from src.DataManager import DataManager
from src.Population import Population
from src.Normalizer import *
no_of_populations = 50 # should be 50 population
no_of_descriptors = 385 # should be 385 descriptors
unfit = 1000
required_r2 = {}
required_r2[SplitTypes.Train] = .6
required_r2[SplitTypes.Valid] = .5
required_r2[SplitTypes.Test] = .5
file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv"
loaded_data = FileManager.load_file(file_path)
output_filename = FileManager.create_output_file()
#rescaling_normalizer = RescalingNormalizer()
#scikit_normalizer = ScikitNormalizer()
#data_manager = DataManager(normalizer=scikit_normalizer)
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data(test_split=0.15, train_split=0.70)
model = svm.SVR()
population = Population()
population.load_data()
from sklearn.preprocessing import MinMaxScaler
from src.Population import Population
from src.ReadData import ReadData
from src.SplitTypes import SplitTypes
from src.FileManager import FileManager
from src.DataManager import DataManager
from src.VariableSetting import VariableSetting
from src.Velocity import Velocity
read_data = ReadData()
loaded_data = read_data.read_data_and_set_variable_settings(
"../Dataset/00-91-Drugs-All-In-One-File.csv",
"../Dataset/VariableSetting.csv")
output_filename = FileManager.create_output_file()
#normalizer = ZeroOneMinMaxNormalizer()
#normalizer = MinMaxScaler()
normalizer = None
data_manager = DataManager(normalizer=normalizer)
data_manager.set_data(loaded_data)
data_manager.split_data_into_train_valid_test_sets()
#data_manager.feature_selector = debpso
#set feature selection algorithm based on variable settings
feature_selection_algo = None
if VariableSetting.Feature_Selection_Algorithm == 'DEBPSO':
feature_selection_algo = DEBPSO()
if VariableSetting.Feature_Selection_Algorithm == 'LinearSVC':
feature_selection_algo = LinearSVC()
