def test_transform_data():
"""
Testing the transformation of the data from raw data to functions
used for fitting a function.
"""
# We start with actual data. We test here just that reading the data in
# different ways ultimately generates the same arrays.
from matplotlib import mlab
ortho = mlab.csv2rec(op.join(data_path, 'ortho.csv'))
para = mlab.csv2rec(op.join(data_path, 'para.csv'))
x1, y1, n1 = sb.transform_data(ortho)
x2, y2, n2 = sb.transform_data(op.join(data_path, 'ortho.csv'))
npt.assert_equal(x1, x2)
npt.assert_equal(y1, y2)
# We can also be a bit more critical, by testing with data that we
# generate, and should produce a particular answer:
my_data = pd.DataFrame(
np.array([[0.1, 2], [0.1, 1], [0.2, 2], [0.2, 2], [0.3, 1],
[0.3, 1]]),
columns=['contrast1', 'answer'])
my_x, my_y, my_n = sb.transform_data(my_data)
npt.assert_equal(my_x, np.array([0.1, 0.2, 0.3]))
npt.assert_equal(my_y, np.array([0.5, 0, 1.0]))
npt.assert_equal(my_n, np.array([2, 2, 2]))
def test_params_regression():
"""
Test for regressions in model parameter values from provided data
"""
model = sb.Model()
ortho_x, ortho_y, ortho_n = sb.transform_data(
op.join(data_path, 'ortho.csv'))
para_x, para_y, para_n = sb.transform_data(op.join(data_path, 'para.csv'))
ortho_fit = model.fit(ortho_x, ortho_y)
para_fit = model.fit(para_x, para_y)
npt.assert_almost_equal(ortho_fit.params[0], 0.46438638)
npt.assert_almost_equal(ortho_fit.params[1], 0.13845926)
npt.assert_almost_equal(para_fit.params[0], 0.57456788)
npt.assert_almost_equal(para_fit.params[1], 0.13684096)
def test_params_regression():
"""
Test for regressions in model parameter values from provided data
"""
model = sb.Model()
ortho_x, ortho_y, ortho_n = sb.transform_data(op.join(data_path,
'ortho.csv'))
para_x, para_y, para_n = sb.transform_data(op.join(data_path,
'para.csv'))
ortho_fit = model.fit(ortho_x, ortho_y)
para_fit = model.fit(para_x, para_y)
npt.assert_almost_equal(ortho_fit.params[0], 0.46438638)
npt.assert_almost_equal(ortho_fit.params[1], 0.13845926)
npt.assert_almost_equal(para_fit.params[0], 0.57456788)
npt.assert_almost_equal(para_fit.params[1], 0.13684096)
def test_transform_data():
"""
Testing the transformation of the data from raw data to functions
used for fitting a function.
"""
# We start with actual data. We test here just that reading the data in
# different ways ultimately generates the same arrays.
from matplotlib import mlab
ortho = mlab.csv2rec(op.join(data_path, 'ortho.csv'))
para = mlab.csv2rec(op.join(data_path, 'para.csv'))
x1, y1, n1 = sb.transform_data(ortho)
x2, y2, n2 = sb.transform_data(op.join(data_path, 'ortho.csv'))
npt.assert_equal(x1, x2)
npt.assert_equal(y1, y2)
# We can also be a bit more critical, by testing with data that we
# generate, and should produce a particular answer:
my_data = pd.DataFrame(np.array([[0.1, 2], [0.1, 1], [0.2, 2], [0.2, 2],
[0.3, 1], [0.3, 1]]),
columns=['contrast1', 'answer'])
my_x, my_y, my_n = sb.transform_data(my_data)
npt.assert_equal(my_x, np.array([0.1, 0.2, 0.3]))
npt.assert_equal(my_y, np.array([0.5, 0, 1.0]))
npt.assert_equal(my_n, np.array([2, 2, 2]))
