from fancyimpute import KNN
# Usa 5NN que tenham um recurso para preencher os valores ausentes de cada linha
previsores = KNN(k=3).fit_transform(previsores)
# Transforma Objeto em DATAFRAME para verificar pre-processamento
result = pd.DataFrame(previsores)
guarda = result
# Cria atributo a ser previsto
classe = result.iloc[:, 0].values
# Exclui o mesmo da base de dados previsora
result = result.drop(columns=0)
# Retorna a modificação
previsores = result.iloc[:, :].values
# Determina o tipo int para todas bases usadas
previsores = previsores.astype('int')
classe = classe.astype('int')
'''
#################################################################################################
######################################## CLASSIFICADORES ########################################
#################################################################################################
'''
from sklearn.model_selection import train_test_split #Função do pacote sklearn que divide automaticamente dados teste e dados de treinamento
from sklearn.model_selection import cross_val_score #importação do algoritmo de validação cruzada
from sklearn.model_selection import cross_validate #Retorna a taxa de previsao, tempo de execução e recall
from sklearn.metrics import confusion_matrix, f1_score #Avalização por meio de Matriz de Confução
from sklearn import metrics
import matplotlib.pyplot as plt
# Criando variaveis para treinamento e teste, usando o metodo de divisao dos dados
pred_data = pred_data.astype(int)
logger.info("初步插补 MSE:{}".format(MSE(imputedData, pred_data)))
logger.info("ae MSE:{}".format(MSE(imputedData, fix_data)))
# logger.info("初步插补 TF:{}".format(TF(imputedData, pred_data)))
# logger.info("ae TF:{}".format(TF(imputedData, fix_data)))
X_filled_knn = KNN(k=3).fit_transform(missData)
re_X = inp.revise(modifier(X_filled_knn, s),
miss_location,
model=os.path.join(modelSavePath,
'{}.pkl'.format(modelName)))
re_X = modifier(re_X, s)
re_X = re_X.astype(int)
X_filled_knn = modifier(X_filled_knn, s)
X_filled_knn = X_filled_knn.astype(int)
logger.info("knn MSE:{}".format(MSE(imputedData, X_filled_knn)))
logger.info("knn res MSE:{}".format(MSE(imputedData, re_X)))
logger.info("res change MSE:{}".format(MSE(X_filled_knn, re_X)))
# X_filled_ii = IterativeImputer().fit_transform(mm_missData)
# re_X = inp.revise(X_filled_ii, miss_location,
# model=os.path.join(modelSavePath, '{}.pkl'.format(modelName)))
# X_filled_ii = restore(min_max_scaler=min_max_scaler,s=s,data=X_filled_ii)
# re_X = restore(min_max_scaler=min_max_scaler, s=s, data=re_X)
# logger.info("ii MSE:{}".format(MSE(imputedData, X_filled_ii)))
# logger.info("ii res MSE:{}".format(MSE(imputedData, re_X)))
X_filled_sf = SimpleFill().fit_transform(missData)
re_X = inp.revise(modifier(X_filled_sf, s),
miss_location,
