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
