perplexity_lenses/data.py

from functools import partial

import numpy as np
import pandas as pd
from datasets import load_dataset
from tqdm import tqdm

from perplexity_lenses.perplexity import KenlmModel


def hub_dataset_to_dataframe(
    path: str, name: str, split: str, sample: int, text_column: str, model: KenlmModel, seed: int = 0, doc_type: str = "Whole document"
) -> pd.DataFrame:
    load_dataset_fn = partial(load_dataset, path=path)
    if name:
        load_dataset_fn = partial(load_dataset_fn, name=name)
    if split:
        load_dataset_fn = partial(load_dataset_fn, split=split)
    dataset = load_dataset_fn(streaming=True).shuffle(buffer_size=10000, seed=seed)
    if doc_type == "Sentence":
        dataset = dataset.map(lambda x: [{text_column: sentence, "perplexity": model.get_perplexity(sentence)} for sentence in x[text_column].split("\n")])
    else:
        dataset = dataset.map(lambda x: {text_column: x[text_column], "perplexity": model.get_perplexity(x[text_column])})
    instances = []
    count = 0
    for instance in tqdm(dataset, total=sample):
        if isinstance(instance, list):
            for sentence in instance:
                instances.append(sentence)
                count += 1
                if count == sample:
                    break
        else:
            instances.append(instance)
            count += 1
        if count == sample:
            break
    return pd.DataFrame(instances)


def documents_df_to_sentences_df(df: pd.DataFrame, text_column: str, sample: int, seed: int = 0):
    df_sentences = pd.DataFrame({text_column: np.array(df[text_column].map(lambda x: x.split("\n")).values.tolist()).flatten()})
    return df_sentences.sample(min(sample, df.shape[0]), random_state=seed)