from nose.tools import eq_, assert_almost_equal import numpy as np from numpy.testing import assert_allclose from scipy.stats import pearsonr from sklearn.utils.testing import assert_greater, assert_less from skll.data import NDJWriter from skll.config import _setup_config_parser from skll.experiments import run_configuration from skll.learner import Learner from skll.learner import _DEFAULT_PARAM_GRIDS from utils import make_regression_data, fill_in_config_paths_for_fancy_output _ALL_MODELS = list(_DEFAULT_PARAM_GRIDS.keys()) _my_dir = abspath(dirname(__file__)) def setup(): """ Create necessary directories for testing. """ train_dir = join(_my_dir, 'train') if not exists(train_dir): os.makedirs(train_dir) test_dir = join(_my_dir, 'test') if not exists(test_dir): os.makedirs(test_dir) output_dir = join(_my_dir, 'output') if not exists(output_dir):
import csv import os from glob import glob from io import open from os.path import abspath, dirname, exists, join import numpy as np from nose.tools import raises from numpy.testing import assert_array_equal from skll.data import NDJWriter from skll.experiments import run_configuration from skll.learner import _DEFAULT_PARAM_GRIDS, Learner from utils import fill_in_config_paths, make_classification_data _ALL_MODELS = list(_DEFAULT_PARAM_GRIDS.keys()) _my_dir = abspath(dirname(__file__)) def setup(): """ Create necessary directories for testing. """ train_dir = join(_my_dir, 'train') if not exists(train_dir): os.makedirs(train_dir) test_dir = join(_my_dir, 'test') if not exists(test_dir): os.makedirs(test_dir) output_dir = join(_my_dir, 'output') if not exists(output_dir):