sap-leonardo - npm Package Compare versions

Comparing version 0.5.2 to 0.5.3

dist/index.d.ts

@@ -15,1 +15,2 @@ export * from "./imageclassification";
 export * from "./product_image_classification";
+export * from "./topic_detection";

dist/index.js

@@ -20,1 +20,2 @@ "use strict";
 __export(require("./product_image_classification"));
+__export(require("./topic_detection"));

dist/test/test_multi_instance_image_segmentation.js

@@ -31,3 +31,3 @@ "use strict";
             }).then(done, done);
-        });
+        }).timeout(60000);
     });
@@ -64,3 +64,3 @@ describe("multi-instance image segmentation image:jpg", () => {
                 }).then(done, done);
-            });
+            }).timeout(60000);
         });
@@ -67,0 +67,0 @@ });

dist/test/test_ocr.js

@@ -19,3 +19,3 @@ "use strict";
             }).then(done, done);
-        });
+        }).timeout(20000);
     });
@@ -33,3 +33,3 @@ describe("image to text with options", () => {
             }).then(done, done);
-        });
+        }).timeout(20000);
         it("should return a German text", (done) => {
@@ -45,3 +45,3 @@ const options = { lang: "de", outputType: "txt", pageSegMode: "1", modelType: "lstmStandard" };
             }).then(done, done);
-        });
+        }).timeout(20000);
     });
@@ -48,0 +48,0 @@ describe("image to text (via job)", () => {

dist/test/test_similarity_scoring.1.js

@@ -32,3 +32,3 @@ "use strict";
         const similarityScoringWithOptions = new index_1.SimilarityScoring(process.env.API_KEY);
-        describe("texts", () => {
+        describe("numSimilarVectors", () => {
             it("should detect similarity scores for 1 similar vector", (done) => {
@@ -48,2 +48,47 @@ const optionsOne = JSON.stringify({ numSimilarVectors: 1 });
         });
+        describe("algorithm", () => {
+            it("should detect similarity scores for naive", (done) => {
+                const optionsOne = JSON.stringify({ numSimilarVectors: 2, algorithm: "naive" });
+                similarityScoringWithOptions.similarityScoring(null, JSON.stringify(data), optionsOne).then((body) => {
+                    chai_1.expect(body).to.have.property("id");
+                    chai_1.expect(body).to.have.property("predictions");
+                    chai_1.expect(body).to.have.property("processedTime");
+                    chai_1.expect(body).to.have.property("status").to.be.equal("DONE");
+                    chai_1.expect(body.predictions).to.be.an("array").have.lengthOf(3);
+                    // for (let i = 0; i < 3; i++) {
+                    //   expect(body.predictions[i].similarVectors[0].score).to.be.equal(predictions[i].similarVectors[0].score);
+                    // }
+                    chai_1.expect(body.predictions).to.be.an("array").have.lengthOf(3).is.eql(predictions);
+                }).then(done, done);
+            });
+            it("should detect similarity scores for matrix_mult", (done) => {
+                const optionsOne = JSON.stringify({ numSimilarVectors: 2, algorithm: "matrix_mult" });
+                similarityScoringWithOptions.similarityScoring(null, JSON.stringify(data), optionsOne).then((body) => {
+                    chai_1.expect(body).to.have.property("id");
+                    chai_1.expect(body).to.have.property("predictions");
+                    chai_1.expect(body).to.have.property("processedTime");
+                    chai_1.expect(body).to.have.property("status").to.be.equal("DONE");
+                    chai_1.expect(body.predictions).to.be.an("array").have.lengthOf(3);
+                    // for (let i = 0; i < 3; i++) {
+                    //   expect(body.predictions[i].similarVectors[0].score).to.be.equal(predictions[i].similarVectors[0].score);
+                    // }
+                    chai_1.expect(body.predictions).to.be.an("array").have.lengthOf(3).is.eql(predictions);
+                }).then(done, done);
+            });
+            it("should detect similarity scores for clustering", (done) => {
+                const optionsOne = JSON.stringify({ numSimilarVectors: 2, algorithm: "clustering" });
+                similarityScoringWithOptions.similarityScoring(null, JSON.stringify(data), optionsOne).then((body) => {
+                    chai_1.expect(body).to.have.property("id");
+                    chai_1.expect(body).to.have.property("predictions");
+                    chai_1.expect(body).to.have.property("processedTime");
+                    chai_1.expect(body).to.have.property("status").to.be.equal("DONE");
+                    chai_1.expect(body.predictions).to.be.an("array").have.lengthOf(3);
+                    // for (let i = 0; i < 3; i++) {
+                    //   expect(body.predictions[i].similarVectors[0].score).to.be.equal(predictions[i].similarVectors[0].score);
+                    // }
+                    chai_1.expect(body.predictions).to.be.an("array").have.lengthOf(3).is.eql(predictions);
+                }).then(done, done);
+            });
+        });
+        // algorithm - Optional. Algorithm to use for calculation, one of [“naive”, “matrix_mult”, “clustering”]
     });
@@ -50,0 +95,0 @@ describe("error coverage", () => {

package.json

 {
   "name": "sap-leonardo",
-  "version": "0.5.2",
+  "version": "0.5.3",
   "description": "NPM module for SAP Leonardo Machine Learning Foundation - Functional Services https://api.sap.com/package/SAPLeonardoMLFunctionalServices",
@@ -8,3 +8,3 @@ "main": "dist/index.js",
     "build": "./node_modules/.bin/tsc",
-    "test": "npm run build && ./node_modules/.bin/istanbul cover ./node_modules/.bin/_mocha --report lcovonly -- -s 30000 -t 30000 dist/test/test_*.js && cat ./coverage/lcov.info | ./node_modules/.bin/coveralls",
+    "test": "npm run build && ./node_modules/.bin/istanbul cover ./node_modules/.bin/_mocha --report lcovonly -- -s 10000 -t 10000 dist/test/test_*.js && cat ./coverage/lcov.info | ./node_modules/.bin/coveralls",
     "lint": "./node_modules/.bin/tslint src/**/*.ts && ./node_modules/.bin/eslint examples/*.js"
@@ -23,14 +23,19 @@ },
   "keywords": [
-    "sap leonardo",
+    "sap",
+    "leonardo",
     "machine learning",
     "ml",
-    "image classification",
-    "image segmentation",
-    "optical character recognition",
+    "image",
+    "text",
+    "classification",
+    "segmentation",
     "ocr",
-    "face detection",
-    "human detection",
-    "machine translation",
-    "language detection",
-    "product text classification"
+    "optical",
+    "character",
+    "recognition",
+    "face",
+    "human",
+    "language",
+    "detection",
+    "translation"
   ],
@@ -37,0 +42,0 @@ "dependencies": {

README.md

@@ -63,3 +63,3 @@ # SAP Leonardo Machine Learning Foundation - Functional Services
 - ~~Inference Service for Customizable Similarity Search~~
-- ~~Inference Service for Topic Detection~~
+- [Inference Service for Topic Detection](https://api.sap.com/api/topic_detection_api/resource)
 
@@ -119,2 +119,3 @@ - [Inference Service for Human Detection](https://api.sap.com/api/human_detection_api/resource)
 - [converse-2069209_640.jpg](https://pixabay.com/en/converse-shoes-grass-outdoors-2069209/)
+- [keyboard-70506_640.jpg](https://pixabay.com/en/keyboard-computer-hardware-keys-70506/)
 
@@ -121,0 +122,0 @@

No alert changes

Improved metrics

Total package byte prevSize

222854

increased by7.8%

Number of package files

70

increased by9.38%

Lines of code

5223

increased by7.36%

Number of lines in readme file

130

increased by0.78%

Worsened metrics

Number of low supply chain risk alerts

52

increased by10.64%

No dependency changes