def _get_all_general_section_names(self):
reporter = Reporter()
default_general_sections_value = DEFAULTS.get('general_sections', '')
default_special_sections_value = DEFAULTS.get('special_sections', '')
default_custom_sections_value = DEFAULTS.get('custom_sections', '')
# if we are told ot use subgroups then just make up a dummy subgroup
# value so that the subgroup-based sections will be included in the
# section list. This value is not actually used in configuration file.
subgroups_value = ['GROUP'] if self.use_subgroups else DEFAULTS.get('subgroups', '')
return reporter.determine_chosen_sections(default_general_sections_value,
default_special_sections_value,
default_custom_sections_value,
subgroups_value,
context=self.context)
def test_run_experiment_lr_with_notebook_rerun():
# basic experiment with LinearRegression model and notebook
# run-run after the experiment after `RSM_REPORT_DIR` is deleted
# to ensure that the `.environ.json` file can be located
source = 'lr-with-notebook-rerun'
experiment_id = 'lr'
config_file = join(rsmtool_test_dir, 'data', 'experiments', source,
'{}.json'.format(experiment_id))
do_run_experiment(source, experiment_id, config_file)
report_ipynb = join('test_outputs', source, 'report',
'{}_report.ipynb'.format(experiment_id))
report_html = join('test_outputs', source, 'report',
'{}_report.html'.format(experiment_id))
del os.environ['RSM_REPORT_DIR']
Reporter.convert_ipynb_to_html(report_ipynb, report_html)
check_report(report_html)
def test_run_experiment_lr_with_notebook_rerun():
# basic experiment with LinearRegression model and notebook
# run-run after the experiment after `RSM_REPORT_DIR` is deleted
# to ensure that the `.environ.json` file can be located
source = 'lr-with-notebook-rerun'
experiment_id = 'lr'
config_file = join(rsmtool_test_dir,
'data',
'experiments',
source,
'{}.json'.format(experiment_id))
do_run_experiment(source, experiment_id, config_file)
report_ipynb = join('test_outputs', source, 'report', '{}_report.ipynb'.format(experiment_id))
report_html = join('test_outputs', source, 'report', '{}_report.html'.format(experiment_id))
del os.environ['RSM_REPORT_DIR']
Reporter.convert_ipynb_to_html(report_ipynb, report_html)
check_report(report_html)
def test_run_experiment_lr_with_notebook_rerun_fail():
# basic experiment with LinearRegression model and notebook
# run-run after the experiment after `RSM_REPORT_DIR` is deleted
# and `.environ.json` is deleted, so the notebook execution will fail
source = 'lr-with-notebook-rerun-fail'
experiment_id = 'lr'
config_file = join(rsmtool_test_dir, 'data', 'experiments', source,
'{}.json'.format(experiment_id))
do_run_experiment(source, experiment_id, config_file)
report_env = join('test_outputs', source, 'report',
'.environ.json'.format(experiment_id))
report_ipynb = join('test_outputs', source, 'report',
'{}_report.ipynb'.format(experiment_id))
report_html = join('test_outputs', source, 'report',
'{}_report.html'.format(experiment_id))
del os.environ['RSM_REPORT_DIR']
os.remove(report_env)
Reporter.convert_ipynb_to_html(report_ipynb, report_html)
def test_run_experiment_lr_with_notebook_rerun_fail():
# basic experiment with LinearRegression model and notebook
# run-run after the experiment after `RSM_REPORT_DIR` is deleted
# and `.environ.json` is deleted, so the notebook execution will fail
source = 'lr-with-notebook-rerun-fail'
experiment_id = 'lr'
config_file = join(rsmtool_test_dir,
'data',
'experiments',
source,
'{}.json'.format(experiment_id))
do_run_experiment(source, experiment_id, config_file)
report_env = join('test_outputs', source, 'report', '.environ.json'.format(experiment_id))
report_ipynb = join('test_outputs', source, 'report', '{}_report.ipynb'.format(experiment_id))
report_html = join('test_outputs', source, 'report', '{}_report.html'.format(experiment_id))
del os.environ['RSM_REPORT_DIR']
os.remove(report_env)
Reporter.convert_ipynb_to_html(report_ipynb, report_html)
def run_comparison(config_file_or_obj, output_dir):
"""
Run an ``rsmcompare`` experiment using the given configuration
file and generate the report in the given directory.
Parameters
----------
config_file_or_obj : str or Configuration
Path to the experiment configuration file.
Users can also pass a `Configuration` object that is in memory.
output_dir : str
Path to the experiment output directory.
Raises
------
ValueError
If any of the required fields are missing or ill-specified.
"""
logger = logging.getLogger(__name__)
# Allow users to pass Configuration object to the
# `config_file_or_obj` argument, rather than read file
if not isinstance(config_file_or_obj, Configuration):
# Instantiate configuration parser object
parser = ConfigurationParser.get_configparser(config_file_or_obj)
configuration = parser.read_normalize_validate_and_process_config(
config_file_or_obj, context='rsmcompare')
# get the directory where the configuration file lives
configpath = dirname(config_file_or_obj)
else:
configuration = config_file_or_obj
if configuration.filepath is not None:
configpath = dirname(configuration.filepath)
else:
configpath = os.getcwd()
logger.info('Saving configuration file.')
configuration.save(output_dir)
# get the information about the "old" experiment
experiment_id_old = configuration['experiment_id_old']
experiment_dir_old = DataReader.locate_files(
configuration['experiment_dir_old'], configpath)
if not experiment_dir_old:
raise FileNotFoundError("The directory {} "
"does not exist.".format(
configuration['experiment_dir_old']))
else:
csvdir_old = normpath(join(experiment_dir_old, 'output'))
figdir_old = normpath(join(experiment_dir_old, 'figure'))
if not exists(csvdir_old) or not exists(figdir_old):
raise FileNotFoundError("The directory {} does not contain "
"the output of an rsmtool "
"experiment.".format(experiment_dir_old))
check_experiment_id(experiment_dir_old, experiment_id_old)
# get the information about the "new" experiment
experiment_id_new = configuration['experiment_id_new']
experiment_dir_new = DataReader.locate_files(
configuration['experiment_dir_new'], configpath)
if not experiment_dir_new:
raise FileNotFoundError("The directory {} "
"does not exist.".format(
configuration['experiment_dir_new']))
else:
csvdir_new = normpath(join(experiment_dir_new, 'output'))
figdir_new = normpath(join(experiment_dir_new, 'figure'))
if not exists(csvdir_new) or not exists(figdir_new):
raise FileNotFoundError("The directory {} does not contain "
"the output of an rsmtool "
"experiment.".format(experiment_dir_new))
check_experiment_id(experiment_dir_new, experiment_id_new)
# are there specific general report sections we want to include?
general_report_sections = configuration['general_sections']
# what about the special or custom sections?
special_report_sections = configuration['special_sections']
custom_report_section_paths = configuration['custom_sections']
# if custom report sections exist, locate sections; otherwise, create empty list
if custom_report_section_paths:
logger.info('Locating custom report sections')
custom_report_sections = Reporter.locate_custom_sections(
custom_report_section_paths, configpath)
else:
custom_report_sections = []
# get the section order
section_order = configuration['section_order']
# get the subgroups if any
subgroups = configuration.get('subgroups')
# Initialize reporter
reporter = Reporter()
chosen_notebook_files = reporter.get_ordered_notebook_files(
general_report_sections,
special_report_sections,
custom_report_sections,
section_order,
subgroups,
model_type=None,
context='rsmcompare')
# add chosen notebook files to configuration
configuration['chosen_notebook_files'] = chosen_notebook_files
# now generate the comparison report
logger.info('Starting report generation.')
reporter.create_comparison_report(configuration, csvdir_old, figdir_old,
csvdir_new, figdir_new, output_dir)
def run_experiment(config_file_or_obj, output_dir):
"""
Run RSMTool experiment using the given configuration
file and generate all outputs in the given directory.
Parameters
----------
config_file_or_obj : str or Configuration
Path to the experiment configuration file.
Users can also pass a `Configuration` object that is in memory.
output_dir : str
Path to the experiment output directory.
Raises
------
ValueError
If any of the required fields are missing or ill-specified.
"""
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)
# Allow users to pass Configuration object to the
# `config_file_or_obj` argument, rather than read from file
if not isinstance(config_file_or_obj, Configuration):
# Instantiate configuration parser object
parser = ConfigurationParser.get_configparser(config_file_or_obj)
configuration = parser.read_normalize_validate_and_process_config(
config_file_or_obj)
# get the directory where the configuration file lives
configpath = dirname(config_file_or_obj)
else:
configuration = config_file_or_obj
if configuration.filepath is not None:
configpath = dirname(configuration.filepath)
else:
configpath = getcwd()
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, configpath)
# 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 generate
# scaled coefficients and save to 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)
# Write out files to disk
writer.write_experiment_output(csvdir,
scaled_data_container,
file_format=file_format)
except AttributeError:
raise ValueError(
"It appears you are trying to save two different "
"experiments to the same directory using the same "
"ID. Please clear the content of the directory and "
"rerun both experiments using different "
"experiment IDs.")
# 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(processed_config, csvdir, figdir)
def run_summary(config_file_or_obj, output_dir):
"""
Run rsmsummarize experiment using the given configuration
file and generate all outputs in the given directory.
Parameters
----------
config_file_or_obj : str or configuration_parser.Configuration
Path to the experiment configuration file.
Users can also pass a `Configuration` object that is in memory.
output_dir : str
Path to the experiment output directory.
Raises
------
ValueError
If any of the required fields are missing or ill-specified.
"""
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
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)
# Allow users to pass Configuration object to the
# `config_file_or_obj` argument, rather than read file
if not isinstance(config_file_or_obj, Configuration):
# Instantiate configuration parser object
parser = ConfigurationParser.get_configparser(config_file_or_obj)
configuration = parser.read_normalize_validate_and_process_config(config_file_or_obj,
context='rsmsummarize')
# get the directory where the configuration file lives
configpath = dirname(config_file_or_obj)
else:
configuration = config_file_or_obj
if configuration.filepath is not None:
configpath = dirname(configuration.filepath)
else:
configpath = os.getcwd()
logger.info('Saving configuration file.')
configuration.save(output_dir)
# get the list of the experiment dirs
experiment_dirs = configuration['experiment_dirs']
# Get experiment names if any
experiment_names = configuration.get('experiment_names')
experiment_names = experiment_names if experiment_names else [None] * len(experiment_dirs)
dirs_with_names = zip(experiment_dirs, experiment_names)
# check the experiment dirs and assemble the list of csvdir and jsons
all_experiments = []
for (experiment_dir, experiment_name) in dirs_with_names:
experiments = check_experiment_dir(experiment_dir,
experiment_name,
configpath)
all_experiments.extend(experiments)
# get the subgroups if any
# Note: at the moment no comparison are reported for subgroups.
# this option is added to the code to make it easier to add
# subgroup comparisons in future versions
subgroups = configuration.get('subgroups')
general_report_sections = configuration['general_sections']
# get any special sections that the user might have specified
special_report_sections = configuration['special_sections']
# get any custom sections and locate them to make sure
# that they exist, otherwise raise an exception
custom_report_section_paths = configuration['custom_sections']
if custom_report_section_paths:
logger.info('Locating custom report sections')
custom_report_sections = Reporter.locate_custom_sections(custom_report_section_paths,
configpath)
else:
custom_report_sections = []
section_order = configuration['section_order']
# Initialize reporter
reporter = Reporter()
# check all sections values and order and get the
# ordered list of notebook files
chosen_notebook_files = reporter.get_ordered_notebook_files(general_report_sections,
special_report_sections,
custom_report_sections,
section_order,
subgroups,
model_type=None,
context='rsmsummarize')
# add chosen notebook files to configuration
configuration['chosen_notebook_files'] = chosen_notebook_files
# now generate the comparison report
logger.info('Starting report generation')
reporter.create_summary_report(configuration,
all_experiments,
csvdir)
def setUp(self):
self.reporter = Reporter()
class TestReporter:
def setUp(self):
self.reporter = Reporter()
def check_section_lists(self, context):
general_sections = master_section_dict['general'][context]
special_sections = master_section_dict['special'][context]
overlap = set(general_sections) & set(special_sections)
# check that there are general section
ok_(len(general_sections) > 0)
# check that there is no overlap between general and special section
# list
eq_(len(overlap), 0)
def test_check_section_lists_rsmtool(self):
# sanity checks to make sure nothing went wrong when generating
# master section list
for context in ['rsmtool', 'rsmeval', 'rsmcompare']:
yield self.check_section_lists, context
@raises(ValueError)
def test_check_section_order_not_enough_sections(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['custom.ipynb']
subgroups = ['prompt', 'gender']
section_order = general_sections
self.reporter.get_ordered_notebook_files(
general_sections,
special_sections=special_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups)
@raises(ValueError)
def test_check_section_order_extra_sections(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['custom.ipynb']
subgroups = []
section_order = general_sections + special_sections + custom_sections + [
'extra_section'
]
self.reporter.get_ordered_notebook_files(
general_sections,
special_sections=special_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups)
@raises(ValueError)
def test_check_section_order_wrong_sections(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['custom.ipynb']
subgroups = []
section_order = ['extra_section1', 'extra_section2']
self.reporter.get_ordered_notebook_files(
general_sections,
special_sections=special_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups)
def test_check_section_order(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['foobar']
section_order = (['foobar'] + special_sections + general_sections)
self.reporter.check_section_order(
general_sections + special_sections + custom_sections,
section_order)
def test_check_general_section_names_rsmtool(self):
specified_list = ['data_description', 'preprocessed_features']
self.reporter.check_section_names(specified_list, 'general')
@raises(ValueError)
def test_check_general_section_names_wrong_names_1(self):
specified_list = ['data_description', 'feature_stats']
self.reporter.check_section_names(specified_list, 'general')
def test_check_general_section_names_rsmeval_1(self):
specified_list = ['data_description', 'evaluation']
self.reporter.check_section_names(specified_list,
'general',
context='rsmeval')
@raises(ValueError)
def test_check_general_section_names_rsmeval_2(self):
specified_list = ['data_description', 'preprocessed_features']
self.reporter.check_section_names(specified_list,
'general',
context='rsmeval')
def test_check_general_section_names_rsmcompare(self):
specified_list = ['feature_descriptives', 'evaluation']
self.reporter.check_section_names(specified_list,
'general',
context='rsmcompare')
@raises(ValueError)
def test_check_general_section_names_wrong_names_2(self):
specified_list = ['data_description', 'evaluation']
self.reporter.check_section_names(specified_list,
'general',
context='rsmcompare')
def test_determine_chosen_sections_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(chosen_sections, general_section_list_rsmtool)
def test_determine_chosen_sections_default_general_no_subgroups(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = []
no_subgroup_list = [
s for s in general_section_list_rsmtool
if not s.endswith('by_group')
]
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(chosen_sections, no_subgroup_list)
@raises(ValueError)
def test_determine_chosen_sections_invalid_general(self):
general_sections = ['data_description', 'foobar']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(chosen_sections, general_section_list_rsmtool)
@raises(ValueError)
def test_determine_chosen_sections_no_subgroups(self):
general_sections = ['data_description', 'data_description_by_group']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(chosen_sections, general_section_list_rsmtool)
def test_determine_chosen_sections_custom_general(self):
general_sections = ['data_description', 'evaluation']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_default_general_with_special(self):
general_sections = ['all']
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(sorted(chosen_sections),
sorted(general_section_list_rsmtool + special_sections))
@raises(ValueError)
def test_determine_chosen_sections_invalid_special(self):
general_sections = ['all']
special_sections = ['placeholder_special_section', 'foobar']
custom_sections = []
subgroups = ['prompt']
self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections, subgroups)
def test_determine_chosen_sections_custom_general_with_special(self):
general_sections = ['data_description', 'evaluation']
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(sorted(chosen_sections),
sorted(general_sections + special_sections))
def test_determine_chosen_sections_default_general_with_subgroups(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(sorted(chosen_sections), sorted(general_section_list_rsmtool))
def test_determine_chosen_sections_custom_general_with_special_subgroups_and_custom(
self):
general_sections = ['evaluation', 'sysinfo', 'evaluation_by_group']
special_sections = ['placeholder_special_section']
custom_sections = ['foobar.ipynb']
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections, special_sections, custom_sections, subgroups)
eq_(sorted(chosen_sections),
sorted(general_sections + special_sections + ['foobar']))
def test_determine_chosen_sections_eval_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections), sorted(general_section_list_rsmeval))
def test_determine_chosen_sections_eval_custom_general(self):
general_sections = ['data_description', 'consistency']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_eval_default_general_with_no_subgroups(
self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = []
no_subgroup_list = [
s for s in general_section_list_rsmeval
if not s.endswith('by_group')
]
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections), sorted(no_subgroup_list))
def test_determine_chosen_sections_eval_custom_general_with_special_and_subgroups(
self):
general_sections = [
'data_description', 'consistency', 'data_description_by_group'
]
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections),
sorted(general_sections + special_sections))
def test_determine_chosen_sections_compare_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(general_section_list_rsmcompare))
def test_determine_chosen_sections_rsmcompare_custom_general(self):
general_sections = ['feature_descriptives', 'evaluation']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_rsmcompare_default_general_with_no_subgroups(
self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = []
no_subgroup_list = [
s for s in general_section_list_rsmcompare
if not s.endswith('by_group')
]
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(no_subgroup_list))
def test_determine_chosen_sections_rsmcompare_custom_general_with_special_and_subgroups(
self):
general_sections = ['feature_descriptives', 'evaluation']
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections),
sorted(general_sections + special_sections))
def test_determine_chosen_sections_rsmsummarize_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmsummarize')
eq_(sorted(chosen_sections), sorted(general_section_list_rsmsummarize))
def test_determine_chosen_sections_rsmsummarize_custom_general(self):
general_sections = ['evaluation']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmsummarize')
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_compare_custom_general_with_special_subgroups_and_custom(
self):
general_sections = ['feature_descriptives', 'evaluation']
special_sections = ['placeholder_special_section']
custom_sections = ['foobar.ipynb']
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(
general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections),
sorted(general_sections + special_sections + ['foobar']))
def test_get_ordered_notebook_files_default_rsmtool(self):
general_sections = ['all']
notebook_files = self.reporter.get_ordered_notebook_files(
general_sections, model_type='skll', context='rsmtool')
no_subgroup_list = [
s for s in general_section_list_rsmtool
if not s.endswith('by_group')
]
section_list = ['header'] + no_subgroup_list + ['footer']
# replace model section with skll_model.
updated_section_list = [
'skll_' + sname if sname == 'model' else sname
for sname in section_list
]
general_section_plus_extension = [
s + '.ipynb' for s in updated_section_list
]
expected_notebook_files = [
join(notebook_path, s) for s in general_section_plus_extension
]
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmtool(self):
# custom and general sections, custom order and subgroups
general_sections = [
'data_description', 'pca', 'data_description_by_group'
]
custom_sections = ['/test_path/custom.ipynb']
special_sections = ['placeholder_special_section']
subgroups = ['prompt']
section_order = [
'custom', 'data_description', 'pca', 'data_description_by_group',
'placeholder_special_section'
]
special_notebook_path = notebook_path_dict['special']['rsmtool']
notebook_files = self.reporter.get_ordered_notebook_files(
general_sections,
custom_sections=custom_sections,
special_sections=special_sections,
section_order=section_order,
subgroups=subgroups,
model_type='skll',
context='rsmtool')
expected_notebook_files = ([join(notebook_path, 'header.ipynb')] + [
'/test_path/custom.ipynb'
] + [
join(notebook_path, s) + '.ipynb'
for s in ['data_description', 'pca', 'data_description_by_group']
] + [join(special_notebook_path, 'placeholder_special_section.ipynb')]
+ [join(notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_default_rsmeval(self):
general_sections = ['all']
notebook_files = self.reporter.get_ordered_notebook_files(
general_sections, context='rsmeval')
no_subgroup_list = [
s for s in general_section_list_rsmeval
if not s.endswith('by_group')
]
section_list = ['header'] + no_subgroup_list + ['footer']
general_section_plus_extension = [
'{}.ipynb'.format(s) for s in section_list
]
expected_notebook_files = [
join(notebook_path_dict['general']['rsmeval'], s)
for s in general_section_plus_extension
]
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmeval(self):
# custom and general sections, custom order and subgroups
general_sections = ['evaluation', 'consistency', 'evaluation_by_group']
custom_sections = ['/test_path/custom.ipynb']
subgroups = ['prompt']
section_order = [
'evaluation', 'consistency', 'custom', 'evaluation_by_group'
]
notebook_path = notebook_path_dict['general']['rsmeval']
notebook_files = self.reporter.get_ordered_notebook_files(
general_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups,
context='rsmeval')
expected_notebook_files = (
[join(notebook_path, 'header.ipynb')] + [
join(notebook_path, s) + '.ipynb'
for s in ['evaluation', 'consistency']
] + ['/test_path/custom.ipynb'] +
[join(notebook_path, 'evaluation_by_group.ipynb')] +
[join(notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_default_rsmcompare(self):
general_sections = ['all']
comparison_notebook_path = notebook_path_dict['general']['rsmcompare']
notebook_files = self.reporter.get_ordered_notebook_files(
general_sections, context='rsmcompare')
no_subgroup_list = [
s for s in general_section_list_rsmcompare
if not s.endswith('by_group')
]
section_list = ['header'] + no_subgroup_list + ['footer']
general_section_plus_extension = [s + '.ipynb' for s in section_list]
expected_notebook_files = [
join(comparison_notebook_path, s)
for s in general_section_plus_extension
]
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmcompare(self):
# custom and general sections, custom order and subgroups
general_sections = [
'feature_descriptives', 'score_distributions', 'features_by_group'
]
custom_sections = ['/test_path/custom.ipynb']
subgroups = ['prompt']
section_order = [
'feature_descriptives', 'score_distributions', 'custom',
'features_by_group'
]
comparison_notebook_path = notebook_path_dict['general']['rsmcompare']
notebook_files = self.reporter.get_ordered_notebook_files(
general_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups,
context='rsmcompare')
expected_notebook_files = (
[join(comparison_notebook_path, 'header.ipynb')] + [
join(comparison_notebook_path, s) + '.ipynb'
for s in ['feature_descriptives', 'score_distributions']
] + ['/test_path/custom.ipynb'] +
[join(comparison_notebook_path, 'features_by_group.ipynb')] +
[join(comparison_notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmsummarize(self):
# custom and general sections, custom order and subgroups
general_sections = ['evaluation']
custom_sections = ['/test_path/custom.ipynb']
subgroups = ['prompt']
section_order = ['custom', 'evaluation']
summary_notebook_path = notebook_path_dict['general']['rsmsummarize']
notebook_files = self.reporter.get_ordered_notebook_files(
general_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups,
context='rsmsummarize')
expected_notebook_files = (
[join(summary_notebook_path, 'header.ipynb')] +
['/test_path/custom.ipynb'] + [
join(summary_notebook_path, s) + '.ipynb'
for s in ['evaluation']
] + [join(summary_notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_section_file_map_rsmtool(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(special_sections,
custom_sections,
model_type='R')
eq_(section_file_map['model'], join(notebook_path, 'r_model.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'],
normpath('special_notebook_path/placeholder.ipynb'))
def test_get_section_file_map_rsmeval(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(
special_sections, custom_sections, context='rsmeval')
eq_(section_file_map['data_description'],
join(notebook_path, 'data_description.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'],
normpath('special_notebook_path/placeholder.ipynb'))
def test_get_section_file_map_rsmcompare(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(
special_sections, custom_sections, context='rsmcompare')
eq_(section_file_map['evaluation'],
join(comparison_notebook_path, 'evaluation.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'],
normpath('special_notebook_path/placeholder.ipynb'))
def test_get_section_file_map_rsmsummarize(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(
special_sections, custom_sections, context='rsmsummarize')
eq_(section_file_map['evaluation'],
join(summary_notebook_path, 'evaluation.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'],
normpath('special_notebook_path/placeholder.ipynb'))
def run_evaluation(config_file_or_obj, output_dir):
"""
Run an `rsmeval` experiment using the given configuration
file and generate all outputs in the given directory.
Parameters
----------
config_file_or_obj : str or configuration_parser.Configuration
Path to the experiment configuration file.
Users can also pass a `Configuration` object that is in memory.
output_dir : str
Path to the experiment output directory.
Raises
------
ValueError
If any of the required fields are missing or ill-specified.
"""
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
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)
# Allow users to pass Configuration object to the
# `config_file_or_obj` argument, rather than read file
if not isinstance(config_file_or_obj, Configuration):
# Instantiate configuration parser object
parser = ConfigurationParser.get_configparser(config_file_or_obj)
configuration = parser.read_normalize_validate_and_process_config(
config_file_or_obj, context='rsmeval')
# get the directory where the configuration file lives
configpath = dirname(config_file_or_obj)
else:
configuration = config_file_or_obj
if configuration.filepath is not None:
configpath = dirname(configuration.filepath)
else:
configpath = os.getcwd()
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'])
# Make sure prediction file can be located
if not DataReader.locate_files(configuration['predictions_file'],
configpath):
raise FileNotFoundError('Error: Predictions file {} '
'not found.\n'.format(
configuration['predictions_file']))
scale_with = configuration.get('scale_with')
# scale_with can be one of the following:
# (a) 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 != 'asis')
# 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, configpath)
if not scale_file_location:
raise FileNotFoundError('Could not find scaling file {}.'
''.format(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, configpath)
converters = {'predictions': configuration.get_default_converter()}
logger.info('Reading predictions: {}.'.format(
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()
(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()
# 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)
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(processed_config, csvdir, figdir, context='rsmeval')
def setUp(self):
self.reporter = Reporter()
class TestReporter:
def setUp(self):
self.reporter = Reporter()
def check_section_lists(self, context):
general_sections = master_section_dict['general'][context]
special_sections = master_section_dict['special'][context]
overlap = set(general_sections) & set(special_sections)
# check that there are general section
ok_(len(general_sections) > 0)
# check that there is no overlap between general and special section
# list
eq_(len(overlap), 0)
def test_check_section_lists_rsmtool(self):
# sanity checks to make sure nothing went wrong when generating
# master section list
for context in ['rsmtool', 'rsmeval', 'rsmcompare']:
yield self.check_section_lists, context
@raises(ValueError)
def test_check_section_order_not_enough_sections(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['custom.ipynb']
subgroups = ['prompt', 'gender']
section_order = general_sections
self.reporter.get_ordered_notebook_files(general_sections,
special_sections=special_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups)
@raises(ValueError)
def test_check_section_order_extra_sections(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['custom.ipynb']
subgroups = []
section_order = general_sections + special_sections + custom_sections + ['extra_section']
self.reporter.get_ordered_notebook_files(general_sections,
special_sections=special_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups)
@raises(ValueError)
def test_check_section_order_wrong_sections(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['custom.ipynb']
subgroups = []
section_order = ['extra_section1', 'extra_section2']
self.reporter.get_ordered_notebook_files(general_sections,
special_sections=special_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups)
def test_check_section_order(self):
general_sections = ['evaluation', 'sysinfo']
special_sections = ['placeholder_special_section']
custom_sections = ['foobar']
section_order = (['foobar'] +
special_sections +
general_sections)
self.reporter.check_section_order(general_sections +
special_sections +
custom_sections,
section_order)
def test_check_general_section_names_rsmtool(self):
specified_list = ['data_description', 'preprocessed_features']
self.reporter.check_section_names(specified_list, 'general')
@raises(ValueError)
def test_check_general_section_names_wrong_names_1(self):
specified_list = ['data_description', 'feature_stats']
self.reporter.check_section_names(specified_list, 'general')
def test_check_general_section_names_rsmeval_1(self):
specified_list = ['data_description', 'evaluation']
self.reporter.check_section_names(specified_list, 'general', context='rsmeval')
@raises(ValueError)
def test_check_general_section_names_rsmeval_2(self):
specified_list = ['data_description', 'preprocessed_features']
self.reporter.check_section_names(specified_list, 'general', context='rsmeval')
def test_check_general_section_names_rsmcompare(self):
specified_list = ['feature_descriptives', 'evaluation']
self.reporter.check_section_names(specified_list, 'general', context='rsmcompare')
@raises(ValueError)
def test_check_general_section_names_wrong_names_2(self):
specified_list = ['data_description', 'evaluation']
self.reporter.check_section_names(specified_list, 'general', context='rsmcompare')
def test_determine_chosen_sections_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(chosen_sections, general_section_list_rsmtool)
def test_determine_chosen_sections_default_general_no_subgroups(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = []
no_subgroup_list = [s for s in general_section_list_rsmtool
if not s.endswith('by_group')]
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(chosen_sections, no_subgroup_list)
@raises(ValueError)
def test_determine_chosen_sections_invalid_general(self):
general_sections = ['data_description', 'foobar']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(chosen_sections, general_section_list_rsmtool)
@raises(ValueError)
def test_determine_chosen_sections_no_subgroups(self):
general_sections = ['data_description', 'data_description_by_group']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(chosen_sections, general_section_list_rsmtool)
def test_determine_chosen_sections_custom_general(self):
general_sections = ['data_description', 'evaluation']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_default_general_with_special(self):
general_sections = ['all']
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(sorted(chosen_sections),
sorted(general_section_list_rsmtool + special_sections))
@raises(ValueError)
def test_determine_chosen_sections_invalid_special(self):
general_sections = ['all']
special_sections = ['placeholder_special_section', 'foobar']
custom_sections = []
subgroups = ['prompt']
self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
def test_determine_chosen_sections_custom_general_with_special(self):
general_sections = ['data_description', 'evaluation']
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(sorted(chosen_sections),
sorted(general_sections + special_sections))
def test_determine_chosen_sections_default_general_with_subgroups(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(sorted(chosen_sections),
sorted(general_section_list_rsmtool))
def test_determine_chosen_sections_custom_general_with_special_subgroups_and_custom(self):
general_sections = ['evaluation', 'sysinfo', 'evaluation_by_group']
special_sections = ['placeholder_special_section']
custom_sections = ['foobar.ipynb']
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups)
eq_(sorted(chosen_sections),
sorted(general_sections +
special_sections +
['foobar']))
def test_determine_chosen_sections_eval_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections), sorted(general_section_list_rsmeval))
def test_determine_chosen_sections_eval_custom_general(self):
general_sections = ['data_description', 'consistency']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_eval_default_general_with_no_subgroups(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = []
no_subgroup_list = [s for s in general_section_list_rsmeval
if not s.endswith('by_group')]
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections), sorted(no_subgroup_list))
def test_determine_chosen_sections_eval_custom_general_with_special_and_subgroups(self):
general_sections = ['data_description', 'consistency', 'data_description_by_group']
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmeval')
eq_(sorted(chosen_sections), sorted(general_sections +
special_sections))
def test_determine_chosen_sections_compare_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(general_section_list_rsmcompare))
def test_determine_chosen_sections_rsmcompare_custom_general(self):
general_sections = ['feature_descriptives',
'evaluation']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_rsmcompare_default_general_with_no_subgroups(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = []
no_subgroup_list = [s for s in general_section_list_rsmcompare
if not s.endswith('by_group')]
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(no_subgroup_list))
def test_determine_chosen_sections_rsmcompare_custom_general_with_special_and_subgroups(self):
general_sections = ['feature_descriptives',
'evaluation']
special_sections = ['placeholder_special_section']
custom_sections = []
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(general_sections +
special_sections))
def test_determine_chosen_sections_rsmsummarize_default_general(self):
general_sections = ['all']
special_sections = []
custom_sections = []
subgroups = ['prompt']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmsummarize')
eq_(sorted(chosen_sections), sorted(general_section_list_rsmsummarize))
def test_determine_chosen_sections_rsmsummarize_custom_general(self):
general_sections = ['evaluation']
special_sections = []
custom_sections = []
subgroups = []
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmsummarize')
eq_(sorted(chosen_sections), sorted(general_sections))
def test_determine_chosen_sections_compare_custom_general_with_special_subgroups_and_custom(self):
general_sections = ['feature_descriptives',
'evaluation']
special_sections = ['placeholder_special_section']
custom_sections = ['foobar.ipynb']
subgroups = ['prompt', 'gender']
chosen_sections = self.reporter.determine_chosen_sections(general_sections,
special_sections,
custom_sections,
subgroups,
context='rsmcompare')
eq_(sorted(chosen_sections), sorted(general_sections +
special_sections +
['foobar']))
def test_get_ordered_notebook_files_default_rsmtool(self):
general_sections = ['all']
notebook_files = self.reporter.get_ordered_notebook_files(general_sections,
model_type='skll',
context='rsmtool')
no_subgroup_list = [s for s in general_section_list_rsmtool
if not s.endswith('by_group')]
section_list = ['header'] + no_subgroup_list + ['footer']
# replace model section with skll_model.
updated_section_list = ['skll_' + sname if sname == 'model' else sname for sname in section_list]
general_section_plus_extension = [s + '.ipynb' for s in updated_section_list]
expected_notebook_files = [join(notebook_path, s)
for s in general_section_plus_extension]
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmtool(self):
# custom and general sections, custom order and subgroups
general_sections = ['data_description', 'pca', 'data_description_by_group']
custom_sections = ['/test_path/custom.ipynb']
special_sections = ['placeholder_special_section']
subgroups = ['prompt']
section_order = ['custom',
'data_description',
'pca',
'data_description_by_group',
'placeholder_special_section']
special_notebook_path = notebook_path_dict['special']['rsmtool']
notebook_files = self.reporter.get_ordered_notebook_files(general_sections,
custom_sections=custom_sections,
special_sections=special_sections,
section_order=section_order,
subgroups=subgroups,
model_type='skll',
context='rsmtool')
expected_notebook_files = ([join(notebook_path, 'header.ipynb')] +
['/test_path/custom.ipynb'] +
[join(notebook_path, s) + '.ipynb' for s in ['data_description',
'pca',
'data_description_by_group']] +
[join(special_notebook_path, 'placeholder_special_section.ipynb')] +
[join(notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_default_rsmeval(self):
general_sections = ['all']
notebook_files = self.reporter.get_ordered_notebook_files(general_sections,
context='rsmeval')
no_subgroup_list = [s for s in general_section_list_rsmeval
if not s.endswith('by_group')]
section_list = ['header'] + no_subgroup_list + ['footer']
general_section_plus_extension = ['{}.ipynb'.format(s) for s in section_list]
expected_notebook_files = [join(notebook_path_dict['general']['rsmeval'], s)
for s in
general_section_plus_extension]
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmeval(self):
# custom and general sections, custom order and subgroups
general_sections = ['evaluation', 'consistency', 'evaluation_by_group']
custom_sections = ['/test_path/custom.ipynb']
subgroups = ['prompt']
section_order = ['evaluation',
'consistency',
'custom',
'evaluation_by_group']
notebook_path = notebook_path_dict['general']['rsmeval']
notebook_files = self.reporter.get_ordered_notebook_files(general_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups,
context='rsmeval')
expected_notebook_files = ([join(notebook_path, 'header.ipynb')] +
[join(notebook_path, s) + '.ipynb' for s in ['evaluation',
'consistency']] +
['/test_path/custom.ipynb'] +
[join(notebook_path, 'evaluation_by_group.ipynb')] +
[join(notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_default_rsmcompare(self):
general_sections = ['all']
comparison_notebook_path = notebook_path_dict['general']['rsmcompare']
notebook_files = self.reporter.get_ordered_notebook_files(general_sections,
context='rsmcompare')
no_subgroup_list = [s for s in general_section_list_rsmcompare
if not s.endswith('by_group')]
section_list = ['header'] + no_subgroup_list + ['footer']
general_section_plus_extension = [s + '.ipynb' for s in section_list]
expected_notebook_files = [join(comparison_notebook_path, s)
for s in general_section_plus_extension]
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmcompare(self):
# custom and general sections, custom order and subgroups
general_sections = ['feature_descriptives',
'score_distributions',
'features_by_group']
custom_sections = ['/test_path/custom.ipynb']
subgroups = ['prompt']
section_order = ['feature_descriptives',
'score_distributions',
'custom',
'features_by_group']
comparison_notebook_path = notebook_path_dict['general']['rsmcompare']
notebook_files = self.reporter.get_ordered_notebook_files(general_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups,
context='rsmcompare')
expected_notebook_files = ([join(comparison_notebook_path, 'header.ipynb')] +
[join(comparison_notebook_path, s) + '.ipynb' for s in ['feature_descriptives',
'score_distributions']] +
['/test_path/custom.ipynb'] +
[join(comparison_notebook_path, 'features_by_group.ipynb')] +
[join(comparison_notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_ordered_notebook_files_custom_rsmsummarize(self):
# custom and general sections, custom order and subgroups
general_sections = ['evaluation']
custom_sections = ['/test_path/custom.ipynb']
subgroups = ['prompt']
section_order = ['custom',
'evaluation']
summary_notebook_path = notebook_path_dict['general']['rsmsummarize']
notebook_files = self.reporter.get_ordered_notebook_files(general_sections,
custom_sections=custom_sections,
section_order=section_order,
subgroups=subgroups,
context='rsmsummarize')
expected_notebook_files = ([join(summary_notebook_path, 'header.ipynb')] +
['/test_path/custom.ipynb'] +
[join(summary_notebook_path, s) + '.ipynb' for s in ['evaluation']] +
[join(summary_notebook_path, 'footer.ipynb')])
eq_(notebook_files, expected_notebook_files)
def test_get_section_file_map_rsmtool(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(special_sections,
custom_sections,
model_type='R')
eq_(section_file_map['model'], join(notebook_path, 'r_model.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'], normpath('special_notebook_path/placeholder.ipynb'))
def test_get_section_file_map_rsmeval(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(special_sections,
custom_sections,
context='rsmeval')
eq_(section_file_map['data_description'], join(notebook_path, 'data_description.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'], normpath('special_notebook_path/placeholder.ipynb'))
def test_get_section_file_map_rsmcompare(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(special_sections,
custom_sections,
context='rsmcompare')
eq_(section_file_map['evaluation'], join(comparison_notebook_path, 'evaluation.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'], normpath('special_notebook_path/placeholder.ipynb'))
def test_get_section_file_map_rsmsummarize(self):
special_sections = ['placeholder']
custom_sections = ['/path/notebook.ipynb']
section_file_map = self.reporter.get_section_file_map(special_sections,
custom_sections,
context='rsmsummarize')
eq_(section_file_map['evaluation'], join(summary_notebook_path, 'evaluation.ipynb'))
eq_(section_file_map['notebook'], '/path/notebook.ipynb')
eq_(section_file_map['placeholder'], normpath('special_notebook_path/placeholder.ipynb'))
def run_experiment(config_file_or_obj,
output_dir):
"""
Run RSMTool experiment using the given configuration
file and generate all outputs in the given directory.
Parameters
----------
config_file_or_obj : str or Configuration
Path to the experiment configuration file.
Users can also pass a `Configuration` object that is in memory.
output_dir : str
Path to the experiment output directory.
Raises
------
ValueError
If any of the required fields are missing or ill-specified.
"""
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)
# Allow users to pass Configuration object to the
# `config_file_or_obj` argument, rather than read from file
if not isinstance(config_file_or_obj, Configuration):
# Instantiate configuration parser object
parser = ConfigurationParser.get_configparser(config_file_or_obj)
configuration = parser.read_normalize_validate_and_process_config(config_file_or_obj)
# get the directory where the configuration file lives
configpath = dirname(config_file_or_obj)
else:
configuration = config_file_or_obj
if configuration.filepath is not None:
configpath = dirname(configuration.filepath)
else:
configpath = getcwd()
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, configpath)
# 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 generate
# scaled coefficients and save to 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)
# Write out files to disk
writer.write_experiment_output(csvdir,
scaled_data_container,
file_format=file_format)
except AttributeError:
raise ValueError("It appears you are trying to save two different "
"experiments to the same directory using the same "
"ID. Please clear the content of the directory and "
"rerun both experiments using different "
"experiment IDs.")
# 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(processed_config,
csvdir,
figdir)
def run_summary(config_file_or_obj, output_dir):
"""
Run rsmsummarize experiment using the given configuration
file and generate all outputs in the given directory.
Parameters
----------
config_file_or_obj : str or configuration_parser.Configuration
Path to the experiment configuration file.
Users can also pass a `Configuration` object that is in memory.
output_dir : str
Path to the experiment output directory.
Raises
------
ValueError
If any of the required fields are missing or ill-specified.
"""
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
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)
# Allow users to pass Configuration object to the
# `config_file_or_obj` argument, rather than read file
if not isinstance(config_file_or_obj, Configuration):
# Instantiate configuration parser object
parser = ConfigurationParser.get_configparser(config_file_or_obj)
configuration = parser.read_normalize_validate_and_process_config(config_file_or_obj,
context='rsmsummarize')
# get the directory where the configuration file lives
configpath = dirname(config_file_or_obj)
else:
configuration = config_file_or_obj
if configuration.filepath is not None:
configpath = dirname(configuration.filepath)
else:
configpath = os.getcwd()
# get the list of the experiment dirs
experiment_dirs = configuration['experiment_dirs']
# check the experiment dirs and assemble the list of csvdir and jsons
all_experiments = []
for experiment_dir in experiment_dirs:
experiments = check_experiment_dir(experiment_dir, configpath)
all_experiments.extend(experiments)
# get the subgroups if any
# Note: at the moment no comparison are reported for subgroups.
# this option is added to the code to make it easier to add
# subgroup comparisons in future versions
subgroups = configuration.get('subgroups')
general_report_sections = configuration['general_sections']
# get any special sections that the user might have specified
special_report_sections = configuration['special_sections']
# get any custom sections and locate them to make sure
# that they exist, otherwise raise an exception
custom_report_section_paths = configuration['custom_sections']
if custom_report_section_paths:
logger.info('Locating custom report sections')
custom_report_sections = Reporter.locate_custom_sections(custom_report_section_paths,
configpath)
else:
custom_report_sections = []
section_order = configuration['section_order']
# Initialize reporter
reporter = Reporter()
# check all sections values and order and get the
# ordered list of notebook files
chosen_notebook_files = reporter.get_ordered_notebook_files(general_report_sections,
special_report_sections,
custom_report_sections,
section_order,
subgroups,
model_type=None,
context='rsmsummarize')
# add chosen notebook files to configuration
configuration['chosen_notebook_files'] = chosen_notebook_files
# now generate the comparison report
logger.info('Starting report generation')
reporter.create_summary_report(configuration,
all_experiments,
csvdir)