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4ce2b3e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | from __future__ import annotations
from pathlib import Path
import pandas as pd
from .config import LABEL2ID
from .text import compact_for_key, normalize_label, normalize_text
BAD_TEXT_VALUES = {"", "x", "-", ".", "n/a", "na", "none", "null"}
def load_binary_dataset(path: str | Path) -> pd.DataFrame:
df = pd.read_csv(path)
required = {"text", "label", "label_name"}
missing = required - set(df.columns)
if missing:
raise ValueError(f"Dataset is missing required columns: {sorted(missing)}")
df = df.copy()
df["text"] = df["text"].map(normalize_text)
df["label"] = df["label"].astype(int)
df["label_name"] = df["label_name"].map(normalize_label)
df = df[df["label_name"].isin(LABEL2ID)]
df = df[df["text"].str.len() > 0]
return df.reset_index(drop=True)
def prepare_binary_dataset(
input_path: str | Path,
output_path: str | Path,
*,
sheet_name: str = "Data",
dedupe: bool = True,
) -> tuple[pd.DataFrame, dict]:
input_path = Path(input_path)
output_path = Path(output_path)
raw = pd.read_excel(input_path, sheet_name=sheet_name)
raw.columns = [normalize_text(c) for c in raw.columns]
rows: list[dict] = []
seen: set[str] = set()
summary = {
"input_file": input_path.name,
"output_file": str(output_path).replace("\\", "/"),
"original_rows": int(len(raw)),
"dropped_netral": 0,
"dropped_other_label": 0,
"dropped_bad_text": 0,
"dropped_duplicates": 0,
}
for _, row in raw.iterrows():
label_name = normalize_label(row.get("sentimen"))
if label_name == "Netral":
summary["dropped_netral"] += 1
continue
if label_name not in LABEL2ID:
summary["dropped_other_label"] += 1
continue
source_column = "perbaikan"
text = normalize_text(row.get("perbaikan"))
if not text:
source_column = "textTranslated"
text = normalize_text(row.get("textTranslated"))
if not text:
source_column = "text"
text = normalize_text(row.get("text"))
if text.lower() in BAD_TEXT_VALUES:
summary["dropped_bad_text"] += 1
continue
key = compact_for_key(text)
if dedupe and key in seen:
summary["dropped_duplicates"] += 1
continue
seen.add(key)
rows.append(
{
"text": text,
"label": LABEL2ID[label_name],
"label_name": label_name,
"kategori": normalize_text(row.get("kategori")),
"stars": normalize_text(row.get("stars")),
"source_column": source_column,
}
)
df = pd.DataFrame(rows)
output_path.parent.mkdir(parents=True, exist_ok=True)
df.to_csv(output_path, index=False)
summary["kept_rows"] = int(len(df))
summary["labels"] = df["label_name"].value_counts().to_dict()
return df, summary
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