docs(samples): move AutoML vision samples from nodejs-vision (#134)

automl/package.json

@@ -14,9 +14,17 @@
       "**/*.test.js"
     ]
   },
-  "scripts": {},
+  "scripts": {
+    "test": "mocha --timeout 600000"
+  },
   "dependencies": {
-    "@google-cloud/automl": "^0.1.3"
+    "@google-cloud/automl": "^0.1.3",
+    "chai": "^4.2.0",
+    "execa": "^1.0.0",
+    "mathjs": "^5.5.0",
+    "yargs": "^13.2.1"
   },
-  "devDependencies": {}
+  "devDependencies": {
+    "mocha": "^6.0.1"
+  }
 }

automl/test/.eslintrc.yml

@@ -0,0 +1,3 @@
+---
+env:
+  mocha: true

automl/test/automlVision.test.js

@@ -0,0 +1,139 @@
+/**
+ * Copyright 2018, Google, LLC.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+'use strict';
+
+const path = require('path');
+const {assert} = require('chai');
+const execa = require('execa');
+
+const exec = async cmd => (await execa.shell(cmd)).stdout;
+const cmdDataset = `node vision/automlVisionDataset.js`;
+const cmdModel = `node vision/automlVisionModel.js`;
+const cmdPredict = `node vision/automlVisionPredict.js`;
+
+const testDataSetName = `testDataSet`;
+const dummyDataSet = `dummyDataSet`;
+const testModelName = `dummyModel`;
+const testImgPath = `./resources/`;
+const sampleImage2 = path.join(testImgPath, `testImage2.jpg`);
+
+describe(`auto ml vision`, () => {
+  it.skip(`should create, list, and delete a dataset`, async () => {
+    // Check to see that this dataset does not yet exist
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.strictEqual(output.includes(testDataSetName), false);
+
+    // Create dataset
+    output = await exec(`${cmdDataset} create-dataset -n "${testDataSetName}"`);
+    const dataSetId = output
+      .split(`\n`)[1]
+      .split(`:`)[1]
+      .trim();
+    assert.match(output, new RegExp(testDataSetName));
+
+    // Delete dataset
+    output = await exec(`${cmdDataset} delete-dataset -i "${dataSetId}"`);
+    assert.match(output, /Dataset deleted./);
+  });
+
+  // See : https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/vision/automl/model_test.py
+  // We make two models running this test, see hard-coded workaround below
+  it.skip(`should create a dataset, import data, and start making a model`, async () => {
+    // Check to see that this dataset does not yet exist
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.strictEqual(output.includes(dummyDataSet), false);
+
+    // Create dataset
+    output = await exec(`${cmdDataset} create-dataset -n "${dummyDataSet}"`);
+    const dataSetId = output
+      .split(`\n`)[1]
+      .split(`:`)[1]
+      .trim();
+    assert.match(output, new RegExp(dummyDataSet));
+
+    // Import Data
+    output = await exec(
+      `${cmdDataset} import-data -i "${dataSetId}" -p "gs://nodejs-docs-samples-vcm/flowerTraindata20lines.csv"`
+    );
+    assert.match(output, /Data imported./);
+
+    // Check to make sure model doesn't already exist
+    output = await exec(`${cmdModel} list-models`);
+    assert.notMatch(output, new RegExp(testModelName));
+
+    // begin training dataset, getting operation ID for next operation
+    output = await exec(`
+      ${cmdModel} create-model -i "${dataSetId}" -m "${testModelName}" -t "2"`);
+    const operationName = output
+      .split(`\n`)[0]
+      .split(`:`)[1]
+      .split(`/`)
+      .pop()
+      .trim();
+    assert.match(output, /Training started.../);
+
+    // poll operation status, here confirming that operation is not complete yet
+    output = await exec(
+      `${cmdModel} get-operation-status -i "${dataSetId}" -o "${operationName}"`
+    );
+    assert.match(output, /done: false/);
+  });
+
+  it.skip(`should display evaluation from prexisting model`, async () => {
+    const flowersModelId = `ICN723541179344731436`;
+    const flowersDisplayName = `flowersTest`;
+
+    // Confirm dataset exists
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.match(output, new RegExp(flowersDisplayName));
+
+    // List model evaluations, confirm model exists
+    output = await exec(
+      `${cmdModel} list-model-evaluations -a "${flowersModelId}"`
+    );
+
+    // Display evaluation
+    output = await exec(
+      `${cmdModel} display-evaluation -a "${flowersModelId}"`
+    );
+    assert.match(output, /Model Precision/);
+  });
+
+  it.skip(`should run Prediction from prexisting model`, async () => {
+    const donotdeleteModelId = `ICN723541179344731436`;
+    const flowersDisplayName = `flowers`;
+
+    // Confirm dataset exists
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.match(output, new RegExp(flowersDisplayName));
+
+    // List model evaluations, confirm model exists
+    output = await exec(
+      `${cmdModel} list-model-evaluations -a "${donotdeleteModelId}"`
+    );
+    // Run prediction on 'testImage.jpg' in resources folder
+    output = await exec(
+      `${cmdPredict} predict -i "${donotdeleteModelId}" -f "${sampleImage2}" -s "0.5"`
+    );
+    assert.match(output, /dandelion/);
+  });
+
+  // List datasets
+  it(`should list datasets`, async () => {
+    const output = await exec(`${cmdDataset} list-datasets`);
+    assert.match(output, /List of datasets:/);
+  });
+});