#!/usr/bin/env python
"""
Run evaluation only experiments.
:author: Jeremy Biggs (jbiggs@ets.org)
:author: Anastassia Loukina (aloukina@ets.org)
:author: Nitin Madnani (nmadnani@ets.org)
:organization: ETS
"""
import logging
import os
import sys
from os import listdir
from os.path import abspath, exists, join
from .analyzer import Analyzer
from .configuration_parser import configure
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 .utils.wandb import init_wandb_run, log_configuration_to_wandb
from .writer import DataWriter
[docs]def run_evaluation(
config_file_or_obj_or_dict, output_dir, overwrite_output=False, logger=None, wandb_run=None
):
"""
Run an rsmeval experiment using the given configuration.
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 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``.
wandb_run : wandb.Run
A wandb run object that will be used to log artifacts and tables.
If ``None`` is passed, a new wandb run will be initialized if
wandb is enabled in the configuration. 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``.
"""
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
csvdir = abspath(join(output_dir, "output"))
figdir = abspath(join(output_dir, "figure"))
reportdir = abspath(join(output_dir, "report"))
os.makedirs(csvdir, exist_ok=True)
os.makedirs(figdir, exist_ok=True)
os.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(f"'{output_dir}' already contains a non-empty 'output' directory.")
else:
logger.warning(
f"{output_dir} already contains a non-empty 'output' directory. "
f"The generated report might contain unexpected information "
f"from a previous experiment."
)
configuration = configure("rsmeval", config_file_or_obj_or_dict)
logger.info("Saving configuration file.")
configuration.save(output_dir)
# If wandb logging is enabled, and wandb_run is not provided,
# start a wandb run and log configuration
if wandb_run is None:
wandb_run = init_wandb_run(configuration)
log_configuration_to_wandb(wandb_run, configuration)
# Get output format
file_format = configuration.get("file_format", "csv")
# Get DataWriter object
writer = DataWriter(configuration["experiment_id"], configuration.context, wandb_run)
# Make sure prediction file can be located
if not DataReader.locate_files(configuration["predictions_file"], configuration.configdir):
raise FileNotFoundError(
f"Error: Predictions file {configuration['predictions_file']} " f"not found."
)
scale_with = configuration.get("scale_with")
# scale_with can be one of the following:
# (a) 'raw' or None : the predictions are assumed to be 'raw' and should be used as is
# when computing the metrics; the names for the final columns are
# 'raw', 'raw_trim' and 'raw_trim_round'.
# (b) 'asis' : the predictions are assumed to be pre-scaled and should be used as is
# when computing the metrics; the names for the final columns are
# 'scale', 'scale_trim' and 'scale_trim_round'.
# (c) a CSV file : the predictions are assumed to be 'raw' and should be scaled
# before computing the metrics; the names for the final columns are
# 'scale', 'scale_trim' and 'scale_trim_round'.
# Check whether we want to do scaling
do_scaling = scale_with is not None and scale_with not in ["asis", "raw"]
# The paths to files and names for data container properties
paths = ["predictions_file"]
names = ["predictions"]
# If we want to do scaling, get the scale file
if do_scaling:
# Make sure scale file can be located
scale_file_location = DataReader.locate_files(scale_with, configuration.configdir)
if not scale_file_location:
raise FileNotFoundError(f"Could not find scaling file {scale_file_location}.")
paths.append("scale_with")
names.append("scale")
# Get the paths, names, and converters for the DataReader
(file_names, file_paths) = configuration.get_names_and_paths(paths, names)
file_paths = DataReader.locate_files(file_paths, configuration.configdir)
converters = {"predictions": configuration.get_default_converter()}
logger.info(f"Reading predictions: {configuration['predictions_file']}.")
# Initialize the reader
reader = DataReader(file_paths, file_names, converters)
data_container = reader.read()
logger.info("Preprocessing predictions.")
# Initialize the processor
processor = FeaturePreprocessor(logger=logger)
(processed_config, processed_container) = processor.process_data(
configuration, data_container, context="rsmeval"
)
logger.info("Saving pre-processed predictions and metadata to disk.")
writer.write_experiment_output(
csvdir,
processed_container,
new_names_dict={
"pred_test": "pred_processed",
"test_excluded": "test_excluded_responses",
},
file_format=file_format,
)
# Initialize the analyzer
analyzer = Analyzer(logger=logger)
# do the data composition stats
(
analyzed_config,
analyzed_container,
) = analyzer.run_data_composition_analyses_for_rsmeval(processed_container, processed_config)
# Write out files
writer.write_experiment_output(csvdir, analyzed_container, file_format=file_format)
for_pred_data_container = analyzed_container + processed_container
# run the analyses on the predictions of the model`
logger.info("Running analyses on predictions.")
(
pred_analysis_config,
pred_analysis_data_container,
) = analyzer.run_prediction_analyses(
for_pred_data_container, analyzed_config, wandb_run=wandb_run
)
writer.write_experiment_output(
csvdir, pred_analysis_data_container, reset_index=True, file_format=file_format
)
# Initialize reporter
reporter = Reporter(logger=logger, wandb_run=wandb_run)
# generate the report
logger.info("Starting report generation.")
reporter.create_report(pred_analysis_config, csvdir, figdir, context="rsmeval")
def main(argv=None): # noqa: D103
# if no arguments are passed, then use sys.argv
if argv is None:
argv = sys.argv[1:]
# 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(
"rsmeval",
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(argv) < 1:
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 argv[0] not in VALID_PARSER_SUBCOMMANDS + [
"-h",
"--help",
"-V",
"--version",
]:
args_to_pass = ["run"] + argv
else:
args_to_pass = argv
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(f"Output directory: {args.output_dir}")
run_evaluation(
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(
"rsmeval",
as_string=True,
suppress_warnings=args.quiet,
use_subgroups=args.subgroups,
)
configuration = (
generator.interact(output_file_name=args.output_file.name if args.output_file else None)
if args.interactive
else generator.generate()
)
print(configuration, file=args.output_file)
if __name__ == "__main__":
main()