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
def predict(self, data): o = self.source_learner.predict(data) I_target = data.is_target if self.opt_succeeded: assert not array_functions.has_invalid(self.g) assert not array_functions.has_invalid(self.h) b = data.R_ul.dot(self.h) w = data.R_ul.dot(self.g) y_old = o.fu[I_target] y_new = (y_old - b) / w I_invalid = array_functions.is_invalid(y_new) y_new[I_invalid] = y_old[I_invalid] o.fu[I_target] = y_new o.y[I_target] = y_new o.b = b o.w = w else: o.b = np.zeros(I_target.sum()) o.w = np.ones(I_target.sum()) o.x = data.x[I_target,:] o.assert_input() return o