Existing Features:
Gap Areas:
Why: Critical for production deployment and model governance
Features to Add:
def save_model(
self,
model: Any,
model_name: str,
version: str,
metadata: Dict = None
) -> str:
"""
Save model with versioning and metadata.
Stores:
- Serialized model (pickle/joblib)
- Model metadata (training date, features, metrics)
- Feature names and encoders
- Version history
"""
def load_model(
self,
model_name: str,
version: str = "latest"
) -> Tuple[Any, Dict]:
"""
Load model by name and version.
Returns model and associated metadata.
"""
def list_model_versions(
self,
model_name: str
) -> List[Dict]:
"""
List all versions of a model with metadata.
"""
Database Schema:
CREATE TABLE def_ml_models (
model_id VARCHAR(36) PRIMARY KEY,
model_name VARCHAR(255) NOT NULL,
version VARCHAR(50) NOT NULL,
model_type VARCHAR(100),
model_binary LONGBLOB,
metadata_json JSON,
feature_names JSON,
training_metrics JSON,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
created_by VARCHAR(255),
is_active BOOLEAN DEFAULT FALSE,
UNIQUE KEY unique_name_version (model_name, version)
);
Benefits:
Why: Automate feature creation and improve model performance
Features to Add:
def create_binned_features(
self,
df: pd.DataFrame,
numeric_columns: List[str],
n_bins: int = 10,
strategy: str = "quantile"
) -> Tuple[pd.DataFrame, Dict]:
"""
Automatically bin numeric features.
Strategies: quantile, uniform, kmeans
Returns binned DataFrame and bin edges
"""
def create_interaction_features(
self,
df: pd.DataFrame,
feature_pairs: List[Tuple[str, str]]
) -> pd.DataFrame:
"""
Create interaction features (product, ratio, difference).
"""
def create_polynomial_features(
self,
df: pd.DataFrame,
columns: List[str],
degree: int = 2
) -> pd.DataFrame:
"""
Generate polynomial features.
"""
def create_aggregate_features(
self,
df: pd.DataFrame,
group_by: str,
agg_columns: List[str],
agg_funcs: List[str]
) -> pd.DataFrame:
"""
Create aggregate features (mean, std, count by group).
Example: Average credit amount by age group
"""
def calculate_psi(
self,
expected: pd.Series,
actual: pd.Series,
bins: int = 10
) -> float:
"""
Calculate Population Stability Index.
Critical for monitoring data drift.
"""
Benefits:
Why: Essential for production ML systems
Features to Add:
def monitor_model_performance(
self,
model_name: str,
predictions: np.ndarray,
actuals: np.ndarray,
prediction_date: datetime
) -> Dict:
"""
Track model performance over time.
Stores:
- Daily/weekly/monthly metrics
- Performance degradation alerts
- Drift indicators
"""
def detect_feature_drift(
self,
baseline_df: pd.DataFrame,
current_df: pd.DataFrame,
features: List[str],
threshold: float = 0.1
) -> Dict[str, float]:
"""
Detect feature drift using PSI or KS test.
Returns drift scores per feature.
"""
def detect_target_drift(
self,
baseline_target: pd.Series,
current_target: pd.Series
) -> Dict:
"""
Detect target variable drift.
"""
def generate_drift_report(
self,
model_name: str,
start_date: datetime,
end_date: datetime
) -> pd.DataFrame:
"""
Generate comprehensive drift analysis report.
"""
Database Schema:
CREATE TABLE def_ml_monitoring (
monitor_id VARCHAR(36) PRIMARY KEY,
model_name VARCHAR(255) NOT NULL,
prediction_date DATE NOT NULL,
metrics_json JSON,
drift_scores JSON,
alert_triggered BOOLEAN DEFAULT FALSE,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
INDEX idx_model_date (model_name, prediction_date)
);
Benefits:
Why: Better model assessment for credit risk
Features to Add:
def calculate_gini_coefficient(
self,
y_true: np.ndarray,
y_pred_proba: np.ndarray
) -> float:
"""
Calculate Gini coefficient (2*AUC - 1).
Standard metric in credit scoring.
"""
def calculate_ks_statistic(
self,
y_true: np.ndarray,
y_pred_proba: np.ndarray
) -> Tuple[float, int]:
"""
Calculate Kolmogorov-Smirnov statistic.
Returns KS value and optimal cutoff.
"""
def calculate_lift_chart(
self,
y_true: np.ndarray,
y_pred_proba: np.ndarray,
n_bins: int = 10
) -> pd.DataFrame:
"""
Generate lift chart data.
"""
def calculate_gains_chart(
self,
y_true: np.ndarray,
y_pred_proba: np.ndarray,
n_bins: int = 10
) -> pd.DataFrame:
"""
Generate cumulative gains chart.
"""
def calculate_business_metrics(
self,
y_true: np.ndarray,
y_pred: np.ndarray,
approval_rate: float,
bad_rate: float,
loan_amount_avg: float
) -> Dict:
"""
Calculate business impact metrics.
Returns:
- Expected profit
- Expected loss
- Risk-adjusted return
"""
def generate_model_report_card(
self,
model: Any,
X_test: pd.DataFrame,
y_test: pd.Series
) -> Dict:
"""
Comprehensive evaluation report.
Includes: AUC, Gini, KS, PSI, Lift, Confusion Matrix,
Classification Report, Feature Importance
"""
Benefits:
Why: Improve model performance automatically
Features to Add:
def optimize_hyperparameters_grid(
self,
X: pd.DataFrame,
y: pd.Series,
model_type: str,
param_grid: Dict,
cv: int = 5,
scoring: str = "roc_auc"
) -> Tuple[Any, Dict]:
"""
Grid search for hyperparameter optimization.
"""
def optimize_hyperparameters_random(
self,
X: pd.DataFrame,
y: pd.Series,
model_type: str,
param_distributions: Dict,
n_iter: int = 100,
cv: int = 5
) -> Tuple[Any, Dict]:
"""
Random search for hyperparameter optimization.
"""
def optimize_hyperparameters_bayesian(
self,
X: pd.DataFrame,
y: pd.Series,
model_type: str,
param_space: Dict,
n_calls: int = 50
) -> Tuple[Any, Dict]:
"""
Bayesian optimization using scikit-optimize.
More efficient than grid/random search.
"""
def auto_tune_model(
self,
X: pd.DataFrame,
y: pd.Series,
model_type: str,
optimization_metric: str = "gini",
time_budget_minutes: int = 60
) -> Tuple[Any, Dict]:
"""
Automated hyperparameter tuning with time budget.
"""
Benefits:
Why: Required for regulatory compliance and trust
Features to Add:
def calculate_shap_values(
self,
model: Any,
X: pd.DataFrame,
background_samples: int = 100
) -> np.ndarray:
"""
Calculate SHAP values for model predictions.
"""
def explain_prediction(
self,
model: Any,
instance: pd.Series,
feature_names: List[str]
) -> Dict:
"""
Explain individual prediction with SHAP.
Returns feature contributions.
"""
def generate_feature_importance_shap(
self,
model: Any,
X: pd.DataFrame
) -> pd.DataFrame:
"""
Calculate global feature importance using SHAP.
"""
def plot_shap_waterfall(
self,
shap_values: np.ndarray,
instance: pd.Series,
output_path: str
):
"""
Generate SHAP waterfall plot for single prediction.
"""
def generate_adverse_action_notice(
self,
prediction: int,
shap_values: np.ndarray,
feature_names: List[str],
top_n: int = 4
) -> Dict:
"""
Generate adverse action reasons per FCRA requirements.
Returns top N factors affecting decision.
"""
Benefits:
Why: Enable real-time credit decisions
Features to Add:
def score_application_realtime(
self,
model_name: str,
application_data: Dict,
return_explanation: bool = True
) -> Dict:
"""
Real-time scoring with <100ms latency.
Returns:
- Score
- Risk category
- Approval recommendation
- Explanation (if requested)
"""
def batch_score_applications(
self,
model_name: str,
applications_df: pd.DataFrame,
chunk_size: int = 1000
) -> pd.DataFrame:
"""
Batch scoring with progress tracking.
"""
def score_with_fallback(
self,
primary_model: str,
fallback_model: str,
application_data: Dict
) -> Dict:
"""
Score with automatic fallback to backup model.
"""
Benefits:
Why: Prevent garbage-in, garbage-out
Features to Add:
def validate_input_schema(
self,
df: pd.DataFrame,
schema: Dict
) -> Tuple[bool, List[str]]:
"""
Validate input data against expected schema.
Checks:
- Required columns present
- Data types correct
- Value ranges valid
- Missing value patterns
"""
def detect_data_quality_issues(
self,
df: pd.DataFrame
) -> Dict:
"""
Comprehensive data quality report.
Detects:
- Missing values
- Outliers (IQR, Z-score)
- Duplicate rows
- Invalid values
- Inconsistent formats
"""
def clean_data_automated(
self,
df: pd.DataFrame,
strategy: Dict
) -> pd.DataFrame:
"""
Automated data cleaning.
Strategies:
- Missing value imputation
- Outlier handling
- Type conversion
- Standardization
"""
Benefits:
Why: Often outperform single models
Features to Add:
def create_stacking_ensemble(
self,
base_models: List[Tuple[str, Any]],
meta_model: Any,
X_train: pd.DataFrame,
y_train: pd.Series
) -> Any:
"""
Create stacking ensemble.
Combines multiple models with meta-learner.
"""
def create_voting_ensemble(
self,
models: List[Tuple[str, Any]],
voting: str = "soft"
) -> Any:
"""
Create voting ensemble (soft or hard voting).
"""
def create_blending_ensemble(
self,
models: List[Any],
X_train: pd.DataFrame,
y_train: pd.Series,
holdout_fraction: float = 0.3
) -> Any:
"""
Create blending ensemble.
"""
Benefits:
Why: Convert scores to probabilities and optimize decisions
Features to Add:
def calibrate_probabilities(
self,
model: Any,
X_cal: pd.DataFrame,
y_cal: pd.Series,
method: str = "isotonic"
) -> Any:
"""
Calibrate model probabilities.
Methods: isotonic, platt (sigmoid)
"""
def optimize_threshold(
self,
y_true: np.ndarray,
y_pred_proba: np.ndarray,
objective: str = "f1",
constraints: Dict = None
) -> float:
"""
Find optimal classification threshold.
Objectives: f1, precision, recall, profit
Constraints: min_approval_rate, max_bad_rate
"""
def optimize_threshold_business(
self,
y_true: np.ndarray,
y_pred_proba: np.ndarray,
profit_good: float,
loss_bad: float
) -> Tuple[float, Dict]:
"""
Optimize threshold for profit maximization.
Returns threshold and expected profit.
"""
Benefits:
Why: Reduce manual work and errors
Features to Add:
def create_ml_pipeline(
self,
steps: List[Tuple[str, Any]],
pipeline_name: str
) -> Any:
"""
Create scikit-learn pipeline.
Steps: preprocessing, feature engineering, model
"""
def train_pipeline_auto(
self,
df: pd.DataFrame,
target_column: str,
categorical_features: List[str],
numeric_features: List[str],
model_type: str = "auto"
) -> Tuple[Any, Dict]:
"""
Automated end-to-end training.
Automatically:
- Handles missing values
- Encodes categoricals
- Scales numerics
- Selects model
- Tunes hyperparameters
"""
def schedule_model_retraining(
self,
model_name: str,
schedule: str,
trigger_conditions: Dict
):
"""
Schedule automated model retraining.
Triggers:
- Time-based (weekly, monthly)
- Performance-based (Gini < threshold)
- Drift-based (PSI > threshold)
"""
Benefits:
Why: Centralize feature management
Features to Add:
def register_feature_set(
self,
feature_set_name: str,
features: List[str],
computation_logic: str,
update_frequency: str
):
"""
Register feature set in feature store.
"""
def get_features_for_scoring(
self,
entity_id: str,
feature_sets: List[str],
point_in_time: datetime = None
) -> pd.DataFrame:
"""
Retrieve features for real-time scoring.
Handles point-in-time correctness.
"""
def materialize_features(
self,
feature_set_name: str,
start_date: datetime,
end_date: datetime
):
"""
Pre-compute and store features.
"""
Benefits:
Why: Required for regulatory compliance
Features to Add:
def log_model_decision(
self,
model_name: str,
application_id: str,
input_features: Dict,
prediction: int,
explanation: Dict
):
"""
Log model decision for audit trail.
"""
def generate_model_documentation(
self,
model_name: str,
version: str
) -> Dict:
"""
Generate SR 11-7 compliant documentation.
Includes:
- Model purpose
- Development process
- Validation results
- Limitations
- Monitoring plan
"""
def test_model_fairness(
self,
model: Any,
X: pd.DataFrame,
y: pd.Series,
protected_attributes: List[str]
) -> Dict:
"""
Test for bias in protected classes.
Tests:
- Disparate impact
- Equal opportunity
- Demographic parity
"""
def generate_validation_report(
self,
model: Any,
X_train: pd.DataFrame,
y_train: pd.Series,
X_test: pd.DataFrame,
y_test: pd.Series,
X_oot: pd.DataFrame,
y_oot: pd.Series
) -> Dict:
"""
Comprehensive validation report.
Includes in-sample, out-of-sample, and out-of-time testing.
"""
Benefits:
def calculate_gini_coefficient(self, y_true, y_pred_proba):
from sklearn.metrics import roc_auc_score
auc = roc_auc_score(y_true, y_pred_proba)
return 2 * auc - 1
def calculate_ks_statistic(self, y_true, y_pred_proba):
from scipy.stats import ks_2samp
pos_scores = y_pred_proba[y_true == 1]
neg_scores = y_pred_proba[y_true == 0]
ks_stat, p_value = ks_2samp(pos_scores, neg_scores)
return ks_stat
def save_model(self, model, filepath):
import joblib
joblib.dump(model, filepath)
def load_model(self, filepath):
import joblib
return joblib.load(filepath)
def calculate_psi(self, expected, actual, bins=10):
"""Population Stability Index"""
exp_bins = pd.cut(expected, bins=bins, duplicates='drop')
act_bins = pd.cut(actual, bins=bins, duplicates='drop')
exp_pct = exp_bins.value_counts(normalize=True)
act_pct = act_bins.value_counts(normalize=True)
psi = ((act_pct - exp_pct) * np.log(act_pct / exp_pct)).sum()
return psi
# requirements.txt additions
shap>=0.42.0 # Model explainability
scikit-optimize>=0.9.0 # Bayesian optimization
imbalanced-learn>=0.11.0 # Handling imbalanced data
category-encoders>=2.6.0 # Advanced encoding
feature-engine>=1.6.0 # Feature engineering
mlflow>=2.8.0 # Experiment tracking (optional)
great-expectations>=0.17.0 # Data validation (optional)
-- Model Registry
CREATE TABLE def_ml_models (
model_id VARCHAR(36) PRIMARY KEY,
model_name VARCHAR(255) NOT NULL,
version VARCHAR(50) NOT NULL,
model_type VARCHAR(100),
model_binary LONGBLOB,
encoder_binary LONGBLOB,
metadata_json JSON,
feature_names JSON,
training_metrics JSON,
validation_metrics JSON,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
created_by VARCHAR(255),
is_active BOOLEAN DEFAULT FALSE,
status VARCHAR(50) DEFAULT 'development',
UNIQUE KEY unique_name_version (model_name, version)
);
-- Model Monitoring
CREATE TABLE def_ml_monitoring (
monitor_id VARCHAR(36) PRIMARY KEY,
model_name VARCHAR(255) NOT NULL,
prediction_date DATE NOT NULL,
n_predictions INT,
metrics_json JSON,
drift_scores JSON,
alert_triggered BOOLEAN DEFAULT FALSE,
alert_details TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
INDEX idx_model_date (model_name, prediction_date)
);
-- Audit Log
CREATE TABLE def_ml_audit_log (
audit_id VARCHAR(36) PRIMARY KEY,
model_name VARCHAR(255) NOT NULL,
application_id VARCHAR(255),
prediction_timestamp TIMESTAMP,
input_features JSON,
prediction INT,
probability FLOAT,
explanation JSON,
user_id VARCHAR(255),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
INDEX idx_model_app (model_name, application_id)
);
For each new feature, add:
Track: