def main():
FEATURE_FILE_PATH = sys.argv[1]
LABEL_FILE_PATH = sys.argv[2]
INPUT_MODEL = sys.argv[3]
OUTPUT_RESULTS = sys.argv[4]
TIMESTAMP = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S").replace(
"'", "")
# base = os.getcwd()
base = INPUT_MODEL
dir = os.path.join(base, "test.pkl")
if not os.path.exists(dir):
raise ValueError("The model file does not exist.")
Mshift = joblib.load(INPUT_MODEL +
'test.pkl') # call the pre-trained model
print(Mshift)
# get data
print("Read file.........")
print("Feature file:{}".format(FEATURE_FILE_PATH))
if not os.path.exists(FEATURE_FILE_PATH):
raise ValueError("The feature file does not exist.")
X = pd.read_excel(FEATURE_FILE_PATH).fillna(0)
for col in X.columns:
X[col] = X[col].apply(lambda x: str(x).replace(" ", ""))
if X.isnull().any().any():
raise ValueError("There is missing data in the feature file.")
# data pre-process
X_ = data_process(X, CHOICE)
# data prediction and results visualization
y_pred = Mshift.predict(X_)
y_pred = pd.DataFrame(y_pred, columns=['predict'])
df = pd.concat([X, y_pred], axis=1)
df.to_csv(OUTPUT_RESULTS + "{}_results.csv".format(TIMESTAMP), index=None)
def main():
FEATURE_FILE_PATH = sys.argv[1]
LABEL_FILE_PATH = sys.argv[2]
INPUT_MODEL = sys.argv[3]
OUTPUT_RESULTS = sys.argv[4]
TIMESTAMP = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S").replace("'","")
# base = os.getcwd()
base = INPUT_MODEL
dir = os.path.join(base,"test.pkl")
if not os.path.exists(dir):
raise ValueError("模型文件不存在")
kmeans = joblib.load(INPUT_MODEL+'test.pkl') # 调用预训练好的模型
print(kmeans)
# 获取数据
print("读取文件.........")
print("特征文件:{}".format(FEATURE_FILE_PATH))
if not os.path.exists(FEATURE_FILE_PATH):
raise ValueError("特征文件不存在")
X = pd.read_excel(FEATURE_FILE_PATH).fillna(0)
for col in X.columns:
X[col] = X[col].apply(lambda x:str(x).replace(" ",""))
if X.isnull().any().any():
raise ValueError("特征文件存在缺失数据")
# 数据预处理
X_ = data_process(X,CHOICE)
# 模型预测并可视化结果
y_pred = kmeans.predict(X_)
y_pred = pd.DataFrame(y_pred,columns=['predict'])
df = pd.concat([X,y_pred],axis=1)
df.to_csv(OUTPUT_RESULTS+"{}_results.csv".format(TIMESTAMP),index=None)