def test_optimize(self):
model = LogisticRegressionModel(Objective.MINIMIZE, log_level=logging.WARN)
assert(model.hyper_params == None)
assert(model.hyper_params_scores == [])
iris = datasets.load_iris()
data = list(iris.data)
targets = list(iris.target)
model.optimize(data, targets)
assert(len(model.hyper_params_scores) == 70)
def test_optimize(self):
model = LogisticRegressionModel(Objective.MINIMIZE,
log_level=logging.WARN)
assert (model.hyper_params == None)
assert (model.hyper_params_scores == [])
iris = datasets.load_iris()
data = list(iris.data)
targets = list(iris.target)
model.optimize(data, targets)
assert (len(model.hyper_params_scores) == 70)
def test_score(self):
model = LogisticRegressionModel(Objective.MINIMIZE, log_level=logging.WARN)
preds, targets = range(10), range(10)
score = model.score(preds, targets)
assert(score == 1.)
preds, targets = [0], [1]
score = model.score(preds, targets)
assert(score == 0.)
preds, targets = range(10), range(10, 20)
score = model.score(preds, targets)
assert(score == 0.)
def test_score(self):
model = LogisticRegressionModel(Objective.MINIMIZE,
log_level=logging.WARN)
preds, targets = range(10), range(10)
score = model.score(preds, targets)
assert (score == 1.)
preds, targets = [0], [1]
score = model.score(preds, targets)
assert (score == 0.)
preds, targets = range(10), range(10, 20)
score = model.score(preds, targets)
assert (score == 0.)
def test_datasets(self):
model = LogisticRegressionModel(Objective.MINIMIZE, log_level=logging.WARN)
data = [[i] for i in range(10)]
targets = range(10)
train_data, cv_data, train_targets, cv_targets = model.create_datasets(data, targets)
expected_data = []
for a,b in zip(train_data, cv_data):
expected_data.append(a)
expected_data.append(b)
expected_targets = []
for a,b in zip(train_targets, cv_targets):
expected_targets.append(a)
expected_targets.append(b)
assert(data == expected_data)
assert(targets == expected_targets)
def test_datasets(self):
model = LogisticRegressionModel(Objective.MINIMIZE,
log_level=logging.WARN)
data = [[i] for i in range(10)]
targets = range(10)
train_data, cv_data, train_targets, cv_targets = model.create_datasets(
data, targets)
expected_data = []
for a, b in zip(train_data, cv_data):
expected_data.append(a)
expected_data.append(b)
expected_targets = []
for a, b in zip(train_targets, cv_targets):
expected_targets.append(a)
expected_targets.append(b)
assert (data == expected_data)
assert (targets == expected_targets)
