def viz(pc, fig=None, show_histogram=False, show=True): import create_data_set from methods import method source_learner = method.NadarayaWatsonMethod() target_learner = method.NadarayaWatsonMethod() #pc = configs_lib.ProjectConfigs() data = helper_functions.load_object('../' + pc.data_file).data data.set_train() source_data = data.get_transfer_subset(pc.source_labels) source_data.set_target() target_data= data.get_transfer_subset(pc.target_labels) target_data.set_target() source_learner.train_and_test(source_data) target_learner.train_and_test(target_data) source_learner.sigma = 10 target_learner.sigma = 10 x = array_functions.vec_to_2d(np.linspace(data.x.min(), data.x.max(), 100)) test_data = data_lib.Data() test_data.x = x test_data.is_regression = True y_s = source_learner.predict(test_data).fu y_t = target_learner.predict(test_data).fu #array_functions.plot_line(x,y_t-y_s,pc.data_set,y_axes=np.asarray([-5,5])) y = y_t-y_s #y = y - y.mean() array_functions.plot_line(x,y, title=None ,fig=fig,show=show) if show_histogram: array_functions.plot_histogram(data.x,20) x=1
def viz_features(x, y, domain_ids, feature_names=None, alpha=.1, learner=None): #y = array_functions.normalize(y) x = array_functions.vec_to_2d(x) for i in range(x.shape[1]): xi = x[:, i] xi_train = xi yi = y ids_i = domain_ids title = str(i) density = None if feature_names is not None: title = str(i) + ': ' + feature_names[i] if learner is not None: xi, yi, ids_i, density = train_on_data(xi, yi, domain_ids, learner) density = density * 100 + 1 I = array_functions.is_invalid(density) density[I] = 200 alpha = 1 array_functions.plot_2d_sub(xi, yi, alpha=alpha, title=title, data_set_ids=ids_i, sizes=density) k = 1 array_functions.plot_histogram(xi_train, 100) k = 1
def viz_features(x,y,domain_ids,feature_names=None,alpha=.1,learner=None): #y = array_functions.normalize(y) x = array_functions.vec_to_2d(x) for i in range(x.shape[1]): xi = x[:,i] xi_train = xi yi = y ids_i = domain_ids title = str(i) density = None if feature_names is not None: title = str(i) + ': ' + feature_names[i] if learner is not None: xi,yi,ids_i,density = train_on_data(xi,yi,domain_ids,learner) density = density*100 + 1 I = array_functions.is_invalid(density) density[I] = 200 alpha = 1 array_functions.plot_2d_sub(xi,yi,alpha=alpha,title=title,data_set_ids=ids_i,sizes=density) k = 1 array_functions.plot_histogram(xi_train,100) k=1