README.md

Introduction

An interface surrounding @howso/amalgam-lang WASM to create a simplified client.

Getting Started

Install dependencies

npm install @howso/engine

Inferring feature attributes

During trainee creation, you'll need to iterate on your data to describe its feature attributes.

This package supplied methods to assist with inference from data generically, or directly through dedicated classes. The primary entry point is through inferFeatureAttributes:

import { inferFeatureAttributes, type ArrayData } from "@howso/engine";

const columns = ["id", "number", "date", "boolean"];
const data: ArrayData = {
  columns,
  data: [
    ["0", 1.2, yesterday.toISOString(), false],
    ["1", 2.4, now.toISOString(), true],
    ["3", 2.4, null, true],
    ["4", 5, now.toISOString(), true],
  ],
};
const featureAttributes = await inferFeatureAttributes(data, "array");

If your data's source is always the same, you may bypass the method, creating and calling a source handler directly. For example, the data above could be used directly with the InferFeatureAttributesFromArray class:

const service = new InferFeatureAttributesFromArray(data);
const features = await service.infer();

This process can be CPU intensive, you are encouraged to use a web Worker if run in a user's browser.

Using a client

Through a web Worker

// @/workers/AmalgamWorker
import { AmalgamWasmService, initRuntime } from "@howso/amalgam-lang";
import wasmDataUri from "@howso/amalgam-lang/lib/amalgam-st.data?url";
import wasmUri from "@howso/amalgam-lang/lib/amalgam-st.wasm?url";

(async function () {
  const svc = new AmalgamWasmService((options) => {
    return initRuntime(options, {
      locateFile: (path: string) => {
        // Override file paths so we can use hashed version in build
        if (path.endsWith("amalgam-st.wasm")) {
          return wasmUri;
        } else if (path.endsWith("amalgam-st.data")) {
          return wasmDataUri;
        }
        return self.location.href + path;
      },
    });
  });
  self.onmessage = async (ev) => {
    svc.dispatch(ev);
  };
  self.postMessage({ type: "event", event: "ready" });
})();

You can then create the worker client using a url import:

import howsoUrl from "@howso/engine/lib/howso.caml?url";
import migrationsUrl from "@howso/engine/lib/migrations.caml?url";
import { type ClientOptions, HowsoWorkerClient, BrowserFileSystem } from "@howso/engine";

const getClient = async (options?: ClientOptions): Promise<HowsoWorkerClient> => {
  const worker = new Worker(new URL("@/workers/AmalgamWorker", import.meta.url), { type: "module" });
  const fs = new BrowserFileSystem(worker);
  return await HowsoWorkerClient.create(worker, fs, {
    howsoUrl,
    migrationsUrl, // Optional, used for upgrading Trainees saved to disk.
    ...options,
  });
};

Once you have a client you can then start by creating a Trainee with some initial features and data:

const client: HowsoWorkerClient = await getClient();
const trainee = await client.createTrainee({ name: "MyTrainee" });
await trainee.setFeatureAttributes({ feature_attributes });
await trainee.batchTrain({ cases: dataset.data, columns: dataset.columns });
await trainee.analyze();
const { payload, warnings } = await trainee.react({
  context_values: [
    [1, 2],
    [3, 4],
  ],
  context_features: ["a", "b"],
  action_features: ["target"],
});

Or loading a trained trainee via an existing .caml file:

import uri from "@/src/trainees/MyTrainee.caml?url";

const options = { id: "MyTrainee", uri };
await client.acquireTraineeResources(options.id, options.uri);
const trainee = await client.getTrainee(options.id);

Publishing

Documentation changes do not require a version publishing. For functional changes, follow SemVer standards updating the package.json and package-lock.json files in your pull request.

When you are ready to publish a new version, use the Github Release action.