diff options
Diffstat (limited to 'backend/microservice/api')
| -rw-r--r-- | backend/microservice/api/controller.py | 7 | ||||
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 96 |
2 files changed, 57 insertions, 46 deletions
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 73f4ed31..7852b63d 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -69,7 +69,7 @@ def train(): #dataset, paramsModel, paramsExperiment, callback) - filepath,result = newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback) + filepath,result,finalMetrics= newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback) """ f = request.json['filepath'] dataset = pd.read_csv(f) @@ -77,6 +77,7 @@ def train(): print(result) """ + url = config.api_url + "/file/h5" files = {'file': open(filepath, 'rb')} r=requests.post(url, files=files,data={"uploaderId":paramsExperiment['uploaderId']}) @@ -94,7 +95,9 @@ def train(): "experimentId" : paramsExperiment["_id"], "modelId" : paramsModel["_id"], "h5FileId" : fileId, - "metrics" : m + "metrics" : m, + "finalMetrics":finalMetrics + } #print(predictor) url = config.api_url + "/Predictor/add" diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 560e575f..0296828a 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -385,7 +385,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): #from ann_visualizer.visualize import ann_viz; #ann_viz(classifier, title="My neural network") - return filepath,hist + return filepath,hist,y_pred,y_test elif(problem_type=='binarni-klasifikacioni'): #print('*************************************************************************binarni') @@ -425,31 +425,37 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): hist=history.history y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') - - #print(y_pred.flatten()) - #print(y_test) scores = classifier.evaluate(x_test, y_test) #print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100)) - # - # - # - # - # - # - # - # - # - # - # - # - # - # - # # ann_viz(classifier, title="My neural network") classifier.save(filepath, save_format='h5') - return filepath,hist + + accuracy = float(sm.accuracy_score(y_test,y_pred)) + precision = float(sm.precision_score(y_test,y_pred)) + recall = float(sm.recall_score(y_test,y_pred)) + tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() + specificity = float(tn / (tn+fp)) + 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 + } + + return filepath,hist,metrics elif(problem_type=='regresioni'): reg=paramsModel['layers'][0]['regularisation'] @@ -489,8 +495,32 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): hist=history.history y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) + classifier.save(filepath, save_format='h5') - return filepath,hist + + + mse = float(sm.mean_squared_error(y_test,y_pred)) + 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))) + rmsle = float(np.sqrt(sm.mean_squared_error(y_test, y_pred))) + r2 = float(sm.r2_score(y_test, y_pred)) + # n - num of observations + # k - num of independent variables + 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 + } + + return filepath,hist,metrics + def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"): #creating a set of all the unique classes using the actual class list @@ -514,29 +544,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): # Metrike # - if(problem_type=="binarni-klasifikacioni"): - accuracy = float(sm.accuracy_score(y_test,y_pred)) - precision = float(sm.precision_score(y_test,y_pred)) - recall = float(sm.recall_score(y_test,y_pred)) - tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() - specificity = float(tn / (tn+fp)) - 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 - } - elif(problem_type=="regresioni"): + if(problem_type=="regresioni"): # https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/ mse = float(sm.mean_squared_error(y_test,y_pred)) mae = float(sm.mean_absolute_error(y_test,y_pred)) |