def train():
machine_data = MySQL_query.get_machine_data_list_for_predict(1)
quality_data = MySQL_query.get_quality_data_list_for_predict(1)
machine_df = pd.DataFrame(machine_data)
quality_df = pd.DataFrame(quality_data)
product_key_list = []
body_l_list = []
body_w_list = []
body_h_list = []
wavyfin_l_list = []
wavyfin_w_list = []
wavyfin_h_list = []
op10_l_list = []
op10_w_list = []
op10_h_list = []
pipe1_l_list = []
pipe1_w_list = []
pipe1_h_list = []
op20_l_list = []
op20_w_list = []
op20_h_list = []
pipe2_l_list = []
pipe2_w_list = []
pipe2_h_list = []
op30_l_list = []
op30_w_list = []
op30_h_list = []
flange1_l_list = []
flange1_w_list = []
flange1_h_list = []
op40_l_list = []
op40_w_list = []
op40_h_list = []
flange2_l_list = []
flange2_w_list = []
flange2_h_list = []
op50_l_list = []
op50_w_list = []
op50_h_list = []
op60_l_list = []
op60_w_list = []
op60_h_list = []
op10_electricity_list = []
op10_process_time_list = []
op20_electricity_list = []
op20_process_time_list = []
op30_electricity_list = []
op30_process_time_list = []
op40_temperature_list = []
op40_process_time_list = []
op50_temperature_list = []
op50_process_time_list = []
op60_process_time_list = []
op10_test_list = []
op20_test_list = []
op30_test_list = []
op40_test_list = []
op50_test_list = []
op60_test_list = []
op10_timestamp = []
op20_timestamp = []
op30_timestamp = []
op40_timestamp = []
op50_timestamp = []
op60_timestamp = []
for instance in quality_data:
if (instance['product_key'][27:29] == 'W6'):
product_key_list.append(instance['product_key'])
if (instance['product_key'][27:] == 'body'):
body_l_list.append(instance['product_size_l'])
body_w_list.append(instance['product_size_w'])
body_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:] == 'wavyfin'):
wavyfin_l_list.append(instance['product_size_l'])
wavyfin_w_list.append(instance['product_size_w'])
wavyfin_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W1'):
op10_l_list.append(instance['product_size_l'])
op10_w_list.append(instance['product_size_w'])
op10_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W1'):
if instance['product_test'] == 'OK':
op10_test_list.append(0)
else:
op10_test_list.append(1)
if (instance['product_key'][27:] == 'pipe1'):
pipe1_l_list.append(instance['product_size_l'])
pipe1_w_list.append(instance['product_size_w'])
pipe1_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W2'):
op20_l_list.append(instance['product_size_l'])
op20_w_list.append(instance['product_size_w'])
op20_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W2'):
if instance['product_test'] == 'OK':
op20_test_list.append(0)
else:
op20_test_list.append(1)
if (instance['product_key'][27:] == 'pipe2'):
pipe2_l_list.append(instance['product_size_l'])
pipe2_w_list.append(instance['product_size_w'])
pipe2_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W3'):
op30_l_list.append(instance['product_size_l'])
op30_w_list.append(instance['product_size_w'])
op30_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W3'):
if instance['product_test'] == 'OK':
op30_test_list.append(0)
else:
op30_test_list.append(1)
if (instance['product_key'][27:] == 'flange1'):
flange1_l_list.append(instance['product_size_l'])
flange1_w_list.append(instance['product_size_w'])
flange1_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W4'):
op40_l_list.append(instance['product_size_l'])
op40_w_list.append(instance['product_size_w'])
op40_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W4'):
if instance['product_test'] == 'OK':
op40_test_list.append(0)
else:
op40_test_list.append(1)
if (instance['product_key'][27:] == 'flange2'):
flange2_l_list.append(instance['product_size_l'])
flange2_w_list.append(instance['product_size_w'])
flange2_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W5'):
op50_l_list.append(instance['product_size_l'])
op50_w_list.append(instance['product_size_w'])
op50_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W5'):
if instance['product_test'] == 'OK':
op50_test_list.append(0)
else:
op50_test_list.append(1)
if (instance['product_key'][27:29] == 'W6'):
op60_l_list.append(instance['product_size_l'])
op60_w_list.append(instance['product_size_w'])
op60_h_list.append(instance['product_size_h'])
if (instance['product_key'][27:29] == 'W6'):
if instance['product_test'] == 'OK':
op60_test_list.append(0)
else:
op60_test_list.append(1)
for instance in machine_data:
if (instance['product_key'][27:29] == 'W1'):
op10_electricity_list.append(instance['machine_data'])
if (instance['product_key'][27:29] == 'W1'):
op10_process_time_list.append(instance['process_time'])
if (instance['product_key'][27:29] == 'W1'):
op10_timestamp.append(instance['end_time'])
if (instance['product_key'][27:29] == 'W2'):
op20_electricity_list.append(instance['machine_data'])
if (instance['product_key'][27:29] == 'W2'):
op20_process_time_list.append(instance['process_time'])
if (instance['product_key'][27:29] == 'W2'):
op20_timestamp.append(instance['end_time'])
if (instance['product_key'][27:29] == 'W3'):
op30_electricity_list.append(instance['machine_data'])
if (instance['product_key'][27:29] == 'W3'):
op30_process_time_list.append(instance['process_time'])
if (instance['product_key'][27:29] == 'W3'):
op30_timestamp.append(instance['end_time'])
if (instance['product_key'][27:29] == 'W4'):
op40_temperature_list.append(instance['machine_data'])
if (instance['product_key'][27:29] == 'W4'):
op40_process_time_list.append(instance['process_time'])
if (instance['product_key'][27:29] == 'W4'):
op40_timestamp.append(instance['end_time'])
if (instance['product_key'][27:29] == 'W5'):
op50_temperature_list.append(instance['machine_data'])
if (instance['product_key'][27:29] == 'W5'):
op50_process_time_list.append(instance['process_time'])
if (instance['product_key'][27:29] == 'W5'):
op50_timestamp.append(instance['end_time'])
if (instance['product_key'][27:29] == 'W6'):
op60_process_time_list.append(instance['process_time'])
if (instance['product_key'][27:29] == 'W6'):
op60_timestamp.append(instance['end_time'])
df = pd.DataFrame({
"product_key": product_key_list,
'body_l': body_l_list,
'body_w': body_w_list,
'body_h': body_h_list,
'wavyfin_l': wavyfin_l_list,
'wavyfin_w': wavyfin_w_list,
'wavyfin_h': wavyfin_h_list,
'op10_l': op10_l_list,
'op10_w': op10_w_list,
'op10_h': op10_h_list,
'op10_electricity': op10_electricity_list,
'op10_process_time': op10_process_time_list,
'op10_test': op10_test_list,
'op10_timestamp': op10_timestamp,
'pipe1_l': pipe1_l_list,
'pipe1_w': pipe1_w_list,
'pipe1_h': pipe1_h_list,
'op20_l': op20_l_list,
'op20_w': op20_w_list,
'op20_h': op20_h_list,
'op20_electricity': op20_electricity_list,
'op20_process_time': op20_process_time_list,
'op20_test': op20_test_list,
'op20_timestamp': op20_timestamp,
'pipe2_l': pipe2_l_list,
'pipe2_w': pipe2_w_list,
'pipe2_h': pipe2_h_list,
'op30_l': op30_l_list,
'op30_w': op30_w_list,
'op30_h': op30_h_list,
'op30_electricity': op30_electricity_list,
'op30_process_time': op30_process_time_list,
'op30_test': op30_test_list,
'op30_timestamp': op30_timestamp,
'flange1_l': flange1_l_list,
'flange1_w': flange1_w_list,
'flange1_h': flange1_h_list,
'op40_l': op40_l_list,
'op40_w': op40_w_list,
'op40_h': op40_h_list,
'op40_temperature': op40_temperature_list,
'op40_process_time': op40_process_time_list,
'op40_test': op40_test_list,
'op40_timestamp': op40_timestamp,
'flange2_l': flange2_l_list,
'flange2_w': flange2_w_list,
'flange2_h': flange2_h_list,
'op50_l': op50_l_list,
'op50_w': op50_w_list,
'op50_h': op50_h_list,
'op50_temperature': op50_temperature_list,
'op50_process_time': op50_process_time_list,
'op50_test': op50_test_list,
'op50_timestamp': op50_timestamp,
'op60_l': op60_l_list,
'op60_w': op60_w_list,
'op60_h': op60_h_list,
'op60_process_time': op60_process_time_list,
'op60_test': op60_test_list,
'op60_timestamp': op60_timestamp
})
X = df.drop([
'op60_timestamp', 'op60_process_time', 'op60_h', 'op60_w', 'op60_l',
'op60_test', 'op50_timestamp', 'op50_process_time', 'op50_h', 'op50_w',
'op50_l', 'op50_temperature', 'op50_test', 'op40_timestamp',
'op40_process_time', 'op40_h', 'op40_w', 'op40_l', 'op40_temperature',
'op40_test', 'flange1_l', 'flange1_w', 'flange1_h', 'flange2_l',
'flange2_w', 'flange2_h', 'op10_timestamp', 'op20_timestamp',
'op30_timestamp', 'product_key'
],
axis=1)
y = df['op60_test']
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
shuffle=False,
random_state=1004)
rf_clf = RandomForestClassifier(random_state=0) # 랜덤 포레스트 모델
rf_clf.fit(X_train, y_train) # 학습
saved_model = pickle.dumps(rf_clf) # 모델 저장
preds = rf_clf.predict(X_test)
accuracy = accuracy_score(y_test, preds)
print('랜덤 포레스트 정확도: {0:.4f}'.format(accuracy))
joblib.dump(rf_clf, 'pretrained_model.pkl')
print("파일 저장완료")
from SQL import MySQL_query import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split machine_data = MySQL_query.get_machine_data_list_for_predict(1) quality_data = MySQL_query.get_quality_data_list_for_predict(1) machine_df = pd.DataFrame(machine_data) quality_df = pd.DataFrame(quality_data) product_key_list = [] body_l_list = [] body_w_list = [] body_h_list = [] wavyfin_l_list = [] wavyfin_w_list = [] wavyfin_h_list = [] op10_l_list = [] op10_w_list = [] op10_h_list = [] pipe1_l_list = [] pipe1_w_list = [] pipe1_h_list = [] op20_l_list = [] op20_w_list = []
