机器学习部分核心代码

最近将机器学习VBA版改写为PYTHON第一版。

FBP_ML.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Author: Daijingbo
+# @Date  : 2019/5/27
+# @Desc  :FBP ML
+# http://www.captainbed.net/blog-acredjb
+import pandas as pd
+import matplotlib.pyplot as plt
+import xgboost as xgb
+from sklearn import preprocessing
+import numpy as np
+from xgboost import plot_importance
+from sklearn.preprocessing import Imputer
+# from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import KFold
+from sklearn.model_selection import train_test_split
+
+
+def featureSet(data):
+    imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
+    imputer.fit(data.loc[:, ['10bet', 'jbb', 'ms', 'ysb', 'Pinnacle', 'SNAI']])
+    x_new = imputer.transform(data.loc[:, ['10bet', 'jbb', 'ms', 'ysb', 'Pinnacle', 'SNAI']])
+
+
+    data_num = len(data)
+    XList = []
+    for row in range(0, data_num):
+        tmp_list = []
+        tmp_list.append(data.iloc[row]['Oddset'])
+        tmp_list.append(data.iloc[row]['li'])
+        tmp_list.append(data.iloc[row]['bet365'])
+        tmp_list.append(data.iloc[row]['interw'])
+        tmp_list.append(data.iloc[row]['wl'])
+        tmp_list.append(data.iloc[row]['w'])
+        tmp_list.append(data.iloc[row]['ao'])
+        # tmp_list.append(data.iloc[row]['10bet'])
+        # tmp_list.append(data.iloc[row]['jbb'])
+        # tmp_list.append(data.iloc[row]['ms'])
+        # tmp_list.append(data.iloc[row]['ysb'])
+        tmp_list.append(x_new[row][0])
+        tmp_list.append(x_new[row][1])
+        tmp_list.append(x_new[row][2])
+        tmp_list.append(x_new[row][3])
+        tmp_list.append(x_new[row][4])
+        tmp_list.append(x_new[row][5])
+        XList.append(tmp_list)
+    yList = data.y.values
+    return XList, yList
+
+
+def loadTestData(filePath):
+    data = pd.read_csv(filepath_or_buffer=filePath)
+    imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
+    imputer.fit(data.loc[:, ['10bet', 'jbb', 'ms', 'ysb', 'Pinnacle', 'SNAI']])
+    x_new = imputer.transform(data.loc[:, ['10bet', 'jbb', 'ms', 'ysb', 'Pinnacle', 'SNAI']])
+
+    data_num = len(data)
+    XList = []
+    for row in range(0, data_num):
+        tmp_list = []
+        tmp_list.append(data.iloc[row]['Oddset'])
+        tmp_list.append(data.iloc[row]['li'])
+        tmp_list.append(data.iloc[row]['bet365'])
+        tmp_list.append(data.iloc[row]['interw'])
+        tmp_list.append(data.iloc[row]['wl'])
+        tmp_list.append(data.iloc[row]['w'])
+        tmp_list.append(data.iloc[row]['ao'])
+        # tmp_list.append(data.iloc[row]['10bet'])
+        # tmp_list.append(data.iloc[row]['jbb'])
+        # tmp_list.append(data.iloc[row]['ms'])
+        # tmp_list.append(data.iloc[row]['ysb'])
+        tmp_list.append(x_new[row][0])
+        tmp_list.append(x_new[row][1])
+        tmp_list.append(x_new[row][2])
+        tmp_list.append(x_new[row][3])
+        tmp_list.append(x_new[row][4])
+        tmp_list.append(x_new[row][5])
+        XList.append(tmp_list)
+    return XList
+
+
+def trainandTest(X_train, y_train, X_test):
+    # XGBoost训练过程
+    model = xgb.XGBRegressor(max_depth=6, learning_rate=0.05, n_estimators=500, silent=False, objective='reg:gamma')
+    model.fit(X_train, y_train)
+
+    # 对测试集进行预测
+    ans = model.predict(X_test)
+    ans_len = len(ans)
+    # print(ans_len)
+    # print('---------------')
+    # print('ans(0):'+str(ans[1]))
+    # print('')
+    id_list = np.arange(5709, 6108)
+    data_arr = []
+    for row in range(0, ans_len):
+        data_arr.append([int(id_list[row]), ans[row]])
+        print(ans[row])
+    np_data = np.array(data_arr)
+
+    # 写入文件
+    pd_data = pd.DataFrame(np_data, columns=['id', 'y'])
+    pd_data.to_csv('FBP_submit.csv', index=None)
+
+    # 显示重要特征
+    plot_importance(model)
+    plt.show()
+
+if __name__ == '__main__':
+    trainFilePath = 'E:/PythonLearn/pc_ex/AdaBoost_PeiLv/FBP_train.csv'
+    testFilePath = 'E:/PythonLearn/pc_ex/AdaBoost_PeiLv/FBP_predict.csv'
+    data = pd.read_csv(trainFilePath)
+    X_train, y_train = featureSet(data)
+    X_test = loadTestData(testFilePath)
+    trainandTest(X_train, y_train, X_test)