Files
p2rank/distro/config/default-predict.groovy
2016-09-27 09:30:03 +02:00

95 lines
1.9 KiB
Groovy

import cz.siret.prank.features.weight.WeightFun
import cz.siret.prank.program.params.Params
(params as Params).with {
/**
* Number of computing threads
*/
threads = Runtime.getRuntime().availableProcessors() + 1
/**
* define this if you want dataset program parameters to be evaluated relative to this directory
* (set absolute path or path relative to install dir, null defaults to working dir)
*/
dataset_base_dir = "./test_data"
/**
* all output of the prorgam will be stored in subdirectores of this directory
* (set absolute path or path relative to install dir, null defaults to working dir)
*/
output_base_dir = "./test_output"
/**
* default model
* (set path relative to install_dir/models/)
*/
model = "p2rank_a.model"
/**
* produce pymol visualisations
*/
visualizations = true
/**
* copy all protein pdb files to visualization folder (making visualizations portable)
*/
vis_copy_proteins = true
/**
* stop processing a datsaset on the first unrecoverable error with a dataset item
*/
fail_fast = false
delete_models = true
delete_vectors = true
cache_datasets = false
predictions = true
out_prefix_date = false
log_cases = false
max_train_instances = 0
classifier="FastRandomForest"
rf_trees = 100
seed = 42
loop = 10
use_volsite_features = true
atom_table_features = ["apRawValids","apRawInvalids","atomicHydrophobicity"]
extra_features = ["protrusion","bfactor"]
residue_table_features = []
protrusion_radius = 10
weight_function = WeightFun.Option.INV
// optimized values
pred_point_threshold = 0.35
pred_clustering_dist = 3
neighbourhood_radius = 6
positive_point_ligand_distance = 2.6
train_pockets = 9
surface_additional_cutoff = 2.5
}