def main(args):
'''Main function for UCI letter and spam datasets.
Args:
- data_name: letter or spam
- miss_rate: probability of missing components
- batch:size: batch size
- hint_rate: hint rate
- alpha: hyperparameter
- iterations: iterations
Returns:
- imputed_data_x: imputed data
- rmse: Root Mean Squared Error
'''
data_name = args.data_name
miss_rate = args.miss_rate
random = args.seed
time = args.time
x1 = data_name
x = x1.split("+")
print(x)
gain_parameters = {
'batch_size': args.batch_size,
'hint_rate': args.hint_rate,
'alpha': args.alpha,
'iterations': args.iterations,
'time': args.time
}
# Load data and introduce missingness
#ori_data_x, miss_data_x, data_m = data_loader2(data_name, miss_rate,random)
miss_rate_caption = "{}% Missing".format(int(miss_rate * 100))
col1 = [
miss_rate_caption, 'RMSE', 'RMSE', 'RMSE', 'RMSE', 'RMSE', 'RMSE',
'RMSPE', 'RMSPE', 'RMSPE', 'RMSPE', 'RMSPE', 'RMSPE', '', 'MLP', 'MLP',
'D.Tree', 'D.Tree', 'LogisticR', 'LogisticR', 'LogisticR', 'LogisticR',
'LogisticR', 'LogisticR', 'SVC', 'SVC', 'SVC', 'SVC', 'SVC', 'SVC',
'SGD', 'SGD', 'SGD', 'SGD', 'SGD', 'SGD'
]
col2 = [
'Method', 'EGAIN', 'GAIN', 'MEAN', 'KNN', 'MICE', 'M.FORE', 'EGAIN',
'GAIN', 'MEAN', 'KNN', 'MICE', 'M.FORE', '', 'EGAIN', 'GAIN', 'EGAIN',
'GAIN', 'EGAIN', 'GAIN', 'MEAN', 'KNN', 'MICE', 'M.FORE', 'EGAIN',
'GAIN', 'MEAN', 'KNN', 'MICE', 'M.FORE', 'EGAIN', 'GAIN', 'MEAN',
'KNN', 'MICE', 'M.FORE'
]
result = [col1, col2]
for data_train in x:
data_name = data_train
dataset = [
'obesity', 'hepatitisC', 'audit', 'letter', 'spam', 'breast',
'credit', 'news', 'blood', 'vowel', 'ecoli', 'ionosphere',
'parkinsons', 'seedst', 'vehicle', 'vertebral', 'wine', 'banknote',
'balance', 'yeast', 'bean', 'shill', 'phishing', 'firewall',
'iBeacon', 'steel'
]
if (data_name not in dataset):
print("Wrong name: {} Dataset. Skip this datasets".format(
data_train))
break
col3 = []
col3.append(data_name)
print("****** {} Dataset ******".format(data_train))
gan_rs, egain_rs, mice_rs,miss_rs, gan_mlp, gan_dt, egan_mlp, egan_dt = [],[],[],[],[],[],[],[]
gan_svc, egan_svc, gan_lr, egan_lr, gan_sgd, egan_sgd, gan_gau, egan_gau = [],[],[],[],[],[],[],[]
knn_rmse, mean_rmse, miss_rmse, mice_rmse = [], [], [], []
gan_rmspe, egan_rmspe, knn_rmspe , mean_rmspe, miss_rmspe, mice_rmspe = [],[],[],[],[],[]
knn_lr, knn_svc, knn_sgd, mean_lr, mean_svc, mean_sgd = [],[],[],[],[],[]
miss_lr, miss_svc, miss_sgd, mice_lr, mice_svc, mice_sgd = [],[],[],[],[],[]
for i in range(time):
# Load data and introduce missingness
# Fix loader i=42
ori_data_x, miss_data_x, data_m, y = data_loader3(
data_name, miss_rate, 42) # 7) #i) block i
train_idx, test_idx = train_test_split(
range(len(y)), test_size=0.2, stratify=y,
random_state=i) #7) #i) block i
miss_data_x2 = miss_data_x #* 10000
if i % 5 == 0:
print('=== Working on {}/{} ==='.format(i, time))
# Impute missing data
imputed_data_x1 = gain(miss_data_x2, gain_parameters)
imputed_data_x_e1 = egain(miss_data_x2, gain_parameters)
imputed_data_x = imputed_data_x1 #* 1/10000
imputed_data_x_e = imputed_data_x_e1 #* 1/10000
imp_MEAN = SimpleImputer(missing_values=np.nan, strategy='mean')
imputed_data_x_mean = imp_MEAN.fit_transform(miss_data_x)
imputed_data_x_mean = imputed_data_x_mean.round()
#imputed_data_x_mean = imp_MEAN.fit_transform(miss_data_x2) *1/10000
imp_KNN = KNNImputer(missing_values=np.nan, n_neighbors=3)
imputed_data_x_knn = imp_KNN.fit_transform(miss_data_x) # *1/10000
imputed_data_x_knn = imputed_data_x_knn.round()
# ExtraTreesRegressor: similar to missForest in R; DecisionTreeRegressor()
imp_mf = IterativeImputer(estimator=ExtraTreesRegressor(),
max_iter=1,
initial_strategy="constant",
n_nearest_features=1,
imputation_order='descending') #20
imputed_data_mf = imp_mf.fit_transform(miss_data_x) #*1/10000
imputed_data_mf = imputed_data_mf.round()
#imp_mf = MissForest(max_iter=1)
#imputed_data_mf = imp_mf.fit_transform(miss_data_x)
imp_mice = IterativeImputer(
estimator=BayesianRidge(),
max_iter=1,
initial_strategy='constant',
n_nearest_features=1,
imputation_order='descending') # 'mean') #20
imputed_data_mice = imp_mice.fit_transform(miss_data_x) #*1/10000
imputed_data_mice = imputed_data_mice.round()
# Report the RMSE performance
rmse = rmse_loss(ori_data_x, imputed_data_x, data_m)
rmse_e = rmse_loss(ori_data_x, imputed_data_x_e, data_m)
rmse_mean = rmse_loss(ori_data_x, imputed_data_x_mean, data_m)
rmse_knn = rmse_loss(ori_data_x, imputed_data_x_knn, data_m)
rmse_mf = rmse_loss(ori_data_x, imputed_data_mf, data_m)
rmse_mice = rmse_loss(ori_data_x, imputed_data_mice, data_m)
gan_rs.append(rmse)
egain_rs.append(rmse_e)
mean_rmse.append(rmse_mean)
knn_rmse.append(rmse_knn)
mice_rmse.append(rmse_mice)
miss_rmse.append(rmse_mf)
# Report the RMSPE performance
rmspe = rmspe_loss(ori_data_x, imputed_data_x, data_m)
rmspe_e = rmspe_loss(ori_data_x, imputed_data_x_e, data_m)
rmspe_mean = rmspe_loss(ori_data_x, imputed_data_x_mean, data_m)
rmspe_knn = rmspe_loss(ori_data_x, imputed_data_x_knn, data_m)
rmspe_mf = rmspe_loss(ori_data_x, imputed_data_mf, data_m)
rmspe_mice = rmspe_loss(ori_data_x, imputed_data_mice, data_m)
#gan_rmspe, egan_rmspe, knn_rmspe , mean_rmspe, miss_rmspe, mice_rmspe
gan_rmspe.append(rmspe)
egan_rmspe.append(rmspe_e)
mean_rmspe.append(rmspe_mean)
knn_rmspe.append(rmspe_knn)
mice_rmspe.append(rmspe_mice)
miss_rmspe.append(rmspe_mf)
mi_data = miss_data_x.astype(float)
no, dim = imputed_data_mice.shape
miss_data = np.reshape(mi_data, (no, dim))
np.savetxt("data/{}missing_data.csv".format(i),
mi_data,
delimiter=',',
fmt='%1.2f')
np.savetxt("data/{}imputed_data_gain.csv".format(i),
imputed_data_x,
delimiter=',',
fmt='%d')
np.savetxt("data/{}imputed_data_egain.csv".format(i),
imputed_data_x_e,
delimiter=',',
fmt='%d')
imputed_data_x, _ = normalization(imputed_data_x)
imputed_data_x_e, _ = normalization(imputed_data_x_e)
imputed_data_x_mean, _ = normalization(imputed_data_x_mean)
imputed_data_x_knn, _ = normalization(imputed_data_x_knn)
imputed_data_mf, _ = normalization(imputed_data_mf)
imputed_data_mice, _ = normalization(imputed_data_mice)
gan_score_mlp = clf_MLP(imputed_data_x, y, train_idx, test_idx)
egan_score_mlp = clf_MLP(imputed_data_x_e, y, train_idx, test_idx)
gan_mlp.append(gan_score_mlp)
egan_mlp.append(egan_score_mlp)
gan_score_dt = clf_DT(imputed_data_x, y, train_idx, test_idx)
egan_score_dt = clf_DT(imputed_data_x_e, y, train_idx, test_idx)
gan_dt.append(gan_score_dt)
egan_dt.append(egan_score_dt)
gan_score_lr = clf_LR(imputed_data_x, y, train_idx, test_idx)
egan_score_lr = clf_LR(imputed_data_x_e, y, train_idx, test_idx)
mean_score_lr = clf_LR(imputed_data_x_mean, y, train_idx, test_idx)
knn_score_lr = clf_LR(imputed_data_x_knn, y, train_idx, test_idx)
miss_score_lr = clf_LR(imputed_data_mf, y, train_idx, test_idx)
mice_score_lr = clf_LR(imputed_data_mice, y, train_idx, test_idx)
gan_lr.append(gan_score_lr)
egan_lr.append(egan_score_lr)
mean_lr.append(mean_score_lr)
knn_lr.append(knn_score_lr)
miss_lr.append(miss_score_lr)
mice_lr.append(mice_score_lr)
mean_score_svc = clf_SVC(imputed_data_x_mean, y, train_idx,
test_idx)
knn_score_svc = clf_SVC(imputed_data_x_knn, y, train_idx, test_idx)
miss_score_svc = clf_SVC(imputed_data_mf, y, train_idx, test_idx)
mice_score_svc = clf_SVC(imputed_data_mice, y, train_idx, test_idx)
mean_svc.append(mean_score_svc)
knn_svc.append(knn_score_svc)
miss_svc.append(miss_score_svc)
mice_svc.append(mice_score_svc)
gan_score_svc = clf_SVC(imputed_data_x, y, train_idx, test_idx)
egan_score_svc = clf_SVC(imputed_data_x_e, y, train_idx, test_idx)
gan_svc.append(gan_score_svc)
egan_svc.append(egan_score_svc)
mean_score_sgd = clf_SGD(imputed_data_x_mean, y, train_idx,
test_idx)
knn_score_sgd = clf_SGD(imputed_data_x_knn, y, train_idx, test_idx)
miss_score_sgd = clf_SGD(imputed_data_mf, y, train_idx, test_idx)
mice_score_sgd = clf_SGD(imputed_data_mice, y, train_idx, test_idx)
mean_sgd.append(mean_score_sgd)
knn_sgd.append(knn_score_sgd)
miss_sgd.append(miss_score_sgd)
mice_sgd.append(mice_score_sgd)
gan_score_sgd = clf_SGD(imputed_data_x, y, train_idx, test_idx)
egan_score_sgd = clf_SGD(imputed_data_x_e, y, train_idx, test_idx)
gan_sgd.append(gan_score_sgd)
egan_sgd.append(egan_score_sgd)
#gan_score_gau = clf_GAU(imputed_data_x , y, train_idx, test_idx)
#egan_score_gau = clf_GAU(imputed_data_x_e , y, train_idx, test_idx)
#gan_gau.append(gan_score_gau)
#egan_gau.append(egan_score_gau)
print()
print("Datasets: ", data_name)
#print(gan_rs,egain_rs, mice_rs,miss_rs)
col3.append(
f"{round(np.mean(egain_rs)*1,2)} ± {round(np.std(egain_rs),4)}")
col3.append(
f"{round(np.mean(gan_rs)*1,2)} ± {round(np.std(gan_rs),4)}")
col3.append(
f"{round(np.mean(mean_rmse)*1,2)} ± {round(np.std(mean_rmse),4)}")
col3.append(
f"{round(np.mean(knn_rmse)*1,2)} ± {round(np.std(knn_rmse),4)}")
col3.append(
f"{round(np.mean(mice_rmse)*1,2)} ± {round(np.std(mice_rmse),4)}")
col3.append(
f"{round(np.mean(miss_rmse)*1,2)} ± {round(np.std(miss_rmse),4)}")
##gan_rmspe, egan_rmspe, knn_rmspe , mean_rmspe, miss_rmspe, mice_rmspe
col3.append(
f"{round(np.mean(egan_rmspe)*1,2)} ± {round(np.std(egan_rmspe),4)}"
)
col3.append(
f"{round(np.mean(gan_rmspe)*1,2)} ± {round(np.std(gan_rmspe),4)}")
col3.append(
f"{round(np.mean(mean_rmspe)*1,2)} ± {round(np.std(mean_rmspe),4)}"
)
col3.append(
f"{round(np.mean(knn_rmspe)*1,2)} ± {round(np.std(knn_rmspe),4)}")
col3.append(
f"{round(np.mean(mice_rmspe)*1,2)} ± {round(np.std(mice_rmspe),4)}"
)
col3.append(
f"{round(np.mean(miss_rmspe)*1,2)} ± {round(np.std(miss_rmspe),4)}"
)
col3.append([])
col3.append(
f"{round(np.mean(egan_mlp)*1,2)} ± {round(np.std(egan_mlp),4)}")
col3.append(
f"{round(np.mean(gan_mlp)*1,2)} ± { round(np.std(gan_mlp),4)}")
col3.append(
f"{round(np.mean(egan_dt)*1,2)} ± { round(np.std(egan_dt),4)}")
col3.append(
f"{round(np.mean(gan_dt)*1,2)} ± { round(np.std(gan_dt),4)}")
col3.append(
f"{round(np.mean(egan_lr)*1,2)} ± {round(np.std(egan_lr),4)}")
col3.append(
f"{round(np.mean(gan_lr)*1,2)} ± {round(np.std(gan_lr),4)}")
col3.append(
f"{round(np.mean(mean_lr)*1,2)} ± {round(np.std(mean_lr),4)}")
col3.append(
f"{round(np.mean(knn_lr)*1,2)} ± {round(np.std(knn_lr),4)}")
col3.append(
f"{round(np.mean(mice_lr)*1,2)} ± {round(np.std(mice_lr),4)}")
col3.append(
f"{round(np.mean(miss_lr)*1,2)} ± {round(np.std(miss_lr),4)}")
col3.append(
f"{round(np.mean(egan_svc)*1,2)} ± { round(np.std(egan_svc),4)}")
col3.append(
f"{round(np.mean(gan_svc)*1,2)} ± { round(np.std(gan_svc),4)}")
col3.append(
f"{round(np.mean(mean_svc)*1,2)} ± { round(np.std(mean_svc),4)}")
col3.append(
f"{round(np.mean(knn_svc)*1,2)} ± {round(np.std(knn_svc),4)}")
col3.append(
f"{round(np.mean(mice_svc)*1,2)} ± {round(np.std(mice_svc),4)}")
col3.append(
f"{round(np.mean(miss_svc)*1,2)} ± { round(np.std(miss_svc),4)}")
col3.append(
f"{round(np.mean(egan_sgd)*1,2)} ± { round(np.std(egan_sgd),4)}")
col3.append(
f"{round(np.mean(gan_sgd)*1,2)} ± { round(np.std(gan_sgd),4)}")
col3.append(
f"{round(np.mean(mean_sgd)*1,2)} ± { round(np.std(mean_sgd),4)}")
col3.append(
f"{round(np.mean(knn_sgd)*1,2)} ± {round(np.std(knn_sgd),4)}")
col3.append(
f"{round(np.mean(mice_sgd)*1,2)} ± { round(np.std(mice_sgd),4)}")
col3.append(
f"{round(np.mean(miss_sgd)*1,2)} ± {round(np.std(miss_sgd),4)}")
'''
print('RMSE GAIN: {} ± {}'.format(round(np.mean(gan_rs)*1,2), round(np.std(gan_rs),4)))
#print(gan_rs)
print('RMSE EGAIN: {} ± {}'.format(round(np.mean(egain_rs)*1,2), round(np.std(egain_rs),4)))
#print(egain_rs)
print('RMSE MEAN: {} ± {}'.format(round(np.mean(mean_rmse)*1,2), round(np.std(mean_rmse),4)))
#print(knn_rmse)
print('RMSE KNN: {} ± {}'.format(round(np.mean(knn_rmse)*1,2), round(np.std(knn_rmse),4)))
#print(mice_rmse)
print('RMSE MICE: {} ± {}'.format(round(np.mean(mice_rmse)*1,2), round(np.std(mice_rmse),4)))
#print(miss_rmse)
print('RMSE MFORE: {} ± {}'.format(round(np.mean(miss_rmse)*1,2), round(np.std(miss_rmse),4)))
#print(miss_rmse)
print()
print('MLP GAIN: {} ± {}'.format(round(np.mean(gan_mlp)*1,2), round(np.std(gan_mlp),4)))
print('MLP EGAIN: {} ± {}'.format(round(np.mean(egan_mlp)*1,2), round(np.std(egan_mlp),4)))
print()
print('DT GAIN: {} ± {}'.format(round(np.mean(gan_dt)*1,2), round(np.std(gan_dt),4)))
print('DT EGAIN: {} ± {}'.format(round(np.mean(egan_dt)*1,2), round(np.std(egan_dt),4)))
print()
print('LR GAIN: {} ± {}'.format(round(np.mean(gan_lr)*1,2), round(np.std(gan_lr),4)))
#print(gan_lr)
print('LR EGAIN: {} ± {}'.format(round(np.mean(egan_lr)*1,2), round(np.std(egan_lr),4)))
#print(egan_lr)
print('LR MEAN: {} ± {}'.format(round(np.mean(mean_lr)*1,2), round(np.std(mean_lr),4)))
#print(mean_lr)
print('LR KNN: {} ± {}'.format(round(np.mean(knn_lr)*1,2), round(np.std(knn_lr),4)))
#print(knn_lr)
print('LR MICE: {} ± {}'.format(round(np.mean(mice_lr)*1,2), round(np.std(mice_lr),4)))
#print(mice_lr)
print('LR MISSFOR: {} ± {}'.format(round(np.mean(miss_lr)*1,2), round(np.std(miss_lr),4)))
#print(miss_lr)
print()
print('SVC GAIN: {} ± {}'.format(round(np.mean(gan_svc)*1,2), round(np.std(gan_svc),4)))
#print(gan_svc)
print('SVC EGAIN: {} ± {}'.format(round(np.mean(egan_svc)*1,2), round(np.std(egan_svc),4)))
#print(egan_svc)
print('SVC MEAN: {} ± {}'.format(round(np.mean(mean_svc)*1,2), round(np.std(mean_svc),4)))
#print(mean_svc)
print('SVC KNN: {} ± {}'.format(round(np.mean(knn_svc)*1,2), round(np.std(knn_svc),4)))
#print(knn_svc)
print('SVC MICE: {} ± {}'.format(round(np.mean(mice_svc)*1,2), round(np.std(mice_svc),4)))
#print(mice_svc)
print('SVC MISS: {} ± {}'.format(round(np.mean(miss_svc)*1,2), round(np.std(miss_svc),4)))
#print(miss_svc)
print()
print('SGD GAIN: {} ± {}'.format(round(np.mean(gan_sgd)*1,2), round(np.std(gan_sgd),4)))
#print(gan_sgd)
print('SGD EGAIN: {} ± {}'.format(round(np.mean(egan_sgd)*1,2), round(np.std(egan_sgd),4)))
#print(egan_sgd)
print('SGD MEAN: {} ± {}'.format(round(np.mean(mean_sgd)*1,2), round(np.std(mean_sgd),4)))
#print(mean_sgd)
print('SGD KNN: {} ± {}'.format(round(np.mean(knn_sgd)*1,2), round(np.std(knn_sgd),4)))
#print(knn_sgd)
print('SGD MICE: {} ± {}'.format(round(np.mean(mice_sgd)*1,2), round(np.std(mice_sgd),4)))
#print(mice_sgd)
print('SGD MISS: {} ± {}'.format(round(np.mean(miss_sgd)*1,2), round(np.std(miss_sgd),4)))
'''
result.append(col3)
my_array = np.asarray(result)
#print(my_array)
df_result = pd.DataFrame(my_array)
df_result_tran = df_result.transpose()
print(df_result_tran.to_string(index=False, header=False))
#df_result_tran.to_csv("result.csv", encoding='utf-8', index=False, header=False)
df_result_tran.to_csv("result.csv", index=False, header=False)
df_result_tran.to_excel("result.xls",
encoding='utf-8',
index=False,
header=False)
#print(miss_sgd)
#print()
#print('GAU GAIN: {} ± {}'.format(round(np.mean(gan_gau)*1,2), round(np.std(gan_dt),4)))
#print('GAU EGAIN: {} ± {}'.format(round(np.mean(egan_gau)*1,2), round(np.std(egan_dt),4)))
# MissForest
#print()
#print('=== MissForest RMSE ===')
#data = miss_data_x
#imp_mean = MissForest(max_iter = 1)
#miss_f = imp_mean.fit_transform(data)
#miss_f = pd.DataFrame(imputed_train_df)
#rmse_MF = rmse_loss (ori_data_x, miss_f, data_m)
#print('RMSE Performance: ' + str(np.round(rmse_MF, 6)))
#np.savetxt("data/imputed_data_MF.csv",miss_f, delimiter=',', fmt='%d')
#print( 'Save results in Imputed_data_MF.csv')
# MICE From Auto Impute
#print()
#print('=== MICE of Auto Impute RMSE ===')
#data_mice = pd.DataFrame(miss_data_x)
#mi = MiceImputer(k=1, imp_kwgs=None, n=1, predictors='all', return_list=True,
# seed=None, strategy='interpolate', visit='default')
#mice_out = mi.fit_transform(data_mice)
#c = [list(x) for x in mice_out]
#c1= c[0]
#c2=c1[1]
#c3=np.asarray(c2)
#mice_x=c3
#print('here :', mice_x, miss_f, miss_f.shape)
#rmse_MICE = rmse_loss (ori_data_x, mice_x, data_m)
#print('=== MICE of Auto Impute RMSE ===')
#print('RMSE Performance: ' + str(np.round(rmse_MICE, 6)))
#np.savetxt("data/imputed_data_MICE.csv",mice_x, delimiter=',', fmt='%d')
#print( 'Save results in Imputed_data_MICE.csv')
return imputed_data_mf, rmse_mf
def main(args):
'''Main function for UCI letter and spam datasets.
Args:
- data_name: letter or spam
- miss_rate: probability of missing components
- batch:size: batch size
- hint_rate: hint rate
- alpha: hyperparameter
- iterations: iterations
Returns:
- imputed_data_x: imputed data
- rmse: Root Mean Squared Error
'''
data_name = args.data_name
miss_rate = args.miss_rate
random = args.seed
time = args.time
gain_parameters = {
'batch_size': args.batch_size,
'hint_rate': args.hint_rate,
'alpha': args.alpha,
'iterations': args.iterations,
'time': args.time
}
# Load data and introduce missingness
#ori_data_x, miss_data_x, data_m = data_loader2(data_name, miss_rate,random)
gan_rs, egain_rs, mice_rs,miss_rs, gan_mlp, gan_dt, egan_mlp, egan_dt = [],[],[],[],[],[],[],[]
gan_svc, egan_svc, gan_lr, egan_lr, gan_sgd, egan_sgd, gan_gau, egan_gau = [],[],[],[],[],[],[],[]
knn_rmse, mean_rmse, miss_rmse, mice_rmse = [], [], [], []
knn_lr, knn_svc, knn_sgd, mean_lr, mean_svc, mean_sgd = [],[],[],[],[],[]
miss_lr, miss_svc, miss_sgd, mice_lr, mice_svc, mice_sgd = [],[],[],[],[],[]
for i in range(time):
# Load data and introduce missingness
ori_data_x, miss_data_x, data_m, y = data_loader3(
data_name, miss_rate, i)
train_idx, test_idx = train_test_split(range(len(y)),
test_size=0.3,
stratify=y,
random_state=42)
miss_data_x2 = miss_data_x * 10000
if i % 5 == 0:
print('=== Working on {}/{} ==='.format(i, time))
# Impute missing data
imputed_data_x1 = gain(miss_data_x2, gain_parameters)
imputed_data_x_e1 = egain(miss_data_x2, gain_parameters)
imputed_data_x = imputed_data_x1 * 1 / 10000
imputed_data_x_e = imputed_data_x_e1 * 1 / 10000
imp_MEAN = SimpleImputer(missing_values=np.nan, strategy='mean')
imputed_data_x_mean = imp_MEAN.fit_transform(miss_data_x)
#imputed_data_x_mean = imp_MEAN.fit_transform(miss_data_x2) *1/10000
imp_KNN = KNNImputer(missing_values=np.nan)
imputed_data_x_knn = imp_KNN.fit_transform(miss_data_x) # *1/10000
imp_mf = IterativeImputer(estimator=DecisionTreeRegressor(),
max_iter=3) #20
imputed_data_mf = imp_mf.fit_transform(miss_data_x) #*1/10000
imp_mice = IterativeImputer(estimator=BayesianRidge(), max_iter=3) #20
imputed_data_mice = imp_mice.fit_transform(miss_data_x) #*1/10000
# Report the RMSE performance
rmse = rmse_loss(ori_data_x, imputed_data_x, data_m)
rmse_e = rmse_loss(ori_data_x, imputed_data_x_e, data_m)
rmse_mean = rmse_loss(ori_data_x, imputed_data_x_mean, data_m)
rmse_knn = rmse_loss(ori_data_x, imputed_data_x_knn, data_m)
rmse_mf = rmse_loss(ori_data_x, imputed_data_mf, data_m)
rmse_mice = rmse_loss(ori_data_x, imputed_data_mice, data_m)
gan_rs.append(rmse)
egain_rs.append(rmse_e)
mean_rmse.append(rmse_mean)
knn_rmse.append(rmse_knn)
mice_rmse.append(rmse_mice)
miss_rmse.append(rmse_mf)
mi_data = miss_data_x.astype(float)
no, dim = imputed_data_mice.shape
miss_data = np.reshape(mi_data, (no, dim))
np.savetxt("data/missing_data.csv",
mi_data,
delimiter=',',
fmt='%1.2f')
np.savetxt("data/imputed_data_gain.csv",
imputed_data_x,
delimiter=',',
fmt='%d')
np.savetxt("data/imputed_data_egain.csv",
imputed_data_x_e,
delimiter=',',
fmt='%d')
imputed_data_x, _ = normalization(imputed_data_x)
imputed_data_x_e, _ = normalization(imputed_data_x_e)
imputed_data_x_mean, _ = normalization(imputed_data_x_mean)
imputed_data_x_knn, _ = normalization(imputed_data_x_knn)
imputed_data_mf, _ = normalization(imputed_data_mf)
imputed_data_mice, _ = normalization(imputed_data_mice)
#gan_score_mlp = clf_MLP(imputed_data_x , y, train_idx, test_idx)
#egan_score_mlp = clf_MLP(imputed_data_x_e, y, train_idx, test_idx)
#gan_mlp.append(gan_score_mlp)
#egan_mlp.append(egan_score_mlp)
#gan_score_dt = clf_DT(imputed_data_x , y, train_idx, test_idx)
#egan_score_dt = clf_DT(imputed_data_x_e , y, train_idx, test_idx)
#gan_dt.append(gan_score_dt)
#egan_dt.append(egan_score_dt)
gan_score_lr = clf_LR(imputed_data_x, y, train_idx, test_idx)
egan_score_lr = clf_LR(imputed_data_x_e, y, train_idx, test_idx)
mean_score_lr = clf_LR(imputed_data_x_mean, y, train_idx, test_idx)
knn_score_lr = clf_LR(imputed_data_x_knn, y, train_idx, test_idx)
miss_score_lr = clf_LR(imputed_data_mf, y, train_idx, test_idx)
mice_score_lr = clf_LR(imputed_data_mice, y, train_idx, test_idx)
gan_lr.append(gan_score_lr)
egan_lr.append(egan_score_lr)
mean_lr.append(mean_score_lr)
knn_lr.append(knn_score_lr)
miss_lr.append(miss_score_lr)
mice_lr.append(mice_score_lr)
mean_score_svc = clf_SVC(imputed_data_x_mean, y, train_idx, test_idx)
knn_score_svc = clf_SVC(imputed_data_x_knn, y, train_idx, test_idx)
miss_score_svc = clf_SVC(imputed_data_mf, y, train_idx, test_idx)
mice_score_svc = clf_SVC(imputed_data_mice, y, train_idx, test_idx)
mean_svc.append(mean_score_svc)
knn_svc.append(knn_score_svc)
miss_svc.append(miss_score_svc)
mice_svc.append(mice_score_svc)
gan_score_svc = clf_SVC(imputed_data_x, y, train_idx, test_idx)
egan_score_svc = clf_SVC(imputed_data_x_e, y, train_idx, test_idx)
gan_svc.append(gan_score_svc)
egan_svc.append(egan_score_svc)
mean_score_sgd = clf_SGD(imputed_data_x_mean, y, train_idx, test_idx)
knn_score_sgd = clf_SGD(imputed_data_x_knn, y, train_idx, test_idx)
miss_score_sgd = clf_SGD(imputed_data_mf, y, train_idx, test_idx)
mice_score_sgd = clf_SGD(imputed_data_mice, y, train_idx, test_idx)
mean_sgd.append(mean_score_sgd)
knn_sgd.append(knn_score_sgd)
miss_sgd.append(miss_score_sgd)
mice_sgd.append(mice_score_sgd)
gan_score_sgd = clf_SGD(imputed_data_x, y, train_idx, test_idx)
egan_score_sgd = clf_SGD(imputed_data_x_e, y, train_idx, test_idx)
gan_sgd.append(gan_score_sgd)
egan_sgd.append(egan_score_sgd)
#gan_score_gau = clf_GAU(imputed_data_x , y, train_idx, test_idx)
#egan_score_gau = clf_GAU(imputed_data_x_e , y, train_idx, test_idx)
#gan_gau.append(gan_score_gau)
#egan_gau.append(egan_score_gau)
print()
print("Datasets: ", data_name)
#print(gan_rs,egain_rs, mice_rs,miss_rs)
print('RMSE GAIN: {} ± {}'.format(round(np.mean(gan_rs) * 1, 2),
round(np.std(gan_rs), 4)))
print('RMSE EGAIN: {} ± {}'.format(round(np.mean(egain_rs) * 1, 2),
round(np.std(egain_rs), 4)))
print('RMSE MEAN: {} ± {}'.format(round(np.mean(mean_rmse) * 1, 2),
round(np.std(mean_rmse), 4)))
print('RMSE KNN: {} ± {}'.format(round(np.mean(knn_rmse) * 1, 2),
round(np.std(knn_rmse), 4)))
print('RMSE MICE: {} ± {}'.format(round(np.mean(mice_rmse) * 1, 2),
round(np.std(mice_rmse), 4)))
print('RMSE MFORE: {} ± {}'.format(round(np.mean(miss_rmse) * 1, 2),
round(np.std(miss_rmse), 4)))
#print()
#print('MLP GAIN: {} ± {}'.format(round(np.mean(gan_mlp)*1,2), round(np.std(gan_mlp),4)))
#print('MLP EGAIN: {} ± {}'.format(round(np.mean(egan_mlp)*1,2), round(np.std(egan_mlp),4)))
#print()
#print('DT GAIN: {} ± {}'.format(round(np.mean(gan_dt)*1,2), round(np.std(gan_dt),4)))
#print('DT EGAIN: {} ± {}'.format(round(np.mean(egan_dt)*1,2), round(np.std(egan_dt),4)))
print()
print('LR GAIN: {} ± {}'.format(round(np.mean(gan_lr) * 1, 2),
round(np.std(gan_lr), 4)))
print('LR EGAIN: {} ± {}'.format(round(np.mean(egan_lr) * 1, 2),
round(np.std(egan_lr), 4)))
print('LR MEAN: {} ± {}'.format(round(np.mean(mean_lr) * 1, 2),
round(np.std(mean_lr), 4)))
print('LR KNN: {} ± {}'.format(round(np.mean(knn_lr) * 1, 2),
round(np.std(knn_lr), 4)))
print('LR MICE: {} ± {}'.format(round(np.mean(mice_lr) * 1, 2),
round(np.std(mice_lr), 4)))
print('LR MISSFOR: {} ± {}'.format(round(np.mean(miss_lr) * 1, 2),
round(np.std(miss_lr), 4)))
print()
print('SVC GAIN: {} ± {}'.format(round(np.mean(gan_svc) * 1, 2),
round(np.std(gan_svc), 4)))
print('SVC EGAIN: {} ± {}'.format(round(np.mean(egan_svc) * 1, 2),
round(np.std(egan_svc), 4)))
print('SVC MEAN: {} ± {}'.format(round(np.mean(mean_svc) * 1, 2),
round(np.std(mean_svc), 4)))
print('SVC KNN: {} ± {}'.format(round(np.mean(knn_svc) * 1, 2),
round(np.std(knn_svc), 4)))
print('SVC MICE: {} ± {}'.format(round(np.mean(mice_svc) * 1, 2),
round(np.std(mice_svc), 4)))
print('SVC MISS: {} ± {}'.format(round(np.mean(miss_svc) * 1, 2),
round(np.std(miss_svc), 4)))
print()
print('SGD GAIN: {} ± {}'.format(round(np.mean(gan_sgd) * 1, 2),
round(np.std(gan_sgd), 4)))
print('SGD EGAIN: {} ± {}'.format(round(np.mean(egan_sgd) * 1, 2),
round(np.std(egan_sgd), 4)))
print('SGD MEAN: {} ± {}'.format(round(np.mean(mean_sgd) * 1, 2),
round(np.std(mean_sgd), 4)))
print('SGD KNN: {} ± {}'.format(round(np.mean(knn_sgd) * 1, 2),
round(np.std(knn_sgd), 4)))
print('SGD MICE: {} ± {}'.format(round(np.mean(mice_sgd) * 1, 2),
round(np.std(mice_sgd), 4)))
print('SGD MISS: {} ± {}'.format(round(np.mean(miss_sgd) * 1, 2),
round(np.std(miss_sgd), 4)))
#print()
#print('GAU GAIN: {} ± {}'.format(round(np.mean(gan_gau)*1,2), round(np.std(gan_dt),4)))
#print('GAU EGAIN: {} ± {}'.format(round(np.mean(egan_gau)*1,2), round(np.std(egan_dt),4)))
# MissForest
#print()
#print('=== MissForest RMSE ===')
#data = miss_data_x
#imp_mean = MissForest(max_iter = 1)
#miss_f = imp_mean.fit_transform(data)
#miss_f = pd.DataFrame(imputed_train_df)
#rmse_MF = rmse_loss (ori_data_x, miss_f, data_m)
#print('RMSE Performance: ' + str(np.round(rmse_MF, 6)))
#np.savetxt("data/imputed_data_MF.csv",miss_f, delimiter=',', fmt='%d')
#print( 'Save results in Imputed_data_MF.csv')
# MICE From Auto Impute
#print()
#print('=== MICE of Auto Impute RMSE ===')
#data_mice = pd.DataFrame(miss_data_x)
#mi = MiceImputer(k=1, imp_kwgs=None, n=1, predictors='all', return_list=True,
# seed=None, strategy='interpolate', visit='default')
#mice_out = mi.fit_transform(data_mice)
#c = [list(x) for x in mice_out]
#c1= c[0]
#c2=c1[1]
#c3=np.asarray(c2)
#mice_x=c3
#print('here :', mice_x, miss_f, miss_f.shape)
#rmse_MICE = rmse_loss (ori_data_x, mice_x, data_m)
#print('=== MICE of Auto Impute RMSE ===')
#print('RMSE Performance: ' + str(np.round(rmse_MICE, 6)))
#np.savetxt("data/imputed_data_MICE.csv",mice_x, delimiter=',', fmt='%d')
#print( 'Save results in Imputed_data_MICE.csv')
return imputed_data_mf, rmse_mf