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61da702 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | """Grading system for AutoClean-Ai data cleaning environment.
Implements deterministic reward calculation, quality scoring, and task grading.
All scores are normalized between 0.0 and 1.0.
"""
import pandas as pd
import numpy as np
from typing import Dict, Any, Tuple
import re
from models import CleaningActionType, DataCleaningAction
def calculate_dataset_quality_score(df: pd.DataFrame, task_id: str) -> float:
"""
Calculate overall dataset quality score from 0.0 (worst) to 1.0 (perfect).
Combines multiple quality metrics:
- Null value percentage
- Duplicate row percentage
- Outlier percentage
- Email validity (if applicable)
- Data type correctness
"""
if df.empty:
return 0.0
total_rows = len(df)
quality_components = []
# 1. Null value handling (0.0 = all nulls, 1.0 = no nulls)
null_percentage = df.isnull().sum().sum() / (df.shape[0] * df.shape[1])
null_score = 1.0 - null_percentage
quality_components.append(null_score)
# 2. Duplicate handling (0.0 = all duplicates, 1.0 = no duplicates)
duplicate_count = df.duplicated().sum()
duplicate_percentage = duplicate_count / total_rows if total_rows > 0 else 0.0
duplicate_score = 1.0 - duplicate_percentage
quality_components.append(duplicate_score)
# 3. Outlier detection for numeric columns
numeric_columns = df.select_dtypes(include=[np.number]).columns
outlier_scores = []
for col in numeric_columns:
if len(df[col].dropna()) < 4:
continue
Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
if IQR == 0:
continue
outlier_mask = (df[col] < (Q1 - 1.5 * IQR)) | (df[col] > (Q3 + 1.5 * IQR))
outlier_percentage = outlier_mask.sum() / len(df[col])
outlier_scores.append(1.0 - outlier_percentage)
if outlier_scores:
outlier_score = sum(outlier_scores) / len(outlier_scores)
quality_components.append(outlier_score)
# 4. Email validation if email column exists
if 'email' in df.columns:
email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
valid_emails = df['email'].astype(str).str.match(email_pattern, na=False).sum()
email_validity = valid_emails / len(df['email']) if len(df['email']) > 0 else 0.0
quality_components.append(email_validity)
# 5. Task specific quality checks
if task_id == "task_1_basic_cleaning":
# Basic task only requires null and duplicate handling
weights = [0.5, 0.5]
final_score = (null_score * weights[0] + duplicate_score * weights[1])
elif task_id == "task_2_intermediate_cleaning":
# Intermediate adds outliers and email validation
components = [null_score, duplicate_score]
if outlier_scores:
components.append(outlier_score)
if 'email' in df.columns:
components.append(email_validity)
final_score = sum(components) / len(components)
elif task_id == "task_3_full_pipeline":
# Advanced task uses all components
final_score = sum(quality_components) / len(quality_components) if quality_components else 0.0
else:
# Default average
final_score = sum(quality_components) / len(quality_components) if quality_components else 0.0
return max(0.0, min(1.0, final_score))
def calculate_reward(
df: pd.DataFrame,
initial_df: pd.DataFrame,
previous_quality: float,
current_quality: float,
action: DataCleaningAction,
task_id: str,
step_count: int
) -> Tuple[float, Dict[str, Any]]:
"""
Calculate reward for an action. Returns (reward, reward_info).
Reward components:
- Quality improvement delta
- Action validity bonus
- Efficiency bonus
- Progress bonus
- Penalties for invalid/ineffective actions
"""
reward_info = {
"quality_improvement": 0.0,
"action_validity": 0.0,
"efficiency_bonus": 0.0,
"progress_bonus": 0.0,
"penalty": 0.0,
"total": 0.0
}
# Base reward is quality improvement
quality_delta = current_quality - previous_quality
reward = quality_delta * 2.0 # Amplify delta for stronger signal
reward_info["quality_improvement"] = quality_delta
# Bonus for valid action execution
reward += 0.05
reward_info["action_validity"] = 0.05
# Bonus for progress
if quality_delta > 0:
progress_bonus = 0.03
reward += progress_bonus
reward_info["progress_bonus"] = progress_bonus
# Efficiency bonus (earlier steps give higher reward)
if step_count < 5 and quality_delta > 0:
efficiency_bonus = 0.02 * (5 - step_count)
reward += efficiency_bonus
reward_info["efficiency_bonus"] = efficiency_bonus
# Penalties
if quality_delta < -0.01:
# Negative quality change penalty
penalty = abs(quality_delta) * 0.5
reward -= penalty
reward_info["penalty"] += penalty
# Penalty for dropping too many rows
if len(df) < len(initial_df) * 0.5:
row_loss_penalty = 0.1
reward -= row_loss_penalty
reward_info["penalty"] += row_loss_penalty
# Clamp final reward between -0.2 and 0.5 per step
final_reward = max(-0.2, min(0.5, reward))
reward_info["total"] = final_reward
return final_reward, reward_info
def grade_task_result(
initial_df: pd.DataFrame,
final_df: pd.DataFrame,
task_id: str,
step_count: int
) -> float:
"""
Grade final task result. Returns score from 0.0 to 1.0.
Grading criteria per task:
- task_1_basic_cleaning: 40% null handling, 40% duplicate handling, 20% efficiency
- task_2_intermediate_cleaning: 25% null, 30% email, 25% outliers, 20% efficiency
- task_3_full_pipeline: Full weighted criteria
"""
if final_df.empty:
return 0.0
final_quality = calculate_dataset_quality_score(final_df, task_id)
initial_quality = calculate_dataset_quality_score(initial_df, task_id)
quality_improvement = final_quality - initial_quality
# Calculate efficiency score (better score for fewer steps)
max_steps = 15
efficiency_score = max(0.0, 1.0 - (step_count / max_steps))
# Task specific grading
if task_id == "task_1_basic_cleaning":
# Basic: 40% null, 40% duplicates, 20% efficiency
null_percentage = final_df.isnull().sum().sum() / (final_df.shape[0] * final_df.shape[1])
null_score = 1.0 - null_percentage
duplicate_percentage = final_df.duplicated().sum() / len(final_df)
duplicate_score = 1.0 - duplicate_percentage
score = (null_score * 0.4) + (duplicate_score * 0.4) + (efficiency_score * 0.2)
elif task_id == "task_2_intermediate_cleaning":
# Intermediate: 25% null, 30% email, 25% outliers, 20% efficiency
null_percentage = final_df.isnull().sum().sum() / (final_df.shape[0] * final_df.shape[1])
null_score = 1.0 - null_percentage
email_score = 0.5
if 'email' in final_df.columns:
email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
valid_emails = final_df['email'].astype(str).str.match(email_pattern, na=False).sum()
email_score = valid_emails / len(final_df['email'])
outlier_score = 0.5
numeric_columns = final_df.select_dtypes(include=[np.number]).columns
for col in numeric_columns:
if len(final_df[col].dropna()) >= 4:
Q1 = final_df[col].quantile(0.25)
Q3 = final_df[col].quantile(0.75)
IQR = Q3 - Q1
if IQR > 0:
outlier_mask = (final_df[col] < (Q1 - 1.5 * IQR)) | (final_df[col] > (Q3 + 1.5 * IQR))
outlier_percentage = outlier_mask.sum() / len(final_df[col])
outlier_score = 1.0 - outlier_percentage
break
score = (null_score * 0.25) + (email_score * 0.30) + (outlier_score * 0.25) + (efficiency_score * 0.20)
elif task_id == "task_3_full_pipeline":
# Advanced: quality * 0.8 + efficiency * 0.2
score = (final_quality * 0.8) + (efficiency_score * 0.2)
else:
# Default grading
score = final_quality
# Ensure score is in valid range
final_score = max(0.0, min(1.0, score))
return final_score |