(self,
                             instruction,
                             callback=False,
                             text=[],
                             ca_threshold=None,
                             drop=None,
                             preprocess=True,
                             test_size=0.2,
                             random_state=49,
                             epochs=50,
                             generate_plots=True,
                             callback_mode='min',
                             maximizer="val_loss",
                             save_model=False,
                             save_path=os.getcwd(),
                             add_layer={})
Detects to see if it's a regression/classification problem and then calls the correct query.
        :param hyperparameters: all of these are hyperparameters that're passed to the algorithm
        :return: a model, plots, accuracy information all stored in the self.models dictionary
Detects to see if it's a regression/classification problem and then calls the correct query.
        :param hyperparameters: all of these are hyperparameters that're passed to the algorithm
        :return: a model, plots, accuracy information all stored in the self.models dictionary
Detects to see if it s a regression / classification problem and then calls the correct query . : param hyperparameters : all of these are hyperparameters that re passed to the algorithm : return : a model plots accuracy information all stored in the self . models dictionary
def neural_network_query(self,
                             instruction,
                             callback=False,
                             text=[],
                             ca_threshold=None,
                             drop=None,
                             preprocess=True,
                             test_size=0.2,
                             random_state=49,
                             epochs=50,
                             generate_plots=True,
                             callback_mode='min',
                             maximizer="val_loss",
                             save_model=False,
                             save_path=os.getcwd(),
                             add_layer={}):
        '''
        Detects to see if it's a regression/classification problem and then calls the correct query.
        :param hyperparameters: all of these are hyperparameters that're passed to the algorithm
        :return: a model, plots, accuracy information all stored in the self.models dictionary
        '''

        data = pd.read_csv(self.dataset)

        if preprocess:

            remove = get_similar_column(
                get_value_instruction(instruction), data)

            if len(data) < 50:
                raise Exception(
                    "Only datasets larger then 50 rows are supported for neural networks")
            if len(data[remove].value_counts()) <= 50:
                callback_mode = 'max'
                maximizer = "val_accuracy"
                self.classification_query_ann(
                    instruction,
                    text=text,
                    callback=callback,
                    ca_threshold=ca_threshold,
                    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)
            else:
                self.regression_query_ann(
                    instruction,
                    callback=callback,
                    text=text,
                    ca_threshold=ca_threshold,
                    preprocess=preprocess,
                    test_size=test_size,
                    random_state=random_state,
                    epochs=epochs,
                    generate_plots=generate_plots,
                    callback_mode=callback_mode,
                    maximizer=maximizer,
                    drop=drop,
                    save_model=save_model,
                    save_path=save_path,
                    add_layer=add_layer)
        clearLog()
def neural_network_query ( self , instruction , callback = False , text = [ ] , ca_threshold = None , drop = None , preprocess = True , test_size = 0.2 , random_state = 49 , epochs = 50 , generate_plots = True , callback_mode = 'min' , maximizer = "val_loss" , save_model = False , save_path = os . getcwd ( ) , add_layer = { } ) : data = pd . read_csv ( self . dataset ) if preprocess : remove = get_similar_column ( get_value_instruction ( instruction ) , data ) if len ( data ) < 50 : raise Exception ( "Only datasets larger then 50 rows are supported for neural networks" ) if len ( data [ remove ] . value_counts ( ) ) <= 50 : callback_mode = 'max' maximizer = "val_accuracy" self . classification_query_ann ( instruction , text = text , callback = callback , ca_threshold = ca_threshold , 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 ) else : self . regression_query_ann ( instruction , callback = callback , text = text , ca_threshold = ca_threshold , preprocess = preprocess , test_size = test_size , random_state = random_state , epochs = epochs , generate_plots = generate_plots , callback_mode = callback_mode , maximizer = maximizer , drop = drop , save_model = save_model , save_path = save_path , add_layer = add_layer ) clearLog ( )