ForeTiS.model._tensorflow_model
Module Contents
Classes
Parent class based on |
- class ForeTiS.model._tensorflow_model.TensorflowModel(optuna_trial, datasets, featureset_name, optimize_featureset, pca_transform=None, target_column=None)
Bases:
ForeTiS.model._base_model.BaseModel
,abc.ABC
Parent class based on
BaseModel
for all TensorFlow models to share functionalities. SeeBaseModel
for more information.Attributes
Inherited attributes
See
BaseModel
.Additional attributes
x_scaler (sklearn.preprocessing.StandardScaler): Standard scaler for the x data
y_scaler (sklearn.preprocessing.StandardScaler): Standard scaler for the y data
- Parameters:
- define_model()
Definition of the actual prediction model.
See
BaseModel
for more information.- Return type:
gpflow.models.GPR
- retrain(retrain)
Implementation of the retraining for models with sklearn-like API. See
BaseModel
for more information- Parameters:
retrain (pandas.DataFrame) –
- update(update, period)
Implementation of the retraining for models with sklearn-like API. See
BaseModel
for more information- Parameters:
update (pandas.DataFrame) –
period (int) –
- predict(X_in)
Implementation of a prediction based on input features for models with sklearn-like API. See
BaseModel
for more information- Parameters:
X_in (pandas.DataFrame) –
- Return type:
numpy.array
- train_val_loop(train, val)
Implementation of a train and validation loop for models with sklearn-like API. See
BaseModel
for more information- Parameters:
train (pandas.DataFrame) –
val (pandas.DataFrame) –
- Return type:
numpy.array
- extend_kernel_combinations()
Function extending kernels list with combinations based on base_kernels