Cancel Training

app/Exceptions/TrainingCancelledException.php

@@ -0,0 +1,9 @@
+<?php
+
+namespace App\Exceptions;
+
+use RuntimeException;
+
+class TrainingCancelledException extends RuntimeException
+{
+}

app/Http/Controllers/PerceptronController.php

@@ -3,6 +3,7 @@
 namespace App\Http\Controllers;
 
 use App\Events\PerceptronInitialization;
+use App\Exceptions\TrainingCancelledException;
 use App\Http\Requests\RunPerceptronRequest;
 use App\Models\NetworksTraining\ADALINEPerceptronTraining;
 use App\Models\NetworksTraining\GradientDescentPerceptronTraining;
@@ -18,9 +19,25 @@ use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 use App\Services\SynapticWeightsProvider\RandomSynapticWeights;
 use App\Services\SynapticWeightsProvider\ZeroSynapticWeights;
 use Illuminate\Http\Request;
+use Illuminate\Support\Facades\Cache;
 
 class PerceptronController extends Controller
 {
+    private function cancellationKey(string $trainingId): string
+    {
+        return "perceptron-training-cancelled:{$trainingId}";
+    }
+
+    public function cancel(Request $request)
+    {
+        $trainingId = $request->validate([
+            'training_id' => ['required', 'string', 'max:100'],
+        ])['training_id'];
+
+        Cache::put($this->cancellationKey($trainingId), true, now()->addHour());
+
+        return response()->noContent();
+    }
     /**
      * Display the specified resource.
      */
@@ -220,6 +237,8 @@ class PerceptronController extends Controller
         $sessionId = $request->input('session_id', session()->getId());
         $trainingId = $request->input('training_id');
 
+        Cache::forget($this->cancellationKey($trainingId));
+
         // Zero initialization prevents hidden layers from receiving a gradient.
         if ($perceptronType === 'multilayer' && $weightInitMethod === 'zeros') {
             $synapticWeightsProvider = new RandomSynapticWeights;
@@ -236,17 +255,22 @@ class PerceptronController extends Controller
         $datasetReader = $this->getDataSetReader($dataSet);
 
         $networkTraining = match ($perceptronType) {
-            'simple' => new SimpleBinaryPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId),
+            'simple' => new SimpleBinaryPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'gradientdescent' => new GradientDescentPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'gradientdescent' => new GradientDescentPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'adaline' => new ADALINEPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'adaline' => new ADALINEPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'monolayer' => new MonoLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'monolayer' => new MonoLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'multilayer' => new MultiLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $hiddenLayers, $hiddenLayersNeurons, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'multilayer' => new MultiLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $hiddenLayers, $hiddenLayersNeurons, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
             default => null,
         };
 
         event(new PerceptronInitialization($datasetReader->lines, $networkTraining->activationFunction, $sessionId, $trainingId));
 
+        try {
             $networkTraining->start();
+        } catch (TrainingCancelledException) {
+            $networkTraining->cancel();
+            Cache::forget($this->cancellationKey($trainingId));
+        }
 
         return back()->with('success', [
             'message' => 'Training completed',

app/Models/NetworksTraining/ADALINEPerceptronTraining.php

@@ -8,6 +8,7 @@ use App\Models\Perceptrons\Perceptron;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+use Closure;
 
 class ADALINEPerceptronTraining extends NetworkTraining
 {
@@ -26,8 +27,9 @@ class ADALINEPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->perceptron = new GradientDescentPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
     }
 

app/Models/NetworksTraining/GradientDescentPerceptronTraining.php

@@ -8,6 +8,7 @@ use App\Models\Perceptrons\Perceptron;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+use Closure;
 
 class GradientDescentPerceptronTraining extends NetworkTraining
 {
@@ -26,8 +27,9 @@ class GradientDescentPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->perceptron = new GradientDescentPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
     }
 

app/Models/NetworksTraining/MonoLayerPerceptronTraining.php

@@ -10,6 +10,7 @@ use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 use App\Services\SynapticWeightsProvider\SimpleNetworkWeightsProvider;
+use Closure;
 use Illuminate\Support\Arr;
 
 class MonoLayerPerceptronTraining extends NetworkTraining
@@ -35,8 +36,9 @@ class MonoLayerPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->isRegression = $datasetReader->getInputSize() === 1;
         $networkWeightsProvider = new SimpleNetworkWeightsProvider($synapticWeightsProvider);
         $this->network = new NetworkPerceptron(

app/Models/NetworksTraining/MultiLayerPerceptronTraining.php

@@ -11,6 +11,7 @@ use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 use App\Services\SynapticWeightsProvider\SimpleNetworkWeightsProvider;
+use Closure;
 use Illuminate\Support\Arr;
 
 class MultiLayerPerceptronTraining extends NetworkTraining
@@ -37,8 +38,9 @@ class MultiLayerPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->labels = $datasetReader->getLabels();
         $this->isRegression = $datasetReader->getOutputSize() === 1
             || ($datasetReader->getOutputSize() > 2

app/Models/NetworksTraining/NetworkTraining.php

@@ -3,9 +3,11 @@
 namespace App\Models\NetworksTraining;
 
 use App\Events\PerceptronTrainingEnded;
+use App\Exceptions\TrainingCancelledException;
 use App\Models\ActivationsFunctions;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
+use Closure;
 
 abstract class NetworkTraining
 {
@@ -24,6 +26,7 @@ abstract class NetworkTraining
         protected IPerceptronIterationEventBuffer $iterationEventBuffer,
         protected string $sessionId,
         protected string $trainingId,
+        protected ?Closure $isCancelled = null,
     ) {}
 
     abstract public function start(): void;
@@ -50,9 +53,18 @@ abstract class NetworkTraining
 
     protected function addIterationToBuffer(float $error, array $synapticWeights)
     {
+        if ($this->isCancelled !== null && ($this->isCancelled)()) {
+            throw new TrainingCancelledException;
+        }
+
         $this->iterationEventBuffer->addIteration($this->epoch, $this->datasetReader->getLastReadLineIndex(), $error, $synapticWeights);
     }
 
+    public function cancel(): void
+    {
+        $this->broadcastTrainingEnded('Entraînement annulé');
+    }
+
     public function getEpoch(): int
     {
         return $this->epoch;

app/Models/NetworksTraining/SimpleBinaryPerceptronTraining.php

@@ -9,6 +9,7 @@ use App\Models\Perceptrons\SimpleBinaryPerceptron;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+use Closure;
 
 class SimpleBinaryPerceptronTraining extends NetworkTraining
 {
@@ -28,8 +29,9 @@ class SimpleBinaryPerceptronTraining extends NetworkTraining
         IPerceptronIterationEventBuffer $iterationEventBuffer,
         string $sessionId,
         string $trainingId,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->perceptron = new SimpleBinaryPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
     }
 

resources/js/components/PerceptronSetup.vue

@@ -1,5 +1,5 @@
 <script setup lang="ts">
-import { useForm } from '@inertiajs/vue3';
+import { router, useForm } from '@inertiajs/vue3';
 import { ref, watch } from 'vue';
 import {
     Form,
@@ -12,6 +12,7 @@ import {
     NativeSelect,
     NativeSelectOption,
 } from '@/components/ui/native-select';
+import { cancel } from '@/routes/perceptron';
 import type {
     Dataset,
     InitializationMethod,
@@ -23,8 +24,9 @@ import Card from './ui/card/Card.vue';
 import CardContent from './ui/card/CardContent.vue';
 import CardHeader from './ui/card/CardHeader.vue';
 import CardTitle from './ui/card/CardTitle.vue';
-import Input from './ui/input/Input.vue';
 import FormError from './ui/form/FormError.vue';
+import Input from './ui/input/Input.vue';
+import Spinner from './ui/spinner/Spinner.vue';
 
 const props = defineProps<{
     type: PerceptronType;
@@ -98,35 +100,50 @@ watch(maxIterations, (value) => {
 watch(selectedDatasetCopy, (newvalue) => {
     form.clearErrors('dataset');
 
-    const selectedDatasetCopy = props.datasets.find(
+    const selectedDatasetCopy =
-        (dataset) => dataset.label === newvalue
+        props.datasets.find((dataset) => dataset.label === newvalue) || null;
-    ) || null;
 
     // LearningRate
     learningRate.value = props.defaultLearningRate;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultLearningRate !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultLearningRate !== undefined
+    ) {
         learningRate.value = selectedDatasetCopy.defaultLearningRate;
     }
     // MinError
     minError.value = props.minError;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultMinError !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultMinError !== undefined
+    ) {
         minError.value = selectedDatasetCopy.defaultMinError;
     }
     // MaxIterations
     maxIterations.value = props.defaultMaxIterations;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultMaxIterations !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultMaxIterations !== undefined
+    ) {
         maxIterations.value = selectedDatasetCopy.defaultMaxIterations;
     }
     // HiddenLayers
     hiddenLayers.value = props.hiddenLayers;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultHiddenLayers !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultHiddenLayers !== undefined
+    ) {
         hiddenLayers.value = selectedDatasetCopy.defaultHiddenLayers;
     }
     hiddenLayersNeurons.value = props.hiddenLayersNeurons;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultHiddenLayersNeurons !== undefined) {
+    if (
-        hiddenLayersNeurons.value = selectedDatasetCopy.defaultHiddenLayersNeurons;
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultHiddenLayersNeurons !== undefined
+    ) {
+        hiddenLayersNeurons.value =
+            selectedDatasetCopy.defaultHiddenLayersNeurons;
     }
-})
+});
 
 const trainingId = ref<string>('');
 
@@ -134,7 +151,7 @@ function startTraining() {
     if (!selectedDatasetCopy.value) {
         form.setError(
             'dataset',
-            'Un dataset est nécessaire avant de lancer l\'entraînement.',
+            "Un dataset est nécessaire avant de lancer l'entraînement.",
         );
         console.log(form.errors);
         return;
@@ -169,6 +186,21 @@ function startTraining() {
 }
 
 const emit = defineEmits(['update:selectedDataset', 'update:trainingId']);
+
+async function cancelTraining() {
+    form.cancel();
+
+    router.post(
+        cancel(),
+        {
+            training_id: trainingId.value,
+        },
+        {
+            preserveScroll: true,
+        },
+    );
+}
+
 watch(selectedDatasetCopy, (newValue) => {
     emit('update:selectedDataset', newValue);
 });
@@ -182,6 +214,7 @@ watch(selectedDatasetCopy, (newValue) => {
         <CardContent>
             <Form
                 class="grid auto-cols-max grid-flow-row grid-cols-1 gap-4 space-y-6 md:grid-cols-2"
+                cancel-on-unmount
             >
                 <!-- DATASET -->
                 <FormField name="dataset">
@@ -206,7 +239,12 @@ watch(selectedDatasetCopy, (newValue) => {
                                 </NativeSelectOption>
                             </NativeSelect>
                         </FormControl>
-                        <FormError :error="form.errors.dataset || props.errors?.selectedDatasetCopy" />
+                        <FormError
+                            :error="
+                                form.errors.dataset ||
+                                props.errors?.selectedDatasetCopy
+                            "
+                        />
                     </FormItem>
                 </FormField>
 
@@ -224,7 +262,9 @@ watch(selectedDatasetCopy, (newValue) => {
                                 class="cursor-pointer"
                             >
                                 <NativeSelectOption
-                                    v-for="method in (props.type == 'multilayer' ? ['random'] : ['zeros', 'random'])"
+                                    v-for="method in props.type == 'multilayer'
+                                        ? ['random']
+                                        : ['zeros', 'random']"
                                     v-bind:key="method"
                                     :value="method"
                                 >
@@ -236,7 +276,10 @@ watch(selectedDatasetCopy, (newValue) => {
                 </FormField>
 
                 <!-- HIDDEN LAYERS -->
-                <FormField name="hidden_layers" v-if="props.type === 'multilayer'">
+                <FormField
+                    name="hidden_layers"
+                    v-if="props.type === 'multilayer'"
+                >
                     <FormItem>
                         <FormLabel>Nombre de couches cachées</FormLabel>
                         <FormControl>
@@ -255,9 +298,14 @@ watch(selectedDatasetCopy, (newValue) => {
                 </FormField>
 
                 <!-- HIDDEN LAYERS NEURONS -->
-                <FormField name="hidden_layers_neurons" v-if="props.type === 'multilayer'">
+                <FormField
+                    name="hidden_layers_neurons"
+                    v-if="props.type === 'multilayer'"
+                >
                     <FormItem>
-                        <FormLabel>Nombre de neurones par couche cachée</FormLabel>
+                        <FormLabel
+                            >Nombre de neurones par couche cachée</FormLabel
+                        >
                         <FormControl>
                             <!-- TODO : MAX input -->
                             <Input
@@ -319,13 +367,39 @@ watch(selectedDatasetCopy, (newValue) => {
                                 @input="handleMaxIterationsInput"
                             />
                         </FormControl>
-                        <div v-if="form.errors.max_iterations || props.errors.max_iterations">
+                        <div
-                            {{ form.errors.max_iterations || props.errors.max_iterations }}
+                            v-if="
+                                form.errors.max_iterations ||
+                                props.errors.max_iterations
+                            "
+                        >
+                            {{
+                                form.errors.max_iterations ||
+                                props.errors.max_iterations
+                            }}
                         </div>
                     </FormItem>
                 </FormField>
             </Form>
-            <Button variant="outline" class="cursor-pointer mt-6" @click="startTraining">Lancer</Button>
+
+            <Transition name="fade">
+                <Button
+                    variant="outline"
+                    class="mt-6 cursor-pointer"
+                    :disabled="form.processing"
+                    @click="startTraining"
+                    >Lancer<Spinner v-if="form.processing" class="ml-1"
+                /></Button>
+            </Transition>
+            <Transition name="fade">
+                <Button
+                    variant="outline"
+                    class="mt-6 ml-4 cursor-pointer"
+                    @click="cancelTraining"
+                    v-if="form.processing"
+                    >Annuler</Button
+                >
+            </Transition>
         </CardContent>
     </Card>
 </template>

resources/js/pages/PerceptronViewer.vue

@@ -15,6 +15,7 @@ import {
     LineController,
     BarController,
     LogarithmicScale,
+    
 } from 'chart.js';
 import { ArrowDown, ArrowUp } from 'lucide-vue-next';
 import { computed, nextTick, ref, watch } from 'vue';

routes/web.php

@@ -3,6 +3,7 @@
 use App\Http\Controllers\PerceptronController;
 use Illuminate\Support\Facades\Broadcast;
 use Illuminate\Support\Facades\Route;
+use Illuminate\Session\Middleware\StartSession;
 use Inertia\Inertia;
 
 // Route::redirect('/', '/perceptron?type=simple')->name('home');
@@ -15,6 +16,10 @@ Route::middleware('throttle:training')->post(
     [PerceptronController::class, 'run'],
 )->name('perceptron.run');
 
+Route::post('perceptron/cancel', [PerceptronController::class, 'cancel'])
+    ->name('perceptron.cancel')
+    ->withoutMiddleware(StartSession::class);
+
 Route::resource('perceptron', PerceptronController::class)->only(['index']);
 
 Broadcast::routes();