AI-Powered Python v3.13 Data Science Workflow Automation

# AI generates comprehensive data science pipeline

# Input specification
pipeline_spec = {
    "data_source": "postgresql://localhost/sales_data",
    "target_variable": "revenue",
    "features": ["customer_age", "purchase_history", "geography"],
    "model_type": "regression",
    "validation": "time_series_split"
}

# AI-generated complete pipeline
import pandas as pd
import numpy as np
from sklearn.model_selection import TimeSeriesSplit
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import mean_absolute_error, r2_score
import mlflow
import logging

class AIDataSciencePipeline:
    def __init__(self, config):
        self.config = config
        self.model = None
        self.preprocessor = None
        self.logger = self._setup_logging()
        
    def _setup_logging(self):
        """AI generates proper logging setup"""
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        return logging.getLogger(__name__)
    
    def load_and_validate_data(self):
        """AI generates data loading with validation"""
        try:
            # AI suggests: Connection pooling and error handling
            self.data = pd.read_sql(
                "SELECT * FROM sales_data WHERE date >= '2020-01-01'",
                self.config["data_source"]
            )
            
            # AI generates data quality checks
            self._validate_data_quality()
            self.logger.info(f"Loaded {len(self.data)} records")
            
        except Exception as e:
            self.logger.error(f"Data loading failed: {e}")
            raise
    
    def _validate_data_quality(self):
        """AI generates comprehensive data validation"""
        # Check for missing values
        missing_pct = self.data.isnull().sum() / len(self.data)
        if missing_pct.max() > 0.3:
            raise ValueError("Too many missing values detected")
        
        # Check for data drift
        if self.data[self.config["target_variable"]].std() == 0:
            raise ValueError("Target variable has no variance")
    
    def feature_engineering(self):
        """AI generates intelligent feature engineering"""
        # AI suggests: Automated feature creation
        self.data['customer_lifetime_months'] = (
            pd.to_datetime('today') - pd.to_datetime(self.data['first_purchase'])
        ).dt.days / 30
        
        # AI suggests: Interaction features
        self.data['age_purchase_interaction'] = (
            self.data['customer_age'] * self.data['purchase_frequency']
        )
        
        # AI suggests: Categorical encoding
        categorical_features = self.data.select_dtypes(include=['object']).columns
        self.data = pd.get_dummies(
            self.data, 
            columns=categorical_features, 
            drop_first=True
        )
    
    def train_model(self):
        """AI generates optimized model training"""
        X = self.data[self.config["features"]]
        y = self.data[self.config["target_variable"]]
        
        # AI suggests: Time series aware splitting
        tscv = TimeSeriesSplit(n_splits=5)
        
        # AI generates pipeline with preprocessing
        self.model = Pipeline([
            ('scaler', StandardScaler()),
            ('regressor', RandomForestRegressor(
                n_estimators=100,
                random_state=42,
                n_jobs=-1  # AI suggests: Use all available cores
            ))
        ])
        
        # AI suggests: Cross-validation with proper metrics
        cv_scores = []
        for train_idx, val_idx in tscv.split(X):
            X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]
            y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]
            
            self.model.fit(X_train, y_train)
            predictions = self.model.predict(X_val)
            score = r2_score(y_val, predictions)
            cv_scores.append(score)
        
        self.logger.info(f"CV Score: {np.mean(cv_scores):.3f} ± {np.std(cv_scores):.3f}")
        
        # AI suggests: Final model training on full dataset
        self.model.fit(X, y)

# AI creates MLflow experiment tracking
import mlflow
import mlflow.sklearn
from mlflow.tracking import MlflowClient

class AIExperimentTracker:
    def __init__(self, experiment_name):
        # AI suggests: Proper MLflow setup
        mlflow.set_experiment(experiment_name)
        self.client = MlflowClient()
    
    def track_experiment(self, model, X_train, X_test, y_train, y_test, params):
        """AI generates comprehensive experiment tracking"""
        with mlflow.start_run():
            # AI suggests: Log all relevant parameters
            mlflow.log_params(params)
            
            # AI suggests: Track data characteristics
            mlflow.log_metric("train_samples", len(X_train))
            mlflow.log_metric("test_samples", len(X_test))
            mlflow.log_metric("feature_count", X_train.shape[1])
            
            # Train and evaluate
            model.fit(X_train, y_train)
            train_pred = model.predict(X_train)
            test_pred = model.predict(X_test)
            
            # AI generates comprehensive metrics
            metrics = {
                "train_mae": mean_absolute_error(y_train, train_pred),
                "test_mae": mean_absolute_error(y_test, test_pred),
                "train_r2": r2_score(y_train, train_pred),
                "test_r2": r2_score(y_test, test_pred),
                "overfitting": abs(r2_score(y_train, train_pred) - r2_score(y_test, test_pred))
            }
            
            # AI suggests: Log all metrics
            for metric, value in metrics.items():
                mlflow.log_metric(metric, value)
            
            # AI suggests: Model and artifact logging
            mlflow.sklearn.log_model(model, "model")
            
            # AI generates feature importance plot
            if hasattr(model, 'feature_importances_'):
                import matplotlib.pyplot as plt
                
                plt.figure(figsize=(10, 6))
                indices = np.argsort(model.feature_importances_)[::-1][:10]
                plt.bar(range(10), model.feature_importances_[indices])
                plt.title("Top 10 Feature Importances")
                plt.tight_layout()
                plt.savefig("feature_importance.png")
                mlflow.log_artifact("feature_importance.png")
                plt.close()
            
            return mlflow.active_run().info.run_id

# AI creates production deployment system
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import pandas as pd
from typing import List, Dict, Any
import uvicorn

# AI generates API models
class PredictionRequest(BaseModel):
    features: Dict[str, float]
    
class PredictionResponse(BaseModel):
    prediction: float
    confidence: float
    model_version: str

# AI creates production API
app = FastAPI(title="AI-Generated ML API", version="1.0.0")

class MLModelAPI:
    def __init__(self, model_path: str):
        # AI suggests: Model loading with error handling
        try:
            self.model = joblib.load(model_path)
            self.logger = logging.getLogger(__name__)
        except Exception as e:
            self.logger.error(f"Failed to load model: {e}")
            raise
    
    @app.post("/predict", response_model=PredictionResponse)
    async def predict(self, request: PredictionRequest):
        """AI generates production prediction endpoint"""
        try:
            # AI suggests: Input validation
            feature_df = pd.DataFrame([request.features])
            
            # AI suggests: Feature alignment check
            expected_features = self.model.feature_names_in_
            if not all(feat in request.features for feat in expected_features):
                raise HTTPException(status_code=400, detail="Missing required features")
            
            # Make prediction
            prediction = self.model.predict(feature_df)[0]
            
            # AI suggests: Confidence estimation
            if hasattr(self.model, 'predict_proba'):
                confidence = max(self.model.predict_proba(feature_df)[0])
            else:
                confidence = 0.95  # Default for regression
            
            return PredictionResponse(
                prediction=float(prediction),
                confidence=float(confidence),
                model_version="v1.0"
            )
            
        except Exception as e:
            self.logger.error(f"Prediction failed: {e}")
            raise HTTPException(status_code=500, detail="Prediction failed")
    
    @app.get("/health")
    async def health_check():
        """AI generates health check endpoint"""
        return {"status": "healthy", "model_loaded": self.model is not None}

# AI suggests: Production configuration
if __name__ == "__main__":
    uvicorn.run(
        "main:app",
        host="0.0.0.0",
        port=8000,
        workers=4,  # AI suggests: Multiple workers for scalability
        log_level="info"
    )