def test_train_model():
X_train = np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1)
y_train = np.array([10, 9, 8, 8, 6, 5])
X_test = np.array([3, 4]).reshape(-1, 1)
y_test = np.array([8, 7])
data = {
"train": {
"X": X_train,
"y": y_train
},
"test": {
"X": X_test,
"y": y_test
}
}
run = Mock(Run)
reg = train_model(run, data, alpha=1.2)
run.log.assert_called_with("mse",
0.029843893480256872,
description='Mean squared error metric')
preds = reg.predict([[1], [2]])
np.testing.assert_equal(preds, [9.93939393939394, 9.03030303030303])
def test_train_model():
X_train = np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1)
y_train = np.array([10, 9, 8, 8, 6, 5])
X_test = np.array([3, 4]).reshape(-1, 1)
y_test = np.array([8, 7])
data = {
"train": {
"X": X_train,
"y": y_train
},
"test": {
"X": X_test,
"y": y_test
}
}
run = Mock(Run)
reg = train_model(run, data, alpha=1.2)
_, call2 = run.log.call_args_list
nameValue, descriptionDict = call2
name, value = nameValue
description = descriptionDict['description']
assert (name == 'mse')
np.testing.assert_almost_equal(value, 0.029843893480257067)
assert (description == 'Mean squared error metric')
preds = reg.predict([[1], [2]])
np.testing.assert_equal(preds, [9.93939393939394, 9.03030303030303])
def test_train_model():
X_train = np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1)
y_train = np.array([10, 9, 8, 8, 6, 5])
data = {"train": {"X": X_train, "y": y_train}}
reg_model = train_model(data, {"alpha": 1.2})
preds = reg_model.predict([[1], [2]])
np.testing.assert_equal(preds, [9.93939393939394, 9.03030303030303])