def test_get_x():
# if using list, get_x should get ints
clf = OGDLR(ndims=100)
clf.fit(X[:100], y[:100])
xt = ['sunny', 'cold', X[0, 2]]
result = clf._get_x(xt)
assert all([isinstance(r, int) for r in result])
# if using dict, get_x should give dictionary keys
xt = ['sunny', 'cold', X[0, 2]]
result = ogdlr_after._get_x(xt)
expected = [
'BIAS', 'weather__sunny', 'temperature__cold', 'noise__' + X[0, 2]
]
assert result == expected
def test_get_x():
# if using list, get_x should get ints
clf = OGDLR(ndims=100)
clf.fit(X[:100], y[:100])
xt = ['sunny', 'cold', X[0, 2]]
result = clf._get_x(xt)
assert all([isinstance(r, int) for r in result])
# if using dict, get_x should give dictionary keys
xt = ['sunny', 'cold', X[0, 2]]
result = ogdlr_after._get_x(xt)
expected = ['BIAS',
'weather__sunny',
'temperature__cold',
'noise__' + X[0, 2]]
assert result == expected
def test_get_p():
# probabilities are between 0 and 1
Xs = [ogdlr_after._get_x(x) for x in X]
prob = [ogdlr_after._get_p(xt) for xt in Xs]
assert all([0 < pr < 1 for pr in prob])
def test_get_p():
# probabilities are between 0 and 1
Xs = [ogdlr_after._get_x(x) for x in X]
prob = [ogdlr_after._get_p(xt) for xt in Xs]
assert all([0 < pr < 1 for pr in prob])