updated readme

dcrescim · dcrescim · commit eef2f5c37605 · 2022-05-18T22:40:19.000-07:00
diff --git a/.gitignore b/.gitignore
@@ -107,4 +107,5 @@ dist
 
 # IDE Files
 .vscode/
-.idea/
+.idea/
+.dccache
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,3 +1,11 @@
+# [1.23.0](https://github.com/javascriptdata/scikit.js/compare/v1.22.0...v1.23.0) (2022-05-19)
+
+
+### Features
+
+* added test case for custom callbacks. works great and somehow serializes. ([7fa5c42](https://github.com/javascriptdata/scikit.js/commit/7fa5c4259902d7dca0a925002cbfaf1937dc2b1b))
+* custom modelfitargs for linear models ([2ddcad9](https://github.com/javascriptdata/scikit.js/commit/2ddcad9053097c75bcdd97d234c1f8b605a88241))
+
 # [1.22.0](https://github.com/javascriptdata/scikit.js/compare/v1.21.0...v1.22.0) (2022-05-18)
 
 
diff --git a/package-lock.json b/package-lock.json
diff --git a/package.json b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "scikitjs",
-  "version": "1.22.0",
+  "version": "1.23.0",
   "description": "Scikit-Learn for JS",
   "output": {
     "node": "dist/node/index.js",
diff --git a/src/linear_model/LinearRegression.test.ts b/src/linear_model/LinearRegression.test.ts
@@ -17,6 +17,38 @@ describe('LinearRegression', function () {
     expect(roughlyEqual(lr.intercept as number, 0)).toBe(true)
   }, 30000)
 
+  it('Works on arrays (small example) with custom callbacks', async function () {
+    let trainingHasStarted = false
+    const onTrainBegin = async (logs: any) => {
+      trainingHasStarted = true
+      console.log('training begins')
+    }
+    const lr = new LinearRegression({
+      modelFitOptions: { callbacks: [new tf.CustomCallback({ onTrainBegin })] }
+    })
+    await lr.fit([[1], [2]], [2, 4])
+    expect(tensorEqual(lr.coef, tf.tensor1d([2]), 0.1)).toBe(true)
+    expect(roughlyEqual(lr.intercept as number, 0)).toBe(true)
+    expect(trainingHasStarted).toBe(true)
+  }, 30000)
+
+  it('Works on arrays (small example) with custom callbacks', async function () {
+    let trainingHasStarted = false
+    const onTrainBegin = async (logs: any) => {
+      trainingHasStarted = true
+      console.log('training begins')
+    }
+    const lr = new LinearRegression({
+      modelFitOptions: { callbacks: [new tf.CustomCallback({ onTrainBegin })] }
+    })
+    await lr.fit([[1], [2]], [2, 4])
+
+    const serialized = await lr.toJSON()
+    const newModel = await fromJSON(serialized)
+    expect(tensorEqual(newModel.coef, tf.tensor1d([2]), 0.1)).toBe(true)
+    expect(roughlyEqual(newModel.intercept as number, 0)).toBe(true)
+  }, 30000)
+
   it('Works on small multi-output example (small example)', async function () {
     const lr = new LinearRegression()
     await lr.fit(
diff --git a/src/linear_model/LinearRegression.ts b/src/linear_model/LinearRegression.ts
@@ -15,6 +15,7 @@
 
 import { SGDRegressor } from './SgdRegressor'
 import { getBackend } from '../tf-singleton'
+import { ModelFitArgs } from '../types'
 
 /**
  * LinearRegression implementation using gradient descent
@@ -39,6 +40,7 @@ export interface LinearRegressionParams {
    * **default = true**
    */
   fitIntercept?: boolean
+  modelFitOptions?: Partial<ModelFitArgs>
 }
 
 /*
@@ -50,7 +52,7 @@ Next steps:
 /** Linear Least Squares
  * @example
  * ```js
- * import {LinearRegression} from 'scikitjs'
+ * import { LinearRegression } from 'scikitjs'
  *
  * let X = [
  *  [1, 2],
@@ -60,13 +62,16 @@ Next steps:
  *  [10, 20]
  * ]
  * let y = [3, 5, 8, 8, 30]
- * const lr = new LinearRegression({fitIntercept: false})
+ * const lr = new LinearRegression({ fitIntercept: false })
   await lr.fit(X, y)
   lr.coef.print() // probably around [1, 1]
  * ```
  */
 export class LinearRegression extends SGDRegressor {
-  constructor({ fitIntercept = true }: LinearRegressionParams = {}) {
+  constructor({
+    fitIntercept = true,
+    modelFitOptions
+  }: LinearRegressionParams = {}) {
     let tf = getBackend()
     super({
       modelCompileArgs: {
@@ -80,7 +85,8 @@ export class LinearRegression extends SGDRegressor {
         verbose: 0,
         callbacks: [
           tf.callbacks.earlyStopping({ monitor: 'mse', patience: 30 })
-        ]
+        ],
+        ...modelFitOptions
       },
       denseLayerArgs: {
         units: 1,
diff --git a/src/linear_model/LogisticRegression.ts b/src/linear_model/LogisticRegression.ts
@@ -15,6 +15,7 @@
 
 import { SGDClassifier } from './SgdClassifier'
 import { getBackend } from '../tf-singleton'
+import { ModelFitArgs } from '../types'
 
 // First pass at a LogisticRegression implementation using gradient descent
 // Trying to mimic the API of scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
@@ -35,6 +36,7 @@ export interface LogisticRegressionParams {
   C?: number
   /** Whether or not the intercept should be estimator not. **default = true** */
   fitIntercept?: boolean
+  modelFitOptions?: Partial<ModelFitArgs>
 }
 
 /** Builds a linear classification model with associated penalty and regularization
@@ -63,7 +65,8 @@ export class LogisticRegression extends SGDClassifier {
   constructor({
     penalty = 'l2',
     C = 1,
-    fitIntercept = true
+    fitIntercept = true,
+    modelFitOptions
   }: LogisticRegressionParams = {}) {
     // Assume Binary classification
     // If we call fit, and it isn't binary then update args
@@ -80,7 +83,8 @@ export class LogisticRegression extends SGDClassifier {
         verbose: 0,
         callbacks: [
           tf.callbacks.earlyStopping({ monitor: 'loss', patience: 50 })
-        ]
+        ],
+        ...modelFitOptions
       },
       denseLayerArgs: {
         units: 1,

Original file line number	Diff line number	Diff line change
`@@ -1,6 +1,6 @@`
`1`	`1`	`{`
`2`	`2`	`"name": "scikitjs",`
`3`		`- "version": "1.22.0",`
	`3`	`+ "version": "1.23.0",`
`4`	`4`	`"description": "Scikit-Learn for JS",`
`5`	`5`	`"output": {`
`6`	`6`	`"node": "dist/node/index.js",`