diff options
| -rw-r--r-- | backend/microservice/api/controller.py | 8 | ||||
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 66 | ||||
| -rw-r--r-- | frontend/src/app/_elements/form-model/form-model.component.ts | 2 |
3 files changed, 39 insertions, 37 deletions
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 7852b63d..6f483008 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -82,9 +82,6 @@ def train(): files = {'file': open(filepath, 'rb')} r=requests.post(url, files=files,data={"uploaderId":paramsExperiment['uploaderId']}) fileId=r.text - m = [] - for attribute, value in result.items(): - m.append({"Name" : attribute, "JsonValue" : value}) predictor = { "_id" : "", "uploaderId" : paramsModel["uploaderId"], @@ -95,13 +92,14 @@ def train(): "experimentId" : paramsExperiment["_id"], "modelId" : paramsModel["_id"], "h5FileId" : fileId, - "metrics" : m, + "metrics" : result, "finalMetrics":finalMetrics - } #print(predictor) + url = config.api_url + "/Predictor/add" r = requests.post(url, json=predictor).text + print(r) return r diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index fd21f8ce..427111a7 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -393,14 +393,14 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): macro_averaged_f1=sm.f1_score(y_test, y_pred, average = 'macro') micro_averaged_f1=sm.f1_score(y_test, y_pred, average = 'micro') - metrics= { - "macro_averaged_precision" :float(macro_averaged_precision), - "micro_averaged_precision" : float(micro_averaged_precision), - "macro_averaged_recall" : float(macro_averaged_recall), - "micro_averaged_recall" : float(micro_averaged_recall), - "macro_averaged_f1" : float(macro_averaged_f1), - "micro_averaged_f1" : float(micro_averaged_f1) - } + metrics= [ + {"Name":"macro_averaged_precision", "JsonValue":str(macro_averaged_precision)}, + {"Name":"micro_averaged_precision" ,"JsonValue":str(micro_averaged_precision)}, + {"Name":"macro_averaged_recall", "JsonValue":str(macro_averaged_recall)}, + {"Name":"micro_averaged_recall" ,"JsonValue":str(micro_averaged_recall)}, + {"Name":"macro_averaged_f1","JsonValue": str(macro_averaged_f1)}, + {"Name":"micro_averaged_f1", "JsonValue": str(micro_averaged_f1)} + ] #vizuelizacija u python-u #from ann_visualizer.visualize import ann_viz; @@ -461,20 +461,20 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): f1 = float(sm.f1_score(y_test,y_pred)) fpr, tpr, _ = sm.roc_curve(y_test,y_pred) logloss = float(sm.log_loss(y_test, y_pred)) - metrics= { - "accuracy" : accuracy, - "precision" : precision, - "recall" : recall, - "specificity" : specificity, - "f1" : f1, - "tn" : float(tn), - "fp" : float(fp), - "fn" : float(fn), - "tp" : float(tp), - "fpr" : fpr.tolist(), - "tpr" : tpr.tolist(), - "logloss" : logloss - } + metrics= [ + {"Name":"accuracy" ,"JsonValue": str(accuracy)}, + {"Name":"precision","JsonValue": str(precision)}, + {"Name":"recall" , "JsonValue":str(recall)}, + {"Name":"specificity" ,"JsonValue":str(specificity)}, + {"Name":"f1" ,"JsonValue": str(f1)}, + {"Name":"tn" , "JsonValue":str(tn)}, + {"Name":"fp" , "JsonValue":str(fp)}, + {"Name":"fn" , "JsonValue":str(fn)}, + {"Name":"tp" , "JsonValue":str(tp)}, + {"Name":"fpr" ,"JsonValue": str(fpr.tolist())}, + {"Name":"tpr" , "JsonValue":str(tpr.tolist())}, + {"Name":"logloss" , "JsonValue":str(logloss)} + ] return filepath,hist,metrics @@ -514,13 +514,15 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) hist=history.history + print() y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) classifier.save(filepath, save_format='h5') - + metrics={} mse = float(sm.mean_squared_error(y_test,y_pred)) + metrics.append mae = float(sm.mean_absolute_error(y_test,y_pred)) mape = float(sm.mean_absolute_percentage_error(y_test,y_pred)) rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred))) @@ -531,14 +533,16 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): n = 40 k = 2 adj_r2 = float(1 - ((1-r2)*(n-1)/(n-k-1))) - metrics= {"mse" : mse, - "mae" : mae, - "mape" : mape, - "rmse" : rmse, - "rmsle" : rmsle, - "r2" : r2, - "adj_r2" : adj_r2 - } + metrics= [ + {"Name":"mse","JsonValue":str(mse)}, + + {"Name":"mae","JsonValue":str(mae)}, + {"Name":"mape","JsonValue":str( mape)}, + {"Name":"rmse","JsonValue":str(rmse)}, + {"Name":"rmsle","JsonValue":str(rmsle)}, + {"Name":"r2","JsonValue":str( r2)}, + {"Name":"adj_r2","JsonValue":str(adj_r2)} + ] return filepath,hist,metrics diff --git a/frontend/src/app/_elements/form-model/form-model.component.ts b/frontend/src/app/_elements/form-model/form-model.component.ts index c9e2fc8e..9e6082c4 100644 --- a/frontend/src/app/_elements/form-model/form-model.component.ts +++ b/frontend/src/app/_elements/form-model/form-model.component.ts @@ -84,7 +84,7 @@ export class FormModelComponent implements AfterViewInit { } addLayer() { - if (this.newModel.hiddenLayers < 128) { + if (this.newModel.hiddenLayers < 150) { this.newModel.layers.push(new Layer(this.newModel.layers.length, this.selectedActivation, this.selectedNumberOfNeurons, this.selectedRegularisation, this.selectedRegularisationRate)); this.newModel.hiddenLayers += 1; |