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demo_deslib.py
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import datetime
import sys
import time
import json
import pickle
import numpy as np
import io
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score, f1_score
from deslib.des.knora_e import KNORAE
from deslib.des.knora_u import KNORAU
from deslib.des.meta_des import METADES
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_recall_fscore_support
from sklearn.tree import DecisionTreeClassifier
from data_helper import data_folder, file_list, result_folder
try:
from_id = int(sys.argv[1])
to_id = int(sys.argv[2])
except:
from_id = 0
to_id = len(file_list)
n_classifiers = int(sys.argv[3])
meta_result_kne = []
meta_result_mdes = []
meta_result_knu = []
def get_xgboost_classifier(n_classes):
if n_classes == 2:
return XGBClassifier(n_estimators=200, object='binary:logistic')
else:
return XGBClassifier(n_estimators=200, object='multi:softmax')
for i_file in range(from_id, to_id):
try:
file_name = file_list[i_file]
print(datetime.datetime.now(), ' File {}: '.format(i_file), file_name)
#-------------------DATA PREPROCESS---------------------
D_train = np.loadtxt(data_folder + '/train1/' + file_name + '_train1.dat', delimiter=',')
D_val = np.loadtxt(data_folder + '/val/' + file_name + '_val.dat', delimiter=',')
D_test = np.loadtxt(data_folder + '/test/' + file_name + '_test.dat', delimiter=',')
X_train = D_train[:, :-1]
Y_train = D_train[:, -1].astype(np.int32)
X_val = D_val[:, :-1]
Y_val = D_val[:, -1].astype(np.int32)
X_train_full = np.concatenate((X_train, X_val), axis=0)
Y_train_full = np.concatenate((Y_train, Y_val))
X_test = D_test[:, :-1]
Y_test = D_test[:, -1].astype(np.int32)
X_train, X_dsel, y_train, y_dsel = train_test_split(X_train_full, Y_train_full,test_size=0.5)
classes = np.unique(Y_train_full)
n_classes = len(classes)
#-------------------- Setting Pool classifiers -------------------------------------
pool_classifiers = []
if n_classifiers == 5:
model_nb = GaussianNB().fit(X_train, y_train)
model_knn = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)
model_lr = LogisticRegression(solver = 'newton-cg').fit(X_train, y_train)
model_xgboost = get_xgboost_classifier(n_classes).fit(X_train, y_train)
model_rf = RandomForestClassifier(n_estimators=200).fit(X_train, y_train)
pool_classifiers = [model_nb,model_knn,model_lr,model_xgboost,model_rf]
# '''If pool_classifiers = None default RandomForestClassifier(n_estimators=200)'''
elif n_classifiers == 0:
pool_classifiers = None
#------------------- KONARAE TRAINING --------------------------------------
train_time_kne_start = time.time()
kne = KNORAE(pool_classifiers)
kne.fit(X_dsel, y_dsel)
train_time_kne_end = time.time()
#------------------- META DES TRAINING -------------------------------------
train_time_mdes_start = time.time()
meta = METADES(pool_classifiers)
meta.fit(X_dsel, y_dsel)
train_time_mdes_end = time.time()
#------------------- KONARAU TRAINING --------------------------------------
train_time_knu_start = time.time()
knu = KNORAU(pool_classifiers)
knu.fit(X_dsel, y_dsel)
train_time_knu_end = time.time()
#---------------------- Test KNE Pharse --------------------------
test_time_kne_start = time.time()
y_kne_pred = kne.predict(X_test)
accuracy_kne = accuracy_score(Y_test, y_kne_pred)
# print('accuracy = ', accuracy_kne)
micro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='micro')
# print('micro_f1 =', micro_f1_kne)
macro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='macro')
# print('macro_f1 =', macro_f1_kne)
test_time_kne_end = time.time()
#---------------------- Test META DES Pharse --------------------------
test_time_mdes_start = time.time()
y_mdes_pred = meta.predict(X_test)
accuracy_mdes = accuracy_score(Y_test, y_mdes_pred)
# print('accuracy = ', accuracy_mdes)
micro_f1_mdes = f1_score(Y_test - 1, y_mdes_pred - 1, average='micro')
# print('micro_f1 =', micro_f1_mdes)
# print('support_macro:',precision_recall_fscore_support(Y_test, y_mdes_pred, average='macro'))
macro_f1_mdes = f1_score(Y_test - 1, y_mdes_pred - 1, average='macro')
# print('macro_f1 =', macro_f1_mdes)
# print('micro:', precision_recall_fscore_support(Y_test, y_mdes_pred, average='micro'))
test_time_mdes_end = time.time()
#---------------------- Test KNU Pharse --------------------------
test_time_knu_start = time.time()
y_knu_pred = knu.predict(X_test)
accuracy_knu = accuracy_score(Y_test, y_knu_pred)
# print('accuracy = ', accuracy_knu)
micro_f1_knu = f1_score(Y_test - 1, y_knu_pred - 1, average='micro')
# print('micro_f1 =', micro_f1_knu)
macro_f1_knu = f1_score(Y_test - 1, y_knu_pred - 1, average='macro')
# print('macro_f1 =', macro_f1_knu)
test_time_knu_end = time.time()
#-------------------------------------- WRITE OUTPUT ---------------------------------------------
result_kne = {'data':file_name,'n_classes': n_classes,'train_time':train_time_kne_end - train_time_kne_start,
'test_time':test_time_kne_end - test_time_kne_start, 'accuracy':accuracy_kne,'micro_f1':micro_f1_kne, 'macro_f1':macro_f1_kne}
meta_result_kne.append(result_kne)
print('result_kne',accuracy_kne)
result_mdes = {'data':file_name,'n_classes': n_classes,'train_time':train_time_mdes_end - train_time_mdes_start,
'test_time':test_time_mdes_end - test_time_mdes_start, 'accuracy':accuracy_mdes,'micro_f1':micro_f1_mdes, 'macro_f1':macro_f1_mdes}
meta_result_mdes.append(result_mdes)
print('accuracy_mdes',accuracy_mdes)
result_knu = {'data':file_name,'n_classes': n_classes,'train_time':train_time_knu_end - train_time_knu_start,
'test_time':test_time_knu_end - test_time_knu_start, 'accuracy':accuracy_knu,'micro_f1':micro_f1_knu , 'macro_f1':macro_f1_knu}
meta_result_knu.append(result_knu)
print('accuracy_knu',accuracy_knu)
except:
print('===========File {}============='.format(file_name))
meta_result_kne.append({'data':file_name,'n_classes': [], 'train_time': [], 'test_time': [], 'accuracy': [], 'micro_f1': [], 'macro_f1': []})
meta_result_mdes.append({'data':file_name,'n_classes': [], 'train_time': [], 'test_time': [], 'accuracy': [], 'micro_f1': [], 'macro_f1': []})
meta_result_knu.append({'data':file_name,'n_classes': [], 'train_time': [], 'test_time': [], 'accuracy': [], 'micro_f1': [], 'macro_f1': []})
if n_classifiers == 5:
with open('result/result_knora-e(5).txt', 'w') as outfile:
json.dump(meta_result_kne, outfile)
with open('result/result_knora-u(5).txt', 'w') as outfile:
json.dump(meta_result_knu, outfile)
with open('result/result_meta-des(5).txt', 'w') as outfile:
json.dump(meta_result_mdes, outfile)
elif n_classifiers == 0:
with open('result/result_knora-e(rf200).txt', 'w') as outfile:
json.dump(meta_result_kne, outfile)
with open('result/result_knora-u(rf200).txt', 'w') as outfile:
json.dump(meta_result_knu, outfile)
with open('result/result_meta-des(rf200).txt', 'w') as outfile:
json.dump(meta_result_mdes, outfile)