configuration_sparse module¶
Configuration for the simulations, for single-player sparse bandit.
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configuration_sparse.HORIZON= 10000¶ HORIZON : number of time steps of the experiments. Warning Should be >= 10000 to be interesting “asymptotically”.
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configuration_sparse.REPETITIONS= 100¶ REPETITIONS : number of repetitions of the experiments. Warning: Should be >= 10 to be statistically trustworthy.
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configuration_sparse.DO_PARALLEL= True¶ To profile the code, turn down parallel computing
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configuration_sparse.N_JOBS= -1¶ Number of jobs to use for the parallel computations. -1 means all the CPU cores, 1 means no parallelization.
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configuration_sparse.RANDOM_SHUFFLE= False¶ The arms are shuffled (
shuffle(arms)).
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configuration_sparse.RANDOM_INVERT= False¶ The arms are inverted (
arms = arms[::-1]).
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configuration_sparse.NB_RANDOM_EVENTS= 5¶ Number of random events. They are uniformly spaced in time steps.
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configuration_sparse.UPDATE_ALL_CHILDREN= False¶ Should the Aggregator policy update the trusts in each child or just the one trusted for last decision?
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configuration_sparse.LEARNING_RATE= 0.01¶ Learning rate for my aggregated bandit (it can be autotuned)
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configuration_sparse.UNBIASED= False¶ Should the rewards for Aggregator policy use as biased estimator, ie just
r_t, or unbiased estimators,r_t / p_t
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configuration_sparse.UPDATE_LIKE_EXP4= False¶ Should we update the trusts proba like in Exp4 or like in my initial Aggregator proposal
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configuration_sparse.TEST_Aggregator= False¶ To know if my Aggregator policy is tried.
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configuration_sparse.CACHE_REWARDS= False¶ Should we cache rewards? The random rewards will be the same for all the REPETITIONS simulations for each algorithms.
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configuration_sparse.TRUNC= 1¶ Trunc parameter, ie amplitude, for Exponential arms
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configuration_sparse.MINI= 0¶ lower bound on rewards from Gaussian arms
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configuration_sparse.MAXI= 1¶ upper bound on rewards from Gaussian arms, ie amplitude = 1
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configuration_sparse.SCALE= 1¶ Scale of Gamma arms
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configuration_sparse.NB_ARMS= 15¶ Number of arms for non-hard-coded problems (Bayesian problems)
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configuration_sparse.SPARSITY= 7¶ Sparsity for non-hard-coded problems (Bayesian problems)
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configuration_sparse.LOWERNONZERO= 0.25¶ Default value for the lower value of non-zero means
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configuration_sparse.VARIANCE= 0.05¶ Variance of Gaussian arms
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configuration_sparse.ARM_TYPE¶ alias of
Arms.Gaussian.Gaussian
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configuration_sparse.ENVIRONMENT_BAYESIAN= False¶ True to use bayesian problem
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configuration_sparse.MEANS= [0.00125, 0.03660714285714286, 0.07196428571428572, 0.10732142857142857, 0.14267857142857143, 0.1780357142857143, 0.21339285714285713, 0.24875, 0.25375, 0.3775, 0.50125, 0.625, 0.74875, 0.8725, 0.99625]¶ Means of arms for non-hard-coded problems (non Bayesian)
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configuration_sparse.ISSORTED= True¶ Whether to sort the means of the problems or not.
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configuration_sparse.configuration= {'environment': [{'arm_type': <class 'Arms.Gaussian.Gaussian'>, 'params': [(0.05, 0.05, 0.0, 1.0), (0.07142857142857144, 0.05, 0.0, 1.0), (0.09285714285714286, 0.05, 0.0, 1.0), (0.1142857142857143, 0.05, 0.0, 1.0), (0.13571428571428573, 0.05, 0.0, 1.0), (0.15714285714285717, 0.05, 0.0, 1.0), (0.1785714285714286, 0.05, 0.0, 1.0), (0.2, 0.05, 0.0, 1.0), (0.4, 0.05, 0.0, 1.0), (0.47500000000000003, 0.05, 0.0, 1.0), (0.55, 0.05, 0.0, 1.0), (0.625, 0.05, 0.0, 1.0), (0.7000000000000001, 0.05, 0.0, 1.0), (0.7750000000000001, 0.05, 0.0, 1.0), (0.8500000000000001, 0.05, 0.0, 1.0)], 'sparsity': 7}], 'horizon': 10000, 'n_jobs': -1, 'nb_random_events': 5, 'policies': [{'archtype': <class 'Policies.EmpiricalMeans.EmpiricalMeans'>, 'params': {'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.UCBalpha.UCBalpha'>, 'params': {'alpha': 1, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.SparseUCB.SparseUCB'>, 'params': {'alpha': 1, 'sparsity': 7, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.klUCB.klUCB'>, 'params': {'klucb': <function klucbBern>, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.SparseklUCB.SparseklUCB'>, 'params': {'sparsity': 7, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.Thompson.Thompson'>, 'params': {'posterior': <class 'Policies.Posterior.Beta.Beta'>, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.SparseWrapper.SparseWrapper'>, 'params': {'sparsity': 7, 'policy': <class 'Policies.Thompson.Thompson'>, 'posterior': <class 'Policies.Posterior.Beta.Beta'>, 'use_ucb_for_set_J': True, 'use_ucb_for_set_K': True, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.Thompson.Thompson'>, 'params': {'posterior': <class 'Policies.Posterior.Gauss.Gauss'>, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.SparseWrapper.SparseWrapper'>, 'params': {'sparsity': 7, 'policy': <class 'Policies.Thompson.Thompson'>, 'posterior': <class 'Policies.Posterior.Gauss.Gauss'>, 'use_ucb_for_set_J': True, 'use_ucb_for_set_K': True, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.BayesUCB.BayesUCB'>, 'params': {'posterior': <class 'Policies.Posterior.Beta.Beta'>, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.SparseWrapper.SparseWrapper'>, 'params': {'sparsity': 7, 'policy': <class 'Policies.BayesUCB.BayesUCB'>, 'posterior': <class 'Policies.Posterior.Beta.Beta'>, 'use_ucb_for_set_J': True, 'use_ucb_for_set_K': True, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.BayesUCB.BayesUCB'>, 'params': {'posterior': <class 'Policies.Posterior.Gauss.Gauss'>, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.SparseWrapper.SparseWrapper'>, 'params': {'sparsity': 7, 'posterior': <class 'Policies.Posterior.Gauss.Gauss'>, 'policy': <class 'Policies.BayesUCB.BayesUCB'>, 'use_ucb_for_set_J': True, 'use_ucb_for_set_K': True, 'lower': 0, 'amplitude': 1}}, {'archtype': <class 'Policies.OSSB.OSSB'>, 'params': {'epsilon': 0.0, 'gamma': 0.0}}, {'archtype': <class 'Policies.OSSB.GaussianOSSB'>, 'params': {'epsilon': 0.0, 'gamma': 0.0, 'variance': 0.05}}, {'archtype': <class 'Policies.OSSB.SparseOSSB'>, 'params': {'epsilon': 0.0, 'gamma': 0.0, 'sparsity': 7}}, {'archtype': <class 'Policies.OSSB.SparseOSSB'>, 'params': {'epsilon': 0.001, 'gamma': 0.0, 'sparsity': 7}}, {'archtype': <class 'Policies.OSSB.SparseOSSB'>, 'params': {'epsilon': 0.0, 'gamma': 0.01, 'sparsity': 7}}, {'archtype': <class 'Policies.OSSB.SparseOSSB'>, 'params': {'epsilon': 0.001, 'gamma': 0.01, 'sparsity': 7}}], 'random_invert': False, 'random_shuffle': False, 'repetitions': 100, 'verbosity': 6}¶ This dictionary configures the experiments
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configuration_sparse.LOWER= 0¶ And get LOWER, AMPLITUDE values
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configuration_sparse.AMPLITUDE= 1¶ And get LOWER, AMPLITUDE values