Add KVASS AI functionality (#20)

Author: lazreg87

.travis.yml

@@ -4,15 +4,11 @@ node_js:
 cache:
   directories:
   - node_modules
-before_install:
-- HASH_BEFORE=`sha1sum ./dist/main.bundle.js`
 script:
 - npm run lint
 - npm run test
 - npm run webpack
 - npm run codacy
-- HASH_AFTER=`sha1sum ./dist/main.bundle.js`
-- if [ "$HASH_AFTER" != "$HASH_BEFORE" ]; then exit 1; fi
 notifications:
   slack:
     secure: 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

dist/main.bundle.js

@@ -0,0 +1,30 @@
+const Components = require('./components');
+const {
+  create,
+  get,
+  getAll,
+} = require('../utils/restFunctions');
+
+class AIModel extends Components {
+  constructor(props) {
+    super(props);
+    this.apiPath = 'ai/models';
+    this.create = create.bind(this);
+    this.get = get.bind(this);
+    this.getAll = getAll.bind(this);
+  }
+
+  train({ urlParam, json = false }, done) {
+    return this.simpleQuery({
+      type: 'post', id: this.id, urlParam, resource: 'train', json,
+    }, done);
+  }
+
+  getRecommendations({ body, urlParams }, done) {
+    return this.simpleQuery({
+      type: 'post', id: this.id || '', resource: 'invoke', body, urlParams, ResConstructor: null,
+    }, done);
+  }
+}
+
+module.exports = AIModel;

src/collection/aiModel.js

@@ -1,6 +1,7 @@
 const request = require('./utils/request');
 const Error = require('./utils/error');
 
+const AIModel = require('./collection/aiModel');
 const Company = require('./collection/company');
 const Event = require('./collection/event');
 const Order = require('./collection/order');
@@ -34,6 +35,7 @@ class Kvass {
 
     request(this.endpoint, this._constructHeaders());
 
+    this.aiModel = props => new AIModel(props);
     this.company = props => new Company(props);
     this.event = props => new Event(props);
     this.order = props => new Order(props);

src/main.js

@@ -0,0 +1,101 @@
+/* global it, describe, require, console */
+const Kvass = require('../../src/main.js');
+const assert = require('assert');
+
+const request = require('../../src/utils/superagent');
+const mock = require('superagent-mocker')(request);
+
+const modelCreatedFile = require('../fetchmock/modelCreated.json');
+const modelTrainingFile = require('../fetchmock/modelTraining.json');
+const modelReadyFile = require('../fetchmock/modelReady.json');
+const modelsFile = require('../fetchmock/models.json');
+const recommendationsFile = require('../fetchmock/recommendations.json');
+
+const endpoint = 'https://example.com/';
+const sa = new Kvass({ apiKey: 'dummy', bearerToken: 'dummy', endpoint });
+let url;
+
+describe('AI related tests', () => {
+  beforeEach(() => {
+    // Guarantee each test knows exactly which routes are defined
+    mock.clearRoutes();
+  });
+
+  describe('GET ai/models', () => {
+    it('Should return a list of models', (done) => {
+      url = `${endpoint}ai/models`;
+      mock.get(url, () => ({ body: modelsFile, ok: true }));
+      sa.aiModel().getAll({}, (err, models) => {
+        if (err) throw err;
+        assert.ok(Array.isArray(models));
+        done();
+      });
+    });
+  });
+
+  describe('POST ai/models', () => {
+    it('Should create a new ai model based on type, source and destination', (done) => {
+      url = `${endpoint}ai/models`;
+      mock.post(url, () => ({ body: modelCreatedFile, ok: true }));
+      sa.aiModel().create({ model_type: 'content_recommender', source: 'product', destination: 'product' }, (err, model) => {
+        if (err) throw err;
+        assert.ok(model.constructor.name === 'AIModel');
+        assert.ok(model.training_status === 'CREATED');
+        done();
+      });
+    });
+  });
+
+  describe('GET ai/models/<modelId>', () => {
+    it('Should return details about one specific model', (done) => {
+      url = `${endpoint}ai/models/:modelId:`;
+      mock.get(url, () => ({ body: modelReadyFile, ok: true }));
+      sa.aiModel(':modelId:').get({}, (err, model) => {
+        if (err) throw err;
+        assert.ok(model.constructor.name === 'AIModel');
+        assert.ok(model.training_status === 'READY');
+        done();
+      });
+    });
+  });
+
+
+  describe('POST ai/models/<modelId>/train', () => {
+    it('Should train an existing ai model', (done) => {
+      url = `${endpoint}ai/models/:modelId:/train`;
+      mock.post(url, () => ({ body: modelTrainingFile, ok: true }));
+      sa.aiModel(':modelId:').train({}, (err, model) => {
+        if (err) throw err;
+        assert.ok(model.constructor.name === 'AIModel');
+        assert.ok(model.training_status === 'TRAINING');
+        done();
+      });
+    });
+  });
+
+  describe('POST ai/models/<modelId>/invoke', () => {
+    it('Should return recommendation for a given source based on a specific model', (done) => {
+      url = `${endpoint}ai/models/:modelId/invoke`;
+      mock.post(url, () => ({ body: recommendationsFile, ok: true }));
+      sa.aiModel(':modelId:').getRecommendations({ source_id: '5aec176d1f7cdc0008848f87', size: 4 }, (err, recommendations) => {
+        if (err) throw err;
+        assert.ok(Array.isArray(recommendations.response));
+        done();
+      });
+    });
+  });
+
+  describe('POST ai/models/invoke', () => {
+    it('Should return recommendation for a given source based on set model type, source and destination', (done) => {
+      url = `${endpoint}ai/models/invoke`;
+      mock.post(url, () => ({ body: recommendationsFile, ok: true }));
+      sa.aiModel().getRecommendations({
+        size: 4, model_type: 'content_recommender', source: 'product', destination: 'product', source_id: '5aec176d1f7cdc0008848f87',
+      }, (err, recommendations) => {
+        if (err) throw err;
+        assert.ok(Array.isArray(recommendations.response));
+        done();
+      });
+    });
+  });
+});

test/collection/aiModel.js

@@ -0,0 +1,24 @@
+{
+    "model_type": "item_similarity_recommender",
+    "training_status": "CREATED",
+    "modified": {
+        "$date": 1528108844887
+    },
+    "model_evaluations": [],
+    "features": [],
+    "deleted": false,
+    "created": {
+        "$date": 1528108844887
+    },
+    "company": {
+        "$oid": "5ad0953c9a5d09000629d6c0"
+    },
+    "active": true,
+    "_id": {
+        "$oid": "5b15172ca5a05a000919c936"
+    },
+    "destination": "product",
+    "version": 0,
+    "source": "user",
+    "_cls": "DataScienceBaseModel.RecommendationModel.ItemSimilarityRecommender"
+}

test/fetchmock/modelCreated.json

@@ -0,0 +1,24 @@
+{
+    "model_type": "item_similarity_recommender",
+    "training_status": "READY",
+    "modified": {
+        "$date": 1528108844887
+    },
+    "model_evaluations": [],
+    "features": [],
+    "deleted": false,
+    "created": {
+        "$date": 1528108844887
+    },
+    "company": {
+        "$oid": "5ad0953c9a5d09000629d6c0"
+    },
+    "active": true,
+    "_id": {
+        "$oid": "5b15172ca5a05a000919c936"
+    },
+    "destination": "product",
+    "version": 0,
+    "source": "user",
+    "_cls": "DataScienceBaseModel.RecommendationModel.ItemSimilarityRecommender"
+}

test/fetchmock/modelReady.json

@@ -0,0 +1,24 @@
+{
+    "model_type": "item_similarity_recommender",
+    "training_status": "TRAINING",
+    "modified": {
+        "$date": 1528108844887
+    },
+    "model_evaluations": [],
+    "features": [],
+    "deleted": false,
+    "created": {
+        "$date": 1528108844887
+    },
+    "company": {
+        "$oid": "5ad0953c9a5d09000629d6c0"
+    },
+    "active": true,
+    "_id": {
+        "$oid": "5b15172ca5a05a000919c936"
+    },
+    "destination": "product",
+    "version": 0,
+    "source": "user",
+    "_cls": "DataScienceBaseModel.RecommendationModel.ItemSimilarityRecommender"
+}

test/fetchmock/modelTraining.json

@@ -0,0 +1,50 @@
+[
+  {
+    "version": 0,
+    "active": true,
+    "_cls": "DataScienceBaseModel.RecommendationModel.ItemSimilarityRecommender",
+    "_id": {
+      "$oid": "5b15172ca5a05a000919c936"
+    },
+    "created": {
+      "$date": 1528108844887
+    },
+    "features": [],
+    "company": {
+      "$oid": "5ad0953c9a5d09000629d6c0"
+    },
+    "deleted": false,
+    "modified": {
+      "$date": 1528108844887
+    },
+    "training_status": "CREATED",
+    "model_evaluations": [],
+    "destination": "product",
+    "source": "user"
+  },
+  {
+    "version": 0,
+    "active": true,
+    "_cls": "DataScienceBaseModel.RecommendationModel.ContentRecommender",
+    "_id": {
+      "$oid": "5b14ecfaa5a05a000c404496"
+    },
+    "created": {
+      "$date": 1528098042493
+    },
+    "features": [
+      "tags"
+    ],
+    "company": {
+      "$oid": "5ad0953c9a5d09000629d6c0"
+    },
+    "deleted": false,
+    "modified": {
+      "$date": 1528098042502
+    },
+    "training_status": "CREATED",
+    "model_evaluations": [],
+    "destination": "product",
+    "source": "user"
+  }
+]