python中的TPOT错误无法使用不同长度的切片索引器进行设置

Question

我正在尝试运行tpot，以使用遗传算法优化随机林的超参数。我收到一个错误，不太确定如何修复。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(与RandomForest相反(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

我用iris数据集尝试了tpot，但没有得到错误的

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)

我认为你的数据集的形状或类型有问题

也许是因为你正在使用Panda DataFrames

尝试这样做：

X = X.to_numpy
y = y.to_numpy