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Aligning sasrec implementation with CIKM2020-S3Rec #5

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137 changes: 63 additions & 74 deletions notebooks/DatasetProcessing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,8 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "3bdb292f",
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"\n",
Expand All @@ -21,69 +19,56 @@
"from torch.utils.data import DataLoader\n",
"\n",
"from tqdm import tqdm as tqdm"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "66d9b312",
"metadata": {},
"outputs": [],
"source": [
"interactions_dataset_path = '../data/Beauty/Beauty_5.json'\n",
"metadata_path = '../data/Beauty/metadata.json'\n",
"\n",
"interactions_output_path = '../data/Beauty/inter_new.json'\n",
"embeddings_output_path = '../data/Beauty/content_embeddings.pkl'"
]
"interactions_output_path = '../data/Beauty/inter.json'\n",
"embeddings_output_path = '../data/Beauty/content_embeddings.pkl'\n"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ed4dffb",
"metadata": {},
"source": "df = defaultdict(list)\n\nwith open(interactions_dataset_path, 'r') as f:\n for line in f.readlines():\n review = json.loads(line)\n df['user_id'].append(review['reviewerID'])\n df['item_id'].append(review['asin'])\n df['timestamp'].append(review['unixReviewTime'])\n\nprint(f'Number of events: {len(df[\"user_id\"])}')\n\ndf = pl.from_dict(df).with_row_index('_row_idx')\n",
"outputs": [],
"source": [
"df = defaultdict(list)\n",
"\n",
"with open(interactions_dataset_path, 'r') as f:\n",
" for line in f.readlines():\n",
" review = json.loads(line)\n",
" df['user_id'].append(review['reviewerID'])\n",
" df['item_id'].append(review['asin'])\n",
" df['timestamp'].append(review['unixReviewTime'])\n",
"\n",
"print(f'Number of events: {len(df[\"user_id\"])}')\n",
"\n",
"df = pl.from_dict(df)"
]
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "c26746c4",
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "adcf5713",
"metadata": {},
"outputs": [],
"source": [
"filtered_df = df.clone()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0bbf9ba",
"metadata": {},
"outputs": [],
"source": [
"# Processing dataset to get core-5 state in case full dataset is provided\n",
"is_changed = True\n",
Expand All @@ -107,22 +92,24 @@
" )\n",
"\n",
" old_size = len(filtered_df)\n",
" filtered_df = filtered_df.join(\n",
" good_users, on='user_id', how='inner',\n",
" ).join(\n",
" good_items, on='item_id', how='inner',\n",
" )\n",
"\n",
" new_df = filtered_df.join(good_users, on='user_id', how='inner')\n",
" new_df = new_df.join(good_items, on='item_id', how='inner')\n",
"\n",
" new_size = len(new_df)\n",
" if len(filtered_df) == old_size:\n",
" is_changed = False\n",
"\n",
" filtered_df = new_df\n",
" is_changed = old_size != new_size\n"
]
"filtered_df = filtered_df.sort('_row_idx').drop('_row_idx')\n"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "218a9348",
"metadata": {},
"outputs": [],
"source": [
"unique_values = filtered_df[\"user_id\"].unique(maintain_order=True).to_list()\n",
"user_ids_mapping = {value: i for i, value in enumerate(unique_values)}\n",
Expand All @@ -139,91 +126,93 @@
")\n",
"\n",
"filtered_df.head()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "34604fe6",
"metadata": {},
"outputs": [],
"source": [
"item_ids_mapping_df = pl.from_dict({\n",
" 'old_item_id': list(item_ids_mapping.keys()),\n",
" 'new_item_id': list(item_ids_mapping.values())\n",
"})\n",
"item_ids_mapping_df.head()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "6017e65c",
"metadata": {},
"outputs": [],
"source": [
"filtered_df.head()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "9efd1983",
"metadata": {},
"outputs": [],
"source": [
"filtered_df = filtered_df.sort([\"user_id\", \"timestamp\"])\n",
"\n",
"grouped_filtered_df = filtered_df.group_by(\"user_id\", maintain_order=True).agg(pl.all())"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd51c525",
"metadata": {},
"outputs": [],
"source": [
"item_ids_mapping_df.head()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b0821da",
"metadata": {},
"outputs": [],
"source": [
"grouped_filtered_df.head()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc222d59",
"metadata": {},
"outputs": [],
"source": [
"print('Users count:', filtered_df.select('user_id').unique().shape[0])\n",
"print('Items count:', filtered_df.select('item_id').unique().shape[0])\n",
"print('Actions count:', filtered_df.shape[0])\n",
"print('Avg user history len:', np.mean(list(map(lambda x: x[0], grouped_filtered_df.select(pl.col('item_id').list.len()).rows()))))"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "d07a2e91",
"metadata": {},
"outputs": [],
"source": [
"json_data = {}\n",
"for user_id, item_ids, _ in grouped_filtered_df.iter_rows():\n",
" json_data[user_id] = item_ids\n",
"\n",
"with open(interactions_output_path, 'w') as f:\n",
" json.dump(json_data, f, indent=2)"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -235,10 +224,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "6361c7a5",
"metadata": {},
"outputs": [],
"source": [
"def getDF(path):\n",
" i = 0\n",
Expand All @@ -252,14 +239,14 @@
"\n",
"df = getDF(metadata_path)\n",
"df.head()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "971fa89c",
"metadata": {},
"outputs": [],
"source": [
"def preprocess(row: pd.Series):\n",
" row = row.fillna(\"None\")\n",
Expand All @@ -278,24 +265,24 @@
" filtered_df[\"combined_text\"] = filtered_df.apply(preprocess, axis=1)\n",
"\n",
" return filtered_df\n"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b0dd5d5",
"metadata": {},
"outputs": [],
"source": [
"data = get_data(pl.from_pandas(df), item_ids_mapping)"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"id": "12e622ff",
"metadata": {},
"outputs": [],
"source": [
"device = torch.device('cuda:6')\n",
"\n",
Expand Down Expand Up @@ -353,7 +340,9 @@
"\n",
"with open(embeddings_output_path, 'wb') as f:\n",
" pickle.dump(new_df, f)\n"
]
],
"outputs": [],
"execution_count": null
}
],
"metadata": {
Expand Down
11 changes: 6 additions & 5 deletions tiger/configs/sasrec_train_config.json
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"experiment_name": "sasrec_beauty",
"dataset": {
"inter_json_path": "../data/Beauty/inter.json",
"max_sequence_length": 20,
"max_sequence_length": 50,
"sampler_type": "sasrec"
},
"dataloader": {
Expand All @@ -14,13 +14,14 @@
"num_heads": 2,
"num_layers": 2,
"dim_feedforward": 256,
"dropout": 0.3,
"activation": "relu",
"dropout": 0.5,
"activation": "gelu",
"top_k": 20,
"layer_norm_eps": 1e-8,
"layer_norm_eps": 1e-12,
"initializer_range": 0.02
},
"optimizer": {
"lr": 0.001
}
},
"early_stopping_threshold": 10
}
2 changes: 1 addition & 1 deletion tiger/modeling/dataset/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,7 @@ def create(cls, inter_json_path, max_sequence_length, sampler_type, is_extended=
LOGGER.info(f'Test dataset size: {len(test_dataset)}')
LOGGER.info(f'Max item id: {max_item_id}')

train_sampler = TrainSampler(train_dataset, sampler_type, max_sequence_length=max_sequence_length)
train_sampler = TrainSampler(train_dataset, sampler_type, max_sequence_length=max_sequence_length, num_items=max_item_id + 1)
validation_sampler = EvalSampler(validation_dataset, max_sequence_length=max_sequence_length)
test_sampler = EvalSampler(test_dataset, max_sequence_length=max_sequence_length)

Expand Down
19 changes: 17 additions & 2 deletions tiger/modeling/dataset/samplers.py
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Original file line number Diff line number Diff line change
@@ -1,27 +1,42 @@
import copy
import random


class TrainSampler:
def __init__(self, dataset, prediction_type, max_sequence_length):
def __init__(self, dataset, prediction_type, max_sequence_length, num_items=None):
self._dataset = dataset
self._prediction_type = prediction_type
self._max_sequence_length = max_sequence_length
self._num_items = num_items

self._transforms = {
'sasrec': self._all_items_transform,
'tiger': self._last_item_transform
}

def _sample_negative(self, exclude: set) -> int:
assert self._num_items is not None, 'num_items required for negative sampling'
while True:
neg = random.randint(0, self._num_items - 1)
if neg not in exclude:
return neg

def _all_items_transform(self, sample):
item_sequence = sample['item.ids'][-self._max_sequence_length:][:-1]
next_item_sequence = sample['item.ids'][-self._max_sequence_length:][1:]

exclude = set(sample['item.ids'])
negative_ids = [self._sample_negative(exclude) for _ in next_item_sequence]

return {
'user.ids': sample['user.ids'],
'user.length': len(sample['user.ids']),
'item.ids': item_sequence,
'item.length': len(item_sequence),
'labels.ids': next_item_sequence,
'labels.length': len(next_item_sequence)
'labels.length': len(next_item_sequence),
'negative.ids': negative_ids,
'negative.length': len(negative_ids),
}

def _last_item_transform(self, sample):
Expand Down
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