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refactor: add more examples

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uetchy 2022-06-03 16:36:32 +09:00
parent 6cd6370a71
commit 4dd186486c
15 changed files with 14961 additions and 1218 deletions
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.gitattributes vendored
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notebooks/*.ipynb linguist-vendored notebooks/*.ipynb linguist-vendored

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.gitignore vendored
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.envrc .envrc
.env .env
.vscode .vscode
TODO
*.csv
*.tangram
/tmp /tmp
# Created by https://www.toptal.com/developers/gitignore/api/python # Created by https://www.toptal.com/developers/gitignore/api/python

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Makefile
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@ -1,7 +1,7 @@
all: build upload all: build
build: build:
python3 -m sensai_dataset.gen python3 -m sensai_dataset.generator
upload: upload:
kaggle datasets version -m "New version" --path $$DATASET_DIR kaggle datasets version -m "New version" --path $$DATASET_DIR

20
README.md
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@ -32,7 +32,7 @@ Ban and deletion are equivalent to `markChatItemsByAuthorAsDeletedAction` and `m
## Dataset Breakdown ## Dataset Breakdown
### Chats (`chats_%Y-%m.csv`) ### Chats (`chats_%Y-%m.parquet`)
| column | type | description | | column | type | description |
| --------------- | ------ | ----------------------------- | | --------------- | ------ | ----------------------------- |
@ -43,6 +43,17 @@ Ban and deletion are equivalent to `markChatItemsByAuthorAsDeletedAction` and `m
## Usage ## Usage
### Pandas
```python
import pandas as pd
from glob import glob
df = pd.concat([pd.read_parquet(x) for x in glob('../input/sensai/*.parquet')], ignore_index=True)
```
### Huggingface Transformers
https://huggingface.co/docs/datasets/loading_datasets.html https://huggingface.co/docs/datasets/loading_datasets.html
```python ```python
@ -75,6 +86,13 @@ trainer = Trainer(
trainer.train() trainer.train()
``` ```
### Tangram
```bash
python3 ./examples/prepare_tangram_dataset.py
tangram train --file ./tangram_input.csv --target label
```
## Consideration ## Consideration
### Anonymization ### Anonymization

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATASET_DIR /home/uetchy/repos/src/github.com/holodata/sensai-huggingface\n"
]
}
],
"source": [
"import pandas as pd\n",
"from os.path import join\n",
"import os\n",
"from glob import glob\n",
"\n",
"DATASET_DIR = os.environ.get(\"DATASET_DIR\", \"../input/sensai\")\n",
"print(\"DATASET_DIR\", DATASET_DIR)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10677038 entries, 0 to 10677037\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 body object\n",
" 1 label object\n",
"dtypes: object(2)\n",
"memory usage: 162.9+ MB\n"
]
}
],
"source": [
"df = pd.concat(\n",
" [pd.read_parquet(x) for x in glob(join(DATASET_DIR, '*.parquet'))],\n",
" ignore_index=True)\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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" body label\n",
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"8259711 草 hidden"
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset, Features\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
"import os\n",
"from datasets import ClassLabel, Value\n",
"\n",
"# https://huggingface.co/docs/datasets/loading_datasets.html\n",
"\n",
"DATASET_DIR = os.environ.get(\"DATASET_DIR\", \"../input/sensai\")\n",
"print(\"DATASET_DIR\", DATASET_DIR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = load_dataset(\"holodata/sensai\", features=Features(\n",
" {\n",
" \"body\": Value(\"string\"),\n",
" \"toxic\": ClassLabel(num_classes=2, names=['0', '1'])\n",
" }\n",
" ))\n",
"dataset = dataset['train']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=2)\n",
"tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"samples = dataset.shuffle().select(range(50000))\n",
"\n",
"def tokenize_function(examples):\n",
" return tokenizer(examples[\"body\"], padding=\"max_length\", truncation=True)\n",
"\n",
"tokenized_datasets = samples.map(tokenize_function, batched=True)\n",
"tokenized_datasets.rename_column_(\"toxic\", \"label\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"splitset = tokenized_datasets.train_test_split(0.2)\n",
"splitset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"training_args = TrainingArguments(\"test_trainer\")\n",
"trainer = Trainer(\n",
" model=model, args=training_args, train_dataset=splitset['train'], eval_dataset=splitset['test']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trainer.train(resume_from_checkpoint=True)"
]
},
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"execution_count": null,
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},
"orig_nbformat": 4
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"source": [
"from datasets import load_dataset, Features\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
"import os\n",
"from os.path import join\n",
"import pandas as pd\n",
"from datasets import ClassLabel, Value\n",
"\n",
"# https://huggingface.co/docs/datasets/loading_datasets.html\n",
"\n",
"DATASET_DIR = os.environ['DATASET_DIR']"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"dataset = load_dataset(\"holodata/sensai\", features=Features(\n",
" {\n",
" \"body\": Value(\"string\"),\n",
" \"toxic\": ClassLabel(num_classes=2, names=['0', '1'])\n",
" }\n",
" ))\n",
"dataset = dataset['train']"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "704789c10f1e44ddbb83262b8a826eec"
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Using custom data configuration sensai-4d9ed81389161083\n",
"Reusing dataset parquet (/home/uetchy/.cache/huggingface/datasets/parquet/sensai-4d9ed81389161083/0.0.0/9296ce43568b20d72ff8ff8ecbc821a16b68e9b8b7058805ef11f06e035f911a)\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8acdde0c4caa4d4698f97ef29993195b"
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
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"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"dataset.features"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'body': Value(dtype='string', id=None),\n",
" 'toxic': ClassLabel(num_classes=2, names=['0', '1'], names_file=None, id=None)}"
]
},
"metadata": {},
"execution_count": 3
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=2)\n",
"tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias']\n",
"- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 5,
"source": [
"samples = dataset.shuffle().select(range(50000))\n",
"\n",
"def tokenize_function(examples):\n",
" return tokenizer(examples[\"body\"], padding=\"max_length\", truncation=True)\n",
"\n",
"tokenized_datasets = samples.map(tokenize_function, batched=True)\n",
"tokenized_datasets.rename_column_(\"toxic\", \"label\")"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Loading cached shuffled indices for dataset at /home/uetchy/.cache/huggingface/datasets/parquet/sensai-4d9ed81389161083/0.0.0/9296ce43568b20d72ff8ff8ecbc821a16b68e9b8b7058805ef11f06e035f911a/cache-24e3dd769ef2f1b7.arrow\n",
"Loading cached processed dataset at /home/uetchy/.cache/huggingface/datasets/parquet/sensai-4d9ed81389161083/0.0.0/9296ce43568b20d72ff8ff8ecbc821a16b68e9b8b7058805ef11f06e035f911a/cache-8395f066c72e57d7.arrow\n",
"/tmp/ipykernel_4082765/2982913603.py:7: FutureWarning: rename_column_ is deprecated and will be removed in the next major version of datasets. Use Dataset.rename_column instead.\n",
" tokenized_datasets.rename_column_(\"toxic\", \"label\")\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"source": [
"splitset = tokenized_datasets.train_test_split(0.2)\n",
"splitset"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['attention_mask', 'body', 'input_ids', 'token_type_ids', 'label'],\n",
" num_rows: 40000\n",
" })\n",
" test: Dataset({\n",
" features: ['attention_mask', 'body', 'input_ids', 'token_type_ids', 'label'],\n",
" num_rows: 10000\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 6
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"source": [
"training_args = TrainingArguments(\"test_trainer\")\n",
"trainer = Trainer(\n",
" model=model, args=training_args, train_dataset=splitset['train'], eval_dataset=splitset['test']\n",
")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"source": [
"trainer.train(resume_from_checkpoint=True)"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Loading model from test_trainer/checkpoint-12500).\n",
"The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: body.\n",
"***** Running training *****\n",
" Num examples = 40000\n",
" Num Epochs = 3\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 15000\n",
" Continuing training from checkpoint, will skip to saved global_step\n",
" Continuing training from epoch 2\n",
" Continuing training from global step 12500\n",
" Will skip the first 2 epochs then the first 2500 batches in the first epoch. If this takes a lot of time, you can add the `--ignore_data_skip` flag to your launch command, but you will resume the training on data already seen by your model.\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d749dea90f914485916ce82c65e73fe6"
},
"text/plain": [
" 0%| | 0/2500 [00:00<?, ?it/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Didn't find an RNG file, if you are resuming a training that was launched in a distributed fashion, reproducibility is not guaranteed.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='15000' max='15000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [15000/15000 31:45, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>13000</td>\n",
" <td>0.687500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13500</td>\n",
" <td>0.686300</td>\n",
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" <td>14500</td>\n",
" <td>0.643200</td>\n",
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" <td>0.627700</td>\n",
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"<IPython.core.display.HTML object>"
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"output_type": "stream",
"name": "stderr",
"text": [
"Saving model checkpoint to test_trainer/checkpoint-13000\n",
"Configuration saved in test_trainer/checkpoint-13000/config.json\n",
"Model weights saved in test_trainer/checkpoint-13000/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-13500\n",
"Configuration saved in test_trainer/checkpoint-13500/config.json\n",
"Model weights saved in test_trainer/checkpoint-13500/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-14000\n",
"Configuration saved in test_trainer/checkpoint-14000/config.json\n",
"Model weights saved in test_trainer/checkpoint-14000/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-14500\n",
"Configuration saved in test_trainer/checkpoint-14500/config.json\n",
"Model weights saved in test_trainer/checkpoint-14500/pytorch_model.bin\n",
"Saving model checkpoint to test_trainer/checkpoint-15000\n",
"Configuration saved in test_trainer/checkpoint-15000/config.json\n",
"Model weights saved in test_trainer/checkpoint-15000/pytorch_model.bin\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=15000, training_loss=0.10941998901367188, metrics={'train_runtime': 1918.0916, 'train_samples_per_second': 62.562, 'train_steps_per_second': 7.82, 'total_flos': 3.24994775580672e+16, 'train_loss': 0.10941998901367188, 'epoch': 3.0})"
]
},
"metadata": {},
"execution_count": 9
}
],
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}
},
"nbformat": 4,
"nbformat_minor": 2
}

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pyproject.toml
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@ -8,18 +8,20 @@ authors = ["Yasuaki Uechi <y@uechi.io>"]
python = "^3.8" python = "^3.8"
datasets = {git = "https://github.com/huggingface/datasets.git", rev = "master"} datasets = {git = "https://github.com/huggingface/datasets.git", rev = "master"}
#datasets = "^1.11.0" #datasets = "^1.11.0"
pyarrow = "^5.0.0" pyarrow = "^7.0.0"
[tool.poetry.dev-dependencies] [tool.poetry.dev-dependencies]
pymongo = "^3.11.3" pymongo = "^4.0.2"
transformers = "^4.10.0" transformers = "^4.17.0"
torch = "^1.9.0" torch = "^1.11.0"
tokenizers = "^0.10.3" tokenizers = "^0.11.6"
kaggle = "^1.5.12" kaggle = "^1.5.12"
pandas = "^1.2.3" pandas = "^1.4.1"
python-dateutil = "^2.8.1" python-dateutil = "^2.8.2"
ipykernel = "^6.2.0" yapf = "^0.32.0"
yapf = "^0.31.0" sentence-transformers = "^2.2.0"
jupyter = "^1.0.0"
tangram = "^0.7.0"
[build-system] [build-system]
requires = ["poetry-core>=1.0.0"] requires = ["poetry-core>=1.0.0"]

15
sensai_dataset/generator/__main__.py
View File

@ -1,16 +1,21 @@
import argparse import argparse
from sensai_dataset.generator.commands import generate_dataset from sensai_dataset.generator.commands import generate_dataset, generate_reduced_dataset
from sensai_dataset.generator.constants import DATASET_DIR, DATASET_SOURCE_DIR from sensai_dataset.generator.constants import SENSAI_COMPLETE_DIR, SENSAI_DIR, DATASET_SOURCE_DIR
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser(description='dataset generator') parser = argparse.ArgumentParser(description='dataset generator')
parser.add_argument('-m', '--matcher', type=str, default='chats_*.csv') parser.add_argument('-m', '--matcher', type=str, default='chats_*.parquet')
args = parser.parse_args() args = parser.parse_args()
print('target: ' + DATASET_DIR)
print('source: ' + DATASET_SOURCE_DIR) print('source: ' + DATASET_SOURCE_DIR)
print('SENSAI_COMPLETE_DIR: ' + SENSAI_COMPLETE_DIR)
print('SENSAI_DIR: ' + SENSAI_DIR)
generate_dataset(source_dir=DATASET_SOURCE_DIR, generate_dataset(source_dir=DATASET_SOURCE_DIR,
target_dir=DATASET_DIR, target_dir=SENSAI_COMPLETE_DIR,
matcher=args.matcher) matcher=args.matcher)
generate_reduced_dataset(source_dir=DATASET_SOURCE_DIR,
target_dir=SENSAI_DIR,
matcher=args.matcher)

107
sensai_dataset/generator/commands.py
View File

@ -6,16 +6,17 @@ import pandas as pd
def generate_dataset(source_dir, target_dir, matcher): def generate_dataset(source_dir, target_dir, matcher):
print('[generate_sensai_dataset]') print('[generate_dataset]')
delet_path = join(source_dir, 'deletion_events.csv') delet_path = join(source_dir, 'deletion_events.parquet')
del_events = pd.read_csv(delet_path, usecols=['id', 'retracted']) del_events = pd.read_parquet(delet_path, columns=['id', 'retracted'])
del_events = del_events.query('retracted == 0').copy() del_events = del_events.query('retracted == 0').copy()
del_events.drop(columns=['retracted'], inplace=True) del_events.drop(columns=['retracted'], inplace=True)
del_events['label'] = 'deleted' del_events['label'] = 'deleted'
ban_path = join(source_dir, 'ban_events.csv') ban_path = join(source_dir, 'ban_events.parquet')
ban_events = pd.read_csv(ban_path, usecols=['authorChannelId', 'videoId']) ban_events = pd.read_parquet(ban_path,
columns=['authorChannelId', 'videoId'])
ban_events['label'] = 'hidden' ban_events['label'] = 'hidden'
for f in sorted(iglob(join(source_dir, matcher))): for f in sorted(iglob(join(source_dir, matcher))):
@ -24,17 +25,93 @@ def generate_dataset(source_dir, target_dir, matcher):
# load chat # load chat
print('>>> Loading chats') print('>>> Loading chats')
chat_path = join(source_dir, 'chats_' + period_string + '.csv') chat_path = join(source_dir, 'chats_' + period_string + '.parquet')
chats = pd.read_csv(chat_path, chats = pd.read_parquet(
na_values='', chat_path,
keep_default_na=False, columns=['authorChannelId', 'videoId', 'id', 'authorName', 'body'])
usecols=[
'authorChannelId', # remove NA
'videoId', chats = chats[chats['body'].notna()]
'id',
'body', # apply mods
]) print('>>> Merging bans')
chats = pd.merge(chats,
ban_events,
on=['authorChannelId', 'videoId'],
how='left')
# apply mods
print('>>> Merging deletion')
chats.loc[chats['id'].isin(del_events['id']), 'label'] = 'deleted'
# apply safe
print('>>> Applying safe')
chats['label'].fillna('nonflagged', inplace=True)
isFlagged = chats['label'] != 'nonflagged'
flagged = chats[isFlagged].copy()
# to make balanced dataset
nbFlagged = flagged.shape[0]
if nbFlagged == 0:
continue
print('>>> Sampling nonflagged chats')
print('nbFlagged', nbFlagged)
nonflag = chats[~isFlagged].sample(nbFlagged)
print('>>> Writing dataset')
# NOTE: do not use categorical type with to_parquest. otherwise, it will be failed to load them with huggingface's Dataset
columns_to_delete = [
'authorChannelId',
'videoId',
'id',
]
flagged.drop(columns=columns_to_delete, inplace=True)
flagged.to_parquet(join(target_dir,
f'chats_flagged_{period_string}.parquet'),
index=False)
nonflag.drop(columns=columns_to_delete, inplace=True)
nonflag.to_parquet(join(target_dir,
f'chats_nonflag_{period_string}.parquet'),
index=False)
# free up memory
del nonflag
del flagged
del chats
gc.collect()
def generate_reduced_dataset(source_dir, target_dir, matcher):
print('[generate_reduced_dataset]')
delet_path = join(source_dir, 'deletion_events.parquet')
del_events = pd.read_parquet(delet_path, columns=['id', 'retracted'])
del_events = del_events.query('retracted == 0').copy()
del_events.drop(columns=['retracted'], inplace=True)
del_events['label'] = 'deleted'
ban_path = join(source_dir, 'ban_events.parquet')
ban_events = pd.read_parquet(ban_path,
columns=['authorChannelId', 'videoId'])
ban_events['label'] = 'hidden'
for f in sorted(iglob(join(source_dir, matcher))):
period_string = splitext(basename(f))[0].split('_')[1]
print('>>> Period:', period_string)
# load chat
print('>>> Loading chats')
chat_path = join(source_dir, 'chats_' + period_string + '.parquet')
chats = pd.read_parquet(
chat_path,
columns=['authorChannelId', 'videoId', 'id', 'authorName', 'body'])
# remove NA # remove NA
chats = chats[chats['body'].notna()] chats = chats[chats['body'].notna()]

6
sensai_dataset/generator/constants.py
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@ -1,7 +1,5 @@
import os import os
DATASET_DIR = os.environ['DATASET_DIR'] SENSAI_DIR = os.environ['SENSAI_DIR']
SENSAI_COMPLETE_DIR = os.environ['SENSAI_COMPLETE_DIR']
DATASET_SOURCE_DIR = os.environ['DATASET_SOURCE_DIR'] DATASET_SOURCE_DIR = os.environ['DATASET_SOURCE_DIR']
os.makedirs(DATASET_DIR, exist_ok=True)
os.makedirs(DATASET_SOURCE_DIR, exist_ok=True)