from transformers import AutoTokenizer, AutoModelForSequenceClassification, get_linear_schedule_with_warmup
import datasets
import pandas as pd
import pyarrow
import pytorch_lightning as pl
import torchmetrics
import torch.nn as nn
import torch
import types
import multiprocessing
from utils.text_cleaning import clean_text_funcs
class RRUMDataset():
scalar_features = ['channel_sim']
_image_features = ['regret_thumbnail',
'recommendation_thumbnail'] # not used atm
def __init__(self, data, with_transcript, cross_encoder_model_name_or_path, label_col="label", label_map=None, balance_label_counts=False, max_length=128, do_train_test_split=False, test_size=0.25, seed=42, keep_video_ids_for_predictions=False, encode_on_the_fly=False, clean_text=False, processing_batch_size=1000, processing_num_proc=1):
self._with_transcript = with_transcript
self.tokenizer = AutoTokenizer.from_pretrained(
cross_encoder_model_name_or_path)
self.label_col = label_col
self.label_map = label_map
self.balance_label_counts = balance_label_counts
self.max_length = max_length
self.seed = seed
self.keep_video_ids_for_predictions = keep_video_ids_for_predictions
self.clean_text = clean_text
self.processing_batch_size = processing_batch_size
self.processing_num_proc = multiprocessing.cpu_count(
) if not processing_num_proc else processing_num_proc
self.text_types = ['title', 'description'] + \
(['transcript'] if self._with_transcript else [])
self._text_features = [
'regret_title', 'recommendation_title', 'regret_description',
'recommendation_description'] + (['regret_transcript', 'recommendation_transcript'] if self._with_transcript else [])
# LOAD DATA INTO DATASET
self.streaming_dataset = False
if isinstance(data, pd.DataFrame):
self.dataset = datasets.Dataset.from_pandas(data)
elif isinstance(data, types.GeneratorType):
examples_iterable = datasets.iterable_dataset.ExamplesIterable(
self._streaming_generate_examples, {"iterable": data})
self.dataset = datasets.IterableDataset(examples_iterable)
self._stream_dataset_example = next(iter(self.dataset))
self._stream_dataset_column_names = list(
self._stream_dataset_example.keys())
self.streaming_dataset = True
elif isinstance(data, pyarrow.Table):
self.dataset = datasets.Dataset(data)
else:
raise ValueError(
f'Type of data is {type(data)} when pd.DataFrame, pyarrow.Table, or generator of pyarrow.RecordBatch is allowed')
# PREPROCESS DATASET
self._preprocess()
# ENCODE DATASET
self.train_dataset = None
self.test_dataset = None
if self.streaming_dataset:
# IterableDataset doesn't have train_test_split method
if self.label_col:
self.train_dataset = self._encode_streaming(self.dataset)
print('Streaming dataset available in .train_dataset')
else:
self.test_dataset = self._encode_streaming(self.dataset)
print(
'Streaming dataset available in .test_dataset because label_col=None')
else:
# dataset into train_dataset and/or test_dataset
if do_train_test_split:
ds = self.dataset.train_test_split(
test_size=test_size, shuffle=True, seed=self.seed, stratify_by_column=self.label_col)
self.train_dataset = ds['train']
self.test_dataset = ds['test']
print(
f'Dataset was splitted into train and test with test_size={test_size}')
else:
if self.label_col:
self.train_dataset = self.dataset
else:
self.test_dataset = self.dataset
if encode_on_the_fly:
if self.train_dataset:
self.train_dataset.set_transform(self._encode_on_the_fly)
print('On-the-fly encoded dataset available in .train_dataset')
if self.test_dataset:
self.test_dataset.set_transform(self._encode_on_the_fly)
print('On-the-fly encoded dataset available in .test_dataset')
else:
if self.train_dataset:
self.train_dataset = self._encode(self.train_dataset)
print('Pre-encoded dataset available in .train_dataset')
if self.test_dataset:
self.test_dataset = self._encode(self.test_dataset)
print('Pre-encoded dataset available in .test_dataset')
def __len__(self):
if self.streaming_dataset:
raise ValueError(
f'Streaming dataset does not support len() method')
return len(self.dataset)
def __getitem__(self, index):
if self.streaming_dataset:
return next(iter(self.dataset))
return self.dataset[index]
def _streaming_generate_examples(self, iterable):
id_ = 0
# TODO: make sure GeneratorType is pyarrow.RecordBatch
if isinstance(iterable, types.GeneratorType):
for examples in iterable:
for ex in examples.to_pylist():
yield id_, ex
id_ += 1
def _preprocess(self):
if self._with_transcript:
self.dataset = self.dataset.filter(
lambda example: example['regret_transcript'] is not None and example['recommendation_transcript'] is not None)
else:
self.dataset = self.dataset.filter(
lambda example: example['regret_transcript'] is None or example['recommendation_transcript'] is None)
if self.label_col:
if self.streaming_dataset:
if self.label_col in self._stream_dataset_column_names and isinstance(self._stream_dataset_example[self.label_col], str):
if not self.label_map:
raise ValueError(
f'"label_map" dict was not provided and is needed to encode string labels for streaming datasets')
# cast_column method had issues with streaming dataset
self.dataset = self.dataset.map(
self._streaming_rename_labels)
else:
if self.dataset.features[self.label_col].dtype == 'string':
if not self.label_map:
self.label_map = {k: v for v, k in enumerate(
self.dataset.unique(self.label_col))}
self.dataset = self.dataset.filter(
lambda example: example[self.label_col] in self.label_map.keys())
self.dataset = self.dataset.cast_column(self.label_col, datasets.ClassLabel(
num_classes=len(self.label_map), names=list(self.label_map.keys())))
self.dataset = self.dataset.filter(lambda example: not any(x in [None, ""] for x in [
example[key] for key in self._text_features + self.scalar_features + ([self.label_col] if self.label_col else [])])) # dropna
if self.balance_label_counts and self.label_col and not self.streaming_dataset:
label_datasets = {}
for label in list(self.label_map.values()):
label_dataset = self.dataset.filter(
lambda example: example[self.label_col] == label)
label_datasets[len(label_dataset)] = label_dataset
min_label_count = min(label_datasets)
sampled_datasets = [dataset.train_test_split(train_size=min_label_count, shuffle=True, seed=self.seed)[
'train'] if len(dataset) != min_label_count else dataset for dataset in label_datasets.values()]
self.dataset = datasets.concatenate_datasets(sampled_datasets)
if self.clean_text:
self.dataset = self.dataset.map(self._clean_text, batched=not self.streaming_dataset,
batch_size=self.processing_batch_size)
self.dataset = self.dataset.map(self._truncate_and_strip_text, batched=not self.streaming_dataset,
batch_size=self.processing_batch_size)
def _streaming_rename_labels(self, example):
# rename labels according to label_map if not already correct labels
if isinstance(example[self.label_col], list):
example[self.label_col] = [self.label_map.get(
ex, None) for ex in example[self.label_col] if ex not in self.label_map.values()]
elif isinstance(example[self.label_col], str) and example[self.label_col] not in self.label_map.values():
example[self.label_col] = self.label_map.get(
example[self.label_col], None)
else:
raise ValueError(
f'Type of example label is {type(example[self.label_col])} when list or string is allowed')
return example
def _clean_text(self, example):
for feat in self._text_features:
example[feat] = clean_text_funcs(example[feat])[0] if isinstance(
example[feat], str) else clean_text_funcs(example[feat])
return example
def _truncate_and_strip_text(self, example):
# tokenizer will truncate to max_length tokens anyway so to save RAM let's truncate to max_length words already beforehand
# one word is usually one or more tokens so should be safe to truncate this way without losing information
for feat in self._text_features:
if isinstance(example[feat], list):
example[feat] = [
' '.join(text.split()[:self.max_length]).strip() for text in example[feat] if text]
elif isinstance(example[feat], str):
example[feat] = ' '.join(example[feat].split()[
:self.max_length]).strip()
elif example[feat] is None:
return None
else:
raise ValueError(
f'Type of example is {type(example[feat])} when list or string is allowed')
return example
def _encode(self, dataset):
encoded_dataset = None
for text_type in self.text_types:
encoded_text_type = dataset.map(lambda regret, recommendation: self.tokenizer(regret, recommendation, padding="max_length", truncation=True, max_length=self.max_length), batched=True,
batch_size=self.processing_batch_size, num_proc=self.processing_num_proc, input_columns=[f'regret_{text_type}', f'recommendation_{text_type}'], remove_columns=dataset.column_names)
encoded_text_type = encoded_text_type.rename_columns(
{col: f'{text_type}_{col}' for col in encoded_text_type.column_names}) # e.g. input_ids -> title_input_ids so we have separate input_ids for each text_type
if encoded_dataset:
encoded_dataset = datasets.concatenate_datasets(
[encoded_dataset, encoded_text_type], axis=1)
else:
encoded_dataset = encoded_text_type
# copy scalar features and label from original dataset to the encoded dataset
for scalar_feat in self.scalar_features:
encoded_dataset = encoded_dataset.add_column(
name=scalar_feat, column=dataset[scalar_feat])
if self.label_col:
encoded_dataset = encoded_dataset.add_column(
name=self.label_col, column=dataset[self.label_col])
if self.keep_video_ids_for_predictions:
for id in ['regret_id', "recommendation_id"]:
encoded_dataset = encoded_dataset.add_column(
name=id, column=dataset[id])
encoded_dataset.set_format(
type='torch', columns=encoded_dataset.column_names)
return encoded_dataset
def _encode_streaming(self, dataset):
encoded_dataset = dataset.map(self._encode_on_the_fly, batched=True,
batch_size=self.processing_batch_size, remove_columns=list(set(self._stream_dataset_column_names)-set(self.scalar_features + (
[self.label_col] if self.label_col else []) + (['regret_id', "recommendation_id"] if self.keep_video_ids_for_predictions else [])))) # IterableDataset doesn't have column_names attribute as normal Dataset
encoded_dataset = encoded_dataset.with_format("torch")
return encoded_dataset
def _encode_on_the_fly(self, batch):
for text_type in self.text_types:
encoded_text_type = dict(self.tokenizer(
batch[f'regret_{text_type}'], batch[f'recommendation_{text_type}'], padding="max_length", truncation=True, max_length=self.max_length, return_tensors="pt"))
for encoded_key in encoded_text_type.copy():
encoded_text_type[f"{text_type}_{encoded_key}"] = encoded_text_type.pop(encoded_key) if not self.streaming_dataset else encoded_text_type.pop(
encoded_key).squeeze(0) # e.g. input_ids -> title_input_ids so we have separate input_ids for each text_type
del batch[f'regret_{text_type}']
del batch[f'recommendation_{text_type}']
batch.update(encoded_text_type)
for scalar_feat in self.scalar_features:
batch[scalar_feat] = torch.as_tensor(
batch[scalar_feat]) if not self.streaming_dataset else torch.as_tensor(batch[scalar_feat]).squeeze(0)
if self.label_col:
batch[self.label_col] = torch.as_tensor(
batch[self.label_col]) if not self.streaming_dataset else torch.as_tensor(batch[self.label_col]).squeeze(0)
return batch
class RRUM(pl.LightningModule):
def __init__(self, text_types, scalar_features, label_col, cross_encoder_model_name_or_path, optimizer_config=None, freeze_policy=None, pos_weight=None):
super().__init__()
self.save_hyperparameters()
self.text_types = text_types
self.scalar_features = scalar_features
self.label_col = label_col
self.optimizer_config = optimizer_config
self.cross_encoder_model_name_or_path = cross_encoder_model_name_or_path
self.cross_encoders = nn.ModuleDict({})
for t in self.text_types:
self.cross_encoders[t] = AutoModelForSequenceClassification.from_pretrained(
self.cross_encoder_model_name_or_path)
if freeze_policy is not None:
for xe in self.cross_encoders.values():
for name, param in xe.named_parameters():
if freeze_policy(name):
param.requires_grad = False
cross_encoder_out_features = list(self.cross_encoders.values())[0](
torch.randint(1, 2, (1, 2))).logits.size(dim=1)
self.lin1 = nn.Linear(len(self.cross_encoders) * cross_encoder_out_features +
len(self.scalar_features), 1)
self.ac_metric = torchmetrics.Accuracy()
self.pr_metric = torchmetrics.Precision()
self.re_metric = torchmetrics.Recall()
self.auc_metric = torchmetrics.AUROC()
if pos_weight:
self.loss = nn.BCEWithLogitsLoss(
pos_weight=torch.Tensor([pos_weight]))
else:
self.loss = nn.BCEWithLogitsLoss()
def forward(self, x):
cross_logits = {}
for f in self.text_types:
inputs = {key.split(f'{f}_')[1]: x[key]
for key in x if f in key} # e.g. title_input_ids -> input_ids since we have separate input_ids for each text_type
cross_logits[f] = self.cross_encoders[f](**inputs).logits
x = torch.cat([*cross_logits.values()] +
[x[scalar][:, None] for scalar in self.scalar_features],
1
)
del cross_logits
x = self.lin1(x)
return x
def configure_optimizers(self):
if self.optimizer_config:
return self.optimizer_config(self)
optimizer = torch.optim.AdamW(self.parameters(), lr=5e-5)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=int(
self.trainer.estimated_stepping_batches * 0.05),
num_training_steps=self.trainer.estimated_stepping_batches,
)
scheduler = {'scheduler': scheduler,
'interval': 'step', 'frequency': 1}
return [optimizer], [scheduler]
def training_step(self, train_batch, batch_idx):
y = train_batch[self.label_col].unsqueeze(1).float()
logits = self(train_batch)
loss = self.loss(logits, y)
self.log('train_loss', loss)
return loss
def validation_step(self, val_batch, batch_idx):
y = val_batch[self.label_col].unsqueeze(1).float()
logits = self(val_batch)
loss = self.loss(logits, y)
self.ac_metric(logits, y.int())
self.pr_metric(logits, y.int())
self.re_metric(logits, y.int())
self.auc_metric(logits, y.int())
self.log('validation_accuracy', self.ac_metric)
self.log('validation_precision', self.pr_metric)
self.log('validation_recall', self.re_metric)
self.log('validation_auc', self.auc_metric)
self.log('val_loss', loss, prog_bar=True)
def validation_epoch_end(self, outputs):
self.log('validation_accuracy_ep', self.ac_metric)
self.log('validation_precision_ep', self.pr_metric)
self.log('validation_recall_ep', self.re_metric)
self.log('validation_auc_ep', self.auc_metric)