# Buggy code
def train_single_full(
    self,
    X_train,
    y_train,
    X_val,
    y_val,
    model: AbstractModel,
    feature_prune=False,
    hyperparameter_tune=True,
    stack_name="core",
    kfolds=None,
    k_fold_start=0,
    k_fold_end=None,
    n_repeats=None,
    n_repeat_start=0,
    level=0,
    time_limit=None,
):
    if (n_repeat_start == 0) and (k_fold_start == 0):
        model.feature_types_metadata = copy.deepcopy(
            self.feature_types_metadata
        )  # TODO: Don't set feature_types_metadata here
    if feature_prune:
        if n_repeat_start != 0:
            raise ValueError(
                "n_repeat_start must be 0 to feature_prune, value = "
                + str(n_repeat_start)
            )
        elif k_fold_start != 0:
            raise ValueError(
                "k_fold_start must be 0 to feature_prune, value = " + str(k_fold_start)
            )
        self.autotune(
            X_train=X_train,
            X_holdout=X_val,
            y_train=y_train,
            y_holdout=y_val,
            model_base=model,
        )  # TODO: Update to use CV instead of holdout
    if hyperparameter_tune:
        if self.scheduler_func is None or self.scheduler_options is None:
            raise ValueError(
                "scheduler_options cannot be None when hyperparameter_tune = True"
            )
        if n_repeat_start != 0:
            raise ValueError(
                "n_repeat_start must be 0 to hyperparameter_tune, value = "
                + str(n_repeat_start)
            )
        elif k_fold_start != 0:
            raise ValueError(
                "k_fold_start must be 0 to hyperparameter_tune, value = "
                + str(k_fold_start)
            )
        # hpo_models (dict): keys = model_names, values = model_paths
        try:
            if isinstance(model, BaggedEnsembleModel):
                hpo_models, hpo_model_performances, hpo_results = (
                    model.hyperparameter_tune(
                        X=X_train,
                        y=y_train,
                        k_fold=kfolds,
                        scheduler_options=(self.scheduler_func, self.scheduler_options),
                        verbosity=self.verbosity,
                    )
                )
            else:
                hpo_models, hpo_model_performances, hpo_results = (
                    model.hyperparameter_tune(
                        X_train=X_train,
                        y_train=y_train,
                        X_val=X_val,
                        y_val=y_val,
                        scheduler_options=(self.scheduler_func, self.scheduler_options),
                        verbosity=self.verbosity,
                    )
                )
        except Exception as err:
            if self.verbosity >= 1:
                traceback.print_tb(err.__traceback__)
            logger.exception(
                "Warning: Exception caused "
                + model.name
                + " to fail during hyperparameter tuning... Skipping this model."
            )
            logger.debug(err)
            del model
            model_names_trained = []
        else:
            self.hpo_results[model.name] = hpo_results
            model_names_trained = []
            for model_hpo_name, model_path in hpo_models.items():
                model_hpo = self.load_model(
                    model_hpo_name, path=model_path, model_type=type(model)
                )
                self.add_model(model=model_hpo, stack_name=stack_name, level=level)
                model_names_trained.append(model_hpo.name)
    else:
        model_names_trained = self.train_and_save(
            X_train,
            y_train,
            X_val,
            y_val,
            model,
            stack_name=stack_name,
            kfolds=kfolds,
            k_fold_start=k_fold_start,
            k_fold_end=k_fold_end,
            n_repeats=n_repeats,
            n_repeat_start=n_repeat_start,
            level=level,
            time_limit=time_limit,
        )
    self.save()
    return model_names_trained


# Traceback (ValueError)
File delimiter for ../input/house-prices-advanced-regression-techniques/test.csv inferred as ',' (comma). If this is incorrect, please manually load the data as a pandas DataFrame.
Loaded data from: ../input/house-prices-advanced-regression-techniques/test.csv | Columns = 80 / 80 | Rows = 1459 -> 1459
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-48-91455d7b9940> in <module>
1 test_data = task.Dataset(file_path=os.path.join(in_dir, job, 'test.csv'))
2
----> 3 preds = model.predict(test_data)

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/autogluon/task/tabular_prediction/predictor.py in predict(self, dataset, model, as_pandas, use_pred_cache, add_to_pred_cache)
130         """
131         dataset = self.__get_dataset(dataset)
--> 132         return self._learner.predict(X=dataset, model=model, as_pandas=as_pandas, use_pred_cache=use_pred_cache, add_to_pred_cache=add_to_pred_cache)
133
134     def predict_proba(self, dataset, model=None, as_pandas=False, as_multiclass=False):

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/autogluon/utils/tabular/ml/learner/abstract_learner.py in predict(self, X, model, as_pandas, use_pred_cache, add_to_pred_cache)
127
128         if len(X_cache_miss) > 0:
--> 129             y_pred_proba = self.predict_proba(X=X_cache_miss, model=model, inverse_transform=False)
130             problem_type = self.trainer_problem_type or self.problem_type
131             y_pred = get_pred_from_proba(y_pred_proba=y_pred_proba, problem_type=problem_type)

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/autogluon/utils/tabular/ml/learner/abstract_learner.py in predict_proba(self, X, model, as_pandas, as_multiclass, inverse_transform)
92     # TODO: Add pred_proba_cache functionality as in predict()
93     def predict_proba(self, X: DataFrame, model=None, as_pandas=False, as_multiclass=False, inverse_transform=True):
---> 94         X = self.transform_features(X)
95         y_pred_proba = self.load_trainer().predict_proba(X, model=model)
96         if inverse_transform:

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/autogluon/utils/tabular/ml/learner/abstract_learner.py in transform_features(self, X)
216     def transform_features(self, X):
217         for feature_generator in self.feature_generators:
--> 218             X = feature_generator.transform(X)
219         return X
220

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/autogluon/utils/tabular/utils/decorators.py in inner1(*args, **kwargs)
12         begin = time.time()
13
---> 14         output = func(*args, **kwargs)
15
16         # storing time after function execution

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/autogluon/utils/tabular/features/abstract_feature_generator.py in transform(self, X)
183             raise ValueError(f'Required columns are missing from the provided dataset. Missing columns: {missing_cols}')
184
--> 185         X = X.astype(self.features_init_types)
186         X.reset_index(drop=True, inplace=True)
187         X_features = self.generate_features(X)

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs)
5863                     results.append(
5864                         col.astype(
-> 5865                             dtype=dtype[col_name], copy=copy, errors=errors, **kwargs
5866                         )
5867                     )

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs)
5880             # else, only a single dtype is given
5881             new_data = self._data.astype(
-> 5882                 dtype=dtype, copy=copy, errors=errors, **kwargs
5883             )
5884             return self._constructor(new_data).__finalize__(self)

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/pandas/core/internals/managers.py in astype(self, dtype, **kwargs)
579
580     def astype(self, dtype, **kwargs):
--> 581         return self.apply("astype", dtype=dtype, **kwargs)
582
583     def convert(self, **kwargs):

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
436                     kwargs[k] = obj.reindex(b_items, axis=axis, copy=align_copy)
437
--> 438             applied = getattr(b, f)(**kwargs)
439             result_blocks = _extend_blocks(applied, result_blocks)
440

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors, values, **kwargs)
557
558     def astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):
--> 559         return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
560
561     def _astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/pandas/core/internals/blocks.py in _astype(self, dtype, copy, errors, values, **kwargs)
641                     # _astype_nansafe works fine with 1-d only
642                     vals1d = values.ravel()
--> 643                     values = astype_nansafe(vals1d, dtype, copy=True, **kwargs)
644
645                 # TODO(extension)

~/miniconda3/envs/autogluon/lib/python3.7/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy, skipna)
698         if not np.isfinite(arr).all():
699             raise ValueError(
--> 700                 "Cannot convert non-finite values (NA or inf) to " "integer"
701             )
702

ValueError: Cannot convert non-finite values (NA or inf) to integer

# Fixed code
def train_single_full(
    self,
    X_train,
    y_train,
    X_val,
    y_val,
    model: AbstractModel,
    feature_prune=False,
    hyperparameter_tune=True,
    stack_name="core",
    kfolds=None,
    k_fold_start=0,
    k_fold_end=None,
    n_repeats=None,
    n_repeat_start=0,
    level=0,
    time_limit=None,
):
    if (n_repeat_start == 0) and (k_fold_start == 0):
        model.feature_metadata = copy.deepcopy(
            self.feature_metadata
        )  # TODO: Don't set feature_metadata here
    if feature_prune:
        if n_repeat_start != 0:
            raise ValueError(
                "n_repeat_start must be 0 to feature_prune, value = "
                + str(n_repeat_start)
            )
        elif k_fold_start != 0:
            raise ValueError(
                "k_fold_start must be 0 to feature_prune, value = " + str(k_fold_start)
            )
        self.autotune(
            X_train=X_train,
            X_holdout=X_val,
            y_train=y_train,
            y_holdout=y_val,
            model_base=model,
        )  # TODO: Update to use CV instead of holdout
    if hyperparameter_tune:
        if self.scheduler_func is None or self.scheduler_options is None:
            raise ValueError(
                "scheduler_options cannot be None when hyperparameter_tune = True"
            )
        if n_repeat_start != 0:
            raise ValueError(
                "n_repeat_start must be 0 to hyperparameter_tune, value = "
                + str(n_repeat_start)
            )
        elif k_fold_start != 0:
            raise ValueError(
                "k_fold_start must be 0 to hyperparameter_tune, value = "
                + str(k_fold_start)
            )
        # hpo_models (dict): keys = model_names, values = model_paths
        try:
            if isinstance(model, BaggedEnsembleModel):
                hpo_models, hpo_model_performances, hpo_results = (
                    model.hyperparameter_tune(
                        X=X_train,
                        y=y_train,
                        k_fold=kfolds,
                        scheduler_options=(self.scheduler_func, self.scheduler_options),
                        verbosity=self.verbosity,
                    )
                )
            else:
                hpo_models, hpo_model_performances, hpo_results = (
                    model.hyperparameter_tune(
                        X_train=X_train,
                        y_train=y_train,
                        X_val=X_val,
                        y_val=y_val,
                        scheduler_options=(self.scheduler_func, self.scheduler_options),
                        verbosity=self.verbosity,
                    )
                )
        except Exception as err:
            if self.verbosity >= 1:
                traceback.print_tb(err.__traceback__)
            logger.exception(
                "Warning: Exception caused "
                + model.name
                + " to fail during hyperparameter tuning... Skipping this model."
            )
            logger.debug(err)
            del model
            model_names_trained = []
        else:
            self.hpo_results[model.name] = hpo_results
            model_names_trained = []
            for model_hpo_name, model_path in hpo_models.items():
                model_hpo = self.load_model(
                    model_hpo_name, path=model_path, model_type=type(model)
                )
                self.add_model(model=model_hpo, stack_name=stack_name, level=level)
                model_names_trained.append(model_hpo.name)
    else:
        model_names_trained = self.train_and_save(
            X_train,
            y_train,
            X_val,
            y_val,
            model,
            stack_name=stack_name,
            kfolds=kfolds,
            k_fold_start=k_fold_start,
            k_fold_end=k_fold_end,
            n_repeats=n_repeats,
            n_repeat_start=n_repeat_start,
            level=level,
            time_limit=time_limit,
        )
    self.save()
    return model_names_trained