How to make subplots consisting of 6 charts to charts from the scikitplot library - subplot

Question

I have six models and want to evaluate them with a ROC chart
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier
from catboost import CatBoostClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LassoCV
NBC = GaussianNB()
LRE = LogisticRegression(solver='lbfgs')
GBC = GradientBoostingClassifier()
RFC = RandomForestClassifier()
LGBM = LGBMClassifier()
CBC = CatBoostClassifier(verbose=0, n_estimators=100)
classifiers = [NBC,LRE,GBC,RFC,LGBM,CBC]
for cls in classifiers:
cls.fit(X_train, y_train)
now I'm making charts for this
import scikitplot as skplt
for cls in classifiers:
skplt.metrics.plot_roc(y_test, cls.predict_proba(X_test),figsize=(6, 3),title=type(cls).__name__)
plt.tight_layout()
plt.show()
But I want subplot charts! Unfortunately, this does not work out. I need this solution for a whole series of problems with scikitplot charts.
classifiers = [NBC,LRE,GBC,RFC,LGBM,CBC]
from sklearn.metrics import confusion_matrix, log_loss, auc, roc_curve, roc_auc_score, recall_score, precision_recall_curve
plt.figure(figsize=(15,7))
grid = plt.GridSpec(2, 3, wspace=0.3, hspace=0.4)
for i in range(6):
col, row = i%3,i//3
ax = plt.subplot(grid[row,col])
ax.title.set_color('blue')
#model = classifiers[i]
#skplt.metrics.plot_roc(y_test, model.predict_proba(X_test),figsize=(6, 3),title=type(cls).__name__)
plt.tight_layout()
plt.show()

Answer 1

you need to specify the ax argument in skplt.metrics.plot_roc
plt.figure(figsize=(15,7))
grid = plt.GridSpec(2, 3, wspace=0.3, hspace=0.4)
for i in range(6):
col, row = i%3,i//3
ax = plt.subplot(grid[row,col])
ax.title.set_color('blue')
model = classifiers[i]
skplt.metrics.plot_roc(y_test, model.predict_proba(X_test), ax=ax, title=type(cls).__name__)
plt.show()