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run.py
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# -*- encoding:utf-8 -*-
import os
import argparse
import pandas as pd
import numpy as np
from method.pre import csv_data_train, csv_data_test
# from sklearn.preprocessing import Imputer换成下边的就可以
import xgboost as xgb
from xgb_model import trainModel, saveModel
def get_parser():
parser = argparse.ArgumentParser(description="Run in csvs")
parser.add_argument("--csvTrain", default="/Users/didi/project/predict/train/", help="path to csv folder")
parser.add_argument("--csvTest", default="/Users/didi/project/predict/test1/", help="path to csv folder")
return parser
def getAllCSV(folderPath):
# 初始化一个列表
outputLst = []
# 输出路径下文件列表，无序的。排序：video_list.sort()
csv_list = os.listdir(folderPath)
# idx为索引，video为列表元素
for idx, csv in enumerate(csv_list):
# 路径拼接，函数会自动加/
csv_path = os.path.join(folderPath, csv)
# 在列表末尾添加新元素
outputLst.append(csv_path)
return outputLst
def test_model():
w = pd.DataFrame({17, 10, 100, 100, 64.639999, 7, 0, 0, 0, 0, 7, 6, 13, 12, 11.545455, 11.454545, 7, 6, 13, 12, 11.545455, 11.454545, 610, 610, 5, 5, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
0.66290897, 0.94605702, 1.7, 900, 1, 1, 0, 11625, 8576, 1, 1, 1, -1, -1, -1, -1, -1, -1})
xx = xgb.DMatrix(w)
bst_new = xgb.Booster({'nthread': 4}) # init model
bst_new.load_model("model/xgboost_v1.3.model")
bst_new.predict(xx)
def predict_test(model, csv_paths_test):
predict_pd = pd.DataFrame()
for csv_path_test in csv_paths_test:
print("test_path:",csv_path_test)
x_test = csv_data_test(csv_path_test)
#print(x_test.columns.tolist)
day_str = str(x_test['time_year'].values[0])+"{:0>2d}".format(x_test['time_month'].values[0]) \
+str(x_test['time_day'].values[0])
#print(day_str)
y_test_predict = model.predict(x_test.values)
predict_pd[day_str] = y_test_predict
# for row in range(0, len(predict_Y)):
# data_arr.append([id_list[row], predict_Y[row]])
# np_data = np.array(data_arr)
#
# #写入文件
# pd_data = pd.DataFrame(np_data, columns=['id', 'y'])
# # print(pd_data)
df2 = pd.DataFrame(predict_pd.values.T, index = predict_pd.columns)
df2.to_csv('submit.csv', sep=',', header=False)
if __name__ == "__main__":
#test_model()
args = get_parser().parse_args()
csv_paths_train = getAllCSV(args.csvTrain)
csv_paths_test = getAllCSV(args.csvTest)
X_train = pd.DataFrame()
Y_train = pd.DataFrame()
X_test = pd.DataFrame()
for csv_path_train in csv_paths_train:
print("train_path:",csv_path_train)
x_train, y_train = csv_data_train(csv_path_train)
X_train = pd.concat([X_train, x_train], axis=0)
Y_train = pd.concat([Y_train, y_train], axis=0)
#print(np.shape(X_train))
#print(X_train.columns.tolist)
# print(X_train.values[0])
# print(Y_train.values[0])
model = trainModel(X_train, Y_train)
saveModel(model)
predict_test(model, csv_paths_test)