29:null 2a:["$","title",null,{"children":"Classifying Penguin Species 🐧 🐧 🐧 | Maddie Bendele | Observable"}] 2f:T5c7,# Classifying Penguin Species 🐧 🐧 🐧 ## Interactive Machine Learning Demo *[dataset](https://github.com/allisonhorst/palmerpenguins) courtesy of [Allison Horst](https://twitter.com/allison_horst?lang=en)* Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. In the example below, we'll use **${N_train}** penguins (with known species) as examples for our computer to learn from. We plot the penguins in a 2D space according to their **${dim1_str}** and **${dim2_str}**. Our computer will the **learn** how to identify the specifies of a penguin by looking at the existing **${N_train}** data points, and their values of **${dim1_str}** and **${dim2_str}**. There's actually lots of ways to **teach** a computer to do this, we tend to refer to these as **"Machine Learning Algorithms"** (see "SVM", "logistic Regression", "naive bayes") but in this case we'll be trying something that is easy conceptualize visually - the KNN algorithm. Our computer will **learn** to do species identification by look at what species the **closest ${K}** penguins on the graph are, and then using "majority-rules" to assign the species to our new penguin. We then will evaluate our computer's ability to assign species on the **test data**, and see out of 100 penguins (of known species) - how many could our computer get correct? The higher the better!30:T411,test_labels = { var test_data = JSON.parse(JSON.stringify(test)); test_data.forEach((td) => { var dists = data.map((p) => { return { dist: Math.sqrt( Math.pow(x(td.x) - x(p[dim1_str]), 2) + Math.pow(y(td.y) - y(p[dim2_str]), 2) ), i: p.idx }; }); var ordered_dists = dists .slice() .sort((a, b) => d3.ascending(a.dist, b.dist)); td.dists = ordered_dists; }); var l = test_data.map((x) => { var filteredpenguins = x.dists.filter((d) => d.i < N_train); var closest_k = filteredpenguins.slice(0, K); var N_a = d3.sum(closest_k, (x) => data[x.i].gf == "genefunction"); var N_c = d3.sum(closest_k, (x) => data[x.i].gf == "genefunction"); var N_g = d3.sum(closest_k, (x) => data[x.i].gf == "genefunction"); var max_N = d3.max([N_a, N_c, N_g]); if (N_a == max_N) { return "Adelie"; } else if (N_c == max_N) { return "Chinstrap"; } else { return "Gentoo"; } return N_a; }); return l; }31:[] 2d:{"id":"6e32eb52d9fa1288","slug":null,"title":"Classifying Penguin Species 🐧 🐧 🐧","thumbnail":"da5891c08e49d4c67e3ae2d6689342b7901293b81c28b2388d940d8eab2982c5","access_level":"public","create_time":"$D2023-10-20T01:03:41.314Z","update_time":"$D2023-10-20T02:10:20.805Z","view_time":null,"first_public_time":"$D2023-10-20T01:03:41.314Z","fork":{"id":"6b916de0bd3999a8","slug":"classifying-penguin-species","title":"Classifying Penguin Species 🐧 🐧 🐧","owner":{"id":"5d1c5c48a3939518","login":"mattdzugan","name":"Matt Dzugan"}},"stars":0,"starred":false,"owner":{"type":"team","id":"f5a60deb983d2ac1","login":"mbendele","name":"Maddie Bendele","avatar_url":"https://avatars-next.observableusercontent.com/eda08aa21264f41360de6456790ae57ec6fa4ab04010c83e854f643516c54538","tier":"starter_2024"},"version":441,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"24c6369a6fc83150","login":"mbendele","name":"Maddie Bendele","avatar_url":"https://avatars-next.observableusercontent.com/df056cb5c50bb95442f5984e912a3fdad13fb13fc423a0b8e54da82de553579a","tier":null}],"members":[{"type":"individual","id":"24c6369a6fc83150","login":"mbendele","name":"Maddie Bendele","avatar_url":"https://avatars-next.observableusercontent.com/df056cb5c50bb95442f5984e912a3fdad13fb13fc423a0b8e54da82de553579a","tier":null,"role":"editor"},{"type":"individual","id":"9f316fa2aba52cf9","login":"scresawn","name":"Steven Cresawn","avatar_url":"https://avatars-next.observableusercontent.com/e2727956835daa3b5e9995b66c1111489c2e403c176b4fb761ec6ad64b97786b","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Classifying Penguin Species 🐧 🐧 🐧","cells":[{"id":0,"value":"$2f","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"viewof shuffle = Inputs.button(\"Shuffle The Data\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"viewof N_train = Inputs.range([10, 244], {value: 200, step: 1, label: \"# Penguins for Training\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"viewof dim1_str = Inputs.select(numericColumns, {\n value: \"genefunction\",\n label: \"Dimension 1\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"viewof dim2_str = Inputs.select(numericColumns, {\n value: \"genefunction\",\n label: \"Dimension 2\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":213,"value":"viewof K = Inputs.range([1, 9], {value: 3, step: 1, label: \"# of Neighbors\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":295,"value":"Accurately assigned species to **${acc} out of 100** Penguins!\n\n_(check the `testing` box below the visual to see these assignments)_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"Swatches(colorScale)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":327,"value":"Plot.plot({\n grid: true,\n height: 500,\n width: width,\n x: {\n label: dim1_str\n },\n y: {\n label: dim2_str\n },\n marks: [\n // P//lot.rect(raster_long, {\n // x1: (d) => d.x_corner,\n //y1: (d) => d.y_corner,\n // x2: (d) => d.x_corner + cell_width_raw,\n // y2: (d) => d.y_corner + cell_height_raw,\n // stroke: \"#ffffff00\",\n //opacity: show.includes(\"learned-pattern 🤖\") ? 0.15 : 0,\n //fill: (d, i) => colorScale(raster_labels[i])\n //}),\n Plot.dot(train, {\n filter: (d) => d.x !== \"NA\",\n x: (d) => d.x,\n y: (d) => d.y,\n opacity: 0.75,\n fill: (d) => colorScale(d.gf),\n title: (d) => `${d.tenant}`,\n r: show.includes(\"training 🐧\") ? 3 : 0\n }),\n Plot.dot(test, {\n filter: (d) => d.x !== \"NA\",\n x: (d) => d.x,\n y: (d) => d.y,\n opacity: 0.75,\n fill: \"#ffffff00\",\n strokeWidth: 3,\n stroke: (d) => colorScale(d.gf),\n title: (d) => `${d.tenant}`,\n r: show.includes(\"testing 🐧\") ? 4 : 0\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"viewof show = Inputs.checkbox([\"training 🐧\", \"testing 🐧\", \"learned-pattern 🤖\"], \n {value: [\"training 🐧\", \"learned-pattern 🤖\"],label: \"Show Dots:\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"## Aesthetics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"colorScale = d3.scaleOrdinal(d3.schemeCategory10).domain([])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"height = width*9/16;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"margin = ({ top: 20, right: 30, bottom: 40, left: 40 });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":162,"value":"## Scales","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"x = d3\n .scaleLinear()\n .domain(d3.extent(data, (d) => d[dim1_str]))\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":170,"value":"y = d3\n .scaleLinear()\n .domain(d3.extent(data, (d) => d[dim2_str]))\n .range([height - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"## Rasterization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"viewof M = Inputs.range([3, 100], {value: 40, step: 1, label: \"Grid Pts\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":143,"value":"cell_x_vals = {\n var x_min = d3.min(data, (d) => d[dim1_str]);\n var x_max = d3.max(data, (d) => d[dim1_str]);\n var x_del = (x_max - x_min) / M;\n return d3.range(M).map((ii) => {\n return {\n x_corner: x_min + ii * x_del,\n x_mid: x_min + ii * x_del + x_del / 2\n };\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":156,"value":"cell_y_vals = {\n var y_min = d3.min(data, (d) => d[dim2_str]);\n var y_max = d3.max(data, (d) => d[dim2_str]);\n var y_del = (y_max - y_min) / M;\n return d3.range(M).map((ii) => {\n return {\n y_corner: y_min + ii * y_del,\n y_mid: y_min + ii * y_del + y_del / 2\n };\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":355,"value":"cell_width_raw = cell_x_vals[1].x_corner - cell_x_vals[0].x_corner","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":359,"value":"cell_height_raw = cell_y_vals[1].y_corner - cell_y_vals[0].y_corner","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"cell_width = x(cell_x_vals[1].x_corner) - x(cell_x_vals[0].x_corner)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"cell_height = -(y(cell_y_vals[1].y_corner) - y(cell_y_vals[0].y_corner))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":181,"value":"//raster_long = {\n // var m = [];\n //for (var xx of cell_x_vals) {\n //for (var yy of cell_y_vals) {\n // var dists = data.map((d) => {\n // return {\n // dist: Math.sqrt(\n // Math.pow(x(xx.x_mid) - x(d[dim1_str]), 2) +\n // Math.pow(y(yy.y_mid) - y(d[dim2_str]), 2)\n // ),\n // i: d.idx\n // };\n //});\n //var ordered_dists = dists\n // .slice()\n // .sort((a, b) => d3.ascending(a.dist, b.dist));\n // m.push({\n // x_corner: xx.x_corner,\n //x_mid: xx.x_mid,\n //y_corner: yy.y_corner,\n //y_mid: yy.y_mid,\n //dists: ordered_dists\n //});\n //}\n //}\n // return m;\n//}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":211,"value":"//raster_labels = {\n //var l = raster_long.map((x) => {\n // var filteredpenguins = x.dists.filter((d) => d.i < N_train);\n //var closest_k = filteredpenguins.slice(0, K);\n // var N_a = d3.sum(closest_k, (x) => data[x.i].gf == \"terminase\");\n //var N_c = d3.sum(closest_k, (x) => data[x.i].gf == \"portal protein\");\n //var N_g = d3.sum(closest_k, (x) => data[x.i].gf == \"\");\n\n //var max_N = d3.max([N_a, N_c, N_g]);\n //if (N_a == max_N) {\n // return \"terminase\";\n //} else if (N_c == max_N) {\n // return \"portal protein\";\n //} else {\n // return \"\";\n //}\n\n //return N_a;\n // });\n\n // return l;\n//}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":298,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"acc = {\n var z = 0;\n for(var ii=0; ii<100; ii++){\n if(test_labels[ii] == test[ii].species){\n z++;\n }\n }\n return z;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"## Import the Data\n#### Load it Raw","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"penguins = {\n shuffle;\n var x = await d3.data\n , d3.autoType);\n x.forEach(d => d.rand = Math.random())\n var sorted = x.slice().sort((a, b) => d3.ascending(a.rand, b.rand))\n sorted.forEach((d,i) => d.idx = i)\n return sorted;\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"numericColumns = [\"gap\", \"start\", \"midpoint\", \"stop\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"#### Split into Training & Testing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"genes_train = data.slice(0, N_train)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"genes_test = data.slice(244, 344)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"train = genes_train.map((d) => {\n return { x: d[dim1_str], y: d[dim2_str], gf: d[\"genefunction\"] };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"test = genes_test.map((d) => {\n return { x: d[dim1_str], y: d[dim2_str], gf: d[\"genefunction\"] };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":272,"value":"## Code Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":263,"value":"import {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":382,"value":"viewof formData = {\n let formData = \"Signed In\";\n formData = formWithSubmit(html`\n \n`);\n return formData;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":378,"value":"import {\n terms,\n getPhameratorData,\n user,\n formWithSubmit,\n styles\n} with { formData } from \"@scresawn/phamerator-api-utilities\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":376,"value":"data = {\n let data = await getPhameratorData(\n dataset,\n \"/genesbycluster/G\",\n user.email,\n user.password\n );\n return data.map((d) => {\n d.phamName = +d.phamName;\n return d;\n });\n // .slice(0, 400)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":374,"value":"viewof dataset = Inputs.select(user.datasets.sort(), {\n label: \"Select a data set\",\n value: \"Actino_Draft\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W31","stdlib":"1","titling":"auto"}} 2e:"$2d:body"

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