research/active_learning/active_learning_methods/constants.py [19:131]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - AL_MAPPING = {} def get_base_AL_mapping(): from active_learning_methods.margin_AL import MarginAL from active_learning_methods.informative_diverse import InformativeClusterDiverseSampler from active_learning_methods.hierarchical_clustering_AL import HierarchicalClusterAL from active_learning_methods.uniform_sampling import UniformSampling from active_learning_methods.represent_cluster_centers import RepresentativeClusterMeanSampling from active_learning_methods.graph_density import GraphDensitySampler from active_learning_methods.kcenter_greedy import kCenterGreedy from active_learning_methods.entropy_sampling import EntropyAL from active_learning_methods.confidence_sampling import ConfidenceAL AL_MAPPING['margin'] = MarginAL AL_MAPPING['informative_diverse'] = InformativeClusterDiverseSampler AL_MAPPING['hierarchical'] = HierarchicalClusterAL AL_MAPPING['uniform'] = UniformSampling AL_MAPPING['margin_cluster_mean'] = RepresentativeClusterMeanSampling AL_MAPPING['graph_density'] = GraphDensitySampler AL_MAPPING['kcenter'] = kCenterGreedy AL_MAPPING['entropy'] = EntropyAL AL_MAPPING['confidence'] = ConfidenceAL def get_all_possible_arms(): from active_learning_methods.mixture_of_samplers import MixtureOfSamplers AL_MAPPING['mixture_of_samplers'] = MixtureOfSamplers def get_wrapper_AL_mapping(): from active_learning_methods.bandit_discrete import BanditDiscreteSampler from active_learning_methods.simulate_batch import SimulateBatchSampler AL_MAPPING['bandit_mixture'] = partial( BanditDiscreteSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [0, 1] }, { 'methods': ['margin', 'uniform'], 'weights': [0.25, 0.75] }, { 'methods': ['margin', 'uniform'], 'weights': [0.5, 0.5] }, { 'methods': ['margin', 'uniform'], 'weights': [0.75, 0.25] }, { 'methods': ['margin', 'uniform'], 'weights': [1, 0] }]) AL_MAPPING['bandit_discrete'] = partial( BanditDiscreteSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [0, 1] }, { 'methods': ['margin', 'uniform'], 'weights': [1, 0] }]) AL_MAPPING['simulate_batch_mixture'] = partial( SimulateBatchSampler, samplers=({ 'methods': ['margin', 'uniform'], 'weights': [1, 0] }, { 'methods': ['margin', 'uniform'], 'weights': [0.5, 0.5] }, { 'methods': ['margin', 'uniform'], 'weights': [0, 1] }), n_sims=5, train_per_sim=10, return_best_sim=False) AL_MAPPING['simulate_batch_best_sim'] = partial( SimulateBatchSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [1, 0] }], n_sims=10, train_per_sim=10, return_type='best_sim') AL_MAPPING['simulate_batch_frequency'] = partial( SimulateBatchSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [1, 0] }], n_sims=10, train_per_sim=10, return_type='frequency') def get_mixture_of_samplers(name): assert 'mixture_of_samplers' in name if 'mixture_of_samplers' not in AL_MAPPING: raise KeyError('Mixture of Samplers not yet loaded.') args = name.split('-')[1:] samplers = args[0::2] weights = args[1::2] weights = [float(w) for w in weights] assert sum(weights) == 1 mixture = {'methods': samplers, 'weights': weights} print(mixture) return partial(AL_MAPPING['mixture_of_samplers'], mixture=mixture) def get_AL_sampler(name): if name in AL_MAPPING and name != 'mixture_of_samplers': return AL_MAPPING[name] if 'mixture_of_samplers' in name: return get_mixture_of_samplers(name) raise NotImplementedError('The specified sampler is not available.') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - research/active_learning/sampling_methods/constants.py [19:131]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - AL_MAPPING = {} def get_base_AL_mapping(): from sampling_methods.margin_AL import MarginAL from sampling_methods.informative_diverse import InformativeClusterDiverseSampler from sampling_methods.hierarchical_clustering_AL import HierarchicalClusterAL from sampling_methods.uniform_sampling import UniformSampling from sampling_methods.represent_cluster_centers import RepresentativeClusterMeanSampling from sampling_methods.graph_density import GraphDensitySampler from sampling_methods.kcenter_greedy import kCenterGreedy from sampling_methods.entropy_sampling import EntropyAL from sampling_methods.confidence_sampling import ConfidenceAL AL_MAPPING['margin'] = MarginAL AL_MAPPING['informative_diverse'] = InformativeClusterDiverseSampler AL_MAPPING['hierarchical'] = HierarchicalClusterAL AL_MAPPING['uniform'] = UniformSampling AL_MAPPING['margin_cluster_mean'] = RepresentativeClusterMeanSampling AL_MAPPING['graph_density'] = GraphDensitySampler AL_MAPPING['kcenter'] = kCenterGreedy AL_MAPPING['entropy'] = EntropyAL AL_MAPPING['confidence'] = ConfidenceAL def get_all_possible_arms(): from sampling_methods.mixture_of_samplers import MixtureOfSamplers AL_MAPPING['mixture_of_samplers'] = MixtureOfSamplers def get_wrapper_AL_mapping(): from sampling_methods.bandit_discrete import BanditDiscreteSampler from sampling_methods.simulate_batch import SimulateBatchSampler AL_MAPPING['bandit_mixture'] = partial( BanditDiscreteSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [0, 1] }, { 'methods': ['margin', 'uniform'], 'weights': [0.25, 0.75] }, { 'methods': ['margin', 'uniform'], 'weights': [0.5, 0.5] }, { 'methods': ['margin', 'uniform'], 'weights': [0.75, 0.25] }, { 'methods': ['margin', 'uniform'], 'weights': [1, 0] }]) AL_MAPPING['bandit_discrete'] = partial( BanditDiscreteSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [0, 1] }, { 'methods': ['margin', 'uniform'], 'weights': [1, 0] }]) AL_MAPPING['simulate_batch_mixture'] = partial( SimulateBatchSampler, samplers=({ 'methods': ['margin', 'uniform'], 'weights': [1, 0] }, { 'methods': ['margin', 'uniform'], 'weights': [0.5, 0.5] }, { 'methods': ['margin', 'uniform'], 'weights': [0, 1] }), n_sims=5, train_per_sim=10, return_best_sim=False) AL_MAPPING['simulate_batch_best_sim'] = partial( SimulateBatchSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [1, 0] }], n_sims=10, train_per_sim=10, return_type='best_sim') AL_MAPPING['simulate_batch_frequency'] = partial( SimulateBatchSampler, samplers=[{ 'methods': ['margin', 'uniform'], 'weights': [1, 0] }], n_sims=10, train_per_sim=10, return_type='frequency') def get_mixture_of_samplers(name): assert 'mixture_of_samplers' in name if 'mixture_of_samplers' not in AL_MAPPING: raise KeyError('Mixture of Samplers not yet loaded.') args = name.split('-')[1:] samplers = args[0::2] weights = args[1::2] weights = [float(w) for w in weights] assert sum(weights) == 1 mixture = {'methods': samplers, 'weights': weights} print(mixture) return partial(AL_MAPPING['mixture_of_samplers'], mixture=mixture) def get_AL_sampler(name): if name in AL_MAPPING and name != 'mixture_of_samplers': return AL_MAPPING[name] if 'mixture_of_samplers' in name: return get_mixture_of_samplers(name) raise NotImplementedError('The specified sampler is not available.') - 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