How to use the verticapy.learn.linear_model.ElasticNet function in verticapy

info = info[0]
	for elem in parameters_dict:
		if type(parameters_dict[elem]) == str:
			parameters_dict[elem] = parameters_dict[elem].replace("'", "")
	if (model_type == "rf_regressor"):
		from verticapy.learn.ensemble import RandomForestRegressor
		model = RandomForestRegressor(name, cursor, int(parameters_dict['ntree']), int(parameters_dict['mtry']), int(parameters_dict['max_breadth']), float(parameters_dict['sampling_size']), int(parameters_dict['max_depth']), int(parameters_dict['min_leaf_size']), float(parameters_dict['min_info_gain']), int(parameters_dict['nbins']))
	elif (model_type == "rf_classifier"):
		from verticapy.learn.ensemble import RandomForestClassifier
		model = RandomForestClassifier(name, cursor, int(parameters_dict['ntree']), int(parameters_dict['mtry']), int(parameters_dict['max_breadth']), float(parameters_dict['sampling_size']), int(parameters_dict['max_depth']), int(parameters_dict['min_leaf_size']), float(parameters_dict['min_info_gain']), int(parameters_dict['nbins']))
	elif (model_type == "logistic_reg"):
		from verticapy.learn.linear_model import LogisticRegression
		model = LogisticRegression(name, cursor, parameters_dict['regularization'], float(parameters_dict['epsilon']), float(parameters_dict['lambda']), int(parameters_dict['max_iterations']), parameters_dict['optimizer'], float(parameters_dict['alpha']))
	elif (model_type == "linear_reg"):
		from verticapy.learn.linear_model import ElasticNet
		model = ElasticNet(name, cursor, parameters_dict['regularization'], float(parameters_dict['epsilon']), float(parameters_dict['lambda']), int(parameters_dict['max_iterations']), parameters_dict['optimizer'], float(parameters_dict['alpha']))
	elif (model_type == "naive_bayes"):
		from verticapy.learn.naive_bayes import MultinomialNB
		model = MultinomialNB(name, cursor, float(parameters_dict['alpha']))
	elif (model_type == "svm_regressor"):
		from verticapy.learn.svm import LinearSVR
		model = LinearSVR(name, cursor, float(parameters_dict['epsilon']), float(parameters_dict['C']), True, float(parameters_dict['intercept_scaling']), parameters_dict['intercept_mode'], float(parameters_dict['error_tolerance']), int(parameters_dict['max_iterations']))
	elif (model_type == "svm_classifier"):
		from verticapy.learn.svm import LinearSVC
		model = LinearSVC(name, cursor, float(parameters_dict['epsilon']), float(parameters_dict['C']), True, float(parameters_dict['intercept_scaling']), parameters_dict['intercept_mode'], [float(item) for item in parameters_dict['class_weights'].split(",")], int(parameters_dict['max_iterations']))
	elif (model_type == "kmeans"):
		from verticapy.learn.cluster import KMeans
		model = KMeans(name, cursor, -1, parameters_dict['init_method'], int(parameters_dict['max_iterations']), float(parameters_dict['epsilon']))
	elif (model_type == "pca"):
		from verticapy.learn.decomposition import PCA
		model = PCA(name, cursor, 0, bool(parameters_dict['scale']))
	elif (model_type == "svd"):

name: str
	Name of the the model. The model will be stored in the DB.
cursor: DBcursor, optional
	Vertica DB cursor.
tol: float, optional
	Determines whether the algorithm has reached the specified accuracy result.
max_iter: int, optional
	Determines the maximum number of iterations the algorithm performs before 
	achieving the specified accuracy result.
solver: str, optional
	The optimizer method used to train the model. 
		Newton : Newton Method
		BFGS   : Broyden Fletcher Goldfarb Shanno
		CGD    : Coordinate Gradient Descent
	"""
	return ElasticNet(name = name,
		  		 	  cursor = cursor,
		  			  penalty = 'L1', 
		  			  tol = tol, 
		  			  max_iter = max_iter, 
		  			  solver = solver)
#---#

coef: tablesample
	Coefficients and their mathematical information (pvalue, std, value...)
input_relation: str
	Train relation.
X: list
	List of the predictors.
y: str
	Response column.
test_relation: str
	Relation used to test the model. All the model methods are abstractions
	which will simplify the process. The test relation will be used by many
	methods to evaluate the model. If empty, the train relation will be 
	used as test. You can change it anytime by changing the test_relation
	attribute of the object.
	"""
	return ElasticNet(name = name,
		  		 	  cursor = cursor,
		  			  penalty = 'None', 
		  			  tol = tol, 
		  			  max_iter = max_iter, 
		  			  solver = solver)
#---#

name: str
	Name of the the model. The model will be stored in the DB.
cursor: DBcursor, optional
	Vertica DB cursor.
tol: float, optional
	Determines whether the algorithm has reached the specified accuracy result.
max_iter: int, optional
	Determines the maximum number of iterations the algorithm performs before 
	achieving the specified accuracy result.
solver: str, optional
	The optimizer method used to train the model. 
		Newton : Newton Method
		BFGS   : Broyden Fletcher Goldfarb Shanno
		CGD    : Coordinate Gradient Descent
	"""
	return ElasticNet(name = name,
		  		 	  cursor = cursor,
		  			  penalty = 'L2', 
		  			  tol = tol, 
		  			  max_iter = max_iter, 
		  			  solver = solver)