TPE Algorithm¶
orion.algo.tpe
– Tree-structured Parzen Estimator Approach¶
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class
orion.algo.tpe.
CategoricalSampler
(tpe, observations, choices)[source]¶ Categorical Sampler for discrete integer and categorical choices
Parameters: - tpe: `TPE` algorithm
The tpe algorithm object which this sampler will be part of.
- observations: list
Observed values in the dimension
- choices: list
Candidate values for the dimension
Methods
get_loglikelis
(points)Return the log likelihood for the points sample
([num])Sample required number of points
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class
orion.algo.tpe.
GMMSampler
(tpe, mus, sigmas, low, high, weights=None)[source]¶ Gaussian Mixture Model Sampler for TPE algorithm
Parameters: - tpe: `TPE` algorithm
The tpe algorithm object which this sampler will be part of.
- mus: list
mus for each Gaussian components in the GMM. Default:
None
- sigmas: list
sigmas for each Gaussian components in the GMM.
- low: real
Lower bound of the sampled points.
- high: real
Upper bound of the sampled points.
- weights: list
Weights for each Gaussian components in the GMM Default:
None
Methods
get_loglikelis
(points)Return the log likelihood for the points sample
([num])Sample required number of points
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class
orion.algo.tpe.
TPE
(space, seed=None, n_initial_points=20, n_ei_candidates=24, gamma=0.25, equal_weight=False, prior_weight=1.0, full_weight_num=25)[source]¶ Tree-structured Parzen Estimator (TPE) algorithm is one of Sequential Model-Based Global Optimization (SMBO) algorithms, which will build models to propose new points based on the historical observed trials.
Instead of modeling p(y|x) like other SMBO algorithms, TPE models p(x|y) and p(y), and p(x|y) is modeled by transforming that generative process, replacing the distributions of the configuration prior with non-parametric densities.
The TPE defines p(x|y) using two such densities l(x) and g(x) while l(x) is distribution of good points and g(x) is the distribution of bad points. New point candidates will be sampled with l(x) and Expected Improvement (EI) optimization scheme will be used to find the most promising point among the candidates.
For more information on the algorithm, see original papers at:
- Algorithms for Hyper-Parameter Optimization
- Making a Science of Model Search: Hyperparameter Optimizationin Hundreds of Dimensions for Vision Architectures
Parameters: - space: `orion.algo.space.Space`
Optimisation space with priors for each dimension.
- seed: None, int or sequence of int
Seed to sample initial points and candidates points. Default:
None
- n_initial_points: int
Number of initial points randomly sampled. Default:
20
- n_ei_candidates: int
Number of candidates points sampled for ei compute. Default:
24
- gamma: real
Ratio to split the observed trials into good and bad distributions. Default:
0.25
- equal_weight: bool
True to set equal weights for observed points. Default:
False
- prior_weight: int
The weight given to the prior point of the input space. Default:
1.0
- full_weight_num: int
The number of the most recent trials which get the full weight where the others will be applied with a linear ramp from 0 to 1.0. It will only take effect if equal_weight is False.
Attributes: configuration
Return tunable elements of this algorithm in a dictionary form appropriate for saving.
is_done
Return True, if an algorithm holds that there can be no further improvement.
should_suspend
Allow algorithm to decide whether a particular running trial is still worth to complete its evaluation, based on information provided by the
judge
method.space
Domain of problem associated with this algorithm’s instance.
state_dict
Return a state dict that can be used to reset the state of the algorithm.
Methods
judge
(point, measurements)Inform an algorithm about online measurements of a running trial. observe
(points, results)Observe evaluation results corresponding to list of points in space. sample_one_dimension
(dimension, shape_size, …)Sample values for a dimension score
(point)Allow algorithm to evaluate point based on a prediction about this parameter set’s performance. seed_rng
(seed)Seed the state of the random number generator. set_state
(state_dict)Reset the state of the algorithm based on the given state_dict split_trials
()Split the observed trials into good and bad ones based on the ratio gamma` suggest
([num])Suggest a num of new sets of parameters. -
observe
(points, results)[source]¶ Observe evaluation results corresponding to list of points in space.
A simple random sampler though does not take anything into account.
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sample_one_dimension
(dimension, shape_size, below_points, above_points, sampler)[source]¶ Sample values for a dimension
Parameters: - dimension – Dimension.
- shape_size – 1D Shape Size of the Real Dimension.
- below_points – good points with shape (m, n), m=shape_size.
- above_points – bad points with shape (m, n), m=shape_size.
- sampler – method to sample one value for upon the dimension.
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seed_rng
(seed)[source]¶ Seed the state of the random number generator.
Parameters: seed – Integer seed for the random number generator.
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set_state
(state_dict)[source]¶ Reset the state of the algorithm based on the given state_dict
Parameters: state_dict – Dictionary representing state of an algorithm
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state_dict
¶ Return a state dict that can be used to reset the state of the algorithm.
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suggest
(num=1)[source]¶ Suggest a num of new sets of parameters. Randomly draw samples from the import space and return them.
Parameters: num – how many sets to be suggested. Note
New parameters must be compliant with the problem’s domain
orion.algo.space.Space
.
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orion.algo.tpe.
adaptive_parzen_estimator
(mus, low, high, prior_weight=1.0, equal_weight=False, flat_num=25)[source]¶ Return the sorted mus, the corresponding sigmas and weights with adaptive kernel estimator.
This adaptive parzen window estimator is based on the original papers and also refer the use of prior mean in this implementation.
Parameters: - mus – list of real values for observed mus.
- low – real value for lower bound of points.
- high – real value for upper bound of points.
- prior_weight – real value for the weight of the prior mean.
- equal_weight – bool value indicating if all points with equal weights.
- flat_num – int value indicating the number of the most recent trials which get the full weight where the others will be applied with a linear ramp from 0 to 1.0. It will only take effect if equal_weight is False.
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orion.algo.tpe.
compute_max_ei_point
(points, below_likelis, above_likelis)[source]¶ Compute ei among points based on their log likelihood and return the point with max ei.
Parameters: - points – list of point with real values.
- below_likelis – list of log likelihood for each point in the good GMM.
- above_likelis – list of log likelihood for each point in the bad GMM.
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orion.algo.tpe.
ramp_up_weights
(total_num, flat_num, equal_weight)[source]¶ Adjust weights of observed trials.
Parameters: - total_num – total number of observed trials.
- flat_num – the number of the most recent trials which get the full weight where the others will be applied with a linear ramp from 0 to 1.0. It will only take effect if equal_weight is False.
- equal_weight – whether all the observed trails share the same weights.