From 92574bc98c00846ea7fa27c782c8702668e0c49b Mon Sep 17 00:00:00 2001
From: Starbeamrainbowlabs <sbrl@starbeamrainbowlabs.com>
Date: Mon, 22 Jul 2019 11:53:30 +0100
Subject: [PATCH] Serialise trained AIs and save them to disk

---
 server/settings.default.toml  |  3 +++
 server/train-ai/AITrainer.mjs | 30 +++++++++++++++++++++++++++---
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/server/settings.default.toml b/server/settings.default.toml
index c828646..606413c 100644
--- a/server/settings.default.toml
+++ b/server/settings.default.toml
@@ -67,6 +67,9 @@ batch_size = 32
 # The number of epochs to train for.
 epochs = 5
 
+# The directory to output trained AIs to, relative to the repository root.
+output_directory = "app/ais/"
+
 [logging]
 # The format the date displayed when logging things should take.
 # Allowed values: relative (e.g like when a Linux machine boots), absolute (e.g. like Nginx server logs), none (omits it entirely))
diff --git a/server/train-ai/AITrainer.mjs b/server/train-ai/AITrainer.mjs
index ea5fcb5..39de0f6 100644
--- a/server/train-ai/AITrainer.mjs
+++ b/server/train-ai/AITrainer.mjs
@@ -1,14 +1,19 @@
 "use strict";
 
+import path from 'path';
+import fs from 'fs';
+
 import tf from '@tensorflow/tfjs-node-gpu';
 
 class AITrainer {
-	constructor({ settings, log, GatewayRepo, DatasetFetcher }) {
+	constructor({ settings, log, root_dir, GatewayRepo, DatasetFetcher }) {
 		this.settings = settings;
+		this.root_dir = root_dir;
 		this.l = log;
 		this.dataset_fetcher = DatasetFetcher;
 		this.repo_gateway = GatewayRepo;
 		this.model = this.generate_model();
+		
 	}
 	
 	generate_model() {
@@ -36,12 +41,29 @@ class AITrainer {
 	}
 	
 	async train_all() {
+		
 		for(let gateway of this.repo_gateway.iterate()) {
-			await this.train_gateway(gateway.id);
+			let filename = path.join(this.root_dir, "..", this.settings.ai.output_directory, `${gateway.id}`);
+			console.log(filename);
+			
+			if(!fs.existsSync(path.dirname(filename)))
+				await fs.promises.mkdir(path.dirname(filename), { recursive: true });
+			
+			await this.train_gateway(
+				gateway.id,
+				filename
+			);
 		}
 	}
 	
-	async train_gateway(gateway_id) {
+	/**
+	 * Trains an AI to predict the coverage of a specific gateway.
+	 * @param	{string}	gateway_id				The id of the gateway to train an AI for.
+	 * @param	{string}	destination_filename	The absolute path to the file to serialise the trained to. Required because we can't serialise and return a TensorFlow model, it has to be sent somewhere because the API is backwards and upside-down :-/
+	 * @return	{Promise}	A promise that resolves when training and serialisation is complete.
+	 */
+	async train_gateway(gateway_id, destination_filename) {
+		// TODO: Add samples here for locations that the gateway does NOT cover too
 		let dataset_input = tf.data.generator(
 			this.dataset_fetcher.fetch_input.bind(this.dataset_fetcher, gateway_id)
 		);
@@ -60,6 +82,8 @@ class AITrainer {
 			epochs: this.settings.ai.epochs,
 			batchSize:  this.settings.ai.batch_size
 		});
+		
+		await this.model.save(`file://${destination_filename}`);
 		console.log(result);
 	}
 }