def mlp_maker_algoritm(trainfolderpath):
dataModel = get_data_from_folder(trainfolderpath)
mlpModel = classify(dataModel.trainFeat, dataModel.trainLabels)
accmlpModel = getAccuracy(mlpModel, dataModel.testFeat,
dataModel.testLabels)
print("MLP Classifier accuracy: {:.2f}%".format(accmlpModel * 100))
saveModel(mlpModel)
def knn_maker_algoritm(trainfolderpath):
dataModel = get_data_from_folder(trainfolderpath)
knnModel = classify(dataModel.trainFeat, dataModel.trainLabels)
accKnnModel = getAccuracy(knnModel, dataModel.testFeat,
dataModel.testLabels)
print("KNNClassifier accuracy: {:.2f}%".format(accKnnModel * 100))
saveModel(knnModel)
def random_forest_maker_algoritm(trainfolderpath):
dataModel = get_data_from_folder(trainfolderpath)
random_forestModel = classify(dataModel.trainFeat, dataModel.trainLabels)
accModel = getAccuracy(random_forestModel, dataModel.testFeat,
dataModel.testLabels)
print("Random Forest Classifier accuracy: {:.2f}%".format(accModel * 100))
saveModel(random_forestModel)
def decision_tree_maker_algoritm(trainfolderpath):
dataModel = get_data_from_folder(trainfolderpath)
decisionTreeModel = classify(dataModel.trainFeat, dataModel.trainLabels)
accDecitionTree = getAccuracy(decisionTreeModel, dataModel.testFeat,
dataModel.testLabels)
print("DecisionTreeClassifier accuracy: {:.2f}%".format(accDecitionTree *
100))
saveModel(decisionTreeModel)
def chooseClassifier(trainfolderpath):
dataModel = get_data_from_folder(trainfolderpath)
decisionTreeModel = dtc(dataModel.trainFeat, dataModel.trainLabels)
knnModel = kc(dataModel.trainFeat, dataModel.trainLabels)
accDecitionTree = getAccuracy(decisionTreeModel, dataModel.testFeat,
dataModel.testLabels)
accKnnModel = getAccuracy(knnModel, dataModel.testFeat,
dataModel.testLabels)
print("DecisionTreeClassifier accuracy: {:.2f}%".format(accDecitionTree *
100))
print("KNNClassifier accuracy: {:.2f}%".format(accKnnModel * 100))
acc = accDecitionTree
if accKnnModel > acc:
acc = accKnnModel
print("Knn is saved")
saveModel(knnModel)
else:
print("Decition tree is saved")
saveModel(decisionTreeModel)
return acc
def logistic_regression_maker_algoritm(trainfolderpath):
dataModel = get_data_from_folder(trainfolderpath)
model = classify(dataModel.trainFeat, dataModel.trainLabels)
acc = getAccuracy(model, dataModel.testFeat, dataModel.testLabels)
print("Logistic Regression accuracy: {:.2f}%".format(acc * 100))
saveModel(model)
def svm_maker_algoritm(trainfolderpath):
dataModel = get_data_from_folder(trainfolderpath)
svmModel = classify(dataModel.trainFeat, dataModel.trainLabels)
accSVMModel = getAccuracy(svmModel, dataModel.testFeat, dataModel.testLabels)
print("SVMClassifier accuracy: {:.2f}%".format(accSVMModel * 100))
saveModel(svmModel)
def testKNNClassifier(self):
dataModel = get_data_from_folder(
"C:/Users/Anna/PycharmProjects/Projekt/train2")
model = classify(dataModel.trainFeat, dataModel.trainLabels)
assert np.array_equal(model.classes_,
['cat', 'dog']), "Błąd klasyfikatora"
def testDecisionTreeClassifier(self):
dataModel = get_data_from_folder(
"C:/Users/Anna/PycharmProjects/Projekt/train2")
model = classify(dataModel.trainFeat, dataModel.trainLabels)
assert model.tree_.node_count > 0, "Błąd klasyfikatora"