我试图将XGBoost的决策树保存为.png文件。当我用随机森林这样做时一切正常，但对于XGBoost却行不通。
我有以下代码：
import xgboost as xgbfrom sklearn.tree import export_graphvizimport warningswarnings.filterwarnings('ignore')from sklearn.metrics import mean_absolute_errorfrom sklearn.metrics import r2_scorefrom sklearn.metrics import mean_squared_errorfrom math import sqrtimport numpy as np import pandas as pd import matplotlib.pyplot as pltfrom sklearn import metricsfrom sklearn.preprocessing import LabelEncoder# Using Skicit-learn to split data into training and testing setsfrom sklearn.model_selection import train_test_splitdf = pd.read_csv("data_clean.csv")del df["Unnamed: 0"]df = df[["gross_square_feet","block","land_square_feet","lot","age_of_building","borough","residential_units","commercial_units","total_units","sale_price"]]df['borough'] = df['borough'].astype('category')X, y = df.iloc[:,:-1],df.iloc[:,-1]one_hot_encoded_X = pd.get_dummies(X)print("# of columns after one-hot encoding: {0}".format(len(one_hot_encoded_X.columns)))from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(one_hot_encoded_X, y, test_size=0.25, random_state=1337)from xgboost import XGBRegressorprint(np.shape(X_train), np.shape(X_test))xg_model = XGBRegressor(n_estimators=500, learning_rate=0.075, max_depth = 7, min_child_weight = 5, eval_metric = 'rmse', seed = 1337, objective = 'reg:squarederror')xg_model.fit(X_train, y_train, early_stopping_rounds=10, eval_set=[(X_test, y_test)], verbose=False)# make predictionspredictions = xg_model.predict(X_test)image = xgb.to_graphviz(xg_model)export_graphviz(image, out_file='treexgb.dot', rounded = True, proportion = False, precision = 2, filled = True)# Convert to png using system command (requires Graphviz)from subprocess import callcall(['dot', '-Tpng', 'tree.dot', '-o', 'tree.png', '-Gdpi=600'])当我执行这段代码时，我得到了以下错误：
---------------------------------------------------------------------------TypeError Traceback (most recent call last)<ipython-input-21-2cde9f840069> in <module>() 3 export_graphviz(image, out_file='treexgb.dot', 4 rounded = True, proportion = False,----> 5 precision = 2, filled = True) 6 7 F:\Softwares\Anaconda\lib\site-packages\sklearn\tree\export.py in export_graphviz(decision_tree, out_file, max_depth, feature_names, class_names, label, filled, leaves_parallel, impurity, node_ids, proportion, rotate, rounded, special_characters, precision) 390 out_file.write('%d -> %d ;\n' % (parent, node_id)) 391 --> 392 check_is_fitted(decision_tree, 'tree_') 393 own_file = False 394 return_string = FalseF:\Softwares\Anaconda\lib\site-packages\sklearn\utils\validation.py in check_is_fitted(estimator, attributes, msg, all_or_any) 760 761 if not hasattr(estimator, 'fit'):--> 762 raise TypeError("%s is not an estimator instance." % (estimator)) 763 764 if not isinstance(attributes, (list, tuple)):TypeError: digraph { graph [rankdir=UT] 0 [label="gross_square_feet<2472.5"] 23 -> 47 [label="yes, missing" color="#0000FF"] ... 112 [label="leaf=22460.7168"]} is not an estimator instance.然而，当我执行xgb.to_graphviz(xg_model)时，它工作得非常好，并且我只得到了一棵树…
有人知道如何将我的树输出为.png文件吗？
回答：
试试这个：
format = 'png' #你应该尝试'svg'image = xgb.to_graphviz(xg_model)#设置不同的dpi（仅在format == 'png'时有效）image.graph_attr = {'dpi':'400'}image.render('filename', format = format)来源：
