def test_compute_score_pass():
model = exponential_model(lower_bound=0, upper_bound=[100, 100])
model.fit(np.array([1, 2, 3]), np.array([1, 2, 8]))
test_data = pd.DataFrame({"x": np.array([5])})
result = compute_score(model, test_data)
expected = pd.DataFrame({"y_pred": np.array([103.300808])})
assert result.round(6).equals(expected)
def test_exponential_model_get_coeff_pass():
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
exp_model.fit(np.array([1, 2, 3]), np.array([1, 2, 8]))
expected_pcov = np.array([[0.00822098, -0.01632878],
[-0.01632878, 0.03276216]])
expected_popt = np.array([0.17081654, 1.28096207])
result_pcov = exp_model.get_coeff()["pcov"]
result_popt = exp_model.get_coeff()["popt"]
assert np.allclose(result_pcov, expected_pcov)
assert np.allclose(result_popt, expected_popt)
def update_prediction(self):
''' with new curren value and span, update the prediction
'''
try:
if self.model_type == "exp":
self.model = exponential_model(
**cattrs.param["prediction_models"]["exponential_model"])
if self.model_type == "lstm":
self.model = lstm_model(
**cattrs.param["prediction_models"]["lstm_model"])
if self.model_type == "logistic":
self.model = logistic_model(
**cattrs.param["prediction_models"]["logistic_model"])
# write model details
if self.model_type == "none":
self.pred_values = [0, 0, 0, 0, 0, 0, 0, 0]
self.model_details["model_name"] = "None"
self.model_details["r2"] = 0.0
self.model_details["msle"] = 0.0
self.model_details["show_prediction"] = False
else:
# update prediction values
x, y, x_test = np.array(self.keys_use), np.array(
self.values_use), np.array(self.pred_keys)
try:
self.model.fit(x, y)
self.pred_values = list(self.model.predict(x_test))
except: # if fail to fit or predict, use the last value as prediction
self.pred_values = list(
np.ones(x_test.shape) * self.values[-1])
# get last x day performance
try:
self.evaluate_model()
except: # if evaluation fails, show the failure on the web app
self.model_details = {
"r2": "Evaluation fails",
"msle": "Evaluation fails"
}
self.model_details["model_name"] = self.model.get_name()
self.model_details["show_prediction"] = True
except Exception as ex:
logger.error(Exception)
def test_compute_score_pass():
with pytest.raises(Exception):
model = exponential_model(lower_bound=0, upper_bound=[100, 100])
model.fit(np.array([1, 2, 3]), np.array([1, 2, 8]))
test_data = pd.DataFrame({"x": np.array(["random"])})
result = compute_score(model, test_data)
def test_exponential_model_get_name_fail():
with pytest.raises(Exception):
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
# invalid input
result = exp_model.get_name("get")
def test_exponential_model_predict_fail():
with pytest.raises(Exception):
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
# predict without fit
exp_model.predict(np.array([-1]))
def test_exponential_model_fit_fail():
with pytest.raises(Exception, match=r"Wrong Input Shape"):
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
exp_model.fit(np.array([1, 2, 3]), np.array([1, 2, 8, 9]))
def test_exponential_model_init_fail():
with pytest.raises(Exception, match=r"Wrong Input Length"):
exp_model = exponential_model(lower_bound=0,
upper_bound=[100, 100, 100])
def test_exponential_model_get_name_pass():
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
result = exp_model.get_name()
expected = "Exponential Model"
assert result == expected
def test_exponential_model_predict_pass():
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
exp_model.fit(np.array([1, 2, 3]), np.array([1, 2, 8]))
result = exp_model.predict(np.array([4, 5, 6]))
expected = np.array([28.694, 103.301, 371.893])
assert np.allclose(np.round(result, 3), expected)
def test_exponential_model_fit_pass():
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
exp_model.fit(np.array([1, 2, 3]), np.array([1, 2, 8]))
expected_pcov = np.array([[0.00822098, -0.01632878],
[-0.01632878, 0.03276216]])
assert np.allclose(exp_model.get_coeff()["pcov"], expected_pcov)
def test_exponential_model_init_pass():
exp_model = exponential_model(lower_bound=0, upper_bound=[100, 100])
assert (exp_model.lower_bound == 0) and (exp_model.upper_bound
== [100, 100])
