ClassificationUniformBlending.py 文件源码

def __init__(self, isTrain, isOutlierRemoval=0):
        super(ClassificationUniformBlending, self).__init__(isTrain, isOutlierRemoval)
        # data preprocessing
        self.dataPreprocessing()

        # create logistic regression object
        self.logreg = linear_model.LogisticRegression(tol=1e-6, penalty='l1', C=0.0010985411419875584)

        # create adaboost object
        self.dt_stump = DecisionTreeClassifier(max_depth=10)
        self.ada = AdaBoostClassifier(
            base_estimator=self.dt_stump,
            learning_rate=1,
            n_estimators=5,
            algorithm="SAMME.R")

        # create knn object
        self.knn = neighbors.KNeighborsClassifier(2, weights='uniform')

        # create decision tree object
        self.decisiontree = DecisionTreeClassifier(max_depth=45, max_features='log2')

        # create neural network object
        self.net1 = NeuralNet(
                        layers=[  # three layers: one hidden layer
                            ('input', layers.InputLayer),
                            ('hidden', layers.DenseLayer),
                            #('hidden2', layers.DenseLayer),
                            ('output', layers.DenseLayer),
                            ],
                        # layer parameters:
                        input_shape=(None, 12),  # inut dimension is 12
                        hidden_num_units=6,  # number of units in hidden layer
                        #hidden2_num_units=3,  # number of units in hidden layer
                        output_nonlinearity=lasagne.nonlinearities.sigmoid,  # output layer uses sigmoid function
                        output_num_units=1,  # output dimension is 1

                        # optimization method:
                        update=nesterov_momentum,
                        update_learning_rate=0.002,
                        update_momentum=0.9,

                        regression=True,  # flag to indicate we're dealing with regression problem
                        max_epochs=25,  # we want to train this many epochs
                        verbose=0,
                        )

        # create PLA object
        self.pla = Perceptron()

        # create random forest object
        self.rf = RandomForestClassifier(max_features='log2', n_estimators=20, max_depth=30)