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541fa1f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | from __future__ import annotations
from pathlib import Path
from typing import Sequence
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 | Sequence[str | Path],
output_path: str | Path,
*,
sheet_name: str = "Data",
dedupe: bool = True,
balance: bool = False,
random_state: int = 42,
) -> tuple[pd.DataFrame, dict]:
if isinstance(input_path, (str, Path)):
input_paths = [Path(input_path)]
else:
input_paths = [Path(path) for path in input_path]
output_path = Path(output_path)
rows: list[dict] = []
seen: set[str] = set()
summary = {
"input_files": [path.name for path in input_paths],
"output_file": str(output_path).replace("\\", "/"),
"original_rows": 0,
"dropped_netral": 0,
"dropped_other_label": 0,
"dropped_bad_text": 0,
"dropped_duplicates": 0,
"balanced": bool(balance),
"random_state": int(random_state),
}
for path in input_paths:
raw = pd.read_excel(path, sheet_name=sheet_name)
raw.columns = [normalize_text(c) for c in raw.columns]
summary["original_rows"] += int(len(raw))
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,
"source_file": path.name,
}
)
columns = ["text", "label", "label_name", "kategori", "stars", "source_column", "source_file"]
df = pd.DataFrame(rows, columns=columns)
summary["kept_rows_before_balance"] = int(len(df))
summary["labels_before_balance"] = df["label_name"].value_counts().to_dict()
if balance and not df.empty:
label_counts = df["label_name"].value_counts()
if len(label_counts) < len(LABEL2ID):
raise ValueError(f"Cannot balance dataset with labels: {label_counts.to_dict()}")
target = int(label_counts.min())
balanced_frames = [
group.sample(n=target, random_state=random_state)
for _, group in df.groupby("label_name", sort=False)
]
df = pd.concat(balanced_frames, ignore_index=True).sample(frac=1, random_state=random_state).reset_index(drop=True)
summary["balance_target_per_label"] = target
summary["dropped_by_balance"] = int(summary["kept_rows_before_balance"] - len(df))
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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