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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"# Regressions\n"
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]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"![name](data:image/png;base64,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
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// deno-lint-ignore-file\n",
"import pl from \"npm:nodejs-polars\";\n",
"import plot from \"../plot/mod.ts\";\n",
"\n",
"const data = await Deno.readTextFile(\"assets/X_Y_Sinusoid_Data.csv\");\n",
"const df = pl.readCSV(data, { sep: \",\" });\n",
"\n",
"const real = pl.DataFrame({ x: new Array(100).fill(0).map((_, i) => i / 100)}).select(\n",
" pl.col('x'),\n",
" pl.col('x').mul(2).mul(3.14).sin().alias('y')\n",
");\n",
"\n",
"const draw = (x, y, title = \"Sinusoid Data\") => \n",
" plot.DrawPlot(\n",
" { \n",
" title,\n",
" width: 6,\n",
" height: 3,\n",
" XLabel: \"X\", \n",
" YLabel: \"Y\", \n",
" }, \n",
" { type: \"line\", data: [real.x, real.y], legend: \"Sinusoid\", lineDashes: [3, 4], lineColor: \"#ff8888\", lineWidth: 1 },\n",
" { type: \"scatter\", data: [x, y], legend: \"Data\", lineDashes: [3, 4], lineWidth: 2, glyphColor: \"#4444ff\", glyphShape: \"circle\" },\n",
" { type: \"trend\", data: [x, y], legend: \"Trend\", lineDashes: [4, 2], lineColor: '#aacccc', lineWidth: .5 },\n",
" );\n",
"\n",
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" const comparePredicted = (x, y, predicted) => plot.DrawPlot(\n",
" { \n",
" width: 7,\n",
" height: 4,\n",
" XLabel: \"X\", \n",
" YLabel: \"Y\", \n",
" }, \n",
" { type: \"line\", data: [real.x, real.y], legend: \"Sinusoid\", lineDashes: [3, 4], lineColor: \"#ff8888\", lineWidth: 1 },\n",
" { type: \"linePoints\", data: [x, y], legend: \"Test Data\", lineDashes: [3, 4], lineColor: \"#8888ff\", glyphColor: \"#4444ff\", glyphShape: \"circle\" },\n",
" { type: \"linePoints\", data: [x, predicted], lineWidth: .5, legend: \"Predicted\", glyphColor: '#f00', glyphShape: \"pyramid\" },\n",
");\n",
"\n",
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"\n",
"draw(df.x, df.y);"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Polynomial Tranformation\n",
"\n",
"First we try to predict values without polynomial transformation:"
]
},
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{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
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"![name](data:image/png;base64,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]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import regr from '../regr/mod.ts';\n",
"\n",
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"const linregWoPoly = regr.Linear();\n",
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"\n",
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"linregWoPoly.fit(df.drop('y').rows(), df.select('y').rows());\n",
"const predWoPoly = linregWoPoly.predict(df.drop('y').rows());\n",
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"\n",
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"comparePredicted(df.x, df.y, predWoPoly);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now augment our dataset with high degree polynomial:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"![name](data:image/png;base64,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
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import { polynomialTransform } from '../encoding.ts';\n",
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"\n",
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"\n",
"\n",
"const polyFeatures = polynomialTransform(df.drop('y'), 36, false, false)\n",
"\n",
"const [polyX, polyY] = [polyFeatures, df.select('y')]\n",
"\n",
"const linregPoly = regr.Linear();\n",
"\n",
"linregPoly.fit(polyX.rows(), polyY.rows());\n",
"const predY = linregPoly.predict(polyX.rows());\n",
"\n",
"comparePredicted(df.x, df.y, predY);\n"
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]
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},
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{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[33m0.9999999469410812\u001b[39m"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linregPoly.score(df.select('y').rows(), pl.DataFrame({\"py\":predY}).rows());"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ElasticNet"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"![name](data:image/png;base64,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
]
},
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"execution_count": 5,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"const elasticNetPoly = regr.ElasticNet(1000, 0.0001);\n",
"elasticNetPoly.fit(polyX.rows(), polyY.rows());\n",
"const predEnetY = elasticNetPoly.predict(polyX.rows());\n",
"\n",
"comparePredicted(df.x, df.y, predEnetY);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lasso"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"![name](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAGACAIAAAD9Gkc5AABxn0lEQVR4nOzdB1hT1xcA8BPC3nsriojgRFEcuEAcVCvuVUEUBUVb9wBtRf+uorVFLYqioKBixVH3RItbREVARKaAgCB7hJHx/0IwQlgJGY/A+X18/fLuW0cacvLeu/dcSQaDAQghhBBqXySJDgAhJFz37t27c+cOmUzevXs30bEghERHgugAEEIC8ObNG3d3dzk5OQsLi/Hjx9vZ2bm4uHz9+hUA7OzsunXrFhwcTHSMCCGRwgSPUHvQv3//7du3V1RULF68+Pbt29euXcvPz7eysqJSqdwf5N9//xVmjAghkcIEj1A7JCsru2zZspSUlMjIyKa24eh/k5mZuWbNGpFEhxASBUzwCLVP8vLyAPDlyxeOdgaDsX//fnt7e2dn51GjRvn6+gJAfHy8s7NzVlbW3LlzT506RVDICCFBwk52CLVP4eHhMjIylpaWHO3BwcEHDhx4//69vLx8bm6uiYmJgYGBg4PD9OnT4+Lizp49S1C8CCEBwwSPULsSGxt7/vz5ly9fBgYG+vn5GRgYcGxw/PjxCRMmsK7vtbS0xo4dGxAQ4ODgQFC8CCFhwVv0CLUrBQUFhYWFtra2cXFxCxYsaLhBamqqtrY2e1FPTy81NVW0MSKERAGv4BFqV4YPH75kyZJmNlBQUKisrGQvVlRUaGpqiiQ0hJBI4RU8Qh1Lv379EhMT2YsfPnzo06cP6zWdTicuLoSQgGGCR6idoFAoAFBeXt6w/jSjBuu1p6fno0ePHjx4QKVSQ0JC0tPT169fDwCmpqZZWVl37tx59+4dEeEjhASMhLXoEWoHEhISgoKCWJfgsrKya9asYXWjY5WqffToEY1GU1VVdXNzU1JSSkpKCgwMzM7O7tu3r4ODQ+fOnQGASqVu3bo1Ozvb3t5+xowZRP+DEEL8wgSPEEIItUN4ix4hhBBqhzDBI4QQQu0QJniEEEKoHcJx8Ejo6HRISAAqFUxNQUqK6GgQQqhjwASPhCs1Fby84PNn5msNDdi8Gb4NukYIISRE2IseCRGdDi4ukJHxvUVFBU6dgm8DuBBCCAkLPoPvcL5+hX37YOFCWLMGHj8W7rmSkupldwAoKoLXr4V7UoQQYktPTw8LCwsPD//06ROVSmU1VlRUvHnzRgRn//z5c1paGkdjVlbWjh07+vTp8/z5c6Geva0k+LKyshUrVjS6KiEhQeThtFulpbBqFdy+zcy70dGwbRvcvSvE09FojTR++xMTFnzDIJ7gG6a9olKp8+bN++WXX5KTk6Ojo11cXObOnctaFRgYOGDAgNzc3FYclqc3jIuLy7x58zga9fT01q9fHxMTU1ZW1ooAuNdWnsEzGIyKiopGV2F9bAG6eRO+fKnXEhgIY8cK63QmJqCqCoWF31vk5GDAAGGdjgXfMIgn+IZpr44dOxYbGxsVFcVadHV1dXFxYb3+6aefevTooaWl1YrD8vSGOXjwIFXY1zRNaytX8Eg0OG6YA0BOTuPX2QIhKQkbNoCiYu2ivDx4eICysrBOhxBCbAkJCWVlZbRvH3BSUlK7d++uubPI1L9/f1YXtKKiosLCQjqdXlZW9vLly5ycHNY1Z2ENVjovLi4uLCxkT8NYWlr6/Pnz6OjokpIS9ukoFMrr169jY2Orq6tZLRUVFdra2p06dWJvw2AwUlNTnzx58unTJxH8BjDBdyxdu3K2GBgAmSzEMw4aBGfPwvbt8NtvEBwMQ4cK8VwIIcQ2ePDgpKQke3v7Z8+esVoMDAxYGX3fvn1qamqZmZkAcObMGTU1tTVr1jg5Oc2bN8/Y2Dg6OrqystLHx0dNTS09PR0Abt261bNnz7/++gsA4uLiBg0aFB4efuXKlUmTJrGOfO7cucGDB4eHh4eEhPTq1evp06cA8PHjxzlz5tjY2LC2yc3NtbGxWblyZVRU1Llz50TxK2C0DSUlJS4uLo2u+vDhg8jDabcoFIaLC8POrvZn3DjGkydExyRo+IZBPME3TDu2bt06CQnmdWyPHj0OHz5Mo9FY7W/fvq25o5nBYDC+fv0KAM+fP2cwGFQqVUtLa/v27QwGIyYmpmagbyprFwsLiz179jAYjJkzZ7q6urIab968yWAwsrOzFRQUHj16xGr8/fffu3fvXl1dzWAwvLy8Bg4cyGp3c3MbMmQInU5nP5K+d++eUP/5eAXfscjKwl9/wbx5oKcHpqbwxx8wbJjgz5KU1Hg7nQ41M5oihJAo7N27Nzk5+X//+19lZeWyZcsWL17c/PZkMrlnz57FxcXNbKOlpXXx4sWQkBAqlTphwgQACAsLo1Aow759mI4ZMyahBseOly5dmjhxIolE4vufxS1M8B1OWhoMHMjM8TY20Lu3gA/OYMC1a/Dvv9DoH8jly/DggYDPiBBCzTAyMtqyZcvHjx+dnJwCAgJY1+vNaDEBu7q6Tps2beHChfr6+ocPHwaAzMxMJSUl1q0CAFBXV2eNhau7V1VVVU5OjqGhYXOHfvECLl6s/al5fPBdRsb3VRERnDuyV128WLeZyAT/tY68vDwsuSMaz55Bbi706AFmZgI+Mp3OTO0JCTBrVuM96YyMIDlZwCdFCKFGXb16lf1aSkpq3bp1Nd2Kc/g8rKysrJ+fX05OjouLi7u7e2xsrJaWVmlpKTuFFRUVAUCXLl3q7iUtLa2mptbCuDgpKZCVrf2RqJ+dyeTvqyQbDH9jr5KVrdtM2DC5qqoqLy8v9mJ1dTXrl4KE6ssX+PoVfvwRtLUFf/D8fMjKgjlzQE+v8Q1MTODePeY2TW2AEEKCcv369b59+xoZGbEW09PTVVRUTExMuNxdQUGBdXXOPgLLlStX1q9fr6SktHPnzgMHDmRlZVlbW5NIpHfv3vXr16/mOvyFpqZm1wZdmvv06XPv3r3ly5c3ecpmxhDr6TX3ufnDD402E5bgpaWlDx06xF4sLS1dtWoVUcF0HLGxoKUllOwOAJqa4ObG+b2zLhUV6NIFSkuFcnaEEKpLVlZ29OjRa9eu7dOnT0pKire397Fjx6SlpUtLS2NjYwHg3bt36urq8fHxABAfHz9gwICcnJzCwsLMzMySkhIjI6Nu3bq5urpOnjy5vLw8Ly8vPT29tLT04cOHFRUVNjY2d+7c6dSp09ChQxUUFLZt27Zs2bLt27d//fp1586doaGhJBIpKysrLS2tuLj4w4cPZmZmBw8eHDp0qLOzs6WlZVpamoSERGJi4qhRoyQbXpELSFupRc9K8P7+/g1XxcfH9+jRg4ig2qHycigpAR0douMQJnzDIJ7gG6Ydi46Ojo2NLSgo0NXVtba21q65uPn06dOHDx9YG/Tt2zc2NpY1Vn7IkCEJCQl5eXkAYGZmZmRklJWVdefOHU1NTRsbm5iYmIKCgp49e6alpWVnZ+fk5Ojq6trb28t+uyv+/v37iIgILS2tIUOGsB7DR0RE5Ofns9ba2dmRyeSvX7+Gh4dXVlaOGzfu3bt3lZWVw4cPV2SXChE0TPCILwwGiLBPKFfwDYN4gm8YxBMxesNgL/oOisGAnByoquLrIGVlcPIkpKYKLCqEEEKC0lZq0SMRYzAgMBCmTQOue5xwKi6GkBCQkhLWE32EEOKJs7NzU6t+/fXXbt26te6wz58/P3v2LHtwfEVFhYWFRbdu3SZNmiRbv9d6W4MJvoOSkAAZGSgvb+XuVCqcPQtycjBrFrTiHV5cDIqKzXXHQwghXqWlpQUGBiopKSUkJAwePPj48eM//vgjhULZu3dvbm4uNwn+69evmpqaHI1DhgzR1NTs3r374cOHp0+f/vTp0/Ly8u3bt69du/bgwYOTJ09u5oBFRUXy8vJSUlJ8/+NaAz9iO4oLF6Cm8OJ3CgqtT/CSkmBrC3PmtCa7Uyhw5Ag0mCIZIYT4MmbMmM6dO6upqcnLy9fMbiWvpaXVuXPnMWPGyMnJcXOEH374gdb07FuKiopaWlpmZmZz58598+ZN//7958yZk9RU5c4aS5YsEc28Mo3CK/gOobAQEhPByqpeo4EBSEu3/
]
},
2024-10-01 13:41:13 +00:00
"execution_count": 6,
2024-10-01 02:37:42 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"const lassoPoly = regr.Lasso(1000, 0.0001);\n",
"lassoPoly.fit(polyX.rows(), polyY.rows());\n",
"const predLassoY = lassoPoly.predict(polyX.rows());\n",
"\n",
"comparePredicted(df.x, df.y, predLassoY);"
]
2024-10-01 13:41:13 +00:00
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[33m0.8637104981289901\u001b[39m"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lassoPoly.score(df.select('y').rows(), pl.DataFrame({\"py\":predLassoY}).rows());\n"
]
2024-10-04 04:09:41 +00:00
},
{
"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,
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},
{
"is_even": 0,
"x": 1,
"x2": 0.00202764718654894,
"x3": 0.0013126936809724655
},
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"is_even": 1,
"x": 2,
"x2": -0.9972687463767552,
"x3": -1.9905576758692132
},
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"is_even": 0,
"x": 3,
"x2": 0.009024117668298463,
"x3": 1.0021275936361942
},
{
"is_even": 1,
"x": 4,
"x2": -0.9968599379688912,
"x3": -0.9947633704461447
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"is_even": 0,
"x": 5,
"x2": 0.0019298798125454942,
"x3": -0.9913865823439642
},
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"is_even": 1,
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"x2": -0.9967558047825875,
"x3": 0.00801993440722967
},
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"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,
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"constraints": null,
"description": null,
"example": null,
"format": null,
"name": "x2",
"rdfType": null,
"title": null,
"type": "number"
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{
"constraints": null,
"description": null,
"example": null,
"format": null,
"name": "x3",
"rdfType": null,
"title": null,
"type": "number"
},
{
"constraints": null,
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"type": "number"
}
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},
"text/html": [
"<table><thead><tr><th>x</th><th>x2</th><th>x3</th><th>is_even</th></tr></thead><tbody><tr><td>0</td><td>-0.9906467224227306</td><td>0.00261179419023964</td><td>1</td></tr><tr><td>1</td><td>0.00202764718654894</td><td>0.0013126936809724655</td><td>0</td></tr><tr><td>2</td><td>-0.9972687463767552</td><td>-1.9905576758692132</td><td>1</td></tr><tr><td>3</td><td>0.009024117668298463</td><td>1.0021275936361942</td><td>0</td></tr><tr><td>4</td><td>-0.9968599379688912</td><td>-0.9947633704461447</td><td>1</td></tr><tr><td>5</td><td>0.0019298798125454942</td><td>-0.9913865823439642</td><td>0</td></tr><tr><td>6</td><td>-0.9967558047825875</td><td>0.00801993440722967</td><td>1</td></tr><tr><td>7</td><td>0.007736464312311071</td><td>0.00034330022067074583</td><td>0</td></tr><tr><td>8</td><td>-0.9959077406033643</td><td>-1.994011690184521</td><td>1</td></tr><tr><td>9</td><td>0.002684616942261051</td><td>1.0072230674765243</td><td>0</td></tr></tbody></table>"
]
},
"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": [
"![name](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAPAAAADACAIAAAC1TXloAAAaF0lEQVR4nOydCVhTV9rHTxJIJOybgCiCYIkDMootbuNu1a+t49KOtNNPB2fQ1qJOfQBFRT9XqkMddUa62oIjo0NFXNCKSgUFC1ZFMYgJICiLZYssYclCcr+HXOYSQ5YbCCa5vr+Hx+eee97z8l755+Q9y73XAsMwRCFoNFJm1LpooBcLYwdgMJSlrEWvuBn+L8iaetAo0EM7OaGmpu6DIUNQZyepJvb2qLW1+4DNRu3tgxyfmVBaWvqf//yntbV148aNrq6uhnVeW1ubmJj4/Pnz8PBwf39/wzpXxuwFTXTMaq7j7Fl08mRP3bJl6L339GhrJly8eDEpKSk7OzsgIIDFYllYWEydOnXTpk0MBqMf3h4/fuzn58fj8fz9/c+fPx8eHs7lct3c3AwSqkwms7CwyMjImD9/vkEcqgczZxDq+VHDhg3YwYOYSNR9LBZjCQnY2rV9reh0zR7MhIyMDIRQfn4+hmHV1dW+vr4ffPBB/1yVlZUhhHg8HoZhpaWlUVFRYrFYi31RUVF1dTVJ511dXQihjIyM/sVGEvogflYGGbx/tbRU17+eOYNGjECffopYrO4ik4k++QRxOCg1VcVQJkNWVoj8aNLE8fT0DAsLS0tLE4lE/WhOp/fqwc/PLz4+nslkarGPjo6uqKgg6Zw2kP/i5mak+DzoxFwHhQUFPQcSibrqH35AiYmqJ1etQv/7v30Tj46OHjX/8EN3YmLu2NjYiMVigUBw9erVo0ePxsTE5OTk/PDDDxUVFY2NjevXr0cIiUSikJCQmJgYxX+gJDY2Ni8vz9vbu+u/onn+/PnBgwczMzNPnTo1fPhwhNC9e/e2bt1qbW3d1tYmEAjOnDlz5MiRS5cuSSQSPz+/r776qqOjIyoqqr6+nkajjRgx4sCBAzRad0IbHx9/7tw5b29vKysrvTUtk6G//Q3l5yMPj25NDxmC4uLQsGHamgxq/z8QsrOz6+rqNNXqyBOWLcMUX8DY5593rlqF7d+PVVZ2nwkN7adDU6WhoUE55VBc+jJPT0/82MHBISwsLDc3NywsDMOwhQsXrlu3DsOw9vZ2JyentLQ0RWq2ITAwUCqVqqQcPB4PIVRWVoZhWE1NjbW1NW6PYdj27dvLysqam5sRQrm5ufjJdevWLVy4EE8tAgIC/v73v2MYdvjwYXd39+bm5v6lHF0rV4pTUnrLT59i77yDaVaFSaccOTk59fX1aqscHJCOkRyDgf79bxQZiSZNqgkLQ9OmoZgYlJSkpQ1eo/UL1hQRCAT4sP7u3bvnzp1bs2ZNVlbWv/71L8Jg7ty5U6dOxWcY0tPTFy9erJjbYU+YMOHGjRtyufyrr75as2aNhYW27+rvvvvOwcFhyZIleHH16tXu7u4qNseOHcOdMxiMKVOm5OTkIIQSEhKWL19ub2/fn2u7fFns5SWcM6f3jJcXOngQ7dihpZFZphwtLbosJk9Ghw+j7Gx06ZLdnTto/Hj0zTdo3ry++YYKUqkBw3yp8Hi8rq6uBQsW7N+/387Orq9BZWUlQujQoUO43FtaWiQSSU1NTWdnp7e3t3bnJSUlPj4+RNHT0xP3QJxpbm5ubW1NTk7GdVxeXu7g4CCXyysqKnQ618i5c2JFgvQCfn6otlZLI8MIur29ff/+/YsXLw4ODlapEolEe/bswb9utm/fzmazDfIbdUy0FRZ2f5onTEAbN3YGB6POThQSgnx9EZer3af5Dg3/+Mc/Tpo0SYsBSzE+jo6OnjZtGnGyoaFBZSyoFjabLRaLdTpfuXLl8uXLiZMYhjGZTJ3ONSIUYvh3sQqWlloaGSDluH79+q5du77++usmfHnjRVatWvXaa6/t27ePw+H85S9/GfivI6W52lpEp6PPPkNXrzomJaFLl9Du3cjWFtXVGca/GeLl5cVisYqKipRPurq6Ojs7379/X3vbsWPHPnr0CE+aVcATHisrK09PTxXnNBqNw+HodK6RwEALYuxP0NWlYR5AKSCDwOFwMjMzVU42NTWxWCx8TNDa2spiserr60k63L17N5fL7Xue1OjN1xd7/Bg/xIc43Tx5gvn4aG9ndkNDHo934cIFfMjRt9bBwSE5OZko/vWvf3Vycjp16lR5efm9e/eePn2KYVhcXJyrq+uFCxd4PF5CQoLaQWFHR4evr++8efNu3rxZXl6elZVVXV0tl8tdXV03b95cWlra0dFx8OBBFov13XfflZWVcblcPp+PYdiJEyfYbHZycnJJScnp06f1GxTW10v+538Ez569cPLzz7FTp7Q0YuzQmmKTJyEh4a233ho1apTySS6Xm5ycvHPnTvxb6dChQ2+++aaXlxcZhzdu3OBwOEOHDlU5v3Mn8vDoHu9pIzERBQQgxRKrQCBwcXHpPpmXh3Jz0ccfa2mXkoIaG7WPOkyLjIyMvLy8kJAQHo9XWloaEhJCVH3//ffDhg1rbm5mMBh4Ijt//nxXV9crCjo6OkJCQths9rRp09zc3C5evJiXlzdz5kxnZ+fKykpHR8fU1NRx48aVlpZ6eHi4u7uvXLmyvr4+PT392rVrikHK5CFDhnh4eOTk5FRVVU2dOnXGjBljxozJysq6ePGiQCAICQmxt7cfO3ZsYGBgZmbmTz/9FBgYOGrUqF9//TUoKMgKn/zXjrV1u4uLdUwMw9kZ2digx49RXByyskIffaStFdmPiy7U9tA//viju7s7URw5ciQx9aMTLT20bpYu7f65dau3hy4owBYtwpYs0dnU7HpoY4cwiDQ2Nj4vL8e+/BL79FNs1y6sqEhnk8Gd5bC1tVUeTIjFYltbW6KYmZmpNi3DwedE+8m4cWjyZHT6NNq3z9XCQtLRIffykoaFWeTldQgEuho7C3TbmApCodCMotWXpqYmR0dH7V+qKgyuoEeMGCEUCru6uiwsLGQyWXNzM77sNOhs2IA++AAdOYJGjKjNz3cfPZpWV2cTGSn85puX8dsB4zEogsYw7ObNm5MnTx45cmRQUNDdu3cnTpx4586dUaNGcTgcwmzu3LlanOCDkn5iY4OOHkWbNiEGY6iDg1NrKxKJUGKik/ZV0//i7Ozc/1/9cmlsbDSjaF8CBhD07du3CwoKBAJBWlpafX39smXLSkpKpk2bdvv27ddff/3o0aObN29euHBhWlpaYt/9Ff1i2DD07JkuIzc3lJSEBIL23FyXKVMQuQ2+Y8YYJEDAaBhgP7RYLO5S2gllbW2NEKqoqCDWlvBtjcOHD9drb8qePXsWL14cGBioGrGeN5vw+XzyO8rN7k4Wva7O7MCHB3p9BRmgh2YpUDmpvFKKb78a+C8CAJ2Y7uYkTQx292lG3TPQF/MTNM5gLFBTddH7lcIsBd2/3Yhk0LrvBTADzFLQ+GqMYTtU3Jv2fS+A6WOWglbsZ0cG1DTuR3HnB2DemNAG/zNnzqSkpBDFqqoq/A4ItRD7ri0sSN49qRFin8yg3l0PvBxMSNBLFBDFPXv2aLfH9+PLZN3/9ntqgk7vaQuTG9TAXFMOHEKF/cs9iE8CqJkymLegcS06OiJcnX2WdzRiY9PzGbC2BjVTChNKOfrN8+cIF7REonvtmuQzHQEzhQqCxsHVqfxw0f+iZqsDSJmqmH3KoQKG9fz03dDi6NhbC1AVqgmaoLGxR7s8Hh8/wDMTgNpQVtDAqwkIGqAUJjQolEqlynfUSs33sVyA8TAhQV+5cuXMmTNE8fHjx3/4wx+MGhFgfpiQoN9WQBR1Ln0DQF8ghwYoBQgaoBQgaIBSgKABSgGCBigFCBqgFCBogFKAoAFKYUILKwUFBb/88gtR5HK5Wm6SBQC1mJCgnZ2dlR+2W1hYaNRwALPEhAQ9UgFRzM3NNWo4gFkCOTRAKUDQAKUAQQOUAgQNUAoQNEApQNAApQBBA5QCBA1QChNaWNHr+dAAoJYeQd+9e3fChAnGDUXf50MDQF96Uo6DBw8O/A2cAGB0egT9888/z549G/YDAeZOj6DDw8O/+OKLmJiY9evXt7S0qBjl5eUZI
]
},
"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": [
"![name](data:image/png;base64,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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();"
]
2024-09-30 23:58:32 +00:00
}
],
"metadata": {
"kernelspec": {
"display_name": "Deno",
"language": "typescript",
"name": "deno"
},
"language_info": {
"codemirror_mode": "typescript",
"file_extension": ".ts",
"mimetype": "text/x.typescript",
"name": "typescript",
"nbconvert_exporter": "script",
"pygments_lexer": "typescript",
"version": "5.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
