#!/usr/bin/env python
"""
The main RSMTool script.
:author: Jeremy Biggs (jbiggs@ets.org)
:author: Anastassia Loukina (aloukina@ets.org)
:author: Nitin Madnani (nmadnani@ets.org)
: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):
"""
Run an ``rsmtool`` experiment using the given configuration
file and generate all outputs in the given directory.
If ``overwrite_output`` is ``True``, overwrite any existing
output in the given ``output_dir``.
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``.
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 ``output_dir`` was previously used to store the output of a linear
model running on the same data with the same experiment ID.
"""
logger = 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)
logger.info('Saving configuration file.')
configuration.save(output_dir)
# 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()}
logger.info('Reading in all data from files.')
# Initialize the reader
reader = DataReader(file_paths, file_names, converters)
data_container = reader.read()
logger.info('Preprocessing all features.')
# Initialize the processor
processor = FeaturePreprocessor()
(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'}
logger.info('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()
(analyzed_config,
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)
logger.info('Training {} model.'.format(processed_config['model_name']))
# Initialize modeler
modeler = Modeler()
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)
logger.info('Running analyses on training set.')
(train_analyzed_config,
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 '.'
logger.info(logged_str)
(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):
logger.info('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
logger.info('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()
# generate the report
logger.info('Starting report generation.')
reporter.create_report(pred_analysis_config,
csvdir,
figdir)
def main():
# 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 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
logger.info('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()