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):
