def METHOD_NAME(
    feature_matrix,
    features,
    top_n=DEFAULT_TOP_N,
    include_unknown=True,
    to_encode=None,
    inplace=False,
    drop_first=False,
    verbose=False,
):
    """Encode categorical features
    Args:
        feature_matrix (pd.DataFrame): Dataframe of features.
        features (list[PrimitiveBase]): Feature definitions in feature_matrix.
        top_n (int or dict[string -> int]): Number of top values to include.
            If dict[string -> int] is used, key is feature name and value is
            the number of top values to include for that feature.
            If a feature's name is not in dictionary, a default value of 10 is used.
        include_unknown (pd.DataFrame): Add feature encoding an unknown class.
            defaults to True
        to_encode (list[str]): List of feature names to encode.
            features not in this list are unencoded in the output matrix
            defaults to encode all necessary features.
        inplace (bool): Encode feature_matrix in place. Defaults to False.
        drop_first (bool): Whether to get k-1 dummies out of k categorical
                levels by removing the first level.
                defaults to False
        verbose (str): Print progress info.
    Returns:
        (pd.Dataframe, list) : encoded feature_matrix, encoded features
    Example:
        .. ipython:: python
            :suppress:
            from featuretools.tests.testing_utils import make_ecommerce_entityset
            import featuretools as ft
            es = make_ecommerce_entityset()
        .. ipython:: python
            f1 = ft.Feature(es["log"].ww["product_id"])
            f2 = ft.Feature(es["log"].ww["purchased"])
            f3 = ft.Feature(es["log"].ww["value"])
            features = [f1, f2, f3]
            ids = [0, 1, 2, 3, 4, 5]
            feature_matrix = ft.calculate_feature_matrix(features, es,
                                                         instance_ids=ids)
            fm_encoded, f_encoded = ft.encode_features(feature_matrix,
                                                       features)
            f_encoded
            fm_encoded, f_encoded = ft.encode_features(feature_matrix,
                                                       features, top_n=2)
            f_encoded
            fm_encoded, f_encoded = ft.encode_features(feature_matrix, features,
                                                       include_unknown=False)
            f_encoded
            fm_encoded, f_encoded = ft.encode_features(feature_matrix, features,
                                                       to_encode=['purchased'])
            f_encoded
            fm_encoded, f_encoded = ft.encode_features(feature_matrix, features,
                                                       drop_first=True)
            f_encoded
    """
    if not isinstance(feature_matrix, pd.DataFrame):
        msg = "feature_matrix must be a Pandas DataFrame"
        raise TypeError(msg)
    if inplace:
        X = feature_matrix
    else:
        X = feature_matrix.copy()
    old_feature_names = set()
    for feature in features:
        for fname in feature.get_feature_names():
            assert fname in X.columns, "Feature %s not found in feature matrix" % (
                fname
            )
            old_feature_names.add(fname)
    pass_through = [col for col in X.columns if col not in old_feature_names]
    if verbose:
        iterator = make_tqdm_iterator(
            iterable=features,
            total=len(features),
            desc="Encoding pass 1",
            unit="feature",
        )
    else:
        iterator = features
    new_feature_list = []
    kept_columns = []
    encoded_columns = []
    columns_info = feature_matrix.ww.columns
    for f in iterator:
        # TODO: features with multiple columns are not encoded by this method,
        # which can cause an "encoded" matrix with non-numeric values
        is_discrete = {"category", "foreign_key"}.intersection(
            f.column_schema.semantic_tags,
        )
        if f.number_output_features > 1 or not is_discrete:
            if f.number_output_features > 1:
                logger.warning(
                    "Feature %s has multiple columns and will not "
                    "be encoded.  This may result in a matrix with"
                    " non-numeric values." % (f),
                )
            new_feature_list.append(f)
            kept_columns.extend(f.get_feature_names())
            continue
        if to_encode is not None and f.get_name() not in to_encode:
            new_feature_list.append(f)
            kept_columns.extend(f.get_feature_names())
            continue
        val_counts = X[f.get_name()].value_counts()
        # Remove 0 count category values
        val_counts = val_counts[val_counts > 0].to_frame()
        index_name = val_counts.index.name
        val_counts = val_counts.rename(columns={val_counts.columns[0]: "count"})
        if index_name is None:
            if "index" in val_counts.columns:
                index_name = "level_0"
            else:
                index_name = "index"
        val_counts.reset_index(inplace=True)
        val_counts = val_counts.sort_values(["count", index_name], ascending=False)
        val_counts.set_index(index_name, inplace=True)
        select_n = top_n
        if isinstance(top_n, dict):
            select_n = top_n.get(f.get_name(), DEFAULT_TOP_N)
        if drop_first:
            select_n = min(len(val_counts), top_n)
            select_n = max(select_n - 1, 1)
        unique = val_counts.head(select_n).index.tolist()
        for label in unique:
            add = f == label
            add_name = add.get_name()
            new_feature_list.append(add)
            new_col = X[f.get_name()] == label
            new_col.rename(add_name, inplace=True)
            encoded_columns.append(new_col)
        if include_unknown:
            unknown = f.isin(unique).NOT().rename(f.get_name() + " is unknown")
            unknown_name = unknown.get_name()
            new_feature_list.append(unknown)
            new_col = ~X[f.get_name()].isin(unique)
            new_col.rename(unknown_name, inplace=True)
            encoded_columns.append(new_col)
        if inplace:
            X.drop(f.get_name(), axis=1, inplace=True)
    kept_columns.extend(pass_through)
    if inplace:
        for encoded_column in encoded_columns:
            X[encoded_column.name] = encoded_column
    else:
        X = pd.concat([X[kept_columns]] + encoded_columns, axis=1)
    entityset = new_feature_list[0].entityset
    ww_init_kwargs = get_ww_types_from_features(new_feature_list, entityset)
    # Grab ww metadata from feature matrix since it may be more exact
    for column in kept_columns:
        ww_init_kwargs["logical_types"][column] = columns_info[column].logical_type
        ww_init_kwargs["semantic_tags"][column] = columns_info[column].semantic_tags
        ww_init_kwargs["column_origins"][column] = columns_info[column].origin
    X.ww.init(**ww_init_kwargs)
    return X, new_feature_list