(
            self,
            instruction,
            callback=False,
            text=[],
            drop=None,
            ca_threshold=None,
            preprocess=True,
            test_size=0.2,
            random_state=49,
            epochs=50,
            generate_plots=True,
            callback_mode='min',
            maximizer="val_loss",
            save_model=True,
            save_path=os.getcwd(),
            add_layer={})
Calls the body of the regression_query__ code in the supplementaries.py file. Used for a regression feed forward neural network.
        :param instruction: The objective that you want to model (str).
        :param callback: Applying a set of functions/actions at various stages of training (bool).
        :param ca_threshold: Threshold for multiple correspondence analysis (float).
        :param dataset: The dataset being used in the regression feed forward neural network (str).
        :param text: A list of columns to perform text embedding on.
        :param drop: A list of the dataset's columns to drop.
        :param preprocess: Preprocess the data (bool).
        :param test_size: Size of the testing set (float).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param epochs: Number of epochs (int).
        :param generate_plots: Generate plots for the model (bool).
        :param callback_mode: The type of callback (str).
        :param maximizer: The accuracy/loss type to optimize (str).
        :param save_model: Save the model (bool).
        :param save_path: Filepath of where to save the model (str).

        :return: a model and information to along with it stored in the self.models dictionary.
Calls the body of the regression_query__ code in the supplementaries.py file. Used for a regression feed forward neural network.
        :param instruction: The objective that you want to model (str).
        :param callback: Applying a set of functions/actions at various stages of training (bool).
        :param ca_threshold: Threshold for multiple correspondence analysis (float).
        :param dataset: The dataset being used in the regression feed forward neural network (str).
        :param text: A list of columns to perform text embedding on.
        :param drop: A list of the dataset's columns to drop.
        :param preprocess: Preprocess the data (bool).
        :param test_size: Size of the testing set (float).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param epochs: Number of epochs (int).
        :param generate_plots: Generate plots for the model (bool).
        :param callback_mode: The type of callback (str).
        :param maximizer: The accuracy/loss type to optimize (str).
        :param save_model: Save the model (bool).
        :param save_path: Filepath of where to save the model (str).
Calls the body of the regression_query__ code in the supplementaries . py file . Used for a regression feed forward neural network . : param instruction : The objective that you want to model ( str ) . : param callback : Applying a set of functions / actions at various stages of training ( bool ) . : param ca_threshold : Threshold for multiple correspondence analysis ( float ) . : param dataset : The dataset being used in the regression feed forward neural network ( str ) . : param text : A list of columns to perform text embedding on . : param drop : A list of the dataset s columns to drop . : param preprocess : Preprocess the data ( bool ) . : param test_size : Size of the testing set ( float ) . : param random_state : Initialize a pseudo - random number generator ( int ) . : param epochs : Number of epochs ( int ) . : param generate_plots : Generate plots for the model ( bool ) . : param callback_mode : The type of callback ( str ) . : param maximizer : The accuracy / loss type to optimize ( str ) . : param save_model : Save the model ( bool ) . : param save_path : Filepath of where to save the model ( str ) .
def regression_query_ann(
            self,
            instruction,
            callback=False,
            text=[],
            drop=None,
            ca_threshold=None,
            preprocess=True,
            test_size=0.2,
            random_state=49,
            epochs=50,
            generate_plots=True,
            callback_mode='min',
            maximizer="val_loss",
            save_model=True,
            save_path=os.getcwd(),
            add_layer={}):
        '''
        Calls the body of the regression_query__ code in the supplementaries.py file. Used for a regression feed forward neural network.
        :param instruction: The objective that you want to model (str).
        :param callback: Applying a set of functions/actions at various stages of training (bool).
        :param ca_threshold: Threshold for multiple correspondence analysis (float).
        :param dataset: The dataset being used in the regression feed forward neural network (str).
        :param text: A list of columns to perform text embedding on.
        :param drop: A list of the dataset's columns to drop.
        :param preprocess: Preprocess the data (bool).
        :param test_size: Size of the testing set (float).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param epochs: Number of epochs (int).
        :param generate_plots: Generate plots for the model (bool).
        :param callback_mode: The type of callback (str).
        :param maximizer: The accuracy/loss type to optimize (str).
        :param save_model: Save the model (bool).
        :param save_path: Filepath of where to save the model (str).

        :return: a model and information to along with it stored in the self.models dictionary.
        '''

        self.models['regression_ANN'] = regression_ann(
            instruction=instruction,
            callback=False,
            ca_threshold=.25 if ca_threshold is None else ca_threshold,
            dataset=self.dataset,
            text=text,
            drop=drop,
            preprocess=preprocess,
            test_size=test_size,
            random_state=random_state,
            epochs=epochs,
            generate_plots=generate_plots,
            callback_mode=callback_mode,
            maximizer=maximizer,
            save_model=save_model,
            save_path=save_path,
            add_layer=add_layer)

        self.latest_model = 'regression_ANN'
        clearLog()
def regression_query_ann ( self , instruction , callback = False , text = [ ] , drop = None , ca_threshold = None , preprocess = True , test_size = 0.2 , random_state = 49 , epochs = 50 , generate_plots = True , callback_mode = 'min' , maximizer = "val_loss" , save_model = True , save_path = os . getcwd ( ) , add_layer = { } ) : self . models [ 'regression_ANN' ] = regression_ann ( instruction = instruction , callback = False , ca_threshold = .25 if ca_threshold is None else ca_threshold , dataset = self . dataset , text = text , drop = drop , preprocess = preprocess , test_size = test_size , random_state = random_state , epochs = epochs , generate_plots = generate_plots , callback_mode = callback_mode , maximizer = maximizer , save_model = save_model , save_path = save_path , add_layer = add_layer ) self . latest_model = 'regression_ANN' clearLog ( )