autocat.learning.sequential
CandidateSelector
Source code in autocat/learning/sequential.py
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__init__(acquisition_function=None, num_candidates_to_pick=None, target_window=None, include_hhi=None, hhi_type='production', include_segregation_energies=None, segregation_energy_data_source=None)
Constructor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
acquisition_function |
str
|
Acquisition function to be used to select the next candidates Options - MLI: maximum likelihood of improvement (default) - Random - MU: maximum uncertainty |
None
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num_candidates_to_pick: Number of candidates to choose from the dataset
target_window: Target window that the candidate should ideally fall within
include_hhi: Whether HHI scores should be used to weight aq scores
hhi_type: Type of HHI index to be used for weighting Options - production (default) - reserves
include_segregation_energies: Whether segregation energies should be used to weight aq scores
segregation_energy_data_source: Which tabulated data should the segregation energies be pulled from. Options: - "raban1999": A.V. Raban, et. al. Phys. Rev. B 59, 15990 (1999) - "rao2020": K. K. Rao, et. al. Topics in Catalysis volume 63, pages728-741 (2020)
Source code in autocat/learning/sequential.py
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choose_candidate(design_space, allowed_idx=None, predictions=None, uncertainties=None)
Choose the next candidate(s) from a design space
Parameters:
Name | Type | Description | Default |
---|---|---|---|
design_space |
DesignSpace
|
DesignSpace where candidates will be selected from |
required |
allowed_idx:
Allowed indices that the selector can choose from when making a recommendation
Defaults to only choosing from systems with np.nan
labels if a DesignSpace
with unknown labels is provided. Otherwise, all structures are considered
predictions: Predictions for all structures in the DesignSpace
uncertainties: Uncertainties for all structures in the DesignSpace
Returns:
Name | Type | Description |
---|---|---|
parent_idx | Index/indices of the selected candidates |
max_scores: Maximum scores (corresponding to the selected candidates)
aq_scores:
Calculated scores using acquisition_function
for the entire DesignSpace
Source code in autocat/learning/sequential.py
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copy()
Returns a copy of the CandidateSelector
Source code in autocat/learning/sequential.py
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to_jsonified_dict()
Returns a jsonified dict representation
Source code in autocat/learning/sequential.py
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write_json_to_disk(json_name=None, write_location='.')
Writes CandidateSelector to disk as a json
Source code in autocat/learning/sequential.py
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DesignSpace
Source code in autocat/learning/sequential.py
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__delitem__(i)
Deletes systems from the design space. If mask provided, deletes wherever True
Source code in autocat/learning/sequential.py
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__init__(design_space_structures, design_space_labels)
Constructor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
design_space_structures |
List[Atoms]
|
List of all structures within the design space |
required |
design_space_labels: Labels corresponding to all structures within the design space. If label not yet known, set to np.nan
Source code in autocat/learning/sequential.py
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copy()
Returns a copy of the design space
Source code in autocat/learning/sequential.py
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to_jsonified_dict()
Returns a jsonified dict representation
Source code in autocat/learning/sequential.py
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update(structures, labels)
Updates design space given structures and corresponding labels. If structure already in design space, the label is updated.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
structures |
List[Atoms]
|
List of Atoms objects structures to be added |
required |
labels:
Corresponding labels to structures
Source code in autocat/learning/sequential.py
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write_json_to_disk(json_name=None, write_location='.')
Writes DesignSpace to disk as a json
Source code in autocat/learning/sequential.py
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SequentialLearner
Source code in autocat/learning/sequential.py
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__init__(design_space, predictor=None, candidate_selector=None, sl_kwargs=None)
Constructor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
design_space |
DesignSpace
|
DesignSpace that is being explored |
required |
predictor: Predictor used for training and predicting on the desired property
candidate_selector: CandidateSelector used for calculating scores and selecting candidates for each iteration
Source code in autocat/learning/sequential.py
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copy()
Returns a copy
Source code in autocat/learning/sequential.py
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iterate()
Runs the next iteration of sequential learning.
This process consists of: - retraining the predictor - predicting candidate properties and calculating candidate scores (if fully explored returns None) - selecting the next batch of candidates for objective evaluation (if fully explored returns None)
Source code in autocat/learning/sequential.py
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to_jsonified_dict()
Returns a jsonified dict representation
Source code in autocat/learning/sequential.py
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write_json_to_disk(write_location='.', json_name=None)
Writes SequentialLearner
to disk as a json
Source code in autocat/learning/sequential.py
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calculate_hhi_scores(structures, hhi_type='production', exclude_species=None)
Calculates HHI scores for structures weighted by their composition. The scores are normalized and inverted such that these should be maximized in the interest of finding a low cost system
Parameters:
Name | Type | Description | Default |
---|---|---|---|
structures |
List[Atoms]
|
List of Atoms objects for which to calculate the scores |
required |
hhi_type: Type of HHI index to be used for the score Options - production (default) - reserves
exclude_species: Species to be excluded when calculating the scores. An example use-case would be comparing transition-metal oxides where we can ignore the presence of O in each.
Returns:
Name | Type | Description |
---|---|---|
hhi_scores | Scores corresponding to each of the provided structures |
Source code in autocat/learning/sequential.py
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calculate_segregation_energy_scores(structures, data_source='raban1999')
Calculates HHI scores for structures weighted by their composition. The scores are normalized and inverted such that these should be maximized in the interest of finding a low cost system
Parameters:
Name | Type | Description | Default |
---|---|---|---|
structures |
List[Atoms]
|
List of Atoms objects for which to calculate the scores |
required |
data_source: Which tabulated data should the segregation energies be pulled from. Options: - "raban1999": A.V. Raban, et. al. Phys. Rev. B 59, 15990 - "rao2020": K. K. Rao, et. al. Topics in Catalysis volume 63, pages728-741 (2020)
Returns:
Name | Type | Description |
---|---|---|
hhi_scores | Scores corresponding to each of the provided structures |
Source code in autocat/learning/sequential.py
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get_overlap_score(mean, std, x2=None, x1=None)
Calculate overlap score given targets x2 (max) and x1 (min)
Source code in autocat/learning/sequential.py
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multiple_simulated_sequential_learning_runs(full_design_space, number_of_runs=5, number_parallel_jobs=None, predictor=None, candidate_selector=None, init_training_size=10, number_of_sl_loops=None, write_to_disk=False, write_location='.', json_name_prefix=None)
Conducts multiple simulated sequential learning runs
Parameters:
Name | Type | Description | Default |
---|---|---|---|
full_design_space |
DesignSpace
|
Fully labelled DesignSpace to simulate being searched over |
required |
predictor: Predictor to be used for predicting properties while iterating.
candidate_selector: CandidateSelector that specifies settings for candidate selection. This is where acquisition function, targets, etc. are specified.
init_training_size: Size of the initial training set to be selected from the full space. Default: 10
number_of_sl_loops:
Integer specifying the number of sequential learning loops to be conducted.
This value cannot be greater than
(DESIGN_SPACE_SIZE - init_training_size)/batch_size_to_add
Default: maximum number of sl loops calculated above
number_of_runs: Integer of number of runs to be done Default: 5
number_parallel_jobs:
Integer giving the number of cores to be paralellized across
using joblib
Default: None (ie. will run in serial)
write_to_disk: Boolean specifying whether runs history should be written to disk as jsons. Default: False
write_location: String with the location where runs history jsons should be written to disk. Default: current directory
json_name_prefix:
Prefix used when writing out each simulated run as a json
The naming convention is {json_name_prefix}_{run #}.json
Default: acsl_run
Returns:
Name | Type | Description |
---|---|---|
runs_history |
List[SequentialLearner]
|
List of SequentialLearner objects for each simulated run |
Source code in autocat/learning/sequential.py
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simulated_sequential_learning(full_design_space, predictor=None, candidate_selector=None, init_training_size=10, number_of_sl_loops=None, write_to_disk=False, write_location='.', json_name=None)
Conducts a simulated sequential learning loop for a fully labelled design space to explore.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
full_design_space |
DesignSpace
|
Fully labelled DesignSpace to simulate being searched over |
required |
predictor: Predictor to be used for predicting properties while iterating.
candidate_selector: CandidateSelector that specifies settings for candidate selection. This is where acquisition function, targets, etc. are specified.
init_training_size: Size of the initial training set to be selected from the full space. Default: 10
number_of_sl_loops:
Integer specifying the number of sequential learning loops to be conducted.
This value cannot be greater than
(DESIGN_SPACE_SIZE - init_training_size)/batch_size_to_add
Default: maximum number of sl loops calculated above
write_to_disk: Boolean specifying whether the resulting sequential learner should be written to disk as a json. Defaults to False.
write_location: String with the location where the resulting sequential learner should be written to disk. Defaults to current directory.
Returns:
Name | Type | Description |
---|---|---|
sl |
SequentialLearner
|
Sequential Learner after having been iterated as specified by the input settings. Contains candidate, prediction, and uncertainty histories for further analysis as desired. |
Source code in autocat/learning/sequential.py
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