docs.sheetjs.com/docz/static/tfjs/SheetJSTF.tsx

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2024-08-16 11:15:33 +00:00
import { useState, useCallback } from "kaioken";
import { TensorContainerObject, data, layers, linspace, train, sequential } from "@tensorflow/tfjs";
import { read, utils } from "xlsx";
import type { Tensor, Rank } from "@tensorflow/tfjs";
import type { WorkSheet } from "xlsx";
interface Data extends TensorContainerObject {
xs: Tensor;
ys: Tensor;
}
type DSet = data.Dataset<Data>;
export default function SheetJSToTFJSCSV() {
const [output, setOutput] = useState("");
const [results, setResults] = useState<[number, number][]>([]);
const [disabled, setDisabled] = useState(false);
function worksheet_to_csv_url(worksheet: WorkSheet) {
/* generate CSV */
const csv = utils.sheet_to_csv(worksheet);
/* CSV -> Uint8Array -> Blob */
const u8 = new TextEncoder().encode(csv);
const blob = new Blob([u8], { type: "text/csv" });
/* generate a blob URL */
return URL.createObjectURL(blob);
}
const doit = useCallback(async () => {
setResults([]); setOutput(""); setDisabled(true);
try {
/* fetch file */
const f = await fetch("https://docs.sheetjs.com/cd.xls");
const ab = await f.arrayBuffer();
/* parse file and get first worksheet */
const wb = read(ab);
const ws = wb.Sheets[wb.SheetNames[0]];
/* generate blob URL */
const url = worksheet_to_csv_url(ws);
/* feed to tf.js */
const dataset = data.csv(url, {
hasHeader: true,
configuredColumnsOnly: true,
columnConfigs:{
"Horsepower": {required: false, default: 0},
"Miles_per_Gallon":{required: false, default: 0, isLabel:true}
}
});
/* pre-process data */
let flat = (dataset as unknown as DSet)
.map(({xs,ys}) =>({xs: Object.values(xs), ys: Object.values(ys)}))
.filter(({xs,ys}) => [...xs,...ys].every(v => v>0));
/* normalize manually :( */
let minX = Infinity, maxX = -Infinity, minY = Infinity, maxY = -Infinity;
await flat.forEachAsync(({xs, ys}) => {
minX = Math.min(minX, xs[0]); maxX = Math.max(maxX, xs[0]);
minY = Math.min(minY, ys[0]); maxY = Math.max(maxY, ys[0]);
});
flat = flat.map(({xs, ys}) => ({xs:xs.map(v => (v-minX)/(maxX - minX)),ys:ys.map(v => (v-minY)/(maxY-minY))}));
let batch = flat.batch(32);
/* build and train model */
const model = sequential();
model.add(layers.dense({inputShape: [1], units: 1}));
model.compile({ optimizer: train.sgd(0.000001), loss: 'meanSquaredError' });
await model.fitDataset(batch, { epochs: 100, callbacks: { onEpochEnd: async (epoch, logs) => {
setOutput(`${epoch}:${logs?.loss}`);
}}});
/* predict values */
const inp = linspace(0, 1, 9);
const pred = model.predict(inp) as Tensor<Rank>;
const xs = await inp.dataSync(), ys = await pred.dataSync();
setResults(Array.from(xs).map((x, i) => [ x * (maxX - minX) + minX, ys[i] * (maxY - minY) + minY ]));
setOutput("");
} catch(e) { setOutput(`ERROR: ${String(e)}`); } finally { setDisabled(false);}
}, []);
return ( <>
<button onclick={doit} disabled={disabled}>Click to run</button><br/>
{output && <pre>{output}</pre> || <></>}
{results.length && <table><thead><tr><th>Horsepower</th><th>MPG</th></tr></thead><tbody>
{results.map((r,i) => <tr key={i}><td>{r[0]}</td><td>{r[1].toFixed(2)}</td></tr>)}
</tbody></table> || <></>}
</> );
}