<commit_before>import re
import warnings

import numpy as np

from glob import glob
from os import remove
from os.path import basename, exists, join
from pathlib import Path

from nose.tools import assert_equal, ok_
from pandas.util.testing import assert_frame_equal

from rsmtool.reader import DataReader
from rsmtool.modeler import Modeler
from rsmtool.rsmtool import run_experiment
from rsmtool.rsmcompare import run_comparison
from rsmtool.rsmeval import run_evaluation
from rsmtool.rsmpredict import compute_and_save_predictions
from rsmtool.rsmsummarize import run_summary

html_error_regexp = re.compile(r'Traceback \(most recent call last\)')
html_warning_regexp = re.compile(r'<div class=".*?output_stderr.*?>')
section_regexp = re.compile(r'<h2>(.*?)</h2>')

# get the directory containing the tests
test_dir = Path(__file__).absolute().parent.parent.joinpath('tests')


def check_run_experiment(source,
                         experiment_id,
                         subgroups=None,
                         consistency=False,
                         skll=False,
                         file_format='csv'):

    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       '{}.json'.format(experiment_id))

    model_type = 'skll' if skll else 'rsmtool'

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', category=RuntimeWarning)
        do_run_experiment(source, experiment_id, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')
    html_report = join('test_outputs', source, 'report', '{}_report.html'.format(experiment_id))

    output_files = glob(join(output_dir, '*.{}'.format(file_format)))
    for output_file in output_files:
        output_filename = basename(output_file)
        expected_output_file = join(expected_output_dir, output_filename)

        if exists(expected_output_file):
            check_file_output(output_file, expected_output_file, file_format=file_format)

    check_generated_output(output_files, experiment_id, model_type, file_format=file_format)

    if not skll:
        check_scaled_coefficients(source, experiment_id, file_format=file_format)

    if subgroups:
        check_subgroup_outputs(output_dir, experiment_id, subgroups, file_format=file_format)

    if consistency:
        check_consistency_files_exist(output_files, experiment_id, file_format=file_format)

    check_report(html_report)


def check_run_evaluation(source,
                         experiment_id,
                         subgroups=None,
                         consistency=False,
                         file_format='csv'):

    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       '{}.json'.format(experiment_id))

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', category=RuntimeWarning)
        do_run_evaluation(source, experiment_id, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')
    html_report = join('test_outputs', source, 'report', '{}_report.html'.format(experiment_id))

    output_files = glob(join(output_dir, '*.{}'.format(file_format)))
    i = 0
    for output_file in output_files:
        output_filename = basename(output_file)
        expected_output_file = join(expected_output_dir, output_filename)

        if exists(expected_output_file):
            check_file_output(output_file, expected_output_file, file_format=file_format)
            i += 1
    foo
    if consistency:
        check_consistency_files_exist(output_files, experiment_id)

    check_report(html_report)


def do_run_experiment(source, experiment_id, config_file):
    """
    Run RSMTool experiment using the given experiment
    configuration file located in the given source directory
    and using the given experiment ID.

    Parameters<commit_msg>Fix typos.<commit_after>import re
import warnings

import numpy as np

from glob import glob
from os import remove
from os.path import basename, exists, join
from pathlib import Path

from nose.tools import assert_equal, ok_
from pandas.util.testing import assert_frame_equal

from rsmtool.reader import DataReader
from rsmtool.modeler import Modeler
from rsmtool.rsmtool import run_experiment
from rsmtool.rsmcompare import run_comparison
from rsmtool.rsmeval import run_evaluation
from rsmtool.rsmpredict import compute_and_save_predictions
from rsmtool.rsmsummarize import run_summary

html_error_regexp = re.compile(r'Traceback \(most recent call last\)')
html_warning_regexp = re.compile(r'<div class=".*?output_stderr.*?>')
section_regexp = re.compile(r'<h2>(.*?)</h2>')

# get the directory containing the tests
test_dir = Path(__file__).absolute().parent.parent.joinpath('tests')


def check_run_experiment(source,
                         experiment_id,
                         subgroups=None,
                         consistency=False,
                         skll=False,
                         file_format='csv'):

    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       '{}.json'.format(experiment_id))

    model_type = 'skll' if skll else 'rsmtool'

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', category=RuntimeWarning)
        do_run_experiment(source, experiment_id, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')
    html_report = join('test_outputs', source, 'report', '{}_report.html'.format(experiment_id))

    output_files = glob(join(output_dir, '*.{}'.format(file_format)))
    for output_file in output_files:
        output_filename = basename(output_file)
        expected_output_file = join(expected_output_dir, output_filename)

        if exists(expected_output_file):
            check_file_output(output_file, expected_output_file, file_format=file_format)

    check_generated_output(output_files, experiment_id, model_type, file_format=file_format)

    if not skll:
        check_scaled_coefficients(source, experiment_id, file_format=file_format)

    if subgroups:
        check_subgroup_outputs(output_dir, experiment_id, subgroups, file_format=file_format)

    if consistency:
        check_consistency_files_exist(output_files, experiment_id, file_format=file_format)

    check_report(html_report)


def check_run_evaluation(source,
                         experiment_id,
                         subgroups=None,
                         consistency=False,
                         file_format='csv'):

    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       '{}.json'.format(experiment_id))

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore', category=RuntimeWarning)
        do_run_evaluation(source, experiment_id, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')
    html_report = join('test_outputs', source, 'report', '{}_report.html'.format(experiment_id))

    output_files = glob(join(output_dir, '*.{}'.format(file_format)))
    for output_file in output_files:
        output_filename = basename(output_file)
        expected_output_file = join(expected_output_dir, output_filename)

        if exists(expected_output_file):
            check_file_output(output_file, expected_output_file, file_format=file_format)

    if consistency:
        check_consistency_files_exist(output_files, experiment_id)

    check_report(html_report)


def do_run_experiment(source, experiment_id, config_file):
    """
    Run RSMTool experiment using the given experiment
    configuration file located in the given source directory
    and using the given experiment ID.

    Parameters
{'fraction_non_alphanumeric': 0.08692485128080611, 'fraction_numerical': 0.0007284205414592692, 'mean_word_length': 3.1046337817638268, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '315379', 'n_tokens_mistral': 2361, 'n_tokens_neox': 2269, 'n_words': 493}