本文整理汇总了Python中xgboost.plot_importance方法的典型用法代码示例。如果您正苦于以下问题：Python xgboost.plot_importance方法的具体用法？Python xgboost.plot_importance怎么用？Python xgboost.plot_importance使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类xgboost的用法示例。
在下文中一共展示了xgboost.plot_importance方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: plot_importance
# 需要导入模块: import xgboost [as 别名]
# 或者: from xgboost import plot_importance [as 别名]
def plot_importance(self, ax=None, height=0.2,
xlim=None, title='Feature importance',
xlabel='F score', ylabel='Features',
grid=True, **kwargs):
"""Plot importance based on fitted trees.
Parameters
----------
ax : matplotlib Axes, default None
Target axes instance. If None, new figure and axes will be created.
height : float, default 0.2
Bar height, passed to ax.barh()
xlim : tuple, default None
Tuple passed to axes.xlim()
title : str, default "Feature importance"
Axes title. To disable, pass None.
xlabel : str, default "F score"
X axis title label. To disable, pass None.
ylabel : str, default "Features"
Y axis title label. To disable, pass None.
kwargs :
Other keywords passed to ax.barh()
Returns
-------
ax : matplotlib Axes
"""
import xgboost as xgb
if not isinstance(self._df.estimator, xgb.XGBModel):
raise ValueError('estimator must be XGBRegressor or XGBClassifier')
# print(type(self._df.estimator.booster), self._df.estimator.booster)
return xgb.plot_importance(self._df.estimator,
ax=ax, height=height, xlim=xlim, title=title,
xlabel=xlabel, ylabel=ylabel, grid=True, **kwargs) 示例2: plot_importance
# 需要导入模块: import xgboost [as 别名]
# 或者: from xgboost import plot_importance [as 别名]
def plot_importance(self):
ax = xgb.plot_importance(self.model)
self.save_topn_features()
return ax 示例3: save_topn_features
# 需要导入模块: import xgboost [as 别名]
# 或者: from xgboost import plot_importance [as 别名]
def save_topn_features(self, fname="XGBRegressor_topn_features.txt", topn=-1):
ax = xgb.plot_importance(self.model)
yticklabels = ax.get_yticklabels()[::-1]
if topn == -1:
topn = len(yticklabels)
else:
topn = min(topn, len(yticklabels))
with open(fname, "w") as f:
for i in range(topn):
f.write("%s\n"%yticklabels[i].get_text()) 示例4: run_train_validation
# 需要导入模块: import xgboost [as 别名]
# 或者: from xgboost import plot_importance [as 别名]
def run_train_validation(self):
x_train, y_train,x_validation,y_validation = self.get_train_validationset()
dtrain = xgb.DMatrix(x_train, label= y_train,feature_names=x_train.columns)
dvalidation = xgb.DMatrix(x_validation, label= y_validation,feature_names=x_validation.columns)
self.set_xgb_parameters()
evals=[(dtrain,'train'),(dvalidation,'eval')]
model = xgb.train(self.xgb_params, dtrain, evals=evals, **self.xgb_learning_params)
xgb.plot_importance(model)
plt.show()
print "features used:\n {}".format(self.get_used_features())
return