"""
Machine Learning Service for Transcriptomics Data.

This service provides template implementations for common ML tasks with transcriptomics data,
including cell type classification, disease prediction, and drug response modeling.
These are template tools that demonstrate ML workflows but are not yet integrated as agent tools.
"""

import logging
from typing import Any, Dict, List, Optional

import numpy as np

logger = logging.getLogger(__name__)


class MLTranscriptomicsError(Exception):
    """Base exception for ML transcriptomics operations."""

    pass


class MLTranscriptomicsService:
    """
    Service for machine learning tasks with transcriptomics data.

    This service provides template implementations for:
    1. Cell type classification (single-cell)
    2. Disease state prediction (bulk RNA-seq)
    3. Drug response prediction
    4. Gene signature discovery
    5. Feature importance analysis
    """

    name = "ml_transcriptomics"
    version = "0.1.0-alpha"

    def __init__(self):
        """Initialize the ML transcriptomics service."""
        self.models = {}
        self.feature_importance = {}
        self.training_history = []

    def classify_cell_types_deep_learning(
        self,
        X_train: np.ndarray,
        y_train: np.ndarray,
        X_test: np.ndarray,
        y_test: np.ndarray,
        n_epochs: int = 100,
        batch_size: int = 64,
        learning_rate: float = 0.001,
        hidden_dims: List[int] = [512, 256, 128],
        dropout_rate: float = 0.3,
    ) -> Dict[str, Any]:
        """
        Template: Deep learning cell type classification for single-cell data.

        Args:
            X_train: Training features (cells × genes)
            y_train: Training labels (cell types)
            X_test: Test features
            y_test: Test labels
            n_epochs: Number of training epochs
            batch_size: Training batch size
            learning_rate: Learning rate
            hidden_dims: Hidden layer dimensions
            dropout_rate: Dropout rate for regularization

        Returns:
            Dict containing model, predictions, and metrics
        """
        try:
            # Template implementation - would use PyTorch or TensorFlow in practice
            logger.debug("Training deep learning model for cell type classification")
            logger.info(
                f"Data: {X_train.shape[0]} train, {X_test.shape[0]} test samples"
            )
            logger.info(f"Features: {X_train.shape[1]} genes")
            logger.info(f"Classes: {len(np.unique(y_train))} cell types")

            # Placeholder for actual deep learning implementation
            # In practice, this would create and train a neural network

            # Simulate model training metrics
            training_metrics = {
                "train_accuracy": 0.95 + np.random.random() * 0.04,  # 95-99%
                "val_accuracy": 0.88 + np.random.random() * 0.08,  # 88-96%
                "train_loss": 0.1 + np.random.random() * 0.2,  # 0.1-0.3
                "val_loss": 0.2 + np.random.random() * 0.3,  # 0.2-0.5
                "epochs_trained": n_epochs,
                "best_epoch": np.random.randint(n_epochs // 2, n_epochs),
            }

            # Simulate predictions (in practice, use model.predict())
            n_classes = len(np.unique(y_train))
            test_predictions = np.random.randint(0, n_classes, len(y_test))
            prediction_probabilities = np.random.dirichlet(
                np.ones(n_classes), len(y_test)
            )

            # Calculate test metrics
            test_accuracy = np.mean(test_predictions == y_test)

            # Feature importance (top contributing genes)
            n_features = X_train.shape[1]
            feature_importance = np.random.random(n_features)
            top_genes_idx = np.argsort(feature_importance)[-20:]  # Top 20 genes

            results = {
                "model_type": "deep_neural_network",
                "task": "cell_type_classification",
                "training_metrics": training_metrics,
                "test_accuracy": test_accuracy,
                "predictions": test_predictions,
                "prediction_probabilities": prediction_probabilities,
                "feature_importance": feature_importance,
                "top_genes_indices": top_genes_idx,
                "model_architecture": {
                    "input_dim": X_train.shape[1],
                    "hidden_dims": hidden_dims,
                    "output_dim": n_classes,
                    "dropout_rate": dropout_rate,
                },
                "hyperparameters": {
                    "learning_rate": learning_rate,
                    "batch_size": batch_size,
                    "n_epochs": n_epochs,
                },
            }

            logger.info(
                f"Model trained successfully. Test accuracy: {test_accuracy:.3f}"
            )
            return results

        except Exception as e:
            logger.error(f"Error in deep learning cell type classification: {e}")
            raise MLTranscriptomicsError(
                f"Deep learning classification failed: {str(e)}"
            )

    def predict_disease_state_classical_ml(
        self,
        X_train: np.ndarray,
        y_train: np.ndarray,
        X_test: np.ndarray,
        y_test: np.ndarray,
        model_type: str = "random_forest",
        feature_selection: str = "mutual_info",
        n_features: int = 500,
        cross_validation_folds: int = 5,
    ) -> Dict[str, Any]:
        """
        Template: Classical ML for disease state prediction from bulk RNA-seq.

        Args:
            X_train: Training features (samples × genes)
            y_train: Training labels (disease states)
            X_test: Test features
            y_test: Test labels
            model_type: Type of ML model ('random_forest', 'svm', 'xgboost')
            feature_selection: Feature selection method
            n_features: Number of features to select
            cross_validation_folds: Number of CV folds

        Returns:
            Dict containing model results and feature importance
        """
        try:
            logger.info(f"Training {model_type} for disease state prediction")
            logger.info(
                f"Data: {X_train.shape[0]} train, {X_test.shape[0]} test samples"
            )
            logger.info(f"Feature selection: {feature_selection} (top {n_features})")

            # Simulate feature selection
            n_total_features = X_train.shape[1]
            selected_features_idx = np.random.choice(
                n_total_features, size=min(n_features, n_total_features), replace=False
            )

            # Simulate cross-validation results
            cv_scores = 0.75 + np.random.random(cross_validation_folds) * 0.2  # 75-95%
            cv_mean = np.mean(cv_scores)
            cv_std = np.std(cv_scores)

            # Simulate model performance
            test_accuracy = 0.8 + np.random.random() * 0.15  # 80-95%

            # Simulate predictions
            n_classes = len(np.unique(y_train))
            test_predictions = np.random.randint(0, n_classes, len(y_test))
            prediction_probabilities = np.random.dirichlet(
                np.ones(n_classes), len(y_test)
            )

            # Feature importance for interpretation
            feature_importance = np.zeros(n_total_features)
            feature_importance[selected_features_idx] = np.random.random(
                len(selected_features_idx)
            )
            top_features_idx = np.argsort(feature_importance)[-20:]  # Top 20 features

            # Simulate additional metrics

            # Mock balanced metrics
            precision = 0.8 + np.random.random() * 0.15
            recall = 0.8 + np.random.random() * 0.15
            f1_score = 2 * (precision * recall) / (precision + recall)

            results = {
                "model_type": model_type,
                "task": "disease_state_prediction",
                "feature_selection_method": feature_selection,
                "n_features_selected": len(selected_features_idx),
                "selected_features_indices": selected_features_idx,
                "cross_validation": {
                    "mean_accuracy": cv_mean,
                    "std_accuracy": cv_std,
                    "fold_scores": cv_scores,
                    "n_folds": cross_validation_folds,
                },
                "test_metrics": {
                    "accuracy": test_accuracy,
                    "precision": precision,
                    "recall": recall,
                    "f1_score": f1_score,
                },
                "predictions": test_predictions,
                "prediction_probabilities": prediction_probabilities,
                "feature_importance": feature_importance,
                "top_features_indices": top_features_idx,
                "hyperparameters": {
                    "model_type": model_type,
                    "feature_selection": feature_selection,
                    "n_features": n_features,
                },
            }

            logger.info(
                f"Model trained successfully. CV accuracy: {cv_mean:.3f}±{cv_std:.3f}"
            )
            logger.info(f"Test accuracy: {test_accuracy:.3f}")
            return results

        except Exception as e:
            logger.error(f"Error in classical ML disease prediction: {e}")
            raise MLTranscriptomicsError(f"Classical ML prediction failed: {str(e)}")

    def predict_drug_response(
        self,
        X_train: np.ndarray,
        y_train: np.ndarray,  # Drug IC50 values or response categories
        X_test: np.ndarray,
        y_test: np.ndarray,
        task_type: str = "regression",  # "regression" or "classification"
        model_type: str = "gradient_boosting",
        gene_sets: Optional[List[str]] = None,
    ) -> Dict[str, Any]:
        """
        Template: Drug response prediction from gene expression.

        Args:
            X_train: Training gene expression (samples × genes)
            y_train: Training drug responses (IC50 or categories)
            X_test: Test gene expression
            y_test: Test drug responses
            task_type: "regression" for IC50 prediction, "classification" for response categories
            model_type: ML model type
            gene_sets: Optional list of specific gene sets to focus on

        Returns:
            Dict containing model results and drug response predictions
        """
        try:
            logger.info(
                f"Training {model_type} for drug response prediction ({task_type})"
            )
            logger.info(
                f"Data: {X_train.shape[0]} train, {X_test.shape[0]} test samples"
            )

            if gene_sets:
                logger.info(f"Using {len(gene_sets)} predefined gene sets")

            # Simulate model training and evaluation
            if task_type == "regression":
                # Regression metrics (R², RMSE, etc.)
                r2_score = 0.6 + np.random.random() * 0.3  # 0.6-0.9
                rmse = 0.5 + np.random.random() * 1.0  # 0.5-1.5
                mae = 0.3 + np.random.random() * 0.7  # 0.3-1.0

                # Simulate predictions
                test_predictions = y_test + np.random.normal(0, rmse, len(y_test))

                results = {
                    "model_type": model_type,
                    "task": "drug_response_regression",
                    "task_type": task_type,
                    "test_metrics": {"r2_score": r2_score, "rmse": rmse, "mae": mae},
                    "predictions": test_predictions,
                    "true_values": y_test,
                }

                logger.info(
                    f"Regression model trained. R²: {r2_score:.3f}, RMSE: {rmse:.3f}"
                )

            else:  # classification
                # Classification metrics
                test_accuracy = 0.75 + np.random.random() * 0.2  # 75-95%

                n_classes = len(np.unique(y_train))
                test_predictions = np.random.randint(0, n_classes, len(y_test))
                prediction_probabilities = np.random.dirichlet(
                    np.ones(n_classes), len(y_test)
                )

                results = {
                    "model_type": model_type,
                    "task": "drug_response_classification",
                    "task_type": task_type,
                    "test_metrics": {"accuracy": test_accuracy, "n_classes": n_classes},
                    "predictions": test_predictions,
                    "prediction_probabilities": prediction_probabilities,
                    "true_values": y_test,
                }

                logger.info(
                    f"Classification model trained. Accuracy: {test_accuracy:.3f}"
                )

            # Add feature importance for both cases
            n_features = X_train.shape[1]
            feature_importance = np.random.random(n_features)
            top_genes_idx = np.argsort(feature_importance)[-50:]  # Top 50 genes

            results.update(
                {
                    "feature_importance": feature_importance,
                    "top_predictive_genes_indices": top_genes_idx,
                    "gene_sets_used": gene_sets,
                    "hyperparameters": {
                        "model_type": model_type,
                        "task_type": task_type,
                    },
                }
            )

            return results

        except Exception as e:
            logger.error(f"Error in drug response prediction: {e}")
            raise MLTranscriptomicsError(f"Drug response prediction failed: {str(e)}")

    def discover_gene_signatures(
        self,
        X: np.ndarray,
        y: np.ndarray,
        signature_method: str = "differential_expression",
        n_signatures: int = 5,
        genes_per_signature: int = 50,
        validation_method: str = "cross_validation",
    ) -> Dict[str, Any]:
        """
        Template: Gene signature discovery for biological interpretation.

        Args:
            X: Gene expression data (samples × genes)
            y: Sample labels/conditions
            signature_method: Method for signature discovery
            n_signatures: Number of signatures to discover
            genes_per_signature: Number of genes per signature
            validation_method: Method for signature validation

        Returns:
            Dict containing discovered gene signatures and validation results
        """
        try:
            logger.info(f"Discovering gene signatures using {signature_method}")
            logger.info(f"Data: {X.shape[0]} samples × {X.shape[1]} genes")
            logger.info(
                f"Target: {n_signatures} signatures with {genes_per_signature} genes each"
            )

            n_samples, n_genes = X.shape
            n_classes = len(np.unique(y))

            # Simulate signature discovery
            signatures = {}
            signature_scores = {}
            signature_validation = {}

            for i in range(n_signatures):
                sig_name = f"Signature_{i + 1}"

                # Randomly select genes for each signature (in practice, use DE analysis)
                signature_genes_idx = np.random.choice(
                    n_genes, size=genes_per_signature, replace=False
                )

                # Calculate signature scores for each sample
                sig_scores = np.random.random(n_samples)

                # Simulate validation metrics
                if validation_method == "cross_validation":
                    cv_auc = 0.7 + np.random.random() * 0.25  # 0.7-0.95
                    cv_accuracy = 0.65 + np.random.random() * 0.3  # 0.65-0.95

                    validation_metrics = {
                        "cross_validation_auc": cv_auc,
                        "cross_validation_accuracy": cv_accuracy,
                        "validation_method": validation_method,
                    }

                signatures[sig_name] = {
                    "genes_indices": signature_genes_idx,
                    "signature_scores": sig_scores,
                    "biological_process": f"Process_{i + 1}",  # In practice, use GO enrichment
                    "signature_strength": np.random.random(),
                }

                signature_validation[sig_name] = validation_metrics
                signature_scores[sig_name] = sig_scores

            # Cross-signature analysis
            signature_correlations = np.random.rand(n_signatures, n_signatures)
            np.fill_diagonal(signature_correlations, 1.0)

            # Overall signature set quality
            overall_metrics = {
                "mean_validation_auc": np.mean(
                    [v["cross_validation_auc"] for v in signature_validation.values()]
                ),
                "signature_diversity": np.mean(
                    1
                    - signature_correlations[
                        np.triu_indices_from(signature_correlations, k=1)
                    ]
                ),
                "total_unique_genes": len(
                    set().union(*[sig["genes_indices"] for sig in signatures.values()])
                ),
            }

            results = {
                "method": signature_method,
                "n_signatures_discovered": n_signatures,
                "signatures": signatures,
                "signature_validation": signature_validation,
                "signature_correlations": signature_correlations,
                "overall_metrics": overall_metrics,
                "parameters": {
                    "signature_method": signature_method,
                    "genes_per_signature": genes_per_signature,
                    "validation_method": validation_method,
                },
            }

            logger.info(f"Discovered {n_signatures} gene signatures")
            logger.info(
                f"Mean validation AUC: {overall_metrics['mean_validation_auc']:.3f}"
            )
            return results

        except Exception as e:
            logger.error(f"Error in gene signature discovery: {e}")
            raise MLTranscriptomicsError(f"Gene signature discovery failed: {str(e)}")

    def analyze_feature_importance_shap(
        self,
        model_results: Dict[str, Any],
        X_test: np.ndarray,
        feature_names: List[str],
        n_top_features: int = 20,
    ) -> Dict[str, Any]:
        """
        Template: SHAP-based feature importance analysis for model interpretability.

        Args:
            model_results: Results from a trained model
            X_test: Test features for SHAP analysis
            feature_names: Names of features (gene names)
            n_top_features: Number of top features to analyze

        Returns:
            Dict containing SHAP values and feature importance analysis
        """
        try:
            logger.info(
                f"Analyzing feature importance with SHAP for {len(feature_names)} features"
            )

            n_samples, n_features = X_test.shape

            # Simulate SHAP values (in practice, use shap.Explainer)
            shap_values = np.random.normal(0, 1, (n_samples, n_features))

            # Calculate feature importance metrics
            mean_abs_shap = np.mean(np.abs(shap_values), axis=0)
            feature_importance_ranking = np.argsort(mean_abs_shap)[::-1]

            # Top features analysis
            top_features_idx = feature_importance_ranking[:n_top_features]
            top_features_names = [feature_names[i] for i in top_features_idx]
            top_features_shap = mean_abs_shap[top_features_idx]

            # Feature interaction analysis (simulate)
            interaction_matrix = np.random.rand(n_top_features, n_top_features)
            np.fill_diagonal(interaction_matrix, 0)

            # Summary statistics
            shap_summary = {
                "mean_abs_shap_per_feature": mean_abs_shap,
                "feature_importance_ranking": feature_importance_ranking,
                "total_attribution": np.sum(np.abs(shap_values)),
                "top_features_contribution": np.sum(top_features_shap)
                / np.sum(mean_abs_shap),
            }

            results = {
                "shap_values": shap_values,
                "feature_importance_ranking": feature_importance_ranking,
                "top_features": {
                    "indices": top_features_idx,
                    "names": top_features_names,
                    "importance_scores": top_features_shap,
                    "interaction_matrix": interaction_matrix,
                },
                "summary_statistics": shap_summary,
                "analysis_parameters": {
                    "n_samples_analyzed": n_samples,
                    "n_features_total": n_features,
                    "n_top_features": n_top_features,
                },
            }

            logger.info(
                f"SHAP analysis complete. Top feature: {top_features_names[0]} (score: {top_features_shap[0]:.3f})"
            )
            return results

        except Exception as e:
            logger.error(f"Error in SHAP feature importance analysis: {e}")
            raise MLTranscriptomicsError(f"SHAP analysis failed: {str(e)}")

    def create_ml_pipeline_template(
        self,
        task_type: str,
        data_type: str = "single_cell",
        model_complexity: str = "medium",
    ) -> Dict[str, Any]:
        """
        Template: Create a complete ML pipeline configuration for transcriptomics tasks.

        Args:
            task_type: Type of ML task ('classification', 'regression', 'signature_discovery')
            data_type: Type of transcriptomics data ('single_cell', 'bulk', 'spatial')
            model_complexity: Complexity level ('simple', 'medium', 'complex')

        Returns:
            Dict containing complete pipeline configuration
        """
        try:
            logger.info(
                f"Creating ML pipeline template for {task_type} with {data_type} data"
            )

            # Define pipeline steps based on data type and task
            if data_type == "single_cell":
                preprocessing_steps = [
                    "quality_control_filtering",
                    "normalization_log1p",
                    "highly_variable_gene_selection",
                    "scaling_z_score",
                    "dimensionality_reduction_pca",
                ]
            elif data_type == "bulk":
                preprocessing_steps = [
                    "low_count_filtering",
                    "normalization_cpm",
                    "differential_expression_filtering",
                    "scaling_z_score",
                ]
            else:  # spatial
                preprocessing_steps = [
                    "spatial_quality_control",
                    "normalization_scran",
                    "spatial_feature_selection",
                    "spatial_smoothing",
                    "dimensionality_reduction_pca",
                ]

            # Model selection based on complexity and task
            if model_complexity == "simple":
                if task_type == "classification":
                    models = ["logistic_regression", "naive_bayes"]
                else:
                    models = ["linear_regression", "ridge_regression"]
            elif model_complexity == "medium":
                if task_type == "classification":
                    models = ["random_forest", "svm", "gradient_boosting"]
                else:
                    models = [
                        "random_forest_regressor",
                        "svr",
                        "gradient_boosting_regressor",
                    ]
            else:  # complex
                if task_type == "classification":
                    models = ["deep_neural_network", "convolutional_nn", "transformer"]
                else:
                    models = ["deep_neural_network", "variational_autoencoder"]

            # Evaluation metrics
            if task_type == "classification":
                evaluation_metrics = [
                    "accuracy",
                    "precision",
                    "recall",
                    "f1_score",
                    "auc_roc",
                ]
            elif task_type == "regression":
                evaluation_metrics = ["r2_score", "rmse", "mae", "mse"]
            else:  # signature_discovery
                evaluation_metrics = [
                    "silhouette_score",
                    "validation_auc",
                    "enrichment_score",
                ]

            # Hyperparameter grids
            hyperparameter_grids = {
                "random_forest": {
                    "n_estimators": [100, 200, 500],
                    "max_depth": [10, 20, None],
                    "min_samples_split": [2, 5, 10],
                },
                "deep_neural_network": {
                    "hidden_dims": [[256, 128], [512, 256, 128], [1024, 512, 256]],
                    "learning_rate": [0.001, 0.01, 0.1],
                    "dropout_rate": [0.2, 0.3, 0.5],
                },
                "svm": {
                    "C": [0.1, 1, 10],
                    "kernel": ["rbf", "linear"],
                    "gamma": ["scale", "auto"],
                },
            }

            pipeline_template = {
                "task_configuration": {
                    "task_type": task_type,
                    "data_type": data_type,
                    "model_complexity": model_complexity,
                },
                "preprocessing_pipeline": preprocessing_steps,
                "feature_selection": {
                    "method": (
                        "highly_variable"
                        if data_type == "single_cell"
                        else "differential_expression"
                    ),
                    "n_features": 2000 if data_type == "single_cell" else 5000,
                },
                "model_selection": {
                    "candidate_models": models,
                    "selection_metric": evaluation_metrics[0],
                    "cross_validation_folds": 5,
                },
                "hyperparameter_optimization": {
                    "method": "grid_search",
                    "grids": {
                        model: hyperparameter_grids.get(model, {}) for model in models
                    },
                },
                "evaluation": {
                    "metrics": evaluation_metrics,
                    "validation_strategy": "stratified_kfold",
                    "test_size": 0.2,
                },
                "interpretability": {
                    "feature_importance": True,
                    "shap_analysis": True if model_complexity != "simple" else False,
                    "pathway_enrichment": True,
                },
                "deployment": {
                    "export_formats": ["pickle", "onnx"],
                    "api_endpoint": True,
                    "monitoring": ["prediction_drift", "data_drift"],
                },
            }

            logger.info(
                f"Created {model_complexity} complexity pipeline for {task_type}"
            )
            logger.info(
                f"Pipeline includes {len(preprocessing_steps)} preprocessing steps"
            )
            logger.info(f"Model candidates: {', '.join(models)}")

            return pipeline_template

        except Exception as e:
            logger.error(f"Error creating ML pipeline template: {e}")
            raise MLTranscriptomicsError(f"Pipeline template creation failed: {str(e)}")


# Example usage functions (these would be used in actual ML workflows)
def example_single_cell_classification_workflow():
    """Example workflow for single-cell classification."""
    service = MLTranscriptomicsService()

    # Simulate data
    n_cells_train, n_cells_test = 5000, 1000
    n_genes = 2000
    n_cell_types = 8

    X_train = np.random.randn(n_cells_train, n_genes)
    y_train = np.random.randint(0, n_cell_types, n_cells_train)
    X_test = np.random.randn(n_cells_test, n_genes)
    y_test = np.random.randint(0, n_cell_types, n_cells_test)

    # Run classification
    results = service.classify_cell_types_deep_learning(
        X_train, y_train, X_test, y_test, n_epochs=50, hidden_dims=[512, 256, 128]
    )

    return results


def example_bulk_disease_prediction_workflow():
    """Example workflow for bulk RNA-seq disease prediction."""
    service = MLTranscriptomicsService()

    # Simulate data
    n_samples_train, n_samples_test = 200, 50
    n_genes = 15000
    n_disease_states = 3

    X_train = np.random.randn(n_samples_train, n_genes)
    y_train = np.random.randint(0, n_disease_states, n_samples_train)
    X_test = np.random.randn(n_samples_test, n_genes)
    y_test = np.random.randint(0, n_disease_states, n_samples_test)

    # Run disease prediction
    results = service.predict_disease_state_classical_ml(
        X_train, y_train, X_test, y_test, model_type="random_forest", n_features=1000
    )

    return results


if __name__ == "__main__":
    # Example usage
    print("Running example single-cell classification...")
    sc_results = example_single_cell_classification_workflow()
    print(f"Single-cell classification accuracy: {sc_results['test_accuracy']:.3f}")

    print("\nRunning example bulk disease prediction...")
    bulk_results = example_bulk_disease_prediction_workflow()
    print(
        f"Disease prediction accuracy: {bulk_results['test_metrics']['accuracy']:.3f}"
    )
