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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()
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