def test_missing_source():
"""Check that all source_apis in training data are in source list."""
lr = LogisticRegression()
source_list = ['reach', 'sparser']
cw = CountsScorer(lr, source_list)
# Should error because test stmts are from signor and signor
# is not in list
cw.stmts_to_matrix(test_stmts)
def test_extra_evidence_content():
"""Should raise ValueError if extra_evidence list entries are not
Evidence objects or empty lists."""
lr = LogisticRegression()
source_list = ['reach', 'sparser', 'signor']
cs = CountsScorer(lr, source_list)
extra_ev = ([[5]] * (len(test_stmts) - 1)) + [[]]
x_arr = cs.stmts_to_matrix(test_stmts, extra_evidence=extra_ev)
def test_extra_evidence_length():
"""Should raise ValueError because the extra_evidence list is not the
same length as the list of statements."""
lr = LogisticRegression()
source_list = ['reach', 'sparser', 'signor']
cs = CountsScorer(lr, source_list)
extra_ev = [[5]]
x_arr = cs.stmts_to_matrix(test_stmts, extra_evidence=extra_ev)
def test_stmts_to_matrix():
"""Check that all source_apis in training data are in source list."""
lr = LogisticRegression()
source_list = ['reach', 'sparser', 'signor']
cw = CountsScorer(lr, source_list)
x_arr = cw.stmts_to_matrix(test_stmts)
assert isinstance(x_arr, np.ndarray), 'x_arr should be a numpy array'
assert x_arr.shape == (len(test_stmts), len(source_list)), \
'stmt matrix dimensions should match test stmts'
assert set(x_arr.sum(axis=0)) == set([0, 0, len(test_stmts)]), \
'Signor col should be 1 in every row, other cols 0.'
# Try again with statement type
cw = CountsScorer(lr, source_list, use_stmt_type=True)
num_types = len(cw.stmt_type_map)
x_arr = cw.stmts_to_matrix(test_stmts)
assert x_arr.shape == (len(test_stmts), len(source_list) + num_types), \
'matrix should have a col for sources and other cols for every ' \
'statement type.'