def normalisation_over_curves(self):
preprocess = PreprocessData()
preprocess.enable_normalisation_scaler = True
preprocess.enable_ignore_price = True
preprocess.feature_range = [0, 1]
sets_training, sets_test, sets_training_scaled, sets_test_scaled, training_dataset_names, test_dataset_names, maturities = preprocess.get_data(
)
self.plotting.plot_some_curves(
"normalisation_over_curves",
sets_test[0],
sets_test_scaled[0], [25, 50, 75, 815],
maturities,
plot_separate=True) # old: [25, 50, 75, 100, 600, 720, 740, 815]
def helper(self, preprocess_type):
preprocess = PreprocessData()
if preprocess_type is None or preprocess_type is PreprocessType.NORMALISATION_OVER_TENORS:
preprocess.enable_normalisation_scaler = True
preprocess.feature_range = [0, 1]
elif preprocess_type is PreprocessType.NORMALISATION_OVER_CURVES:
preprocess.enable_normalisation_scaler = True
preprocess.feature_range = [0, 1]
preprocess.enable_ignore_price = True
elif preprocess_type is PreprocessType.STANDARDISATION_OVER_TENORS:
preprocess.enable_standardisation_scaler = True
elif preprocess_type is PreprocessType.LOG_RETURNS_OVER_TENORS:
preprocess.enable_log_returns = True
sets_training, sets_test, sets_training_scaled, sets_test_scaled, training_dataset_names, test_dataset_names, maturities = preprocess.get_data(
)
rescaled_first_test_set = preprocess.rescale_data(
sets_test_scaled[0], test_dataset_names[0])
# check that assert_allclose is working:
# rand = np.random.random_sample(sets_test[0].shape)
# np.testing.assert_allclose(rescaled_first_test_set, rand)
np.testing.assert_allclose(rescaled_first_test_set, sets_test[0])