From 690e24271ecb1c089711c4e339a532876fd574a0 Mon Sep 17 00:00:00 2001 From: Anton Nesterov Date: Fri, 4 Oct 2024 06:09:41 +0200 Subject: [PATCH] logreg js and examples --- notebooks/regressions.ipynb | 317 ++++++++++++++++++++++++++++++- plot/mod.wasm | Bin 12241039 -> 12241303 bytes regr/main.go | 1 + regr/mod.wasm | Bin 1170445 -> 1211089 bytes regr/src/LogisticRegressionJS.go | 40 ++++ regr/src/utils_js.go | 25 +++ 6 files changed, 376 insertions(+), 7 deletions(-) create mode 100644 regr/src/LogisticRegressionJS.go diff --git a/notebooks/regressions.ipynb b/notebooks/regressions.ipynb index f889c55..461a4d4 100644 --- a/notebooks/regressions.ipynb +++ b/notebooks/regressions.ipynb @@ -4,11 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Regressions\n", - "\n", - "$X = (0,1)$\n", - "\n", - "$Y = sin(2\\pi X)$" + "# Regressions\n" ] }, { @@ -29,8 +25,6 @@ ], "source": [ "// deno-lint-ignore-file\n", - "\n", - "import { display } from \"https://deno.land/x/display@v0.1.1/mod.ts\";\n", "import pl from \"npm:nodejs-polars\";\n", "import plot from \"../plot/mod.ts\";\n", "\n", @@ -249,6 +243,315 @@ "source": [ "lassoPoly.score(df.select('y').rows(), pl.DataFrame({\"py\":predLassoY}).rows());\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression\n", + "\n", + "Logistic regression is applicable for classification problems when there are linear relationships in the data.\n", + "\n", + "For example we'll use a simple linear pattern for predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.dataresource+json": { + "bytes": null, + "data": [ + { + "is_even": 1, + "x": 0, + "x2": -0.9906467224227306, + "x3": 0.00261179419023964 + }, + { + "is_even": 0, + "x": 1, + "x2": 0.00202764718654894, + "x3": 0.0013126936809724655 + }, + { + "is_even": 1, + "x": 2, + "x2": -0.9972687463767552, + "x3": -1.9905576758692132 + }, + { + "is_even": 0, + "x": 3, + "x2": 0.009024117668298463, + "x3": 1.0021275936361942 + }, + { + "is_even": 1, + "x": 4, + "x2": -0.9968599379688912, + "x3": -0.9947633704461447 + }, + { + "is_even": 0, + "x": 5, + "x2": 0.0019298798125454942, + "x3": -0.9913865823439642 + }, + { + "is_even": 1, + "x": 6, + "x2": -0.9967558047825875, + "x3": 0.00801993440722967 + }, + { + "is_even": 0, + "x": 7, + "x2": 0.007736464312311071, + "x3": 0.00034330022067074583 + }, + { + "is_even": 1, + "x": 8, + "x2": -0.9959077406033643, + "x3": -1.994011690184521 + }, + { + "is_even": 0, + "x": 9, + "x2": 0.002684616942261051, + "x3": 1.0072230674765243 + } + ], + "description": null, + "dialect": null, + "encoding": null, + "format": null, + "hash": null, + "homepage": null, + "licenses": null, + "mediatype": null, + "path": null, + "schema": { + "fields": [ + { + "constraints": null, + "description": null, + "example": null, + "format": null, + "name": "x", + "rdfType": null, + "title": null, + "type": "number" + }, + { + "constraints": null, + "description": null, + "example": null, + "format": null, + "name": "x2", + "rdfType": null, + "title": null, + "type": "number" + }, + { + "constraints": null, + "description": null, + "example": null, + "format": null, + "name": "x3", + "rdfType": null, + "title": null, + "type": "number" + }, + { + "constraints": null, + "description": null, + "example": null, + "format": null, + "name": "is_even", + "rdfType": null, + "title": null, + "type": "number" + } + ], + "foreignKeys": null, + "missingValues": null, + "primaryKey": null + }, + "sources": null, + "title": null + }, + "text/html": [ + "
xx2x3is_even
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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "const clsDf = pl.DataFrame({ \n", + " x: new Array(100).fill(0).map((_, i) => i ),\n", + " x2: new Array(100).fill(0).map((_, i) => i % 2 - 1 + Math.random() / 100),\n", + " x3: new Array(100).fill(0).map((_, i) => i % 2 - i % 3 + Math.random() / 100),\n", + " }).select(\n", + " pl.all(),\n", + " pl.col('x').modulo(2).eq(0).add(0).alias('is_even'),\n", + ");\n", + "clsDf.head(10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that I left `x` in training data which is a continuos sequence that couln't be generalized by this model, however the model should guess a correct class in most cases. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "hide_code", + "parameters" + ] + }, + "outputs": [], + "source": [ + "const drawBin = (x1, y1, t = \"Example\") => {\n", + " const xTrue = clsDf.x.toArray().map((v, i) => v % 2)\n", + " const yTrue = clsDf.is_even.toArray()\n", + " y1 = y1.map((v, i) => v + i * 0.02)\n", + " x1 = x1.map((v, i) => v % 2)\n", + " return plot.DrawPlot(\n", + " { \n", + " title: \"\",\n", + " width: 2.5,\n", + " height: 2,\n", + " XLabel: \"X\", \n", + " YLabel: \"Y\", \n", + " }, \n", + " { type: \"scatter\", data: [xTrue, yTrue], lineDashes: [3, 4], glypRadius: 12, glyphColor: \"#00f\", glyphShape: \"ring\" },\n", + " { type: \"scatter\", data: [x1, y1], legend: t, lineDashes: [3, 4], glypRadius: 3, glyphColor: \"#f00\", glyphShape: \"ring\" },\n", + " );\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import {trainTestSplit} from \"../split.ts\";\n", + "\n", + "\n", + "const {testX, trainX, testY, trainY} = trainTestSplit(clsDf, 0.05, true, \"is_even\");\n", + "\n", + "const drawTestBin = () => drawBin(testX.x.toArray(), testY.is_even.toArray(), \"Test Data\");\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + 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)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "const logreg = regr.Logistic({\n", + " epochs: 5000,\n", + " learningRate: 0.001,\n", + "});\n", + "logreg.fit(trainX.rows(), trainY.rows());\n", + "\n", + "const predLogReg = logreg.predict(testX.rows());\n", + "\n", + "const yPred1 = predLogReg.map((x) => x[0]);\n", + "\n", + "drawBin(\n", + " testX.x.toArray(), \n", + " yPred1,\n", + " \"Predicted\"\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "hide_code", + "parameters" + ] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + 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)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drawTestBin()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0, 1, 1, 0, 1 ] should be [ 0, 1, 1, 0, 1 ]\n" + ] + } + ], + "source": [ + "console.log(yPred1, \"should be\", testY.is_even.toArray());" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[33m1.6635532311438688\u001b[39m" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg.loss();" + ] } ], "metadata": { diff --git a/plot/mod.wasm b/plot/mod.wasm index b4c33f778fc4dbbb9e3a6c55f584814a4491e619..46a2fc49159fa27c9a11d1ddbcf57ef81ff24757 100755 GIT binary patch delta 60183 zcmZ@>3w#V$7oUl2WJMy#5|6Up+M+=a5^M;G1{>lLrM1Y+M5#v6D$074APB;V_o`R4 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= args[0].Get("learningRate").Float() + ) + reg := &MCLogisticRegression{ + Epochs: epochs, + LearningRate: learningRate, + } + loss := 1.0 + obj := js.Global().Get("Object").New() + obj.Set("fit", js.FuncOf(func(this js.Value, args []js.Value) interface{} { + X := JSFloatArray2D(args[0]) + Y := JSFloatArray2D(args[1]) + XDense := Array2DToDense(X) + YDense := Array2DToDense(Y) + reg.Fit(XDense, YDense) + return nil + })) + obj.Set("predict", js.FuncOf(func(this js.Value, args []js.Value) interface{} { + X := JSFloatArray2D(args[0]) + XDense := Array2DToDense(X) + Y := reg.Predict(XDense) + loss = reg.Loss(XDense, Y) + return MatrixToJSArray2D(Y) + })) + obj.Set("loss", js.FuncOf(func(this js.Value, args []js.Value) interface{} { + return loss + })) + return obj +} diff --git a/regr/src/utils_js.go b/regr/src/utils_js.go index 903470a..d7f5125 100644 --- a/regr/src/utils_js.go +++ b/regr/src/utils_js.go @@ -5,6 +5,8 @@ package src import ( "syscall/js" + + "gonum.org/v1/gonum/mat" ) func JSFloatArray(arg js.Value) []float64 { @@ -35,3 +37,26 @@ func ToJSArray[T any](arr []T) []interface{} { } return jsArr } + +func MatrixToJSArray(m mat.Matrix) []interface{} { + r, c := m.Dims() + data := make([]float64, r*c) + for i := 0; i < r; i++ { + for j := 0; j < c; j++ { + data[i*c+j] = m.At(i, j) + } + } + return ToJSArray(data) +} + +func MatrixToJSArray2D(m mat.Matrix) []interface{} { + r, c := m.Dims() + data := make([][]interface{}, r) + for i := 0; i < r; i++ { + data[i] = make([]interface{}, c) + for j := 0; j < c; j++ { + data[i][j] = m.At(i, j) + } + } + return ToJSArray(data) +}

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