could not determine class_counts_ from previously fitted classifier

Describe the bug When running visualizer, I am receiving a warning :
yellowbrick/classifier/base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier

which results in fitting again the classifier.

To Reproduce

from yellowbrick.classifier import ClassificationReport, ConfusionMatrix
from sklearn import datasets
from sklearn.model_selection import train_test_split
import xgboost as xgb
import matplotlib.pyplot as plt

X, y = datasets.load_iris(return_X_y=True)

x_train, x_val, y_train, y_val = train_test_split(
						                        X,
						                        y,
						                        stratify=y,
						                        test_size = 0.2)

model = xgb.XGBClassifier(objective ='multi:softprob',
                                            num_class=3,
                                            use_label_encoder=False,
                                            enable_categorical=False,
                                            n_estimators=10) 

model.fit(x_train,
               y_train,
               early_stopping_rounds=10,
              eval_set=[(x_train, y_train), (x_val, y_val)])
   

fig, ax = plt.subplots()
visualizer = ClassificationReport(model,
                                                   is_fitted=True)
visualizer.score(x_val, y_val)
visualizer.show()

Dataset from sklearn import datasets

Expected behavior Since, we declare is_fitted=True, it should not fit again.

Traceback

/home/ggous/miniconda3/envs/sklearn/lib/python3.9/site-packages/yellowbrick/classifier/base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier
  warnings.warn(

Desktop (please complete the following information):

• OS: Linux Mint
• Python Version : 3.9.12 miniconda
• Yellowbrick Version : 1.5

Additional context I think sometimes it takes too much time to fit again?

[bbengfort]

@lwgray thank you for adding those suggestions!

@ggous if you’re in a Jupyter notebook, this StackOverflow post has some suggestions for preventing the image from being rendered. Otherwise clear_figure as @lwgray mentioned is probably your best bet.

[lwgray]

This code address both concerns but @bbengfort maybe you have a better trick to stop showing the plot. I feel like we answered this question before.

from yellowbrick.classifier import ClassificationReport, ConfusionMatrix
from sklearn import datasets
from sklearn.model_selection import train_test_split
import xgboost as xgb
import matplotlib.pyplot as plt

X, y = datasets.load_iris(return_X_y=True)

x_train, x_val, y_train, y_val = train_test_split(X,y,stratify=y,test_size = 0.2)

model = xgb.XGBClassifier(objective ='multi:softprob',
                                            num_class=3,
                                            use_label_encoder=False,
                                            enable_categorical=False,
                                            n_estimators=10) 

model.fit(x_train,
          y_train,
          early_stopping_rounds=10,
          eval_set=[(x_train, y_train), (x_val, y_val)])

# Specify class counts on the model
model.class_counts_ = 3
fig, ax = plt.subplots()
visualizer = ClassificationReport(model, is_fitted=True)
visualizer.score(x_val, y_val)

# Clear Figure works but @bbengfort might have a better approach
visualizer.show('test.png', clear_figure=True);