Source code for rsmtool.rsmtool

#!/usr/bin/env python

Run an rsmtool experiment.

:author: Jeremy Biggs (
:author: Anastassia Loukina (
:author: Nitin Madnani (

:organization: ETS

import logging
import sys
from os import listdir, makedirs
from os.path import abspath, exists, join

from .analyzer import Analyzer
from .configuration_parser import configure
from .modeler import Modeler
from .preprocessor import FeaturePreprocessor
from .reader import DataReader
from .reporter import Reporter
from .utils.commandline import ConfigurationGenerator, setup_rsmcmd_parser
from .utils.constants import VALID_PARSER_SUBCOMMANDS
from .utils.logging import LogFormatter
from .writer import DataWriter

[docs]def run_experiment(config_file_or_obj_or_dict, output_dir, overwrite_output=False, logger=None): """ Run an rsmtool experiment using the given configuration. Run rsmtool experiment using the given configuration file, object, or dictionary. All outputs are generated under ``output_dir``. If ``overwrite_output`` is ``True``, any existing output in ``output_dir`` is overwritten. Parameters ---------- config_file_or_obj_or_dict : str or pathlib.Path or dict or Configuration Path to the experiment configuration file either a a string or as a ``pathlib.Path`` object. Users can also pass a ``Configuration`` object that is in memory or a Python dictionary with keys corresponding to fields in the configuration file. Given a configuration file, any relative paths in the configuration file will be interpreted relative to the location of the file. Given a ``Configuration`` object, relative paths will be interpreted relative to the ``configdir`` attribute, that _must_ be set. Given a dictionary, the reference path is set to the current directory. output_dir : str Path to the experiment output directory. overwrite_output : bool, optional If ``True``, overwrite any existing output under ``output_dir``. Defaults to ``False``. logger : logging object, optional A logging object. If ``None`` is passed, get logger from ``__name__``. Defaults to ``None``. Raises ------ FileNotFoundError If any of the files contained in ``config_file_or_obj_or_dict`` cannot be located. IOError If ``output_dir`` already contains the output of a previous experiment and ``overwrite_output`` is ``False``. ValueError If the current configuration specifies a non-linear model but ``output_dir`` already contains the output of a previous experiment that used a linear model with the same experiment ID. """ logger = logger if logger else logging.getLogger(__name__) # create the 'output' and the 'figure' sub-directories # where all the experiment output such as the CSV files # and the box plots will be saved # Get absolute paths to output directories csvdir = abspath(join(output_dir, 'output')) figdir = abspath(join(output_dir, 'figure')) reportdir = abspath(join(output_dir, 'report')) featuredir = abspath(join(output_dir, 'feature')) # Make directories, if necessary makedirs(csvdir, exist_ok=True) makedirs(figdir, exist_ok=True) makedirs(reportdir, exist_ok=True) # Raise an error if the specified output directory # already contains a non-empty `output` directory, unless # `overwrite_output` was specified, in which case we assume # that the user knows what she is doing and simply # output a warning saying that the report might # not be correct. non_empty_csvdir = exists(csvdir) and listdir(csvdir) if non_empty_csvdir: if not overwrite_output: raise IOError("'{}' already contains a non-empty 'output' " "directory.".format(output_dir)) else: logger.warning("{} already contains a non-empty 'output' directory. " "The generated report might contain " "unexpected information from a previous " "experiment.".format(output_dir)) configuration = configure('rsmtool', config_file_or_obj_or_dict)'Saving configuration file.') # Get output format file_format = configuration.get('file_format', 'csv') # Get DataWriter object writer = DataWriter(configuration['experiment_id']) # Get the paths and names for the DataReader (file_names, file_paths_org) = configuration.get_names_and_paths(['train_file', 'test_file', 'features', 'feature_subset_file'], ['train', 'test', 'feature_specs', 'feature_subset_specs']) file_paths = DataReader.locate_files(file_paths_org, configuration.configdir) # if there are any missing files after trying to locate # all expected files, raise an error if None in file_paths: missing_file_paths = [file_paths_org[idx] for idx, path in enumerate(file_paths) if path is None] raise FileNotFoundError('The following files were not found: ' '{}'.format(repr(missing_file_paths))) # Use the default converter for both train and test converters = {'train': configuration.get_default_converter(), 'test': configuration.get_default_converter()}'Reading in all data from files.') # Initialize the reader reader = DataReader(file_paths, file_names, converters) data_container ='Preprocessing all features.') # Initialize the processor processor = FeaturePreprocessor(logger=logger) (processed_config, processed_container) = processor.process_data(configuration, data_container) # Rename certain frames with more descriptive names # for writing out experiment files rename_dict = {'train_excluded': 'train_excluded_responses', 'test_excluded': 'test_excluded_responses', 'train_length': 'train_response_lengths', 'train_flagged': 'train_responses_with_excluded_flags', 'test_flagged': 'test_responses_with_excluded_flags'}'Saving training and test set data to disk.') # Write out files writer.write_experiment_output(csvdir, processed_container, ['train_features', 'test_features', 'train_metadata', 'test_metadata', 'train_other_columns', 'test_other_columns', 'train_preprocessed_features', 'test_preprocessed_features', 'train_excluded', 'test_excluded', 'train_length', 'test_human_scores', 'train_flagged', 'test_flagged'], rename_dict, file_format=file_format) # Initialize the analyzer analyzer = Analyzer(logger=logger) (_, analyzed_container) = analyzer.run_data_composition_analyses_for_rsmtool(processed_container, processed_config) # Write out files writer.write_experiment_output(csvdir, analyzed_container, file_format=file_format)'Training {} model.'.format(processed_config['model_name'])) # Initialize modeler modeler = Modeler(logger=logger) modeler.train(processed_config, processed_container, csvdir, figdir, file_format) # Identify the features used by the model selected_features = modeler.get_feature_names() # Add selected features to processed configuration processed_config['selected_features'] = selected_features # Write out files writer.write_feature_csv(featuredir, processed_container, selected_features, file_format=file_format) features_data_container = processed_container.copy() # Get selected feature info, and write out to file df_feature_info = features_data_container.feature_info.copy() df_selected_feature_info = df_feature_info[df_feature_info['feature'].isin(selected_features)] selected_feature_dataset_dict = {'name': 'selected_feature_info', 'frame': df_selected_feature_info} features_data_container.add_dataset(selected_feature_dataset_dict, update=True) writer.write_experiment_output(csvdir, features_data_container, dataframe_names=['selected_feature_info'], new_names_dict={'selected_feature_info': 'feature'}, file_format=file_format)'Running analyses on training set.') (_, train_analyzed_container) = analyzer.run_training_analyses(processed_container, processed_config) # Write out files writer.write_experiment_output(csvdir, train_analyzed_container, reset_index=True, file_format=file_format) # Use only selected features for predictions columns_for_prediction = ['spkitemid', 'sc1'] + selected_features train_for_prediction = processed_container.train_preprocessed_features[columns_for_prediction] test_for_prediction = processed_container.test_preprocessed_features[columns_for_prediction] logged_str = 'Generating training and test set predictions' logged_str += ' (expected scores).' if configuration['predict_expected_scores'] else '.' (pred_config, pred_data_container) = modeler.predict_train_and_test(train_for_prediction, test_for_prediction, processed_config) # Write out files writer.write_experiment_output(csvdir, pred_data_container, new_names_dict={'pred_test': 'pred_processed'}, file_format=file_format) original_coef_file = join(csvdir, '{}_coefficients.{}'.format(pred_config['experiment_id'], file_format)) # If coefficients file exists, then try to generate the scaled # coefficients and save them to a file if exists(original_coef_file):'Scaling the coefficients and saving them to disk') try: # scale coefficients, and return DataContainer w/ scaled coefficients scaled_data_container = modeler.scale_coefficients(pred_config) # raise an error if the coefficient file exists but the # coefficients are not available for the current model # which can happen if the user is re-running the same experiment # with the same ID but with a non-linear model whereas the previous # run of the same ID was with a linear model and the user has not # cleared the directory except RuntimeError: raise ValueError("It appears you previously ran an experiment with the " "same ID using a linear model and saved its output to " "the same directory. That output is interfering with " "the current experiment. Either clear the contents " "of the output directory or re-run the current " "experiment using a different experiment ID.") else: # Write out scaled coefficients to disk writer.write_experiment_output(csvdir, scaled_data_container, file_format=file_format) # Add processed data_container frames to pred_data_container new_pred_data_container = pred_data_container + processed_container'Running prediction analyses.') (pred_analysis_config, pred_analysis_data_container) = analyzer.run_prediction_analyses(new_pred_data_container, pred_config) # Write out files writer.write_experiment_output(csvdir, pred_analysis_data_container, reset_index=True, file_format=file_format) # Initialize reporter reporter = Reporter(logger=logger) # generate the report'Starting report generation.') reporter.create_report(pred_analysis_config, csvdir, figdir)
def main(): # noqa: D103 # set up the basic logging configuration formatter = LogFormatter() # we need two handlers, one that prints to stdout # for the "run" command and one that prints to stderr # from the "generate" command; the latter is important # because do not want the warning to show up in the # generated configuration file stdout_handler = logging.StreamHandler(sys.stdout) stdout_handler.setFormatter(formatter) stderr_handler = logging.StreamHandler(sys.stderr) stderr_handler.setFormatter(formatter) logging.root.setLevel(logging.INFO) logger = logging.getLogger(__name__) # set up an argument parser via our helper function parser = setup_rsmcmd_parser('rsmtool', uses_output_directory=True, allows_overwriting=True, uses_subgroups=True) # if we have no arguments at all then just show the help message if len(sys.argv) < 2: sys.argv.append("-h") # if the first argument is not one of the valid sub-commands # or one of the valid optional arguments, then assume that they # are arguments for the "run" sub-command. This allows the # old style command-line invocations to work without modification. if sys.argv[1] not in VALID_PARSER_SUBCOMMANDS + ['-h', '--help', '-V', '--version']: args_to_pass = ['run'] + sys.argv[1:] else: args_to_pass = sys.argv[1:] args = parser.parse_args(args=args_to_pass) # call the appropriate function based on which sub-command was run if args.subcommand == 'run': # when running, log to stdout logging.root.addHandler(stdout_handler) # run the experiment'Output directory: {}'.format(args.output_dir)) run_experiment(abspath(args.config_file), abspath(args.output_dir), overwrite_output=args.force_write) else: # when generating, log to stderr logging.root.addHandler(stderr_handler) # auto-generate an example configuration and print it to STDOUT generator = ConfigurationGenerator('rsmtool', as_string=True, suppress_warnings=args.quiet, use_subgroups=args.subgroups) configuration = generator.interact() if args.interactive else generator.generate() print(configuration) if __name__ == '__main__': main()