src/convert.py

from typing import Optional

import Levenshtein
import pandas as pd

from .data import read_aste_file, read_sem_eval_file
from .types import CharacterIndices, WordSpan


def get_original_text(
    aste_file: str,
    sem_eval_file: str,
    debug: bool = False,
) -> pd.DataFrame:
    approximate_matches = 0

    def best_match(text: str) -> str:
        comparison = text.replace(" ", "")
        if comparison in comparison_to_text:
            return comparison_to_text[comparison]

        nonlocal approximate_matches
        approximate_matches += 1
        distances = sem_eval_comparison.apply(
            lambda se_comparison: Levenshtein.distance(comparison, se_comparison)
        )
        best = sem_eval_df.iloc[distances.argmin()].text
        return best

    sem_eval_df = read_sem_eval_file(sem_eval_file)
    sem_eval_comparison = sem_eval_df.text.str.replace(" ", "")
    comparison_to_text = dict(zip(sem_eval_comparison, sem_eval_df.text))

    aste_df = read_aste_file(aste_file)
    aste_df = aste_df.rename(columns={"text": "preprocessed_text"})
    aste_df["text"] = aste_df.preprocessed_text.apply(best_match)
    if debug:
        print(f"Read {len(aste_df):,} rows")
        print(f"Had to use {approximate_matches:,} approximate matches")
    return aste_df[["text", "preprocessed_text", "triples"]]


def edit(original: str, preprocessed: str) -> list[Optional[int]]:
    indices: list[Optional[int]] = list(range(len(preprocessed)))
    for operation, _source_position, destination_position in Levenshtein.editops(
        preprocessed, original
    ):
        if operation == "replace":
            indices[destination_position] = None
        elif operation == "insert":
            indices.insert(destination_position, None)
        elif operation == "delete":
            del indices[destination_position]
    return indices


def has_unmapped(indicies: list[Optional[int]]) -> bool:
    return any(index is None for index in indicies)


def has_unmapped_non_space(row: pd.Series) -> bool:
    letter_and_index: list[tuple[str, Optional[int]]] = list(
        zip(row.text, row.text_indices)
    )
    return any(index is None for letter, index in letter_and_index if letter != " ")


def row_to_character_indices(row: pd.Series) -> pd.Series:
    try:
        return pd.Series(
            to_character_indices(
                triplet=row.triples,
                preprocessed=row.preprocessed_text,
                text=row.text,
                text_indices=row.text_indices,
            )
        )
    except:
        print(f"failed to process row {row.name}")
        print(row)
        raise


def to_character_indices(
    *,
    triplet: tuple[tuple[int], tuple[int], str],
    preprocessed: str,
    text: str,
    text_indices: list[Optional[int]],
) -> CharacterIndices:
    def find_start_index(span: WordSpan) -> int:
        # the starting letter in the lookup can be missing or None
        # this would cause a lookup failure
        # to recover from this we can find the following letter index and backtrack
        for index in range(span.start_index, span.end_index):
            try:
                text_index = text_indices.index(index)
                for _ in range(index - span.start_index):
                    if text_index - 1 <= 0:
                        break
                    if text_indices[text_index - 1] is not None:
                        break
                    text_index -= 1
                return text_index
            except ValueError:
                pass
                # not present in list
        raise ValueError(f"cannot find any part of {span}")

    def find_end_index(span: WordSpan) -> int:
        # the ending letter in the lookup can be missing or None
        # this would cause a lookup failure
        # to recover from this we can find the preceding letter index and backtrack
        for index in range(span.end_index - 1, span.start_index - 1, -1):
            try:
                text_index = text_indices.index(index)
                for _ in range(span.end_index - index):
                    if text_index + 1 >= len(text_indices):
                        break
                    if text_indices[text_index + 1] is not None:
                        break
                    text_index += 1
                return text_index
            except ValueError:
                pass
                # not present in list
        raise ValueError(f"cannot find any part of {span}")

    def to_indices(span: tuple[int]) -> tuple[int, int]:
        word_start = span[0]
        word_start_span = word_indices[word_start]

        word_end = span[-1]
        word_end_span = word_indices[word_end]

        start_index = find_start_index(word_start_span)
        end_index = find_end_index(word_end_span)
        return start_index, end_index

    aspect_span, opinion_span, sentiment = triplet
    assert is_sequential(aspect_span), f"aspect span not sequential: {aspect_span}"
    assert is_sequential(opinion_span), f"opinion span not sequential: {opinion_span}"
    assert sentiment in {"POS", "NEG", "NEU"}, f"unknown sentiment: {sentiment}"

    word_indices = WordSpan.to_spans(preprocessed)

    aspect_start_index, aspect_end_index = to_indices(aspect_span)
    aspect_term = text[aspect_start_index : aspect_end_index + 1]
    opinion_start_index, opinion_end_index = to_indices(opinion_span)
    opinion_term = text[opinion_start_index : opinion_end_index + 1]

    nice_sentiment = {
        "POS": "positive",
        "NEG": "negative",
        "NEU": "neutral",
    }[sentiment]

    return CharacterIndices(
        aspect_start_index=aspect_start_index,
        aspect_end_index=aspect_end_index,
        aspect_term=aspect_term,
        opinion_start_index=opinion_start_index,
        opinion_end_index=opinion_end_index,
        opinion_term=opinion_term,
        sentiment=nice_sentiment,
    )


def convert_sem_eval_text(
    aste_file: str,
    sem_eval_file: str,
    debug: bool = False,
) -> pd.DataFrame:
    df = get_original_text(
        aste_file=aste_file,
        sem_eval_file=sem_eval_file,
        debug=debug,
    )
    df = df.explode("triples")
    df = df.reset_index(drop=False)
    df["text_indices"] = df.apply(
        lambda row: edit(original=row.text, preprocessed=row.preprocessed_text),
        axis="columns",
    )
    df = df.merge(
        df.apply(row_to_character_indices, axis="columns"),
        left_index=True,
        right_index=True,
    )
    df = df.drop(columns=["preprocessed_text", "triples", "text_indices"])
    return df


def convert_aste_text(aste_file: str) -> pd.DataFrame:
    df = read_aste_file(aste_file)
    df = df.explode("triples")
    df = df.reset_index(drop=False)
    df = df.merge(
        df.apply(aste_row_to_character_indices, axis="columns"),
        left_index=True,
        right_index=True,
    )
    df = df.drop(columns=["triples"])
    return df


def aste_row_to_character_indices(row: pd.Series) -> pd.Series:
    try:
        return pd.Series(
            aste_to_character_indices(
                triplet=row.triples,
                text=row.text,
            )
        )
    except:
        print(f"failed to process row {row.name}")
        print(row)
        raise


def is_sequential(span: tuple[int]) -> bool:
    return all(span[index + 1] - span[index] == 1 for index in range(len(span) - 1))


def aste_to_character_indices(
    *,
    triplet: tuple[tuple[int], tuple[int], str],
    text: str,
) -> CharacterIndices:
    def to_indices(span: tuple[int]) -> tuple[int, int]:
        word_start = span[0]
        word_start_span = word_indices[word_start]

        word_end = span[-1]
        word_end_span = word_indices[word_end]

        return word_start_span.start_index, word_end_span.end_index - 1

    aspect_span, opinion_span, sentiment = triplet
    assert is_sequential(aspect_span), f"aspect span not sequential: {aspect_span}"
    assert is_sequential(opinion_span), f"opinion span not sequential: {opinion_span}"
    assert sentiment in {"POS", "NEG", "NEU"}, f"unknown sentiment: {sentiment}"

    word_indices = WordSpan.to_spans(text)

    aspect_start_index, aspect_end_index = to_indices(aspect_span)
    aspect_term = text[aspect_start_index : aspect_end_index + 1]
    opinion_start_index, opinion_end_index = to_indices(opinion_span)
    opinion_term = text[opinion_start_index : opinion_end_index + 1]

    nice_sentiment = {
        "POS": "positive",
        "NEG": "negative",
        "NEU": "neutral",
    }[sentiment]

    return CharacterIndices(
        aspect_start_index=aspect_start_index,
        aspect_end_index=aspect_end_index,
        aspect_term=aspect_term,
        opinion_start_index=opinion_start_index,
        opinion_end_index=opinion_end_index,
        opinion_term=opinion_term,
        sentiment=nice_sentiment,
    )


label_to_sentiment = {
    "POS": "positive",
    "NEG": "negative",
    "NEU": "neutral",
}


def to_nice_sentiment(label: str) -> str:
    return label_to_sentiment[sentiment]