使用 cross_val_score 和 StackingClassifier 或投票分类器获取 "nan"
Getting "nan" with cross_val_score and StackingClassifier or Voting Classifier
我想将 StackingClassifier 和 VotingClassifier 与 StratifiedKFold 和 cross_val_score 一起使用。如果我使用 StackingClassifier 或 VotingClassifier，我会在 cross_val_score 中得到 nan 值。如果我使用任何其他算法而不是 StackingClassifier 或 VotingClassifier，cross_val_score 工作正常。
我正在使用 python 3.8.5 & sklearn 0.23.2.
正在更新工作示例的代码。请使用来自 kaggle 的这个 Parkinons 数据集 Parkinsons Dataset 这是我一直在处理的数据集，下面是我遵循的确切步骤。
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn import preprocessing
from sklearn import metrics
from sklearn import model_selection
from sklearn import feature_selection
from imblearn.over_sampling import SMOTE
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')
dataset = pd.read_csv('parkinsons.csv')
FS_X=dataset.iloc[:,:-1]
FS_y=dataset.iloc[:,-1:]
FS_X.drop(['name'],axis=1,inplace=True)
select_k_best = feature_selection.SelectKBest(score_func=feature_selection.f_classif,k=15)
X_k_best = select_k_best.fit_transform(FS_X,FS_y)
supportList = select_k_best.get_support().tolist()
p_valuesList = select_k_best.pvalues_.tolist()
toDrop=[]
for i in np.arange(len(FS_X.columns)):
bool = supportList[i]
if(bool == False):
toDrop.append(FS_X.columns[i])
FS_X.drop(toDrop,axis=1,inplace=True)
smote = SMOTE(random_state=7)
Balanced_X,Balanced_y = smote.fit_sample(FS_X,FS_y)
before = pd.merge(FS_X,FS_y,right_index=True, left_index=True)
after = pd.merge(Balanced_X,Balanced_y,right_index=True, left_index=True)
b=before['status'].value_counts()
a=after['status'].value_counts()
print('Before')
print(b)
print('After')
print(a)
SkFold = model_selection.StratifiedKFold(n_splits=10, random_state=7, shuffle=False)
estimators_list = list()
KNN = KNeighborsClassifier()
RF = RandomForestClassifier(criterion='entropy',random_state = 1)
DT = DecisionTreeClassifier(criterion='entropy',random_state = 1)
GNB = GaussianNB()
LR = LogisticRegression(random_state = 1)
estimators_list.append(LR)
estimators_list.append(RF)
estimators_list.append(DT)
estimators_list.append(GNB)
SCLF = StackingClassifier(estimators = estimators_list,final_estimator = KNN,stack_method = 'predict_proba',cv=SkFold,n_jobs = -1)
VCLF = VotingClassifier(estimators = estimators_list,voting = 'soft',n_jobs = -1)
scores1 = model_selection.cross_val_score(estimator = SCLF,X=Balanced_X.values,y=Balanced_y.values,scoring='accuracy',cv=SkFold)
print('StackingClassifier Scores',scores1)
scores2 = model_selection.cross_val_score(estimator = VCLF,X=Balanced_X.values,y=Balanced_y.values,scoring='accuracy',cv=SkFold)
print('VotingClassifier Scores',scores2)
scores3 = model_selection.cross_val_score(estimator = DT,X=Balanced_X.values,y=Balanced_y.values,scoring='accuracy',cv=SkFold)
print('DecisionTreeClassifier Scores',scores3)
输出
Before
1 147
0 48
Name: status, dtype: int64
After
1 147
0 147
Name: status, dtype: int64
StackingClassifier Scores [nan nan nan nan nan nan nan nan nan nan]
VotingClassifier Scores [nan nan nan nan nan nan nan nan nan nan]
DecisionTreeClassifier Scores [0.86666667 0.9 0.93333333 0.86666667 0.96551724 0.82758621
0.75862069 0.86206897 0.86206897 0.93103448]
我查看了 Whosebug 上的其他一些相关帖子，但无法解决我的问题。我无法理解我哪里出错了。
传递给 StackingClassifier 或 VotingClassifier 的 estimators_list 不正确。 documentation on sklearn for StackingClassifier 表示：
Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.
因此正确的列表如下所示：
KNN = KNeighborsClassifier()
DT = DecisionTreeClassifier(criterion="entropy")
GNB = GaussianNB()
estimators_list = [("KNN", KNN), ("DT", DT), ("GNB", GNB)]
使用帕金森病数据的完整最小工作示例可能如下所示：
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import StackingClassifier
dataset = pd.read_csv("parkinsons.csv")
FS_X = dataset.drop(["name", "status"], axis=1)
FS_y = dataset["status"]
estimators_list = [("KNN", KNeighborsClassifier()), ("DT", DecisionTreeClassifier(criterion="entropy")), ("GNB", GaussianNB())]
SCLF = StackingClassifier(estimators=estimators_list)
X_train, X_test, y_train, y_test = train_test_split(FS_X, FS_y)
SCLF.fit(X_train, y_train)
print("SCLF: ", accuracy_score(y_test, SCLF.predict(X_test)))
我想将 StackingClassifier 和 VotingClassifier 与 StratifiedKFold 和 cross_val_score 一起使用。如果我使用 StackingClassifier 或 VotingClassifier，我会在 cross_val_score 中得到 nan 值。如果我使用任何其他算法而不是 StackingClassifier 或 VotingClassifier，cross_val_score 工作正常。 我正在使用 python 3.8.5 & sklearn 0.23.2.
正在更新工作示例的代码。请使用来自 kaggle 的这个 Parkinons 数据集 Parkinsons Dataset 这是我一直在处理的数据集，下面是我遵循的确切步骤。
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn import preprocessing
from sklearn import metrics
from sklearn import model_selection
from sklearn import feature_selection
from imblearn.over_sampling import SMOTE
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')
dataset = pd.read_csv('parkinsons.csv')
FS_X=dataset.iloc[:,:-1]
FS_y=dataset.iloc[:,-1:]
FS_X.drop(['name'],axis=1,inplace=True)
select_k_best = feature_selection.SelectKBest(score_func=feature_selection.f_classif,k=15)
X_k_best = select_k_best.fit_transform(FS_X,FS_y)
supportList = select_k_best.get_support().tolist()
p_valuesList = select_k_best.pvalues_.tolist()
toDrop=[]
for i in np.arange(len(FS_X.columns)):
bool = supportList[i]
if(bool == False):
toDrop.append(FS_X.columns[i])
FS_X.drop(toDrop,axis=1,inplace=True)
smote = SMOTE(random_state=7)
Balanced_X,Balanced_y = smote.fit_sample(FS_X,FS_y)
before = pd.merge(FS_X,FS_y,right_index=True, left_index=True)
after = pd.merge(Balanced_X,Balanced_y,right_index=True, left_index=True)
b=before['status'].value_counts()
a=after['status'].value_counts()
print('Before')
print(b)
print('After')
print(a)
SkFold = model_selection.StratifiedKFold(n_splits=10, random_state=7, shuffle=False)
estimators_list = list()
KNN = KNeighborsClassifier()
RF = RandomForestClassifier(criterion='entropy',random_state = 1)
DT = DecisionTreeClassifier(criterion='entropy',random_state = 1)
GNB = GaussianNB()
LR = LogisticRegression(random_state = 1)
estimators_list.append(LR)
estimators_list.append(RF)
estimators_list.append(DT)
estimators_list.append(GNB)
SCLF = StackingClassifier(estimators = estimators_list,final_estimator = KNN,stack_method = 'predict_proba',cv=SkFold,n_jobs = -1)
VCLF = VotingClassifier(estimators = estimators_list,voting = 'soft',n_jobs = -1)
scores1 = model_selection.cross_val_score(estimator = SCLF,X=Balanced_X.values,y=Balanced_y.values,scoring='accuracy',cv=SkFold)
print('StackingClassifier Scores',scores1)
scores2 = model_selection.cross_val_score(estimator = VCLF,X=Balanced_X.values,y=Balanced_y.values,scoring='accuracy',cv=SkFold)
print('VotingClassifier Scores',scores2)
scores3 = model_selection.cross_val_score(estimator = DT,X=Balanced_X.values,y=Balanced_y.values,scoring='accuracy',cv=SkFold)
print('DecisionTreeClassifier Scores',scores3)
输出
Before
1 147
0 48
Name: status, dtype: int64
After
1 147
0 147
Name: status, dtype: int64
StackingClassifier Scores [nan nan nan nan nan nan nan nan nan nan]
VotingClassifier Scores [nan nan nan nan nan nan nan nan nan nan]
DecisionTreeClassifier Scores [0.86666667 0.9 0.93333333 0.86666667 0.96551724 0.82758621
0.75862069 0.86206897 0.86206897 0.93103448]
我查看了 Whosebug 上的其他一些相关帖子，但无法解决我的问题。我无法理解我哪里出错了。
传递给 StackingClassifier 或 VotingClassifier 的 estimators_list 不正确。 documentation on sklearn for StackingClassifier 表示：
Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.
因此正确的列表如下所示：
KNN = KNeighborsClassifier()
DT = DecisionTreeClassifier(criterion="entropy")
GNB = GaussianNB()
estimators_list = [("KNN", KNN), ("DT", DT), ("GNB", GNB)]
使用帕金森病数据的完整最小工作示例可能如下所示：
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import StackingClassifier
dataset = pd.read_csv("parkinsons.csv")
FS_X = dataset.drop(["name", "status"], axis=1)
FS_y = dataset["status"]
estimators_list = [("KNN", KNeighborsClassifier()), ("DT", DecisionTreeClassifier(criterion="entropy")), ("GNB", GaussianNB())]
SCLF = StackingClassifier(estimators=estimators_list)
X_train, X_test, y_train, y_test = train_test_split(FS_X, FS_y)
SCLF.fit(X_train, y_train)
print("SCLF: ", accuracy_score(y_test, SCLF.predict(X_test)))