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[demo] Add ability to deploy any of the supported model types (#85)
Resolves #82 Improve smoothness. Dismiss some notifications. Add warning if deploying to main.
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178 changes: 178 additions & 0 deletions
178
demo/client/src/ml-models/__tests__/deploy-model.test.ts
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,178 @@ | ||
| import assert from 'assert' | ||
| import Web3 from 'web3' | ||
| import { convertNum } from '../../float-utils' | ||
| import { CentroidInfo, ModelDeployer, NearestCentroidModel } from '../deploy-model' | ||
|
|
||
| declare const web3: Web3 | ||
|
|
||
| function assertEqualNumbers(actual: any, expected: any, message?: string | Error): void { | ||
| if (web3.utils.isBN(actual)) { | ||
| if (web3.utils.isBN(expected)) { | ||
| if (message === undefined) { | ||
| message = `actual: ${actual} (${typeof actual})\nexpected: ${expected} (${typeof expected})` | ||
| } | ||
| return assert(actual.eq(expected), message) | ||
| } else { | ||
| const expectedBN = web3.utils.toBN(expected) | ||
| if (message === undefined) { | ||
| message = `actual: ${actual} (${typeof actual})\nexpected: ${expected} (${typeof expected}) => BN: ${expectedBN}` | ||
| } | ||
| return assert(actual.eq(expectedBN), message) | ||
| } | ||
| } else if (web3.utils.isBN(expected)) { | ||
| const actualBN = web3.utils.toBN(actual) | ||
| if (message === undefined) { | ||
| message = `actual: ${actual} (${typeof actual}) => BN: ${actualBN}\nexpected: ${expected} (${typeof expected})` | ||
| } | ||
| return assert(actualBN.eq(expected), message) | ||
| } else { | ||
| if (typeof actual === 'string') { | ||
| actual = parseInt(actual) | ||
| } | ||
| return assert.equal(actual, expected, message) | ||
| } | ||
| } | ||
|
|
||
| describe("ModelDeployer", () => { | ||
| let account: string | ||
| const deployer = new ModelDeployer(web3) | ||
|
|
||
| beforeAll(async () => { | ||
| const accounts = await web3.eth.getAccounts() | ||
| // Pick a random account between 2 and 9 since 0 and 1 are usually used in the browser. | ||
| account = accounts[2 + Math.min(Math.floor(Math.random() * 8), 7)] | ||
| }) | ||
|
|
||
| it("should deploy Naive Bayes", async () => { | ||
| const model = { | ||
| classifications: [ | ||
| "A", | ||
| "B" | ||
| ], | ||
| classCounts: [ | ||
| 2, | ||
| 3 | ||
| ], | ||
| featureCounts: [ | ||
| [[0, 2], [1, 1]], | ||
| [[1, 3], [2, 2]], | ||
| ], | ||
| totalNumFeatures: 9, | ||
| smoothingFactor: 1.0, | ||
| type: "naive bayes" | ||
| } | ||
| const m = await deployer.deployModel( | ||
| model, | ||
| { | ||
| account, | ||
| }) | ||
|
|
||
| for (let i = 0; i < model.classifications.length; ++i) { | ||
| assert.equal(await m.methods.classifications(i).call(), model.classifications[i]) | ||
| assertEqualNumbers(await m.methods.getNumSamples(i).call(), model.classCounts[i]) | ||
| for (const [featureIndex, count] of model.featureCounts[i]) { | ||
| assertEqualNumbers(await m.methods.getFeatureCount(i, featureIndex).call(), count) | ||
| } | ||
| } | ||
| assertEqualNumbers(await m.methods.getClassTotalFeatureCount(0).call(), 3) | ||
| assertEqualNumbers(await m.methods.getClassTotalFeatureCount(1).call(), 5) | ||
| }) | ||
|
|
||
| it("should deploy dense Nearest Centroid", async () => { | ||
| const model = new NearestCentroidModel( | ||
| 'dense nearest centroid classifier', | ||
| { | ||
| "AA": new CentroidInfo([-1, -1], 2), | ||
| "BB": new CentroidInfo([+1, +1], 2), | ||
| } | ||
| ) | ||
| const m = await deployer.deployModel( | ||
| model, | ||
| { | ||
| account, | ||
| }) | ||
|
|
||
| let i = -1 | ||
| for (let [classification, centroidInfo] of Object.entries(model.intents)) { | ||
| ++i | ||
| assert.equal(await m.methods.classifications(i).call(), classification) | ||
| assertEqualNumbers(await m.methods.getNumSamples(i).call(), centroidInfo.dataCount) | ||
| for (let j = 0; j < centroidInfo.centroid.length; ++j) { | ||
| assertEqualNumbers(await m.methods.getCentroidValue(i, j).call(), convertNum(centroidInfo.centroid[j], web3)) | ||
| } | ||
| } | ||
| }) | ||
|
|
||
| it("should deploy sparse Nearest Centroid", async () => { | ||
| const model = new NearestCentroidModel( | ||
| 'sparse nearest centroid classifier', | ||
| { | ||
| "AA": new CentroidInfo([0, +1], 2), | ||
| "BB": new CentroidInfo([+1, 0], 2), | ||
| } | ||
| ) | ||
| const m = await deployer.deployModel( | ||
| model, | ||
| { | ||
| account, | ||
| }) | ||
|
|
||
| let i = -1 | ||
| for (let [classification, centroidInfo] of Object.entries(model.intents)) { | ||
| ++i | ||
| assert.equal(await m.methods.classifications(i).call(), classification) | ||
| assertEqualNumbers(await m.methods.getNumSamples(i).call(), centroidInfo.dataCount) | ||
| for (let j = 0; j < centroidInfo.centroid.length; ++j) { | ||
| assertEqualNumbers(await m.methods.getCentroidValue(i, j).call(), convertNum(centroidInfo.centroid[j], web3)) | ||
| } | ||
| } | ||
| }) | ||
|
|
||
| it("should deploy dense Perceptron", async () => { | ||
| const classifications = ["A", "B"] | ||
| const weights = [1, -1] | ||
| const intercept = 0 | ||
| const m = await deployer.deployModel( | ||
| { | ||
| type: 'dense perceptron', | ||
| classifications, | ||
| weights, | ||
| intercept, | ||
| }, | ||
| { | ||
| account, | ||
| }) | ||
|
|
||
| for (let i = 0; i < classifications.length; ++i) { | ||
| assert.equal(await m.methods.classifications(i).call(), classifications[i]) | ||
| } | ||
| for (let i = 0; i < weights.length; ++i) { | ||
| assertEqualNumbers(await m.methods.weights(i).call(), convertNum(weights[i], web3)) | ||
| } | ||
| assertEqualNumbers(await m.methods.intercept().call(), convertNum(intercept, web3)) | ||
| }) | ||
|
|
||
| it("should deploy sparse Perceptron", async () => { | ||
| const classifications = ["AA", "BB"] | ||
| const weights = [2, -2] | ||
| const intercept = 3 | ||
| const m = await deployer.deployModel( | ||
| { | ||
| type: 'sparse perceptron', | ||
| classifications, | ||
| weights, | ||
| intercept, | ||
| }, | ||
| { | ||
| account, | ||
| }) | ||
|
|
||
| for (let i = 0; i < classifications.length; ++i) { | ||
| assert.equal(await m.methods.classifications(i).call(), classifications[i]) | ||
| } | ||
| for (let i = 0; i < weights.length; ++i) { | ||
| assertEqualNumbers(await m.methods.weights(i).call(), convertNum(weights[i], web3)) | ||
| } | ||
| assertEqualNumbers(await m.methods.intercept().call(), convertNum(intercept, web3)) | ||
| }) | ||
| }) |
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