user_input#

sparx.user_input.get_ffnn_model(x_data, y_data, hidden_layers_size=[4])#: Legacy code for BASIC MODEL for the FF-NN

sparx.user_input.get_ffnn_model_general(x_data: DataFrame, y_data: DataFrame, activation_funcs: list[str], hidden_layers_size: list[int], output_activaiton: Optional[str] = 'sigmoid') → Model#: BASIC MODEL for the FF-NN

sparx.user_input.import_dataset(filepath: str, features: Optional[list[str]] = None, has_index: bool = True) → tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]#: Import dataset from file path to pandas dataframe.

sparx.user_input.import_model(framework: Framework, model: any) → Model#: Approach 1: User inputs a pre-trained model

sparx.user_input.load_preset_dataset(dataset: str) → tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]#: Load and plot

sparx.user_input.net_train(model, x_train, y_train_onehot, x_validate, y_validate_onehot, epochs=1000)#: Train the model

sparx.user_input.precision_m(y_true, y_pred)#: Precision function.

sparx.user_input.recall_m(y_true, y_pred)#: Recall function.

sparx.user_input.train_model(dataset: str, activation_functions: list[str], hidden_layers_size: list[int], epochs: int = 10) → Model#: Train model.

sparx.user_input.verify_keras_model_is_fnn(model: Model) → bool#: Verify that the model is a feed-forward neural network.