def test_no_labels(store, label_store):
'''Make sure the learner can handle zero labels.'''
# Overwrite the query so we get some hits.
store.put([('q', counter_fc({'x': 5, 'y': 90}))])
results = (mod_pairwise.similar(store, label_store)
.set_query_id('q')
.set_query_params({'limit': 1})
.recommendations())
assert len(results['results']) == 0
def test_no_labels(store, label_store):
'''Make sure the learner can handle zero labels.'''
# Overwrite the query so we get some hits.
store.put([('q', counter_fc({'x': 5, 'y': 90}))])
results = (mod_pairwise.similar(
store, label_store).set_query_id('q').set_query_params({
'limit': 1
}).recommendations())
assert len(results['results']) == 0
def test_search_engine_limit(store, label_store):
'''Pretty much the same as classify, but for the search engine.'''
content_objs, labels = interesting_training_data()
store.put(content_objs)
for lab in labels:
label_store.put(lab)
# Overwrite the query so we get some hits.
store.put([('q', counter_fc({'x': 5, 'y': 90}))])
# And assign some labels to the query.
# (label expansion should fill in the rest)
label_store.put(neg_label('q', 'a'))
label_store.put(pos_label('q', 'b'))
results = (mod_pairwise.similar(store, label_store)
.set_query_id('q')
.set_query_params({'limit': 2})
.recommendations())
assert len(results['results']) == 2
def test_search_engine_limit(store, label_store):
'''Pretty much the same as classify, but for the search engine.'''
content_objs, labels = interesting_training_data()
store.put(content_objs)
for lab in labels:
label_store.put(lab)
# Overwrite the query so we get some hits.
store.put([('q', counter_fc({'x': 5, 'y': 90}))])
# And assign some labels to the query.
# (label expansion should fill in the rest)
label_store.put(neg_label('q', 'a'))
label_store.put(pos_label('q', 'b'))
results = (mod_pairwise.similar(
store, label_store).set_query_id('q').set_query_params({
'limit': 2
}).recommendations())
assert len(results['results']) == 2
def test_only_negative_labels(store, label_store):
'''Make sure the learner can handle one class of labels.'''
content_objs, labels = interesting_training_data()
store.put(content_objs)
for lab in labels:
if lab.value == CorefValue.Negative:
label_store.put(lab)
# Overwrite the query so we get some hits.
store.put([('q', counter_fc({'x': 5, 'y': 90}))])
# And assign a label to the query.
label_store.put(neg_label('q', 'a'))
results = (mod_pairwise.similar(store, label_store)
.set_query_id('q')
.set_query_params({'limit': 100})
.recommendations())
# The search engine will fall back to a plain index scan if it has
# insufficient training data.
assert len(results['results']) >= 0
def test_only_negative_labels(store, label_store):
'''Make sure the learner can handle one class of labels.'''
content_objs, labels = interesting_training_data()
store.put(content_objs)
for lab in labels:
if lab.value == CorefValue.Negative:
label_store.put(lab)
# Overwrite the query so we get some hits.
store.put([('q', counter_fc({'x': 5, 'y': 90}))])
# And assign a label to the query.
label_store.put(neg_label('q', 'a'))
results = (mod_pairwise.similar(
store, label_store).set_query_id('q').set_query_params({
'limit': 100
}).recommendations())
# The search engine will fall back to a plain index scan if it has
# insufficient training data.
assert len(results['results']) >= 0
