# Buggy code
def spunge_augment(
    X,
    feature_types_metadata: FeatureTypesMetadata,
    num_augmented_samples=10000,
    frac_perturb=0.1,
    continuous_feature_noise=0.1,
    **kwargs,
):
    """Generates synthetic datapoints for learning to mimic teacher model in distillation
    via simplified version of MUNGE strategy (that does not require near-neighbor search).

    Args:
        num_augmented_samples: number of additional augmented data points to return
        frac_perturb: fraction of features/examples that are perturbed during augmentation. Set near 0 to ensure augmented sample distribution remains closer to real data.
        continuous_feature_noise: we noise numeric features by this factor times their std-dev. Set near 0 to ensure augmented sample distribution remains closer to real data.
    """
    if frac_perturb > 1.0:
        raise ValueError("frac_perturb must be <= 1")
    logger.log(
        20,
        f"SPUNGE: Augmenting training data with {num_augmented_samples} synthetic samples for distillation...",
    )
    num_feature_perturb = max(1, int(frac_perturb * len(X.columns)))
    X_aug = pd.concat([X.iloc[[0]].copy()] * num_augmented_samples)
    X_aug.reset_index(drop=True, inplace=True)
    continuous_types = ["float", "int"]
    continuous_featnames = []  # these features will have shuffled values with added noise
    for contype in continuous_types:
        if contype in feature_types_metadata.feature_types_raw:
            continuous_featnames += feature_types_metadata.feature_types_raw[contype]

    for i in range(
        num_augmented_samples
    ):  # hot-deck sample some features per datapoint
        og_ind = i % len(X)
        augdata_i = X.iloc[og_ind].copy()
        num_feature_perturb_i = np.random.choice(
            range(1, num_feature_perturb + 1)
        )  # randomly sample number of features to perturb
        cols_toperturb = np.random.choice(
            list(X.columns), size=num_feature_perturb_i, replace=False
        )
        for feature in cols_toperturb:
            feature_data = X[feature]
            augdata_i[feature] = feature_data.sample(n=1).values[0]
        X_aug.iloc[i] = augdata_i

    for feature in X.columns:
        if feature in continuous_featnames:
            feature_data = X[feature]
            aug_data = X_aug[feature]
            noise = np.random.normal(
                scale=np.nanstd(feature_data) * continuous_feature_noise,
                size=num_augmented_samples,
            )
            mask = np.random.binomial(n=1, p=frac_perturb, size=num_augmented_samples)
            aug_data = aug_data + noise * mask
            X_aug[feature] = pd.Series(aug_data, index=X_aug.index)

    return X_aug


# 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 spunge_augment(
    X,
    feature_metadata: FeatureMetadata,
    num_augmented_samples=10000,
    frac_perturb=0.1,
    continuous_feature_noise=0.1,
    **kwargs,
):
    """Generates synthetic datapoints for learning to mimic teacher model in distillation
    via simplified version of MUNGE strategy (that does not require near-neighbor search).

    Args:
        num_augmented_samples: number of additional augmented data points to return
        frac_perturb: fraction of features/examples that are perturbed during augmentation. Set near 0 to ensure augmented sample distribution remains closer to real data.
        continuous_feature_noise: we noise numeric features by this factor times their std-dev. Set near 0 to ensure augmented sample distribution remains closer to real data.
    """
    if frac_perturb > 1.0:
        raise ValueError("frac_perturb must be <= 1")
    logger.log(
        20,
        f"SPUNGE: Augmenting training data with {num_augmented_samples} synthetic samples for distillation...",
    )
    num_feature_perturb = max(1, int(frac_perturb * len(X.columns)))
    X_aug = pd.concat([X.iloc[[0]].copy()] * num_augmented_samples)
    X_aug.reset_index(drop=True, inplace=True)
    continuous_types = ["float", "int"]
    continuous_featnames = feature_metadata.get_features(
        valid_raw_types=continuous_types
    )  # these features will have shuffled values with added noise

    for i in range(
        num_augmented_samples
    ):  # hot-deck sample some features per datapoint
        og_ind = i % len(X)
        augdata_i = X.iloc[og_ind].copy()
        num_feature_perturb_i = np.random.choice(
            range(1, num_feature_perturb + 1)
        )  # randomly sample number of features to perturb
        cols_toperturb = np.random.choice(
            list(X.columns), size=num_feature_perturb_i, replace=False
        )
        for feature in cols_toperturb:
            feature_data = X[feature]
            augdata_i[feature] = feature_data.sample(n=1).values[0]
        X_aug.iloc[i] = augdata_i

    for feature in X.columns:
        if feature in continuous_featnames:
            feature_data = X[feature]
            aug_data = X_aug[feature]
            noise = np.random.normal(
                scale=np.nanstd(feature_data) * continuous_feature_noise,
                size=num_augmented_samples,
            )
            mask = np.random.binomial(n=1, p=frac_perturb, size=num_augmented_samples)
            aug_data = aug_data + noise * mask
            X_aug[feature] = pd.Series(aug_data, index=X_aug.index)

    return X_aug