def __init__(self, configs=MethodConfigs()):
super(StackingTransfer, self).__init__(configs)
#from far_transfer_methods import GraphTransferNW
self.base_learner = method.SKLRidgeRegression(deepcopy(configs))
self.source_learner = method.NadarayaWatsonMethod(deepcopy(configs))
self.target_learner = method.NadarayaWatsonMethod(deepcopy(configs))
self.joint_cv = getattr(configs, 'joint_cv', False)
self.only_use_source_prediction = False
self.use_all_source = True
self.source_only = False
self.target_only = False
self.just_bias = False
self.linear_source = False
if self.target_only or self.source_only or self.linear_source:
self.joint_cv = False
if self.just_bias:
self.base_learner.cv_params = {'alpha': [1e16]}
self.joint_cv = False
if self.linear_source:
self.target_learner.cv_params = {'sigma': [1]}
if self.joint_cv:
self.cv_params = self.base_learner.cv_params.copy()
self.cv_params.update(self.target_learner.cv_params)
self.base_learner.cv_params = None
self.target_learner.cv_params = None
sub_configs = deepcopy(configs)
#self.source_learner = method.NadarayaWatsonKNNMethod(deepcopy(sub_configs))
#self.target_learner = method.NadarayaWatsonKNNMethod(deepcopy(sub_configs))
self.use_validation = configs.use_validation
def __init__(self, configs=None):
super(GraphTransferNW, self).__init__(configs)
self.cv_params = dict()
step = 2
self.use_rbf = True
if self.use_rbf:
self.cv_params['C'] = self.create_cv_params(-4,
5,
step,
append_zero=True)
self.cv_params['sigma_nw'] = self.create_cv_params(-5, 6, 1)
self.cv_params['sigma_tr'] = self.create_cv_params(-5, 6, 1)
else:
self.cv_params['C'] = self.create_cv_params(-5,
10,
step,
append_zero=True)
self.cv_params['sigma_nw'] = np.asarray(
[1, .5, .25, .1, .05, .025, .01])
self.cv_params['sigma_tr'] = np.asarray(
[1, .5, .25, .1, .05, .025])
self.use_prediction_graph_sparsification = False
self.k_sparsification = 25
self.use_prediction_graph_radius = True
self.radius = .1
if self.use_prediction_graph_radius:
self.cv_params['radius'] = np.asarray([1, .3, .1, .05])
self.use_oracle_graph = False
self.oracle_guidance = getattr(configs, 'oracle_guidance', None)
self.oracle_guidance_binary = getattr(configs,
'oracle_guidance_binary', True)
self.nystrom_percentage = getattr(configs, 'nystrom_percentage')
self.sigma_nw = None
self.C = None
self.sigma_tr = None
self.just_nw = getattr(configs, 'just_nw', False)
if self.just_nw:
del self.cv_params['sigma_tr']
del self.cv_params['C']
self.C = 0
self.sigma_tr = 0
configs = deepcopy(configs)
self.source_learner = method.NadarayaWatsonMethod(deepcopy(configs))
self.source_learner.configs.source_labels = None
self.source_learner.configs.target_labels = None
self.transform = StandardScaler()
self.predict_sample = None
self.use_validation = getattr(configs, 'use_validation', False)
self.nw_learner = method.NadarayaWatsonMethod(deepcopy(configs))
self.nw_learner.configs.source_labels = None
self.nw_learner.configs.target_labels = None
self.source_learner.configs.use_validation = False
self.nw_learner.configs.use_validation = False
self.sampled_inds = None
self.predict_time = None
def __init__(self, configs=MethodConfigs()):
super(ReweightedTransfer, self).__init__(configs)
self.target_kde = None
self.source_kde = None
self.kde_bandwidths = 10**np.asarray(range(-6, 6), dtype='float64')
c = deepcopy(configs)
c.temp_dir = None
self.base_learner = method.NadarayaWatsonMethod(configs)
self.cv_params = {'B': np.asarray([2, 4, 8, 16, 32])}
self.base_learner_cv_keys = []
def __init__(self, configs=None):
super(GraphTransfer, self).__init__(configs)
self.cv_params = dict()
self.alpha = 0
self.just_transfer = getattr(configs, 'just_transfer', False)
self.just_target = getattr(configs, 'just_target', False)
self.cv_params['alpha'] = [0, .2, .4, .6, .8, 1]
configs = deepcopy(configs)
self.source_learner = method.NadarayaWatsonMethod(deepcopy(configs))
self.nw_transfer = method.NadarayaWatsonMethod(deepcopy(configs))
self.nw_target = method.NadarayaWatsonMethod(deepcopy(configs))
self.nw_target.quiet = True
assert not (self.just_transfer and self.just_target)
if self.just_transfer:
del self.cv_params['alpha']
self.alpha = 0
if self.just_target:
del self.cv_params['alpha']
self.alpha = 1
def __init__(self, configs=MethodConfigs()):
super(ModelSelectionTransfer, self).__init__(configs)
self.methods.append(TargetTranfer(configs))
self.methods.append(FuseTransfer(configs))
for m in self.methods:
m.base_learner = method.NadarayaWatsonMethod(configs)
def __init__(self, configs=MethodConfigs()):
super(MixedFeatureGuidanceMethod, self).__init__(configs)
#self.cv_params['C'] = self.create_cv_params(-5, 5, append_zero=True)
self.cv_params['C'] = self.create_cv_params(-5, 5, append_zero=False)
#self.cv_params['C2'] = self.create_cv_params(-8, 8, append_zero=True, prepend_inf=True)
self.cv_params['C2'] = self.create_cv_params(-8,
8,
append_zero=True,
prepend_inf=False)
#self.cv_params['C2'] = np.asarray([np.inf])
self.cv_params['C3'] = self.create_cv_params(-5, 5, append_zero=True)
self.transform = StandardScaler()
#self.preprocessor = preprocessing.BasisQuadraticFewPreprocessor()
if hasattr(configs, 'method'):
self.method = configs.method
else:
self.method = MixedFeatureGuidanceMethod.METHOD_RELATIVE
#self.method = MixedFeatureGuidanceMethod.METHOD_RIDGE
#self.method = MixedFeatureGuidanceMethod.METHOD_ORACLE
#self.method = MixedFeatureGuidanceMethod.METHOD_ORACLE_SPARSITY
self.use_sign = getattr(configs, 'use_sign', True)
self.use_corr = getattr(configs, 'use_corr', False)
self.use_training_corr = getattr(configs, 'use_training_corr', False)
self.use_oracle = getattr(configs, 'use_oracle', False)
self.use_nonneg = getattr(configs, 'use_nonneg', False)
self.use_stacking = getattr(configs, 'use_stacking', False)
self.can_use_test_error_for_model_selection = True
self.use_test_error_for_model_selection = configs.use_test_error_for_model_selection
self.use_validation = configs.use_validation
self.num_random_pairs = getattr(configs, 'num_random_pairs', 0)
self.num_random_signs = getattr(configs, 'num_random_signs', 0)
self.disable_relaxed_guidance = getattr(configs,
'disable_relaxed_guidance',
False)
self.disable_tikhonov = getattr(configs, 'disable_tikhonov', False)
self.random_guidance = getattr(configs, 'random_guidance', False)
self.use_transfer = getattr(configs, 'use_transfer', False)
self.w = None
self.b = None
self.stacking_method = method.NadarayaWatsonMethod(configs)
self.trained_stacked_methods = list()
self.cvx_method = getattr(configs, 'cvx_method', 'SCS')
self.num_features = getattr(configs, 'num_features', -1)
self.use_l1 = getattr(configs, 'use_l1', False)
self.solve_dual = getattr(configs, 'solve_dual', False)
self.mean_b = getattr(configs, 'mean_b', False)
self.solve_scipy = getattr(configs, 'solve_scipy', False)
self.use_pairwise_same_signs = getattr(configs,
'use_pairwise_same_signs',
False)
self.use_hinge_primal = getattr(configs, 'use_hinge_primal', False)
self.fix_C2 = getattr(configs, 'fix_C2', False)
self.fix_C3 = getattr(configs, 'fix_C3', False)
if self.method == MixedFeatureGuidanceMethod.METHOD_HARD_CONSTRAINT:
self.configs.scipy_opt_method = 'SLSQP'
if self.method in MixedFeatureGuidanceMethod.METHODS_UNIFORM_C:
self.C = 1
del self.cv_params['C']
if (self.method in MixedFeatureGuidanceMethod.METHODS_NO_C3
and not self.use_hinge_primal) or self.fix_C3:
self.C3 = 1
del self.cv_params['C3']
if self.method in MixedFeatureGuidanceMethod.METHODS_NO_C2 or self.fix_C2:
self.C2 = 1
del self.cv_params['C2']
if self.disable_relaxed_guidance:
self.C2 = np.inf
if 'C2' in self.cv_params:
del self.cv_params['C2']
if self.disable_tikhonov:
self.C = 0
if 'C' in self.cv_params:
del self.cv_params['C']
self.quiet = False
self.pairs = None
self.feats_to_constrain = None
self.learner_lasso = Lasso()