def test_train(self, init):
init.return_value = None
p = factor_analysis.FactorAnalysisPreprocessor()
p.nfactors = 5
d = np.random.normal(size=(500, 50))
p._train(d)
assert p.fa_node
def test_call(self, init):
init.return_value = None
p = factor_analysis.FactorAnalysisPreprocessor()
p.fa_node = Mock()
rv = object()
p.fa_node.execute.return_value = rv
d = object()
r = p(d)
assert r == rv
p.fa_node.execute.assert_called_once_with(d)
def test_get_variable_selector(self, init, nvariables):
init.return_value = None
nvariables.__get__ = Mock(return_value=4)
p = factor_analysis.FactorAnalysisPreprocessor()
# 4 variables, 3 factors
p.fa_node = Mock()
p.fa_node.E_y_mtx = np.array([[2, 1, 0], [1, 0, 0], [0, 0, 1],
[0, 0, 2]])
p.input_variables = ['f1', 'f2', 'f3', 'f4']
sel = p.get_variable_selector()
data = np.array([0, 1, 2, 3], dtype='i4')
assert np.array_equal(sel(data), np.array([0, 3]))
def test_nvariables(self, init):
init.return_value = None
p = factor_analysis.FactorAnalysisPreprocessor()
p.fa_node = Mock()
p.fa_node.E_y_mtx = np.zeros((3, 2))
assert p.nvariables == 3
def test_init_too_many_factors(self):
d = np.random.normal(size=(500, 5))
variables = ['f%d' % (i + 1) for i in range(5)]
self.assertRaises(
ValueError, lambda: factor_analysis.FactorAnalysisPreprocessor(
d, variables, 6))
def test_init(self, train):
d = np.random.normal(size=(500, 50))
variables = ['f%d' % (i + 1) for i in range(50)]
p = factor_analysis.FactorAnalysisPreprocessor(d, variables, 5)
p._train.assert_called_once_with(d)