def wisconsin_breast_experiment(): ExperimentSetup.wisconsin_breast_cancer_data({ 'method': 'full', 'sparse_factor': 1.0, 'run_id': 1, 'log_level': logging.DEBUG })
def abalone_experiment(): ExperimentSetup.abalone_data({ 'method': 'full', 'sparse_factor': 1.0, 'run_id': 1, 'log_level': logging.DEBUG })
def mining_experiment(): ExperimentSetup.mining_data({ 'method': 'mix1', 'sparse_factor': 1.0, 'run_id': 1, 'log_level': logging.DEBUG })
def boston_experiment(): ExperimentSetup.boston_data({ 'method': 'mix2', 'sparse_factor': 0.8, 'run_id': 3, 'log_level': logging.DEBUG })
def sarcos_experiment(): ExperimentSetup.sarcos_data({'method': 'full', 'sparse_factor': 0.04, 'run_id': 0, 'log_level': logging.DEBUG, 'n_thread': 15, 'partition_size': 2000, # 'image': '../results/all/' })
def main(args): algorithm = args['algo'] with tf.Session() as sess: print(args['env']) exp_setup = ExperimentSetup(algorithm, args['env'], sess, args['random_seed']) exp_setup.setup_experiment(args) train_experiment(algorithm, exp_setup)
def USPS_experiment(): ExperimentSetup.USPS_data({ 'method': 'full', 'sparse_factor': 0.1, 'run_id': 1, 'log_level': logging.DEBUG })
def mnist_binary_inducing_experiment(): ExperimentSetup.MNIST_binary_inducing_data({'method': 'full', 'sparse_factor': 200. / 60000, 'run_id': 1, 'log_level': logging.DEBUG, 'n_thread': 8, 'partition_size': 1000, # 'image': '../results/mnist_1/' })
def creep_experiment(): ExperimentSetup.creep_data({'method': 'full', 'sparse_factor': 1.0, 'run_id': 1, 'log_level': logging.DEBUG})
def wisconsin_breast_experiment(): ExperimentSetup.wisconsin_breast_cancer_data( {'method': 'full', 'sparse_factor': 1.0, 'run_id': 1, 'log_level': logging.DEBUG})
def boston_experiment(): ExperimentSetup.boston_data({'method': 'mix2', 'sparse_factor': 0.8, 'run_id': 3, 'log_level': logging.DEBUG})
", $\Theta = \{\stackrel{6.5}{0}\}$")[0], ) tar_labels.add(target_to_cls_dict[i]) ax.legend(handles=patches, fontsize=18, loc='best') if show: plt.show() return fig if __name__ == "__main__": env = 'phantom2' key = list(pca_sample_numb_change.keys())[0] exp_setup = ExperimentSetup(data_path[env], baseline="./../data/baseline.mat") exp_setup.set_class_names(properties_exp_state[env]) exp_metric_data, exp_fig_data = exp_setup.run_experiments( which_type='properties', which_clustering='k_means', show=SHOW, save_local=SAVE_LOCAL, save_global=SAVE_GLOBAL, where=env, resolution=RESOLUTION, format=SAVE_FORMAT) # ------------------------------------------------------------------------- # --------------------------- FIGURES ----------------------------------- # -------------------------------------------------------------------------
} if __name__ == "__main__": cluster_numbers = list(class_types.keys()) exp_metric_data = dict() exp_fig_data = dict() exp_setup = dict() env = list(properties_exp_state.keys())[0] data_path = data_path[list(data_path.keys())[0]] for clst_no in cluster_numbers: exp_setup[clst_no] = ExperimentSetup(data_path, baseline="./../data/baseline.mat") exp_setup[clst_no].set_class_names(properties_exp_state[env]) exp_setup[clst_no].set_class_type(class_types[clst_no]) exp_metric_data[clst_no], _ = exp_setup[clst_no].run_experiments( which_type='properties', which_clustering='k_means', show=SHOW, save_local=SAVE_LOCAL, save_global=SAVE_GLOBAL, where=env, resolution=RESOLUTION, format=SAVE_FORMAT) ############# BAR PLOT FIGURE SILHOUETTE ################## clst_no_fig = plt.figure(figsize=(18, 10))
from experiment_setup import ExperimentSetup task2 = frozenset( {frozenset({'Sphere', 'HSphere', 'HCube'}), frozenset({'Cube'})}) task4 = frozenset( {frozenset({'Sphere', 'HSphere'}), frozenset({'HCube', 'Cube'})}) task5 = frozenset( {frozenset({'Sphere', 'Cube'}), frozenset({'HCube', 'HSphere'})}) exp_setup = ExperimentSetup("./../data/skin_experiments.mat", best_pressure_time=140) # exp_setup.run_experiment(experiment_name='3mil', which_clustering='k_means', task=task4, show=True, save=True) exp_setup.run_experiment(experiment_name='6mil', which_clustering='k_means', task=task2, show=True, save=True) # exp_setup.run_experiment(experiment_name='10mil', which_clustering='k_means', task=task5, show=True, save=True)
'no SS': ['1-1', '2-1', '3-1', '4-1', '2-2', '3-2', '4-2', '1-3', '3-3', '4-3', '2-4', '3-4', '4-4'], 'no SD': ['1-1', '2-1', '3-1', '4-1', '1-2', '2-2', '3-2', '4-2', '1-3', '2-3', '3-3', '4-3', '1-4'], } data_path = { 'phantom1': "./../data/presence_exp.mat", 'phantom2': "./../data/properties_exp.mat" } exp_metric_data = dict() exp_fig_data = dict() exp_setup = dict() env = 'phantom2' for key in pca_sample_numb_change.keys(): exp_setup[key] = ExperimentSetup(data_path[env], baseline="./../data/baseline.mat") exp_setup[key].set_class_names(properties_exp_state[env]) exp_metric_data[key], _ = exp_setup[key].run_experiments( which_type='presence', which_clustering='k_means', show=SHOW, save_local=SAVE_LOCAL, save_global=SAVE_GLOBAL, where=env, resolution=RESOLUTION, format=SAVE_FORMAT, # bad extra parameters pca_sample_numb_change=pca_sample_numb_change[key] ) # -------------------------------------------------------------------------
def mining_experiment(): ExperimentSetup.mining_data({'method': 'mix1', 'sparse_factor': 1.0, 'run_id': 1, 'log_level': logging.DEBUG})