Datasets:

matthewfranglen
/

aste-v2

import ast
from pathlib import Path

import pandas as pd
from lxml import etree


def read_sem_eval_file(file: str | Path) -> pd.DataFrame:
    root = etree.parse(file)
    documents = root.xpath("//text/text()")
    assert isinstance(documents, list), f"cannot parse text from {file}"
    df = pd.DataFrame({"text": documents})
    return df


def read_aste_file(file: str | Path) -> pd.DataFrame:
    df = pd.read_csv(
        file,
        sep="####",
        header=None,
        names=["text", "triples"],
        engine="python",
    )

    # There are duplicate rows, some of which have the same triples and some don't
    # This deals with that by
    # * first dropping the pure duplicates,
    # * then parsing the triples and exploding them to one per row
    # * then dropping the exploded duplicates (have to convert triples back to string for this)
    # * then grouping the triples up again
    # * finally sorting the distinct triples

    df = df.drop_duplicates()
    df["triples"] = df.triples.apply(ast.literal_eval)
    df = df.explode("triples")
    df["triples"] = df.triples.apply(_triple_to_hashable)
    df = df.drop_duplicates()
    df = df.groupby("text").agg(list)
    df = df.reset_index(drop=False)
    df["triples"] = df.triples.apply(set).apply(sorted)

    return df


def _triple_to_hashable(
    triple: tuple[list[int], list[int], str]
) -> tuple[tuple[int, ...], tuple[int, ...], str]:
    aspect_span, opinion_span, sentiment = triple
    return tuple(aspect_span), tuple(opinion_span), sentiment

	import ast
	from pathlib import Path

	import pandas as pd
	from lxml import etree


	def read_sem_eval_file(file: str \| Path) -> pd.DataFrame:
	root = etree.parse(file)
	documents = root.xpath("//text/text()")
	assert isinstance(documents, list), f"cannot parse text from {file}"
	df = pd.DataFrame({"text": documents})
	return df


	def read_aste_file(file: str \| Path) -> pd.DataFrame:
	df = pd.read_csv(
	file,
	sep="####",
	header=None,
	names=["text", "triples"],
	engine="python",
	)

	# There are duplicate rows, some of which have the same triples and some don't
	# This deals with that by
	# * first dropping the pure duplicates,
	# * then parsing the triples and exploding them to one per row
	# * then dropping the exploded duplicates (have to convert triples back to string for this)
	# * then grouping the triples up again
	# * finally sorting the distinct triples

	df = df.drop_duplicates()
	df["triples"] = df.triples.apply(ast.literal_eval)
	df = df.explode("triples")
	df["triples"] = df.triples.apply(_triple_to_hashable)
	df = df.drop_duplicates()
	df = df.groupby("text").agg(list)
	df = df.reset_index(drop=False)
	df["triples"] = df.triples.apply(set).apply(sorted)

	return df


	def _triple_to_hashable(
	triple: tuple[list[int], list[int], str]
	) -> tuple[tuple[int, ...], tuple[int, ...], str]:
	aspect_span, opinion_span, sentiment = triple
	return tuple(aspect_span), tuple(opinion_span), sentiment