def load_datasets(self): ''' load train and validation dataset ''' self.train_data = data_frame.DataFrame( self.in_path+"_train.h5", output_label = self.class_label, phi_padding = self.phi_padding ) self.val_data = data_frame.DataFrame( self.in_path+"_val.h5", output_label = self.class_label, phi_padding = self.phi_padding ) self.num_classes = self.train_data.num_classes
def load_datasets(self): ''' load train and validation dataset ''' self.train_data = data_frame.DataFrame( self.in_path + "_train.h5", output_label=self.class_label, variables=self.input_variables, n_particles=self.n_particles, normed_inputs=self.normed_inputs) self.val_data = data_frame.DataFrame(self.in_path + "_val.h5", output_label=self.class_label, variables=self.input_variables, n_particles=self.n_particles, normed_inputs=self.normed_inputs) self.num_classes = self.train_data.num_classes
def eval_model(self): # loading test examples self.test_data = data_frame.DataFrame( self.in_path+"_test.h5", output_label = self.class_label, phi_padding = self.phi_padding ) self.target_names = [self.test_data.inverted_label_dict[i] for i in range( self.test_data.min_jets, self.test_data.max_jets+1)] self.test_eval = self.model.evaluate( self.test_data.X, self.test_data.one_hot) print("test loss: {}".format( self.test_eval[0] )) for im, metric in enumerate(self.eval_metrics): print("test {}: {}".format( metric, self.test_eval[im+1] )) self.history = self.trained_model.history self.predicted_vector = self.model.predict( self.test_data.X ) self.predicted_classes = np.argmax( self.predicted_vector, axis = 1) self.predicted_classes = np.array([self.test_data.max_jets if j >= self.test_data.max_jets \ else self.test_data.min_jets if j <= self.test_data.min_jets \ else j for j in self.predicted_classes]) self.confusion_matrix = confusion_matrix( self.test_data.Y, self.predicted_classes )
def _load_datasets(self): ''' load data set ''' return data_frame.DataFrame(input_samples=self.input_samples, event_category=self.event_category, train_variables=self.train_variables, test_percentage=self.test_percentage, norm_variables=True, additional_cut=self.additional_cut)
def _load_datasets(self): ''' load dataset ''' return data_frame.DataFrame(path_to_input_files=self.in_path, classes=self.event_classes, event_category=self.event_category, train_variables=self.train_variables, prenet_targets=self.prenet_targets, test_percentage=self.test_percentage, norm_variables=True, additional_cut=self.additional_cut)
def _load_datasets(self): ''' load data set ''' return data_frame.DataFrame( input_samples = self.input_samples, input_features = self.inputs, target_features = self.targets, feature_scaling = self.feature_scaling, test_percentage = self.test_percentage, val_percentage = self.val_percentage)
def _load_datasets(self, shuffle_seed, balanceSamples): ''' load data set ''' return data_frame.DataFrame(input_samples=self.input_samples, event_category=self.category_cutString, train_variables=self.train_variables, test_percentage=self.test_percentage, norm_variables=self.norm_variables, shuffleSeed=shuffle_seed, balanceSamples=balanceSamples, evenSel=self.evenSel, addSampleSuffix=self.addSampleSuffix)
def eval_model(self): self.test_data = data_frame.DataFrame(self.in_path + "_test.h5") self.test_eval = self.model.evaluate(self.test_data.X, self.test_data.X) print("test loss: {}".format(self.test_eval[0])) for im, metric in enumerate(self.eval_metrics): print("test {}: {}".format(metric, self.test_eval[im + 1])) self.history = self.trained_model.history self.test_encoded_images = self.encoder.predict(self.test_data.X) self.test_decoded_images = self.model.predict(self.test_data.X)
def eval_model(self): # loading test examples self.test_data = data_frame.DataFrame( self.in_path+"_test.h5", output_label = self.class_label, one_hot = False, phi_padding = self.phi_padding ) self.test_eval = self.model.evaluate( self.test_data.X, self.test_data.Y) print("test loss: {}".format( self.test_eval[0] )) for im, metric in enumerate(self.eval_metrics): print("test {}: {}".format( metric, self.test_eval[im+1] )) self.history = self.trained_model.history self.predicted_vector = self.model.predict( self.test_data.X ) # correct predictons (kind of arbitrary) # everything so far only implemented for a single output neuron with integer targets self.predicted_classes = [int(val+0.5) for val in self.predicted_vector[:,0]] self.confusion_matrix = confusion_matrix( self.test_data.Y, self.predicted_classes)
def load_datasets(self): ''' load train and validation dataset ''' self.train_data = data_frame.DataFrame(self.in_path + "_train.h5") self.val_data = data_frame.DataFrame(self.in_path + "_val.h5")