调用XGBoost.fit后的Python sklearn NotFitteError

Question

我试图在XGBoost fitted模型上使用sklearn plot_partial_dependence函数，即调用.fit之后。但我一直在犯错误：

未安装错误：此XGBRegressionor实例尚未安装。在使用此估计器之前，使用适当的参数调用“fit”

下面是我使用虚拟数据集采取的步骤

使用虚拟数据完成示例：

import numpy as np
# dummy dataset
from sklearn.datasets import make_regression
X_train, y_train = make_regression(n_samples = 1000, n_features = 10)


# Import xgboost
import xgboost as xgb

# Initialize the model 
model_xgb_1 = xgb.XGBRegressor(max_depth = 5, 
                               learning_rate = 0.01, 
                               n_estimators = 100, 
                               objective = 'reg:squarederror', 
                               booster = 'gbtree') 

# Fit the model 
# Not assigning to a new variable 
model_xgb_1.fit(X_train, y_train)

# Just to check that .predict can be called and works
# without error 
print(np.sum(model_xgb_1.predict(X_train)))
# the above works ok and prints the output

#This next step throws an error:
from sklearn.inspection import plot_partial_dependence
plot_partial_dependence(model_xgb_1, X_train, [0])

输出：

662.3468

未安装错误：此XGBRegressionor实例尚未安装。在使用此估计器之前，使用适当的参数调用“fit”

更新

增压器='gblinear'时的解决方法

# CHANGE 1/2: Use booster = 'gblinear'
# as no coef are returned for the case of 'gbtree' 
model_xgb_1 = xgb.XGBRegressor(max_depth = 5, 
                               learning_rate = 0.01, 
                               n_estimators = 100, 
                               objective = 'reg:squarederror', 
                               booster = 'gblinear') 

# Fit the model 
# Not assigning to a new variable 
model_xgb_1.fit(X_train, y_train)

# Just to check that .predict can be called and works
# without error 
print(np.sum(model_xgb_1.predict(X_train)))
# the above works ok and prints the output


#This next step throws an error:
from sklearn.inspection import plot_partial_dependence
plot_partial_dependence(model_xgb_1, X_train, [0])

# CHANGE 2/2
# Add the following:
model_xgb_1.coef__ = model_xgb_1.coef_
model_xgb_1.intercept__ = model_xgb_1.intercept_

# Now call plot_partial_dependence --- It works ok
from sklearn.inspection import plot_partial_dependence
plot_partial_dependence(model_xgb_1, X_train, [0])

Answer 1

from sklearn.ensemble import VotingRegressor
XGB_v=VotingRegressor([("reg",XGB)],)
XGB_RMR=PartialDependenceDisplay.from_estimator(
    XGB_v, x_train, features,
    feature_names=["a"],line_kw={"color": "blue"}
)

这将帮助您解决您的帮助

Answer 2

为避免此错误，请不要影响变量的拟合模型

# Import xgboost
import xgboost as xgb

# Initialize the model 
model_xgb_1 = xgb.XGBRegressor(max_depth = max_depth, 
                               learning_rate = shrinkage, 
                               n_estimators = nTrees, 
                               objective = 'reg:squarederror', 
                               booster = 'gbtree') 

# Fit the model 
model_xgb_1.fit(X_train, y_train)

# Just to check that .predict can be called and works
# without error 
model_xgb_1.predict(X_train)
# the above works ok and prints the output

#This next step throws an error:
from sklearn.inspection import plot_partial_dependence
plot_partial_dependence(model_xgb_1, X_train, [0])