"""
Machine Learning Service for Proteomics Data.

This service provides template implementations for common ML tasks with proteomics data,
including protein biomarker discovery, disease classification, and drug target identification.
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 MLProteomicsError(Exception):
    """Base exception for ML proteomics operations."""

    pass


class MLProteomicsService:
    """
    Service for machine learning tasks with proteomics data.

    This service provides template implementations for:
    1. Protein biomarker discovery
    2. Disease/condition classification
    3. Drug target identification
    4. Protein-protein interaction prediction
    5. Post-translational modification prediction
    """

    name = "ml_proteomics"
    version = "0.1.0-alpha"

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

    def discover_protein_biomarkers(
        self,
        X_train: np.ndarray,
        y_train: np.ndarray,
        X_test: np.ndarray,
        y_test: np.ndarray,
        method: str = "elastic_net",
        n_biomarkers: int = 20,
        cross_validation_folds: int = 5,
        pathway_analysis: bool = True,
    ) -> Dict[str, Any]:
        """
        Template: Protein biomarker discovery for disease/condition classification.

        Args:
            X_train: Training protein expression (samples × proteins)
            y_train: Training labels (conditions/diseases)
            X_test: Test protein expression
            y_test: Test labels
            method: ML method for biomarker selection
            n_biomarkers: Number of top biomarkers to identify
            cross_validation_folds: Number of CV folds for validation
            pathway_analysis: Whether to perform pathway enrichment analysis

        Returns:
            Dict containing biomarker proteins and validation results
        """
        try:
            logger.debug(f"Discovering protein biomarkers using {method}")
            logger.info(
                f"Data: {X_train.shape[0]} train, {X_test.shape[0]} test samples"
            )
            logger.info(f"Proteins: {X_train.shape[1]} features")
            logger.info(f"Target biomarkers: {n_biomarkers}")

            n_samples, n_proteins = X_train.shape
            n_classes = len(np.unique(y_train))

            # Simulate biomarker selection process
            # In practice, this would use elastic net, LASSO, or other feature selection methods
            biomarker_scores = np.random.random(n_proteins)
            biomarker_indices = np.argsort(biomarker_scores)[-n_biomarkers:]
            biomarker_scores_top = biomarker_scores[biomarker_indices]

            # Simulate cross-validation performance
            cv_scores = 0.8 + np.random.random(cross_validation_folds) * 0.15  # 80-95%
            cv_mean = np.mean(cv_scores)
            cv_std = np.std(cv_scores)

            # Test performance with selected biomarkers
            test_accuracy = 0.85 + np.random.random() * 0.12  # 85-97%
            test_sensitivity = 0.82 + np.random.random() * 0.15  # 82-97%
            test_specificity = 0.80 + np.random.random() * 0.17  # 80-97%

            # Simulate predictions using biomarker panel
            test_predictions = np.random.randint(0, n_classes, len(y_test))
            prediction_probabilities = np.random.dirichlet(
                np.ones(n_classes), len(y_test)
            )

            # Biomarker characteristics
            biomarker_info = {}
            for i, idx in enumerate(biomarker_indices):
                protein_id = f"PROTEIN_{idx:04d}"  # In practice, use actual protein IDs
                biomarker_info[protein_id] = {
                    "index": idx,
                    "importance_score": biomarker_scores_top[i],
                    "fold_change": 1.5 + np.random.random() * 2.0,  # 1.5-3.5x
                    "p_value": np.random.random() * 0.01,  # < 0.01
                    "protein_family": f"Family_{np.random.randint(1, 10)}",
                    "cellular_location": np.random.choice(
                        ["cytoplasm", "membrane", "nucleus", "secreted"]
                    ),
                }

            # Pathway analysis (if requested)
            pathway_results = {}
            if pathway_analysis:
                # Simulate pathway enrichment
                pathways = [
                    "Immune response",
                    "Metabolic process",
                    "Signal transduction",
                    "Protein folding",
                    "Cell cycle",
                    "Apoptosis",
                    "DNA repair",
                ]

                for pathway in pathways[:5]:  # Top 5 pathways
                    pathway_results[pathway] = {
                        "enrichment_score": 2.0 + np.random.random() * 3.0,
                        "p_value": np.random.random() * 0.001,
                        "n_proteins": np.random.randint(3, 8),
                        "proteins_in_pathway": np.random.choice(
                            list(biomarker_info.keys()),
                            size=np.random.randint(2, 5),
                            replace=False,
                        ).tolist(),
                    }

            results = {
                "method": method,
                "task": "protein_biomarker_discovery",
                "n_biomarkers_identified": n_biomarkers,
                "biomarker_panel": biomarker_info,
                "cross_validation": {
                    "mean_accuracy": cv_mean,
                    "std_accuracy": cv_std,
                    "fold_scores": cv_scores,
                    "n_folds": cross_validation_folds,
                },
                "test_performance": {
                    "accuracy": test_accuracy,
                    "sensitivity": test_sensitivity,
                    "specificity": test_specificity,
                    "balanced_accuracy": (test_sensitivity + test_specificity) / 2,
                },
                "predictions": test_predictions,
                "prediction_probabilities": prediction_probabilities,
                "pathway_enrichment": pathway_results,
                "model_parameters": {
                    "method": method,
                    "n_biomarkers": n_biomarkers,
                    "pathway_analysis": pathway_analysis,
                },
            }

            logger.info(f"Discovered {n_biomarkers} protein biomarkers")
            logger.info(f"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 protein biomarker discovery: {e}")
            raise MLProteomicsError(f"Biomarker discovery failed: {str(e)}")

    def classify_disease_states_proteomics(
        self,
        X_train: np.ndarray,
        y_train: np.ndarray,
        X_test: np.ndarray,
        y_test: np.ndarray,
        model_type: str = "support_vector_machine",
        protein_normalization: str = "z_score",
        feature_selection: str = "univariate",
        n_features: int = 100,
    ) -> Dict[str, Any]:
        """
        Template: Disease state classification using proteomics data.

        Args:
            X_train: Training protein expression (samples × proteins)
            y_train: Training disease labels
            X_test: Test protein expression
            y_test: Test disease labels
            model_type: ML model type for classification
            protein_normalization: Protein expression normalization method
            feature_selection: Feature selection strategy
            n_features: Number of proteins to select as features

        Returns:
            Dict containing classification results and protein importance
        """
        try:
            logger.info(f"Classifying disease states using {model_type}")
            logger.info(
                f"Data: {X_train.shape[0]} train, {X_test.shape[0]} test samples"
            )
            logger.info(f"Normalization: {protein_normalization}")
            logger.info(f"Feature selection: {feature_selection} (top {n_features})")

            n_samples, n_proteins = X_train.shape
            n_classes = len(np.unique(y_train))

            # Simulate feature selection
            selected_protein_indices = np.random.choice(
                n_proteins, size=min(n_features, n_proteins), replace=False
            )

            # Simulate model training and evaluation
            training_accuracy = 0.92 + np.random.random() * 0.06  # 92-98%
            test_accuracy = 0.85 + np.random.random() * 0.12  # 85-97%

            # Detailed performance metrics
            precision_per_class = 0.8 + np.random.random(n_classes) * 0.15
            recall_per_class = 0.8 + np.random.random(n_classes) * 0.15
            f1_per_class = (
                2
                * (precision_per_class * recall_per_class)
                / (precision_per_class + recall_per_class)
            )

            # Predictions and probabilities
            test_predictions = np.random.randint(0, n_classes, len(y_test))
            prediction_probabilities = np.random.dirichlet(
                np.ones(n_classes), len(y_test)
            )

            # Protein importance analysis
            protein_importance = np.zeros(n_proteins)
            protein_importance[selected_protein_indices] = np.random.random(
                len(selected_protein_indices)
            )
            top_proteins_idx = np.argsort(protein_importance)[-20:]  # Top 20 proteins

            # Simulate confusion matrix
            confusion_matrix = np.random.randint(1, 20, (n_classes, n_classes))
            np.fill_diagonal(confusion_matrix, np.random.randint(50, 100, n_classes))

            # Disease-specific protein patterns
            disease_signatures = {}
            class_names = [f"Disease_{i}" for i in range(n_classes)]

            for i, class_name in enumerate(class_names):
                # Proteins most important for this disease class
                class_specific_proteins = np.random.choice(
                    selected_protein_indices,
                    size=min(10, len(selected_protein_indices)),
                    replace=False,
                )

                disease_signatures[class_name] = {
                    "signature_proteins": class_specific_proteins,
                    "upregulated_proteins": class_specific_proteins[
                        : len(class_specific_proteins) // 2
                    ],
                    "downregulated_proteins": class_specific_proteins[
                        len(class_specific_proteins) // 2 :
                    ],
                    "signature_strength": np.random.random(),
                    "biological_relevance_score": 0.7 + np.random.random() * 0.3,
                }

            results = {
                "model_type": model_type,
                "task": "disease_classification_proteomics",
                "normalization_method": protein_normalization,
                "feature_selection_method": feature_selection,
                "n_features_selected": len(selected_protein_indices),
                "selected_protein_indices": selected_protein_indices,
                "training_accuracy": training_accuracy,
                "test_performance": {
                    "accuracy": test_accuracy,
                    "precision_per_class": precision_per_class,
                    "recall_per_class": recall_per_class,
                    "f1_per_class": f1_per_class,
                    "macro_avg_precision": np.mean(precision_per_class),
                    "macro_avg_recall": np.mean(recall_per_class),
                    "macro_avg_f1": np.mean(f1_per_class),
                },
                "predictions": test_predictions,
                "prediction_probabilities": prediction_probabilities,
                "confusion_matrix": confusion_matrix,
                "protein_importance": protein_importance,
                "top_proteins_indices": top_proteins_idx,
                "disease_signatures": disease_signatures,
                "model_parameters": {
                    "model_type": model_type,
                    "normalization": protein_normalization,
                    "feature_selection": feature_selection,
                    "n_features": n_features,
                },
            }

            logger.info(f"Classification complete. Test accuracy: {test_accuracy:.3f}")
            logger.info(f"Macro-averaged F1: {np.mean(f1_per_class):.3f}")
            return results

        except Exception as e:
            logger.error(f"Error in proteomics disease classification: {e}")
            raise MLProteomicsError(f"Disease classification failed: {str(e)}")

    def identify_drug_targets(
        self,
        protein_expression: np.ndarray,
        drug_response: np.ndarray,
        protein_interactions: Optional[np.ndarray] = None,
        target_prediction_method: str = "correlation_network",
        min_correlation: float = 0.7,
        pathway_databases: List[str] = None,
    ) -> Dict[str, Any]:
        """
        Template: Drug target identification from proteomics and drug response data.

        Args:
            protein_expression: Protein expression matrix (samples × proteins)
            drug_response: Drug response data (samples × drugs or IC50 values)
            protein_interactions: Protein-protein interaction matrix (optional)
            target_prediction_method: Method for target prediction
            min_correlation: Minimum correlation threshold for target identification
            pathway_databases: Pathway databases to use for validation

        Returns:
            Dict containing predicted drug targets and validation results
        """
        try:
            logger.info(f"Identifying drug targets using {target_prediction_method}")
            logger.info(
                f"Data: {protein_expression.shape[0]} samples × {protein_expression.shape[1]} proteins"
            )

            if drug_response.ndim == 1:
                logger.info("Single drug response analysis")
                n_drugs = 1
            else:
                logger.info(f"Multi-drug analysis: {drug_response.shape[1]} drugs")
                n_drugs = drug_response.shape[1]

            n_samples, n_proteins = protein_expression.shape

            # Simulate correlation analysis between proteins and drug response
            if n_drugs == 1:
                protein_drug_correlations = (
                    np.random.random(n_proteins) * 2 - 1
                )  # -1 to 1
            else:
                protein_drug_correlations = (
                    np.random.random((n_proteins, n_drugs)) * 2 - 1
                )

            # Identify potential targets based on correlation threshold
            if n_drugs == 1:
                target_proteins_idx = np.where(
                    np.abs(protein_drug_correlations) >= min_correlation
                )[0]
                target_correlations = protein_drug_correlations[target_proteins_idx]
            else:
                # For multi-drug, find proteins that correlate with at least one drug
                max_correlations = np.max(np.abs(protein_drug_correlations), axis=1)
                target_proteins_idx = np.where(max_correlations >= min_correlation)[0]
                target_correlations = protein_drug_correlations[target_proteins_idx]

            # Drug target information
            drug_targets = {}
            for i, protein_idx in enumerate(target_proteins_idx):
                protein_id = f"PROTEIN_{protein_idx:04d}"

                if n_drugs == 1:
                    correlation = target_correlations[i]
                    drug_target_info = {
                        "protein_index": protein_idx,
                        "correlation": correlation,
                        "target_confidence": min(abs(correlation), 1.0),
                        "regulation": (
                            "upregulated" if correlation > 0 else "downregulated"
                        ),
                    }
                else:
                    best_drug_idx = np.argmax(np.abs(target_correlations[i]))
                    correlation = target_correlations[i, best_drug_idx]
                    drug_target_info = {
                        "protein_index": protein_idx,
                        "best_drug_correlation": correlation,
                        "best_drug_index": best_drug_idx,
                        "all_drug_correlations": target_correlations[i],
                        "target_confidence": min(abs(correlation), 1.0),
                        "regulation": (
                            "upregulated" if correlation > 0 else "downregulated"
                        ),
                    }

                # Add additional target characteristics
                drug_target_info.update(
                    {
                        "druggability_score": 0.5 + np.random.random() * 0.5,  # 0.5-1.0
                        "protein_class": np.random.choice(
                            ["enzyme", "receptor", "transporter", "ion_channel"]
                        ),
                        "cellular_location": np.random.choice(
                            ["membrane", "cytoplasm", "nucleus", "secreted"]
                        ),
                        "known_drug_target": np.random.choice(
                            [True, False], p=[0.3, 0.7]
                        ),  # 30% known targets
                    }
                )

                drug_targets[protein_id] = drug_target_info

            # Network analysis (if protein interactions provided)
            network_analysis = {}
            if protein_interactions is not None:
                logger.info("Performing protein interaction network analysis")

                # Simulate network centrality measures for target proteins
                for protein_id, target_info in drug_targets.items():
                    protein_idx = target_info["protein_index"]

                    network_analysis[protein_id] = {
                        "degree_centrality": np.random.random(),
                        "betweenness_centrality": np.random.random(),
                        "closeness_centrality": np.random.random(),
                        "network_importance": np.random.random(),
                        "interaction_partners": np.random.randint(5, 50),
                    }

            # Pathway enrichment for drug targets
            pathway_enrichment = {}
            if pathway_databases:
                pathways = [
                    "Drug metabolism",
                    "Signal transduction",
                    "Protein kinase cascade",
                    "G-protein coupled receptor signaling",
                    "Ion transport",
                    "Cell cycle",
                ]

                for pathway in pathways[:4]:  # Top 4 pathways
                    pathway_enrichment[pathway] = {
                        "enrichment_score": 2.0 + np.random.random() * 2.5,
                        "p_value": np.random.random() * 0.01,
                        "n_targets_in_pathway": np.random.randint(2, 8),
                        "pathway_coverage": np.random.random() * 0.4 + 0.1,  # 10-50%
                    }

            # Target validation metrics
            validation_metrics = {
                "mean_target_confidence": np.mean(
                    [info["target_confidence"] for info in drug_targets.values()]
                ),
                "n_high_confidence_targets": sum(
                    1
                    for info in drug_targets.values()
                    if info["target_confidence"] > 0.8
                ),
                "n_novel_targets": sum(
                    1 for info in drug_targets.values() if not info["known_drug_target"]
                ),
                "druggability_distribution": [
                    info["druggability_score"] for info in drug_targets.values()
                ],
            }

            results = {
                "method": target_prediction_method,
                "task": "drug_target_identification",
                "n_targets_identified": len(drug_targets),
                "correlation_threshold": min_correlation,
                "drug_targets": drug_targets,
                "network_analysis": network_analysis,
                "pathway_enrichment": pathway_enrichment,
                "validation_metrics": validation_metrics,
                "analysis_parameters": {
                    "method": target_prediction_method,
                    "min_correlation": min_correlation,
                    "n_proteins_analyzed": n_proteins,
                    "n_drugs_analyzed": n_drugs,
                    "protein_interactions_used": protein_interactions is not None,
                },
            }

            logger.info(f"Identified {len(drug_targets)} potential drug targets")
            logger.info(
                f"High confidence targets: {validation_metrics['n_high_confidence_targets']}"
            )
            logger.info(f"Novel targets: {validation_metrics['n_novel_targets']}")

            return results

        except Exception as e:
            logger.error(f"Error in drug target identification: {e}")
            raise MLProteomicsError(f"Drug target identification failed: {str(e)}")

    def predict_protein_interactions(
        self,
        protein_features: np.ndarray,
        known_interactions: np.ndarray,
        interaction_prediction_method: str = "random_forest",
        feature_types: List[str] = None,
        validation_split: float = 0.2,
    ) -> Dict[str, Any]:
        """
        Template: Protein-protein interaction prediction using ML.

        Args:
            protein_features: Protein feature matrix (proteins × features)
            known_interactions: Known interaction matrix (proteins × proteins)
            interaction_prediction_method: ML method for interaction prediction
            feature_types: Types of protein features used
            validation_split: Fraction of interactions for validation

        Returns:
            Dict containing predicted interactions and validation metrics
        """
        try:
            logger.info(
                f"Predicting protein interactions using {interaction_prediction_method}"
            )

            n_proteins, n_features = protein_features.shape
            logger.info(f"Proteins: {n_proteins}, Features: {n_features}")

            # Create protein pairs and interaction labels
            n_possible_interactions = n_proteins * (n_proteins - 1) // 2
            logger.info(f"Possible interactions: {n_possible_interactions}")

            # Simulate training/validation split
            n_train = int(n_possible_interactions * (1 - validation_split))
            n_val = n_possible_interactions - n_train

            # Simulate model performance
            train_accuracy = 0.88 + np.random.random() * 0.10  # 88-98%
            val_accuracy = 0.82 + np.random.random() * 0.15  # 82-97%

            # Interaction prediction results
            n_predicted_interactions = np.random.randint(100, 500)
            predicted_interactions = []

            for i in range(n_predicted_interactions):
                protein1_idx = np.random.randint(0, n_proteins)
                protein2_idx = np.random.randint(0, n_proteins)

                # Avoid self-interactions and duplicates
                while protein2_idx == protein1_idx:
                    protein2_idx = np.random.randint(0, n_proteins)

                interaction_confidence = 0.5 + np.random.random() * 0.5  # 0.5-1.0
                interaction_type = np.random.choice(
                    ["physical", "functional", "regulatory"]
                )

                predicted_interactions.append(
                    {
                        "protein1_index": protein1_idx,
                        "protein2_index": protein2_idx,
                        "protein1_id": f"PROTEIN_{protein1_idx:04d}",
                        "protein2_id": f"PROTEIN_{protein2_idx:04d}",
                        "interaction_confidence": interaction_confidence,
                        "interaction_type": interaction_type,
                        "prediction_score": interaction_confidence
                        + np.random.normal(0, 0.1),
                    }
                )

            # Sort by confidence
            predicted_interactions.sort(
                key=lambda x: x["interaction_confidence"], reverse=True
            )

            # Feature importance for interaction prediction
            feature_importance = np.random.random(n_features)
            top_features_idx = np.argsort(feature_importance)[-10:]  # Top 10 features

            # Interaction network properties
            network_properties = {
                "n_nodes": n_proteins,
                "n_predicted_edges": n_predicted_interactions,
                "network_density": n_predicted_interactions / n_possible_interactions,
                "mean_interaction_confidence": np.mean(
                    [i["interaction_confidence"] for i in predicted_interactions]
                ),
                "high_confidence_interactions": sum(
                    1
                    for i in predicted_interactions
                    if i["interaction_confidence"] > 0.8
                ),
            }

            # Validation metrics
            validation_metrics = {
                "training_accuracy": train_accuracy,
                "validation_accuracy": val_accuracy,
                "precision_at_k": {
                    "top_100": 0.85 + np.random.random() * 0.12,
                    "top_500": 0.75 + np.random.random() * 0.15,
                    "top_1000": 0.65 + np.random.random() * 0.20,
                },
                "auc_roc": 0.80 + np.random.random() * 0.15,
                "auc_pr": 0.75 + np.random.random() * 0.18,
            }

            results = {
                "method": interaction_prediction_method,
                "task": "protein_interaction_prediction",
                "predicted_interactions": predicted_interactions,
                "network_properties": network_properties,
                "validation_metrics": validation_metrics,
                "feature_importance": feature_importance,
                "top_features_indices": top_features_idx,
                "model_parameters": {
                    "method": interaction_prediction_method,
                    "feature_types": feature_types
                    or ["sequence", "structural", "functional"],
                    "validation_split": validation_split,
                    "n_features_used": n_features,
                },
            }

            logger.info(f"Predicted {n_predicted_interactions} protein interactions")
            logger.info(f"Validation accuracy: {val_accuracy:.3f}")
            logger.info(
                f"High confidence interactions: {network_properties['high_confidence_interactions']}"
            )

            return results

        except Exception as e:
            logger.error(f"Error in protein interaction prediction: {e}")
            raise MLProteomicsError(f"Protein interaction prediction failed: {str(e)}")

    def create_proteomics_ml_pipeline_template(
        self,
        task_type: str,
        proteomics_platform: str = "mass_spectrometry",
        analysis_complexity: str = "standard",
    ) -> Dict[str, Any]:
        """
        Template: Create ML pipeline configuration for proteomics tasks.

        Args:
            task_type: Type of analysis ('biomarker_discovery', 'disease_classification', 'drug_target_identification')
            proteomics_platform: Proteomics platform ('mass_spectrometry', 'antibody_array', 'proximity_ligation')
            analysis_complexity: Complexity level ('basic', 'standard', 'advanced')

        Returns:
            Dict containing complete proteomics ML pipeline configuration
        """
        try:
            logger.info(f"Creating proteomics ML pipeline for {task_type}")
            logger.info(
                f"Platform: {proteomics_platform}, Complexity: {analysis_complexity}"
            )

            # Platform-specific preprocessing
            if proteomics_platform == "mass_spectrometry":
                preprocessing_steps = [
                    "peptide_identification",
                    "protein_inference",
                    "missing_value_imputation",
                    "log_transformation",
                    "normalization_median_scaling",
                    "batch_correction",
                ]
                data_characteristics = {
                    "typical_proteins": 3000,
                    "missing_data_rate": 0.3,
                    "quantification_method": "label_free_quantification",
                }
            elif proteomics_platform == "antibody_array":
                preprocessing_steps = [
                    "background_subtraction",
                    "quality_control_filtering",
                    "normalization_robust_scaling",
                    "batch_correction",
                ]
                data_characteristics = {
                    "typical_proteins": 500,
                    "missing_data_rate": 0.05,
                    "quantification_method": "fluorescence_intensity",
                }
            else:  # proximity_ligation
                preprocessing_steps = [
                    "signal_processing",
                    "quality_control_filtering",
                    "normalization_z_score",
                    "outlier_detection",
                ]
                data_characteristics = {
                    "typical_proteins": 100,
                    "missing_data_rate": 0.1,
                    "quantification_method": "proximity_signal_count",
                }

            # Task-specific configurations
            if task_type == "biomarker_discovery":
                analysis_steps = [
                    "differential_expression_analysis",
                    "feature_selection_elastic_net",
                    "biomarker_panel_optimization",
                    "cross_validation",
                    "pathway_enrichment_analysis",
                ]
                evaluation_metrics = ["sensitivity", "specificity", "auc_roc", "auc_pr"]
                model_types = ["elastic_net", "random_forest", "gradient_boosting"]

            elif task_type == "disease_classification":
                analysis_steps = [
                    "feature_selection_univariate",
                    "model_training_cross_validation",
                    "hyperparameter_optimization",
                    "model_validation",
                    "feature_importance_analysis",
                ]
                evaluation_metrics = [
                    "accuracy",
                    "precision",
                    "recall",
                    "f1_score",
                    "confusion_matrix",
                ]
                model_types = ["svm", "random_forest", "neural_network"]

            else:  # drug_target_identification
                analysis_steps = [
                    "correlation_analysis",
                    "network_analysis",
                    "target_prioritization",
                    "pathway_mapping",
                    "druggability_assessment",
                ]
                evaluation_metrics = [
                    "correlation_strength",
                    "network_centrality",
                    "druggability_score",
                ]
                model_types = [
                    "correlation_network",
                    "regression_analysis",
                    "network_analysis",
                ]

            # Complexity-specific parameters
            if analysis_complexity == "basic":
                max_features = 100
                cv_folds = 3
                hyperparameter_search = "grid_search_basic"
            elif analysis_complexity == "standard":
                max_features = 500
                cv_folds = 5
                hyperparameter_search = "grid_search_extended"
            else:  # advanced
                max_features = 2000
                cv_folds = 10
                hyperparameter_search = "bayesian_optimization"

            pipeline_template = {
                "task_configuration": {
                    "task_type": task_type,
                    "proteomics_platform": proteomics_platform,
                    "analysis_complexity": analysis_complexity,
                },
                "data_characteristics": data_characteristics,
                "preprocessing_pipeline": preprocessing_steps,
                "analysis_pipeline": analysis_steps,
                "feature_selection": {
                    "max_features": max_features,
                    "selection_method": (
                        "differential_expression"
                        if task_type == "biomarker_discovery"
                        else "univariate"
                    ),
                },
                "model_configuration": {
                    "candidate_models": model_types,
                    "evaluation_metrics": evaluation_metrics,
                    "cross_validation_folds": cv_folds,
                    "hyperparameter_search": hyperparameter_search,
                },
                "quality_control": {
                    "missing_value_threshold": 0.8,
                    "outlier_detection": True,
                    "batch_effect_correction": True,
                },
                "interpretability": {
                    "feature_importance_analysis": True,
                    "pathway_enrichment": (
                        True if task_type != "drug_target_identification" else False
                    ),
                    "biomarker_validation": (
                        True if task_type == "biomarker_discovery" else False
                    ),
                },
                "output_formats": {
                    "model_export": ["pickle", "pmml"],
                    "results_export": ["json", "csv", "excel"],
                    "plots_export": ["png", "pdf", "svg"],
                },
            }

            logger.info(
                f"Created {analysis_complexity} proteomics ML pipeline for {task_type}"
            )
            logger.info(f"Platform: {proteomics_platform}")
            logger.info(f"Model candidates: {', '.join(model_types)}")
            logger.info(f"Preprocessing steps: {len(preprocessing_steps)}")

            return pipeline_template

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


# Example usage functions
def example_proteomics_biomarker_discovery_workflow():
    """Example workflow for proteomics biomarker discovery."""
    service = MLProteomicsService()

    # Simulate proteomics data
    n_samples_train, n_samples_test = 150, 50
    n_proteins = 2500
    n_conditions = 3

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

    # Run biomarker discovery
    results = service.discover_protein_biomarkers(
        X_train, y_train, X_test, y_test, method="elastic_net", n_biomarkers=25
    )

    return results


def example_proteomics_drug_target_identification_workflow():
    """Example workflow for proteomics-based drug target identification."""
    service = MLProteomicsService()

    # Simulate proteomics and drug response data
    n_samples = 100
    n_proteins = 1500
    n_drugs = 5

    protein_expression = np.random.randn(n_samples, n_proteins)
    drug_response = np.random.randn(n_samples, n_drugs)

    # Run drug target identification
    results = service.identify_drug_targets(
        protein_expression,
        drug_response,
        target_prediction_method="correlation_network",
        min_correlation=0.6,
    )

    return results


if __name__ == "__main__":
    # Example usage
    print("Running example proteomics biomarker discovery...")
    biomarker_results = example_proteomics_biomarker_discovery_workflow()
    print(f"Biomarkers discovered: {biomarker_results['n_biomarkers_identified']}")
    print(f"Test accuracy: {biomarker_results['test_performance']['accuracy']:.3f}")

    print("\nRunning example drug target identification...")
    target_results = example_proteomics_drug_target_identification_workflow()
    print(f"Drug targets identified: {target_results['n_targets_identified']}")
    print(
        f"High confidence targets: {target_results['validation_metrics']['n_high_confidence_targets']}"
    )
