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JasonKChowJasonKChow
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Update config examples (#771)
Summary: Pull Request resolved: #771 Updated config examples to be more modern in its descriptions. Also added extra examples for new features Reviewed By: adellari Differential Revision: D72883100 fbshipit-source-id: 2c0a9e477c4b713c3038663bca6a2a77343d11fe
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‎configs/independent_gps_example.ini‎

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### Example config for a multi outcome experiment
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[common]
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parnames = [freq, amp] # names of the parameters
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outcome_names = [detection, strength] # names of outcomes, needed for multioutcome
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outcome_types = [binary, continuous]
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stimuli_per_trial = 1
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strategy_names = [init_strat, opt_strat]
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[freq]
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par_type = continuous
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lower_bound = 0
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upper_bound = 500
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[amp]
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par_type = continuous
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lower_bound = 0
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upper_bound = 1
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[init_strat]
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generator = SobolGenerator
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min_asks = 40
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[opt_strat]
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generator = IndependentOptimizeAcqfGenerator # Used alongside independent gps model
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model = IndependentGPsModel
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min_asks = 1
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[IndependentOptimizeAcqfGenerator]
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generators = [DetectionGen, StrengthGen] # A generator for each outcome, in order, defined using aliases
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[DetectionGen] # Using aliases to define the objects
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class = OptimizeAcqfGenerator # Requires a class
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acqf = EAVC # Configured as normal
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[EAVC]
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target = 0.75
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[StrengthGen] # Using aliases to define the objects
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class = OptimizeAcqfGenerator # Requires a class
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acqf = qLogNoisyExpectedImprovement
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[IndependentGPsModel]
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models = [GPClassificationModel, GPRegressionModel] # A model for each outcome, in order, can be aliases as above

‎configs/mixed_likelihood_constraints_example.ini‎

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### Example config for mixed likelihood constraints
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[common]
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parnames = [par1, par2]
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strategy_names = [init_strat, opt_strat]
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stimuli_per_trial = 1
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outcome_types = [binary]
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[par1]
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par_type = continuous
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lower_bound = 0
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upper_bound = 500
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[par2]
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par_type = continuous
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lower_bound = 0
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upper_bound = 2
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[init_strat]
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min_asks = 1
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generator = SobolGenerator
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[opt_strat]
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min_asks = 1
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model = GPClassificationModel
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generator = OptimizeAcqfGenerator
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[GPClassificationModel] # These types of constraints are only available for GPClassification
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constraint_locations = [[100, 0.4], [200, 1.0], [300, 1.5]] # Individual locations can be defined
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constraint_values = [1, 0.7, 0.3] # For each location, a specific value is needed, defined in the probability space
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constraint_strengths = [1e-3, 2e-3, 3e-3] # The strength of the constraints (the smaller the stronger), this is optional, without it, a heuristic is used
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constraint_factory = constraint_factory # Constraint factory can be defined to constraint a subspace
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[constraint_factory] # Constraint factory is used to define constraints over a space
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constraint_lower = [[0, 0], [0, 0]] # The subspace is defined by coordinates
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constraint_upper = [[0, 2], [500, 0]] # Multiple subspaces can be defined
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constraint_values = [0.5, 0.5] # Each subspace needs a value
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constraint_strengths = [1e-2, 1e-2] # Each subspace can have an optional strength
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points_per_dim = 2 # The space is constructed via a grid of points, the more points the tighter the grid
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[OptimizeAcqfGenerator]
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acqf = qLogNoisyExpectedImprovement

‎configs/nonmonotonic_optimization_example.ini‎

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### Example config for optimization from single observations without any
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# monotonicity assumptions.
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# Assuming you are doing threshold estimation and the intensity dimension
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# is the final dimension, the only things that need to be changed for a
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# typical experiment are:
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# 1. parnames, lb and ub under [common], and optionally target.
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# 2. min_asks under init_strat
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# 3. min_asks under opt_strat.
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## The common section includes global server parameters and parameters
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## reused in multiple other classes

‎configs/ordinal_exploration_example.ini‎

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### Example config for ordinal (likert) data
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# Assuming you are learning a latent value from k-point scores, the
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# only things that need to be changed for a
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# typical experiment are:
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# 1. parnames, lb and ub under [common], and optionally target.
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# 2. min_asks under init_strat
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# 3. n_levels under OrdinalLikelihood
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# Assuming you are learning a latent value from k-point scores
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## The common section includes global server parameters and parameters
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## reused in multiple other classes

‎configs/pairwise_al_example.ini‎

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### Example config for optimization (tuning) from pairwise obsevations
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# The only things that need to be changed for a
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# typical experiment are:
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# 1. parnames, lb and ub under [common], and optionally target.
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# 2. min_asks under init_strat.
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# 3. min_asks under opt_strat.
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## The common section includes global server parameters and parameters
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## reused in multiple other classes

‎configs/pairwise_opt_example.ini‎

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### Example config for active learning (JND estimation) from pairwise obsevations
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# The only things that need to be changed for a
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# typical experiment are:
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# 1. parnames, lb and ub under [common], and optionally target.
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# 2. min_asks under init_strat.
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# 3. min_asks under opt_strat.
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## The common section includes global server parameters and parameters
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## reused in multiple other classes

‎configs/regression_example.ini‎

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### Example config for optimization from single observations without any
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# monotonicity assumptions.
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# Assuming you are doing threshold estimation and the intensity dimension
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# is the final dimension, the only things that need to be changed for a
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# typical experiment are:
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# 1. parnames, lb and ub under [common], and optionally target.
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# 2. min_asks under init_strat
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# 3. min_asks under opt_strat.
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## The common section includes global server parameters and parameters
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## reused in multiple other classes

‎configs/single_lse_example.ini‎

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### Example config for threshold estimation from single observations.
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# Assuming you are doing threshold estimation and the intensity dimension
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# is the final dimension, the only things that need to be changed for a
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# typical experiment are:
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# 1. parnames, lb and ub under [common], and optionally target.
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# 2. min_asks under init_strat
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# 3. min_asks under opt_strat.
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## The common section includes global server parameters and parameters
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## reused in multiple other classes

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