From f9a8a24387cefc4b713d3f375e75773751894015 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Wed, 4 May 2022 18:53:37 +0200 Subject: Dodato parsiranje BatchSize parametra. --- backend/microservice/api/newmlservice.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 3244e82f..9e26b03a 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -349,7 +349,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt, metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history #plt.plot(hist['accuracy']) @@ -403,7 +403,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') @@ -452,7 +452,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) -- cgit v1.2.3 From 4858cd15ec093245e5febc39f3176370c9947ab4 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Wed, 4 May 2022 21:02:50 +0200 Subject: Dodato računanje korelacione matrice. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/api/controller.py | 7 ++----- backend/microservice/api/newmlservice.py | 17 +++++++++++++++-- 2 files changed, 17 insertions(+), 7 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 41035cc7..988ad987 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -118,7 +118,6 @@ def returnColumnsInfo(): ''' preprocess = newmlservice.returnColumnsInfo(data) #samo 10 jedinstvenih posto ih ima previse, bilo bi dobro da promenimo ovo da to budu 10 najzastupljenijih vrednosti - for col in preprocess["columnInfo"]: col["uniqueValues"] = col["uniqueValues"][0:6] col["uniqueValuesCount"] = col["uniqueValuesCount"][0:6] @@ -128,11 +127,9 @@ def returnColumnsInfo(): dataset["nullRows"] = preprocess["allNullRows"] dataset["colCount"] = preprocess["colCount"] dataset["rowCount"] = preprocess["rowCount"] + dataset["cMatrix"]=preprocess['cMatrix'] dataset["isPreProcess"] = True - #print(dataset) - - - + return jsonify(dataset) print("App loaded.") diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 9e26b03a..f5e5abcc 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -27,13 +27,26 @@ import matplotlib.pyplot as plt #from ann_visualizer.visualize import ann_viz; def returnColumnsInfo(dataset): dict=[] + datafront=dataset.copy() + dataMatrix=dataset.copy() + + svekolone=datafront.columns kategorijskekolone=datafront.select_dtypes(include=['object']).columns + allNullCols=0 rowCount=datafront.shape[0]#ukupan broj redova colCount=len(datafront.columns)#ukupan broj kolona + for kolona in svekolone: + if(kolona in kategorijskekolone): + encoder=LabelEncoder() + dataMatrix[kolona]=encoder.fit_transform(dataMatrix[kolona]) + + #print(dataMatrix.dtypes) + cMatrix=dataMatrix.corr() + for kolona in svekolone: if(kolona in kategorijskekolone): unique=datafront[kolona].value_counts() @@ -86,7 +99,7 @@ def returnColumnsInfo(dataset): #pretvaranje u kategorijsku datafront = datafront.astype({kolona: str}) - print(datafront.dtypes) + #print(datafront.dtypes) unique=datafront[kolona].value_counts() uniquevaluesn=[] uniquevaluescountn=[] @@ -117,7 +130,7 @@ def returnColumnsInfo(dataset): #print(NullRows) #print(len(NullRows)) allNullRows=len(NullRows) - return {'columnInfo':dict,'allNullColl':int(allNullCols),'allNullRows':int(allNullRows),'rowCount':int(rowCount),'colCount':int(colCount)} + return {'columnInfo':dict,'allNullColl':int(allNullCols),'allNullRows':int(allNullRows),'rowCount':int(rowCount),'colCount':int(colCount),'cMatrix':str(np.matrix(cMatrix))} @dataclass class TrainingResultClassification: -- cgit v1.2.3