def main_14():
if not os.path.isdir('./outputs'):
os.mkdir('./outputs')
if not os.path.isdir('./models'):
os.mkdir('./models')
"""
preprocessing
"""
_inputs, _outputs, input_dict = regression_preprocessing(
) # 범주형 데이터 리스트, 사람 수 데이터 리스트, 벡터화 dictionary
# delete columns
_outputs = deleteColumn(_outputs, [1]) # 사람 수 데이터에서 사상자 수 column을 제외
_inputs, _outputs = shuffleList(
_inputs, _outputs) # 데이터 리스트가 고루 섞이도록 _inputs와 _outputs를 함께 섞음
inputs, outputs, input_test, input_train, input_val, output_test, output_train, output_val \
= separate_set_one_output(_inputs, _outputs) # 범주형 데이터와 사람 수 데이터를 각각 test, train, validate를 위해 분류
# ensemble
num_models = 11 # 11
cnt = 0
models = []
while cnt < num_models:
"""
create model
"""
model = createModel(inputs, outputs)
"""
training
"""
model.compile(loss="mse", optimizer="adam", metrics=['accuracy'])
# model.summary()
# early stopping
# val_loss값이 10번 동안 개선되지 않으면 해당 model의 학습을 중단
early_stopping = EarlyStopping(monitor='val_loss',
patience=10,
verbose=0)
# train
# model의 학습 이력 정보로 train의 loss와 accuracy, val의 loss와 accuracy 값을 받음
hist = model.fit([np.array(i) for i in input_train],
np.array(output_train),
epochs=1000,
batch_size=pow(2, 13),
validation_data=([np.array(i) for i in input_val],
np.array(output_val)),
callbacks=[early_stopping],
verbose=0)
try:
# plot_hist(hist)
pass
except:
pass
"""
test
"""
# model의 성능 평가
# score = model.evaluate([np.array(i) for i in input_test], np.array(output_test), verbose=0)
# print('complete: %s = %.2f%%' % (model.metrics_names[1], score[1] * 100))
_preds = model.predict([np.array(i) for i in input_test])
preds = []
for i, _pred in enumerate(_preds):
preds.append([])
for val in _pred:
# 반올림된 예측값이 0보다 클 경우 preds 리스트에 추가, 음수일경우 0을 추가
preds[i].append(int(max(0, round(val))))
# 성능 평가
res = evaluate_lists(preds, output_test)
# TH = 0.90
if res < 0.90:
print('fail : model %d: %.2f%%' % (cnt, res * 100))
continue
# PASS
print('complete: model %d: %.2f%%' % (cnt, res * 100))
"""
save model
"""
model.save('./models/14_model_' + str(cnt) + '.h5')
try:
plot_model(model,
to_file='./models/14_model_' + str(cnt) + '.png',
show_shapes=True,
show_layer_names=True)
except:
pass
models.append(model)
# model의 개수가 10개가 될 때
cnt += 1
"""
predict
"""
f1 = open('./outputs/13_output.csv', 'r')
f2 = open('./outputs/14_output.csv', 'w', newline='')
r1 = csv.reader(f1)
r2 = csv.writer(f2)
for i, row in enumerate(r1):
# blank
if ((0 < i) & (i < 11)) | ((30 < i) & (i < 36)) | (45 < i):
"""
preprocessing
"""
# copy
_row = row[:]
# C~G열 제외
for k, j in enumerate([2, 3, 4, 5, 6]):
del (_row[j - k])
input_pred = []
for j, elem in enumerate(_row):
try:
if isinstance(elem, int):
input_pred.append(elem)
else:
input_pred.append(input_dict[j][elem])
except:
input_pred.append('')
"""
predict
"""
preds = []
for k, model in enumerate(models):
_pred = list(
model.predict([np.array([j]) for j in input_pred])[0])
preds.append([])
for val in _pred:
# 반올림된 예측값이 0보다 클 경우 preds 리스트에 추가, 음수일경우 0을 추가
preds[k].append(int(max(0, round(val))))
collect = []
for j in range(len(preds[0])):
collect.append([])
for k in range(len(preds)):
collect[j].append(preds[k][j])
# majority
val1, rate1 = majority(collect[0])
print('save : row %2d: 1_val = %.2f%%' % (i + 1, rate1 * 100))
val2, rate2 = majority(collect[1])
print('save : row %2d: 2_val = %.2f%%' % (i + 1, rate2 * 100))
val3, rate3 = majority(collect[2])
print('save : row %2d: 3_val = %.2f%%' % (i + 1, rate3 * 100))
val4, rate4 = majority(collect[3])
print('save : row %2d: 4_val = %.2f%%' % (i + 1, rate4 * 100))
"""
write
"""
if row[2] == '':
row[2] = val1
if row[4] == '':
row[4] = val2
if row[5] == '':
row[5] = val3
if row[6] == '':
row[6] = val4
r2.writerow(row)
else:
r2.writerow(row)
f1.close()
f2.close()
def main_2():
if not os.path.isdir('./outputs'):
os.mkdir('./outputs')
if not os.path.isdir('./models'):
os.mkdir('./models')
"""
preprocessing
"""
# 범주형 데이터 리스트, 사람 수 데이터 리스트, 벡터화 dictionary
_inputs, _outputs, input_dict, output_dict = category_L_preprocessing()
# delete columns
_inputs = deleteColumn(_inputs, [1, 3, 10]) # 요일, 사상자 수, K열 제외
# make list
tmp = []
for _input in _inputs:
ttmp = []
nums = []
for i, elem in enumerate(_input):
if i < 1:
ttmp.append(elem)
elif i < 5:
nums.append(elem)
elif i == 5:
ttmp.append(norm_list(nums))
ttmp.append(elem)
else:
ttmp.append(elem)
tmp.append(ttmp)
_inputs, _outputs = shuffleList(
tmp, _outputs) # 데이터 리스트가 고루 섞이도록 _inputs와 _outputs를 함께 섞음
inputs, outputs, input_test, input_train, input_val, output_test, output_train, output_val \
= separate_set_multiple_outputs(_inputs, _outputs) # 범주형 데이터와 사람 수 데이터를 각각 test, train, validate를 위해 분류
# ensemble
num_models = 11 # 11
cnt = 0
models = []
while cnt < num_models:
"""
create model
"""
model = createModel(inputs, outputs)
"""
training
"""
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=['accuracy'])
# model.summary()
# early stopping
# val_loss값이 10번 동안 개선되지 않으면 해당 model의 학습을 중단
early_stopping = EarlyStopping(monitor='val_loss',
patience=10,
verbose=0)
# train
# model의 학습 이력 정보로 train의 loss와 accuracy, val의 loss와 accuracy 값을 받음
hist = model.fit([np.array(i) for i in input_train],
[np.array(i) for i in output_train],
epochs=1000,
batch_size=pow(2, 13),
validation_data=([np.array(i) for i in input_val],
[np.array(i) for i in output_val]),
callbacks=[early_stopping],
verbose=0)
try:
# plot_hist(hist)
pass
except:
pass
"""
test
"""
# model의 성능 평가
score = model.evaluate([np.array(i) for i in input_test],
[np.array(i) for i in output_test],
verbose=0)
# TH = 0.72
if score[1] < 0.72:
print('fail : model %d: %s = %.2f%%' %
(cnt, model.metrics_names[1], score[1] * 100))
continue
# PASS
print('complete: model %d: %s = %.2f%%' %
(cnt, model.metrics_names[1], score[1] * 100))
"""
save model
"""
model.save('./models/2_model_' + str(cnt) + '.h5')
try:
plot_model(model,
to_file='./models/2_model_' + str(cnt) + '.png',
show_shapes=True,
show_layer_names=True)
except:
pass
models.append(model)
# model의 개수가 10개가 될 때
cnt += 1
"""
predict
"""
f1 = open('./outputs/1_output.csv', 'r')
f2 = open('./outputs/2_output.csv', 'w', newline='')
r1 = csv.reader(f1)
r2 = csv.writer(f2)
for i, row in enumerate(r1):
# blank
if (10 < i & i < 21) | (42 < i & i < 46):
"""
preprocessing
"""
# copy
_row = row[:]
# L열 제외
for k, j in enumerate([11]):
del (_row[j - k])
for j, elem in enumerate(_row):
if 1 < j & j < 7:
_row[j] = int(elem)
_input_pred = []
for j, elem in enumerate(_row):
try:
if isinstance(elem, int):
_input_pred.append(elem)
else:
_input_pred.append(input_dict[j][elem])
except:
_input_pred.append('')
# _row = deleteColumn(row, [1, 3, 10]) # 요일, 사상자 수, K열 제외
for k, j in enumerate([1, 3, 10]):
del (_input_pred[j - k])
# make list
input_pred = []
nums = []
for j, elem in enumerate(_input_pred):
if j < 1:
input_pred.append(elem)
elif j < 5:
nums.append(elem)
elif j == 5:
input_pred.append(norm_list(nums))
input_pred.append(elem)
else:
input_pred.append(elem)
"""
predict
"""
pred = []
for model in models:
_pred = list(
model.predict([np.array([j]) for j in input_pred])[0])
pred.append(
value_key_map(
output_dict, 0,
oneHotEncoding(len(_pred), _pred.index(max(_pred)))))
# majority
val, rate = majority(pred)
print('save : row %2d: val = %.2f%%' % (i + 1, rate * 100))
"""
write
"""
row[11] = val
r2.writerow(row)
else:
r2.writerow(row)
f1.close()
f2.close()