch_size: 4086 -metrics: ["Recall", "NDCG"] -topk: [10] -valid_metric: Recall@10 -embedding_size : 2 +similarity: cosine # 유사도 메트릭스로 사용할 유사도 함수 + +topk: 10 # 가장 유사한 아이템을 선택할 개수 eval_args: group_by: user @@ -34,10 +30,7 @@ eval_args: LS: valid_and_test mode: full -train_neg_sample_args: - distribution: popularity - candidate_num: 1 -training_neg_sample_num: 1 - log_wandb: True wandb_project: recbole_deepfm + #user: [user, type] + #item: [item, genre, director, title, writer, year, year_bin, popularity_bin] \ No newline at end of file diff --git a/code/recbole/configs/fm.yaml b/code/recbole/configs/fm.yaml new file mode 100644 index 0000000..39eab20 --- /dev/null +++ b/code/recbole/configs/fm.yaml @@ -0,0 +1,45 @@ + +model: FM + +data_path: /data/data/train +dataset: movie +field_separator: " " +seq_separator: " " +USER_ID_FIELD: user +ITEM_ID_FIELD: item +#TIME_FIELD: time +LABEL_FIELD: label +show_progress: false +use_gpu: true + +load_col: + inter: [user, item, label] + user: [user, type] + item: [item, genre, popularity_bin] + +user_inter_num_interval: "[0,inf)" +item_inter_num_interval: "[0,inf)" + +epochs: 10 +train_batch_size: 2048 +learning_rate: 0.01 +metrics: ["Recall", "NDCG"] +topk: [10] +valid_metric: Recall@10 +#embedding_size : 2 + + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +train_neg_sample_args: + distribution: popularity + candidate_num: 1 +training_neg_sample_num: 1 + +log_wandb: True +wandb_project: recbole_seq diff --git a/code/recbole/configs/seq.yaml b/code/recbole/configs/seq.yaml index 4ce60fb..f098e92 100644 --- a/code/recbole/configs/seq.yaml +++ b/code/recbole/configs/seq.yaml @@ -1,5 +1,5 @@ -model: SASRec +model: GRU4Rec #GRU4Rec, BERT4Rec, SASRec data_path: /data/data/train dataset: movie @@ -38,3 +38,5 @@ neg_sampling : Null log_wandb: True wandb_project: recbole_seq + #user: [user, type] + #item: [item, genre, director, title, writer, year, year_bin, popularity_bin] \ No newline at end of file diff --git a/code/recbole/inference.py b/code/recbole/inference.py index 90f18ef..acd7bcd 100644 --- a/code/recbole/inference.py +++ b/code/recbole/inference.py @@ -7,28 +7,18 @@ from recbole.utils.case_study import full_sort_topk from args import parse_args + def seq_sort(dataset,topk_iid_list): topk_iid_list_1000 = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()) data_path = '/data/data/train' interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) grouped_items = interactions.groupby('user')['item'].apply(list) - - # 결과를 저장할 텐서 생성 result_tensor = torch.zeros((topk_iid_list_1000.shape[0], 10), dtype=torch.int32, device='cuda:0') - - # 각 사용자에 대해 반복 for user_idx in range(topk_iid_list_1000.shape[0]): - # 해당 사용자의 topk_iid_list와 grouped_items 가져오기 user_topk_iid = topk_iid_list_1000[user_idx,:] user_grouped_items = grouped_items.iloc[user_idx] - - # topk_iid_list에서 grouped_items에 있는 아이템을 제외 filtered_items = user_topk_iid[~np.isin(user_topk_iid, user_grouped_items)] - - # 상위 10개의 아이템 선택 top_10_items = filtered_items[:10].astype(int) - - # 결과를 텐서에 저장 result_tensor[user_idx] = torch.from_numpy(top_10_items) return result_tensor @@ -41,9 +31,12 @@ def main(model_path): external_user_ids = dataset.id2token(dataset.uid_field, list(range(dataset.user_num)))[1:] uid_series = dataset.token2id(dataset.uid_field, external_user_ids) - topk_score, topk_iid_list = full_sort_topk(uid_series, model, test_data, k=3000, device=config["device"]) - #external_item_list = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()).flatten() - external_item_list = seq_sort(dataset,topk_iid_list).flatten() #Seq의 과거시청이력 제거 + if args.sequence: #Recbole의 Sequence model에서는 추가로 과거시청이력 제거해줘야한다. + topk_score, topk_iid_list = full_sort_topk(uid_series, model, test_data, k=3000, device=config["device"]) + external_item_list = seq_sort(dataset,topk_iid_list).flatten() + else: #Recbole의 General model은 library에서 과거시청이력을 제거해준다. + topk_score, topk_iid_list = full_sort_topk(uid_series, model, test_data, k=10, device=config["device"]) + external_item_list = dataset.id2token(dataset.iid_field, topk_iid_list.cpu()).flatten() output = pd.read_csv('/data/data/eval/sample_submission.csv') output['item'] = [int(item) for item in external_item_list] if not os.path.exists('output/'): From c73c50fb31614c259225dddd573155d4e9df8026 Mon Sep 17 00:00:00 2001 From: Seungbin Date: Thu, 22 Feb 2024 01:55:31 +0000 Subject: [PATCH 34/49] feat: add DiffRec --- code/recbole/configs/DiffRec.yaml | 49 +++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 code/recbole/configs/DiffRec.yaml diff --git a/code/recbole/configs/DiffRec.yaml b/code/recbole/configs/DiffRec.yaml new file mode 100644 index 0000000..96e45c6 --- /dev/null +++ b/code/recbole/configs/DiffRec.yaml @@ -0,0 +1,49 @@ +#environment config +seed : 42 +log_wandb: True +wandb_project: diffrec +use_gpu: True + +data_path: /data/ephemeral/movie/data/train +#data_path: /data/train +dataset: movie + +# dataset config : General Recommendation +USER_ID_FIELD: user +ITEM_ID_FIELD: item +#LABEL_FIELD: label +RATING_FIELD: similarity +#field_separator: ',' +field_separator: " " +seq_separator: " " +load_col: + inter: [user, item, similarity] #inter file에서 사용할 cols + +rating_threshold: null + +# model config +model: DiffRec + + +noise_schedule: 'linear' #[‘linear’, ‘linear-var’, ‘cosine’, ‘binomial’] +noise_scale: 0.01 +noise_max: 0.005 +noise_min: 0 +time-aware: False + + + +# Training and evaluation config +epochs: 25 +train_batch_size: 4096 +eval_args: + group_by: none #굳이 user로 할 필요가 있나? + order: RO + split: #{'RS': [0.8,0.1,0.1]} + LS: 'valid_and_test' + mode: full + +metrics: ['Recall', 'NDCG', 'Precision'] +topk: 10 +valid_metric: Recall@10 +metric_decimal_place: 4 From b7b2f75a8c64ce199ff5d0c892c2de85191f41a1 Mon Sep 17 00:00:00 2001 From: jaegwon Date: Thu, 22 Feb 2024 02:07:14 +0000 Subject: [PATCH 35/49] feat: add LightGCN config --- code/recbole/configs/lightgcn.yaml | 40 ++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 code/recbole/configs/lightgcn.yaml diff --git a/code/recbole/configs/lightgcn.yaml b/code/recbole/configs/lightgcn.yaml new file mode 100644 index 0000000..722ac80 --- /dev/null +++ b/code/recbole/configs/lightgcn.yaml @@ -0,0 +1,40 @@ +#environment config +seed : 42 +log_wandb: True +wandb_project: movielens +use_gpu: True + +data_path: ../../data/train +dataset: movie + +# dataset config : General Recommendation +USER_ID_FIELD: user +ITEM_ID_FIELD: item +LABEL_FIELD: label +field_separator: " " +seq_separator: " " +load_col: + inter: [user, item] + +# model config +model: LightGCN +embedding_size: 128 +n_layers: 1 + +# Training and evaluation config +epochs: 1000 +stopping_step: 10 +train_batch_size: 2048 +eval_batch_size: 2048 + +eval_args: + group_by: user + order: RO + split: + LS: valid_and_test + mode: full + +metrics: ['Recall', 'NDCG', 'Precision'] +topk: 10 +valid_metric: Recall@10 +metric_decimal_place: 4 \ No newline at end of file From 47b72ac6973b5d425a36272b1fb4c6a5a42a95d0 Mon Sep 17 00:00:00 2001 From: arctic890 <95343771+arctic890@users.noreply.github.com> Date: Thu, 22 Feb 2024 11:59:32 +0900 Subject: [PATCH 36/49] Update DiffRec.yaml remove comments --- code/recbole/configs/DiffRec.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/code/recbole/configs/DiffRec.yaml b/code/recbole/configs/DiffRec.yaml index 96e45c6..9c9706c 100644 --- a/code/recbole/configs/DiffRec.yaml +++ b/code/recbole/configs/DiffRec.yaml @@ -17,7 +17,7 @@ RATING_FIELD: similarity field_separator: " " seq_separator: " " load_col: - inter: [user, item, similarity] #inter file에서 사용할 cols + inter: [user, item, similarity] rating_threshold: null @@ -25,7 +25,7 @@ rating_threshold: null model: DiffRec -noise_schedule: 'linear' #[‘linear’, ‘linear-var’, ‘cosine’, ‘binomial’] +noise_schedule: 'linear' noise_scale: 0.01 noise_max: 0.005 noise_min: 0 @@ -37,7 +37,7 @@ time-aware: False epochs: 25 train_batch_size: 4096 eval_args: - group_by: none #굳이 user로 할 필요가 있나? + group_by: user order: RO split: #{'RS': [0.8,0.1,0.1]} LS: 'valid_and_test' From a5b41d6b78b21977991786adba494d354b8a181b Mon Sep 17 00:00:00 2001 From: Jang <8chris8@naver.com> Date: Fri, 23 Feb 2024 00:45:40 +0000 Subject: [PATCH 37/49] feat:README --- code/EDA/README.md | 28 ++ code/EDA/requirements.txt | 3 + .../rulebased.py => EDA/rule_based_model.py} | 2 - code/EDA/submission_EDA.py | 15 +- code/recbole/EDA.py | 356 ------------------ 5 files changed, 45 insertions(+), 359 deletions(-) create mode 100644 code/EDA/README.md create mode 100644 code/EDA/requirements.txt rename code/{recbole/rulebased.py => EDA/rule_based_model.py} (98%) delete mode 100644 code/recbole/EDA.py diff --git a/code/EDA/README.md b/code/EDA/README.md new file mode 100644 index 0000000..c37842f --- /dev/null +++ b/code/EDA/README.md @@ -0,0 +1,28 @@ +# EDA : data와 submission 분석 + +## Setup +``` +bash +cd code/EDA +conda init +(base) . ~/.bashrc +(base) conda create -n eda python=3.10 -y +(base) conda activate eda +(eda) pip install -r requirements.txt +``` + +## Files +`code/EDA` +* `data_EDA.py` : item,user에 대한 EDA파일을 생성합니다. +* `submission_EDA.py` : submission에 대한 EDA파일을 생성합니다. +* `rule_based_model.py` : User type을 기반으로 하는 rule based model입니다. + +## 실행방법 +* EDA_items.csv, EDA_users.csv 파일 생성 +``` +python data_EDA.py +``` +* submission_model_EDA.csv 파일 생성 +``` +python submission_EDA.py +``` \ No newline at end of file diff --git a/code/EDA/requirements.txt b/code/EDA/requirements.txt new file mode 100644 index 0000000..4021870 --- /dev/null +++ b/code/EDA/requirements.txt @@ -0,0 +1,3 @@ +numpy +pandas +matplotlib diff --git a/code/recbole/rulebased.py b/code/EDA/rule_based_model.py similarity index 98% rename from code/recbole/rulebased.py rename to code/EDA/rule_based_model.py index b487a8e..7d12486 100644 --- a/code/recbole/rulebased.py +++ b/code/EDA/rule_based_model.py @@ -69,5 +69,3 @@ recommendations_df = pd.DataFrame(recommendations, columns=['user', 'item']) print(recommendations_df) recommendations_df.to_csv('output/submission_rule_types2.csv',index=False) - -directors[directors['item']==3471] \ No newline at end of file diff --git a/code/EDA/submission_EDA.py b/code/EDA/submission_EDA.py index e588710..910af95 100644 --- a/code/EDA/submission_EDA.py +++ b/code/EDA/submission_EDA.py @@ -49,9 +49,22 @@ def calculate_pivot(sub, Users, column_name, submission_EDA): submission_EDA['model'] = submission_name submission_EDA.to_csv(os.path.join(output_path,'submission_'+submission_name+'_EDA.csv'), sep=',', index=False) +def submission_merge(): + #2개의 submission file을 각 type에 맞게 merge + data_path = 'data/train' + items = pd.read_csv(os.path.join(data_path, 'EDA_items.csv'), sep=',') + Users = pd.read_csv(os.path.join(data_path, 'EDA_users.csv'), sep=',') + + output_path = 'code/recbole/output/' + sub1 = pd.read_csv(output_path+'submission_EASER_final.csv') # popular type 반영 + sub2 = pd.read_csv(output_path+'submission_ensemble_final.csv') # 나머지 type 반영 + + popular_users = Users.loc[Users['type']=='popular','user'].unique() + sub2.loc[sub2['user'].isin(popular_users), 'item'] = sub1.loc[sub1['user'].isin(popular_users), 'item'] + sub2.to_csv(os.path.join(output_path, 'submission_final.csv'), sep=',',index = False) if __name__ == '__main__': data_path = '../../data/train' output_path = '../recbole/output/' - submission_name='ease' + submission_name='EASER' main(data_path,output_path,submission_name) \ No newline at end of file diff --git a/code/recbole/EDA.py b/code/recbole/EDA.py deleted file mode 100644 index 49ceb8e..0000000 --- a/code/recbole/EDA.py +++ /dev/null @@ -1,356 +0,0 @@ -import os -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import re - -data_path = '../../data/train' -interactions = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) -directors = pd.read_csv(os.path.join(data_path, 'directors.tsv'), sep='\t') -genres = pd.read_csv(os.path.join(data_path, 'genres.tsv'), sep='\t') -titles = pd.read_csv(os.path.join(data_path, 'titles.tsv'), sep='\t') -writers = pd.read_csv(os.path.join(data_path, 'writers.tsv'), sep='\t') -years = pd.read_csv(os.path.join(data_path, 'years.tsv'), sep='\t') - - -## items 파일 - -#Genre, Writer, Director 한칸에 넣기 -grouped_genres= genres.groupby('item')['genre'].apply(list) -grouped_writers = writers.groupby('item')['writer'].apply(list) -grouped_directors = directors.groupby('item')['director'].apply(list) -items = pd.merge(titles, years, on='item') -items = pd.merge(items, grouped_genres, on='item', how = 'left') -items = pd.merge(items, grouped_writers, on='item', how = 'left') -items = pd.merge(items, grouped_directors, on='item', how = 'left') -# year_bin 추가 -year_counts = interactions.merge(items[['item','year']], on='item',how='left')['year'].value_counts() -year_counts = pd.DataFrame(year_counts) -year_counts.reset_index(inplace=True) -year_counts.columns = ['year', 'count'] -year_counts['cumulative_count'] = year_counts['count'].cumsum() -labels = [f'year_{i}' for i in range(3,0,-1)] # 10개 구간 레이블 -year_counts['year_bin'] = pd.cut(year_counts['cumulative_count'], bins=3, labels=labels) -items = items.merge(year_counts[['year', 'year_bin']], on='year', how='left') -# popularity_bin 추가 -item_counts = interactions['item'].value_counts() -item_counts = pd.DataFrame(item_counts) -item_counts.reset_index(inplace=True) -item_counts.columns = ['item', 'count'] -#item count 기준 -item_counts['cumulative_count'] = item_counts['count'].cumsum() -labels = [f'pop_{i}' for i in range(3,0,-1)] # 10개 구간 레이블 -item_counts['popularity_bin'] = pd.cut(item_counts['cumulative_count'], bins=3, labels=labels) -items = items.merge(item_counts[['item', 'popularity_bin']], on='item', how='left') - - - - -## Users 파일 - -#item 추가 -Users = interactions.groupby('user')['item'].agg(list).reset_index() -Users['item_count'] = Users['item'].apply(len) -merged_interactions = interactions.merge(items, on='item', how='left') -#popularity 추가 -popularity_filled = merged_interactions.groupby(['user', 'popularity_bin']).size().unstack(fill_value=0) -#new_column_names = {pop_bin: f'pop_{int(pop_bin)}' for pop_bin in popularity_filled.columns} -#popularity_filled = popularity_filled.rename(columns=new_column_names) -Users = pd.merge(Users, popularity_filled, on='user') -#year 추가 -year_filled = merged_interactions.groupby(['user', 'year_bin']).size().unstack(fill_value=0) -#new_column_names = {year_bin: f'year_{int(year_bin)}' for year_bin in year_filled.columns} -#year_filled = year_filled.rename(columns=new_column_names) -Users = pd.merge(Users, year_filled, on='user') -#genre,writer,director 추가 -def process_genres(df, top_n=3, label='genre'): - rows = [] # 결과를 저장할 리스트 초기화 - # 각 사용자별로 반복 - for user, row in df.iterrows(): - # 각 장르(또는 대체 용어)의 상호작용 횟수를 내림차순으로 정렬 - sorted_df = row.sort_values(ascending=False) - # 상위 N개 장르와 해당 상호작용 횟수 추출 - top_categories = sorted_df[:top_n].index.tolist() - top_counts = sorted_df[:top_n].values.tolist() - # 결과 딕셔너리 생성 - row_dict = {'user': user} - for i in range(top_n): - row_dict[f'{label}_{i+1}'] = top_categories[i] if i < len(top_categories) else None - row_dict[f'{label}_{i+1}_count'] = top_counts[i] if i < len(top_counts) else 0 - # 나머지 장르(또는 대체 용어)의 상호작용 횟수 합산 - etc_count = sorted_df[top_n:].sum() - row_dict[f'{label}_etc'] = 'etc' - row_dict[f'{label}_etc_count'] = etc_count - # 결과 리스트에 추가 - rows.append(row_dict) - # 결과 리스트를 DataFrame으로 변환 - result_df = pd.DataFrame(rows) - result_df.set_index('user', inplace=True) - return result_df - -interactions_genres = interactions.merge(genres, on='item', how='left') -genre_filled = interactions_genres.groupby(['user', 'genre']).size().unstack(fill_value=0) -interactions_directors = interactions.merge(directors, on='item', how='left') -director_filled = interactions_directors.groupby(['user', 'director']).size().unstack(fill_value=0) -interactions_writers = interactions.merge(writers, on='item', how='left') -writer_filled = interactions_writers.groupby(['user', 'writer']).size().unstack(fill_value=0) - -Users = pd.merge(Users, process_genres(genre_filled,label='genre'), on='user') -Users = pd.merge(Users, process_genres(director_filled,label='director'), on='user') -Users = pd.merge(Users, process_genres(writer_filled,label='writer'), on='user') - -# percent 추가 -for column in Users.columns: - if column in ['pop_3','pop_2','pop_1','year_1','year_2','year_3','genre_1_count','genre_2_count','genre_3_count' - ,'director_1_count','director_2_count','director_3_count','writer_1_count','writer_2_count','writer_3_count']: - Users[column+'_percent'] = Users[column].div(Users['item_count'],axis=0) - -# type 추가 -Users['type_popular'] = Users['pop_3_percent'] >= 0.7 -Users['type_unpopular'] = Users['pop_1_percent'] >= 0.6 -Users['type_new'] = Users['year_3_percent'] >= 0.7 -Users['type_old'] = Users['year_1_percent'] >= 0.6 -Users['type_genre'] = Users['genre_1_count_percent'] >= 0.7 -Users['type_director'] = Users['director_1_count_percent'] >= 0.08 -Users['type_writer'] = Users['writer_1_count_percent'] >= 0.08 -Users['type_mania'] = Users['item_count']>300 -Users['type_novice'] = Users['item_count']<50 - -true_counts = { - 'type_popular': Users['type_popular'].sum(), - 'type_unpopular': Users['type_unpopular'].sum(), - 'type_new': Users['type_new'].sum(), - 'type_old': Users['type_old'].sum(), - 'type_genre': Users['type_genre'].sum(), - 'type_director': Users['type_director'].sum(), - 'type_writer': Users['type_writer'].sum(), - 'type_mania': Users['type_mania'].sum(), - 'type_novice': Users['type_novice'].sum() -} - -print(true_counts) - -Users['All_users'] = True - -Users['type'] = 'popular' -Users['type'] = Users.apply(lambda Users: 'genre' if Users['type_genre'] else Users['type'], axis=1) -Users['type'] = Users.apply(lambda Users: 'writer' if Users['type_writer'] else Users['type'], axis=1) -Users['type'] = Users.apply(lambda Users: 'director' if Users['type_director'] else Users['type'], axis=1) -Users['type'].value_counts() - -Users = Users.drop('item', axis=1) -items.to_csv(os.path.join(data_path, 'EDA_items.csv'), sep=',', index=False) -Users.to_csv(os.path.join(data_path, 'EDA_users.csv'), sep=',', index=False) - - - -## Submission 분석 # -import os -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import re - -data_path = 'data/train' -items = pd.read_csv(os.path.join(data_path, 'EDA_items.csv'), sep=',') -Users = pd.read_csv(os.path.join(data_path, 'EDA_users.csv'), sep=',') - -output_path = 'code/recbole/output/' - -submission_name='easer' -sub = pd.read_csv(output_path+'submission_'+submission_name+'.csv') -sub = pd.merge(sub, items, on='item') - -import ast -# 문자열을 리스트로 변환하는 함수 정의 -def convert_str_to_list(str_list): - try: - return ast.literal_eval(str_list) - except ValueError: # 문자열이 올바른 리스트 형식이 아닐 경우 - return [] # 빈 리스트 반환 - end - -submission_EDA = pd.DataFrame({'user': Users['user'].copy()}) - - - -year_bin_pivot = sub.pivot_table(index='user', columns='year_bin', aggfunc='size', fill_value=0) -popularity_bin_pivot = sub.pivot_table(index='user', columns='popularity_bin', aggfunc='size', fill_value=0) - -submission_EDA = pd.merge(submission_EDA, year_bin_pivot, on='user') -submission_EDA = pd.merge(submission_EDA, popularity_bin_pivot, on='user') - -def calculate_pivot(sub, Users, column_name, submission_EDA): - sub[column_name] = sub[column_name].apply(convert_str_to_list) - sub_exploded = sub.explode(column_name) - counts = sub_exploded.pivot_table(index='user', columns=column_name, aggfunc='size', fill_value=0) - counts_merge = pd.merge(counts, Users, on='user') - pivot_1 = counts_merge.apply(lambda row: row[row[column_name+'_1']] if row[column_name+'_1'] in counts_merge.columns else 0, axis=1) - submission_EDA[column_name+'_1']=pivot_1.values - pivot_2 = counts_merge.apply(lambda row: row[row[column_name+'_2']] if row[column_name+'_2'] in counts_merge.columns else 0, axis=1) - submission_EDA[column_name+'_2']=pivot_2.values - pivot_3 = counts_merge.apply(lambda row: row[row[column_name+'_3']] if row[column_name+'_3'] in counts_merge.columns else 0, axis=1) - submission_EDA[column_name+'_3']=pivot_3.values - return submission_EDA - -submission_EDA = calculate_pivot(sub,Users,'genre',submission_EDA) -submission_EDA = calculate_pivot(sub,Users,'writer',submission_EDA) -submission_EDA = calculate_pivot(sub,Users,'director',submission_EDA) - -submission_EDA = pd.merge(submission_EDA, Users[['user','type']], on='user') -submission_EDA['model'] = submission_name -submission_EDA.to_csv(os.path.join(output_path,'submission_'+submission_name+'_EDA.csv'), sep=',', index=False) - - -## EDA ## - -#year, popularity PLOT -item_counts_by_year_bin = items.groupby('year_bin').size() -plt.bar(item_counts_by_year_bin.index, item_counts_by_year_bin.values) -plt.savefig('year-item수.png') - - -plt.figure(figsize=(10, 6)) -item_counts_by_popularity_bin = items.groupby('popularity_bin').size() -plt.bar(item_counts_by_popularity_bin.index, item_counts_by_popularity_bin.values) -plt.savefig('popularity-item수.png') - -# interactions에서의 item 수 : 6807개 -# Title에서의 item 수 : 6807개 (위와 전부 동일) -> 중복 제외 6616 -# Years에서의 item수 : 6799개 -# Directors에서의 item수 : 5503개 -# Writers에서의 item수 : 5648개 -# 모든 Item은 Ineractions(Title)에 포함된다 - - -## title 리메이크 확인 -titles['title'] = titles['title'].apply(lambda x: re.sub(r'\s*\(\d{4}\)', '', x)) -titles['title'].nunique() -titles.to_csv(os.path.join(data_path, 'title_sorted'), sep='\t', index=False) - -## Directors -# 5905개의 data, 5503개의 영화 -# 1340개의 감독중 1169개의 감독이 10편이상 촬영 -# 58개의 영화는 2명이상의 감독이 있다. 한영화의 최대 감독수는 14명 -directors['director'].value_counts -directors['item'].nunique() - -## Writers -# 11307개의 data, 5648개의 영화 -# Writers와 Directors는 4973개 일치 -# 1307개의 영화는 2명이상의 작가가 있고. 한영화의 최대 작가수는 24명 -writers['writer'].value_counts() -writers['item'].nunique() - -merged_df = pd.merge(directors, writers, on='item', how='inner') -common_items_count = merged_df['item'].nunique() -print("두 데이터프레임에서 공통된 item의 수:", common_items_count) - - -item_counts = writers.groupby('item').size() -print(len(item_counts[item_counts>10])) - - - - -interactions.to_csv(os.path.join(data_path, 'EDAinter.csv'), sep='\t', index=False) -print("Save interaction data ...") -print("Save user data ...") - -items = pd.merge(titles, years, on='item',) -items = pd.merge(items, genres, on='item') -items = pd.merge(items, writers, on='item', how = 'left') -items = pd.merge(items, directors, on='item', how = 'left') -items = items.fillna("none") -items.to_csv(os.path.join(data_path,'EDAitem.csv'), sep='\t', index=False) -print("Save item data ...") - - -#Data 확인 -min_time = interactions['time'].min() -max_time = interactions['time'].max() -# 1970년 1월 1일 자정(UTC)을 기준 : 2005년 3월 ~ 2015년 3월 -# min : 1113220585 / max : 1427781052 - -#item_genre -item_genre_group = genres.groupby('item')['genre'].apply(list) -print(item_genre_group) -# interactions과 genres를 item을 기준으로 병합 -merged = interactions.merge(genres, on='item') -#사용자별로 본 영화의 년도수 묶기 -merged_df = pd.merge(interactions, items, on='item') -user_year_count = merged_df.groupby(['user', 'year']).size().reset_index(name='counts') -user_year_count.to_csv(os.path.join(data_path, 'user_year_count.csv')) -# 년도를 기준으로 2000년 이하와 이후로 구분 -user_year_count['period'] = pd.cut(user_year_count['year'], bins=[0, 2000, user_year_count['year'].max()], labels=['2000년 이하', '2000년 이후']) - -# 사용자별로 2000년 이하와 이후의 아이템을 본 카운트 -user_period_count = user_year_count.groupby(['user', 'period'])['counts'].sum().reset_index() -user_period_count.to_csv(os.path.join(data_path, 'user_year_count_bin.csv')) - - -# 사용자별로 본 장르를 리스트로 묶기 -user_genre_counts = merged.groupby('user')['genre'].value_counts() -print(user_genre_counts) -user_genre_counts.to_csv(os.path.join(data_path, 'item_genre_group.csv')) - - -# item별 시청횟수 -# 6807개의 item중 2000개는 100회 이하 시청했다. -num_items = interactions['item'].nunique() -print('아이템의 개수:', num_items) - -item_counts = interactions['item'].value_counts() -print(item_counts) - -item_counts_over_100 = item_counts[item_counts >= 5000] -print(item_counts_over_100) - -item_counts = interactions['item'].value_counts() -plt.figure(figsize=(12,6)) -plt.bar(item_counts.index, item_counts.values) -plt.title('Item Interaction Counts') -plt.xlabel('Item ID') -plt.ylabel('Count') -plt.savefig('item_counts.png') - -print(item_counts[10]) - - - - -#User별 시청횟수 -user_view_counts = interactions['user'].value_counts() -print(user_view_counts) - -user_view_counts_over = user_view_counts[user_view_counts >= 100] -print(user_view_counts_over) - - -### Submission 결과 확인 - -submission = pd.read_csv(os.path.join('output/submission_EASE.csv')) -train = pd.read_csv('data/data/train/train_ratings.csv') - -## 각 Train과 Submission 내에는 중복이 없음 - -submission_duplicates = submission.duplicated(subset=['user', 'item']) -train_duplicates = train.duplicated(subset=['user', 'item']) -print('submission 내부에 중복된 경우가 있는지:', submission_duplicates.any()) -print('train 내부에 중복된 경우가 있는지:', train_duplicates.any()) - - -## Sequential submission과 train에는 중복이 있음 - -# submission과 train 데이터셋에서 'user'와 'item' 열만 추출합니다. -submission_user_item = submission[['user', 'item']] -train_user_item = train[['user', 'item']] -# 두 데이터셋을 병합하고, 'user'와 'item' 열이 중복되는지 확인합니다. -merged = pd.concat([submission_user_item, train_user_item]) -duplicates = merged.duplicated(subset=['user', 'item']) -# 중복된 경우가 있는지 확인합니다. -print('Sequential submission과 train에 중복된 경우가 있는지:', duplicates.any()) - -# 중복된 데이터의 개수를 확인합니다. -num_duplicates = duplicates.sum() -print('Sequential submission과 train에서 중복된 데이터의 개수:', num_duplicates) \ No newline at end of file From 5f72bc123c71f7f66ed95dadd41df94850c1d525 Mon Sep 17 00:00:00 2001 From: Jang <8chris8@naver.com> Date: Fri, 23 Feb 2024 01:02:30 +0000 Subject: [PATCH 38/49] fix:conflicts --- code/recbole/configs/deepfm.yaml | 36 -------------------------------- 1 file changed, 36 deletions(-) delete mode 100644 code/recbole/configs/deepfm.yaml diff --git a/code/recbole/configs/deepfm.yaml b/code/recbole/configs/deepfm.yaml deleted file mode 100644 index 025a832..0000000 --- a/code/recbole/configs/deepfm.yaml +++ /dev/null @@ -1,36 +0,0 @@ -model: ItemKNN - -data_path: /data/data/train -dataset: movie -field_separator: " " -seq_separator: " " -USER_ID_FIELD: user -ITEM_ID_FIELD: item -#TIME_FIELD: time -LABEL_FIELD: label -show_progress: false -use_gpu: true - -load_col: - inter: [user, item, label] - user: [user] - item: [item, genre, director, title, writer, year, popularity_bin] - -user_inter_num_interval: "[0,inf)" -item_inter_num_interval: "[0,inf)" - -similarity: cosine # 유사도 메트릭스로 사용할 유사도 함수 - -topk: 10 # 가장 유사한 아이템을 선택할 개수 - -eval_args: - group_by: user - order: RO - split: - LS: valid_and_test - mode: full - -log_wandb: True -wandb_project: recbole_deepfm - #user: [user, type] - #item: [item, genre, director, title, writer, year, year_bin, popularity_bin] \ No newline at end of file From 86ad313f87ecdc8dd96334bfb4908685369f84eb Mon Sep 17 00:00:00 2001 From: arctic890 <95343771+arctic890@users.noreply.github.com> Date: Fri, 23 Feb 2024 13:35:48 +0900 Subject: [PATCH 39/49] Update DiffRec.yaml --- code/recbole/configs/DiffRec.yaml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/code/recbole/configs/DiffRec.yaml b/code/recbole/configs/DiffRec.yaml index 9c9706c..255ab6e 100644 --- a/code/recbole/configs/DiffRec.yaml +++ b/code/recbole/configs/DiffRec.yaml @@ -5,15 +5,12 @@ wandb_project: diffrec use_gpu: True data_path: /data/ephemeral/movie/data/train -#data_path: /data/train dataset: movie # dataset config : General Recommendation USER_ID_FIELD: user ITEM_ID_FIELD: item -#LABEL_FIELD: label RATING_FIELD: similarity -#field_separator: ',' field_separator: " " seq_separator: " " load_col: @@ -29,8 +26,6 @@ noise_schedule: 'linear' noise_scale: 0.01 noise_max: 0.005 noise_min: 0 -time-aware: False - # Training and evaluation config @@ -39,7 +34,7 @@ train_batch_size: 4096 eval_args: group_by: user order: RO - split: #{'RS': [0.8,0.1,0.1]} + split: LS: 'valid_and_test' mode: full From eba4400fd74be6420f5b0939b8816da6635bc0d9 Mon Sep 17 00:00:00 2001 From: Seungbin Date: Fri, 23 Feb 2024 04:51:34 +0000 Subject: [PATCH 40/49] feat: add weight and score --- code/voting/args.py | 1 + code/voting/voting.py | 13 ++++++++++--- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/code/voting/args.py b/code/voting/args.py index fa9d522..33e4b2f 100644 --- a/code/voting/args.py +++ b/code/voting/args.py @@ -5,6 +5,7 @@ def parse_args(): #ensemble configs parser.add_argument("--outputs", default=',', type=str, help='output files that will be ensembled') + parser.add_argument("--weights", default='0.5,0.5', type=str, help='output files weight') args = parser.parse_args() return args \ No newline at end of file diff --git a/code/voting/voting.py b/code/voting/voting.py index 85561ee..7597396 100644 --- a/code/voting/voting.py +++ b/code/voting/voting.py @@ -8,14 +8,21 @@ def main(args): outputs = list(map(lambda x: x.strip(), args.outputs.split(','))) + weights = list(map(lambda x: float(x.strip()), args.weights.split(','))) df = pd.DataFrame(columns = ['user','item']) - for output in outputs: - new_df = pd.read_csv(os.path.join(output),index_col=0,header=0) + for i, output in enumerate(outputs): + new_df = pd.read_csv(output,index_col=0,header=0) + new_df = new_df.groupby('user').head(30).reset_index() + score_list = list(map(lambda x: x*weights[i]*0.5, range(40, 10, -1))) + new_df['score'] = score_list * len(new_df['user'].unique()) df = pd.concat([df, new_df], ignore_index=True) - count_df = df.groupby('user')['item'].value_counts().reset_index(name='count') + sum_df = df.groupby(['user', 'item']).sum().reset_index() + sum_df = sum_df.drop_duplicates(['user','item']) + count_df = sum_df.sort_values(by=['user', 'score'], ascending=[True, False]) count_df = count_df.groupby('user').head(10).reset_index() + output = pd.read_csv('../movie/data/eval/sample_submission.csv') output['item'] = count_df['item'] if not os.path.exists('output/'): From 251b0fb3bce40a094dc9279f8c9e46be67044ffd Mon Sep 17 00:00:00 2001 From: Seungbin Date: Fri, 23 Feb 2024 05:07:34 +0000 Subject: [PATCH 41/49] feat: add new user features --- code/custom/user_feature.py | 51 +++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 code/custom/user_feature.py diff --git a/code/custom/user_feature.py b/code/custom/user_feature.py new file mode 100644 index 0000000..9631512 --- /dev/null +++ b/code/custom/user_feature.py @@ -0,0 +1,51 @@ +import pandas as pd +import numpy as np +import os +from itertools import combinations +from multiprocessing import Pool + +def calculate_correlation(pair): + idx1, idx2 = pair + correlation = np.corrcoef(user_data[:, idx1], user_data[:, idx2])[0, 1] + return correlation + +def calculation_per_user(user1): + idx1 = user_pairs.index(user1) + pairs = [(idx1, user_pairs.index(user2)) for user2 in inactive_user_ids] + with Pool() as pool: + correlations = pool.map(calculate_correlation, pairs) + #calculate mean taste similarity + avg = np.mean(correlations) + #calculate taste dispersion + std = np.std(correlations) + user_correlation[idx1] = avg + taste_dispersion[idx1] = std + + +def mean_taste_sim(train_df): + for user1 in user_pairs: + calculation_per_user(user1) + + pearson_df = pd.DataFrame({'user':user_pairs, 'similarity':user_correlation, 'dispersion':taste_dispersion}) + new_df = pd.merge(train_df, pearson_df, on=['user'], how='left') + return new_df + +#get data +data_path = '/data/ephemeral/movie/data/train' +train_df = pd.read_csv(os.path.join(data_path, 'train_ratings.csv')) +#except 3000 active user +inactive_user_ids = train_df.value_counts('user').index[3000:] +#user-item interaction table +table = pd.pivot_table(train_df, index=['item'], columns=['user'], aggfunc=lambda x: 1, fill_value=0) +#prepare data +user_pairs = train_df['user'].unique().tolist() +user_num = len(user_pairs) +user_correlation = np.zeros((user_num)) +taste_dispersion = np.zeros((user_num)) +user_data = table.to_numpy() +#save new df +new_df = mean_taste_sim(train_df) +new_df.to_csv(os.path.join(data_path, 'train_pearson_inactive.csv'), index=False) + + + From f16ba497930f656d0d4cde4e5133184b661f5924 Mon Sep 17 00:00:00 2001 From: Kim_SuBeen <83735049+ksb3966@users.noreply.github.com> Date: Mon, 26 Feb 2024 09:32:29 +0900 Subject: [PATCH 42/49] Feat/Modifying Bert4rec Directory Path --- code/bert4rec/readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 code/bert4rec/readme.md diff --git a/code/bert4rec/readme.md b/code/bert4rec/readme.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/code/bert4rec/readme.md @@ -0,0 +1 @@ + From dcf3444f30553294ba34d0b26dc3cb64dce2eea7 Mon Sep 17 00:00:00 2001 From: subeenkim Date: Mon, 26 Feb 2024 09:40:58 +0900 Subject: [PATCH 43/49] Feat/Move File to New Directory --- code/{ => bert4rec}/data_loaders.py | 0 code/{ => bert4rec}/logger/__init__.py | 0 code/{ => 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b/code/bert4rec/train_args.py similarity index 100% rename from code/train_args.py rename to code/bert4rec/train_args.py diff --git a/code/trainer/__init__.py b/code/bert4rec/trainer/__init__.py similarity index 100% rename from code/trainer/__init__.py rename to code/bert4rec/trainer/__init__.py diff --git a/code/trainer/trainer.py b/code/bert4rec/trainer/trainer.py similarity index 100% rename from code/trainer/trainer.py rename to code/bert4rec/trainer/trainer.py diff --git a/code/utils/__init__.py b/code/bert4rec/utils/__init__.py similarity index 100% rename from code/utils/__init__.py rename to code/bert4rec/utils/__init__.py diff --git a/code/utils/util.py b/code/bert4rec/utils/util.py similarity index 100% rename from code/utils/util.py rename to code/bert4rec/utils/util.py From 7b7e8bcf37676f7a6c04d7cf40afd880d730eebf Mon Sep 17 00:00:00 2001 From: Kim_SuBeen <83735049+ksb3966@users.noreply.github.com> Date: Mon, 26 Feb 2024 09:42:31 +0900 Subject: [PATCH 44/49] Feat/Add New Model EASER --- code/EASER/dataloader.py | 77 ++++++++++++++++++++++ code/EASER/model.py | 128 ++++++++++++++++++++++++++++++++++++ code/EASER/preprocessing.py | 95 ++++++++++++++++++++++++++ code/EASER/train.py | 37 +++++++++++ code/EASER/train_args.py | 22 +++++++ code/EASER/utils.py | 43 ++++++++++++ 6 files changed, 402 insertions(+) create mode 100644 code/EASER/dataloader.py create mode 100644 code/EASER/model.py create mode 100644 code/EASER/preprocessing.py create mode 100644 code/EASER/train.py create mode 100644 code/EASER/train_args.py create mode 100644 code/EASER/utils.py diff --git a/code/EASER/dataloader.py b/code/EASER/dataloader.py new file mode 100644 index 0000000..9bb8829 --- /dev/null +++ b/code/EASER/dataloader.py @@ -0,0 +1,77 @@ +import pandas as pd +import os +from pytz import timezone +from train_args import parse_args +from collections import defaultdict +import numpy as np + +import warnings +warnings.filterwarnings(action="ignore") + +class Dataloader(): + def __init__(self, config): + self.config = config + self.df = pd.read_csv(os.path.join(self.config.data_path, 'train_ratings.csv')) + + self.users = self.df.user.unique() + self.items = self.df.item.unique() + + self.item_encoder, self.item_decoder = self.generate_encoder_decoder('item') + self.user_encoder, self.user_decoder = self.generate_encoder_decoder('user') + self.num_item, self.num_user = len(self.item_encoder), len(self.user_encoder) + + self.df['item_idx'] = self.df['item'].apply(lambda x : self.item_encoder[x]) + self.df['user_idx'] = self.df['user'].apply(lambda x : self.user_encoder[x]) + + self.user_train, self.user_valid = self.generate_sequence_data() + + def dataloader(self): + return self.df, self.users, self.items + + def generate_encoder_decoder(self, col : str) -> dict: + """ + encoder, decoder 생성 + + Args: + col (str): 생성할 columns 명 + Returns: + dict: 생성된 user encoder, decoder + """ + + encoder = {} + decoder = {} + ids = self.df[col].unique() + + for idx, _id in enumerate(ids): + encoder[_id] = idx + decoder[idx] = _id + + return encoder, decoder + + def generate_sequence_data(self) -> dict: + """ + sequence_data 생성 + + Returns: + dict: train user sequence / valid user sequence + """ + users = defaultdict(list) + user_train = {} + user_valid = {} + for user, item, time in zip(self.df['user_idx'], self.df['item_idx'], self.df['time']): + users[user].append(item) + + for user in users: + np.random.seed(self.config.seed) + + user_total = users[user] + valid = np.random.choice(user_total, size = self.config.valid_samples, replace = False).tolist() + train = list(set(user_total) - set(valid)) + + user_train[user] = train + user_valid[user] = valid # valid_samples 개수 만큼 검증에 활용 (현재 Task와 가장 유사하게) + + return user_train, user_valid + + def get_train_valid_data(self): + return self.user_train, self.user_valid diff --git a/code/EASER/model.py b/code/EASER/model.py new file mode 100644 index 0000000..180fe48 --- /dev/null +++ b/code/EASER/model.py @@ -0,0 +1,128 @@ +from scipy.sparse import csr_matrix +from scipy import sparse + +import numpy as np +import pandas as pd +from sklearn.preprocessing import LabelEncoder +from tqdm import tqdm +from copy import deepcopy +import torch + +class EASER: + def __init__(self, args): + self.user_enc = LabelEncoder() + self.item_enc = LabelEncoder() + + self.threshold = args.threshold + self.lambdaBB = args.lambdaBB + self.lambdaCC = args.lambdaCC + self.rho = args.rho + self.epochs = args.epochs + + def _get_users_and_items(self, df): + users = self.user_enc.fit_transform(df.loc[:, 'user']) + items = self.item_enc.fit_transform(df.loc[:, 'item']) + + return users, items + + def create_list_feature_pairs(self, XtX): + AA = np.triu(np.abs(XtX)) + AA[ np.diag_indices(AA.shape[0]) ]=0.0 + ii_pairs = np.where((AA > self.threshold) == True) + return ii_pairs + + def create_matrix_Z(self, ii_pairs, X): + MM = np.zeros( (len(ii_pairs[0]), X.shape[1]), dtype=np.float64) + MM[np.arange(MM.shape[0]) , ii_pairs[0] ]=1.0 + MM[np.arange(MM.shape[0]) , ii_pairs[1] ]=1.0 + CCmask = 1.0-MM + MM = sparse.csc_matrix(MM.T) + Z= X * MM + Z= (Z == 2.0 ) + Z=Z*1.0 + return Z, CCmask + + def train_higher(self, XtX, XtXdiag, ZtZ, ZtZdiag, CCmask, ZtX): + ii_diag=np.diag_indices(XtX.shape[0]) + XtX[ii_diag] = XtXdiag + self.lambdaBB + PP = np.linalg.inv(XtX) + ii_diag_ZZ=np.diag_indices(ZtZ.shape[0]) + ZtZ[ii_diag_ZZ] = ZtZdiag + self.lambdaCC + self.rho + QQ=np.linalg.inv(ZtZ) + CC = np.zeros( (ZtZ.shape[0], XtX.shape[0]),dtype=np.float64 ) + DD = np.zeros( (ZtZ.shape[0], XtX.shape[0]),dtype=np.float64 ) + UU = np.zeros( (ZtZ.shape[0], XtX.shape[0]),dtype=np.float64 ) + + for iter in tqdm(range(self.epochs)): + # learn BB + XtX[ii_diag] = XtXdiag + BB= PP.dot(XtX-ZtX.T.dot(CC)) + gamma = np.diag(BB) / np.diag(PP) + BB-= PP * gamma + # learn CC + CC= QQ.dot(ZtX-ZtX.dot(BB) + self.rho * (DD-UU)) + # learn DD + DD = CC * CCmask + # DD = np.maximum(0.0, DD) # if you want to enforce non-negative parameters + # learn UU (is Gamma in paper) + UU+= CC-DD + + return BB, DD + + def fit(self, df): + users, items = self._get_users_and_items(df) + values = ( + np.ones(df.shape[0]) + ) + X = csr_matrix((values, (users, items))) + self.X = X + + print(' --- init --- ') + XtX = (X.transpose() * X).toarray() + XtXdiag = deepcopy(np.diag(XtX)) + ii_pairs = self.create_list_feature_pairs(XtX) + Z, CCmask = self.create_matrix_Z(ii_pairs, X) + + ZtZ = (Z.transpose() * Z).toarray() + ZtZdiag = deepcopy(np.diag(ZtZ)) + + ZtX = (Z.transpose() * X).toarray() + + print(' --- iteration start.--- ') + BB, CC = self.train_higher(XtX, XtXdiag, ZtZ, ZtZdiag, CCmask, ZtX) + print(' --- iteration end.--- ') + + self.pred = torch.from_numpy(X.toarray().dot(BB) + Z.toarray().dot(CC)) + + return self.pred + + + def predict(self, train, users, items, k): + items = self.item_enc.transform(items) + train_df = train.loc[train.user.isin(users)] + train_df['ci'] = self.item_enc.transform(train_df.item) + train_df['cu'] = self.user_enc.transform(train_df.user) + groupby_user = train_df.groupby('user') + + user_preds = pd.DataFrame() + for user, group in tqdm(groupby_user): + watched = set(group['ci']) # The movie users watched + candidates = [item for item in items if item not in watched] + + predict_user = group.cu.iloc[0] + pred = np.take(self.pred[predict_user,:], candidates) + res = np.argpartition(pred, -k)[-k:] # top-K + r = pd.DataFrame( + { + "user": [user] * len(res), + "item": np.take(candidates, res), + "score": np.take(pred, res), + } + ).sort_values('score', ascending=False) + user_preds = pd.concat([user_preds,r]) + + user_preds['item'] = self.item_enc.inverse_transform(user_preds['item']) + + + return user_preds + \ No newline at end of file diff --git a/code/EASER/preprocessing.py b/code/EASER/preprocessing.py new file mode 100644 index 0000000..79a78c0 --- /dev/null +++ b/code/EASER/preprocessing.py @@ -0,0 +1,95 @@ +import torch +import numpy as np +import pandas as pd +import scipy.sparse as sp +import os +from collections import defaultdict +from torch.utils.data import Dataset, DataLoader + + + +class MakeMatrixDataSet(): + """ + MatrixDataSet 생성 + """ + def __init__(self, config): + self.config = config + self.df = pd.read_csv(os.path.join(self.config.data_path, 'train_ratings.csv')) + + self.item_encoder, self.item_decoder = self.generate_encoder_decoder('item') + self.user_encoder, self.user_decoder = self.generate_encoder_decoder('user') + self.num_item, self.num_user = len(self.item_encoder), len(self.user_encoder) + + self.df['item_idx'] = self.df['item'].apply(lambda x : self.item_encoder[x]) + self.df['user_idx'] = self.df['user'].apply(lambda x : self.user_encoder[x]) + + self.user_train, self.user_valid = self.generate_sequence_data() + + def generate_encoder_decoder(self, col : str) -> dict: + """ + encoder, decoder 생성 + + Args: + col (str): 생성할 columns 명 + Returns: + dict: 생성된 user encoder, decoder + """ + + encoder = {} + decoder = {} + ids = self.df[col].unique() + + for idx, _id in enumerate(ids): + encoder[_id] = idx + decoder[idx] = _id + + return encoder, decoder + + def generate_sequence_data(self) -> dict: + """ + sequence_data 생성 + + Returns: + dict: train user sequence / valid user sequence + """ + users = defaultdict(list) + user_train = {} + user_valid = {} + for user, item, time in zip(self.df['user_idx'], self.df['item_idx'], self.df['time']): + users[user].append(item) + + for user in users: + np.random.seed(self.config.seed) + + user_total = users[user] + valid = np.random.choice(user_total, size = self.config.valid_samples, replace = False).tolist() + train = list(set(user_total) - set(valid)) + + user_train[user] = train + user_valid[user] = valid # valid_samples 개수 만큼 검증에 활용 (현재 Task와 가장 유사하게) + + return user_train, user_valid + + def get_train_valid_data(self): + return self.user_train, self.user_valid + + def make_matrix(self, user_list, train = True): + """ + user_item_dict를 바탕으로 행렬 생성 + """ + mat = torch.zeros(size = (user_list.size(0), self.num_item)) + for idx, user in enumerate(user_list): + if train: + mat[idx, self.user_train[user.item()]] = 1 + else: + mat[idx, self.user_train[user.item()] + self.user_valid[user.item()]] = 1 + return mat + + def make_sparse_matrix(self): + X = sp.dok_matrix((self.num_user, self.num_item), dtype=np.float32) + for user in self.user_train.keys(): + item_list = self.user_train[user] + X[user, item_list] = 1.0 + + return X.tocsr() + \ No newline at end of file diff --git a/code/EASER/train.py b/code/EASER/train.py new file mode 100644 index 0000000..755280e --- /dev/null +++ b/code/EASER/train.py @@ -0,0 +1,37 @@ +import sys +sys.path.append("../") + +from model import EASER +import pandas as pd +import os +from datetime import datetime +from pytz import timezone +from train_args import parse_args +from dataloader import * +from datetime import datetime +from preprocessing import MakeMatrixDataSet +from utils import evaluate + +import warnings +warnings.filterwarnings(action="ignore") + + +def main(args): + data_loader = Dataloader(config = args) + train_df, users, items = data_loader.dataloader() + + model = EASER(args) + model.fit(train_df) + # HIT_RATE, NDCG = evaluate(pred, train_df, users) + # print("Hit Rate: ", HIT_RATE, "NDCG : ", NDCG) + result_df = model.predict(train_df, users, items, args.K) + + file_name = datetime.now(timezone('Asia/Seoul')).strftime('%Y%m%d_%H%M%S') + result_df[["user", "item"]].to_csv(args.output_dir + f"{file_name}_{args.K}.csv", index=False) + + +if __name__ == "__main__": + args = parse_args() + os.makedirs(args.output_dir, exist_ok=True) + main(args) + \ No newline at end of file diff --git a/code/EASER/train_args.py b/code/EASER/train_args.py new file mode 100644 index 0000000..76bca9d --- /dev/null +++ b/code/EASER/train_args.py @@ -0,0 +1,22 @@ +import argparse + +def parse_args(): + parser = argparse.ArgumentParser("arguments for EASER") + parser.add_argument('--K', default=10, help="decision top-K", type=int) + parser.add_argument('--output_dir', default='./submission/') + parser.add_argument('--data_path', default="../../data/train/", type=str) + + ###################### EASER ########################## + parser.add_argument('--threshold', default=3500, type=float) + parser.add_argument('--lambdaBB', default=500, type=float) + parser.add_argument('--lambdaCC', default=10000, type=float) + parser.add_argument('--rho', default=50000, type=float) + parser.add_argument('--epochs', default=100, type=int) + parser.add_argument('--seed', default=99, type=float) + parser.add_argument('--valid_samples', default=10, type=float) + + + + args = parser.parse_args() + + return args \ No newline at end of file diff --git a/code/EASER/utils.py b/code/EASER/utils.py new file mode 100644 index 0000000..1c769a3 --- /dev/null +++ b/code/EASER/utils.py @@ -0,0 +1,43 @@ + +import torch +import numpy as np +from tqdm import tqdm + +def get_ndcg(pred_list, true_list): + idcg = sum((1 / np.log2(rank + 2) for rank in range(1, len(pred_list)))) + dcg = 0 + for rank, pred in enumerate(pred_list): + if pred in true_list: + dcg += 1 / np.log2(rank + 2) + ndcg = dcg / idcg + return ndcg + +# hit == recall == precision +def get_hit(pred_list, true_list): + hit_list = set(true_list) & set(pred_list) + hit = len(hit_list) / len(true_list) + return hit + + +def evaluate(pred, X, user): + mat = torch.from_numpy(X.to_numpy) + + NDCG = 0.0 # NDCG@10 + HIT = 0.0 # HIT@10 + + recon_mat1 = pred.cpu() + recon_mat1[mat == 1] = -np.inf + rec_list1 = recon_mat1.argsort(dim = 1) + + for user, rec1 in tqdm(enumerate(rec_list1)): + uv = user[user] + # ranking + up = rec1[-10:].cpu().numpy().tolist()[::-1] + + NDCG += get_ndcg(pred_list = up, true_list = uv) + HIT += get_hit(pred_list = up, true_list = uv) + + NDCG /= len(user) + HIT /= len(user) + + return NDCG, HIT \ No newline at end of file From 26c615d770cbe5042f066df65d4ece890d17fd99 Mon Sep 17 00:00:00 2001 From: Kim_SuBeen <83735049+ksb3966@users.noreply.github.com> Date: Mon, 26 Feb 2024 16:36:41 +0900 Subject: [PATCH 45/49] Create README.md --- README.md | 136 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..e663f2b --- /dev/null +++ b/README.md @@ -0,0 +1,136 @@ +# Movie Recommendation + +## OverView +본 프로젝트는 'Movie Recommendation'으로, 사용자의 영화 시청 이력 데이터를 토대로 사용자가 다음에 시청할 영화 및 좋아할 영화를 예측하는 기존 대회들과는 다르게 사용자의 영화 시청 이력에서 10개를 랜덤으로 제거한 후 제거된 시청 이력이 무엇인지를 맞춰야 하는 대회이다. + +모든 사용자에게 10개의 영화를 추천하며, 이때 추천 리스트의 정확성(Recall@10)을 평가 기준으로 삼는다. + +평가를 위한 정답(ground-truth) 데이터는 Sequential Recommendation 시나리오를 바탕으로 사용자의 Time-Ordered Sequence에서 일부 Item이 누락(dropout)된 상황을 상정한다 +

+ +## Component + +### 프로젝트 디렉토리 구조 +``` +📦level2-movierecommendation-recsys-03-main + ┗ 📂code + ┣ 📂EASER + ┣ 📂EDA + ┣ 📂bert4rec + ┣ 📂custom + ┣ 📂multi + ┣ 📂recbole + ┃ ┗ 📂configs - .yaml + ┃ ┣ ADMMSLIM, DIFFRec, EASE, deepfM + ┃ ┣ fm, lightgcn, ract, recvae, + ┃ ┗ seq, slim + ┣ 📂s3rec + ┗ 📂voting +``` +### 데이터셋 구조 +``` +📦level2-movierecommendation-recsys-03-main + ┗ 📂train + ┣ 📜Ml_item2attributes.json + ┣ 📜directors.tsv + ┣ 📜genres.tsv + ┣ 📜titles.tsv + ┣ 📜train_ratings.csv + ┣ 📜writers.tsv + ┗ 📜years.tsv +``` + + +## Team +
+ + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + +
+ 김수빈_T6021 + + 박시우_T6060 + + 백승빈_T6075 + + 이재권_T6131 + + 이진민_T6139 + + 장재원_T6149 +
+  +
+ +## Role + +| 이름 | 역할 | +| --- | --- | +| 김수빈 | EDA, 모델 선정 및 튜닝, EASER, SASRec, Bert4Rec 실험 수행 | +| 박시우 | EDA, s3rec baseline 정리, recbole baseline 구축, RecVAE, ADMMSLIM, soft voting 앙상블 구현 | +| 백승빈 | EDA, EASE, SLIMElastic, DiffRec 모델 실험 및 튜닝, hard voting기반 앙상블 구현 | +| 이재권 | EDA, LightGCN 모델 실험 및 튜닝 | +| 이진민 | EDA, Multi-DAE, Multi-VAE 코드 모듈화 및 실험 | +| 장재원 | EDA, Sequential models, Type based model, 모델들 성능 비교 및 분석 | +
+ +## Experiment Result + +### Single Model Result +| | Public Recall@10 | Private Recall@10 | +| --- | --- | --- | +| Popular item rule based model | 0.0673 | 0.0671 | +| Genre rule based model | 0.0619 | 0.0626 | +| Type based model | 0.0687 | 0.0696 | +| GRU4Rec | 0.0970 | 0.0809 | +| SASRec | 0.0884 | 0.0833 | +| S3Rec(Pretrained) | 0.0829 | 0.0743 | +| BERT4Rec | 0.0687 | 0.0676 | +| LightGCN | 0.1302 | 0.1316 | +| DiffRec | 0.1413 | 0.1431 | +| RecVAE | 0.1349 | 0.1362 | +| Multi-VAE | 0.1394 | 0.1377 | +| Multi-DAE (with side information) | 0.1427 | 0.1413 | +| EASE | 0.1566 | 0.1565 | +| ADMMSLIM | 0.1524 | 0.1541 | +| SLIMElastic | 0.1562 | 0.1562 | +| EASER | 0.1612 | 0.1603 | + +### Ensemble Result +| | Private Recall@10 | Public Recall@10 | +| --- | --- | --- | +| EASER, 앙상블 모델*에 type별 추천 적용 | 0.1614 | 0.1605 | +| 앙상블 모델*(EASE, ADMMSLIM, RecVAE) | 0.1613 | 0.1611 | + +최종적으로는 가장 높은 Public score 값에 해당하는 두 결과 값을 제출하였다. +

+ +## Wrap-Up Report +[MovieREc Wrap-up Report - Suggestify.pdf](./DKT.Wrap-up.Report.-.Suggestify.pdf) +
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2001 From: Kim_SuBeen <83735049+ksb3966@users.noreply.github.com> Date: Mon, 26 Feb 2024 16:40:49 +0900 Subject: [PATCH 48/49] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e663f2b..e2cef9e 100644 --- a/README.md +++ b/README.md @@ -132,5 +132,5 @@

## Wrap-Up Report -[MovieREc Wrap-up Report - Suggestify.pdf](./DKT.Wrap-up.Report.-.Suggestify.pdf) +[MovieRec Wrap-up Report - Suggestify.pdf](./Movie_Recommendation_Wrap-up_Report-Suggestify.pdf)
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