Source code for category_encoders.gray

"""Gray encoding"""
from functools import partialmethod

import pandas as pd

from category_encoders import utils
from category_encoders.basen import BaseNEncoder
from typing import List

__author__ = 'paulwestenthanner'

[docs]class GrayEncoder(BaseNEncoder): """Gray encoding for categorical variables. Gray encoding is a form of binary encoding where consecutive values only differ by a single bit. Hence, gray encoding only makes sense for ordinal features. This has benefits in privacy preserving data publishing. Parameters ---------- verbose: int integer indicating verbosity of the output. 0 for none. cols: list a list of columns to encode, if None, all string columns will be encoded. drop_invariant: bool boolean for whether or not to drop columns with 0 variance. return_df: bool boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array). handle_unknown: str options are 'error', 'return_nan', 'value', and 'indicator'. The default is 'value'. Warning: if indicator is used, an extra column will be added in if the transform matrix has unknown categories. This can cause unexpected changes in dimension in some cases. handle_missing: str options are 'error', 'return_nan', 'value', and 'indicator'. The default is 'value'. Warning: if indicator is used, an extra column will be added in if the transform matrix has nan values. This can cause unexpected changes in dimension in some cases. Example ------- >>> from category_encoders import GrayEncoder >>> import pandas as pd >>> from sklearn.datasets import fetch_openml >>> bunch = fetch_openml(name="house_prices", as_frame=True) >>> display_cols = ["Id", "MSSubClass", "MSZoning", "LotFrontage", "YearBuilt", "Heating", "CentralAir"] >>> y = >>> X = pd.DataFrame(, columns=bunch.feature_names)[display_cols] >>> enc = GrayEncoder(cols=['CentralAir', 'Heating']).fit(X, y) >>> numeric_dataset = enc.transform(X) >>> print( <class 'pandas.core.frame.DataFrame'> RangeIndex: 1460 entries, 0 to 1459 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 1460 non-null float64 1 MSSubClass 1460 non-null float64 2 MSZoning 1460 non-null object 3 LotFrontage 1201 non-null float64 4 YearBuilt 1460 non-null float64 5 Heating_0 1460 non-null int64 6 Heating_1 1460 non-null int64 7 Heating_2 1460 non-null int64 8 CentralAir_0 1460 non-null int64 9 CentralAir_1 1460 non-null int64 dtypes: float64(4), int64(5), object(1) memory usage: 114.2+ KB None References ---------- .. [1] .. [2] Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2017. PrivBayes: Private Data Release via Bayesian Networks. ACM Trans. Database Syst. 42, 4, Article 25 (October 2017) """ encoding_relation = utils.EncodingRelation.ONE_TO_M __init__ = partialmethod(BaseNEncoder.__init__, base=2) @staticmethod def gray_code(n, n_bit) -> List[int]: gray = n ^ (n >> 1) gray_formatted = "{0:0{1}b}".format(gray, n_bit) return [int(bit) for bit in gray_formatted] def _fit(self, X, y=None, **kwargs): super(GrayEncoder, self)._fit(X, y, **kwargs) gray_mapping = [] # convert binary mapping to Gray mapping and reorder for col_to_encode in self.mapping: col = col_to_encode["col"] bin_mapping = col_to_encode["mapping"] n_cols_out = bin_mapping.shape[1] null_cond = (bin_mapping.index < 0) | (bin_mapping.isna().all(1)) map_null = bin_mapping[null_cond] map_non_null = bin_mapping[~null_cond].copy() ordinal_mapping = [m for m in self.ordinal_encoder.mapping if m.get("col") == col] if len(ordinal_mapping) != 1: raise ValueError("Cannot find ordinal encoder mapping of Gray encoder") ordinal_mapping = ordinal_mapping[0]["mapping"] reverse_ordinal_mapping = {v: k for k, v in ordinal_mapping.to_dict().items()} map_non_null["orig_value"] = map_non_null.index.to_series().map(reverse_ordinal_mapping) map_non_null = map_non_null.sort_values(by="orig_value") gray_encoding = [self.gray_code(i + 1, n_cols_out) for i in range(map_non_null.shape[0])] gray_encoding = pd.DataFrame(data=gray_encoding, index=map_non_null.index, columns=bin_mapping.columns) gray_encoding = pd.concat([gray_encoding, map_null]) gray_mapping.append({"col": col, "mapping": gray_encoding}) self.mapping = gray_mapping