from scipy.stats import spearmanr
import numpy as np
import dataML_Prepro as dmlPre
from sklearn.datasets import load_boston, load_iris, load_diabetes, load_digits, load_linnerud
from sklearn.naive_bayes import GaussianNB
from sklearn import linear_model
from sklearn.metrics import f1_score
import funcs as fun
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import General as gF
#Datasets
expe=gF.experimentVariables('unlabeledModelS')
exec(expe['variables'])
data=expe['data']
labels=expe['labels']
cont=0
ranks=[]
f1_score_mv_predval_agmnt=[]
oldAgmntLevels=[]
bestF1s=[]
while cont<expe['numTests']:
#Divide the data in training, testing and validation
splitData=dmlPre.dataSplitBalancedClass(data, labels)
trainData=splitData['trainData']
