easypheno.model._tensorflow_model

Module Contents

Classes

TensorflowModel

Parent class based on BaseModel for all TensorFlow models to share functionalities.
class easypheno.model._tensorflow_model.TensorflowModel(task, optuna_trial, encoding=None, n_outputs=1, n_features=None, width_onehot=None, batch_size=None, n_epochs=None, early_stopping_point=None)

Bases: easypheno.model._base_model.BaseModel, abc.ABC

Parent class based on BaseModel for all TensorFlow models to share functionalities. See BaseModel for more information.

Attributes

Inherited attributes

See BaseModel.

Additional attributes

  • n_features (int): Number of input features to the model

  • width_onehot (int): Number of input channels in case of onehot encoding

  • batch_size (int): Batch size for batch-based training

  • n_epochs (int): Number of epochs for optimization

  • optimizer (tf.keras.optimizers.Optimizer): optimizer for model fitting

  • loss_fn: loss function for model fitting

  • early_stopping_patience (int): epochs without improvement before early stopping

  • early_stopping_point (int): epoch at which early stopping occured

  • early_stopping_callback (tf.keras.callbacks.EarlyStopping): callback for early stopping

Parameters

  • task (str) – ML task (regression or classification) depending on target variable

  • optuna_trial (optuna.trial.Trial) – optuna.trial.Trial : trial of optuna for optimization

  • encoding (str) – the encoding to use (standard encoding or user-defined)

  • n_features (int) – Number of input features to the model

  • width_onehot (int) – Number of input channels in case of onehot encoding

  • batch_size (int) – Batch size for batch-based training

  • n_epochs (int) – Number of epochs for optimization

  • early_stopping_point (int) – Stop training at defined epoch

  • n_outputs (int) –

train_val_loop(self, X_train, y_train, X_val, y_val)

Implementation of a train and validation loop for TensorFlow models. See BaseModel for more information

Parameters

  • X_train (numpy.array) –

  • y_train (numpy.array) –

  • X_val (numpy.array) –

  • y_val (numpy.array) –

Return type

numpy.array

retrain(self, X_retrain, y_retrain)

Implementation of the retraining for PyTorch models. See BaseModel for more information

Parameters

  • X_retrain (numpy.array) –

  • y_retrain (numpy.array) –

predict(self, X_in)

Implementation of a prediction based on input features for PyTorch models. See BaseModel for more information

Parameters

X_in (numpy.array) –

Return type

numpy.array

get_dataloader(self, X, y=None, shuffle=True)

Get a dataloader using the specified data and batch size

Parameters

  • X (numpy.array) – feature matrix to use

  • y (numpy.array) – optional target vector to use

  • shuffle (bool) – shuffle parameter for DataLoader

Returns

batched dataset

Return type

tensorflow.data.Dataset

static common_hyperparams()

Add hyperparameters that are common for PyTorch models. Do not need to be included in optimization for every child model. Also See BaseModel for more information

save_model(self, path, filename)

Method to persist the whole model object on a hard drive (can be loaded with load_model)

Parameters

  • path (pathlib.Path) – path where the model will be saved

  • filename (str) – filename of the model