289 lines
12 KiB
Python
289 lines
12 KiB
Python
"""
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This module contains the view generators that take care of computing the view specific document embeddings:
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- VanillaFunGen (-X) cast document representations encoded via TFIDF into posterior probabilities by means of SVM.
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- WordClassGen (-W): generates document representation via Word-Class-Embeddings.
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Document embeddings are obtained via weighted sum of document's constituent embeddings.
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- MuseGen (-M):
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- RecurrentGen (-G): generates document embedding by means of a Gated Recurrent Units. The model can be
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initialized with different (multilingual/aligned) word representations (e.g., MUSE, WCE, ecc.,).
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Output dimension is (n_docs, 512).
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- View generator (-B): generates document embedding via mBERT model.
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"""
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from abc import ABC, abstractmethod
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from models.learners import *
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from util.embeddings_manager import MuseLoader, XdotM, wce_matrix
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from util.common import TfidfVectorizerMultilingual, _normalize
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from models.pl_gru import RecurrentModel
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from models.pl_bert import BertModel
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from pytorch_lightning import Trainer
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from data.datamodule import RecurrentDataModule, BertDataModule, tokenize
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from pytorch_lightning.loggers import TensorBoardLogger, CSVLogger
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from time import time
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class ViewGen(ABC):
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@abstractmethod
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def fit(self, lX, ly):
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pass
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@abstractmethod
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def transform(self, lX):
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pass
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@abstractmethod
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def fit_transform(self, lX, ly):
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pass
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class VanillaFunGen(ViewGen):
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def __init__(self, base_learner, n_jobs=-1):
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"""
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Original funnelling architecture proposed by Moreo, Esuli and Sebastiani in DOI: https://doi.org/10.1145/3326065
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:param base_learner: naive monolingual learners to be deployed as first-tier learners. Should be able to
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return posterior probabilities.
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:param n_jobs: integer, number of concurrent workers
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"""
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super().__init__()
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self.learners = base_learner
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self.n_jobs = n_jobs
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self.doc_projector = NaivePolylingualClassifier(self.learners)
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self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
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def fit(self, lX, lY):
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print('# Fitting VanillaFunGen (X)...')
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lX = self.vectorizer.fit_transform(lX)
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self.doc_projector.fit(lX, lY)
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return self
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def transform(self, lX):
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lX = self.vectorizer.transform(lX)
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lZ = self.doc_projector.predict_proba(lX)
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return lZ
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def fit_transform(self, lX, ly):
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return self.fit(lX, ly).transform(lX)
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class MuseGen(ViewGen):
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def __init__(self, muse_dir='../embeddings', n_jobs=-1):
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"""
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generates document representation via MUSE embeddings (Fasttext multilingual word
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embeddings). Document embeddings are obtained via weighted sum of document's constituent embeddings.
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:param muse_dir: string, path to folder containing muse embeddings
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:param n_jobs: int, number of concurrent workers
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"""
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super().__init__()
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self.muse_dir = muse_dir
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self.n_jobs = n_jobs
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self.langs = None
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self.lMuse = None
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self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
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def fit(self, lX, ly):
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print('# Fitting MuseGen (M)...')
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self.vectorizer.fit(lX)
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self.langs = sorted(lX.keys())
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self.lMuse = MuseLoader(langs=self.langs, cache=self.muse_dir)
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lVoc = self.vectorizer.vocabulary()
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self.lMuse = self.lMuse.extract(lVoc) # overwriting lMuse with dict {lang : embed_matrix} with only known words
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# TODO: featureweight.fit
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return self
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def transform(self, lX):
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lX = self.vectorizer.transform(lX)
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XdotMUSE = Parallel(n_jobs=self.n_jobs)(
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delayed(XdotM)(lX[lang], self.lMuse[lang], sif=True) for lang in self.langs)
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lZ = {lang: XdotMUSE[i] for i, lang in enumerate(self.langs)}
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lZ = _normalize(lZ, l2=True)
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return lZ
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def fit_transform(self, lX, ly):
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return self.fit(lX, ly).transform(lX)
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class WordClassGen(ViewGen):
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def __init__(self, n_jobs=-1):
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"""
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generates document representation via Word-Class-Embeddings.
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Document embeddings are obtained via weighted sum of document's constituent embeddings.
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:param n_jobs: int, number of concurrent workers
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"""
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super().__init__()
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self.n_jobs = n_jobs
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self.langs = None
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self.lWce = None
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self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
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def fit(self, lX, ly):
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print('# Fitting WordClassGen (W)...')
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lX = self.vectorizer.fit_transform(lX)
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self.langs = sorted(lX.keys())
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wce = Parallel(n_jobs=self.n_jobs)(
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delayed(wce_matrix)(lX[lang], ly[lang]) for lang in self.langs)
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self.lWce = {l: wce[i] for i, l in enumerate(self.langs)}
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# TODO: featureweight.fit()
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return self
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def transform(self, lX):
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lX = self.vectorizer.transform(lX)
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XdotWce = Parallel(n_jobs=self.n_jobs)(
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delayed(XdotM)(lX[lang], self.lWce[lang], sif=True) for lang in self.langs)
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lWce = {l: XdotWce[i] for i, l in enumerate(self.langs)}
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lWce = _normalize(lWce, l2=True)
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return lWce
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def fit_transform(self, lX, ly):
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return self.fit(lX, ly).transform(lX)
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class RecurrentGen(ViewGen):
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# TODO: save model https://forums.pytorchlightning.ai/t/how-to-save-hparams-when-not-provided-as-argument-apparently-assigning-to-hparams-is-not-recomended/339/5
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# Problem: we are passing lPretrained to init the RecurrentModel -> incredible slow at saving (checkpoint).
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# if we do not save it is impossible to init RecurrentModel by calling RecurrentModel.load_from_checkpoint()
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def __init__(self, multilingualIndex, pretrained_embeddings, wce, batch_size=512, nepochs=50,
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gpus=0, n_jobs=-1, stored_path=None):
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"""
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generates document embedding by means of a Gated Recurrent Units. The model can be
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initialized with different (multilingual/aligned) word representations (e.g., MUSE, WCE, ecc.,).
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Output dimension is (n_docs, 512).
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:param multilingualIndex:
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:param pretrained_embeddings:
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:param wce:
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:param gpus:
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:param n_jobs:
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"""
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super().__init__()
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self.multilingualIndex = multilingualIndex
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self.langs = multilingualIndex.langs
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self.batch_size = batch_size
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self.gpus = gpus
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self.n_jobs = n_jobs
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self.stored_path = stored_path
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self.nepochs = nepochs
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# EMBEDDINGS to be deployed
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self.pretrained = pretrained_embeddings
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self.wce = wce
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self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
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self.multilingualIndex.embedding_matrices(self.pretrained, supervised=self.wce)
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self.model = self._init_model()
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self.logger = TensorBoardLogger(save_dir='tb_logs', name='rnn', default_hp_metric=False)
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# self.logger = CSVLogger(save_dir='csv_logs', name='rnn_dev')
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def _init_model(self):
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if self.stored_path:
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lpretrained = self.multilingualIndex.l_embeddings()
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return RecurrentModel.load_from_checkpoint(self.stored_path, lPretrained=lpretrained)
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else:
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lpretrained = self.multilingualIndex.l_embeddings()
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langs = self.multilingualIndex.langs
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output_size = self.multilingualIndex.get_target_dim()
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hidden_size = 512
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lvocab_size = self.multilingualIndex.l_vocabsize()
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learnable_length = 0
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return RecurrentModel(
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lPretrained=lpretrained,
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langs=langs,
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output_size=output_size,
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hidden_size=hidden_size,
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lVocab_size=lvocab_size,
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learnable_length=learnable_length,
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drop_embedding_range=self.multilingualIndex.sup_range,
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drop_embedding_prop=0.5,
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gpus=self.gpus
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)
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def fit(self, lX, ly):
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"""
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lX and ly are not directly used. We rather get them from the multilingual index used in the instantiation
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of the Dataset object (RecurrentDataset) in the GfunDataModule class.
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:param lX:
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:param ly:
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:return:
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"""
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print('# Fitting RecurrentGen (G)...')
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recurrentDataModule = RecurrentDataModule(self.multilingualIndex, batchsize=self.batch_size)
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trainer = Trainer(gradient_clip_val=1e-1, gpus=self.gpus, logger=self.logger, max_epochs=self.nepochs,
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checkpoint_callback=False)
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# vanilla_torch_model = torch.load(
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# '/home/andreapdr/funneling_pdr/checkpoint/gru_viewgen_-jrc_doclist_1958-2005vs2006_all_top300_noparallel_processed_run0.pickle')
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# self.model.linear0 = vanilla_torch_model.linear0
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# self.model.linear1 = vanilla_torch_model.linear1
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# self.model.linear2 = vanilla_torch_model.linear2
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# self.model.rnn = vanilla_torch_model.rnn
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trainer.fit(self.model, datamodule=recurrentDataModule)
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trainer.test(self.model, datamodule=recurrentDataModule)
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return self
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def transform(self, lX):
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"""
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Project documents to the common latent space
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:param lX:
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:return:
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"""
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l_pad = self.multilingualIndex.l_pad()
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data = self.multilingualIndex.l_devel_index()
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self.model.to('cuda' if self.gpus else 'cpu')
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self.model.eval()
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time_init = time()
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l_embeds = self.model.encode(data, l_pad, batch_size=256)
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transform_time = round(time() - time_init, 3)
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print(f'Executed! Transform took: {transform_time}')
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return l_embeds
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def fit_transform(self, lX, ly):
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return self.fit(lX, ly).transform(lX)
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class BertGen(ViewGen):
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def __init__(self, multilingualIndex, batch_size=128, nepochs=50, gpus=0, n_jobs=-1, stored_path=None):
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super().__init__()
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self.multilingualIndex = multilingualIndex
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self.nepochs = nepochs
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self.gpus = gpus
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self.batch_size = batch_size
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self.n_jobs = n_jobs
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self.stored_path = stored_path
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self.model = self._init_model()
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self.logger = TensorBoardLogger(save_dir='tb_logs', name='bert', default_hp_metric=False)
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def _init_model(self):
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output_size = self.multilingualIndex.get_target_dim()
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return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus)
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def fit(self, lX, ly):
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print('# Fitting BertGen (M)...')
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self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
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bertDataModule = BertDataModule(self.multilingualIndex, batchsize=self.batch_size, max_len=512)
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trainer = Trainer(gradient_clip_val=1e-1, max_epochs=self.nepochs, gpus=self.gpus,
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logger=self.logger, checkpoint_callback=False)
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trainer.fit(self.model, datamodule=bertDataModule)
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trainer.test(self.model, datamodule=bertDataModule)
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return self
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def transform(self, lX):
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# lX is raw text data. It has to be first indexed via Bert Tokenizer.
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data = self.multilingualIndex.l_devel_raw_index()
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data = tokenize(data, max_len=512)
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self.model.to('cuda' if self.gpus else 'cpu')
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self.model.eval()
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time_init = time()
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l_emebds = self.model.encode(data, batch_size=64)
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transform_time = round(time() - time_init, 3)
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print(f'Executed! Transform took: {transform_time}')
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return l_emebds
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def fit_transform(self, lX, ly):
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# we can assume that we have already indexed data for transform() since we are first calling fit()
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return self.fit(lX, ly).transform(lX)
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