如何在Windows上的python中安装XGBoost软件包

按照上述资源，我已经在Windows操作系统中安装了xgboost，到目前为止，在pip中尚不可用。但是，我尝试使用以下功能代码来调整CV参数：

#import libraries:import pandas as pdimport numpy as npimport xgboost as xgbfrom xgboost.sklearn import XGBClassifierfrom sklearn import cross_validation, metrics   #Additional sklearn functionsfrom sklearn.grid_search import GridSearchCV   #Perforing grid searchimport matplotlib.pylab as plt%matplotlib inlinefrom matplotlib.pylab import rcParamsrcParams['figure.figsize'] = 12, 4train = pd.read_csv('train_data.csv')target = 'target_value'IDcol = 'ID'

创建一个函数以获取最佳参数并以可视形式显示输出。

def modelfit(alg, dtrain, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):if useTrainCV:    xgb_param = alg.get_xgb_params()    xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,        metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress=False)    alg.set_params(n_estimators=cvresult.shape[0])#Fit the algorithm on the dataalg.fit(dtrain[predictors], dtrain[target_label],eval_metric='auc')#Predict training set:dtrain_predictions = alg.predict(dtrain[predictors])dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]#Print model report:print "nModel Report"print "Accuracy : %.4g" % metrics.accuracy_score(dtrain[target_label].values, dtrain_predictions)print "AUC Score (Train): %f" % metrics.roc_auc_score(dtrain[target_label], dtrain_predprob)feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)feat_imp.plot(kind='bar', title='Feature importances')plt.ylabel('Feature importance Score')

现在，当调用函数以获取最佳参数时：

  #Choose all predictors except target & IDcols  predictors = [x for x in train.columns if x not in [target]]  xgb = XGBClassifier(  learning_rate =0.1,  n_estimators=1000,  max_depth=5,  min_child_weight=1,  gamma=0,  subsample=0.7,  colsample_bytree=0.7,  objective= 'binary:logistic',  nthread=4,  scale_pos_weight=1,  seed=198) modelfit(xgb, train, predictors)

虽然显示了功能重要性图表，但是缺少图表顶部红色框中的参数信息：已

咨询使用linux / mac
OS并安装了xgboost的人员。他们正在获取以上信息。我想知道是否是由于特定的实现，所以我在Windows中构建并安装了它。以及如何获取显示在图表上方的参数信息。到目前为止，我正在获取图表，而不是其中的红色框和信息。谢谢。