schrodinger.active_learning.al_driver module¶
Implementation of screening large library with active learning scheme.
Active learning scheme 1. Select N ligands from the library 2. Dock the selected portion of the library. 3. Train a ligand_ml model with the scores. 4. Evaluate the whole library with the generated ligand_ml model. 5. Pick N from the top M best ligands predicted by the ligand_ml model. 6. Dock the ligands picked in step 5 and repeat step 3 until it reaches num_iter.
Copyright Schrodinger Inc, All Rights Reserved.
- class schrodinger.active_learning.al_driver.Option(*names, dest=None, help=None, type=<class 'str'>, metavar=None, default=None, action=None, nargs=None, choices=None, required=False)¶
Bases:
object
A class to represent “options” which may be translated into argparse command-line arguments or an InputConfig spec for parsing input files. This is used to support the behavior of the legacy SiteMap driver, where every option could be specified in an input file or on the command line, with the latter taking precedence.
- __init__(*names, dest=None, help=None, type=<class 'str'>, metavar=None, default=None, action=None, nargs=None, choices=None, required=False)¶
The arguments all have the same meaning as for argparse.ArgumentParser.add_argument(), except
min
andmax
which are only used by ConfigObj and limit the range of allowed values for numeric types.
- toArgparse(parser)¶
Add an option to an argument parser.
- Parameters
parser (argparse.ArgumentParser) – argument parser
- toConfigObj()¶
Return a ConfigObj validator spec for self.
- Returns
validation spec
- Return type
str
- toSubparser(subparsers)¶
Create a subparser for certain task.
- Returns
argument parser
- Return type
argparse.ArgumentParser
- schrodinger.active_learning.al_driver.get_workflow_node_names(task, num_iter, use_known_score, run_rescore_ligand, al_node_supplier)¶
Return a list of stages needed to complete the workflow based on the task type, number of iteration, whether score is known and whether to run rescore stage.
- Parameters
task (str in [SCREEN_TASK, PILOT_TASK or EVAL_TASK]) – workflow task type
num_iter (int) – number of iterations
use_known_score (bool) – Use known scores in score_file to obtain the score.
run_rescore_ligand (bool) – run rescore stage for ligand.
al_node_supplier (ActiveLearningNodeSupplier) – Supplier of active learning nodes
- Returns
list of names of stages needed to complete the workflow
- Return type
list(str)
- schrodinger.active_learning.al_driver.validate_stop_after(stop_after_node, task, num_iter, use_known_score, run_rescore_ligand, restart_file, al_node_supplier)¶
Check whether the node name user specified in -stop_after is valid.
- Parameters
stop_after_node (str in [ActiveLearningNode name] or 'FinishAll') – name of the node where workflow will exit when it was finished.
task (str in [SCREEN_TASK, PILOT_TASK or EVAL_TASK]) – workflow task type
num_iter (int) – number of iterations
use_known_score (bool) – Use known scores in use_known_score to obtain the score.
run_rescore_ligand (bool) – run rescore stage for ligands.
al_node_supplier (ActiveLearningNodeSupplier) – Supplier of active learning nodes
- Returns
error message if validation failed; None if it passed
- Return type
str or None
- class schrodinger.active_learning.al_driver.ActiveLearningJob(args, al_node_supplier)¶
Bases:
object
- __init__(args, al_node_supplier)¶
Initialize the ActiveLearningJob from the cmd arguments.
- Parameters
args (argparse.Namespace) – argument namespace with command line options
- static LoadPreviousNodes(restart_file)¶
Load nodes that were finished in previous job.
- Parameters
restart_file (str) – filename of the AL .pkl restart file
- Returns
Nodes that were finished in previous job.
- Return type
OrderedDict that maps node name to node instance.
- static getNodeClasses(use_known_score, al_node_supplier)¶
Return a list of node classes to run based on the job type.
- Parameters
use_known_score (bool) – Use known scores in score_file to obtain the score.
al_node_supplier (ActiveLearningNodeSupplier) – Supplier of active learning nodes
- Returns
a list of ActiveLearningNode subclass
- Return type
list
- LoadOptionalRestartFiles()¶
Load the restart files for the possible restarting of the running node.
- Returns
list of filenames
- Type
list(str) or None
- LoadOptionalRestartFilesDict()¶
Load a dict of the optional restart files. The dictionary maps the jobname of the subjob to a list of optional files for restarting the subjob.
- Returns
dict of optional files
- Return type
{str: list}
- addOptionalRestartFilesDict()¶
Pass variable optional_restart_files_dict to the restart dictionary. Dump the restart dictionary as a pickle file.
- configure()¶
Prepare the active learning job.
- property scored_csv_file_list¶
Get all the .csv files that contain scored ligands from ScoreProviderNode.
- Returns
list of .csv files contain score ligands.
- Return type
list(str)
- property restart_files¶
Get all the necessary files for restarting the workflow from finished nodes.
- Returns
a set of files for restarting.
- Return type
set(str)
- getPilotScoreFile()¶
Reorder the columns in the pilot ligand score file for the use of machine learning model training input.
- Returns
name of reorder .csv file.
- Return type
str
- checkOSFileLimit()¶
Check the system file descriptors limit.
- getNodesToRun()¶
Return nodes to run and finished nodes for current active learning job.
- splitInputfiles()¶
Separate the input files into small blocks randomly.
- getRestartNode()¶
Get the node for restarting the workflow.
- Returns
last finished node
- Return type
- getInitialInputs()¶
Get the inputs for the runNode() method of the first node in the workflow.
- Returns
dict that contains the keyword arguments and values for the runNode() of the first node.
- Return type
dict{keyword argument: value}
- getLocalArgs()¶
- Returns
arguments on local machine.
- Return type
argparse.Namespace
- runNodes()¶
Run all the ActiveLearningNode instances in self.nodes_to_run.
- postprocessing()¶
Combine the log files of all subjobs.
- schrodinger.active_learning.al_driver.read_paths_listed_in_file(old_paths, paths_list_file)¶
Add the paths specified in the paths_list_file to old_paths.
- Parameters
old_paths (list) – None or list of original paths
paths_list_file (string) – path of the file that contains paths to be added
- Returns
list of paths
- Return type
list(str)
- schrodinger.active_learning.al_driver.restart_args_handler(args)¶
Load the previous arguments stored in args.restart_file.
- Parameters
args (argparse.Namespace) – argument namespace with command line options
- Returns
updated argument namespace, argument namespace of previous job or None
- Return type
argument namespace, argument namespace or None
- schrodinger.active_learning.al_driver.common_parse_args(args, script_name=None)¶
Parses command-line arguments.
- Parameters
args (argparse.Namespace) – argument namespace with command line options
- Returns
argument namespace with command line options
- Return type
argparse.Namespace
- schrodinger.active_learning.al_driver.common_validate_args(args, skip_validate_infile=False)¶
Validate command-line arguments
- Parameters
args (argparse.Namespace) – argument namespace with command line options
skip_validate_infile (bool) – Whether to skip validation of the infile list.
- Returns
Whether the args were validated. If they were not, the corresponding message is given.
- Return type
tuple[bool, str]
- schrodinger.active_learning.al_driver.common_get_job_spec_from_args(args, jsb)¶