def test_fit_pca(fn):
dates = generate_random_dates(4)
assets = get_assets(3)
fn_inputs = {
'returns':
pd.DataFrame([[0.02769242, 1.34872387, 0.23460972],
[-0.94728692, 0.68386883, -1.23987235],
[1.93769376, -0.48275934, 0.34957348],
[0.23985234, 0.35897345, 0.34598734]], dates, assets),
'num_factor_exposures':
2,
'svd_solver':
'full'
}
fn_correct_values = {
'PCA':
PCA(),
'PCA.components_':
np.array([[0.81925896, -0.40427891, 0.40666118],
[-0.02011128, 0.68848693, 0.72496985]])
}
pca_fit = PCA.fit
with patch.object(PCA, 'fit', autospec=True) as mock_fit:
mock_fit.side_effect = pca_fit
fn_return_value = fn(**fn_inputs)
assert_structure(fn_return_value, fn_correct_values['PCA'], 'PCA')
try:
# print(dir(fn_return_value.fit))
fn_return_value.fit.assert_called()
# old python:
# fn_return_value.fit.assert_any_call()
except AssertionError:
raise Exception('Test Failure: PCA.fit not called')
try:
fn_return_value.fit.assert_called_with(self=fn_return_value,
X=fn_inputs['returns'])
except Exception:
raise Exception(
'Test Failure: PCA.fit called with the wrong arguments')
assert_structure(fn_return_value.components_,
fn_correct_values['PCA.components_'],
'PCA.components_')
if not does_data_match(fn_return_value.components_,
fn_correct_values['PCA.components_']):
raise Exception('Test Failure: PCA not fitted correctly\n\n'
'PCA.components_:\n'
'{}\n\n'
'Expected PCA.components_:\n'
'{}'.format(fn_return_value.components_,
fn_correct_values['PCA.components_']))
def test_fit_pca(fn):
dates = generate_random_dates(4)
assets = get_assets(3)
fn_inputs = {
'returns': pd.DataFrame(
[
[0.02769242, 1.34872387, 0.23460972],
[-0.94728692, 0.68386883, -1.23987235],
[1.93769376, -0.48275934, 0.34957348],
[0.23985234, 0.35897345, 0.34598734]],
dates, assets),
'num_factor_exposures': 2,
'svd_solver': 'full'}
fn_correct_values = {
'PCA': PCA(),
'PCA.components_': np.array([
[0.81925896, -0.40427891, 0.40666118],
[-0.02011128, 0.68848693, 0.72496985]])}
pca_fit = PCA.fit
with patch.object(PCA, 'fit', autospec=True) as mock_fit:
mock_fit.side_effect = pca_fit
fn_return_value = fn(**fn_inputs)
assert_structure(fn_return_value, fn_correct_values['PCA'], 'PCA')
try:
fn_return_value.fit.assert_called()
except AssertionError:
raise Exception('Test Failure: PCA.fit not called')
try:
fn_return_value.fit.assert_called_with(self=fn_return_value, X=fn_inputs['returns'])
except Exception:
raise Exception('Test Failure: PCA.fit called with the wrong arguments')
assert_structure(fn_return_value.components_, fn_correct_values['PCA.components_'], 'PCA.components_')
if not does_data_match(fn_return_value.components_, fn_correct_values['PCA.components_']):
raise Exception('Test Failure: PCA not fitted correctly\n\n'
'PCA.components_:\n'
'{}\n\n'
'Expected PCA.components_:\n'
'{}'.format(fn_return_value.components_, fn_correct_values['PCA.components_']))
def test_fit_pca(fn):
dates = generate_random_dates(4)
assets = get_assets(3)
fn_inputs = {
"returns":
pd.DataFrame(
[
[0.02769242, 1.34872387, 0.23460972],
[-0.94728692, 0.68386883, -1.23987235],
[1.93769376, -0.48275934, 0.34957348],
[0.23985234, 0.35897345, 0.34598734],
],
dates,
assets,
),
"num_factor_exposures":
2,
"svd_solver":
"full",
}
fn_correct_values = {
"PCA":
PCA(),
"PCA.components_":
np.array([
[0.81925896, -0.40427891, 0.40666118],
[-0.02011128, 0.68848693, 0.72496985],
]),
}
pca_fit = PCA.fit
with patch.object(PCA, "fit", autospec=True) as mock_fit:
mock_fit.side_effect = pca_fit
fn_return_value = fn(**fn_inputs)
assert_structure(fn_return_value, fn_correct_values["PCA"], "PCA")
try:
fn_return_value.fit.assert_called()
except AssertionError:
raise Exception("Test Failure: PCA.fit not called")
try:
fn_return_value.fit.assert_called_with(self=fn_return_value,
X=fn_inputs["returns"])
except Exception:
raise Exception(
"Test Failure: PCA.fit called with the wrong arguments")
assert_structure(
fn_return_value.components_,
fn_correct_values["PCA.components_"],
"PCA.components_",
)
if not does_data_match(fn_return_value.components_,
fn_correct_values["PCA.components_"]):
raise Exception("Test Failure: PCA not fitted correctly\n\n"
"PCA.components_:\n"
"{}\n\n"
"Expected PCA.components_:\n"
"{}".format(fn_return_value.components_,
fn_correct_values["PCA.components_"]))