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_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 __init__(self, config: dict, dotbot: dict) -> None:
"""
Initialize the chatbot engine.
:param config: Configuration values for the instance.
"""
self.config = config
self.dotbot = dotbot
self.dotdb = None
self.logger = None
self.cache = None
self.extensions = []
self.pipeline = []
self.is_fallback = False
self.user_id = ''
self.bot_id = ''
self.org_id = ''
self.logger_level = ''
self.request = {}
self.executed_functions = []
self.bot = None
self.functions_map = {} # Registered template functions
self.bbot = BBotFunctionsProxy(self)
smokesignal.clear_all() # this shouldnt be needed. @TODO build the whole obj hierarchy, store it in a static var and decouple request session data
def test_EvaluatingListener():
@smokesignal.on("evaluation_finished")
def t(evaluation, dataset, predictor):
assert dataset == "dataset"
assert isinstance(evaluation, EvaluationSheet)
assert_array_equal(evaluation.tp, [1, 1, 2, 2])
assert_array_equal(evaluation.fp, [0, 1, 1, 2])
assert_array_equal(evaluation.fn, [1, 1, 0, 0])
assert_array_equal(evaluation.tn, [2, 1, 1, 0])
assert predictor == "predictor"
t.called = True
t.called = False
relevant = {1, 2}
universe = {1, 2, 3, 4}
scoresheet = BaseScoresheet({1: 10, 3: 5, 2: 2, 4: 1})
EvaluatingListener(relevant=relevant, universe=universe)
smokesignal.emit(
"prediction_finished",
scoresheet=scoresheet,
dataset="dataset",
predictor="predictor",
)
assert t.called
smokesignal.clear_all()
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_clear_all(self):
smokesignal.on(('foo', 'bar'), self.callback)
assert len(smokesignal._receivers['foo']) == 1
assert len(smokesignal._receivers['bar']) == 1
smokesignal.clear_all()
assert len(smokesignal._receivers['foo']) == 0
assert len(smokesignal._receivers['bar']) == 0
def test_clear_all(self):
smokesignal.on(('foo', 'bar'), self.callback)
assert len(smokesignal._receivers['foo']) == 1
assert len(smokesignal._receivers['bar']) == 1
smokesignal.clear_all()
assert len(smokesignal._receivers['foo']) == 0
assert len(smokesignal._receivers['bar']) == 0
def test_CacheEvaluationListener():
l = CacheEvaluationListener()
scores = BaseScoresheet({1: 10, 2: 5})
ev = EvaluationSheet(scores, {1})
smokesignal.emit('evaluation_finished', ev, 'd', 'p')
ev2 = EvaluationSheet.from_file(l.fname)
assert_array_equal(ev.data, ev2.data)
smokesignal.clear_all()
os.unlink(l.fname)
def test_CachePredictionListener():
l = CachePredictionListener()
scoresheet = BaseScoresheet({1: 10, 2: 5, 3: 2, 4: 1})
smokesignal.emit("prediction_finished", scoresheet, "d", "p")
with open(l.fname) as fh:
# Line endings may be different across platforms
assert fh.read().replace("\r\n", "\n") == "1\t10\n2\t5\n3\t2\n4\t1\n"
smokesignal.clear_all()
os.unlink(l.fname)
def test_CacheEvaluationListener():
l = CacheEvaluationListener()
scores = BaseScoresheet({1: 10, 2: 5})
ev = EvaluationSheet(scores, {1})
smokesignal.emit("evaluation_finished", ev, "d", "p")
ev2 = EvaluationSheet.from_file(l.fname)
assert_array_equal(ev.data, ev2.data)
smokesignal.clear_all()
os.unlink(l.fname)
def test_CachePredictionListener():
l = CachePredictionListener()
scoresheet = BaseScoresheet({1: 10, 2: 5, 3: 2, 4: 1})
smokesignal.emit('prediction_finished', scoresheet, 'd', 'p')
with open(l.fname) as fh:
# Line endings may be different across platforms
assert_equal(fh.read().replace("\r\n", "\n"),
"1\t10\n2\t5\n3\t2\n4\t1\n")
smokesignal.clear_all()
os.unlink(l.fname)
def test_clear_all(self):
smokesignal.on(('foo', 'bar'), self.fn)
assert smokesignal.receivers == {
'foo': set([self.fn]),
'bar': set([self.fn]),
}
smokesignal.clear_all()
assert smokesignal.receivers == {
'foo': set(),
'bar': set(),
}
def test_clear_all(self):
smokesignal.on(('foo', 'bar'), self.fn)
assert smokesignal.receivers == {
'foo': set([self.fn]),
'bar': set([self.fn]),
}
smokesignal.clear_all()
assert smokesignal.receivers == {
'foo': set(),
'bar': set(),
}
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_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_EvaluatingListener():
@smokesignal.on('evaluation_finished')
def t(evaluation, dataset, predictor):
assert_equal(dataset, 'dataset')
assert_is_instance(evaluation, EvaluationSheet)
assert_array_equal(evaluation.tp, [1, 1, 2, 2])
assert_array_equal(evaluation.fp, [0, 1, 1, 2])
assert_array_equal(evaluation.fn, [1, 1, 0, 0])
assert_array_equal(evaluation.tn, [2, 1, 1, 0])
assert_equal(predictor, 'predictor')
t.called = True
t.called = False
relevant = {1, 2}
universe = {1, 2, 3, 4}
scoresheet = BaseScoresheet({1: 10, 3: 5, 2: 2, 4: 1})
EvaluatingListener(relevant=relevant, universe=universe)
smokesignal.emit('prediction_finished', scoresheet=scoresheet,
dataset='dataset', predictor='predictor')
assert t.called
smokesignal.clear_all()
def test_EvaluatingListener():
@smokesignal.on('evaluation_finished')
def t(evaluation, dataset, predictor):
assert_equal(dataset, 'dataset')
assert_is_instance(evaluation, EvaluationSheet)
assert_array_equal(evaluation.tp, [1, 1, 2, 2])
assert_array_equal(evaluation.fp, [0, 1, 1, 2])
assert_array_equal(evaluation.fn, [1, 1, 0, 0])
assert_array_equal(evaluation.tn, [2, 1, 1, 0])
assert_equal(predictor, 'predictor')
t.called = True
t.called = False
relevant = {1, 2}
universe = {1, 2, 3, 4}
scoresheet = BaseScoresheet({1: 10, 3: 5, 2: 2, 4: 1})
EvaluatingListener(relevant=relevant, universe=universe)
smokesignal.emit('prediction_finished',
scoresheet=scoresheet,
dataset='dataset',
predictor='predictor')
assert t.called
smokesignal.clear_all()
def teardown(self):
smokesignal.clear_all()
def onStop(self):
self.save()
output.out.usage('%s (%s) going down' % (self.info.pdid, self.meta.type))
smokesignal.clear_all()
output.out.endLogging()
def onStop(self):
self.save()
output.out.usage('%s going down' % (self.info.pdid))
smokesignal.clear_all()
output.out.endLogging()
def onStop(self):
self.save()
output.out.usage('%s going down' % (self.info.pdid))
smokesignal.clear_all()
output.out.endLogging()
def on_game_over(self):
self._game_over = True
smokesignal.clear_all()
def tearDown(self):
smokesignal.clear_all()
