def _create_dummy_lr_model(self, path, encoded_data, label): # dummy logistic regression with 100 observations with 3 features belonging to 2 classes dummy_lr = LogisticRegression() dummy_lr.fit_by_cross_validation(encoded_data, number_of_splits=2, label_name=label) return dummy_lr
def _create_dummy_lr_model(self): dummy_lr = LogisticRegression() encoded_tr = EncodedData(np.random.rand(100, 20), {"l1": [i % 2 for i in range(0, 100)]}) dummy_lr.fit_by_cross_validation(encoded_tr, number_of_splits=2, label=Label("l1", values=[0, 1])) return dummy_lr, encoded_tr
def test_fit_by_cross_validation(self): x = EncodedData( np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1], [1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]]), { "test1": [1, 0, 2, 0, 1, 0, 2, 0], "test2": [1, 0, 2, 0, 1, 0, 2, 0] }) lr = LogisticRegression() lr.fit_by_cross_validation(x, number_of_splits=2, label=Label("test2"))
def _create_dummy_lr_model(self, path): # dummy logistic regression with 100 observations with 20 features belonging to 2 classes dummy_lr = LogisticRegression() dummy_lr.fit_by_cross_validation(EncodedData(np.random.rand(100, 5), {"l1": [i % 2 for i in range(0, 100)]}), number_of_splits=2, label=Label("l1")) # Change coefficients to values 1-20 dummy_lr.model.coef_ = np.array(list(range(0, 5))).reshape(1, -1) with open(path / "ml_details.yaml", "w") as file: yaml.dump({"l1": {"feature_names": ["AAA", "AAC", "CKJ", "KSA", "AKJ"]}}, file) return dummy_lr
def _create_dummy_lr_model(self, path): # dummy logistic regression with 100 observations with 20 features belonging to 2 classes dummy_lr = LogisticRegression() dummy_lr.fit_by_cross_validation(EncodedData(np.random.rand( 100, 20), {"l1": [i % 2 for i in range(0, 100)]}), number_of_splits=2, label_name="l1") # Change coefficients to values 1-20 dummy_lr.models["l1"].coef_ = np.array(list(range(0, 20))).reshape(1, -1) file_path = path / "ml_details.yaml" with file_path.open("w") as file: yaml.dump( {"l1": { "feature_names": [f"feature{i}" for i in range(20)] }}, file) return dummy_lr