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🧹 Data Quality Agent
Detects and fixes dataset issues:
- Missing values (multiple strategies)
- Outliers (IsolationForest + IQR)
- Data leakage detection
- Class imbalance detection
- Duplicate detection
Self-corrects by trying multiple strategies and validating improvements.
"""
import pandas as pd
import numpy as np
from typing import Dict, List, Any, Tuple, Optional
from dataclasses import dataclass
import logging
from .base import BaseAgent, AgentResult, AgentStatus, Phase, MessageType
logger = logging.getLogger(__name__)
@dataclass
class DataIssue:
"""Represents a detected data issue"""
type: str # missing, outlier, leakage, imbalance, duplicate
column: str
severity: str # low, medium, high, critical
details: Dict[str, Any]
class DataQualityAgent(BaseAgent):
"""
Autonomous Data Quality Agent
Detects issues → Applies strategies → Validates improvements → Reports
"""
name = "data_quality"
description = "Detects and fixes data quality issues"
def __init__(self, memory=None):
super().__init__(memory)
self.issues_found: List[DataIssue] = []
self.fixes_applied: List[str] = []
def execute(self, **kwargs) -> AgentResult:
"""Main execution: detect issues, apply fixes, validate"""
# Get dataset from memory
df = self.read_state("dataset")
target_col = self.read_state("target_column")
if df is None:
return AgentResult(
status=AgentStatus.FAILED,
agent_name=self.name,
phase=self.current_phase,
errors=["No dataset found in memory"]
)
df = df.copy()
original_shape = df.shape
self.logger.info(f"📊 Dataset: {df.shape[0]} rows, {df.shape[1]} columns")
# =====================================================================
# PHASE-AWARE PROCESSING
# =====================================================================
if self.is_fast_phase():
# Fast phase: Quick detection and basic fixes
df, issues = self._fast_quality_check(df, target_col)
else:
# Deep phase: Thorough analysis with multiple strategies
df, issues = self._deep_quality_check(df, target_col)
# Store results
self.write_state("dataset_cleaned", df, self.name)
self.write_state("data_issues", [i.__dict__ for i in issues], self.name)
# Build result
result = AgentResult(
status=AgentStatus.SUCCESS,
agent_name=self.name,
phase=self.current_phase,
data={
"original_shape": original_shape,
"cleaned_shape": df.shape,
"issues_found": len(issues),
"fixes_applied": self.fixes_applied
},
metrics={
"rows_removed": original_shape[0] - df.shape[0],
"columns_removed": original_shape[1] - df.shape[1],
"completeness": 1 - (df.isnull().sum().sum() / (df.shape[0] * df.shape[1]))
}
)
return result
# =========================================================================
# FAST PHASE - Quick cleaning
# =========================================================================
def _fast_quality_check(self, df: pd.DataFrame, target_col: str) -> Tuple[pd.DataFrame, List[DataIssue]]:
"""Fast quality check with basic fixes"""
issues = []
# 1. Remove duplicates
dup_count = df.duplicated().sum()
if dup_count > 0:
df = df.drop_duplicates()
issues.append(DataIssue("duplicate", "_all_", "medium", {"count": dup_count}))
self.fixes_applied.append(f"Removed {dup_count} duplicates")
self.logger.info(f" ✅ Removed {dup_count} duplicates")
# 2. Handle missing values
missing_cols = df.columns[df.isnull().any()].tolist()
for col in missing_cols:
missing_pct = df[col].isnull().mean()
if col == target_col:
# Drop rows with missing target
df = df.dropna(subset=[col])
issues.append(DataIssue("missing", col, "high", {"pct": missing_pct}))
self.fixes_applied.append(f"Dropped missing target rows")
elif missing_pct > 0.5:
# Drop column if >50% missing
df = df.drop(columns=[col])
issues.append(DataIssue("missing", col, "high", {"pct": missing_pct}))
self.fixes_applied.append(f"Dropped column {col} (>{missing_pct:.0%} missing)")
else:
# Fill with median/mode
if pd.api.types.is_numeric_dtype(df[col]):
df[col] = df[col].fillna(df[col].median())
else:
df[col] = df[col].fillna(df[col].mode().iloc[0] if len(df[col].mode()) > 0 else "_UNKNOWN_")
issues.append(DataIssue("missing", col, "low", {"pct": missing_pct}))
if missing_cols:
self.logger.info(f" ✅ Fixed missing values in {len(missing_cols)} columns")
# 3. Quick outlier cap (99th percentile)
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
if target_col in numeric_cols:
numeric_cols.remove(target_col)
for col in numeric_cols:
p99 = df[col].quantile(0.99)
p1 = df[col].quantile(0.01)
outliers = ((df[col] > p99) | (df[col] < p1)).sum()
if outliers > 0:
df[col] = df[col].clip(p1, p99)
issues.append(DataIssue("outlier", col, "low", {"count": outliers}))
self.logger.info(f" ✅ Capped outliers in {len(numeric_cols)} numeric columns")
return df, issues
# =========================================================================
# DEEP PHASE - Thorough analysis
# =========================================================================
def _deep_quality_check(self, df: pd.DataFrame, target_col: str) -> Tuple[pd.DataFrame, List[DataIssue]]:
"""Deep quality check with multiple strategies"""
issues = []
# 1. Run fast phase first
df, fast_issues = self._fast_quality_check(df, target_col)
issues.extend(fast_issues)
# 2. Detect data leakage
leakage_cols = self._detect_leakage(df, target_col)
for col in leakage_cols:
df = df.drop(columns=[col])
issues.append(DataIssue("leakage", col, "critical", {"correlation": 1.0}))
self.fixes_applied.append(f"Removed leaky column: {col}")
if leakage_cols:
self.logger.info(f" ⚠️ Removed {len(leakage_cols)} leaky columns")
# 3. Advanced outlier detection with IsolationForest
df, outlier_issues = self._detect_outliers_isolation(df, target_col)
issues.extend(outlier_issues)
# 4. Detect class imbalance
if target_col in df.columns:
imbalance = self._detect_imbalance(df[target_col])
if imbalance:
issues.append(imbalance)
self.logger.info(f" ⚠️ Class imbalance detected: {imbalance.details}")
return df, issues
def _detect_leakage(self, df: pd.DataFrame, target_col: str) -> List[str]:
"""Detect columns that might be leaking target information"""
leaky_cols = []
if target_col not in df.columns:
return leaky_cols
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
for col in numeric_cols:
if col == target_col:
continue
try:
corr = abs(df[col].corr(pd.to_numeric(df[target_col], errors='coerce')))
if corr > 0.95: # Very high correlation
leaky_cols.append(col)
except:
pass
return leaky_cols
def _detect_outliers_isolation(self, df: pd.DataFrame, target_col: str) -> Tuple[pd.DataFrame, List[DataIssue]]:
"""Detect outliers using IsolationForest"""
issues = []
try:
from sklearn.ensemble import IsolationForest
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
if target_col in numeric_cols:
numeric_cols.remove(target_col)
if len(numeric_cols) < 2 or len(df) < 100:
return df, issues
# Fit IsolationForest
iso = IsolationForest(contamination=0.05, random_state=42, n_jobs=-1)
numeric_data = df[numeric_cols].fillna(0).values
outlier_pred = iso.fit_predict(numeric_data)
outlier_count = (outlier_pred == -1).sum()
if outlier_count > 0:
# Don't remove, just flag
issues.append(DataIssue("outlier", "_multivariate_", "medium", {"count": outlier_count}))
self.logger.info(f" 📊 Detected {outlier_count} multivariate outliers")
except Exception as e:
self.logger.warning(f" ⚠️ IsolationForest failed: {str(e)[:50]}")
return df, issues
def _detect_imbalance(self, target: pd.Series) -> Optional[DataIssue]:
"""Detect class imbalance in target"""
try:
target_clean = target.dropna()
# For classification targets
if target_clean.dtype == object or target_clean.nunique() <= 20:
value_counts = target_clean.value_counts()
if len(value_counts) >= 2:
max_class = value_counts.max()
min_class = value_counts.min()
ratio = max_class / min_class if min_class > 0 else float('inf')
if ratio > 3: # Significant imbalance
return DataIssue(
"imbalance",
"_target_",
"high" if ratio > 10 else "medium",
{"ratio": ratio, "distribution": value_counts.to_dict()}
)
except:
pass
return None
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