Source code for asreview.entry_points.base

import argparse
from argparse import RawTextHelpFormatter

from abc import ABC, abstractclassmethod

from asreview.config import DEFAULT_MODEL, DEFAULT_FEATURE_EXTRACTION
from asreview.config import DEFAULT_QUERY_STRATEGY
from asreview.config import DEFAULT_BALANCE_STRATEGY
from asreview.config import DEFAULT_N_INSTANCES
from asreview import __version__


[docs]class BaseEntryPoint(ABC): """Base class for defining entry points.""" description = "Base Entry point." extension_name = "asreview" version = __version__
[docs] @abstractclassmethod def execute(self, argv): """Perform the functionality of the entry point. Arguments --------- argv: list Argument list, with the entry point and program removed. For example, if `asreview plot X` is executed, then argv == ['X']. """ raise NotImplementedError
[docs] def format(self, entry_name="?"): """Create a short formatted description of the entry point. Arguments --------- entry_name: str Name of the entry point. For example 'plot' in `asreview plot X` """ description = self.description version = getattr(self, "version", "?") extension_name = getattr(self, "extension_name", "?") display_name = f"{entry_name} [{extension_name}-{version}]" return f"{display_name}\n {description}"
def _base_parser(prog=None, description=None): """Argument parser for simulate. Parameters ---------- mode : str The mode to run ASReview. prog : str The program name. For example 'asreview'. Returns ------- argparse.ArgumentParser Configured argparser. """ # parse arguments if available parser = argparse.ArgumentParser( prog=prog, description=description, formatter_class=RawTextHelpFormatter ) parser.add_argument( "-m", "--model", type=str, default=DEFAULT_MODEL, help=f"The prediction model for Active Learning. " f"Default: '{DEFAULT_MODEL}'.") #noqa parser.add_argument( "-q", "--query_strategy", type=str, default=DEFAULT_QUERY_STRATEGY, help=f"The query strategy for Active Learning. " f"Default: '{DEFAULT_QUERY_STRATEGY}'.") #noqa parser.add_argument( "-b", "--balance_strategy", type=str, default=DEFAULT_BALANCE_STRATEGY, help="Data rebalancing strategy mainly for RNN methods. Helps against" " imbalanced dataset with few inclusions and many exclusions. " f"Default: '{DEFAULT_BALANCE_STRATEGY}'") parser.add_argument( "-e", "--feature_extraction", type=str, default=DEFAULT_FEATURE_EXTRACTION, help="Feature extraction method. Some combinations of feature" " extraction method and prediction model are impossible/ill" " advised." f"Default: '{DEFAULT_FEATURE_EXTRACTION}'" ) parser.add_argument( "--n_instances", default=DEFAULT_N_INSTANCES, type=int, help="Number of papers queried each query." f"Default {DEFAULT_N_INSTANCES}.") parser.add_argument( "--n_queries", type=int, default=None, help="The number of queries. By default, the program " "stops after all documents are reviewed or is " "interrupted by the user." ) parser.add_argument( "-n", "--n_papers", type=int, default=None, help="The number of papers to be reviewed. By default, " "the program stops after all documents are reviewed or is " "interrupted by the user." ) parser.add_argument( "--embedding", type=str, default=None, dest='embedding_fp', help="File path of embedding matrix. Required for LSTM models." ) # Configuration file with model/balance/query parameters. parser.add_argument( "--config_file", type=str, default=None, help="Configuration file with model parameters" ) parser.add_argument( "--included_dataset", default=[], nargs="*", type=str, help="A dataset with papers that should be included" "Can be used multiple times." ) parser.add_argument( "--excluded_dataset", default=[], nargs="*", type=str, help="A dataset with papers that should be excluded" "Can be used multiple times." ) parser.add_argument( "--prior_dataset", default=[], nargs="*", type=str, help="A dataset with papers from prior studies." ) # logging and verbosity parser.add_argument( "--state_file", "-s", "--log_file", "-l", default=None, type=str, help="Location to store the state of the simulation." ) parser.add_argument( "--seed", default=None, type=int, help="Seed for models. Use integer between 0 and 2^32 - 1." ) parser.add_argument( "--abstract_only", default=False, action='store_true', help="Use after abstract screening as the inclusions/exclusions." ) return parser