163 lines
5.0 KiB
Python
163 lines
5.0 KiB
Python
# !/usr/bin/env/python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""BERT Data Module Script."""
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import numpy as np
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import pyarrow.parquet as pq
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import pytorch_lightning as pl
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import torch
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from sklearn.model_selection import train_test_split
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from torch.utils.data import DataLoader
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from transformers import BertTokenizer
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from news_dataset import NewsDataset
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class BertDataModule(pl.LightningDataModule): # pylint: disable=too-many-instance-attributes
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"""Data Module Class."""
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def __init__(self, **kwargs):
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"""Initialization of inherited lightning data module."""
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super(BertDataModule, self).__init__() # pylint: disable=super-with-arguments
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self.pre_trained_model_name = "bert-base-uncased"
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self.df_train = None
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self.df_val = None
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self.df_test = None
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self.train_data_loader = None
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self.val_data_loader = None
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self.test_data_loader = None
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self.max_length = 100
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self.encoding = None
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self.tokenizer = None
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self.args = kwargs
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def prepare_data(self):
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"""Implementation of abstract class."""
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@staticmethod
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def process_label(rating):
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"""Puts labels to ratings"""
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rating = int(rating)
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return rating - 1
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def setup(self, stage=None):
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"""Downloads the data, parse it and split the data into train, test,
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validation data.
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Args:
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stage: Stage - training or testing
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"""
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num_samples = self.args.get("num_samples", 1000)
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data_path = self.args["train_glob"]
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print("\n\nTRAIN GLOB")
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print(data_path)
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print("\n\n")
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df_parquet = pq.ParquetDataset(self.args["train_glob"])
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dataframe = df_parquet.read_pandas().to_pandas()
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dataframe.columns = ["label", "title", "description"]
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dataframe.sample(frac=1)
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dataframe = dataframe.iloc[:num_samples]
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dataframe["label"] = dataframe.label.apply(self.process_label)
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self.tokenizer = BertTokenizer.from_pretrained(
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self.pre_trained_model_name
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)
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random_seed = 42
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np.random.seed(random_seed)
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torch.manual_seed(random_seed)
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self.df_train, self.df_test = train_test_split(
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dataframe,
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test_size=0.2,
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random_state=random_seed,
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stratify=dataframe["label"],
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)
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self.df_val, self.df_test = train_test_split(
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self.df_test,
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test_size=0.2,
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random_state=random_seed,
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stratify=self.df_test["label"],
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)
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def create_data_loader(self, dataframe, tokenizer, max_len, batch_size): # pylint: disable=unused-argument
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"""Generic data loader function.
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Args:
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dataframe: Input dataframe
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tokenizer: bert tokenizer
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max_len: Max length of the news datapoint
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batch_size: Batch size for training
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Returns:
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Returns the constructed dataloader
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"""
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dataset = NewsDataset(
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reviews=dataframe.description.to_numpy(),
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targets=dataframe.label.to_numpy(),
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tokenizer=tokenizer,
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max_length=max_len,
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)
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return DataLoader(
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dataset,
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batch_size=self.args.get("batch_size", 4),
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num_workers=self.args.get("num_workers", 1),
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)
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def train_dataloader(self):
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"""Train data loader
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Returns:
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output - Train data loader for the given input
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"""
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self.train_data_loader = self.create_data_loader(
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self.df_train,
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self.tokenizer,
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self.max_length,
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self.args.get("batch_size", 4),
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)
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return self.train_data_loader
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def val_dataloader(self):
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"""Validation data loader.
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Returns:
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output - Validation data loader for the given input
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"""
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self.val_data_loader = self.create_data_loader(
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self.df_val,
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self.tokenizer,
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self.max_length,
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self.args.get("batch_size", 4),
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)
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return self.val_data_loader
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def test_dataloader(self):
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"""Test data loader.
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Return:
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output - Test data loader for the given input
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"""
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self.test_data_loader = self.create_data_loader(
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self.df_test,
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self.tokenizer,
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self.max_length,
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self.args.get("batch_size", 4),
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)
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return self.test_data_loader
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