3c:null 3d:["$","title",null,{"children":"Logistic regression playground | Lincoln | Observable"}] 43:[] 41:{"id":"c43f267ce17d1cf6","slug":null,"title":"Logistic regression playground","thumbnail":"49e3b8e7b5c25c76853ac31b4ed41e895e2facb91573182f4e68e995d0ccb899","access_level":"public","create_time":"$D2023-03-29T12:28:26.675Z","update_time":"$D2023-04-29T20:51:15.863Z","view_time":null,"first_public_time":"$D2023-03-29T12:28:26.675Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"d0ef53fac0ab29d5","login":"ljcolling","name":"Lincoln","avatar_url":"https://avatars-next.observableusercontent.com/bdf2577c1c205964ad4657464c0fbaf1f1fe5b95b1440fefb07cf62cace3cc26","tier":"starter_2024","bio":"Cognitive scientist based at the University of Sussex"},"version":334,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"0bcce7a4d3127de7","login":"ljcolling","name":"Lincoln","avatar_url":"https://avatars-next.observableusercontent.com/bdf2577c1c205964ad4657464c0fbaf1f1fe5b95b1440fefb07cf62cace3cc26","tier":null,"bio":"Cognitive scientist based at the University of Sussex"}],"members":[{"type":"individual","id":"0bcce7a4d3127de7","login":"ljcolling","name":"Lincoln","avatar_url":"https://avatars-next.observableusercontent.com/bdf2577c1c205964ad4657464c0fbaf1f1fe5b95b1440fefb07cf62cace3cc26","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Logistic regression playground","cells":[{"id":0,"value":"# Logistic regression playground","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"The task is to make a decision about m is positive. But there are two other features v1 and v2 that also influence this decision. On half the trails m and v1 are congruent. On the trails where m and v1 are congruent, and half these trials v1 and v2 are congruent, and on half they are incongurent.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"m = [\n 0.5,\n 0.62,\n 0.74,\n 0.81,\n 0.88,\n 1 / 0.88,\n 1 / 0.81,\n 1 / 0.74,\n 1 / 0.62,\n 1 / 0.5\n].map((x) => Math.log2(x))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"v1 = [\n 0.5,\n 0.62,\n 0.74,\n 0.81,\n 0.88,\n 1 / 0.88,\n 1 / 0.81,\n 1 / 0.74,\n 1 / 0.62,\n 1 / 0.5\n].map((x) => Math.log2(x))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"v2 = [\n 0.5,\n 0.62,\n 0.74,\n 0.81,\n 0.88,\n 1 / 0.88,\n 1 / 0.81,\n 1 / 0.74,\n 1 / 0.62,\n 1 / 0.5\n].map((x) => Math.log2(x))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"df_c_full = m.map((v, i) => {\n return { m: v, v1: v1[i], v2: v2[i] };\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"df_inc_p1 = m.map((v, i) => {\n return { m: v, v1: v1[9 - i], v2: v2[i] };\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"df_inc_p2 = m.map((v, i) => {\n return { m: v, v1: v1[i], v2: v2[9 - i] };\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"df_inc_full = m.map((v, i) => {\n return { m: v, v1: v1[9 - i], v2: v2[9 - i] };\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":115,"value":"df = _.flatten([\n df_c_full,\n df_c_full,\n df_inc_full,\n df_inc_full,\n df_inc_full,\n df_inc_p2,\n df_inc_p1\n])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"prob = (b_m, b_v1, b_v2, m, v1, v2) => {\n b_m = parseFloat(b_m);\n b_v1 = parseFloat(b_v1);\n b_v2 = parseFloat(b_v2);\n let exponent = (b_m * m + b_v1 * v1 + b_v2 * v2) * -1;\n let prob = 1 / (1 + Math.exp(exponent));\n return prob;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"df_c = aq\n .from(df_obj)\n .groupby(\"m\")\n .rollup({\n y: (d) => _.mean(d.y),\n m: (d) => _.mean(d.m),\n v1: (d) => _.mean(d.v1),\n v2: (d) => _.mean(d.v2)\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"df_obj = df.map((x) => {\n let y = prob(b_m, b_v1, b_v2, x.m, x.v1, x.v2);\n return { m: x.m, v1: x.v1, v2: x.v2, y: y };\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"viewof b_m = Inputs.range([-20, 20], { step: 0.1 })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"viewof b_v1 = Inputs.range([-20, 20], { step: 0.1 })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"viewof b_v2 = Inputs.range([-20, 20], { step: 0.1 })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":283,"value":"htl.html`

${v1_con}${v1_inc}${v2_con}${v2_inc}

`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"// now actually draw the plot\nPlot.plot({\n x: { domain: [-1, 1] },\n y: { domain: [0, 1] },\n marks: [Plot.line(df_c, { x: \"m\", y: \"y\" })],\n caption: \"All trials\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"// now actually draw the plot\nv1_con = {\n return Plot.plot({\n x: { domain: [-1, 1] },\n y: { domain: [0, 1] },\n marks: [\n Plot.line(\n aq\n .from(df_obj)\n .filter(\n (d) =>\n ((d.m < 0) & (d.v1 < 0) & (d.v2 < 0)) |\n ((d.m > 0) & (d.v1 > 0) & (d.v2 > 0))\n )\n .groupby(\"m\")\n .rollup({\n y: (d) => _.mean(d.y),\n m: (d) => _.mean(d.m),\n v1: (d) => _.mean(d.v1),\n v2: (d) => _.mean(d.v2)\n }),\n { x: \"m\", y: \"y\" }\n )\n ],\n caption: \"v1 congruent/ v2 congruent\"\n });\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"// now actually draw the plot\nv1_inc = Plot.plot({\n x: { domain: [-1, 1] },\n y: { domain: [0, 1] },\n marks: [\n Plot.line(\n aq\n .from(df_obj)\n .filter(\n (d) =>\n ((d.m < 0) & (d.v1 > 0) & (d.v2 > 0)) |\n ((d.m > 0) & (d.v1 < 0) & (d.v2 < 0))\n )\n .groupby(\"m\")\n .rollup({\n y: (d) => _.mean(d.y),\n m: (d) => _.mean(d.m),\n v1: (d) => _.mean(d.v1),\n v2: (d) => _.mean(d.v2)\n }),\n { x: \"m\", y: \"y\" }\n )\n ],\n caption: \"v1 incongurent / v2 incongruent\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"// now actually draw the plot\nv2_inc = Plot.plot({\n x: { domain: [-1, 1] },\n y: { domain: [0, 1] },\n marks: [\n Plot.line(\n aq\n .from(df_obj)\n .filter(\n (d) =>\n ((d.m < 0) & (d.v1 > 0) & (d.v2 < 0)) |\n ((d.m > 0) & (d.v1 < 0) & (d.v2 > 0))\n )\n .groupby(\"m\")\n .rollup({\n y: (d) => _.mean(d.y),\n m: (d) => _.mean(d.m),\n v1: (d) => _.mean(d.v1),\n v2: (d) => _.mean(d.v2)\n }),\n { x: \"m\", y: \"y\" }\n )\n ],\n caption: \"v1 incongruent / v2 congurent\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":270,"value":"// now actually draw the plot\nv2_con = Plot.plot({\n x: { domain: [-1, 1] },\n y: { domain: [0, 1] },\n marks: [\n Plot.line(\n aq\n .from(df_obj)\n .filter(\n (d) =>\n ((d.m < 0) & (d.v1 < 0) & (d.v2 > 0)) |\n ((d.m > 0) & (d.v1 > 0) & (d.v2 < 0))\n )\n .groupby(\"m\")\n .rollup({\n y: (d) => _.mean(d.y),\n m: (d) => _.mean(d.m),\n v1: (d) => _.mean(d.v1),\n v2: (d) => _.mean(d.v2)\n }),\n { x: \"m\", y: \"y\" }\n )\n ],\n caption: \"v1 congruent / v2 incongurent\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"sd = (x) => {\n let m = _.mean(x);\n let dev = x.map((x) => (x - m) * (x - m));\n let ssd = _.sum(dev);\n let n = x.length;\n return Math.sqrt(ssd / (n -1 ));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W43","stdlib":"1"}} 42:"$41:body"