Improve ondobarding steps
This commit is contained in:
Urtzi Alfaro
@@ -9,11 +9,11 @@ export * from './inventory.service';
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export * from './production.service';
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export * from './sales.service';
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export * from './forecasting.service';
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export * from './training.service';
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export * from './orders.service';
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export * from './procurement.service';
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export * from './pos.service';
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export * from './data.service';
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export * from './training.service';
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export * from './notification.service';
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export * from './subscription.service';
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@@ -28,7 +28,6 @@ export { ordersService } from './orders.service';
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export { procurementService } from './procurement.service';
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export { posService } from './pos.service';
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export { dataService } from './data.service';
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export { trainingService } from './training.service';
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export { notificationService } from './notification.service';
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export { subscriptionService } from './subscription.service';
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@@ -1,447 +1,70 @@
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import { apiClient, ApiResponse } from './client';
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/**
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* Training service for ML model training operations
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*/
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import { ApiClient } from './client';
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import { ApiResponse } from '../../types/api.types';
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// Model and training types
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export interface TrainingJob {
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id: string;
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tenant_id: string;
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name: string;
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model_type: 'demand_forecasting' | 'sales_prediction' | 'inventory_optimization' | 'production_planning';
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status: 'pending' | 'initializing' | 'training' | 'validating' | 'completed' | 'failed' | 'cancelled';
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model_id: string;
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status: 'pending' | 'running' | 'completed' | 'failed';
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progress: number;
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parameters: {
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data_start_date: string;
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data_end_date: string;
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validation_split: number;
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hyperparameters: Record<string, any>;
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};
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metrics?: {
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accuracy: number;
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mse: number;
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mae: number;
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r2_score: number;
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validation_accuracy: number;
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};
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training_duration_seconds?: number;
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model_size_mb?: number;
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created_at: string;
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started_at?: string;
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completed_at?: string;
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error_message?: string;
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created_by: string;
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parameters: Record<string, any>;
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metrics?: Record<string, number>;
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}
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export interface ModelInfo {
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id: string;
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tenant_id: string;
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name: string;
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model_type: string;
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version: string;
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status: 'training' | 'active' | 'deprecated' | 'archived';
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is_production: boolean;
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performance_metrics: {
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accuracy: number;
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precision: number;
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recall: number;
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f1_score: number;
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last_evaluated: string;
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};
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training_data_info: {
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record_count: number;
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date_range: {
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start: string;
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end: string;
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};
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features_used: string[];
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};
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deployment_info?: {
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deployed_at: string;
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prediction_count: number;
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avg_response_time_ms: number;
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};
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created_at: string;
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updated_at: string;
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export interface TrainingJobCreate {
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model_id: string;
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parameters?: Record<string, any>;
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}
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export interface TrainingConfiguration {
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id: string;
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tenant_id: string;
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model_type: string;
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name: string;
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description?: string;
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is_default: boolean;
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parameters: {
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algorithm: string;
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hyperparameters: Record<string, any>;
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feature_selection: string[];
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validation_method: string;
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cross_validation_folds?: number;
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};
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data_requirements: {
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minimum_records: number;
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required_columns: string[];
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date_range_days: number;
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};
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created_at: string;
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updated_at: string;
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export interface TrainingJobUpdate {
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parameters?: Record<string, any>;
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}
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class TrainingService {
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private readonly baseUrl = '/training';
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// Training job management
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async getTrainingJobs(params?: {
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page?: number;
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size?: number;
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status?: string;
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model_type?: string;
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}): Promise<ApiResponse<{ items: TrainingJob[]; total: number; page: number; size: number; pages: number }>> {
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const queryParams = new URLSearchParams();
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if (params) {
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Object.entries(params).forEach(([key, value]) => {
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if (value !== undefined) {
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queryParams.append(key, value.toString());
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}
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});
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}
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const url = queryParams.toString()
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? `${this.baseUrl}/jobs?${queryParams.toString()}`
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: `${this.baseUrl}/jobs`;
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return apiClient.get(url);
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export class TrainingService extends ApiClient {
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constructor() {
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super('/ml/training');
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}
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async getTrainingJob(jobId: string): Promise<ApiResponse<TrainingJob>> {
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return apiClient.get(`${this.baseUrl}/jobs/${jobId}`);
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async getTrainingJobs(modelId?: string): Promise<ApiResponse<TrainingJob[]>> {
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const params = modelId ? { model_id: modelId } : {};
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return this.get('/', params);
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}
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async createTrainingJob(jobData: {
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name: string;
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model_type: string;
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config_id?: string;
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parameters: {
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data_start_date: string;
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data_end_date: string;
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validation_split?: number;
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hyperparameters?: Record<string, any>;
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};
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}): Promise<ApiResponse<TrainingJob>> {
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return apiClient.post(`${this.baseUrl}/jobs`, jobData);
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async getTrainingJob(id: string): Promise<ApiResponse<TrainingJob>> {
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return this.get(`/${id}`);
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}
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async cancelTrainingJob(jobId: string): Promise<ApiResponse<{ message: string }>> {
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return apiClient.post(`${this.baseUrl}/jobs/${jobId}/cancel`);
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async createTrainingJob(data: TrainingJobCreate): Promise<ApiResponse<TrainingJob>> {
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return this.post('/', data);
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}
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async retryTrainingJob(jobId: string): Promise<ApiResponse<TrainingJob>> {
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return apiClient.post(`${this.baseUrl}/jobs/${jobId}/retry`);
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async updateTrainingJob(id: string, data: TrainingJobUpdate): Promise<ApiResponse<TrainingJob>> {
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return this.put(`/${id}`, data);
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}
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async getTrainingLogs(jobId: string, params?: {
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level?: 'debug' | 'info' | 'warning' | 'error';
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limit?: number;
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}): Promise<ApiResponse<Array<{
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timestamp: string;
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level: string;
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message: string;
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details?: Record<string, any>;
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}>>> {
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const queryParams = new URLSearchParams();
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if (params) {
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Object.entries(params).forEach(([key, value]) => {
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if (value !== undefined) {
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queryParams.append(key, value.toString());
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}
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});
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}
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const url = queryParams.toString()
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? `${this.baseUrl}/jobs/${jobId}/logs?${queryParams.toString()}`
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: `${this.baseUrl}/jobs/${jobId}/logs`;
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return apiClient.get(url);
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async deleteTrainingJob(id: string): Promise<ApiResponse<void>> {
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return this.delete(`/${id}`);
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}
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// Model management
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async getModels(params?: {
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page?: number;
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size?: number;
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model_type?: string;
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status?: string;
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is_production?: boolean;
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}): Promise<ApiResponse<{ items: ModelInfo[]; total: number; page: number; size: number; pages: number }>> {
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const queryParams = new URLSearchParams();
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if (params) {
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Object.entries(params).forEach(([key, value]) => {
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if (value !== undefined) {
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queryParams.append(key, value.toString());
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}
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});
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}
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const url = queryParams.toString()
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? `${this.baseUrl}/models?${queryParams.toString()}`
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: `${this.baseUrl}/models`;
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return apiClient.get(url);
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async startTraining(id: string): Promise<ApiResponse<TrainingJob>> {
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return this.post(`/${id}/start`);
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}
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async getModel(modelId: string): Promise<ApiResponse<ModelInfo>> {
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return apiClient.get(`${this.baseUrl}/models/${modelId}`);
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async stopTraining(id: string): Promise<ApiResponse<TrainingJob>> {
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return this.post(`/${id}/stop`);
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}
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async deployModel(modelId: string): Promise<ApiResponse<{ message: string; deployment_id: string }>> {
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return apiClient.post(`${this.baseUrl}/models/${modelId}/deploy`);
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async getTrainingLogs(id: string): Promise<ApiResponse<string[]>> {
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return this.get(`/${id}/logs`);
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}
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async undeployModel(modelId: string): Promise<ApiResponse<{ message: string }>> {
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return apiClient.post(`${this.baseUrl}/models/${modelId}/undeploy`);
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async getTrainingMetrics(id: string): Promise<ApiResponse<Record<string, number>>> {
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return this.get(`/${id}/metrics`);
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}
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async deleteModel(modelId: string): Promise<ApiResponse<{ message: string }>> {
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return apiClient.delete(`${this.baseUrl}/models/${modelId}`);
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}
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async compareModels(modelIds: string[]): Promise<ApiResponse<{
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models: ModelInfo[];
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comparison: {
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accuracy_comparison: Array<{ model_id: string; accuracy: number; rank: number }>;
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performance_metrics: Record<string, Array<{ model_id: string; value: number }>>;
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recommendation: {
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best_model_id: string;
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reason: string;
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confidence: number;
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};
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};
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}>> {
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return apiClient.post(`${this.baseUrl}/models/compare`, { model_ids: modelIds });
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}
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// Model evaluation
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async evaluateModel(modelId: string, evaluationData?: {
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test_data_start?: string;
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test_data_end?: string;
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metrics?: string[];
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}): Promise<ApiResponse<{
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evaluation_id: string;
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status: 'pending' | 'running' | 'completed' | 'failed';
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}>> {
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return apiClient.post(`${this.baseUrl}/models/${modelId}/evaluate`, evaluationData);
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}
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async getEvaluationResults(evaluationId: string): Promise<ApiResponse<{
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model_id: string;
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status: string;
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metrics: Record<string, number>;
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predictions_sample: Array<{
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actual: number;
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predicted: number;
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date: string;
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error: number;
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}>;
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feature_importance?: Array<{
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feature: string;
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importance: number;
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}>;
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completed_at: string;
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}>> {
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return apiClient.get(`${this.baseUrl}/evaluations/${evaluationId}`);
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}
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// Training configuration
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async getTrainingConfigs(modelType?: string): Promise<ApiResponse<TrainingConfiguration[]>> {
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const url = modelType
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? `${this.baseUrl}/configs?model_type=${encodeURIComponent(modelType)}`
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: `${this.baseUrl}/configs`;
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return apiClient.get(url);
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}
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async getTrainingConfig(configId: string): Promise<ApiResponse<TrainingConfiguration>> {
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return apiClient.get(`${this.baseUrl}/configs/${configId}`);
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}
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async createTrainingConfig(configData: {
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model_type: string;
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name: string;
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description?: string;
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parameters: TrainingConfiguration['parameters'];
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data_requirements?: Partial<TrainingConfiguration['data_requirements']>;
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}): Promise<ApiResponse<TrainingConfiguration>> {
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return apiClient.post(`${this.baseUrl}/configs`, configData);
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}
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async updateTrainingConfig(configId: string, configData: Partial<TrainingConfiguration>): Promise<ApiResponse<TrainingConfiguration>> {
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return apiClient.put(`${this.baseUrl}/configs/${configId}`, configData);
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}
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async deleteTrainingConfig(configId: string): Promise<ApiResponse<{ message: string }>> {
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return apiClient.delete(`${this.baseUrl}/configs/${configId}`);
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}
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// Data analysis and preparation
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async analyzeTrainingData(params: {
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model_type: string;
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data_start_date: string;
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data_end_date: string;
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}): Promise<ApiResponse<{
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data_quality: {
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total_records: number;
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complete_records: number;
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missing_data_percentage: number;
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duplicate_records: number;
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};
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feature_analysis: Array<{
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feature: string;
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data_type: string;
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completeness: number;
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unique_values: number;
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correlation_with_target?: number;
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}>;
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recommendations: Array<{
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type: 'data_cleaning' | 'feature_engineering' | 'model_selection';
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message: string;
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priority: 'high' | 'medium' | 'low';
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}>;
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training_feasibility: {
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can_train: boolean;
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minimum_requirements_met: boolean;
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estimated_training_time_minutes: number;
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};
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}>> {
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return apiClient.post(`${this.baseUrl}/analyze-data`, params);
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}
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async getDataAlignmentStatus(): Promise<ApiResponse<{
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status: 'aligned' | 'misaligned' | 'processing';
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last_alignment: string;
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data_sources: Array<{
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source: string;
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status: 'synced' | 'out_of_sync' | 'error';
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last_sync: string;
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record_count: number;
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}>;
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issues?: Array<{
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source: string;
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issue_type: string;
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description: string;
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severity: 'low' | 'medium' | 'high';
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}>;
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}>> {
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return apiClient.get(`${this.baseUrl}/data-alignment/status`);
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}
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async triggerDataAlignment(): Promise<ApiResponse<{ message: string; task_id: string }>> {
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return apiClient.post(`${this.baseUrl}/data-alignment/trigger`);
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}
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// Training insights and recommendations
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async getTrainingInsights(): Promise<ApiResponse<{
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model_performance_trends: Array<{
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model_type: string;
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accuracy_trend: 'improving' | 'stable' | 'declining';
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last_training_date: string;
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recommendation: string;
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}>;
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training_frequency_suggestions: Array<{
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model_type: string;
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current_frequency: string;
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suggested_frequency: string;
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reason: string;
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}>;
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data_quality_alerts: Array<{
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alert_type: string;
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severity: 'info' | 'warning' | 'critical';
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message: string;
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affected_models: string[];
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}>;
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}>> {
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return apiClient.get(`${this.baseUrl}/insights`);
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}
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// Utility methods
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getModelTypes(): { value: string; label: string; description: string }[] {
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return [
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{
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value: 'demand_forecasting',
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label: 'Demand Forecasting',
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description: 'Predict future demand for products based on historical sales and external factors'
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},
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{
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value: 'sales_prediction',
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label: 'Sales Prediction',
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description: 'Forecast sales revenue and patterns'
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},
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{
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value: 'inventory_optimization',
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label: 'Inventory Optimization',
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description: 'Optimize inventory levels and reorder points'
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},
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{
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value: 'production_planning',
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label: 'Production Planning',
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description: 'Optimize production schedules and capacity planning'
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}
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];
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}
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getAlgorithmOptions(): { value: string; label: string; suitable_for: string[] }[] {
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return [
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{
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value: 'prophet',
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label: 'Prophet',
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suitable_for: ['demand_forecasting', 'sales_prediction']
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},
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{
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value: 'arima',
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label: 'ARIMA',
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suitable_for: ['demand_forecasting', 'sales_prediction']
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},
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{
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value: 'random_forest',
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label: 'Random Forest',
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suitable_for: ['inventory_optimization', 'production_planning']
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},
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{
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value: 'xgboost',
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label: 'XGBoost',
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suitable_for: ['demand_forecasting', 'inventory_optimization']
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},
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{
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value: 'lstm',
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label: 'LSTM Neural Network',
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suitable_for: ['demand_forecasting', 'sales_prediction']
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}
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];
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}
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getValidationMethods(): { value: string; label: string; description: string }[] {
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return [
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{
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||||
value: 'time_series_split',
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label: 'Time Series Split',
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||||
description: 'Split data chronologically for time series validation'
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||||
},
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||||
{
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||||
value: 'k_fold',
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||||
label: 'K-Fold Cross Validation',
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||||
description: 'Standard k-fold cross validation'
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||||
},
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||||
{
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||||
value: 'stratified_k_fold',
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||||
label: 'Stratified K-Fold',
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||||
description: 'Stratified sampling for cross validation'
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||||
},
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||||
{
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||||
value: 'holdout',
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||||
label: 'Holdout Validation',
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||||
description: 'Simple train/validation split'
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||||
}
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||||
];
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||||
}
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||||
}
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||||
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export const trainingService = new TrainingService();
|
||||
}
|
||||
Reference in New Issue
Block a user