def test_ModelMetrics(sample_data):
series_one, series_two = sample_data
model_metrics = ModelMetrics(series_one, series_two, num_parameters=2)
assert model_metrics.observed_length == 5
assert model_metrics.predicted_length == 5
assert model_metrics.merged_length == 5
assert model_metrics.observed_mean == 3.0
assert model_metrics.predicted_mean == 2.8
assert round(model_metrics.observed_skew, 3) == 0.524
assert round(model_metrics.predicted_skew, 3) == 0.512
assert round(model_metrics.observed_kurtosis, 3) == -0.963
assert round(model_metrics.predicted_kurtosis, 3) == -0.612
assert round(model_metrics.observed_cvstd, 3) == 0.707
assert round(model_metrics.predicted_cvstd, 3) == 0.299
assert round(model_metrics.r_squared, 3) == 0.972
assert round(model_metrics.r_squared_adj, 3) == 0.944
assert round(model_metrics.cvrmse, 3) == 0.394
assert round(model_metrics.cvrmse_adj, 3) == 0.509
assert round(model_metrics.mape, 3) == 0.517
assert round(model_metrics.mape_no_zeros, 3) == 0.517
assert model_metrics.num_meter_zeros == 0
assert round(model_metrics.nmae, 3) == 0.333
assert round(model_metrics.nmbe, 3) == -0.067
assert round(model_metrics.autocorr_resid, 3) == -0.674
assert repr(model_metrics) is not None
assert json.dumps(model_metrics.json()) is not None
def test_ModelMetrics_invalid_confidence_level(sample_data):
series_one, series_two = sample_data
with pytest.raises(Exception) as e:
model_metrics = ModelMetrics(
series_one, series_two, num_parameters=2, confidence_level=1.1
)
with pytest.raises(Exception) as e:
model_metrics = ModelMetrics(
series_one, series_two, num_parameters=2, confidence_level=-1
)
def test_model_results_json_with_model_metrics():
candidate_model = CandidateModel(model_type="model_type",
formula="formula",
status="status",
r_squared_adj=0.5)
model_results = ModelResults(
status="status",
method_name="method_name",
model=candidate_model,
r_squared_adj=np.nan,
)
model_metrics = ModelMetrics(observed_input=pd.Series([0, 1, 2]),
predicted_input=pd.Series([1, 0, 2]))
json_result = model_results.json()
json.dumps(json_result) # just make sure it's valid json
json_result["metrics"] = {} # overwrite because of floats
assert json_result == {
"candidates": None,
"metadata": {},
"method_name": "method_name",
"metrics": {},
"model": {
"formula": "formula",
"model_params": {},
"model_type": "model_type",
"r_squared_adj": 0.5,
"status": "status",
"warnings": [],
},
"settings": {},
"status": "status",
"r_squared_adj": None,
"warnings": [],
}
def test_ModelMetrics_diff_length_error_with_nan(sample_data_diff_length_with_nan):
series_one, series_two = sample_data_diff_length_with_nan
model_metrics = ModelMetrics(series_one, series_two)
assert len(model_metrics.warnings) == 1
warning = model_metrics.warnings[0]
assert warning.qualified_name.startswith("eemeter.metrics.input_series_are_of")
assert warning.description.startswith("Input series")
assert warning.data == {
"merged_length": 5,
"observed_input_length": 7,
"observed_length_without_nan": 7,
"predicted_input_length": 7,
"predicted_length_without_nan": 5,
}
def test_ModelMetrics_autocorr_lags_error(sample_data):
series_one, series_two = sample_data
with pytest.raises(ValueError):
model_metrics = ModelMetrics(series_one, series_two, autocorr_lags=0)
def test_ModelMetrics_num_parameter_error(sample_data):
series_one, series_two = sample_data
with pytest.raises(ValueError):
model_metrics = ModelMetrics(series_one, series_two, num_parameters=-1)
def test_ModelMetrics_zeros(sample_data_zeros):
series_one, series_two = sample_data_zeros
model_metrics = ModelMetrics(series_one, series_two, num_parameters=2)
assert np.isinf(model_metrics.mape)
assert model_metrics.num_meter_zeros == 2
def model_metrics(sample_data):
series_one, series_two = sample_data
return ModelMetrics(series_one, series_two, num_parameters=2)
def test_ModelMetrics_inputs_unchanged(sample_data):
series_one, series_two = sample_data
model_metrics = ModelMetrics(series_one, series_two)
assert sample_data[0].name == "NameOne"
assert sample_data[1].name == "NameTwo"
def test_ModelMetrics(sample_data):
series_one, series_two = sample_data
model_metrics = ModelMetrics(series_one, series_two, num_parameters=2)
test_sample_model_metrics(model_metrics)
assert repr(model_metrics) is not None
assert json.dumps(model_metrics.json()) is not None
def test_model_metrics_json_covert(sample_data):
series_one, series_two = sample_data
model_metrics = ModelMetrics(series_one, series_two, num_parameters=2)
json_rep = model_metrics.json()
test_sample_model_metrics(ModelMetrics.from_json(json_rep))
def test_ModelMetrics_diff_length_error(sample_data_diff_length):
series_one, series_two = sample_data_diff_length
with pytest.raises(ValueError):
model_metrics = ModelMetrics(series_one, series_two)