Upload load_dataset.py

load_dataset.py

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+"""
+Simple script to load the Misery Index Dataset using pandas or datasets library.
+"""
+
+import pandas as pd
+from typing import Dict, List, Optional
+
+def load_misery_dataset(file_path: str = "Misery_Data.csv") -> pd.DataFrame:
+    """
+    Load the Misery Index Dataset from CSV file.
+    
+    Args:
+        file_path: Path to the CSV file (default: "Misery_Data.csv")
+        
+    Returns:
+        pandas.DataFrame with cleaned column names and proper data types
+    """
+    df = pd.read_csv(file_path)
+    
+    # Rename columns to be more user-friendly
+    column_mapping = {
+        "Ep #": "episode",
+        "Misery": "scenario", 
+        "Score": "misery_score",
+        "VNTO": "vnto",
+        "Reward": "reward",
+        "Win": "win",
+        "Comments": "comments",
+        "question_tag": "question_tag",
+        "level": "level"
+    }
+    
+    df = df.rename(columns=column_mapping)
+    
+    # Convert misery_score to numeric
+    df["misery_score"] = pd.to_numeric(df["misery_score"], errors="coerce")
+    
+    # Convert reward to numeric, handling empty values
+    df["reward"] = pd.to_numeric(df["reward"], errors="coerce").fillna(0).astype(int)
+    
+    # Clean up string columns
+    string_columns = ["episode", "scenario", "vnto", "win", "comments", "question_tag", "level"]
+    for col in string_columns:
+        if col in df.columns:
+            df[col] = df[col].astype(str).str.strip()
+            df[col] = df[col].replace("nan", "")
+    
+    return df
+
+def get_dataset_statistics(df: pd.DataFrame) -> Dict:
+    """
+    Get basic statistics about the dataset.
+    
+    Args:
+        df: DataFrame containing the dataset
+        
+    Returns:
+        Dictionary with dataset statistics
+    """
+    stats = {
+        "total_samples": len(df),
+        "mean_misery": df["misery_score"].mean(),
+        "std_misery": df["misery_score"].std(),
+        "min_misery": df["misery_score"].min(),
+        "max_misery": df["misery_score"].max(),
+        "percentiles": {
+            "25th": df["misery_score"].quantile(0.25),
+            "50th": df["misery_score"].quantile(0.50),
+            "75th": df["misery_score"].quantile(0.75),
+        },
+        "vnto_types": df["vnto"].value_counts().to_dict() if "vnto" in df.columns else {},
+        "episodes": df["episode"].nunique(),
+        "question_tags": df["question_tag"].value_counts().to_dict() if "question_tag" in df.columns else {},
+    }
+    return stats
+
+def filter_by_vnto(df: pd.DataFrame, vnto_type: str) -> pd.DataFrame:
+    """
+    Filter dataset by VNTO type.
+    
+    Args:
+        df: DataFrame containing the dataset
+        vnto_type: VNTO type to filter by (T, V, N, O, P)
+        
+    Returns:
+        Filtered DataFrame
+    """
+    return df[df["vnto"] == vnto_type].copy()
+
+def filter_by_misery_range(df: pd.DataFrame, min_score: float = 0, max_score: float = 100) -> pd.DataFrame:
+    """
+    Filter dataset by misery score range.
+    
+    Args:
+        df: DataFrame containing the dataset
+        min_score: Minimum misery score (inclusive)
+        max_score: Maximum misery score (inclusive)
+        
+    Returns:
+        Filtered DataFrame
+    """
+    return df[(df["misery_score"] >= min_score) & (df["misery_score"] <= max_score)].copy()
+
+def get_sample_scenarios(df: pd.DataFrame, vnto_type: Optional[str] = None, n: int = 5) -> List[Dict]:
+    """
+    Get sample scenarios from the dataset.
+    
+    Args:
+        df: DataFrame containing the dataset
+        vnto_type: Optional VNTO type to filter by
+        n: Number of samples to return
+        
+    Returns:
+        List of dictionaries with scenario information
+    """
+    if vnto_type:
+        df_filtered = filter_by_vnto(df, vnto_type)
+    else:
+        df_filtered = df
+    
+    samples = df_filtered.sample(n=min(n, len(df_filtered)))
+    
+    return [
+        {
+            "scenario": row["scenario"],
+            "misery_score": row["misery_score"],
+            "vnto": row["vnto"],
+            "episode": row["episode"]
+        }
+        for _, row in samples.iterrows()
+    ]
+
+def main():
+    """Example usage of the dataset loading functions."""
+    
+    # Load the dataset
+    print("Loading Misery Index Dataset...")
+    df = load_misery_dataset()
+    
+    # Get basic statistics
+    stats = get_dataset_statistics(df)
+    print(f"\nDataset Statistics:")
+    print(f"Total samples: {stats['total_samples']}")
+    print(f"Mean misery score: {stats['mean_misery']:.2f}")
+    print(f"Standard deviation: {stats['std_misery']:.2f}")
+    print(f"Score range: {stats['min_misery']}-{stats['max_misery']}")
+    print(f"Number of episodes: {stats['episodes']}")
+    
+    print(f"\nPercentiles:")
+    for p, value in stats['percentiles'].items():
+        print(f"  {p}: {value:.2f}")
+    
+    print(f"\nVNTO Types distribution:")
+    for vnto_type, count in stats['vnto_types'].items():
+        percentage = (count / stats['total_samples']) * 100
+        print(f"  {vnto_type}: {count} ({percentage:.1f}%)")
+    
+    print(f"\nTop Question Tags:")
+    for tag, count in list(stats['question_tags'].items())[:5]:
+        percentage = (count / stats['total_samples']) * 100
+        print(f"  {tag}: {count} ({percentage:.1f}%)")
+    
+    # Show some examples
+    print(f"\nSample low misery scenarios (< 30):")
+    low_misery = filter_by_misery_range(df, 0, 30)
+    samples = get_sample_scenarios(low_misery, n=3)
+    for sample in samples:
+        print(f"  Score {sample['misery_score']}: {sample['scenario']}")
+    
+    print(f"\nSample high misery scenarios (> 80):")
+    high_misery = filter_by_misery_range(df, 80, 100)
+    samples = get_sample_scenarios(high_misery, n=3)
+    for sample in samples:
+        print(f"  Score {sample['misery_score']}: {sample['scenario']}")
+    
+    print(f"\nSample Video scenarios (VNTO=V):")
+    video_scenarios = filter_by_vnto(df, "V")
+    samples = get_sample_scenarios(video_scenarios, n=3)
+    for sample in samples:
+        print(f"  Score {sample['misery_score']}: {sample['scenario']}")
+
+if __name__ == "__main__":
+    main()