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import { tensor2D } from "../mod.ts";import type { DataLike } from "./types.ts";import { CsvParseStream } from "./deps.ts";
export interface CsvColumnConfig {  /**   * Whether this column is a label column.   */  label?: boolean;}
export interface CsvLoaderConfig {  columns?: Record<string, CsvColumnConfig>;}
export async function loadCsv(  url: string | URL,  config: CsvLoaderConfig = {},): Promise<DataLike> {  const data = await fetch(url).then((res) =>    res      .body!.pipeThrough(new TextDecoderStream())      .pipeThrough(new CsvParseStream())  );  const colConfigs = Object.entries(config.columns ?? {});  const train_x: number[][] = [];  const train_y: number[][] = [];  const labelCols = colConfigs.filter(([, col]) => col.label);  if (labelCols.length === 0) {    throw new Error("No label column was set");  }  let columnNames!: string[];  let columnIndices!: Record<string, number>;  for await (const row of data) {    if (!columnNames) {      columnNames = row;      columnIndices = columnNames.reduce((acc, col, i) => {        acc[col] = i;        return acc;      }, {} as Record<string, number>);      continue;    }    const x = [];    const y = [];    for (const col in columnIndices) {      const colConfig = config.columns?.[col];      const i = columnIndices[col];      const value = row[i];      if (colConfig?.label) {        y.push(Number(value));      } else {        x.push(Number(value));      }    }    train_x.push(x);    train_y.push(y);  }  return {    train_x: tensor2D(train_x),    train_y: tensor2D(train_y),  };}