def trainingMethod(self, kindDataSet): self.model = GaussianNB() self.GaussianNBAlgorithm = self.model.fit(self.dataset, self.target) if kindDataSet == 1: params = 'Params:default' self.performanceData = responseTraining.responseTraining( self.GaussianNBAlgorithm, 'GaussianNB', params, self.validation) self.performanceData.estimatedMetricsPerformance( self.dataset, self.target) else: params = 'Params:default' self.performanceData = responseTraining.responseTraining( self.GaussianNBAlgorithm, 'GaussianNB', params, self.validation) self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.target)
def trainingMethod(self, kindDataSet): self.model = AdaBoostClassifier(n_estimators=self.n_estimators, algorithm=self.algorithm) self.AdaBoostAlgorithm = self.model.fit(self.dataset, self.target) if kindDataSet == 1: #binary params = "algorithm:%s-n_estimators:%d" % (self.algorithm, self.n_estimators) self.performanceData = responseTraining.responseTraining( self.AdaBoostAlgorithm, 'AdaBoost', params, self.validation) self.performanceData.estimatedMetricsPerformance( self.dataset, self.target) else: params = "algorithm:%s-n_estimators:%d" % (self.algorithm, self.n_estimators) self.performanceData = responseTraining.responseTraining( self.AdaBoostAlgorithm, 'AdaBoost', params, self.validation) self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.target)
def trainingMethod(self, kindDataSet): self.model = BaggingClassifier(n_estimators=self.n_estimators, bootstrap=self.bootstrap, n_jobs=-1) self.BagginAlgorithm = self.model.fit(self.dataset, self.target) if kindDataSet == 1: #binary params = "n_estimators:%d-bootstrap:%s" % (self.n_estimators, self.bootstrap) self.performanceData = responseTraining.responseTraining( self.BagginAlgorithm, 'Baggin', params, self.validation) self.performanceData.estimatedMetricsPerformance( self.dataset, self.target) else: params = "n_estimators:%d-bootstrap:%s" % (self.n_estimators, self.bootstrap) self.performanceData = responseTraining.responseTraining( self.BagginAlgorithm, 'Baggin', params, self.validation) self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.target)
def trainingMethod(self, kindDataSet): self.model=MLPClassifier(hidden_layer_sizes=self.hidden_layer_sizes,activation=self.activation,solver=self.solver,learning_rate=self.learning_rate) self.MLPAlgorithm=self.model.fit(self.dataset,self.target) params = "activation:%s-learning_rate:%s-solver:%s-hidden_layer_sizes_a:%d-hidden_layer_sizes_b:%d-hidden_layer_sizes_c:%d-alpha:%f-max_iter:%d-shuffle:%s" % (self.activation, self.learning_rate, self.solver,self.hidden_layer_sizes[0], self.hidden_layer_sizes[1], self.hidden_layer_sizes[2], self.alpha, self.max_iter, self.shuffle) self.performanceData = responseTraining.responseTraining(self.MLPAlgorithm, 'MLP', params, self.validation) if kindDataSet == 1: self.performanceData.estimatedMetricsPerformance(self.dataset, self.target) else: self.performanceData.estimatedMetricsPerformanceMultilabels(self.dataset, self.target)
def trainingMethod(self, kindDataSet): self.model = tree.DecisionTreeClassifier(criterion=self.criterion, splitter=self.splitter) self.DecisionTreeAlgorithm = self.model.fit(self.dataset, self.target) if kindDataSet == 1: params = "criterion:%s-splitter:%s" % (self.criterion, self.splitter) self.performanceData = responseTraining.responseTraining( self.DecisionTreeAlgorithm, 'DecisionTree', params, self.validation) self.performanceData.estimatedMetricsPerformance( self.dataset, self.target) else: params = "criterion:%s-splitter:%s" % (self.criterion, self.splitter) self.performanceData = responseTraining.responseTraining( self.DecisionTreeAlgorithm, 'DecisionTree', params, self.validation) self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.target)
def trainingMethod(self, kindDataSet): self.model = GradientBoostingClassifier(n_estimators=self.n_estimators) self.GradientAlgorithm = self.model.fit(self.dataset, self.target) params = "n_estimators:%d-loss:%s-min_samples_leaf:%d-min_samples_split:%d" % ( self.n_estimators, self.loss, self.min_samples_leaf, self.min_samples_split) self.performanceData = responseTraining.responseTraining( self.GradientAlgorithm, 'Gradient', params, self.validation) if kindDataSet == 1: self.performanceData.estimatedMetricsPerformance( self.dataset, self.target) else: self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.target)
def trainingMethod(self, kindDataSet): self.model = NuSVC(kernel=self.kernel, degree=self.degree, gamma=self.gamma, nu=self.nu, probability=True) self.NuSVMAlgorithm = self.model.fit(self.dataset, self.target) params = "kernel:%s-degree:%f-gamma:%f-nu:%f-probability:True" % ( self.kernel, self.degree, self.gamma, self.nu) self.performanceData = responseTraining.responseTraining( self.NuSVMAlgorithm, 'NuSVM', params, self.validation) if kindDataSet == 1: self.performanceData.estimatedMetricsPerformance( self.dataset, self.target) else: self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.target)
def trainingMethod(self, kindDataSet): self.model = KNeighborsClassifier(n_neighbors=self.n_neighbors, weights=self.weights, algorithm=self.algorithm, metric=self.metric, n_jobs=-1) #instancia self.knnAlgorithm = self.model.fit(self.dataset, self.response) #training... params = "algorithm:%s-metric:%s-neighbors:%d-weights:%s" % ( self.algorithm, self.metric, self.n_neighbors, self.weights) self.performanceData = responseTraining.responseTraining( self.knnAlgorithm, 'KNN', params, self.validation) if kindDataSet == 1: self.performanceData.estimatedMetricsPerformance( self.dataset, self.response) else: self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.response)
def trainingMethod(self, kindDataSet): self.model = RandomForestClassifier( n_estimators=self.n_estimators, criterion=self.criterion, min_samples_leaf=self.min_samples_leaf, min_samples_split=self.min_samples_split, bootstrap=self.bootstrap, n_jobs=-1) self.RandomForestAlgorithm = self.model.fit(self.dataset, self.target) params = "criterion:%s-n_estimators:%d-min_samples_leaf:%d-min_samples_split:%d-bootstrap:%s" % ( self.criterion, self.n_estimators, self.min_samples_leaf, self.min_samples_split, self.bootstrap) self.performanceData = responseTraining.responseTraining( self.RandomForestAlgorithm, 'RandomForest', params, self.validation) if kindDataSet == 1: self.performanceData.estimatedMetricsPerformance( self.dataset, self.target) else: self.performanceData.estimatedMetricsPerformanceMultilabels( self.dataset, self.target)