93 lines
3.4 KiB
TypeScript
93 lines
3.4 KiB
TypeScript
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import { useState, useCallback } from "kaioken";
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import { TensorContainerObject, data, layers, linspace, train, sequential } from "@tensorflow/tfjs";
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import { read, utils } from "xlsx";
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import type { Tensor, Rank } from "@tensorflow/tfjs";
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import type { WorkSheet } from "xlsx";
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interface Data extends TensorContainerObject {
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xs: Tensor;
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ys: Tensor;
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}
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type DSet = data.Dataset<Data>;
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export default function SheetJSToTFJSCSV() {
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const [output, setOutput] = useState("");
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const [results, setResults] = useState<[number, number][]>([]);
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const [disabled, setDisabled] = useState(false);
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function worksheet_to_csv_url(worksheet: WorkSheet) {
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/* generate CSV */
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const csv = utils.sheet_to_csv(worksheet);
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/* CSV -> Uint8Array -> Blob */
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const u8 = new TextEncoder().encode(csv);
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const blob = new Blob([u8], { type: "text/csv" });
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/* generate a blob URL */
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return URL.createObjectURL(blob);
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}
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const doit = useCallback(async () => {
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setResults([]); setOutput(""); setDisabled(true);
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try {
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/* fetch file */
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const f = await fetch("https://docs.sheetjs.com/cd.xls");
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const ab = await f.arrayBuffer();
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/* parse file and get first worksheet */
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const wb = read(ab);
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const ws = wb.Sheets[wb.SheetNames[0]];
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/* generate blob URL */
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const url = worksheet_to_csv_url(ws);
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/* feed to tf.js */
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const dataset = data.csv(url, {
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hasHeader: true,
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configuredColumnsOnly: true,
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columnConfigs:{
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"Horsepower": {required: false, default: 0},
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"Miles_per_Gallon":{required: false, default: 0, isLabel:true}
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}
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});
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/* pre-process data */
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let flat = (dataset as unknown as DSet)
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.map(({xs,ys}) =>({xs: Object.values(xs), ys: Object.values(ys)}))
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.filter(({xs,ys}) => [...xs,...ys].every(v => v>0));
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/* normalize manually :( */
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let minX = Infinity, maxX = -Infinity, minY = Infinity, maxY = -Infinity;
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await flat.forEachAsync(({xs, ys}) => {
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minX = Math.min(minX, xs[0]); maxX = Math.max(maxX, xs[0]);
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minY = Math.min(minY, ys[0]); maxY = Math.max(maxY, ys[0]);
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});
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flat = flat.map(({xs, ys}) => ({xs:xs.map(v => (v-minX)/(maxX - minX)),ys:ys.map(v => (v-minY)/(maxY-minY))}));
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let batch = flat.batch(32);
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/* build and train model */
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const model = sequential();
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model.add(layers.dense({inputShape: [1], units: 1}));
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model.compile({ optimizer: train.sgd(0.000001), loss: 'meanSquaredError' });
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await model.fitDataset(batch, { epochs: 100, callbacks: { onEpochEnd: async (epoch, logs) => {
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setOutput(`${epoch}:${logs?.loss}`);
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}}});
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/* predict values */
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const inp = linspace(0, 1, 9);
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const pred = model.predict(inp) as Tensor<Rank>;
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const xs = await inp.dataSync(), ys = await pred.dataSync();
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setResults(Array.from(xs).map((x, i) => [ x * (maxX - minX) + minX, ys[i] * (maxY - minY) + minY ]));
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setOutput("");
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} catch(e) { setOutput(`ERROR: ${String(e)}`); } finally { setDisabled(false);}
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}, []);
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return ( <>
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<button onclick={doit} disabled={disabled}>Click to run</button><br/>
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{output && <pre>{output}</pre> || <></>}
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{results.length && <table><thead><tr><th>Horsepower</th><th>MPG</th></tr></thead><tbody>
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{results.map((r,i) => <tr key={i}><td>{r[0]}</td><td>{r[1].toFixed(2)}</td></tr>)}
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</tbody></table> || <></>}
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</> );
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}
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