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| """ | |
| liar_loader.py - LIAR Dataset Loader and Parser for TruthLens AI. | |
| This module handles loading, parsing, and analyzing the LIAR benchmark dataset | |
| (Wang, ACL 2017) across train, valid, and test splits. It robustly parses the | |
| 14 tab-delimited columns and computes statistics on statements, speakers, | |
| metadata, and 6-class truth labels. | |
| """ | |
| import csv | |
| import os | |
| from typing import Dict, List, Any, Optional, Tuple | |
| import pandas as pd | |
| import numpy as np | |
| LIAR_COLUMNS = [ | |
| "id", | |
| "label", | |
| "statement", | |
| "subject", | |
| "speaker", | |
| "speaker_job_title", | |
| "state_info", | |
| "party_affiliation", | |
| "barely_true_counts", | |
| "false_counts", | |
| "half_true_counts", | |
| "mostly_true_counts", | |
| "pants_on_fire_counts", | |
| "context", | |
| ] | |
| NUMERIC_COUNT_COLS = [ | |
| "barely_true_counts", | |
| "false_counts", | |
| "half_true_counts", | |
| "mostly_true_counts", | |
| "pants_on_fire_counts", | |
| ] | |
| # Standard 6-point scale defined by PolitiFact and LIAR | |
| LIAR_6_LABELS = [ | |
| "pants-fire", | |
| "false", | |
| "barely-true", | |
| "half-true", | |
| "mostly-true", | |
| "true", | |
| ] | |
| def load_liar_split(filepath: str) -> Tuple[pd.DataFrame, int]: | |
| """ | |
| Loads a single LIAR TSV split file. | |
| Uses csv.QUOTE_NONE to ensure unescaped quotes inside speech statements | |
| do not cause row misalignment or multi-line consumption. | |
| Args: | |
| filepath: Path to train.tsv, valid.tsv, or test.tsv. | |
| Returns: | |
| Tuple of (DataFrame of parsed records, count of malformed lines). | |
| """ | |
| if not os.path.exists(filepath): | |
| raise FileNotFoundError(f"LIAR file not found: {filepath}") | |
| malformed_count = 0 | |
| valid_rows = [] | |
| with open(filepath, "r", encoding="utf-8", errors="replace") as f: | |
| reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
| for line_idx, row in enumerate(reader, start=1): | |
| if len(row) == 14: | |
| valid_rows.append(row) | |
| elif len(row) > 14: | |
| # Tab within context or statement; merge trailing elements into context | |
| merged_row = row[:13] + ["\t".join(row[13:])] | |
| valid_rows.append(merged_row) | |
| malformed_count += 1 | |
| else: | |
| malformed_count += 1 | |
| df = pd.DataFrame(valid_rows, columns=LIAR_COLUMNS) | |
| # Cast count columns to numeric | |
| for col in NUMERIC_COUNT_COLS: | |
| df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0).astype(int) | |
| return df, malformed_count | |
| def load_all_liar_splits(data_dir: str) -> Dict[str, pd.DataFrame]: | |
| """ | |
| Loads all available LIAR splits from a directory (train, valid, test). | |
| """ | |
| splits = {} | |
| for split_name, filename in [("train", "train.tsv"), ("valid", "valid.tsv"), ("test", "test.tsv")]: | |
| full_path = os.path.join(data_dir, filename) | |
| if os.path.exists(full_path): | |
| df, _ = load_liar_split(full_path) | |
| splits[split_name] = df | |
| return splits | |
| def get_liar_statistics(df: pd.DataFrame, split_name: str = "dataset") -> Dict[str, Any]: | |
| """ | |
| Calculates detailed statistics for a LIAR DataFrame. | |
| """ | |
| total_records = len(df) | |
| if total_records == 0: | |
| return {"total_records": 0} | |
| statement_words = df["statement"].astype(str).apply(lambda s: len(s.split())) | |
| statement_chars = df["statement"].astype(str).apply(len) | |
| # Missing / empty string counts | |
| missing_counts = {} | |
| for col in df.columns: | |
| null_cnt = int(df[col].isnull().sum()) | |
| empty_cnt = int((df[col].astype(str).str.strip() == "").sum()) | |
| missing_counts[col] = null_cnt + empty_cnt | |
| # Duplicates | |
| unique_stmts = df["statement"].nunique() | |
| duplicate_stmts = total_records - unique_stmts | |
| # Label counts | |
| label_counts = df["label"].value_counts().to_dict() | |
| # Speaker & party metrics | |
| top_speakers = df["speaker"].value_counts().head(10).to_dict() | |
| top_parties = df["party_affiliation"].value_counts().head(5).to_dict() | |
| top_subjects = df["subject"].value_counts().head(10).to_dict() | |
| return { | |
| "split": split_name, | |
| "total_records": total_records, | |
| "columns": list(df.columns), | |
| "label_counts": label_counts, | |
| "missing_or_empty_values": missing_counts, | |
| "unique_statements": unique_stmts, | |
| "duplicate_statements": duplicate_stmts, | |
| "statement_word_length": { | |
| "min": int(statement_words.min()), | |
| "max": int(statement_words.max()), | |
| "mean": float(round(statement_words.mean(), 2)), | |
| "median": float(round(statement_words.median(), 2)), | |
| "std": float(round(statement_words.std(), 2)), | |
| }, | |
| "statement_char_length": { | |
| "min": int(statement_chars.min()), | |
| "max": int(statement_chars.max()), | |
| "mean": float(round(statement_chars.mean(), 2)), | |
| "median": float(round(statement_chars.median(), 2)), | |
| }, | |
| "top_speakers": top_speakers, | |
| "top_parties": top_parties, | |
| "top_subjects": top_subjects, | |
| } | |