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