# Buggy code
def _get_types_of_features(
    self, df, skew_threshold, embed_min_categories, use_ngram_features
):
    """Returns dict with keys: : 'continuous', 'skewed', 'onehot', 'embed', 'language', values = ordered list of feature-names falling into each category.
    Each value is a list of feature-names corresponding to columns in original dataframe.
    TODO: ensure features with zero variance have already been removed before this function is called.
    """
    if self.types_of_features is not None:
        Warning(
            "Attempting to _get_types_of_features for TabularNeuralNetModel, but previously already did this."
        )

    feature_types = self.feature_types_metadata.feature_types_raw

    categorical_featnames = (
        feature_types["category"] + feature_types["object"] + feature_types["bool"]
    )
    continuous_featnames = (
        feature_types["float"] + feature_types["int"]
    )  # + self.__get_feature_type_if_present('datetime')
    language_featnames = []  # TODO: not implemented. This should fetch text features present in the data
    valid_features = categorical_featnames + continuous_featnames + language_featnames
    if (
        len(categorical_featnames) + len(continuous_featnames) + len(language_featnames)
        != df.shape[1]
    ):
        unknown_features = [
            feature for feature in df.columns if feature not in valid_features
        ]
        # print('unknown features:', unknown_features)
        df = df.drop(columns=unknown_features)
        self.features = list(df.columns)
        # raise ValueError("unknown feature types present in DataFrame")

    types_of_features = {
        "continuous": [],
        "skewed": [],
        "onehot": [],
        "embed": [],
        "language": [],
    }
    # continuous = numeric features to rescale
    # skewed = features to which we will apply power (ie. log / box-cox) transform before normalization
    # onehot = features to one-hot encode (unknown categories for these features encountered at test-time are encoded as all zeros). We one-hot encode any features encountered that only have two unique values.
    for feature in self.features:
        feature_data = df[feature]  # pd.Series
        num_unique_vals = len(feature_data.unique())
        if (
            num_unique_vals == 2
        ):  # will be onehot encoded regardless of proc.embed_min_categories value
            types_of_features["onehot"].append(feature)
        elif feature in continuous_featnames:
            if np.abs(feature_data.skew()) > skew_threshold:
                types_of_features["skewed"].append(feature)
            else:
                types_of_features["continuous"].append(feature)
        elif feature in categorical_featnames:
            if (
                num_unique_vals >= embed_min_categories
            ):  # sufficiently many categories to warrant learned embedding dedicated to this feature
                types_of_features["embed"].append(feature)
            else:
                types_of_features["onehot"].append(feature)
        elif feature in language_featnames:
            types_of_features["language"].append(feature)
    return types_of_features


# 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 _get_types_of_features(
    self, df, skew_threshold, embed_min_categories, use_ngram_features
):
    """Returns dict with keys: : 'continuous', 'skewed', 'onehot', 'embed', 'language', values = ordered list of feature-names falling into each category.
    Each value is a list of feature-names corresponding to columns in original dataframe.
    TODO: ensure features with zero variance have already been removed before this function is called.
    """
    if self.types_of_features is not None:
        Warning(
            "Attempting to _get_types_of_features for TabularNeuralNetModel, but previously already did this."
        )

    feature_types = self.feature_metadata.get_type_group_map_raw()

    categorical_featnames = (
        feature_types[R_CATEGORY] + feature_types[R_OBJECT] + feature_types["bool"]
    )
    continuous_featnames = (
        feature_types[R_FLOAT] + feature_types[R_INT]
    )  # + self.__get_feature_type_if_present('datetime')
    language_featnames = []  # TODO: not implemented. This should fetch text features present in the data
    valid_features = categorical_featnames + continuous_featnames + language_featnames
    if (
        len(categorical_featnames) + len(continuous_featnames) + len(language_featnames)
        != df.shape[1]
    ):
        unknown_features = [
            feature for feature in df.columns if feature not in valid_features
        ]
        # print('unknown features:', unknown_features)
        df = df.drop(columns=unknown_features)
        self.features = list(df.columns)
        # raise ValueError("unknown feature types present in DataFrame")

    types_of_features = {
        "continuous": [],
        "skewed": [],
        "onehot": [],
        "embed": [],
        "language": [],
    }
    # continuous = numeric features to rescale
    # skewed = features to which we will apply power (ie. log / box-cox) transform before normalization
    # onehot = features to one-hot encode (unknown categories for these features encountered at test-time are encoded as all zeros). We one-hot encode any features encountered that only have two unique values.
    for feature in self.features:
        feature_data = df[feature]  # pd.Series
        num_unique_vals = len(feature_data.unique())
        if (
            num_unique_vals == 2
        ):  # will be onehot encoded regardless of proc.embed_min_categories value
            types_of_features["onehot"].append(feature)
        elif feature in continuous_featnames:
            if np.abs(feature_data.skew()) > skew_threshold:
                types_of_features["skewed"].append(feature)
            else:
                types_of_features["continuous"].append(feature)
        elif feature in categorical_featnames:
            if (
                num_unique_vals >= embed_min_categories
            ):  # sufficiently many categories to warrant learned embedding dedicated to this feature
                types_of_features["embed"].append(feature)
            else:
                types_of_features["onehot"].append(feature)
        elif feature in language_featnames:
            types_of_features["language"].append(feature)
    return types_of_features