workspace-v1/projects/erp-construccion/docs/02-definicion-modulos/MAI-006-reportes-analytics/especificaciones/ET-BI-003-implementacion-analisis-predictivo.md

ET-BI-003: Implementación de Análisis Predictivo

Épica: MAI-006 - Reportes y Business Intelligence Módulo: Análisis Predictivo y Machine Learning Responsable Técnico: Data Science + Backend + Frontend Fecha: 2025-11-17 Versión: 1.0

---

1. Objetivo Técnico

Implementar el sistema de análisis predictivo con:

• Predicción de costos y cronogramas usando ML
• Modelos de regresión lineal, ARIMA, Random Forest
• Simulación de escenarios (optimista, realista, pesimista)
• Detección de anomalías en costos
• Forecasting de flujo de caja
• Entrenamiento y re-entrenamiento automático de modelos
• Integración con scikit-learn y TensorFlow.js
• Dashboard de métricas de modelos (accuracy, RMSE, MAE)

---

2. Stack Tecnológico

Backend

- NestJS 10+ con TypeScript
- PostgreSQL 15+ (schema: predictions)
- Python 3.11+ para ML (Flask micro-service)
- Bull/BullMQ para procesamiento asíncrono
- node-cron para re-entrenamiento programado

ML/Data Science

- scikit-learn 1.3+ (regresión, clasificación)
- statsmodels (ARIMA, time series)
- pandas, numpy para data processing
- joblib para serialización de modelos
- TensorFlow 2.14+ (opcional para deep learning)

Frontend

- React 18 con TypeScript
- Chart.js / Recharts para visualizaciones
- react-query para cache de predicciones
- Zustand para state management

---

3. Modelo de Datos SQL

3.1 Schema Principal

-- =====================================================
-- SCHEMA: predictions
-- Descripción: Análisis predictivo y ML
-- =====================================================

CREATE SCHEMA IF NOT EXISTS predictions;

-- =====================================================
-- TABLE: predictions.ml_models
-- Descripción: Registro de modelos de ML
-- =====================================================

CREATE TABLE predictions.ml_models (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  constructora_id UUID NOT NULL REFERENCES public.constructoras(id) ON DELETE CASCADE,

  -- Identificación
  model_code VARCHAR(50) NOT NULL,
  model_name VARCHAR(255) NOT NULL,
  description TEXT,

  -- Tipo de modelo
  model_type VARCHAR(30) NOT NULL,
  -- linear_regression, random_forest, arima, xgboost, neural_network

  prediction_target VARCHAR(50) NOT NULL,
  -- cost_overrun, schedule_delay, margin_erosion, cash_flow, risk_score

  -- Algoritmo
  algorithm VARCHAR(50) NOT NULL,
  hyperparameters JSONB DEFAULT '{}',
  /*
  {
    "n_estimators": 100,
    "max_depth": 10,
    "learning_rate": 0.01
  }
  */

  -- Features utilizadas
  features JSONB NOT NULL,
  /*
  [
    {"name": "project_size", "type": "numeric", "importance": 0.35},
    {"name": "project_complexity", "type": "categorical", "importance": 0.25},
    {"name": "historical_variance", "type": "numeric", "importance": 0.20}
  ]
  */

  -- Métricas de desempeño
  training_metrics JSONB,
  /*
  {
    "train_score": 0.85,
    "test_score": 0.78,
    "cross_val_score": 0.80,
    "rmse": 125000,
    "mae": 98000,
    "r2_score": 0.82
  }
  */

  -- Versión del modelo
  version INTEGER NOT NULL DEFAULT 1,
  parent_model_id UUID REFERENCES predictions.ml_models(id),

  -- Archivos
  model_file_path VARCHAR(500), -- /models/cost_prediction_v1.joblib
  training_data_path VARCHAR(500),

  -- Estado
  status VARCHAR(20) NOT NULL DEFAULT 'training',
  -- training, trained, validated, deployed, deprecated

  is_active BOOLEAN DEFAULT false,

  -- Metadata
  trained_by UUID REFERENCES auth.users(id),
  trained_at TIMESTAMP,
  last_retrained_at TIMESTAMP,
  deployed_at TIMESTAMP,

  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

  CONSTRAINT valid_model_type CHECK (model_type IN (
    'linear_regression', 'random_forest', 'arima', 'xgboost', 'neural_network'
  )),
  CONSTRAINT valid_status CHECK (status IN (
    'training', 'trained', 'validated', 'deployed', 'deprecated'
  )),
  UNIQUE(constructora_id, model_code, version)
);

CREATE INDEX idx_ml_models_constructora ON predictions.ml_models(constructora_id);
CREATE INDEX idx_ml_models_type ON predictions.ml_models(model_type);
CREATE INDEX idx_ml_models_active ON predictions.ml_models(is_active) WHERE is_active = true;


-- =====================================================
-- TABLE: predictions.cost_predictions
-- Descripción: Predicciones de costos
-- =====================================================

CREATE TABLE predictions.cost_predictions (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  project_id UUID NOT NULL REFERENCES projects.projects(id) ON DELETE CASCADE,
  model_id UUID NOT NULL REFERENCES predictions.ml_models(id),

  -- Predicción
  prediction_date DATE NOT NULL,
  prediction_horizon INTEGER NOT NULL, -- días hacia el futuro

  -- Costos predichos
  predicted_total_cost DECIMAL(15,2) NOT NULL,
  predicted_cost_variance DECIMAL(15,2), -- diferencia vs presupuesto
  predicted_cost_variance_pct DECIMAL(5,2),

  -- Intervalos de confianza
  confidence_level DECIMAL(5,2) DEFAULT 95.00,
  lower_bound DECIMAL(15,2),
  upper_bound DECIMAL(15,2),

  -- Features utilizadas en la predicción
  input_features JSONB,
  /*
  {
    "current_progress": 0.45,
    "budget_utilization": 0.52,
    "current_spi": 0.98,
    "current_cpi": 1.05,
    "remaining_duration": 120
  }
  */

  -- Resultado real (para validación posterior)
  actual_cost DECIMAL(15,2),
  prediction_error DECIMAL(15,2),
  prediction_accuracy DECIMAL(5,2),

  -- Metadata
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_cost_pred_project ON predictions.cost_predictions(project_id);
CREATE INDEX idx_cost_pred_model ON predictions.cost_predictions(model_id);
CREATE INDEX idx_cost_pred_date ON predictions.cost_predictions(prediction_date DESC);


-- =====================================================
-- TABLE: predictions.schedule_predictions
-- Descripción: Predicciones de cronograma
-- =====================================================

CREATE TABLE predictions.schedule_predictions (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  project_id UUID NOT NULL REFERENCES projects.projects(id) ON DELETE CASCADE,
  schedule_id UUID NOT NULL REFERENCES schedules.schedules(id),
  model_id UUID NOT NULL REFERENCES predictions.ml_models(id),

  -- Predicción
  prediction_date DATE NOT NULL,

  -- Fechas predichas
  predicted_completion_date DATE NOT NULL,
  predicted_delay_days INTEGER,
  baseline_completion_date DATE NOT NULL,

  -- Probabilidades
  on_time_probability DECIMAL(5,2),
  delay_probability DECIMAL(5,2),

  -- Actividades críticas en riesgo
  critical_activities_at_risk JSONB,
  /*
  [
    {
      "activity_id": "uuid-1",
      "activity_name": "Cimentación",
      "delay_risk_score": 0.75,
      "predicted_delay_days": 5
    }
  ]
  */

  -- Intervalos de confianza
  confidence_level DECIMAL(5,2) DEFAULT 95.00,
  earliest_completion DATE,
  latest_completion DATE,

  -- Features
  input_features JSONB,

  -- Resultado real
  actual_completion_date DATE,
  prediction_error_days INTEGER,

  -- Metadata
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_schedule_pred_project ON predictions.schedule_predictions(project_id);
CREATE INDEX idx_schedule_pred_schedule ON predictions.schedule_predictions(schedule_id);
CREATE INDEX idx_schedule_pred_date ON predictions.schedule_predictions(prediction_date DESC);


-- =====================================================
-- TABLE: predictions.scenario_simulations
-- Descripción: Simulaciones de escenarios
-- =====================================================

CREATE TABLE predictions.scenario_simulations (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  project_id UUID NOT NULL REFERENCES projects.projects(id) ON DELETE CASCADE,

  -- Identificación
  simulation_name VARCHAR(255) NOT NULL,
  description TEXT,

  -- Tipo de escenario
  scenario_type VARCHAR(20) NOT NULL,
  -- optimistic, realistic, pessimistic, custom

  -- Parámetros del escenario
  parameters JSONB NOT NULL,
  /*
  {
    "costVarianceFactor": 0.10, // +10%
    "scheduleVarianceFactor": 0.15, // +15%
    "productivityFactor": 0.95, // -5%
    "weatherImpact": true,
    "resourceAvailability": 0.90
  }
  */

  -- Resultados de la simulación
  results JSONB,
  /*
  {
    "projectedCost": 10500000,
    "projectedCompletionDate": "2025-12-31",
    "projectedMargin": 950000,
    "projectedMarginPct": 9.0,
    "riskScore": 65.5,
    "criticalRisks": [...]
  }
  */

  -- Comparación con baseline
  baseline_comparison JSONB,
  /*
  {
    "costVariance": 500000,
    "costVariancePct": 5.0,
    "scheduleVarianceDays": 15,
    "marginImpact": -100000
  }
  */

  -- Metadata
  created_by UUID REFERENCES auth.users(id),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

  CONSTRAINT valid_scenario_type CHECK (scenario_type IN (
    'optimistic', 'realistic', 'pessimistic', 'custom'
  ))
);

CREATE INDEX idx_simulations_project ON predictions.scenario_simulations(project_id);
CREATE INDEX idx_simulations_type ON predictions.scenario_simulations(scenario_type);


-- =====================================================
-- TABLE: predictions.training_data
-- Descripción: Datasets para entrenamiento
-- =====================================================

CREATE TABLE predictions.training_data (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  constructora_id UUID NOT NULL REFERENCES public.constructoras(id) ON DELETE CASCADE,

  -- Identificación
  dataset_name VARCHAR(255) NOT NULL,
  description TEXT,

  -- Tipo de dataset
  dataset_type VARCHAR(30) NOT NULL,
  -- historical_projects, cost_actuals, schedule_actuals, external_factors

  -- Configuración
  data_source JSONB NOT NULL,
  /*
  {
    "sourceType": "sql_query",
    "query": "SELECT...",
    "filters": {...}
  }
  */

  -- Estadísticas del dataset
  row_count INTEGER,
  feature_count INTEGER,
  date_range_start DATE,
  date_range_end DATE,

  statistics JSONB,
  /*
  {
    "mean_cost": 8500000,
    "std_cost": 2100000,
    "min_cost": 3000000,
    "max_cost": 25000000
  }
  */

  -- Archivos
  file_path VARCHAR(500),
  file_format VARCHAR(20), -- csv, parquet, json

  -- Metadata
  created_by UUID REFERENCES auth.users(id),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_training_data_constructora ON predictions.training_data(constructora_id);


-- =====================================================
-- TABLE: predictions.forecast_history
-- Descripción: Historial de forecasts
-- =====================================================

CREATE TABLE predictions.forecast_history (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  project_id UUID NOT NULL REFERENCES projects.projects(id) ON DELETE CASCADE,

  -- Tipo de forecast
  forecast_type VARCHAR(30) NOT NULL,
  -- cash_flow, revenue, cost, resource_demand

  forecast_date DATE NOT NULL,
  forecast_period_start DATE NOT NULL,
  forecast_period_end DATE NOT NULL,

  -- Forecast
  forecast_values JSONB NOT NULL,
  /*
  [
    {"date": "2025-01", "value": 850000, "lower": 800000, "upper": 900000},
    {"date": "2025-02", "value": 920000, "lower": 870000, "upper": 970000}
  ]
  */

  -- Método
  forecasting_method VARCHAR(30) NOT NULL,
  -- arima, exponential_smoothing, prophet, linear_regression

  model_id UUID REFERENCES predictions.ml_models(id),

  -- Precisión (calculada después)
  mape DECIMAL(5,2), -- Mean Absolute Percentage Error
  forecast_bias DECIMAL(10,2),

  -- Metadata
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_forecast_project ON predictions.forecast_history(project_id);
CREATE INDEX idx_forecast_type ON predictions.forecast_history(forecast_type);
CREATE INDEX idx_forecast_date ON predictions.forecast_history(forecast_date DESC);


-- =====================================================
-- TABLE: predictions.anomaly_detections
-- Descripción: Detección de anomalías
-- =====================================================

CREATE TABLE predictions.anomaly_detections (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  project_id UUID NOT NULL REFERENCES projects.projects(id) ON DELETE CASCADE,

  -- Detección
  detection_date DATE NOT NULL,
  anomaly_type VARCHAR(30) NOT NULL,
  -- cost_spike, schedule_slippage, margin_erosion, resource_overutilization

  -- Severidad
  severity VARCHAR(20) NOT NULL,
  -- low, medium, high, critical
  anomaly_score DECIMAL(5,2), -- 0-100

  -- Detalles
  description TEXT,
  affected_metric VARCHAR(100),
  expected_value DECIMAL(15,2),
  actual_value DECIMAL(15,2),
  deviation_pct DECIMAL(5,2),

  -- Contexto
  context JSONB,
  /*
  {
    "activity_id": "uuid",
    "budget_item_id": "uuid",
    "period": "2025-11"
  }
  */

  -- Recomendaciones
  recommendations TEXT[],

  -- Estado
  status VARCHAR(20) NOT NULL DEFAULT 'new',
  -- new, acknowledged, investigating, resolved, false_positive

  acknowledged_by UUID REFERENCES auth.users(id),
  acknowledged_at TIMESTAMP,
  resolution_notes TEXT,

  -- Metadata
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

  CONSTRAINT valid_severity CHECK (severity IN ('low', 'medium', 'high', 'critical')),
  CONSTRAINT valid_status CHECK (status IN (
    'new', 'acknowledged', 'investigating', 'resolved', 'false_positive'
  ))
);

CREATE INDEX idx_anomalies_project ON predictions.anomaly_detections(project_id);
CREATE INDEX idx_anomalies_type ON predictions.anomaly_detections(anomaly_type);
CREATE INDEX idx_anomalies_severity ON predictions.anomaly_detections(severity);
CREATE INDEX idx_anomalies_status ON predictions.anomaly_detections(status) WHERE status = 'new';

3.2 Functions y Triggers

-- =====================================================
-- FUNCTION: Calcular error de predicción
-- =====================================================

CREATE OR REPLACE FUNCTION predictions.calculate_prediction_error()
RETURNS TRIGGER AS $$
BEGIN
  IF NEW.actual_cost IS NOT NULL AND OLD.actual_cost IS NULL THEN
    NEW.prediction_error := ABS(NEW.actual_cost - NEW.predicted_total_cost);
    NEW.prediction_accuracy := (1 - (NEW.prediction_error / NULLIF(NEW.actual_cost, 0))) * 100;
  END IF;

  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER trg_calculate_cost_prediction_error
  BEFORE UPDATE ON predictions.cost_predictions
  FOR EACH ROW
  EXECUTE FUNCTION predictions.calculate_prediction_error();


-- =====================================================
-- FUNCTION: Detectar anomalías en costos
-- =====================================================

CREATE OR REPLACE FUNCTION predictions.detect_cost_anomalies(
  p_project_id UUID,
  p_threshold_pct DECIMAL DEFAULT 15.0
) RETURNS TABLE (
  metric VARCHAR,
  expected_value DECIMAL,
  actual_value DECIMAL,
  deviation_pct DECIMAL,
  is_anomaly BOOLEAN
) AS $$
BEGIN
  RETURN QUERY
  WITH project_stats AS (
    SELECT
      AVG(weekly_cost) AS avg_weekly_cost,
      STDDEV(weekly_cost) AS stddev_weekly_cost
    FROM (
      SELECT
        DATE_TRUNC('week', t.transaction_date) AS week,
        SUM(t.amount) AS weekly_cost
      FROM costs.transactions t
      WHERE t.project_id = p_project_id
        AND t.transaction_date >= CURRENT_DATE - INTERVAL '12 weeks'
      GROUP BY DATE_TRUNC('week', t.transaction_date)
    ) weekly_costs
  ),
  current_week AS (
    SELECT
      SUM(t.amount) AS current_weekly_cost
    FROM costs.transactions t
    WHERE t.project_id = p_project_id
      AND t.transaction_date >= DATE_TRUNC('week', CURRENT_DATE)
  )
  SELECT
    'weekly_cost'::VARCHAR AS metric,
    ps.avg_weekly_cost AS expected_value,
    cw.current_weekly_cost AS actual_value,
    ((cw.current_weekly_cost - ps.avg_weekly_cost) / NULLIF(ps.avg_weekly_cost, 0) * 100) AS deviation_pct,
    (ABS(cw.current_weekly_cost - ps.avg_weekly_cost) > ps.stddev_weekly_cost * 2) AS is_anomaly
  FROM project_stats ps, current_week cw;
END;
$$ LANGUAGE plpgsql;

---

4. TypeORM Entities

4.1 MLModel Entity

// src/modules/predictions/entities/ml-model.entity.ts

import {
  Entity,
  PrimaryGeneratedColumn,
  Column,
  ManyToOne,
  JoinColumn,
  CreateDateColumn,
  UpdateDateColumn,
  Index,
} from 'typeorm';
import { Constructora } from '../../constructoras/entities/constructora.entity';
import { User } from '../../auth/entities/user.entity';

export enum ModelType {
  LINEAR_REGRESSION = 'linear_regression',
  RANDOM_FOREST = 'random_forest',
  ARIMA = 'arima',
  XGBOOST = 'xgboost',
  NEURAL_NETWORK = 'neural_network',
}

export enum ModelStatus {
  TRAINING = 'training',
  TRAINED = 'trained',
  VALIDATED = 'validated',
  DEPLOYED = 'deployed',
  DEPRECATED = 'deprecated',
}

export interface Feature {
  name: string;
  type: 'numeric' | 'categorical';
  importance?: number;
}

export interface TrainingMetrics {
  train_score: number;
  test_score: number;
  cross_val_score?: number;
  rmse?: number;
  mae?: number;
  r2_score?: number;
}

@Entity('ml_models', { schema: 'predictions' })
@Index(['constructoraId', 'modelCode', 'version'], { unique: true })
export class MLModel {
  @PrimaryGeneratedColumn('uuid')
  id: string;

  @Column({ name: 'constructora_id', type: 'uuid' })
  @Index()
  constructoraId: string;

  @ManyToOne(() => Constructora)
  @JoinColumn({ name: 'constructora_id' })
  constructora: Constructora;

  // Identificación
  @Column({ name: 'model_code', type: 'varchar', length: 50 })
  modelCode: string;

  @Column({ name: 'model_name', type: 'varchar', length: 255 })
  modelName: string;

  @Column({ type: 'text', nullable: true })
  description?: string;

  // Tipo
  @Column({ name: 'model_type', type: 'enum', enum: ModelType })
  @Index()
  modelType: ModelType;

  @Column({ name: 'prediction_target', type: 'varchar', length: 50 })
  predictionTarget: string;

  // Algoritmo
  @Column({ type: 'varchar', length: 50 })
  algorithm: string;

  @Column({ type: 'jsonb', default: {} })
  hyperparameters: any;

  // Features
  @Column({ type: 'jsonb' })
  features: Feature[];

  // Métricas
  @Column({ name: 'training_metrics', type: 'jsonb', nullable: true })
  trainingMetrics?: TrainingMetrics;

  // Versión
  @Column({ type: 'integer', default: 1 })
  version: number;

  @Column({ name: 'parent_model_id', type: 'uuid', nullable: true })
  parentModelId?: string;

  @ManyToOne(() => MLModel)
  @JoinColumn({ name: 'parent_model_id' })
  parentModel?: MLModel;

  // Archivos
  @Column({ name: 'model_file_path', type: 'varchar', length: 500, nullable: true })
  modelFilePath?: string;

  @Column({ name: 'training_data_path', type: 'varchar', length: 500, nullable: true })
  trainingDataPath?: string;

  // Estado
  @Column({ type: 'enum', enum: ModelStatus, default: ModelStatus.TRAINING })
  status: ModelStatus;

  @Column({ name: 'is_active', type: 'boolean', default: false })
  @Index()
  isActive: boolean;

  // Metadata
  @Column({ name: 'trained_by', type: 'uuid', nullable: true })
  trainedBy?: string;

  @ManyToOne(() => User)
  @JoinColumn({ name: 'trained_by' })
  trainer?: User;

  @Column({ name: 'trained_at', type: 'timestamp', nullable: true })
  trainedAt?: Date;

  @Column({ name: 'last_retrained_at', type: 'timestamp', nullable: true })
  lastRetrainedAt?: Date;

  @Column({ name: 'deployed_at', type: 'timestamp', nullable: true })
  deployedAt?: Date;

  @CreateDateColumn({ name: 'created_at' })
  createdAt: Date;

  @UpdateDateColumn({ name: 'updated_at' })
  updatedAt: Date;
}

4.2 CostPrediction Entity

// src/modules/predictions/entities/cost-prediction.entity.ts

import {
  Entity,
  PrimaryGeneratedColumn,
  Column,
  ManyToOne,
  JoinColumn,
  CreateDateColumn,
  Index,
} from 'typeorm';
import { Project } from '../../projects/entities/project.entity';
import { MLModel } from './ml-model.entity';

@Entity('cost_predictions', { schema: 'predictions' })
export class CostPrediction {
  @PrimaryGeneratedColumn('uuid')
  id: string;

  @Column({ name: 'project_id', type: 'uuid' })
  @Index()
  projectId: string;

  @ManyToOne(() => Project)
  @JoinColumn({ name: 'project_id' })
  project: Project;

  @Column({ name: 'model_id', type: 'uuid' })
  @Index()
  modelId: string;

  @ManyToOne(() => MLModel)
  @JoinColumn({ name: 'model_id' })
  model: MLModel;

  // Predicción
  @Column({ name: 'prediction_date', type: 'date' })
  @Index()
  predictionDate: Date;

  @Column({ name: 'prediction_horizon', type: 'integer' })
  predictionHorizon: number;

  // Costos predichos
  @Column({ name: 'predicted_total_cost', type: 'decimal', precision: 15, scale: 2 })
  predictedTotalCost: number;

  @Column({ name: 'predicted_cost_variance', type: 'decimal', precision: 15, scale: 2, nullable: true })
  predictedCostVariance?: number;

  @Column({ name: 'predicted_cost_variance_pct', type: 'decimal', precision: 5, scale: 2, nullable: true })
  predictedCostVariancePct?: number;

  // Intervalos de confianza
  @Column({ name: 'confidence_level', type: 'decimal', precision: 5, scale: 2, default: 95.00 })
  confidenceLevel: number;

  @Column({ name: 'lower_bound', type: 'decimal', precision: 15, scale: 2, nullable: true })
  lowerBound?: number;

  @Column({ name: 'upper_bound', type: 'decimal', precision: 15, scale: 2, nullable: true })
  upperBound?: number;

  // Features
  @Column({ name: 'input_features', type: 'jsonb', nullable: true })
  inputFeatures?: any;

  // Resultado real
  @Column({ name: 'actual_cost', type: 'decimal', precision: 15, scale: 2, nullable: true })
  actualCost?: number;

  @Column({ name: 'prediction_error', type: 'decimal', precision: 15, scale: 2, nullable: true })
  predictionError?: number;

  @Column({ name: 'prediction_accuracy', type: 'decimal', precision: 5, scale: 2, nullable: true })
  predictionAccuracy?: number;

  // Metadata
  @CreateDateColumn({ name: 'created_at' })
  createdAt: Date;
}

4.3 ScenarioSimulation Entity

// src/modules/predictions/entities/scenario-simulation.entity.ts

import {
  Entity,
  PrimaryGeneratedColumn,
  Column,
  ManyToOne,
  JoinColumn,
  CreateDateColumn,
  Index,
} from 'typeorm';
import { Project } from '../../projects/entities/project.entity';
import { User } from '../../auth/entities/user.entity';

export enum ScenarioType {
  OPTIMISTIC = 'optimistic',
  REALISTIC = 'realistic',
  PESSIMISTIC = 'pessimistic',
  CUSTOM = 'custom',
}

@Entity('scenario_simulations', { schema: 'predictions' })
export class ScenarioSimulation {
  @PrimaryGeneratedColumn('uuid')
  id: string;

  @Column({ name: 'project_id', type: 'uuid' })
  @Index()
  projectId: string;

  @ManyToOne(() => Project)
  @JoinColumn({ name: 'project_id' })
  project: Project;

  // Identificación
  @Column({ name: 'simulation_name', type: 'varchar', length: 255 })
  simulationName: string;

  @Column({ type: 'text', nullable: true })
  description?: string;

  // Tipo
  @Column({ name: 'scenario_type', type: 'enum', enum: ScenarioType })
  @Index()
  scenarioType: ScenarioType;

  // Parámetros
  @Column({ type: 'jsonb' })
  parameters: any;

  // Resultados
  @Column({ type: 'jsonb', nullable: true })
  results?: any;

  // Comparación
  @Column({ name: 'baseline_comparison', type: 'jsonb', nullable: true })
  baselineComparison?: any;

  // Metadata
  @Column({ name: 'created_by', type: 'uuid', nullable: true })
  createdBy?: string;

  @ManyToOne(() => User)
  @JoinColumn({ name: 'created_by' })
  creator?: User;

  @CreateDateColumn({ name: 'created_at' })
  createdAt: Date;
}

---

5. Services (Lógica de Negocio)

5.1 PredictionService

// src/modules/predictions/services/prediction.service.ts

import { Injectable, Logger } from '@nestjs/common';
import { InjectRepository } from '@nestjs/typeorm';
import { Repository } from 'typeorm';
import { CostPrediction } from '../entities/cost-prediction.entity';
import { SchedulePrediction } from '../entities/schedule-prediction.entity';
import { MLModel, ModelStatus } from '../entities/ml-model.entity';
import { HttpService } from '@nestjs/axios';
import { ConfigService } from '@nestjs/config';
import { lastValueFrom } from 'rxjs';

@Injectable()
export class PredictionService {
  private readonly logger = new Logger(PredictionService.name);
  private readonly mlServiceUrl: string;

  constructor(
    @InjectRepository(CostPrediction)
    private costPredictionRepo: Repository<CostPrediction>,
    @InjectRepository(SchedulePrediction)
    private schedulePredictionRepo: Repository<SchedulePrediction>,
    @InjectRepository(MLModel)
    private mlModelRepo: Repository<MLModel>,
    private httpService: HttpService,
    private configService: ConfigService,
  ) {
    this.mlServiceUrl = this.configService.get('ML_SERVICE_URL') || 'http://localhost:5000';
  }

  /**
   * Predecir costo final de un proyecto
   */
  async predictProjectCost(
    projectId: string,
    predictionHorizon: number = 90,
  ): Promise<CostPrediction> {
    // Obtener modelo activo para cost prediction
    const model = await this.mlModelRepo.findOne({
      where: {
        predictionTarget: 'cost_overrun',
        isActive: true,
        status: ModelStatus.DEPLOYED,
      },
    });

    if (!model) {
      throw new Error('No active cost prediction model found');
    }

    // Preparar features
    const features = await this.prepareCostFeatures(projectId);

    // Llamar al servicio de ML (Python)
    const prediction = await this.callMLService('predict/cost', {
      modelId: model.id,
      features,
    });

    // Guardar predicción
    const costPrediction = this.costPredictionRepo.create({
      projectId,
      modelId: model.id,
      predictionDate: new Date(),
      predictionHorizon,
      predictedTotalCost: prediction.predicted_cost,
      predictedCostVariance: prediction.variance,
      predictedCostVariancePct: prediction.variance_pct,
      confidenceLevel: 95,
      lowerBound: prediction.lower_bound,
      upperBound: prediction.upper_bound,
      inputFeatures: features,
    });

    return this.costPredictionRepo.save(costPrediction);
  }

  /**
   * Predecir fecha de finalización de un proyecto
   */
  async predictProjectSchedule(
    projectId: string,
    scheduleId: string,
  ): Promise<SchedulePrediction> {
    const model = await this.mlModelRepo.findOne({
      where: {
        predictionTarget: 'schedule_delay',
        isActive: true,
        status: ModelStatus.DEPLOYED,
      },
    });

    if (!model) {
      throw new Error('No active schedule prediction model found');
    }

    const features = await this.prepareScheduleFeatures(projectId, scheduleId);

    const prediction = await this.callMLService('predict/schedule', {
      modelId: model.id,
      features,
    });

    const schedulePrediction = this.schedulePredictionRepo.create({
      projectId,
      scheduleId,
      modelId: model.id,
      predictionDate: new Date(),
      predictedCompletionDate: new Date(prediction.predicted_completion_date),
      predictedDelayDays: prediction.delay_days,
      baselineCompletionDate: features.baseline_completion_date,
      onTimeProbability: prediction.on_time_probability,
      delayProbability: prediction.delay_probability,
      criticalActivitiesAtRisk: prediction.critical_activities_at_risk,
      confidenceLevel: 95,
      earliestCompletion: new Date(prediction.earliest_completion),
      latestCompletion: new Date(prediction.latest_completion),
      inputFeatures: features,
    });

    return this.schedulePredictionRepo.save(schedulePrediction);
  }

  /**
   * Preparar features para predicción de costos
   */
  private async prepareCostFeatures(projectId: string): Promise<any> {
    const result = await this.costPredictionRepo.query(`
      SELECT
        p.total_budget,
        p.contracted_value,
        pm.spent_amount,
        pm.budget_utilization_pct,
        pm.physical_progress_pct,
        pm.schedule_progress_pct,
        pm.spi,
        pm.cpi,
        pm.gross_margin_pct,
        EXTRACT(EPOCH FROM (p.end_date - CURRENT_DATE)) / 86400 AS remaining_days,
        EXTRACT(EPOCH FROM (CURRENT_DATE - p.start_date)) / 86400 AS elapsed_days,
        COUNT(DISTINCT sa.id) AS total_activities,
        COUNT(DISTINCT sa.id) FILTER (WHERE sa.status = 'delayed') AS delayed_activities
      FROM projects.projects p
      LEFT JOIN analytics_reports.project_performance_metrics pm ON pm.project_id = p.id
        AND pm.snapshot_date = (
          SELECT MAX(snapshot_date)
          FROM analytics_reports.project_performance_metrics
          WHERE project_id = p.id
        )
      LEFT JOIN schedules.schedule_activities sa ON sa.schedule_id = (
        SELECT id FROM schedules.schedules
        WHERE project_id = p.id AND status = 'active'
        LIMIT 1
      )
      WHERE p.id = $1
      GROUP BY p.id, p.total_budget, p.contracted_value, pm.spent_amount,
               pm.budget_utilization_pct, pm.physical_progress_pct, pm.schedule_progress_pct,
               pm.spi, pm.cpi, pm.gross_margin_pct, p.end_date, p.start_date
    `, [projectId]);

    return result[0];
  }

  /**
   * Preparar features para predicción de cronograma
   */
  private async prepareScheduleFeatures(projectId: string, scheduleId: string): Promise<any> {
    const result = await this.schedulePredictionRepo.query(`
      SELECT
        s.end_date AS baseline_completion_date,
        AVG(sa.percent_complete) AS avg_progress,
        COUNT(*) FILTER (WHERE sa.is_critical_path = true) AS critical_path_activities,
        COUNT(*) FILTER (WHERE sa.status = 'delayed') AS delayed_activities,
        AVG(sa.total_float) AS avg_total_float,
        pm.spi,
        pm.schedule_variance_pct
      FROM schedules.schedules s
      LEFT JOIN schedules.schedule_activities sa ON sa.schedule_id = s.id
      LEFT JOIN analytics_reports.project_performance_metrics pm ON pm.project_id = s.project_id
        AND pm.snapshot_date = (
          SELECT MAX(snapshot_date)
          FROM analytics_reports.project_performance_metrics
          WHERE project_id = s.project_id
        )
      WHERE s.id = $1
      GROUP BY s.id, s.end_date, pm.spi, pm.schedule_variance_pct
    `, [scheduleId]);

    return result[0];
  }

  /**
   * Llamar al servicio de ML (Python Flask)
   */
  private async callMLService(endpoint: string, data: any): Promise<any> {
    try {
      const response = await lastValueFrom(
        this.httpService.post(`${this.mlServiceUrl}/${endpoint}`, data),
      );

      return response.data;
    } catch (error) {
      this.logger.error(`Error calling ML service: ${error.message}`, error.stack);
      throw new Error('Failed to get prediction from ML service');
    }
  }
}

5.2 MLModelService

// src/modules/predictions/services/ml-model.service.ts

import { Injectable, Logger } from '@nestjs/common';
import { InjectRepository } from '@nestjs/typeorm';
import { Repository } from 'typeorm';
import { MLModel, ModelStatus, ModelType } from '../entities/ml-model.entity';
import { TrainingData } from '../entities/training-data.entity';
import { Cron, CronExpression } from '@nestjs/schedule';
import { HttpService } from '@nestjs/axios';
import { lastValueFrom } from 'rxjs';

@Injectable()
export class MLModelService {
  private readonly logger = new Logger(MLModelService.name);

  constructor(
    @InjectRepository(MLModel)
    private mlModelRepo: Repository<MLModel>,
    @InjectRepository(TrainingData)
    private trainingDataRepo: Repository<TrainingData>,
    private httpService: HttpService,
  ) {}

  /**
   * Entrenar nuevo modelo
   */
  async trainModel(
    modelCode: string,
    modelType: ModelType,
    predictionTarget: string,
    trainingDataId: string,
    hyperparameters: any,
    userId: string,
  ): Promise<MLModel> {
    // Crear registro del modelo
    const model = this.mlModelRepo.create({
      modelCode,
      modelType,
      predictionTarget,
      algorithm: this.getAlgorithmForType(modelType),
      hyperparameters,
      status: ModelStatus.TRAINING,
      trainedBy: userId,
    });

    const savedModel = await this.mlModelRepo.save(model);

    // Disparar entrenamiento asíncrono
    this.triggerTraining(savedModel.id, trainingDataId);

    return savedModel;
  }

  /**
   * Disparar entrenamiento (procesamiento asíncrono)
   */
  private async triggerTraining(modelId: string, trainingDataId: string): Promise<void> {
    try {
      // Llamar al servicio de ML para entrenar
      const response = await lastValueFrom(
        this.httpService.post('http://localhost:5000/train', {
          modelId,
          trainingDataId,
        }),
      );

      // Actualizar modelo con métricas
      await this.mlModelRepo.update(modelId, {
        status: ModelStatus.TRAINED,
        trainedAt: new Date(),
        trainingMetrics: response.data.metrics,
        modelFilePath: response.data.model_path,
      });

      this.logger.log(`Model ${modelId} trained successfully`);
    } catch (error) {
      this.logger.error(`Error training model ${modelId}`, error.stack);

      await this.mlModelRepo.update(modelId, {
        status: ModelStatus.TRAINING,
      });
    }
  }

  /**
   * Desplegar modelo
   */
  async deployModel(modelId: string): Promise<MLModel> {
    const model = await this.mlModelRepo.findOne({ where: { id: modelId } });

    if (!model) {
      throw new Error('Model not found');
    }

    if (model.status !== ModelStatus.VALIDATED) {
      throw new Error('Model must be validated before deployment');
    }

    // Desactivar modelos anteriores del mismo tipo
    await this.mlModelRepo.update(
      {
        predictionTarget: model.predictionTarget,
        isActive: true,
      },
      {
        isActive: false,
        status: ModelStatus.DEPRECATED,
      },
    );

    // Activar nuevo modelo
    model.status = ModelStatus.DEPLOYED;
    model.isActive = true;
    model.deployedAt = new Date();

    return this.mlModelRepo.save(model);
  }

  /**
   * Re-entrenar modelos (CRON mensual)
   */
  @Cron(CronExpression.EVERY_1ST_DAY_OF_MONTH_AT_MIDNIGHT)
  async retrainModels(): Promise<void> {
    this.logger.log('Starting monthly model retraining...');

    const activeModels = await this.mlModelRepo.find({
      where: { isActive: true },
    });

    for (const model of activeModels) {
      try {
        // Obtener dataset actualizado
        const trainingData = await this.prepareTrainingData(model.predictionTarget);

        // Re-entrenar
        await this.triggerTraining(model.id, trainingData.id);

        await this.mlModelRepo.update(model.id, {
          lastRetrainedAt: new Date(),
        });

        this.logger.log(`Model ${model.modelCode} retrained successfully`);
      } catch (error) {
        this.logger.error(`Error retraining model ${model.modelCode}`, error.stack);
      }
    }

    this.logger.log('Monthly model retraining completed');
  }

  /**
   * Helpers
   */
  private getAlgorithmForType(modelType: ModelType): string {
    const algorithmMap = {
      [ModelType.LINEAR_REGRESSION]: 'linear_regression',
      [ModelType.RANDOM_FOREST]: 'random_forest_regressor',
      [ModelType.ARIMA]: 'arima',
      [ModelType.XGBOOST]: 'xgboost_regressor',
      [ModelType.NEURAL_NETWORK]: 'mlp_regressor',
    };

    return algorithmMap[modelType];
  }

  private async prepareTrainingData(predictionTarget: string): Promise<TrainingData> {
    // TODO: Implementar lógica de preparación de dataset
    throw new Error('Not implemented');
  }
}

5.3 ScenarioSimulationService

// src/modules/predictions/services/scenario-simulation.service.ts

import { Injectable } from '@nestjs/common';
import { InjectRepository } from '@nestjs/typeorm';
import { Repository } from 'typeorm';
import { ScenarioSimulation, ScenarioType } from '../entities/scenario-simulation.entity';

@Injectable()
export class ScenarioSimulationService {
  constructor(
    @InjectRepository(ScenarioSimulation)
    private simulationRepo: Repository<ScenarioSimulation>,
  ) {}

  /**
   * Simular escenarios
   */
  async simulateScenarios(projectId: string, userId: string): Promise<ScenarioSimulation[]> {
    const scenarios: ScenarioSimulation[] = [];

    // Escenario Optimista
    const optimistic = await this.simulateScenario(
      projectId,
      ScenarioType.OPTIMISTIC,
      {
        costVarianceFactor: -0.05, // -5% de costos
        scheduleVarianceFactor: -0.10, // -10% de tiempo
        productivityFactor: 1.10, // +10% productividad
        weatherImpact: false,
        resourceAvailability: 1.0,
      },
      userId,
    );
    scenarios.push(optimistic);

    // Escenario Realista
    const realistic = await this.simulateScenario(
      projectId,
      ScenarioType.REALISTIC,
      {
        costVarianceFactor: 0.00,
        scheduleVarianceFactor: 0.00,
        productivityFactor: 1.00,
        weatherImpact: true,
        resourceAvailability: 0.95,
      },
      userId,
    );
    scenarios.push(realistic);

    // Escenario Pesimista
    const pessimistic = await this.simulateScenario(
      projectId,
      ScenarioType.PESSIMISTIC,
      {
        costVarianceFactor: 0.15, // +15% de costos
        scheduleVarianceFactor: 0.20, // +20% de tiempo
        productivityFactor: 0.85, // -15% productividad
        weatherImpact: true,
        resourceAvailability: 0.80,
      },
      userId,
    );
    scenarios.push(pessimistic);

    return scenarios;
  }

  /**
   * Simular un escenario específico
   */
  private async simulateScenario(
    projectId: string,
    scenarioType: ScenarioType,
    parameters: any,
    userId: string,
  ): Promise<ScenarioSimulation> {
    // Obtener datos base del proyecto
    const baseData = await this.simulationRepo.query(`
      SELECT
        p.total_budget,
        p.end_date,
        pm.gross_margin,
        pm.gross_margin_pct
      FROM projects.projects p
      LEFT JOIN analytics_reports.project_performance_metrics pm ON pm.project_id = p.id
        AND pm.snapshot_date = (
          SELECT MAX(snapshot_date)
          FROM analytics_reports.project_performance_metrics
          WHERE project_id = p.id
        )
      WHERE p.id = $1
    `, [projectId]);

    const base = baseData[0];

    // Aplicar factores del escenario
    const projectedCost = base.total_budget * (1 + parameters.costVarianceFactor);
    const projectedMargin = base.gross_margin * (1 - Math.abs(parameters.costVarianceFactor));
    const projectedMarginPct = (projectedMargin / projectedCost) * 100;

    const daysToAdd = Math.floor(
      ((base.end_date - new Date()) / (1000 * 60 * 60 * 24)) * parameters.scheduleVarianceFactor,
    );
    const projectedCompletionDate = new Date(base.end_date);
    projectedCompletionDate.setDate(projectedCompletionDate.getDate() + daysToAdd);

    // Calcular risk score
    const riskScore = this.calculateRiskScore(parameters, projectedMarginPct);

    // Crear simulación
    const simulation = this.simulationRepo.create({
      projectId,
      simulationName: `${scenarioType} Scenario`,
      scenarioType,
      parameters,
      results: {
        projectedCost,
        projectedCompletionDate,
        projectedMargin,
        projectedMarginPct,
        riskScore,
      },
      baselineComparison: {
        costVariance: projectedCost - base.total_budget,
        costVariancePct: parameters.costVarianceFactor * 100,
        scheduleVarianceDays: daysToAdd,
        marginImpact: projectedMargin - base.gross_margin,
      },
      createdBy: userId,
    });

    return this.simulationRepo.save(simulation);
  }

  /**
   * Calcular risk score
   */
  private calculateRiskScore(parameters: any, marginPct: number): number {
    let score = 50; // Base

    // Penalizar por cost variance
    score += Math.abs(parameters.costVarianceFactor) * 100;

    // Penalizar por schedule variance
    score += Math.abs(parameters.scheduleVarianceFactor) * 80;

    // Penalizar por baja productividad
    score += (1 - parameters.productivityFactor) * 100;

    // Penalizar por margen bajo
    if (marginPct < 10) score += 20;
    if (marginPct < 5) score += 30;

    return Math.min(100, Math.max(0, score));
  }
}

---

6. Python ML Service (Flask)

# ml_service/app.py

from flask import Flask, request, jsonify
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import joblib
import os

app = Flask(__name__)

MODELS_DIR = '/models'
DATA_DIR = '/data'

@app.route('/train', methods=['POST'])
def train_model():
    """
    Entrenar modelo de ML
    """
    data = request.json
    model_id = data.get('model_id')
    training_data_id = data.get('training_data_id')

    # Cargar datos de entrenamiento
    df = load_training_data(training_data_id)

    # Preparar features y target
    X = df.drop(['target'], axis=1)
    y = df['target']

    # Split
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )

    # Entrenar Random Forest
    model = RandomForestRegressor(
        n_estimators=100,
        max_depth=10,
        random_state=42
    )
    model.fit(X_train, y_train)

    # Evaluar
    train_score = model.score(X_train, y_train)
    test_score = model.score(X_test, y_test)

    y_pred = model.predict(X_test)
    rmse = np.sqrt(mean_squared_error(y_test, y_pred))
    mae = mean_absolute_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    # Cross-validation
    cv_scores = cross_val_score(model, X, y, cv=5)
    cv_score = cv_scores.mean()

    # Guardar modelo
    model_path = f'{MODELS_DIR}/{model_id}.joblib'
    joblib.dump(model, model_path)

    return jsonify({
        'model_id': model_id,
        'model_path': model_path,
        'metrics': {
            'train_score': float(train_score),
            'test_score': float(test_score),
            'cross_val_score': float(cv_score),
            'rmse': float(rmse),
            'mae': float(mae),
            'r2_score': float(r2)
        }
    })


@app.route('/predict/cost', methods=['POST'])
def predict_cost():
    """
    Predecir costo final del proyecto
    """
    data = request.json
    model_id = data.get('model_id')
    features = data.get('features')

    # Cargar modelo
    model_path = f'{MODELS_DIR}/{model_id}.joblib'
    model = joblib.load(model_path)

    # Preparar features
    X = pd.DataFrame([features])

    # Predecir
    prediction = model.predict(X)[0]

    # Intervalos de confianza (aproximado)
    # En producción, usar prediction intervals más sofisticados
    variance = prediction * 0.10
    lower_bound = prediction - (1.96 * variance)
    upper_bound = prediction + (1.96 * variance)

    return jsonify({
        'predicted_cost': float(prediction),
        'variance': float(prediction - features['total_budget']),
        'variance_pct': float((prediction - features['total_budget']) / features['total_budget'] * 100),
        'lower_bound': float(lower_bound),
        'upper_bound': float(upper_bound)
    })


@app.route('/predict/schedule', methods=['POST'])
def predict_schedule():
    """
    Predecir fecha de finalización
    """
    data = request.json
    model_id = data.get('model_id')
    features = data.get('features')

    model_path = f'{MODELS_DIR}/{model_id}.joblib'
    model = joblib.load(model_path)

    X = pd.DataFrame([features])
    delay_days = int(model.predict(X)[0])

    baseline_date = pd.to_datetime(features['baseline_completion_date'])
    predicted_date = baseline_date + pd.Timedelta(days=delay_days)

    # Probabilidades (basado en distribución histórica)
    on_time_prob = max(0, 100 - (abs(delay_days) * 5))
    delay_prob = 100 - on_time_prob

    return jsonify({
        'predicted_completion_date': predicted_date.strftime('%Y-%m-%d'),
        'delay_days': delay_days,
        'on_time_probability': float(on_time_prob),
        'delay_probability': float(delay_prob),
        'critical_activities_at_risk': [],  # TODO: Implementar lógica
        'earliest_completion': (predicted_date - pd.Timedelta(days=7)).strftime('%Y-%m-%d'),
        'latest_completion': (predicted_date + pd.Timedelta(days=14)).strftime('%Y-%m-%d')
    })


def load_training_data(training_data_id):
    """
    Cargar datos de entrenamiento desde CSV
    """
    file_path = f'{DATA_DIR}/{training_data_id}.csv'
    return pd.read_csv(file_path)


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)

---

7. React Components

// src/pages/Predictions/PredictionDashboard.tsx

import React, { useEffect, useState } from 'react';
import { usePredictionStore } from '../../stores/predictionStore';
import { Card } from '../../components/ui/Card';
import { LineChart } from '../../components/charts/LineChart';
import { Button } from '../../components/ui/Button';
import { TrendingUp, AlertTriangle, Target } from 'lucide-react';

interface PredictionDashboardProps {
  projectId: string;
}

export const PredictionDashboard: React.FC<PredictionDashboardProps> = ({ projectId }) => {
  const { costPrediction, schedulePrediction, scenarios, loading, fetchPredictions, simulateScenarios } = usePredictionStore();

  useEffect(() => {
    fetchPredictions(projectId);
  }, [projectId]);

  const handleSimulateScenarios = async () => {
    await simulateScenarios(projectId);
  };

  if (loading) {
    return <div>Cargando predicciones...</div>;
  }

  return (
    <div className="prediction-dashboard">
      <div className="page-header mb-6">
        <h1 className="text-2xl font-bold">Análisis Predictivo</h1>
        <Button onClick={handleSimulateScenarios} variant="primary">
          Simular Escenarios
        </Button>
      </div>

      {/* Predicción de Costos */}
      {costPrediction && (
        <Card title="Predicción de Costo Final" className="mb-6">
          <div className="grid grid-cols-3 gap-4">
            <div className="metric">
              <label className="text-sm text-gray-600">Costo Predicho</label>
              <div className="text-2xl font-bold">
                ${costPrediction.predictedTotalCost.toLocaleString('es-MX')}
              </div>
              <div className="text-sm text-gray-500">
                Confianza: {costPrediction.confidenceLevel}%
              </div>
            </div>

            <div className="metric">
              <label className="text-sm text-gray-600">Variación vs Presupuesto</label>
              <div className={`text-2xl font-bold ${costPrediction.predictedCostVariancePct > 0 ? 'text-red-600' : 'text-green-600'}`}>
                {costPrediction.predictedCostVariancePct > 0 ? '+' : ''}
                {costPrediction.predictedCostVariancePct?.toFixed(2)}%
              </div>
            </div>

            <div className="metric">
              <label className="text-sm text-gray-600">Rango (95% confianza)</label>
              <div className="text-sm">
                ${costPrediction.lowerBound?.toLocaleString('es-MX')} -
                ${costPrediction.upperBound?.toLocaleString('es-MX')}
              </div>
            </div>
          </div>
        </Card>
      )}

      {/* Predicción de Cronograma */}
      {schedulePrediction && (
        <Card title="Predicción de Finalización" className="mb-6">
          <div className="grid grid-cols-3 gap-4">
            <div className="metric">
              <label className="text-sm text-gray-600">Fecha Predicha</label>
              <div className="text-2xl font-bold">
                {new Date(schedulePrediction.predictedCompletionDate).toLocaleDateString('es-MX')}
              </div>
            </div>

            <div className="metric">
              <label className="text-sm text-gray-600">Retraso Estimado</label>
              <div className={`text-2xl font-bold ${schedulePrediction.predictedDelayDays > 0 ? 'text-red-600' : 'text-green-600'}`}>
                {schedulePrediction.predictedDelayDays} días
              </div>
            </div>

            <div className="metric">
              <label className="text-sm text-gray-600">Probabilidad a Tiempo</label>
              <div className="text-2xl font-bold">
                {schedulePrediction.onTimeProbability?.toFixed(1)}%
              </div>
            </div>
          </div>
        </Card>
      )}

      {/* Simulación de Escenarios */}
      {scenarios && scenarios.length > 0 && (
        <Card title="Simulación de Escenarios" className="mb-6">
          <div className="grid grid-cols-3 gap-4">
            {scenarios.map((scenario) => (
              <div key={scenario.id} className="scenario-card border rounded-lg p-4">
                <h3 className="font-bold mb-2 capitalize">
                  {scenario.scenarioType}
                </h3>

                <div className="space-y-2">
                  <div>
                    <label className="text-sm text-gray-600">Costo Proyectado</label>
                    <div className="font-semibold">
                      ${scenario.results.projectedCost.toLocaleString('es-MX')}
                    </div>
                  </div>

                  <div>
                    <label className="text-sm text-gray-600">Margen Proyectado</label>
                    <div className="font-semibold">
                      {scenario.results.projectedMarginPct.toFixed(2)}%
                    </div>
                  </div>

                  <div>
                    <label className="text-sm text-gray-600">Risk Score</label>
                    <div className={`font-semibold ${
                      scenario.results.riskScore > 70 ? 'text-red-600' :
                      scenario.results.riskScore > 50 ? 'text-yellow-600' : 'text-green-600'
                    }`}>
                      {scenario.results.riskScore.toFixed(1)}
                    </div>
                  </div>
                </div>
              </div>
            ))}
          </div>
        </Card>
      )}
    </div>
  );
};

---

8. Testing

// src/modules/predictions/services/__tests__/prediction.service.spec.ts

import { Test, TestingModule } from '@nestjs/testing';
import { PredictionService } from '../prediction.service';
import { getRepositoryToken } from '@nestjs/typeorm';
import { CostPrediction } from '../../entities/cost-prediction.entity';
import { HttpService } from '@nestjs/axios';
import { ConfigService } from '@nestjs/config';
import { of } from 'rxjs';

describe('PredictionService', () => {
  let service: PredictionService;
  let httpService: HttpService;

  beforeEach(async () => {
    const module: TestingModule = await Test.createTestingModule({
      providers: [
        PredictionService,
        {
          provide: getRepositoryToken(CostPrediction),
          useValue: {
            create: jest.fn(),
            save: jest.fn(),
            query: jest.fn(),
          },
        },
        {
          provide: HttpService,
          useValue: {
            post: jest.fn(),
          },
        },
        {
          provide: ConfigService,
          useValue: {
            get: jest.fn().mockReturnValue('http://localhost:5000'),
          },
        },
      ],
    }).compile();

    service = module.get<PredictionService>(PredictionService);
    httpService = module.get<HttpService>(HttpService);
  });

  it('should predict project cost', async () => {
    const mlResponse = {
      predicted_cost: 10500000,
      variance: 500000,
      variance_pct: 5.0,
      lower_bound: 10000000,
      upper_bound: 11000000,
    };

    jest.spyOn(httpService, 'post').mockReturnValue(of({ data: mlResponse }) as any);

    const result = await service.predictProjectCost('project-1', 90);

    expect(result.predictedTotalCost).toBe(10500000);
    expect(result.predictedCostVariancePct).toBe(5.0);
  });
});

---

9. Criterios de Aceptación Técnicos

• Schema predictions creado con todas las tablas
• Entities TypeORM con relaciones correctas
• Services para predicciones y ML
• Python Flask micro-service para ML
• Modelos de Random Forest, ARIMA implementados
• Simulación de escenarios (optimista, realista, pesimista)
• Detección de anomalías en costos
• CRON jobs para re-entrenamiento mensual
• Controllers con endpoints RESTful
• React components para visualización
• Triggers para cálculo de errores de predicción
• Tests unitarios con >75% coverage

---

Fecha: 2025-11-17 Preparado por: Equipo Técnico Versión: 1.0 Estado: ✅ Listo para Implementación