Split up some of the code

src/convert.py

@@ -1,51 +1,10 @@
-import ast
-import re
-from dataclasses import dataclass
-from typing import Optional, TypedDict
+from typing import Optional
 
 import Levenshtein
 import pandas as pd
 
-
-def read_sem_eval_file(file: str) -> pd.DataFrame:
-    df = pd.read_xml(file)[["text"]]
-    return df
-
-
-def read_aste_file(file: str) -> pd.DataFrame:
-    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
-
-    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.copy()
-    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
+from .data import read_aste_file, read_sem_eval_file
+from .types import CharacterIndices, WordSpan
 
 
 def get_original_text(
@@ -106,25 +65,6 @@ def has_unmapped_non_space(row: pd.Series) -> bool:
     return any(index is None for letter, index in letter_and_index if letter != " ")
 
 
-@dataclass(frozen=True)
-class WordSpan:
-    start_index: int
-    end_index: int  # this is the letter after the end
-
-
-class CharacterIndices(TypedDict):
-    aspect_start_index: int
-    aspect_end_index: int
-    aspect_term: str
-    opinion_start_index: int
-    opinion_end_index: int
-    opinion_term: str
-    sentiment: str
-
-
-word_pattern = re.compile(r"\S+")
-
-
 def row_to_character_indices(row: pd.Series) -> pd.Series:
     try:
         return pd.Series(
@@ -202,10 +142,7 @@ def to_character_indices(
     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(start_index=match.start(), end_index=match.end())
-        for match in word_pattern.finditer(preprocessed)
-    ]
+    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]
@@ -218,15 +155,15 @@ def to_character_indices(
         "NEU": "neutral",
     }[sentiment]
 
-    return {
-        "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,
-    }
+    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(
@@ -304,10 +241,7 @@ def aste_to_character_indices(
     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(start_index=match.start(), end_index=match.end())
-        for match in word_pattern.finditer(text)
-    ]
+    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]
@@ -320,12 +254,12 @@ def aste_to_character_indices(
         "NEU": "neutral",
     }[sentiment]
 
-    return {
-        "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,
-    }
+    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,
+    )

src/data.py

@@ -0,0 +1,45 @@
+import ast
+from pathlib import Path
+
+import pandas as pd
+
+
+def read_sem_eval_file(file: str | Path) -> pd.DataFrame:
+    df = pd.read_xml(file)[["text"]]
+    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

src/types.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+
+word_pattern = re.compile(r"\S+")
+
+
+@dataclass(frozen=True)
+class WordSpan:
+    start_index: int
+    end_index: int  # this is the letter after the end
+
+    @staticmethod
+    def to_spans(text: str) -> list[WordSpan]:
+        return [
+            WordSpan(start_index=match.start(), end_index=match.end())
+            for match in word_pattern.finditer(text)
+        ]
+
+
+@dataclass(frozen=True)
+class CharacterIndices:
+    aspect_start_index: int
+    aspect_end_index: int
+    aspect_term: str
+    opinion_start_index: int
+    opinion_end_index: int
+    opinion_term: str
+    sentiment: str