def setup_belief(include_more_specific=False):
# Make a model
lr = LogisticRegression()
# Get all the sources
source_list = CountsScorer.get_all_sources(test_stmts_cur)
cs = CountsScorer(lr,
source_list,
include_more_specific=include_more_specific)
# Train on curated stmt data
if include_more_specific:
extra_evidence = [[
ev for supp in stmt.supports for ev in supp.evidence
] for stmt in test_stmts_cur]
else:
extra_evidence = None
# Fit with extra evidence, if any
cs.fit(test_stmts_cur, y_arr_stmts_cur, extra_evidence)
# Run predictions on test statements without extra evidence to get prior
# probs
probs = cs.predict_proba(test_stmts_cur)[:, 1]
# Now check if we get these same beliefs set on the statements when we
# run with the belief engine:
# Get scorer and belief engine instances for trained model
be = BeliefEngine(scorer=cs)
# Make a shallow copy of the test stmts so that we don't change beliefs
# of the global instances as a side-effect of this test
test_stmts_copy = copy(test_stmts_cur)
return be, test_stmts_copy, probs
def test_fit_stmts_predict_stmts():
lr = LogisticRegression()
source_list = ['reach', 'sparser', 'signor']
cw = CountsScorer(lr, source_list)
cw.fit(test_stmts, y_arr_stmts)
probs = cw.predict_proba(test_stmts)
assert probs.shape == (len(test_stmts), 2), \
'prediction results should have dimension (# stmts, # classes)'
log_probs = cw.predict_log_proba(test_stmts)
assert log_probs.shape == (len(test_stmts), 2), \
'prediction results should have dimension (# stmts, # classes)'
preds = cw.predict(test_stmts)
assert preds.shape == (len(test_stmts),), \
'prediction results should have dimension (# stmts)'
def test_hybrid_scorer():
# First instantiate and train the SimpleScorer on readers
# Make a model
lr = LogisticRegression()
# Get all the sources
source_list = CountsScorer.get_all_sources(test_stmts_cur)
# The sources for this sample (test_stmts_cur) include only: trips,
# sparser, medscan, hprd, and reach. Of these, we'll set aside hprd to be
# scored by the simplescorer and the other to be scored by the CountsScorer
skl_sources = ['trips', 'sparser', 'medscan', 'reach']
cs = CountsScorer(lr, skl_sources)
# Train on curated stmt data
cs.fit(test_stmts_cur, y_arr_stmts_cur)
# Run predictions on test statements for later comparison
cs_beliefs = cs.predict_proba(test_stmts_cur)[:, 1]
# Next, get the default SimpleScorer:
ss = default_scorer
# Let's check the prior probability associated with HPRD
hprd_rand = ss.prior_probs['rand']['hprd']
hprd_syst = ss.prior_probs['syst']['hprd']
# Now instantiate a HybridScorer
hs = HybridScorer(cs, ss)
# Check that sources are accounted for
hs.check_prior_probs(test_stmts_cur)
# Score the statements with the HybridScorer
hybrid_beliefs = hs.score_statements(test_stmts_cur)
# Look at each statement and check that the belief is what's expected
# based on the skl-predicted belief and the HPRD evidence from the
# simple scorer
expected_beliefs = []
for ix, stmt in enumerate(test_stmts_cur):
# Check the sources
stmt_sources = Counter([ev.source_api for ev in stmt.evidence])
# If statement has no HPRD evidence, we expect the belief to be
# the same as the skl-predicted belief
if 'hprd' not in stmt_sources:
expected_beliefs.append(cs_beliefs[ix])
# Otherwise, calculate belief incorporating HPRD evidences
else:
# How many HPRD evidences?
hprd_count = stmt_sources['hprd']
print("hprd_count", hprd_count)
hprd_belief = 1 - (hprd_syst + hprd_rand**hprd_count)
expected_beliefs.append(1 - (1 - cs_beliefs[ix]) *
(1 - hprd_belief))
print(expected_beliefs[ix], hybrid_beliefs[ix])
assert np.allclose(hybrid_beliefs, expected_beliefs)
def setup_belief():
# Make a model
lr = LogisticRegression()
# Get all the sources
source_list = CountsScorer.get_all_sources(test_stmts_cur)
cs = CountsScorer(lr, source_list)
# Train on curated stmt data
cs.fit(test_stmts_cur, y_arr_stmts_cur)
# Run predictions on test statements
probs = cs.predict_proba(test_stmts_cur)[:, 1]
# Now check if we get these same beliefs set on the statements when we
# run with the belief engine:
# Get scorer and belief engine instances for trained model
be = BeliefEngine(scorer=cs)
# Make a shallow copy of the test stmts so that we don't change beliefs
# of the global instances as a side-effect of this test
test_stmts_copy = copy(test_stmts_cur)
return be, test_stmts_copy, probs
