def fit(self, X, y=None):
        """Compute the lower and upper quantile cutoffs, columns to transform, and each column's quantiles.

        Parameters
        ----------
        X : array-like, shape [n_samples, n_features]
            The data array to transform. Must be numeric, non-sparse, and two-dimensional.

        Returns
        -------
        self : QuantileExtremeValueTransformer
        """
        super().fit(X)
        X = check_array(X)
        self.quantile_transformer_ = QuantileTransformer(random_state=0, copy=True)
        self.quantile_transformer_.fit(X)
        return self 

def do_feature_engineering(list_text):
    df = pd.DataFrame(list_text, columns=["col2"])

    feature_list = []
    feature = get_length_related_features_col2(df)
    feature_list.append(feature)
    feature = get_col2_re_features(df)
    feature_list.append(feature)
    index = feature_list[0].index

    for feature_dataset in feature_list[1:]:
        pd.testing.assert_index_equal(index, feature_dataset.index)

    data = pd.concat(feature_list, axis=1)
    qt = QuantileTransformer(random_state=2019)
    for col in data.columns:
        data[col] = qt.fit_transform(data[[col]])
    return data 

def fit(self, X):
        """ Fit the quantile mapping model.

        Parameters
        ----------
        X : array-like, shape  [n_samples, n_features]
            Training data.
        """
        X = ensure_samples_features(X)

        qt_kws = self.qt_kwargs.copy()

        if "n_quantiles" not in qt_kws:
            qt_kws["n_quantiles"] = len(X)

        # maybe detrend the input datasets
        if self.detrend:
            x_to_cdf = LinearTrendTransformer(**self.lt_kwargs).fit_transform(X)
        else:
            x_to_cdf = X

        # calculate the cdfs for X
        # TODO: replace this transformer with something that uses robust
        # empirical cdf plotting positions
        self.x_cdf_fit_ = QuantileTransformer(**qt_kws).fit(x_to_cdf)

        return self 

def _transform_function(self, x, idx=None):
        """Applies single column quantile transform from ``sklearn.preprocessing.QuantileTransformer``.

        Uses ``quantile_transformer_.quantiles_`` calculated during ``fit`` if given an index, otherwise the quantiles
        will be calculated from input ``x``.
        """
        if idx:
            return self.quantile_transformer_._transform_col(  # pylint: disable=protected-access
                x, self.quantile_transformer_.quantiles_[:, idx], False
            )
        return quantile_transform_nonrandom(x) 

def __init__(self, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=True):
        self._hyperparams = {
            'n_quantiles': n_quantiles,
            'output_distribution': output_distribution,
            'ignore_implicit_zeros': ignore_implicit_zeros,
            'subsample': subsample,
            'random_state': random_state,
            'copy': copy}
        self._wrapped_model = SKLModel(**self._hyperparams) 

def __init__(self, options):
        self.handle_options(options)

        out_params = convert_params(
            options.get('params', {}),
            bools=['copy'],
            ints=['n_quantiles'],
            strs=['output_distribution']
        )
        self.estimator = _QuantileTransformer(**out_params)
        self.columns = None 

def quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="normal", n_quantiles=100)
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets, transformer 

def bad_quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="normal", n_quantiles=100)
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets, "i am the wrong type for an inversion result" 

def my_quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="uniform")
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets 

def my_quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="uniform")
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets 

def quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="normal", n_quantiles=100)
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets, transformer 

def test_basic(self, output_distribution):
        rs = da.random.RandomState(0)
        a = dpp.QuantileTransformer(output_distribution=output_distribution)
        b = spp.QuantileTransformer(output_distribution=output_distribution)

        X = rs.uniform(size=(1000, 3), chunks=50)
        a.fit(X)
        b.fit(X)
        assert_estimator_equal(a, b, atol=0.02)

        # set the quantiles, so that from here out, we're exact
        a.quantiles_ = b.quantiles_
        assert_eq_ar(a.transform(X), b.transform(X), atol=1e-7)
        assert_eq_ar(X, a.inverse_transform(a.transform(X))) 

def test_types(self, type_, kwargs):
        X = np.random.uniform(size=(1000, 3))
        dX = type_(X, **kwargs)
        qt = spp.QuantileTransformer()
        qt.fit(X)
        dqt = dpp.QuantileTransformer()
        dqt.fit(dX) 

def test_fit_transform_frame(self):
        df = pd.DataFrame(np.random.randn(1000, 3))
        ddf = dd.from_pandas(df, 2)

        a = spp.QuantileTransformer()
        b = dpp.QuantileTransformer()

        expected = a.fit_transform(df)
        result = b.fit_transform(ddf)
        assert_eq_ar(result, expected, rtol=1e-3, atol=1e-3) 

def preprocess_train(self, X, feature_types, vect_max_features):
        transformer_list = []
        if len(feature_types['language']) > 0:
            pipeline = Pipeline(steps=[
                ("preparator", NlpDataPreprocessor(nlp_cols=feature_types['language'])),
                ("vectorizer",
                 TfidfVectorizer(ngram_range=self.params['proc.ngram_range'], sublinear_tf=True, max_features=vect_max_features, tokenizer=self.tokenize))
            ])
            transformer_list.append(('vect', pipeline))
        if len(feature_types['onehot']) > 0:
            pipeline = Pipeline(steps=[
                ('generator', OheFeaturesGenerator(cats_cols=feature_types['onehot'])),
            ])
            transformer_list.append(('cats', pipeline))
        if len(feature_types['continuous']) > 0:
            pipeline = Pipeline(steps=[
                ('generator', NumericDataPreprocessor(cont_cols=feature_types['continuous'])),
                ('imputer', SimpleImputer(strategy=self.params['proc.impute_strategy'])),
                ('scaler', StandardScaler())
            ])
            transformer_list.append(('cont', pipeline))
        if len(feature_types['skewed']) > 0:
            pipeline = Pipeline(steps=[
                ('generator', NumericDataPreprocessor(cont_cols=feature_types['skewed'])),
                ('imputer', SimpleImputer(strategy=self.params['proc.impute_strategy'])),
                ('quantile', QuantileTransformer(output_distribution='normal')),  # Or output_distribution = 'uniform'
            ])
            transformer_list.append(('skew', pipeline))
        self.pipeline = FeatureUnion(transformer_list=transformer_list)
        self.pipeline.fit(X)