Batcher added
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GassiGiuseppe
parent
bed9718f27
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96610612fe
@ -31,7 +31,7 @@ class TokeNanoCore:
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def vocabulary_size(self):
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BPE_VOC_SIZE = self.__bpe_encoder.vocabulary_size
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SPECIAL_VOC_SIZE = self.__special_encoder.vocabulary_size
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return BPE_VOC_SIZE + SPECIAL_VOC_SIZE
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return BPE_VOC_SIZE + SPECIAL_VOC_SIZE + 1
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def encode(self, corpus: str) -> list[int]:
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output: list[int] = []
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@ -1,49 +1,68 @@
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import random
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from typing import Generator
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import sys
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from typing import Any, Generator
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import pandas as pd
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from pathlib import Path
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from Project_Model.Libs.Batch.Enums.TaskType import TaskType
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import Project_Model.Libs.BPE as BPE
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from Scripts.Libs.CleaningPipeline.special_token import SpecialToken
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from Project_Model.Libs.Transformer.Classes.SpannedMasker import SpannedMasker
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# from Scripts.Libs.CleaningPipeline.special_token import SpecialToken
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from Project_Model.Libs.Transformer import SpannedMasker, truncate_rdf_list, normalize_sequence
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from TokenCompletation import TokenCompletationTransformer
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from Project_Model.Libs.BPE.Enums.SpecialToken import SpecialToken
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from Project_Model.Libs.BPE import SpecialToken
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MAX_LENGHT = 128
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class Batcher:
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def __init__(self, dataset_path: str, batch_size:int, tokenizer: BPE.TokeNanoCore, masker: SpannedMasker) -> None:
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def __init__(self, dataset_path: Path, tokenizer: BPE.TokeNanoCore, masker: SpannedMasker, seed:int = 0) -> None:
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# ABSTRACT, TRIPLE
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# tasks:
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# rdf2text: X: TRIPLE, Y: ABSTRACT
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# text2rdf: X: ABSTRACT, X:TRIPLE
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# masking ( call masker): X: incomplete_triple Y: complete_triple (as exam)
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# completation: X: TRIPLE SUBSET, Y: related TRIPLE SUBSET
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# it will truncate
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# it will instantiate spanmaskter and truncator
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self._dataset_path = dataset_path
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self._batch_size = batch_size
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self._tokenizer = tokenizer
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self._masker = masker
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sotl = self._tokenizer.encode(SpecialToken.START_TRIPLE_LIST.value)
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eos = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)
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self._token_completation = TokenCompletationTransformer(sotl,eos)
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self._seed = seed
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# self._token_completation = TokenCompletationTransformer(sotl,eos)
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self._completation_task_token_truncator = truncate_rdf_list
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def get_batch(self)-> Generator[pd.DataFrame]:
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for batch in pd.read_csv(self._dataset_path, chunksize= int(self._batch_size/4)): #now we support 3 task
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def batch(self, batch_size)-> Generator[tuple[list[list[int]], list[list[int]], list[list[int]],list[list[int]], TaskType],Any,Any]:
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"""
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Yields: X,Y,padding_X
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"""
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RNG = random.Random(self._seed)
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self._masker.reseed(self._seed)
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for batch in pd.read_csv(self._dataset_path, chunksize= int(batch_size)): #now we support 3 task
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tokenized_batch = pd.DataFrame()
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# encode
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tokenized_batch[["Abstract","RDFs"]] = (
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batch[["Abstract","RDFs"]]
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.map(lambda t: self._tokenizer.encode(t))
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)
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rdf2txt_batch = self.__rdf2txt_transformation(tokenized_batch)
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txt2rdf_batch = self.__txt2rdf_transformation(tokenized_batch)
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mask_batch = self.__masking_trasformation(tokenized_batch)
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completation_batch = self.__token_completation_task(tokenized_batch)
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X,Y, padding_X, padding_Y = self.__rdf2txt_transformation(tokenized_batch)
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yield X,Y, padding_X, padding_Y, TaskType.RDF2TXT
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X,Y, padding_X, padding_Y, = self.__txt2rdf_transformation(tokenized_batch)
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yield X,Y, padding_X, padding_Y, TaskType.TEXT2RDF
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X,Y, padding_X, padding_Y, = self.__masking_trasformation(tokenized_batch)
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yield X,Y, padding_X, padding_Y, TaskType.MASKING
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X,Y, padding_X, padding_Y, = self.__token_completation_task(tokenized_batch, RNG.randint(0,sys.maxsize))
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yield X,Y, padding_X, padding_Y, TaskType.COMPLETATION
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output = pd.concat([rdf2txt_batch,txt2rdf_batch,mask_batch,completation_batch],ignore_index=True)
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output = output.sample(frac=1).reset_index(drop=True)
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yield output
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# output = pd.concat([rdf2txt_batch,txt2rdf_batch,completation_batch],ignore_index=True)
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# output = output.sample(frac=1).reset_index(drop=True)
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# self.decode_debug(output)
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# yield output
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def __random_subset_rdfs(self, batch: pd.DataFrame, seed = 0):
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@ -57,35 +76,77 @@ class Batcher:
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to_list
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)
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def decode_debug(self, batch: pd.DataFrame):
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decoded = pd.DataFrame()
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decoded[["X","Y"]] = (
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batch[["X","Y"]]
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.map(lambda t: self._tokenizer.decode(t))
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)
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print(decoded)
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def __normalization(self, X:list[list[int]], Y: list[list[int]])-> tuple[list[list[int]], list[list[int]], list[list[int]], list[list[int]]]:
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pad_token = self._tokenizer.encode(SpecialToken.PAD.value)[0]
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end_token = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)[0]
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out_X = []
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padding_X = []
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out_Y = []
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padding_Y = []
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for x in X:
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out_x, padding_x = normalize_sequence(x,MAX_LENGHT,pad_token,end_token,True)
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out_X.append(out_x)
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padding_X.append(padding_x)
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for y in Y:
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out_y, padding_y = normalize_sequence(y,MAX_LENGHT,pad_token,end_token,True)
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out_Y.append(out_y)
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padding_Y.append(padding_y)
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return out_X,out_Y,padding_X,padding_Y
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def __rdf2txt_transformation(self, batch: pd.DataFrame):
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batch = batch.rename(columns={"RDFs": "X", "Abstract": "Y"})
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return batch[["X", "Y"]]
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task_token = self._tokenizer.encode(SpecialToken.RDF_TO_TEXT.value)
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out = batch.rename(columns={"RDFs":"X","Abstract":"Y"})[["X","Y"]]
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out["X"] = [task_token + x for x in out["X"]]
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return self.__normalization(out["X"].to_list(),out["Y"].to_list())
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def __txt2rdf_transformation(self, batch: pd.DataFrame):
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batch = batch.rename(columns={ "Abstract": "X","RDFs": "Y"})
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return batch[["X", "Y"]]
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task_token = self._tokenizer.encode(SpecialToken.TEXT_TO_RDF.value)
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out = batch.rename(columns={"Abstract":"X","RDFs":"Y"})[["X","Y"]]
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out["X"] = [task_token + x for x in out["X"]]
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return self.__normalization(out["X"].to_list(),out["Y"].to_list())
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def __masking_trasformation(self, batch: pd.DataFrame):
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# mask_sequence: List[int] -> Tuple[List[int], List[int]]
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xy_tuples = batch["RDFs"].apply(self._masker.mask_sequence) # Series of (X, Y)
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output = batch.copy()
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# Expand into two columns preserving the original index
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output[["X", "Y"]] = pd.DataFrame(xy_tuples.tolist(), index=batch.index)
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return output[["X", "Y"]]
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X = []
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Y = []
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for rdf in batch["RDFs"]:
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x,y = self._masker.mask_sequence(rdf)
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X.append(x)
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Y.append(y)
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return self.__normalization(X,Y)
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def __token_completation_task(self, batch: pd.DataFrame):
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xy_tuples = batch["RDFs"].apply(self._token_completation.get_completation_tuple)
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output = batch.copy()
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output[["X", "Y"]] = pd.DataFrame(xy_tuples.tolist(), index=batch.index)
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return output[["X", "Y"]]
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def __token_completation_task(self, batch: pd.DataFrame, minibatch_seed: int):
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continue_triple_token = self._tokenizer.encode(SpecialToken.CONTINUE_RDF.value)[0]
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eot = self._tokenizer.encode(SpecialToken.END_TRIPLE.value)[0]
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X = []
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Y = []
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for rdf in batch["RDFs"]:
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x,y = self._completation_task_token_truncator(rdf, 0.5, continue_triple_token, eot, minibatch_seed)
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X.append(x)
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Y.append(y)
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return self.__normalization(X,Y)
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"""
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DATASET_PATH = "Assets/Dataset/Tmp/rdf_text.csv"
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if __name__ == "__main__":
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DATASET_PATH = Path("Assets/Dataset/Tmp/rdf_text.csv")
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VOCABULARY_path = "Assets/Dataset/Tmp/trimmed.json"
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from pathlib import Path
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@ -98,7 +159,6 @@ MASKER = SpannedMasker(TOKENANO.vocabulary_size,SPECIAL_TOKENS)
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prova = "<ABS>Cactus Flower is a 1969 American screwball comedy film directed by Gene Saks, and starring Walter Matthau, Ingrid Bergman and Goldie Hawn, who won an Academy Award for her performance.The screenplay was adapted by I. A. L. Diamond from the 1965 Broadway play of the same title written by Abe Burrows, which, in turn, is based on the French play Fleur de cactus by Pierre Barillet and Jean-Pierre Gredy. Cactus Flower was the ninth highest-grossing film of 1969."
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print(TOKENANO.encode(prova))
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batcher = Batcher(DATASET_PATH,8,TOKENANO,MASKER)
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for batch in batcher.get_batch():
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batcher = Batcher(DATASET_PATH,TOKENANO,MASKER)
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for batch in batcher.batch(8):
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print(batch)
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"""
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@ -25,6 +25,11 @@ class SpannedMasker:
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self.__forbidden_tokens = forbidden_tokens
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def reseed(self, seed:int):
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self.__rng = random.Random(seed)
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def mask_sequence(
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self,
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token_sequence: list[int],
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