def test_integration_nonperfect_linear():
X, y = data_gen(nrows=100, Non_perfect=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
scores = cross_validation(lm(), X=X_train, y=y_train, shuffle=False)
summary = summary_cv(scores)
assert summary['mean'] < 1 and summary['median'] < 1 and summary[
'sd'] > 0, 'Non-perfect linear relation test does not give correct summary'
def test_integration_nonperfect_linear():
X, y = data_gen(nrows=100, Non_perfect=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
scores = cross_validation(lm(), X=X_train, y=y_train, shuffle=False)
summary = summary_cv(scores)
assert summary['mean'] < 1 and summary['median'] < 1 and summary[
'sd'] > 0, 'Non-perfect linear relation test does not give correct summary'
def test_sd_is_float():
assert isinstance(summary_cv(gen_summary())['sd'], float)
def test_output_length():
assert len(summary_cv(gen_summary())) == 3
def test_input_contains_over_1():
with pytest.raises(ValueError):
summary_cv(scores=[0.96, 0.97, 1.98])
def test_zero_length_input():
with pytest.raises(TypeError):
summary_cv(scores=[])
def test_input_as_tuple():
with pytest.raises(TypeError):
summary_cv(scores=(0.96, 0.97, 0.98, 0.99))
def test_summary_cv_sd():
assert summary_cv(gen_summary())['sd'] == 0.009
def test_sd_is_float():
assert isinstance(summary_cv(gen_summary())['sd'], float)
def test_summary_cv():
assert summary_cv(gen_summary())['mean'] == 0.9666667
assert summary_cv(gen_summary())['sd'] == 0.01032796
assert summary_cv(gen_summary())['mode'] == 0.97
assert summary_cv(gen_summary())['median'] == 0.97
def test_is_float():
assert isinstance(summary_cv(gen_summary())['mean'], float)
assert isinstance(summary_cv(gen_summary())['sd'], float)
assert isinstance(summary_cv(gen_summary())['mode'], float)
assert isinstance(summary_cv(gen_summary())['median'], float)
def test_input_contains_over_1():
with pytest.raises(ValueError('`scores` must be between 0 and 1.')):
summary_cv(scores=[0.96, 0.97, 1.98])
def test_input_contains_negative():
with pytest.raises(ValueError('`scores` must be a nonnegative number.')):
summary_cv(scores=[0.96, 0.97, -0.98])
def test_zero_length_input():
with pytest.raises(DimensionError('`scores` cannot be of length zero.')):
summary_cv(scores=[])
def test_summary_cv_sd():
assert summary_cv(gen_summary())['sd'] == 0.009
def test_input_contains_string():
with pytest.raises(TypeError('Elements of `scores` must be numbers.')):
summary_cv(scores=c(0.96, 0.97, "0.98"))
def test_input_as_dataframe():
with pytest.raises(TypeError):
summary_cv(scores=pd.DataFrame(
data={'cv_scores': [0.97, 0.96, 0.98, 0.97, 0.95, 0.97]}))
def test_summary_cv_median():
assert summary_cv(gen_summary())['median'] == 0.97
def test_input_as_tuple():
with pytest.raises(TypeError):
summary_cv(scores=(0.96, 0.97, 0.98, 0.99))
def test_input_as_dataframe():
with pytest.raises(TypeError):
summary_cv(scores=pd.DataFrame(data={'cv_scores': [0.97, 0.96, 0.98, 0.97, 0.95, 0.97]}))
def test_input_contains_string():
with pytest.raises(TypeError):
summary_cv(scores=[0.96, 0.97, "0.98"])
def test_input_contains_string():
with pytest.raises(TypeError):
summary_cv(scores=[0.96, 0.97, "0.98"])
def test_zero_length_input():
with pytest.raises(TypeError):
summary_cv(scores=[])
def test_input_contains_negative():
with pytest.raises(ValueError):
summary_cv(scores=[0.96, 0.97, -0.98])
def test_input_contains_negative():
with pytest.raises(ValueError):
summary_cv(scores=[0.96, 0.97, -0.98])
def test_is_dict():
assert isinstance(summary_cv(gen_summary()), dict)
def test_input_contains_over_1():
with pytest.raises(ValueError):
summary_cv(scores=[0.96, 0.97, 1.98])
def test_median_is_float():
assert isinstance(summary_cv(gen_summary())['median'], float)
def test_is_dict():
assert isinstance(summary_cv(gen_summary()), dict)
def test_summary_cv_median():
assert summary_cv(gen_summary())['median'] == 0.97
def test_output_length():
assert len(summary_cv(gen_summary())) == 3
def test_median_is_float():
assert isinstance(summary_cv(gen_summary())['median'], float)
def test_input_as_tuple():
with pytest.raises(TypeError('`scores` must be a list.')):
summary_cv(scores=(0.96, 0.97, 0.98, 0.99))
