Fix naming collision: rename Feature to ModelFeature in predictive analytics

Co-authored-by: hotlong <50353452+hotlong@users.noreply.github.com>

Author: Copilot

content/docs/references/ai/ModelFeature.mdx

@@ -0,0 +1,19 @@
+---
+title: ModelFeature
+description: ModelFeature Schema Reference
+---
+
+## Properties
+
+| Property | Type | Required | Description |
+| :--- | :--- | :--- | :--- |
+| **name** | `string` | ✅ | Feature name (snake_case) |
+| **label** | `string` | optional | Human-readable label |
+| **field** | `string` | ✅ | Source field name |
+| **object** | `string` | optional | Source object (if different from target) |
+| **dataType** | `Enum<'numeric' \| 'categorical' \| 'text' \| 'datetime' \| 'boolean'>` | ✅ | Feature data type |
+| **transformation** | `Enum<'none' \| 'normalize' \| 'standardize' \| 'one_hot_encode' \| 'label_encode' \| 'log_transform' \| 'binning' \| 'embedding'>` | optional |  |
+| **required** | `boolean` | optional |  |
+| **defaultValue** | `any` | optional |  |
+| **description** | `string` | optional |  |
+| **importance** | `number` | optional | Feature importance score (0-1) |

content/docs/references/ai/config/Feature.mdx

@@ -0,0 +1,71 @@
+{
+  "$ref": "#/definitions/ModelFeature",
+  "definitions": {
+    "ModelFeature": {
+      "type": "object",
+      "properties": {
+        "name": {
+          "type": "string",
+          "pattern": "^[a-z_][a-z0-9_]*$",
+          "description": "Feature name (snake_case)"
+        },
+        "label": {
+          "type": "string",
+          "description": "Human-readable label"
+        },
+        "field": {
+          "type": "string",
+          "description": "Source field name"
+        },
+        "object": {
+          "type": "string",
+          "description": "Source object (if different from target)"
+        },
+        "dataType": {
+          "type": "string",
+          "enum": [
+            "numeric",
+            "categorical",
+            "text",
+            "datetime",
+            "boolean"
+          ],
+          "description": "Feature data type"
+        },
+        "transformation": {
+          "type": "string",
+          "enum": [
+            "none",
+            "normalize",
+            "standardize",
+            "one_hot_encode",
+            "label_encode",
+            "log_transform",
+            "binning",
+            "embedding"
+          ],
+          "default": "none"
+        },
+        "required": {
+          "type": "boolean",
+          "default": true
+        },
+        "defaultValue": {},
+        "description": {
+          "type": "string"
+        },
+        "importance": {
+          "type": "number",
+          "description": "Feature importance score (0-1)"
+        }
+      },
+      "required": [
+        "name",
+        "field",
+        "dataType"
+      ],
+      "additionalProperties": false
+    }
+  },
+  "$schema": "http://json-schema.org/draft-07/schema#"
+}

packages/spec/json-schema/ModelFeature.json

@@ -2,15 +2,15 @@ import { describe, it, expect } from 'vitest';
 import {
   PredictiveModelSchema,
   PredictiveModelTypeSchema,
-  FeatureSchema,
+  ModelFeatureSchema,
   HyperparametersSchema,
   TrainingConfigSchema,
   EvaluationMetricsSchema,
   PredictionRequestSchema,
   PredictionResultSchema,
   ModelDriftSchema,
   type PredictiveModel,
-  type Feature,
+  type ModelFeature,
   type PredictionRequest,
 } from './predictive.zod';
 
@@ -32,23 +32,23 @@ describe('PredictiveModelTypeSchema', () => {
   });
 });
 
-describe('FeatureSchema', () => {
+describe('ModelFeatureSchema', () => {
   it('should accept minimal feature', () => {
-    const feature: Feature = {
+    const feature: ModelFeature = {
       name: 'user_age',
       field: 'age',
       dataType: 'numeric',
     };
 
-    const result = FeatureSchema.parse(feature);
+    const result = ModelFeatureSchema.parse(feature);
     expect(result.transformation).toBe('none');
     expect(result.required).toBe(true);
   });
 
   it('should enforce snake_case for feature name', () => {
     const validNames = ['user_age', 'total_revenue', '_internal_score'];
     validNames.forEach(name => {
-      expect(() => FeatureSchema.parse({
+      expect(() => ModelFeatureSchema.parse({
         name,
         field: 'test',
         dataType: 'numeric',
@@ -57,7 +57,7 @@ describe('FeatureSchema', () => {
 
     const invalidNames = ['userAge', 'User-Age', '123age'];
     invalidNames.forEach(name => {
-      expect(() => FeatureSchema.parse({
+      expect(() => ModelFeatureSchema.parse({
         name,
         field: 'test',
         dataType: 'numeric',
@@ -66,39 +66,39 @@ describe('FeatureSchema', () => {
   });
 
   it('should accept feature with transformation', () => {
-    const feature: Feature = {
+    const feature: ModelFeature = {
       name: 'normalized_revenue',
       field: 'annual_revenue',
       dataType: 'numeric',
       transformation: 'normalize',
       label: 'Normalized Annual Revenue',
     };
 
-    expect(() => FeatureSchema.parse(feature)).not.toThrow();
+    expect(() => ModelFeatureSchema.parse(feature)).not.toThrow();
   });
 
   it('should accept categorical feature', () => {
-    const feature: Feature = {
+    const feature: ModelFeature = {
       name: 'industry_encoded',
       field: 'industry',
       dataType: 'categorical',
       transformation: 'one_hot_encode',
       description: 'Industry category with one-hot encoding',
     };
 
-    expect(() => FeatureSchema.parse(feature)).not.toThrow();
+    expect(() => ModelFeatureSchema.parse(feature)).not.toThrow();
   });
 
   it('should accept feature from related object', () => {
-    const feature: Feature = {
+    const feature: ModelFeature = {
       name: 'account_revenue',
       field: 'annual_revenue',
       object: 'account',
       dataType: 'numeric',
       transformation: 'log_transform',
     };
 
-    expect(() => FeatureSchema.parse(feature)).not.toThrow();
+    expect(() => ModelFeatureSchema.parse(feature)).not.toThrow();
   });
 });
 

packages/spec/src/ai/predictive.test.ts

@@ -31,10 +31,10 @@ export const PredictiveModelTypeSchema = z.enum([
 ]);
 
 /**
- * Feature Definition
- * Describes an input feature for the model
+ * Model Feature Definition
+ * Describes an input feature for a predictive model
  */
-export const FeatureSchema = z.object({
+export const ModelFeatureSchema = z.object({
   /** Feature Identity */
   name: z.string().regex(/^[a-z_][a-z0-9_]*$/).describe('Feature name (snake_case)'),
   label: z.string().optional().describe('Human-readable label'),
@@ -183,7 +183,7 @@ export const PredictiveModelSchema = z.object({
   targetType: z.enum(['numeric', 'categorical', 'binary']).optional().describe('Target field type'),
 
   /** Features */
-  features: z.array(FeatureSchema).describe('Input features for the model'),
+  features: z.array(ModelFeatureSchema).describe('Input features for the model'),
 
   /** Hyperparameters */
   hyperparameters: HyperparametersSchema.optional(),
@@ -285,7 +285,7 @@ export const ModelDriftSchema = z.object({
 
 // Type exports
 export type PredictiveModelType = z.infer<typeof PredictiveModelTypeSchema>;
-export type Feature = z.infer<typeof FeatureSchema>;
+export type ModelFeature = z.infer<typeof ModelFeatureSchema>;
 export type Hyperparameters = z.infer<typeof HyperparametersSchema>;
 export type TrainingConfig = z.infer<typeof TrainingConfigSchema>;
 export type EvaluationMetrics = z.infer<typeof EvaluationMetricsSchema>;