源码：fromsklearn.model_selectionimportKFoldfromsklearn.metricsimportrecall_scorefold=KFold(5,shuffle=False)recall_accs=[]c_param_range=[0.01,0.1,1,10,100]results_table=p...

源码：

from sklearn.model_selection import KFold

from sklearn.metrics import recall_score

fold = KFold(5, shuffle=False)

recall_accs = []

c_param_range = [0.01, 0.1, 1, 10, 100]

results_table = pd.DataFrame(columns = ['C值', '平均召回率得分'])

results_table['C值'] = c_param_range

j = 0

for c_param in c_param_range:

print('C值:',c_param)

recall_accs = []

for iteration, indices in enumerate(fold.split(y_train)):

lr = LR(C = c_param, penalty = '12')

X_train = X_train.reset_index(drop=True)

y_train = y_train.reset_index(drop=True)

lr.fit(X_train.iloc[indices[0],:], y_train.iloc[indices[0]].values.ravel())

y_pred = lr.predict(X_train.iloc[indices[1],:].values)

recall_acc = recall_score(y_train.iloc[indices[1]].values, y_pred)

recall_accs.append(recall_acc)

print('迭代次数', iteration, ':召回率得分=', recall_acc)

results_table.ix[j, '平均召回率得分'] = np.mean(recall_accs)

j += 1

print('平均召回率得分:', np.mean(recall_accs))

best_c = results_table.loc[results_table['平均召回率得分'].idxmax()]['C值']

print('交叉验证最好的C值是',best_c)

错误提示：ValueError Traceback (most recent call last)

in ()

14 X_train = X_train.reset_index(drop=True)

15 y_train = y_train.reset_index(drop=True)

---> 16 lr.fit(X_train.iloc[indices[0],:], y_train.iloc[indices[0]].values.ravel())

17 y_pred = lr.predict(X_train.iloc[indices[1],:].values)

18 recall_acc = recall_score(y_train.iloc[indices[1]].values, y_pred)

D:\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight)

1491 The SAGA solver supports both float64 and float32 bit arrays.

1492 """

-> 1493 solver = _check_solver(self.solver, self.penalty, self.dual)

1494

1495 if not isinstance(self.C, numbers.Number) or self.C < 0:

D:\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in _check_solver(solver, penalty, dual)

440 if penalty not in all_penalties:

441 raise ValueError("Logistic Regression supports only penalties in %s,"

--> 442 " got %s." % (all_penalties, penalty))

443

444 if solver not in ['liblinear', 'saga'] and penalty not in ('l2', 'none'):

ValueError: Logistic Regression supports only penalties in ['l1', 'l2', 'elasticnet', 'none'], got 12.

求大神帮忙。

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