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)