TransformedTargetRegressor保存和加载错误

2024-05-20 • 问答

我正在使用TransformedTargetRegressor定义我的自定义回归器，将其添加到管道中并将模型保存在“ joblib”文件中。但是，当我尝试加载模型时，出现错误

模块“ 主要”没有属性“ transform_targets”

其中transform_targets是为回归器定义的功能之一

def transform_targets(targets):
   targets = (targets - min_t)/(max_t-min_t)
   return targets

def inv_transform_targets(outputs):
   outputs = (outputs)*(max_t-min_t)+min_t
   return outputs

# Define the model 

mlp_model = MLPRegressor(activation = 'relu',validation_fraction = 0.2,hidden_layer_sizes=(1000,))
full_model = TransformedTargetRegressor(regressor = mlp_model,func = transform_targets,inverse_func = inv_transform_targets)

# Incorporate feature scaling via pipeline

pipeline = make_pipeline(MinmaxScaler(),full_model)
nn_model = pipeline.fit(X_train,y_train)

# Fit the model which uses the transformed target regressor + maxmin pipeline

nn_model.fit(X_train,y_train)

from joblib import dump,load
dump(nn_model,'fitness_nn_C1.joblib')

该模型运行良好且可以很好地进行预测，可以保存且没有错误，但不会加载回去。如果我用泡菜将其保存，它也会返回类似的错误

AttributeError：模块' main '>
上无法获取属性'transform_targets'

有人知道如何保存一个包含TransformedTargetRegressor的模型到一个文件中，然后可以成功地重新加载它吗？我意识到我可以将与转换目标相关的参数/函数转储到一个单独的文件中，但这正是我要避免的

编辑：

当前解决方法是将MinmaxScaler用作转换器或预处理批次中的任何其他转换器，但仍不知道是否可以在此工作流程中包含自定义功能

问题是，当您尝试重新加载文件时，它无法解析最初未转储的transform_targets。您可以使用dill对其进行序列化。因此，基本上，您必须创建要转储的项目的列表，然后使用dill和joblib对其进行序列化，如下所示：

from sklearn.neural_network import MLPRegressor
from sklearn.compose import TransformedTargetRegressor
from sklearn.pipeline import make_pipeline
from sklearn.datasets import make_friedman1
from sklearn.preprocessing import MinMaxScaler
import dill
X,y = make_friedman1(n_samples=50,n_features=10,random_state=0)

min_t = 10
max_t = 300
def transform_targets(targets):
   targets = (targets - min_t)/(max_t-min_t)
   return targets

def inv_transform_targets(outputs):
   outputs = (outputs)*(max_t-min_t)+min_t
   return outputs

# Define the model 

mlp_model = MLPRegressor(activation = 'relu',validation_fraction = 0.2,hidden_layer_sizes=(1000,))
full_model = TransformedTargetRegressor(regressor = mlp_model,func = transform_targets,inverse_func = inv_transform_targets)

# Incorporate feature scaling via pipeline

pipeline = make_pipeline(MinMaxScaler(),full_model)
nn_model = pipeline.fit(X,y)

# Fit the model which uses the transformed target regressor + maxmin pipeline

nn_model.fit(X,y)
to_save = [transform_targets,inv_transform_targets,nn_model]
r = dill.dumps(to_save)
from joblib import dump,load
dump(r,'fitness_nn_C1.joblib')

现在您可以按如下所示加载它：

from joblib import dump,load
import dill
Q = load('fitness_nn_C1.joblib')
T = dill.loads(Q)

T看起来像这样：

[<function __main__.transform_targets(targets)>,<function __main__.inv_transform_targets(outputs)>,Pipeline(memory=None,steps=[('minmaxscaler',MinMaxScaler(copy=True,feature_range=(0,1))),('transformedtargetregressor',TransformedTargetRegressor(check_inverse=True,func=<function transform_targets at 0x000001F486D27048>,inverse_func=<function inv_transform_targets at 0x000001F4882E6C80>,regressor=MLPRegressor(activation='relu',alpha=0.0001,batch_size='a...
                                                                    beta_2=0.999,early_stopping=False,epsilon=1e-08,),learning_rate='constant',learning_rate_init=0.001,max_iter=200,momentum=0.9,n_iter_no_change=10,nesterovs_momentum=True,power_t=0.5,random_state=None,shuffle=True,solver='adam',tol=0.0001,validation_fraction=0.2,verbose=False,warm_start=False),transformer=None))],verbose=False)]

希望这会有所帮助！

TransformedTargetRegressor保存和加载错误

puenshou 回答：TransformedTargetRegressor保存和加载错误

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