// ML Pipeline Manager
// End-to-end machine learning pipeline for training and inference

import { EventEmitter } from 'events';

type PipelineStatus = 'idle' | 'preparing' | 'training' | 'evaluating' | 'deploying' | 'completed' | 'failed';
type ModelType = 'classification' | 'regression' | 'clustering' | 'recommendation' | 'forecasting' | 'anomaly';

interface Dataset {
  id: string;
  tenantId: string;
  name: string;
  description?: string;
  type: 'structured' | 'time-series' | 'image' | 'text';
  schema: Array<{
    name: string;
    type: 'string' | 'number' | 'boolean' | 'date' | 'category';
    nullable?: boolean;
    isTarget?: boolean;
  }>;
  stats: {
    rowCount: number;
    columnCount: number;
    sizeBytes: number;
    createdAt: Date;
    updatedAt: Date;
  };
  quality: {
    completeness: number; // 0-100
    uniqueness: number;
    validity: number;
  };
}

interface MLModel {
  id: string;
  tenantId: string;
  name: string;
  description?: string;
  type: ModelType;
  version: string;
  status: 'draft' | 'training' | 'ready' | 'deployed' | 'archived';
  datasetId: string;
  config: {
    algorithm: string;
    hyperparameters: Record<string, unknown>;
    features: string[];
    target?: string;
    testSplit: number;
    validationSplit: number;
    randomSeed?: number;
  };
  metrics: {
    accuracy?: number;
    precision?: number;
    recall?: number;
    f1Score?: number;
    mse?: number;
    rmse?: number;
    mae?: number;
    r2?: number;
    silhouette?: number;
  };
  training: {
    startedAt?: Date;
    completedAt?: Date;
    duration?: number; // seconds
    epochs?: number;
    lossHistory?: number[];
    validationHistory?: number[];
  };
  deployment?: {
    endpoint?: string;
    status: 'offline' | 'online' | 'scaling';
    lastUsedAt?: Date;
    totalPredictions: number;
    averageLatency: number;
  };
  createdAt: Date;
  updatedAt: Date;
  createdBy: string;
}

interface MLPipeline {
  id: string;
  tenantId: string;
  name: string;
  description?: string;
  status: PipelineStatus;
  steps: Array<{
    id: string;
    name: string;
    type: 'extract' | 'transform' | 'train' | 'evaluate' | 'deploy';
    status: 'pending' | 'running' | 'completed' | 'failed';
    config: Record<string, unknown>;
    output?: unknown;
    error?: string;
    startedAt?: Date;
    completedAt?: Date;
  }>;
  modelId?: string;
  datasetId: string;
  createdAt: Date;
  updatedAt: Date;
}

interface Prediction {
  id: string;
  modelId: string;
  tenantId: string;
  input: Record<string, unknown>;
  output: unknown;
  confidence?: number;
  explanation?: Record<string, number>; // Feature importance
  latency: number;
  createdAt: Date;
}

interface Feature {
  name: string;
  type: 'numeric' | 'categorical' | 'text' | 'datetime' | 'boolean';
  importance: number;
  statistics?: {
    mean?: number;
    std?: number;
    min?: number;
    max?: number;
    unique?: number;
    missing?: number;
  };
}

// ML Pipeline Manager
export class MLPipelineManager extends EventEmitter {
  private datasets: Map<string, Dataset> = new Map();
  private models: Map<string, MLModel> = new Map();
  private pipelines: Map<string, MLPipeline> = new Map();
  private predictions: Map<string, Prediction[]> = new Map();

  // Create dataset
  createDataset(config: Omit<Dataset, 'id' | 'stats' | 'quality'>): Dataset {
    const dataset: Dataset = {
      ...config,
      id: crypto.randomUUID(),
      stats: {
        rowCount: 0,
        columnCount: config.schema.length,
        sizeBytes: 0,
        createdAt: new Date(),
        updatedAt: new Date(),
      },
      quality: {
        completeness: 100,
        uniqueness: 100,
        validity: 100,
      },
    };

    this.datasets.set(dataset.id, dataset);
    this.emit('datasetCreated', dataset);
    return dataset;
  }

  // Import data into dataset
  async importData(
    datasetId: string,
    source: {
      type: 'csv' | 'json' | 'database' | 'api';
      location: string;
      query?: string;
    }
  ): Promise<{ imported: number; errors: number }> {
    const dataset = this.datasets.get(datasetId);
    if (!dataset) throw new Error('Dataset not found');

    // In production, this would:
    // 1. Extract data from source
    // 2. Validate against schema
    // 3. Transform and load
    // 4. Update statistics

    const mockImported = Math.floor(Math.random() * 10000) + 1000;
    
    dataset.stats.rowCount = mockImported;
    dataset.stats.updatedAt = new Date();
    dataset.quality.completeness = Math.random() * 10 + 90; // 90-100%

    this.emit('dataImported', { datasetId, imported: mockImported });

    return { imported: mockImported, errors: 0 };
  }

  // Create ML model
  createModel(config: Omit<MLModel, 'id' | 'version' | 'status' | 'metrics' | 'training' | 'deployment' | 'createdAt' | 'updatedAt'>): MLModel {
    const model: MLModel = {
      ...config,
      id: crypto.randomUUID(),
      version: '1.0.0',
      status: 'draft',
      metrics: {},
      training: {},
      deployment: {
        status: 'offline',
        totalPredictions: 0,
        averageLatency: 0,
      },
      createdAt: new Date(),
      updatedAt: new Date(),
    };

    this.models.set(model.id, model);
    this.emit('modelCreated', model);
    return model;
  }

  // Create and run pipeline
  async createAndRunPipeline(config: Omit<MLPipeline, 'id' | 'status' | 'steps' | 'createdAt' | 'updatedAt'>): Promise<MLPipeline> {
    const pipeline: MLPipeline = {
      ...config,
      id: crypto.randomUUID(),
      status: 'idle',
      steps: [
        {
          id: 'step-1',
          name: 'Data Extraction',
          type: 'extract',
          status: 'pending',
          config: {},
        },
        {
          id: 'step-2',
          name: 'Data Transformation',
          type: 'transform',
          status: 'pending',
          config: {},
        },
        {
          id: 'step-3',
          name: 'Model Training',
          type: 'train',
          status: 'pending',
          config: {},
        },
        {
          id: 'step-4',
          name: 'Model Evaluation',
          type: 'evaluate',
          status: 'pending',
          config: {},
        },
        {
          id: 'step-5',
          name: 'Model Deployment',
          type: 'deploy',
          status: 'pending',
          config: {},
        },
      ],
      createdAt: new Date(),
      updatedAt: new Date(),
    };

    this.pipelines.set(pipeline.id, pipeline);

    // Run pipeline asynchronously
    this.runPipeline(pipeline.id).catch(console.error);

    return pipeline;
  }

  // Run pipeline
  private async runPipeline(pipelineId: string): Promise<void> {
    const pipeline = this.pipelines.get(pipelineId);
    if (!pipeline) throw new Error('Pipeline not found');

    const model = this.models.get(pipeline.modelId!);
    if (!model) throw new Error('Model not found');

    pipeline.status = 'preparing';
    this.emit('pipelineStarted', pipeline);

    try {
      for (const step of pipeline.steps) {
        step.status = 'running';
        step.startedAt = new Date();
        pipeline.status = this.getPipelineStatusFromStep(step.type);

        this.emit('stepStarted', { pipelineId, step });

        try {
          switch (step.type) {
            case 'extract':
              step.output = await this.runExtraction(pipeline.datasetId);
              break;
            case 'transform':
              step.output = await this.runTransformation(step.output as Dataset);
              break;
            case 'train':
              step.output = await this.runTraining(model);
              break;
            case 'evaluate':
              step.output = await this.runEvaluation(model);
              break;
            case 'deploy':
              step.output = await this.runDeployment(model);
              break;
          }

          step.status = 'completed';
          step.completedAt = new Date();
          this.emit('stepCompleted', { pipelineId, step });

        } catch (error) {
          step.status = 'failed';
          step.error = String(error);
          step.completedAt = new Date();
          this.emit('stepFailed', { pipelineId, step, error });
          throw error;
        }
      }

      pipeline.status = 'completed';
      this.emit('pipelineCompleted', pipeline);

    } catch (error) {
      pipeline.status = 'failed';
      this.emit('pipelineFailed', { pipeline, error });
    }

    pipeline.updatedAt = new Date();
  }

  // Make prediction
  async predict(
    modelId: string,
    input: Record<string, unknown>
  ): Promise<{
    prediction: unknown;
    confidence: number;
    explanation: Record<string, number>;
    latency: number;
  }> {
    const model = this.models.get(modelId);
    if (!model) throw new Error('Model not found');

    if (model.status !== 'deployed' || model.deployment?.status !== 'online') {
      throw new Error('Model is not deployed');
    }

    const startTime = Date.now();

    // In production, this would:
    // 1. Validate input
    // 2. Preprocess features
    // 3. Call model endpoint
    // 4. Post-process output

    const latency = Date.now() - startTime;

    // Store prediction
    const prediction: Prediction = {
      id: crypto.randomUUID(),
      modelId,
      tenantId: model.tenantId,
      input,
      output: {},
      confidence: Math.random(),
      explanation: this.generateExplanation(model),
      latency,
      createdAt: new Date(),
    };

    const predictions = this.predictions.get(modelId) || [];
    predictions.push(prediction);
    this.predictions.set(modelId, predictions.slice(-10000)); // Keep last 10k

    // Update deployment stats
    if (model.deployment) {
      model.deployment.lastUsedAt = new Date();
      model.deployment.totalPredictions++;
      model.deployment.averageLatency = 
        (model.deployment.averageLatency * (model.deployment.totalPredictions - 1) + latency) / 
        model.deployment.totalPredictions;
    }

    return {
      prediction: prediction.output,
      confidence: prediction.confidence,
      explanation: prediction.explanation,
      latency,
    };
  }

  // Batch prediction
  async predictBatch(
    modelId: string,
    inputs: Record<string, unknown>[]
  ): Promise<Array<{
    input: Record<string, unknown>;
    prediction: unknown;
    confidence: number;
  }>> {
    // In production, use batch endpoint for efficiency
    const results = [];
    for (const input of inputs) {
      const result = await this.predict(modelId, input);
      results.push({
        input,
        prediction: result.prediction,
        confidence: result.confidence,
      });
    }
    return results;
  }

  // Get feature importance
  async getFeatureImportance(modelId: string): Promise<Feature[]> {
    const model = this.models.get(modelId);
    if (!model) throw new Error('Model not found');

    // Return features sorted by importance
    return model.config.features.map(name => ({
      name,
      type: 'numeric',
      importance: Math.random(),
      statistics: {
        mean: Math.random() * 100,
        std: Math.random() * 20,
        min: Math.random() * 50,
        max: Math.random() * 150 + 50,
      },
    })).sort((a, b) => b.importance - a.importance);
  }

  // Get model performance over time
  async getPerformanceHistory(
    modelId: string,
    period: { from: Date; to: Date }
  ): Promise<Array<{
    date: string;
    accuracy: number;
    predictions: number;
    avgLatency: number;
  }>> {
    const history = [];
    const days = Math.ceil((period.to.getTime() - period.from.getTime()) / (1000 * 60 * 60 * 24));

    for (let i = 0; i < days; i++) {
      const date = new Date(period.from);
      date.setDate(date.getDate() + i);

      history.push({
        date: date.toISOString().split('T')[0],
        accuracy: Math.random() * 0.2 + 0.8, // 80-100%
        predictions: Math.floor(Math.random() * 1000),
        avgLatency: Math.random() * 100 + 50, // 50-150ms
      });
    }

    return history;
  }

  // Retrain model with new data
  async retrain(
    modelId: string,
    options: {
      incrementally?: boolean;
      newDatasetId?: string;
    } = {}
  ): Promise<MLPipeline> {
    const model = this.models.get(modelId);
    if (!model) throw new Error('Model not found');

    // Create new version
    const versionParts = model.version.split('.').map(Number);
    versionParts[2]++; // Increment patch version
    model.version = versionParts.join('.');

    model.status = 'training';

    return this.createAndRunPipeline({
      tenantId: model.tenantId,
      name: `Retrain ${model.name} v${model.version}`,
      datasetId: options.newDatasetId || model.datasetId,
      modelId: model.id,
    });
  }

  // A/B test models
  async createABTest(
    modelIds: string[],
    config: {
      trafficSplit: number[];
      duration: number; // days
      metric: 'accuracy' | 'latency' | 'userSatisfaction';
    }
  ): Promise<{
    testId: string;
    status: 'running' | 'completed';
    results: Array<{
      modelId: string;
      traffic: number;
      metric: number;
      confidence: number;
    }>;
    winner?: string;
  }> {
    // In production, set up shadow deployment
    return {
      testId: crypto.randomUUID(),
      status: 'running',
      results: modelIds.map((id, i) => ({
        modelId: id,
        traffic: config.trafficSplit[i],
        metric: Math.random(),
        confidence: Math.random(),
      })),
    };
  }

  // Get all models for tenant
  getModels(tenantId: string, options: {
    status?: MLModel['status'];
    type?: ModelType;
  } = {}): MLModel[] {
    let models = Array.from(this.models.values()).filter(m => m.tenantId === tenantId);

    if (options.status) {
      models = models.filter(m => m.status === options.status);
    }

    if (options.type) {
      models = models.filter(m => m.type === options.type);
    }

    return models.sort((a, b) => b.createdAt.getTime() - a.createdAt.getTime());
  }

  // Get pipeline status
  getPipelineStatus(pipelineId: string): MLPipeline | null {
    return this.pipelines.get(pipelineId) || null;
  }

  // Private methods for pipeline steps
  private async runExtraction(datasetId: string): Promise<Dataset> {
    const dataset = this.datasets.get(datasetId);
    if (!dataset) throw new Error('Dataset not found');

    // Simulate extraction
    await new Promise(resolve => setTimeout(resolve, 1000));

    return dataset;
  }

  private async runTransformation(dataset: Dataset): Promise<Dataset> {
    // Simulate feature engineering
    await new Promise(resolve => setTimeout(resolve, 2000));

    return dataset;
  }

  private async runTraining(model: MLModel): Promise<MLModel> {
    model.training.startedAt = new Date();
    model.status = 'training';

    // Simulate training
    const epochs = 100;
    model.training.lossHistory = [];
    model.training.validationHistory = [];

    for (let i = 0; i < epochs; i++) {
      model.training.lossHistory.push(1 / (i + 1));
      model.training.validationHistory.push(1 / (i + 2));
      
      // Emit progress every 10 epochs
      if (i % 10 === 0) {
        this.emit('trainingProgress', { modelId: model.id, epoch: i, total: epochs });
      }

      await new Promise(resolve => setTimeout(resolve, 50));
    }

    model.training.completedAt = new Date();
    model.training.duration = (model.training.completedAt.getTime() - model.training.startedAt.getTime()) / 1000;
    model.training.epochs = epochs;

    return model;
  }

  private async runEvaluation(model: MLModel): Promise<MLModel['metrics']> {
    // Simulate evaluation
    await new Promise(resolve => setTimeout(resolve, 1000));

    const metrics: MLModel['metrics'] = {
      accuracy: Math.random() * 0.15 + 0.85,
      precision: Math.random() * 0.15 + 0.85,
      recall: Math.random() * 0.15 + 0.85,
      f1Score: Math.random() * 0.15 + 0.85,
    };

    model.metrics = metrics;
    model.status = 'ready';

    return metrics;
  }

  private async runDeployment(model: MLModel): Promise<{ endpoint: string }> {
    model.status = 'deployed';
    model.deployment = {
      status: 'online',
      endpoint: `/api/v1/ml/models/${model.id}/predict`,
      totalPredictions: 0,
      averageLatency: 0,
    };

    return { endpoint: model.deployment.endpoint! };
  }

  private getPipelineStatusFromStep(stepType: MLPipeline['steps'][0]['type']): PipelineStatus {
    const map: Record<string, PipelineStatus> = {
      extract: 'preparing',
      transform: 'preparing',
      train: 'training',
      evaluate: 'evaluating',
      deploy: 'deploying',
    };
    return map[stepType] || 'idle';
  }

  private generateExplanation(model: MLModel): Record<string, number> {
    const explanation: Record<string, number> = {};
    for (const feature of model.config.features) {
      explanation[feature] = Math.random();
    }
    return explanation;
  }
}

// Predefined ML templates
export const ML_TEMPLATES: Array<{
  name: string;
  type: ModelType;
  description: string;
  config: Partial<MLModel['config']>;
}> = [
  {
    name: 'Churn Prediction',
    type: 'classification',
    description: 'Predict customer churn probability',
    config: {
      algorithm: 'xgboost',
      features: ['tenure', 'monthly_charges', 'total_charges', 'contract', 'payment_method'],
      hyperparameters: {
        max_depth: 6,
        learning_rate: 0.1,
        n_estimators: 100,
      },
      testSplit: 0.2,
      validationSplit: 0.1,
    },
  },
  {
    name: 'Sales Forecasting',
    type: 'forecasting',
    description: 'Predict future sales based on historical data',
    config: {
      algorithm: 'prophet',
      features: ['date', 'sales', 'promotions', 'seasonality'],
      hyperparameters: {
        changepoint_prior_scale: 0.05,
        seasonality_prior_scale: 10,
      },
      testSplit: 0.2,
      validationSplit: 0.1,
    },
  },
  {
    name: 'Customer Segmentation',
    type: 'clustering',
    description: 'Group customers by behavior',
    config: {
      algorithm: 'kmeans',
      features: ['recency', 'frequency', 'monetary', 'age', 'location'],
      hyperparameters: {
        n_clusters: 5,
        max_iter: 300,
      },
      testSplit: 0,
      validationSplit: 0,
    },
  },
  {
    name: 'Anomaly Detection',
    type: 'anomaly',
    description: 'Detect unusual patterns in transactions',
    config: {
      algorithm: 'isolation_forest',
      features: ['amount', 'time', 'location', 'merchant', 'category'],
      hyperparameters: {
        contamination: 0.1,
        n_estimators: 100,
      },
      testSplit: 0.2,
      validationSplit: 0.1,
    },
  },
];

// Export singleton
export const mlPipelineManager = new MLPipelineManager();

export { Dataset, MLModel, MLPipeline, Prediction, ModelType, Feature };