92 lines
4.1 KiB
Python
92 lines
4.1 KiB
Python
import pandas as pd
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from pathlib import Path
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import Project_Model.Libs.BPE as BPE
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#from BPE import TokeNanoCore as Tokenizer
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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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import random
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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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# 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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# self._DATASET = pd.read_csv(dataset_path)
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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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def get_batch(self):
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for batch in pd.read_csv(self._dataset_path, chunksize= int(self._batch_size/3)): #now we support 3 task
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# each batch get 4 transformation for the 4 tasks and then shuffled
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# now a batch is ["Abstract"], ["Triples"]
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# tokenize the strings:
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# batch = batch.drop(columns=['MovieID'])
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tokenized_batch = pd.DataFrame()
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# bho = batch.map(lambda x: self._tokenizer.encode(x))
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tokenized_batch[["Abstract","RDFs"]] = batch[["Abstract","RDFs"]].map(
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lambda t: self._tokenizer.encode(t))
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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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output = pd.concat([rdf2txt_batch,txt2rdf_batch,mask_batch],ignore_index=True)
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output.sample(frac=1).reset_index(drop=True)
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yield output
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def __random_subset_rdfs(self, batch: pd.DataFrame, seed = 0):
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rng = random.Random(seed)
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def to_list(x):
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return x.split(SpecialToken.START_TRIPLE.value)[1:]
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batch["RDFs"] = batch["RDFs"].map(
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to_list
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)
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def __rdf2txt_transformation(self, batch: pd.DataFrame):
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# rename ["Triples"] as ["X"]
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# rename ["Abstract"] as ["Y"]
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# return just them
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batch = batch.rename(columns={"RDFs": "X", "Abstract": "Y"})
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return batch[["X", "Y"]] #.sample(frac=1).reset_index(drop=True)
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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"]]# .sample(frac=1).reset_index(drop=True)
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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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DATASET_PATH = "Assets/Dataset/Tmp/rdf_text.csv"
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VOCABULARY_path = "Assets/Dataset/Tmp/trimmed.json"
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VOCABULARY = BPE.load_nanos_vocabulary(Path(VOCABULARY_path))
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SPECIAL_LIST = BPE.default_special_tokens()
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TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_LIST)
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SPECIAL_TOKENS: set[int] = set(TOKENANO.encode("".join(SPECIAL_LIST)))
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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,9,TOKENANO,MASKER)
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for batch in batcher.get_batch():
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print(batch) |