我遇到一个奇怪的错误，在使用GridSearchCV时，我的代码失败了，但仅运行sklearnMLPRegressor时却没有。
以下代码：
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from sklearn import preprocessing
import pandas as pd
import numpy as np
def str_to_num(arr):
le = preprocessing.LabelEncoder()
new_arr = le.fit_transform(arr)
return new_arr
def compare_values(arr1, arr2):
thediff = 0
thediffs = []
for thing1, thing2 in zip(arr1, arr2):
thediff = abs(thing1 - thing2)
thediffs.append(thediff)
return thediffs
def print_to_file(filepath, arr):
with open(filepath, 'w') as f:
for item in arr:
f.write("%s\n" % item)
data = pd.read_csv('data2.csv')
# create the labels, or field we are trying to estimate
label = data['TOTAL']
# remove the header
label = label[1:]
# create the data, or the data that is to be estimated
data = data.drop('TOTAL', axis=1)
data = data.drop('SERIALNUM', axis=1)
# remove the header
data = data[1:]
# # split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data, label, test_size = 0.2)
mlp = MLPRegressor(activation = 'relu', solver = 'lbfgs', verbose=False)
mlp.fit(X_train, y_train)
mlp_predictions = mlp.predict(X_test)
mlp_differences = compare_values(y_test, mlp_predictions)
mlp_Avg = np.average(mlp_differences)
print(mlp_Avg)
打印以下内容：
32.92041129078561 （是的，我知道平均错误很严重）
但是，当尝试优化参数时，相同的参数设置会产生错误：
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neural_network import MLPRegressor
from sklearn import preprocessing
import pandas as pd
import numpy as np
def str_to_num(arr):
le = preprocessing.LabelEncoder()
new_arr = le.fit_transform(arr)
return new_arr
def compare_values(arr1, arr2):
thediff = 0
thediffs = []
for thing1, thing2 in zip(arr1, arr2):
thediff = abs(thing1 - thing2)
thediffs.append(thediff)
return thediffs
def print_to_file(filepath, arr):
with open(filepath, 'w') as f:
for item in arr:
f.write("%s\n" % item)
data = pd.read_csv('data2.csv')
# create the labels, or field we are trying to estimate
label = data['TOTAL_DAYS_TO_COMPLETE']
# remove the header
label = label[1:]
# create the data, or the data that is to be estimated
data = data.drop('TOTAL_DAYS_TO_COMPLETE', axis=1)
data = data.drop('SERIALNUM', axis=1)
# remove the header
data = data[1:]
# # split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data, label, test_size = 0.2)
param_grid = {
#'hidden_layer_sizes': [(1,),(2,),(3,),(10,),(15,),(20,),(25,)],
'activation': ['identity', 'logistic', 'relu'],
#'activation': ['relu'],
'solver': ['lbfgs', 'sgd', 'adam'],
#'solver': ['adam']
#'alpha': [0.0001, 0.0005, 0.0009],
#'learning_rate': ['constant', 'invscaling', 'adaptive'],
#'learning_rate_init': [0.001, 0.01, 0.99],
#'warm_start': [True, False]
#'momentum': [0.1, 0.9, 0.99]
# Did not solver-specifics...yet
}# Create a based model
mlp = MLPRegressor()# Instantiate the grid search model
grid_search = GridSearchCV(estimator = mlp, param_grid = param_grid,
cv = 3, n_jobs = -1, verbose = 2)
grid_search.fit(X_train, y_train)
print()
print(grid_search.best_params_)
print(grid_search.best_score_)
print()
print("Grid scores on development set: ")
print()
answers = grid_search.predict(X_test)
results = compare_values(answers, y_test)
print("Accuracy: ", np.average(results))
print()
满足以下条件：
对9名候选人各进行3折，合计27套 [Parallel（n_jobs = -1）]：使用后端LokyBackend并发8个 工人。 [CV]激活=身份，求解器= lbfgs ............................... [CV]激活=身份，求解器= lbfgs ............................... [CV]激活=身份，求解器= sgd .................................... C：\ Python367-64 \ lib \ site-packages \ sklearn \ neural_network_base.py：195： RuntimeWarning：平方返回（（y_true- y_pred）** 2）.mean（）/ 2 [CV]激活=身份，求解器= adam ................................ [CV]激活=身份， resolver = lbfgs ............................... [CV]激活=身份， resolver = sgd .................................. [CV]激活=身份， resolver = sgd .................................
<删除了工作正常的多余行>
！！！这是它开始失败的地方[CV] !!!!
....................激活= relu，求解器= lbfgs，总计= 0.5s
joblib.externals.loky.process_executor._RemoteTraceback：“”“追溯 （最近通话最近）：文件 “ C：\ Python367-64 \ lib \ site-packages \ joblib \ externals \ loky \ process_executor.py”， _process_worker中的第418行 r = call_item（）文件“ C：\ Python367-64 \ lib \ site-packages \ joblib \ externals \ loky \ process_executor.py”， 第272行，在致电中 返回self.fn（* self.args，** self.kwargs）文件“ C：\ Python367-64 \ lib \ site-packages \ joblib_parallel_backends.py”，行 567，在致电中 返回self.func（* args，** kwargs）文件“ C：\ Python367-64 \ lib \ site-packages \ joblib \ parallel.py”，第225行，在 致电 用于func，args，self.items中的kwargs]文件“ C：\ Python367-64 \ lib \ site-packages \ joblib \ parallel.py”，第225行，在 用于self.items中的func，args，kwargs]文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ model_selection_validation.py”， _fit_and_score中的第554行 test_scores = _score（估算器，X_test，y_test，计分器，is_multimetric）文件 “ C：\ Python367-64 \ lib \ site-packages \ sklearn \ model_selection_validation.py”， _score中的第597行 返回_multimetric_score（estimator，X_test，y_test，scorer）文件 “ C：\ Python367-64 \ lib \ site-packages \ sklearn \ model_selection_validation.py”， _multimetric_score中的第627行 得分=得分手（估计器，X_test，y_test）文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ metrics \ scorer.py”，行 240，在_passthrough_scorer中 返回estimator.score（* args，** kwargs）文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ base.py”，行410，在 得分了 y_type，_，_，_ = _check_reg_targets（y，y_pred，无）文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ metrics \ regression.py”， 第79行，在_check_reg_targets中 y_pred = check_array（y_pred，sure_2d = False）文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ utils \ validation.py”，行 542，在check_array中 allow_nan = force_all_finite =='allow-nan'）文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ utils \ validation.py”，行 56，位于_assert_all_finite中 引发ValueError（msg_err.format（type_err，X.dtype））ValueError：输入包含NaN，无穷大或值对于 dtype（'float64'）。 “”“
上述异常是以下异常的直接原因：
回溯（最近一次通话最后一次）：文件“ mlp_optimizer.py”，第93行， 在 grid_search.fit（X_train，y_train）文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ model_selection_search.py”， 687行，适合 self._run_search（evaluate_candidates）文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ model_selection_search.py”， _run_search中的第1148行 Evaluation_candidates（ParameterGrid（self.param_grid））文件“ C：\ Python367-64 \ lib \ site-packages \ sklearn \ model_selection_search.py”， 第666行，在Evaluation_candidates中 cv.split（X，y，groups）））文件“ C：\ Python367-64 \ lib \ site-packages \ joblib \ parallel.py”，第934行，在 致电 self.retrieve（）文件“ C：\ Python367-64 \ lib \ site-packages \ joblib \ parallel.py”，行833，在 找回 self._output.extend（job.get（timeout = self.timeout））文件“ C：\ Python367-64 \ lib \ site-packages \ joblib_parallel_backends.py”，行 521，在wrap_future_result中 返回future.result（timeout = timeout）文件“ C：\ Python367-64 \ lib \ concurrent \ futures_base.py”，结果为432行 返回self .__ get_result（）文件“ C：\ Python367-64 \ lib \ concurrent \ futures_base.py”，行384，在 __get_result 引发self._exception ValueError：输入包含NaN，无穷大或对于dtype（'float64'）而言太大的值。
为什么不使用GridSearchCV却不能使用GridSearchCV导致失败？
答案 0 :(得分：0)
问题与此行有关：
'solver': ['lbfgs', 'sgd', 'adam'],
sgd选项要求每个the documentation的某个阈值内的某些参数
只需更改
'solver': ['lbfgs', 'sgd', 'adam'],
到
'solver': ['lbfgs', 'adam'],
解决了问题