/Users/glemaitre/Documents/packages/scikit-learn/sklearn/model_selection/_split.py:779: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[1], line 6
      4 y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 3]
      5 logreg = LogisticRegressionCV(cv=5, scoring='neg_log_loss')
----> 6 logreg.fit(X, y)

File ~/Documents/packages/scikit-learn/sklearn/base.py:1244, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1237     estimator._validate_params()
   1239 with config_context(
   1240     skip_parameter_validation=(
   1241         prefer_skip_nested_validation or global_skip_validation
   1242     )
   1243 ):
-> 1244     return fit_method(estimator, *args, **kwargs)

File ~/Documents/packages/scikit-learn/sklearn/linear_model/_logistic.py:2000, in LogisticRegressionCV.fit(self, X, y, sample_weight, **params)
   1997 else:
   1998     prefer = "processes"
-> 2000 fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer=prefer)(
   2001     path_func(
   2002         X,
   2003         y,
   2004         train,
   2005         test,
   2006         pos_class=label,
   2007         Cs=self.Cs,
   2008         fit_intercept=self.fit_intercept,
   2009         penalty=self.penalty,
   2010         dual=self.dual,
   2011         solver=solver,
   2012         tol=self.tol,
   2013         max_iter=self.max_iter,
   2014         verbose=self.verbose,
   2015         class_weight=class_weight,
   2016         scoring=self.scoring,
   2017         multi_class=multi_class,
   2018         intercept_scaling=self.intercept_scaling,
   2019         random_state=self.random_state,
   2020         max_squared_sum=max_squared_sum,
   2021         sample_weight=sample_weight,
   2022         l1_ratio=l1_ratio,
   2023         score_params=routed_params.scorer.score,
   2024     )
   2025     for label in iter_encoded_labels
   2026     for train, test in folds
   2027     for l1_ratio in l1_ratios_
   2028 )
   2030 # _log_reg_scoring_path will output different shapes depending on the
   2031 # multi_class param, so we need to reshape the outputs accordingly.
   2032 # Cs is of shape (n_classes . n_folds . n_l1_ratios, n_Cs) and all the
   (...)
   2039 #  (n_classes, n_folds, n_Cs . n_l1_ratios) or
   2040 #  (1, n_folds, n_Cs . n_l1_ratios)
   2041 coefs_paths, Cs, scores, n_iter_ = zip(*fold_coefs_)

File ~/Documents/packages/scikit-learn/sklearn/utils/parallel.py:77, in Parallel.__call__(self, iterable)
     72 config = get_config()
     73 iterable_with_config = (
     74     (_with_config(delayed_func, config), args, kwargs)
     75     for delayed_func, args, kwargs in iterable
     76 )
---> 77 return super().__call__(iterable_with_config)

File ~/mambaforge/envs/sklearn_dev/lib/python3.10/site-packages/joblib/parallel.py:1863, in Parallel.__call__(self, iterable)
   1861     output = self._get_sequential_output(iterable)
   1862     next(output)
-> 1863     return output if self.return_generator else list(output)
   1865 # Let's create an ID that uniquely identifies the current call. If the
   1866 # call is interrupted early and that the same instance is immediately
   1867 # re-used, this id will be used to prevent workers that were
   1868 # concurrently finalizing a task from the previous call to run the
   1869 # callback.
   1870 with self._lock:

File ~/mambaforge/envs/sklearn_dev/lib/python3.10/site-packages/joblib/parallel.py:1792, in Parallel._get_sequential_output(self, iterable)
   1790 self.n_dispatched_batches += 1
   1791 self.n_dispatched_tasks += 1
-> 1792 res = func(*args, **kwargs)
   1793 self.n_completed_tasks += 1
   1794 self.print_progress()

File ~/Documents/packages/scikit-learn/sklearn/utils/parallel.py:139, in _FuncWrapper.__call__(self, *args, **kwargs)
    137     config = {}
    138 with config_context(**config):
--> 139     return self.function(*args, **kwargs)

File ~/Documents/packages/scikit-learn/sklearn/linear_model/_logistic.py:803, in _log_reg_scoring_path(X, y, train, test, pos_class, Cs, scoring, fit_intercept, max_iter, tol, class_weight, verbose, solver, penalty, dual, intercept_scaling, multi_class, random_state, max_squared_sum, sample_weight, l1_ratio, score_params)
    801         score_params = score_params or {}
    802         score_params = _check_method_params(X=X, params=score_params, indices=test)
--> 803         scores.append(scoring(log_reg, X_test, y_test, **score_params))
    804 return coefs, Cs, np.array(scores), n_iter

File ~/Documents/packages/scikit-learn/sklearn/metrics/_scorer.py:288, in _BaseScorer.__call__(self, estimator, X, y_true, sample_weight, **kwargs)
    285 if sample_weight is not None:
    286     _kwargs["sample_weight"] = sample_weight
--> 288 return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)

File ~/Documents/packages/scikit-learn/sklearn/metrics/_scorer.py:388, in _Scorer._score(self, method_caller, estimator, X, y_true, **kwargs)
    380 y_pred = method_caller(
    381     estimator,
    382     _get_response_method_name(response_method),
    383     X,
    384     pos_label=pos_label,
    385 )
    387 scoring_kwargs = {**self._kwargs, **kwargs}
--> 388 return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)

File ~/Documents/packages/scikit-learn/sklearn/utils/_param_validation.py:189, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    187 global_skip_validation = get_config()["skip_parameter_validation"]
    188 if global_skip_validation:
--> 189     return func(*args, **kwargs)
    191 func_sig = signature(func)
    193 # Map *args/**kwargs to the function signature

File ~/Documents/packages/scikit-learn/sklearn/metrics/_classification.py:3033, in log_loss(y_true, y_pred, normalize, sample_weight, labels)
   3031 if len(lb.classes_) != y_pred.shape[1]:
   3032     if labels is None:
-> 3033         raise ValueError(
   3034             "y_true and y_pred contain different number of "
   3035             "classes {0}, {1}. Please provide the true "
   3036             "labels explicitly through the labels argument. "
   3037             "Classes found in "
   3038             "y_true: {2}".format(
   3039                 transformed_labels.shape[1], y_pred.shape[1], lb.classes_
   3040             )
   3041         )
   3042     else:
   3043         raise ValueError(
   3044             "The number of classes in labels is different "
   3045             "from that in y_pred. Classes found in "
   3046             "labels: {0}".format(lb.classes_)
   3047         )

ValueError: y_true and y_pred contain different number of classes 2, 3. Please provide the true labels explicitly through the labels argument. Classes found in y_true: [0 1]