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‎.gitignore‎

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# Logs
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logs
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*.log
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npm-debug.log*
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yarn-debug.log*
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yarn-error.log*
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pnpm-debug.log*
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lerna-debug.log*
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node_modules
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dist
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dist-ssr
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*.local
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# Editor directories and files
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.vscode/*
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!.vscode/extensions.json
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.idea
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.DS_Store
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*.suo
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*.ntvs*
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*.njsproj
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*.sln
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*.sw?
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model.json

‎App.vue‎

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<style>
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canvas {
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width: 100%;
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}
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</style>
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<template>
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<v-app>
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<v-app-bar app flat>
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<v-toolbar-title> Bayesian Binomial Test Calculator</v-toolbar-title>
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</v-app-bar>
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<v-main>
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<v-container>
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<v-row>
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<v-col cols="12" lg="4">
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<v-container>
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<v-row>
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<v-col cols="12">
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<Group
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v-model="a"
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groupName="A"
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:posInitial="30"
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:totInitial="100"
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></Group>
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</v-col>
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</v-row>
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<v-row>
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<v-col cols="12">
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<Group
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v-model="b"
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groupName="B"
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:posInitial="b_init.pos"
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:totInitial="b_init.tot"
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></Group>
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</v-col>
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</v-row>
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<v-row>
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<v-col cols="12">
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<v-card outlined>
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<v-card-title> Detailed settings </v-card-title>
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<v-card-text>
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<v-row>
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<v-col cols="6">
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<v-text-field
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label="alpha"
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type="Number"
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:error-messages="alphaError"
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v-model.number="alpha"
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></v-text-field>
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</v-col>
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<v-col cols="6">
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<v-text-field
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label="beta"
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type="Number"
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v-model.number="beta"
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:error-messages="betaError"
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></v-text-field>
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</v-col>
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<v-col cols="6">
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<v-text-field
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label="Number of samples"
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type="Number"
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v-model.number="nSamples"
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:error-messages="nSamplesError"
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></v-text-field>
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</v-col>
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</v-row>
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</v-card-text>
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</v-card>
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</v-col>
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</v-row>
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</v-container>
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</v-col>
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<v-col
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cols="12"
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lg="8"
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:style="{ opacity: payload === null ? 0.4 : 1.0 }"
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>
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<v-row>
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<v-col cols="12">
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<div class="text-center pt-4 text-h5">
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Probability of B being the winner:
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{{ (100 * this.result).toFixed(2) }} %
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</div>
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<v-row class="pt-4">
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<v-col cols="1"></v-col>
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<v-col cols="10">
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<canvas
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ref="canvas_a_b"
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:width="800 * dpr"
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:height="300 * dpr"
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></canvas>
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</v-col>
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<v-col cols="1"></v-col>
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<v-col cols="1"></v-col>
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<v-col cols="10">
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<canvas
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ref="canvas_diff"
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:width="800 * dpr"
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:height="300 * dpr"
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></canvas>
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</v-col>
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<v-col cols="1"></v-col>
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</v-row>
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</v-col>
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</v-row>
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</v-col>
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</v-row>
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</v-container>
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</v-main>
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</v-app>
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</template>
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<script>
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import { compute } from "./bayesian-wasm/pkg";
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import Group from "./components/Group.vue";
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export default {
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data() {
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return {
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a: null,
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b: null,
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a_init: {
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tot: 100,
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pos: 30,
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},
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b_init: {
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tot: 10,
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pos: 5,
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},
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alpha: 1.0,
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alphaError: [],
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beta: 1.0,
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betaError: [],
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nSamples: 100000,
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nSamplesError: [],
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n_bins: 100,
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result: null,
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dpr: 3,
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};
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},
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computed: {
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payload() {
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let invalid = false;
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if (this.a === null) invalid = true;
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if (this.b === null) invalid = true;
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let alpha = parseFloat(this.alpha);
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let beta = parseFloat(this.beta);
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let nSamples = parseInt(this.nSamples);
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if (isNaN(alpha) || alpha < 0) {
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this.alphaError = ["valid, positive float required."];
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invalid = true;
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} else {
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this.alphaError = null;
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}
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if (isNaN(beta) || beta < 0) {
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this.betaError = ["valid, positive float required."];
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invalid = true;
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} else {
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this.betaError = [];
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}
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if (isNaN(nSamples) || nSamples <= 0) {
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this.nSamplesError = ["this field must be strictly positive."];
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invalid = true;
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} else {
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this.nSamplesError = [];
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}
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if (invalid) {
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return null;
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}
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let data = {
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prior_pos: alpha,
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prior_neg: beta,
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a_tot: this.a.tot,
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a_pos: this.a.pos,
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b_tot: this.b.tot,
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b_pos: this.b.pos,
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n_samples: nSamples,
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n_bins: this.n_bins,
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};
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return JSON.stringify(data);
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},
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},
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watch: {
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payload(nv) {
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this.compute(nv);
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},
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},
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methods: {
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async compute(payload) {
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console.log(payload);
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if (payload === null) {
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return;
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}
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let res = compute(
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payload,
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this.dpr,
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this.$refs.canvas_a_b,
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this.$refs.canvas_diff
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);
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this.result = res;
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},
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},
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async mounted() {
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//this.compute(this.payload);
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},
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components: {
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Group,
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},
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};
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</script>

‎ReadMe.md‎

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# Bayesian binomial test using WebAssembly
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A/B test significance judgement using bayesian beta-binomial distribution is simple. In Python, we can do that like:
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```python
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import numpy as np
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alpha_prior = 1.0
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beta_prior = 1.0
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n_events_a = 100
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n_success_a = 30
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n_events_b = 10
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n_success_b = 5
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n_samples = 10000
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rng = np.random.default_rng(0)
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posterior_a = rng.beta(alpha_prior + n_success_a, beta_prior + n_events_a - n_success_a, size=n_samples)
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posterior_b = rng.beta(alpha_prior + n_success_b, beta_prior + n_events_b - n_success_b, size=n_samples)
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# 0.90576
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(posterior_a < posterior_b).mean()
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```
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This simple demo visualizes the Bayesian binomial test, using Rust + WebAssembly to sample from the posterior distribution (obviously overkill, but fun).
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You can find a similar example app [here](https://making.lyst.com/bayesian-calculator/).

‎bayesian-wasm/.cargo-ok‎

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‎bayesian-wasm/.gitignore‎

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/target
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**/*.rs.bk
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Cargo.lock
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bin/
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pkg/
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wasm-pack.log
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node_modules
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dist/
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rust-version.html
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dist_rollup

‎bayesian-wasm/Cargo.toml‎

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[package]
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authors = ["Tomoki <tomoki.ohtsuki129@gmail.com>"]
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edition = "2018"
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name = "bayesian-binomial-visualization"
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version = "0.1.0"
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[lib]
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crate-type = ["cdylib", "rlib"]
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[features]
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default = ["console_error_panic_hook"]
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[dependencies]
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wasm-bindgen = "0.2.63"
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# The `console_error_panic_hook` crate provides better debugging of panics by
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# logging them with `console.error`. This is great for development, but requires
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# all the `std::fmt` and `std::panicking` infrastructure, so isn't great for
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# code size when deploying.
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console_error_panic_hook = {version = "0.1.6", optional = true}
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# `wee_alloc` is a tiny allocator for wasm that is only ~1K in code size
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# compared to the default allocator's ~10K. It is slower than the default
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# allocator, however.
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#
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# Unfortunately, `wee_alloc` requires nightly Rust when targeting wasm for now.
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getrandom = {version = "*", features = ["js"]}
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plotters = "0.3.1"
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plotters-bitmap = "0.3.1"
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plotters-canvas = "0.3.0"
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rand = "0.8.5"
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rand_pcg = "0.3.1"
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serde = {version = "1.0", features = ["derive"]}
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serde_json = "1.0.81"
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statrs = "0.15.0"
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web-sys = {version = "0.3.57", features = ['console']}
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wee_alloc = {version = "0.4.5", optional = true}
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[dev-dependencies]
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wasm-bindgen-test = "0.3.13"
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[profile.release]
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# Tell `rustc` to optimize for small code size.
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opt-level = "s"

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