AutoMLSearchException: All pipelines in the current AutoML batch produced a score of np.nan on the primary objective

I just put the problem_ Type="binary" becomes "multiclass"
####

*****************************
* Beginning pipeline search *
*****************************

Optimizing for Log Loss Multiclass.
Lower score is better.

Using SequentialEngine to train and score pipelines.
Searching up to 3 batches for a total of None pipelines.
Allowed model families:

Evaluating Baseline Pipeline: Mode Baseline Multiclass Classification Pipeline
Mode Baseline Multiclass Classification Pipeline fold 0: Encountered an error.
Mode Baseline Multiclass Classification Pipeline fold 0: All scores will be replaced with nan.
Fold 0: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 0: Parameters:
{'Label Encoder': {'positive_label': None}, 'Baseline Classifier': {'strategy': 'mode'}}
Fold 0: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Mode Baseline Multiclass Classification Pipeline fold 1: Encountered an error.
Mode Baseline Multiclass Classification Pipeline fold 1: All scores will be replaced with nan.
Fold 1: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 1: Parameters:
{'Label Encoder': {'positive_label': None}, 'Baseline Classifier': {'strategy': 'mode'}}
Fold 1: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Mode Baseline Multiclass Classification Pipeline fold 2: Encountered an error.
Mode Baseline Multiclass Classification Pipeline fold 2: All scores will be replaced with nan.
Fold 2: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 2: Parameters:
{'Label Encoder': {'positive_label': None}, 'Baseline Classifier': {'strategy': 'mode'}}
Fold 2: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Mode Baseline Multiclass Classification Pipeline:
Starting cross validation
Finished cross validation - mean Log Loss Multiclass: nan

*****************************
* Evaluating Batch Number 1 *
*****************************

Logistic Regression Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder + Standard Scaler fold 0: Encountered an error.
Logistic Regression Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder + Standard Scaler fold 0: All scores will be replaced with nan.
Fold 0: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 0: Parameters:
{'Label Encoder': {'positive_label': None}, 'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': 'if_binary', 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Logistic Regression Classifier': {'penalty': 'l2', 'C': 1.0, 'n_jobs': -1, 'multi_class': 'auto', 'solver': 'lbfgs'}}
Fold 0: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Logistic Regression Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder + Standard Scaler fold 1: Encountered an error.
Logistic Regression Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder + Standard Scaler fold 1: All scores will be replaced with nan.
Fold 1: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 1: Parameters:
{'Label Encoder': {'positive_label': None}, 'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': 'if_binary', 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Logistic Regression Classifier': {'penalty': 'l2', 'C': 1.0, 'n_jobs': -1, 'multi_class': 'auto', 'solver': 'lbfgs'}}
Fold 1: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Logistic Regression Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder + Standard Scaler fold 2: Encountered an error.
Logistic Regression Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder + Standard Scaler fold 2: All scores will be replaced with nan.
Fold 2: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 2: Parameters:
{'Label Encoder': {'positive_label': None}, 'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': 'if_binary', 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Logistic Regression Classifier': {'penalty': 'l2', 'C': 1.0, 'n_jobs': -1, 'multi_class': 'auto', 'solver': 'lbfgs'}}
Fold 2: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Logistic Regression Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder + Standard Scaler:
Starting cross validation
Finished cross validation - mean Log Loss Multiclass: nan
Random Forest Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder fold 0: Encountered an error.
Random Forest Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder fold 0: All scores will be replaced with nan.
Fold 0: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 0: Parameters:
{'Label Encoder': {'positive_label': None}, 'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': 'if_binary', 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Random Forest Classifier': {'n_estimators': 100, 'max_depth': 6, 'n_jobs': -1}}
Fold 0: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Random Forest Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder fold 1: Encountered an error.
Random Forest Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder fold 1: All scores will be replaced with nan.
Fold 1: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 1: Parameters:
{'Label Encoder': {'positive_label': None}, 'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': 'if_binary', 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Random Forest Classifier': {'n_estimators': 100, 'max_depth': 6, 'n_jobs': -1}}
Fold 1: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Random Forest Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder fold 2: Encountered an error.
Random Forest Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder fold 2: All scores will be replaced with nan.
Fold 2: Exception during automl search: Multiclass pipelines require y to have 3 or more unique classes!
Fold 2: Parameters:
{'Label Encoder': {'positive_label': None}, 'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': 'if_binary', 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Random Forest Classifier': {'n_estimators': 100, 'max_depth': 6, 'n_jobs': -1}}
Fold 2: Traceback:
File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 238, in _train_and_score
fitted_pipeline, hashes = train_pipeline(

File "D:\conda\envs\gradio\lib\site-packages\evalml\automl\engine\engine_base.py", line 176, in train_pipeline
cv_pipeline.fit(X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\utils\base_meta.py", line 19, in _set_fit
return_value = method(self, X, y)

File "D:\conda\envs\gradio\lib\site-packages\evalml\pipelines\classification_pipeline.py", line 66, in fit
raise ValueError(

Random Forest Classifier w/ Label Encoder + Replace Nullable Types Transformer + Imputer + One Hot Encoder:
Starting cross validation
Finished cross validation - mean Log Loss Multiclass: nan
---------------------------------------------------------------------------
AutoMLSearchException Traceback (most recent call last)
Cell In [20], line 1
----> 1 automl.search(interactive_plot=False)

File D:\conda\envs\gradio\lib\site-packages\evalml\automl\automl_search.py:1159, in AutoMLSearch.search(self, interactive_plot)
1152 if (
1153 len(current_batch_pipeline_scores)
1154 and current_batch_pipeline_scores.isna().all()
1155 ):
1156 error_msgs = set(
1157 [str(pl_fold["Exception"]) for pl_fold in self.errors.values()],
1158 )
-> 1159 raise AutoMLSearchException(
1160 f"All pipelines in the current AutoML batch produced a score of np.nan on the primary objective {self.objective}. Exception(s) raised: {error_msgs}. Check the 'errors' attribute of the AutoMLSearch object for a full breakdown of errors and tracebacks.",
1161 )
1162 if len(pipeline_times) > 0:
1163 pipeline_times["Total time of batch"] = time_elapsed(start_batch_time)

AutoMLSearchException: All pipelines in the current AutoML batch produced a score of np.nan on the primary objective . Exception(s) raised: {'Multiclass pipelines require y to have 3 or more unique classes!'}. Check the 'errors' attribute of the AutoMLSearch object for a full breakdown of errors and tracebacks.
```python
# Your code here
y_train.dtypes
```
CategoricalDtype(categories=['<=5%', '>5%'], ordered=False)
```

automl = AutoMLSearch(
X_train=X_train,
y_train=y_train,
problem_type="multiclass",
verbose=True,
)
automl.search(interactive_plot=False)

```