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
