def train(self, data):
assert data.is_regression
y_s, y_true = self.get_predictions(data)
I = data.is_target & data.is_labeled
#y_s = y_s[I]
y_s = data.y[data.is_source]
y_true = data.true_y[I]
x_s = data.x[data.is_source]
x_s = array_functions.append_column(x_s, data.y[data.is_source])
x_s = array_functions.standardize(x_s)
x_t = data.x[I]
x_t = array_functions.append_column(x_t, data.y[I])
x_t = array_functions.standardize(x_t)
Wrbf = array_functions.make_rbf(x_t, self.sigma, self.metric, x2=x_s)
S = array_functions.make_smoothing_matrix(Wrbf)
w = cvx.Variable(x_s.shape[0])
constraints = [w >= 0]
reg = cvx.norm(w)**2
loss = cvx.sum_entries(
cvx.power(
S*cvx.diag(w)*y_s - y_true,2
)
)
obj = cvx.Minimize(loss + self.C*reg)
prob = cvx.Problem(obj,constraints)
assert prob.is_dcp()
try:
prob.solve()
#g_value = np.reshape(np.asarray(g.value),n_labeled)
w_value = w.value
except:
k = 0
#assert prob.status is None
print 'CVX problem: setting g = ' + str(k)
print '\tsigma=' + str(self.sigma)
print '\tC=' + str(self.C)
w_value = k*np.ones(x_s.shape[0])
all_data = data.get_transfer_subset(self.configs.labels_to_keep,include_unlabeled=True)
all_data.instance_weights = np.ones(all_data.n)
all_data.instance_weights[all_data.is_source] = w.value
self.instance_weights = all_data.instance_weights
self.target_learner.train_and_test(all_data)
self.x = all_data.x[all_data.is_source]
self.w = all_data.instance_weights[all_data.is_source]
def create_wine(data_to_create=WINE_RED):
red_file = "wine/winequality-red.csv"
white_file = "wine/winequality-white.csv"
field_names, red_data = load_csv(red_file, delim=";")
white_data = load_csv(white_file, delim=";")[1]
if data_to_create == WINE_TRANSFER:
red_ids = np.zeros((red_data.shape[0], 1))
white_ids = np.ones((white_data.shape[0], 1))
red_data = np.hstack((red_data, red_ids))
white_data = np.hstack((white_data, white_ids))
wine_data = np.vstack((red_data, white_data))
ids = wine_data[:, -1]
x = wine_data[:, :-2]
y = wine_data[:, -2]
used_field_names = field_names[:-1]
viz = True
if viz:
learner = make_learner()
# learner = None
viz_features(x, y, ids, used_field_names, alpha=0.01, learner=learner)
suffix = "transfer"
else:
if data_to_create == WINE_RED:
wine_data = red_data
suffix = "red"
elif data_to_create == WINE_WHITE:
wine_data = white_data
suffix = "white"
else:
assert False
ids = None
x = wine_data[:, :-1]
y = wine_data[:, -1]
used_field_names = field_names[:-1]
data = data_class.Data()
data.x = data.x = array_functions.standardize(x)
if data_to_create == WINE_TRANSFER:
pass
# feat_idx = 1
# data.x = array_functions.vec_to_2d(x[:,feat_idx])
data.y = y
data.set_train()
data.set_target()
data.set_true_y()
data.data_set_ids = ids
data.is_regression = True
"""
data = data.rand_sample(.25, data.data_set_ids == 0)
data = data.rand_sample(.1, data.data_set_ids == 1)
s = wine_file % ('-small-' + str(data.p))
"""
s = wine_file % ("-" + suffix)
helper_functions.save_object(s, data)
def create_concrete(transfer=False):
file = 'concrete/Concrete_Data.csv'
used_field_names, concrete_data = load_csv(file)
data = data_class.Data()
t = ''
if transfer:
feat_ind = 0
domain_ind = (used_field_names == 'age').nonzero()[0][0]
ages = concrete_data[:, domain_ind]
domain_ids = np.zeros(ages.shape)
domain_ids[ages < 10] = 1
domain_ids[(ages >= 10) & (ages <= 28)] = 2
domain_ids[ages > 75] = 3
data.x = concrete_data[:, 0:(concrete_data.shape[1] - 2)]
#0,3,5
#data.x = preprocessing.scale(data.x)
if concrete_num_feats == 1:
data.x = array_functions.vec_to_2d(data.x[:, feat_ind])
t = '-feat=' + str(feat_ind)
elif concrete_num_feats >= data.x.shape[1]:
t = '-' + str(min(data.x.shape[1], concrete_num_feats))
else:
assert False
data.data_set_ids = domain_ids
else:
data.x = concrete_data[:, 0:-1]
data.y = concrete_data[:, -1]
data.set_train()
data.set_target()
data.set_true_y()
data.is_regression = True
viz = False
if viz:
to_use = domain_ids > 0
domain_ids = domain_ids[to_use]
concrete_data = concrete_data[to_use, :]
np.delete(concrete_data, domain_ind, 1)
viz_features(concrete_data, concrete_data[:, -1], domain_ids,
used_field_names)
return
data.x = array_functions.standardize(data.x)
#viz_features(data.x,data.y,data.data_set_ids)
s = concrete_file % t
helper_functions.save_object(s, data)
def create_concrete(transfer=False):
file = "concrete/Concrete_Data.csv"
used_field_names, concrete_data = load_csv(file)
data = data_class.Data()
t = ""
if transfer:
feat_ind = 0
domain_ind = (used_field_names == "age").nonzero()[0][0]
ages = concrete_data[:, domain_ind]
domain_ids = np.zeros(ages.shape)
domain_ids[ages < 10] = 1
domain_ids[(ages >= 10) & (ages <= 28)] = 2
domain_ids[ages > 75] = 3
data.x = concrete_data[:, 0 : (concrete_data.shape[1] - 2)]
# 0,3,5
# data.x = preprocessing.scale(data.x)
if concrete_num_feats == 1:
data.x = array_functions.vec_to_2d(data.x[:, feat_ind])
t = "-feat=" + str(feat_ind)
elif concrete_num_feats >= data.x.shape[1]:
t = "-" + str(min(data.x.shape[1], concrete_num_feats))
else:
assert False
data.data_set_ids = domain_ids
else:
data.x = concrete_data[:, 0:-1]
data.y = concrete_data[:, -1]
data.set_train()
data.set_target()
data.set_true_y()
data.is_regression = True
viz = False
if viz:
to_use = domain_ids > 0
domain_ids = domain_ids[to_use]
concrete_data = concrete_data[to_use, :]
np.delete(concrete_data, domain_ind, 1)
viz_features(concrete_data, concrete_data[:, -1], domain_ids, used_field_names)
return
data.x = array_functions.standardize(data.x)
# viz_features(data.x,data.y,data.data_set_ids)
s = concrete_file % t
helper_functions.save_object(s, data)
def create_bike_sharing():
file = "bike_sharing/day.csv"
columns = [0] + range(2, 16)
all_field_names = pd.read_csv(file, nrows=1, dtype="string")
all_field_names = np.asarray(all_field_names.keys())
used_field_names = all_field_names[columns]
bike_data = np.loadtxt(file, skiprows=1, delimiter=",", usecols=columns)
domain_ind = used_field_names == "yr"
domain_ids = np.squeeze(bike_data[:, domain_ind])
# inds_to_keep = (used_field_names == 'temp') | (used_field_names == 'atemp')
# bike_data = bike_data[:,inds_to_keep]
# used_field_names = used_field_names[inds_to_keep]
viz = True
to_use = np.asarray([8, 9, 10, 11])
x = bike_data[:, to_use]
used_field_names = used_field_names[to_use]
y = bike_data[:, -1]
if viz:
# learner = make_learner()
learner = None
viz_features(x, y, domain_ids, used_field_names, learner=learner)
field_to_use = 1
x = x[:, field_to_use]
data = data_class.Data()
data.is_regression = True
data.x = array_functions.vec_to_2d(x)
data.x = array_functions.standardize(data.x)
data.y = y
data.y = array_functions.normalize(data.y)
data.set_defaults()
data.data_set_ids = domain_ids
s = bike_file % ("-feat=" + str(field_to_use))
helper_functions.save_object(s, data)
pass
def create_boston_housing(file_dir=""):
boston_data = datasets.load_boston()
data = data_class.Data()
data.x = boston_data.data
data.y = boston_data.target
data.feature_names = list(boston_data.feature_names)
data.set_train()
data.set_target()
data.set_true_y()
data.is_regression = True
s = boston_housing_raw_data_file
x = data.x
y = data.y
if create_transfer_data:
x_ind = 5
domain_ind = 12
domain_ids = np.ones(x.shape[0])
domain_ids = array_functions.bin_data(x[:, domain_ind], num_bins=4)
x = np.delete(x, domain_ind, 1)
# viz_features(x,y,domain_ids,boston_data.feature_names)
data.data_set_ids = domain_ids
if boston_num_feats == 1:
data.x = data.x[:, x_ind]
data.x = array_functions.vec_to_2d(data.x)
s = s % ""
elif boston_num_feats >= data.x.shape[1]:
data.x = array_functions.standardize(data.x)
p = min(boston_num_feats, data.x.shape[1])
s = s % ("-" + str(p))
else:
assert False
else:
s %= ""
if file_dir != "":
s = file_dir + "/" + s
helper_functions.save_object(s, data)
def create_bike_sharing():
file = 'bike_sharing/day.csv'
columns = [0] + range(2, 16)
all_field_names = pd.read_csv(file, nrows=1, dtype='string')
all_field_names = np.asarray(all_field_names.keys())
used_field_names = all_field_names[columns]
bike_data = np.loadtxt(file, skiprows=1, delimiter=',', usecols=columns)
domain_ind = used_field_names == 'yr'
domain_ids = np.squeeze(bike_data[:, domain_ind])
#inds_to_keep = (used_field_names == 'temp') | (used_field_names == 'atemp')
#bike_data = bike_data[:,inds_to_keep]
#used_field_names = used_field_names[inds_to_keep]
viz = True
to_use = np.asarray([8, 9, 10, 11])
x = bike_data[:, to_use]
used_field_names = used_field_names[to_use]
y = bike_data[:, -1]
if viz:
#learner = make_learner()
learner = None
viz_features(x, y, domain_ids, used_field_names, learner=learner)
field_to_use = 1
x = x[:, field_to_use]
data = data_class.Data()
data.is_regression = True
data.x = array_functions.vec_to_2d(x)
data.x = array_functions.standardize(data.x)
data.y = y
data.y = array_functions.normalize(data.y)
data.set_defaults()
data.data_set_ids = domain_ids
s = bike_file % ('-feat=' + str(field_to_use))
helper_functions.save_object(s, data)
pass
def create_boston_housing(file_dir=''):
boston_data = datasets.load_boston()
data = data_class.Data()
data.x = boston_data.data
data.y = boston_data.target
data.feature_names = list(boston_data.feature_names)
data.set_train()
data.set_target()
data.set_true_y()
data.is_regression = True
s = boston_housing_raw_data_file
x = data.x
y = data.y
create_transfer_data = False
create_y_split = True
if create_y_split:
from base import transfer_project_configs as configs_lib
pc = configs_lib.ProjectConfigs()
main_configs = configs_lib.MainConfigs(pc)
learner = main_configs.learner
learner.quiet = True
learner.target_learner[0].quiet = True
learner.source_learner.quiet = True
learner.g_learner.quiet = False
domain_ids = array_functions.bin_data(data.y, num_bins=2)
data.data_set_ids = domain_ids
data.is_train[:] = True
corrs = []
for i in range(x.shape[1]):
corrs.append(scipy.stats.pearsonr(x[:, i], y)[0])
learner.train_and_test(data)
print 'Just playing with data - not meant to save it'
for i, name in enumerate(data.feature_names):
v = learner.g_learner.g[i]
if abs(v) < 1e-6:
v = 0
print name + ': ' + str(v)
exit()
elif create_transfer_data:
x_ind = 5
domain_ind = 12
domain_ids = np.ones(x.shape[0])
domain_ids = array_functions.bin_data(x[:, domain_ind], num_bins=4)
x = np.delete(x, domain_ind, 1)
#viz_features(x,y,domain_ids,boston_data.feature_names)
data.data_set_ids = domain_ids
if boston_num_feats == 1:
data.x = data.x[:, x_ind]
data.x = array_functions.vec_to_2d(data.x)
s = s % ''
elif boston_num_feats >= data.x.shape[1]:
data.x = array_functions.standardize(data.x)
p = min(boston_num_feats, data.x.shape[1])
s = s % ('-' + str(p))
else:
assert False
else:
s %= ''
if file_dir != '':
s = file_dir + '/' + s
helper_functions.save_object(s, data)
def create_wine(data_to_create=WINE_RED):
red_file = 'wine/winequality-red.csv'
white_file = 'wine/winequality-white.csv'
field_names, red_data = load_csv(red_file, delim=';')
white_data = load_csv(white_file, delim=';')[1]
if data_to_create == WINE_TRANSFER:
red_ids = np.zeros((red_data.shape[0], 1))
white_ids = np.ones((white_data.shape[0], 1))
red_data = np.hstack((red_data, red_ids))
white_data = np.hstack((white_data, white_ids))
wine_data = np.vstack((red_data, white_data))
ids = wine_data[:, -1]
x = wine_data[:, :-2]
y = wine_data[:, -2]
used_field_names = field_names[:-1]
viz = True
if viz:
learner = make_learner()
#learner = None
viz_features(x,
y,
ids,
used_field_names,
alpha=.01,
learner=learner)
suffix = 'transfer'
else:
if data_to_create == WINE_RED:
wine_data = red_data
suffix = 'red'
elif data_to_create == WINE_WHITE:
wine_data = white_data
suffix = 'white'
else:
assert False
ids = None
x = wine_data[:, :-1]
y = wine_data[:, -1]
used_field_names = field_names[:-1]
data = data_class.Data()
data.x = data.x = array_functions.standardize(x)
if data_to_create == WINE_TRANSFER:
pass
#feat_idx = 1
#data.x = array_functions.vec_to_2d(x[:,feat_idx])
data.y = y
data.set_train()
data.set_target()
data.set_true_y()
data.data_set_ids = ids
data.is_regression = True
'''
data = data.rand_sample(.25, data.data_set_ids == 0)
data = data.rand_sample(.1, data.data_set_ids == 1)
s = wine_file % ('-small-' + str(data.p))
'''
s = wine_file % ('-' + suffix)
helper_functions.save_object(s, data)
def train(self, data):
assert data.is_regression
y_s, y_true = self.get_predictions(data)
I_target = data.is_target
I_target_labeled = data.is_target & data.is_labeled & data.is_train
y_s = data.y[I_target_labeled]
y_true = data.true_y[I_target_labeled]
x = array_functions.standardize(data.x)
x_t = x[I_target]
x_tl = x[I_target_labeled]
C = self.C
C2 = self.C2
W_ll = array_functions.make_rbf(x_tl, self.sigma, self.metric)
W_ll_reg_inv = np.linalg.inv(W_ll+C2*np.eye(W_ll.shape[0]))
W_ul = array_functions.make_rbf(x_t, self.sigma, self.metric, x2=x_tl)
R_ll = W_ll.dot(W_ll_reg_inv)
R_ul = W_ul.dot(W_ll_reg_inv)
assert not array_functions.has_invalid(R_ll)
assert not array_functions.has_invalid(R_ul)
reg = lambda gh: SMSTransfer.reg(gh, R_ul)
#f = lambda gh: SMSTransfer.eval(gh, R_ll, R_ul, y_s, y_true, C, reg)
f = SMSTransfer.eval
jac = SMSTransfer.gradient
g0 = np.zeros((R_ll.shape[0] * 2, 1))
gh_ids = np.zeros(g0.shape)
gh_ids[R_ll.shape[0]:] = 1
maxfun = np.inf
maxitr = np.inf
constraints = []
options = {
'disp': False,
'maxiter': maxitr,
'maxfun': maxfun
}
method = 'L-BFGS-B'
#R_ll = np.eye(R_ll.shape[0])
#R_ul = np.eye(R_ll.shape[0])
#y_s = 1*np.ones(y_s.shape)
#y_true = 1*np.ones(y_s.shape)
args = (R_ll, R_ul, y_s, y_true, C, reg)
results = optimize.minimize(
f,
g0,
method=method,
jac=jac,
options=options,
constraints=constraints,
args=args
)
check_results = False
if check_results:
results2 = optimize.minimize(
f,
g0,
method=method,
jac=None,
options=options,
constraints=constraints,
args=args
)
print self.params
scipy_opt_methods.compare_results(results, results2, gh_ids)
diff = results.x-results2.x
print results.x
print results2.x
g, h = SMSTransfer.unpack_gh(results.x, R_ll.shape[0])
self.opt_succeeded = results.success
if not results.success:
print 'SMS Opt failed'
data.R_ul = R_ul
self.g = g
self.h = h
#assert results.success
pass
