diff options
Diffstat (limited to 'backend/microservice')
| -rw-r--r-- | backend/microservice/api/controller.py | 15 | ||||
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 24 |
2 files changed, 21 insertions, 18 deletions
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 9b83b8e7..f870b2b1 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -53,7 +53,7 @@ class train_callback(tf.keras.callbacks.Callback): @app.route('/train', methods = ['POST']) def train(): - print("******************************TRAIN*************************************************") + #print("******************************TRAIN*************************************************") f = request.files.get("file") data = pd.read_csv(f) @@ -88,10 +88,11 @@ def train(): "h5FileId" : fileId, "metrics" : m } - print(predictor) + #print(predictor) + #print('\n') url = config.api_url + "/Predictor/add" r = requests.post(url, json=predictor).text - print(r) + #print(r) return r @app.route('/predict', methods = ['POST']) @@ -100,13 +101,13 @@ def predict(): model = tf.keras.models.load_model(h5) paramsExperiment = json.loads(request.form["experiment"]) paramsPredictor = json.loads(request.form["predictor"]) - print("********************************model loaded*******************************") + #print("********************************model loaded*******************************") result = newmlservice.predict(paramsExperiment, paramsPredictor, model) return result @app.route('/preprocess',methods=['POST']) def returnColumnsInfo(): - print("********************************PREPROCESS*******************************") + #print("********************************PREPROCESS*******************************") dataset = json.loads(request.form["dataset"]) file = request.files.get("file") data=pd.read_csv(file) @@ -126,8 +127,8 @@ def returnColumnsInfo(): dataset["colCount"] = preprocess["colCount"] dataset["rowCount"] = preprocess["rowCount"] dataset["isPreProcess"] = True - print(dataset) + #print(dataset) return jsonify(dataset) -print("App loaded.") +#print("App loaded.") app.run()
\ No newline at end of file diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 6cbda69c..6e65c876 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -129,7 +129,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): ###NULL null_value_options = paramsExperiment["nullValues"] null_values_replacers = paramsExperiment["nullValuesReplacers"] - + kategorijskekolone=data.select_dtypes(include=['object']).columns.copy() + #print(kategorijskekolone) if(null_value_options=='replace'): #print("replace null") # dict=null_values_replacers @@ -143,11 +144,18 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): val = np.int64(val) elif(data[col].dtype == 'float64'): val = np.float64(val) - #elif(data[col].dtype == 'object'): data[col]=data[col].fillna(val) elif(null_value_options=='delete_rows'): data=data.dropna() elif(null_value_options=='delete_columns'): + if(data[output_column].isnull().sum()>0): + if(output_column in kategorijskekolone): + replace=data[output_column].value_counts().index[0] + #print(replace) + else: + replace=data[output_column].mean() + data[output_column]=data[output_column].fillna(replace) + #print(data[output_column].isnull().sum()) data=data.dropna(axis=1) #print(data.shape) @@ -175,7 +183,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): encodings=paramsExperiment["encodings"] datafront=dataset.copy() svekolone=datafront.columns - kategorijskekolone=datafront.select_dtypes(include=['object']).columns + + for kolonaEncoding in encodings: kolona = kolonaEncoding["columnName"] @@ -237,13 +246,6 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): #print(x_columns) x = data[x_columns].values y = data[output_column].values - print('-----------------dfghfhgfhfg-------------------------------') - print(x) - print('-----------------dfghfhgfhfg-------------------------------') - print(y) - print('-----------------dfghfhgfhfg-------------------------------') - print(output_column) - print('-----------------dfghfhgfhfg-------------------------------') # # Podela na test i trening skupove @@ -360,7 +362,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history |