python 中的 TPOT 错误无法使用具有不同长度的切片索引器进行设置

Question

我正在尝试运行 tpot 使用遗传算法优化随机森林的超参数。我收到一条错误消息，但不太确定如何修复它。下面是我正在使用的基本代码。

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from tpot import TPOTClassifier

X = my_df_features
y = my_df_target

X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)

model_parameters = {'n_estimators': [100,200],
                      "max_depth" : [None, 5, 10],
                      "max_features" : [10]}  

# This seems to work perfectly fine when I run it
# model_tuned = GridSearchCV(RandomForestClassifier(),model_parameters, cv=5)

# This does not seem to work 
model_tuned = TPOTClassifier(generations= 2, population_size= 2, offspring_size= 2,
                                      verbosity= 2, early_stop= 10,
                                      config_dict=
                                      {'sklearn.ensemble.RandomForestClassifier': model_parameters}, 
                                      cv = 5)
 

model_tuned.fit(X_train,y_train)

当使用 TPOT（而不是 RandomForest）时，上面的最后一行会产生以下错误：

ValueError: cannot set using a slice indexer with a different length than the value"

Answer 1

我尝试使用 iris 数据集进行 tpot，但没有收到任何错误

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from tpot import TPOTClassifier
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data 
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)

model_parameters = {'n_estimators': [100,200],
                      "max_depth" : [None, 5, 10],
                      "max_features" : [len(X_train[0])]}  


model_tuned = TPOTClassifier(generations= 2, 
                             population_size= 2, 
                             offspring_size= 2,
                             verbosity= 2, 
                             early_stop= 10, 
                             config_dict={'sklearn.ensemble.RandomForestClassifier': 
                             model_parameters}, 
                             cv = 5)
 

model_tuned.fit(X_train,y_train)

我认为您的数据集的形状或类型有问题

可能是因为您正在使用 pandas DataFrames

尝试这样做：

X = X.to_numpy
y = y.to_numpy

python 中的 TPOT 错误无法使用具有不同长度的切片索引器进行设置

TPOT error in python cannot set using a slice indexer with a different length

python

machine-learning

tpot