def test_excluded(self):
for value, expected in zip(('', 'old', 'new'),
(set(), {('A', 'B')}, {('B', 'C'),
('A', 'C')})):
lp = linkpred.LinkPred(
self.config_file(training=True, exclude=value))
assert_equal({tuple(sorted(p)) for p in lp.excluded}, expected)
with assert_raises(linkpred.exceptions.LinkPredError):
lp = linkpred.LinkPred(self.config_file(exclude='bla'))
lp.excluded
def test_excluded(self):
for value, expected in zip(("", "old", "new"),
(set(), {("A", "B")}, {("B", "C"),
("A", "C")})):
lp = linkpred.LinkPred(
self.config_file(training=True, exclude=value))
assert {tuple(sorted(p)) for p in lp.excluded} == expected
with pytest.raises(linkpred.exceptions.LinkPredError):
lp = linkpred.LinkPred(self.config_file(exclude="bla"))
lp.excluded
def test_init(self):
lp = linkpred.LinkPred(self.config_file())
assert_equal(lp.config['label'], 'testing')
assert lp.training is None
lp = linkpred.LinkPred(self.config_file(training=True))
assert_is_instance(lp.training, nx.Graph)
assert_equal(len(lp.training.nodes()), 3)
assert_equal(len(lp.training.edges()), 1)
assert lp.test is None
def test_init(self):
lp = linkpred.LinkPred(self.config_file())
assert lp.config["label"] == "testing"
assert lp.training is None
lp = linkpred.LinkPred(self.config_file(training=True))
assert isinstance(lp.training, nx.Graph)
assert len(lp.training.nodes()) == 3
assert len(lp.training.edges()) == 1
assert lp.test is None
def test_process_predictions(self):
@smokesignal.on('prediction_finished')
def a(scoresheet, dataset, predictor):
assert scoresheet.startswith('scoresheet')
assert predictor.startswith('pred')
assert_equal(dataset, 'testing')
a.called = True
@smokesignal.on('dataset_finished')
def b(dataset):
assert_equal(dataset, 'testing')
b.called = True
@smokesignal.on('run_finished')
def c():
c.called = True
a.called = b.called = c.called = False
lp = linkpred.LinkPred(self.config_file())
lp.predictions = [('pred1', 'scoresheet1'), ('pred2', 'scoresheet2')]
lp.process_predictions()
assert a.called
assert b.called
assert c.called
smokesignal.clear_all()
def test_predict_all(self):
# Mock out linkpred.predictors
class Stub:
def __init__(self, training, eligible, excluded):
self.training = training
self.eligible = eligible
self.excluded = excluded
def predict(self, **params):
self.params = params
return 'scoresheet'
linkpred.predictors.A = Stub
linkpred.predictors.B = Stub
config = self.config_file(training=True)
config['predictors'] = [{
'name': 'A',
'parameters': {
'X': 'x'
},
'displayname': 'prettyA'
}, {
'name': 'B',
'displayname': 'prettyB'
}]
lp = linkpred.LinkPred(config)
results = list(lp.predict_all())
assert_equal(results, [('A', 'scoresheet'), ('B', 'scoresheet')])
def test_process_predictions(self):
@smokesignal.on("prediction_finished")
def a(scoresheet, dataset, predictor):
assert scoresheet.startswith("scoresheet")
assert predictor.startswith("pred")
assert dataset == "testing"
a.called = True
@smokesignal.on("dataset_finished")
def b(dataset):
assert dataset == "testing"
b.called = True
@smokesignal.on("run_finished")
def c():
c.called = True
a.called = b.called = c.called = False
lp = linkpred.LinkPred(self.config_file())
lp.predictions = [("pred1", "scoresheet1"), ("pred2", "scoresheet2")]
lp.process_predictions()
assert a.called
assert b.called
assert c.called
smokesignal.clear_all()
def test_predict_all(self):
# Mock out linkpred.predictors
class Stub:
def __init__(self, training, eligible, excluded):
self.training = training
self.eligible = eligible
self.excluded = excluded
def predict(self, **params):
self.params = params
return "scoresheet"
linkpred.predictors.A = Stub
linkpred.predictors.B = Stub
config = self.config_file(training=True)
config["predictors"] = [
{
"name": "A",
"parameters": {
"X": "x"
},
"displayname": "prettyA"
},
{
"name": "B",
"displayname": "prettyB"
},
]
lp = linkpred.LinkPred(config)
results = list(lp.predict_all())
assert results == [("A", "scoresheet"), ("B", "scoresheet")]
def test_setup_output(self):
# Make sure this also works is $DISPLAY is not set.
# Should probably mock this out...
for name, klass in (
('recall-precision', RecallPrecisionPlotter),
('f-score', FScorePlotter),
# Should be able to handle uppercase
('ROC', ROCPlotter),
('fmax', FMaxListener),
('cache-evaluations', CacheEvaluationListener)):
config = self.config_file(training=True, test=True, output=[name])
lp = linkpred.LinkPred(config)
lp.setup_output()
assert_is_instance(lp.listeners[0], klass)
smokesignal.clear_all()
# Has an evaluator been set up?
assert_equal(len(lp.evaluator.params['relevant']), 1)
assert_equal(lp.evaluator.params['universe'], 2)
assert_is_instance(lp.evaluator.params['universe'], int)
def test_setup_output(self):
# Make sure this also works is $DISPLAY is not set.
# Should probably mock this out...
for name, klass in (
("recall-precision", RecallPrecisionPlotter),
("f-score", FScorePlotter),
# Should be able to handle uppercase
("ROC", ROCPlotter),
("fmax", FMaxListener),
("cache-evaluations", CacheEvaluationListener),
):
config = self.config_file(training=True, test=True, output=[name])
lp = linkpred.LinkPred(config)
lp.setup_output()
assert isinstance(lp.listeners[0], klass)
smokesignal.clear_all()
# Has an evaluator been set up?
assert len(lp.evaluator.params["relevant"]) == 1
assert lp.evaluator.params["universe"] == 2
assert isinstance(lp.evaluator.params["universe"], int)
def test_setup_output_evaluating_without_test(self):
lp = linkpred.LinkPred(self.config_file(training=True))
lp.setup_output()
def test_preprocess_training_and_test(self):
lp = linkpred.LinkPred(self.config_file(training=True, test=True))
lp.preprocess()
assert_equal(set(lp.training.nodes()), {"B"})
assert_equal(set(lp.test.nodes()), {"B"})
def test_preprocess_only_training(self):
lp = linkpred.LinkPred(self.config_file(training=True))
lp.preprocess()
assert_equal(set(lp.training.nodes()), set("AB"))
def test_LinkPred_without_predictors():
linkpred.LinkPred()
def test_setup_output_evaluating_without_test(self):
lp = linkpred.LinkPred(self.config_file(training=True))
with pytest.raises(linkpred.exceptions.LinkPredError):
lp.setup_output()
def test_LinkPred_without_predictors():
with pytest.raises(linkpred.exceptions.LinkPredError):
linkpred.LinkPred()
