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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"):
from verticapy.learn.decomposition import SVD
model = SVD(name, cursor)
elif (model_type == "one_hot_encoder_fit"):
from verticapy.learn.preprocessing import OneHotEncoder
model = OneHotEncoder(name, cursor)
model.input_relation = info.split(",")[1].replace("'", '').replace('\\', '')
model.test_relation = test_relation if (test_relation) else model.input_relation
if (model_type not in ("kmeans", "pca", "svd", "one_hot_encoder_fit")):
model.X = info.split(",")[3:len(info.split(","))]
model.X = [item.replace("'", '').replace('\\', '') for item in model.X]
model.y = info.split(",")[2].replace("'", '').replace('\\', '')
elif (model_type in ("pca")):
model.X = info.split(",")[2:len(info.split(","))]
model.X = [item.replace("'", '').replace('\\', '') for item in model.X]
model.components = to_tablesample(query = "SELECT GET_MODEL_ATTRIBUTE(USING PARAMETERS model_name = '{}', attr_name = 'principal_components')".format(name.replace("'", "''")), cursor = cursor)
model.components.table_info = False
model.explained_variance = to_tablesample(query = "SELECT GET_MODEL_ATTRIBUTE(USING PARAMETERS model_name = '{}', attr_name = 'singular_values')".format(name.replace("'", "''")), cursor = cursor)
model.explained_variance.table_info = False
model.mean = to_tablesample(query = "SELECT GET_MODEL_ATTRIBUTE(USING PARAMETERS model_name = '{}', attr_name = 'columns')".format(name.replace("'", "''")), cursor = cursor)
model.mean.table_info = False
elif (model_type in ("svd")):