CompositionBasedCSPEngine

See Javadoc for complete documentation of this class.

Usage: $<model template>
model template: Template of a BaseClassifier used to predict which structure is most likely

Available Operations

These commands can be used to perform a variety of tasks, ranging from defining important settings about the object to actually using it.

attributes [<command...>] – Configure how attributes are generated
command: Attribute command from PrototypeDataset

examples <composition> – Get a list of other possible prototypes for a certain composition and all known examples for each prototype
composition: Composition in question

exclude <elements...> – Remove entries containing certain elements from the list of known compounds
elements: List of elements abbreviations

get model = $<model> – Get the classifier used to predict last crystal structure

predict <composition> [<# to show>] – Predict what structural prototypes are most likely for a certain compound
composition: Composition of compound of interest
# to show: Number of the top candidates to print (default = 10)

prototypes <filename> – Import list of known prototypes
filename: Path to file containing composition of known compounds, and name of their prototype structure

validate <ncomp> [<folds>] – Evaluate performance of CSP algorithm using cross-validation.
ncomp: Only attempt to predict structures of compounds with this number of constituents
folds: Number of folds in which to split known compounds. Can use "loocv" to perform leave-one-out cross-validation (default = 20)

Available Print Commands

These commands are run by calling "print <variable name> <command> [<options>]". Any output from that command will be printed to standard output.

stats list-length [<min prob>] [<max length>] – Print out the minimum number of prototypes that need to be calculated for a certain prediction success probability
min prob: Minimum success probability to print (default = 0.7)
max length: Maximum length list to print (default = 20)

stats – Print out the number of predictions used to generate performance statistics.