Source code for category_encoders.basen

"""BaseX encoding"""

import pandas as pd
import numpy as np
import math
import re
from sklearn.base import BaseEstimator, TransformerMixin
from category_encoders.ordinal import OrdinalEncoder
import category_encoders.utils as util
import warnings

__author__ = 'willmcginnis'


[docs]class BaseNEncoder(BaseEstimator, TransformerMixin): """Base-N encoder encodes the categories into arrays of their base-N representation. A base of 1 is equivalent to one-hot encoding (not really base-1, but useful), a base of 2 is equivalent to binary encoding. N=number of actual categories is equivalent to vanilla ordinal encoding. 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). base: int when the downstream model copes well with nonlinearities (like decision tree), use higher base. 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 * >>> import pandas as pd >>> from sklearn.datasets import load_boston >>> bunch = load_boston() >>> y = bunch.target >>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names) >>> enc = BaseNEncoder(cols=['CHAS', 'RAD']).fit(X, y) >>> numeric_dataset = enc.transform(X) >>> print(numeric_dataset.info()) <class 'pandas.core.frame.DataFrame'> RangeIndex: 506 entries, 0 to 505 Data columns (total 18 columns): CRIM 506 non-null float64 ZN 506 non-null float64 INDUS 506 non-null float64 CHAS_0 506 non-null int64 CHAS_1 506 non-null int64 NOX 506 non-null float64 RM 506 non-null float64 AGE 506 non-null float64 DIS 506 non-null float64 RAD_0 506 non-null int64 RAD_1 506 non-null int64 RAD_2 506 non-null int64 RAD_3 506 non-null int64 RAD_4 506 non-null int64 TAX 506 non-null float64 PTRATIO 506 non-null float64 B 506 non-null float64 LSTAT 506 non-null float64 dtypes: float64(11), int64(7) memory usage: 71.3 KB None """ def __init__(self, verbose=0, cols=None, mapping=None, drop_invariant=False, return_df=True, base=2, handle_unknown='value', handle_missing='value'): self.return_df = return_df self.drop_invariant = drop_invariant self.drop_cols = [] self.verbose = verbose self.handle_unknown = handle_unknown self.handle_missing = handle_missing self.cols = cols self.mapping = mapping self.ordinal_encoder = None self._dim = None self.base = base self._encoded_columns = None self.feature_names = None
[docs] def fit(self, X, y=None, **kwargs): """Fit encoder according to X and y. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. Returns ------- self : encoder Returns self. """ # if the input dataset isn't already a dataframe, convert it to one (using default column names) X = util.convert_input(X) self._dim = X.shape[1] # if columns aren't passed, just use every string column if self.cols is None: self.cols = util.get_obj_cols(X) else: self.cols = util.convert_cols_to_list(self.cols) if self.handle_missing == 'error': if X[self.cols].isnull().any().any(): raise ValueError('Columns to be encoded can not contain null') # train an ordinal pre-encoder self.ordinal_encoder = OrdinalEncoder( verbose=self.verbose, cols=self.cols, handle_unknown='value', handle_missing='value' ) self.ordinal_encoder = self.ordinal_encoder.fit(X) self.mapping = self.fit_base_n_encoding(X) # do a transform on the training data to get a column list X_temp = self.transform(X, override_return_df=True) self._encoded_columns = X_temp.columns.values self.feature_names = list(X_temp.columns) # drop all output columns with 0 variance. if self.drop_invariant: self.drop_cols = [] generated_cols = util.get_generated_cols(X, X_temp, self.cols) self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5] try: [self.feature_names.remove(x) for x in self.drop_cols] except KeyError as e: if self.verbose > 0: print("Could not remove column from feature names." "Not found in generated cols.\n{}".format(e)) return self
def fit_base_n_encoding(self, X): mappings_out = [] for switch in self.ordinal_encoder.category_mapping: col = switch.get('col') values = switch.get('mapping') if self.handle_missing == 'value': values = values[values > 0] if self.handle_unknown == 'indicator': values = np.append(values, -1) digits = self.calc_required_digits(values) X_unique = pd.DataFrame(index=values, columns=[str(col) + '_%d' % x for x in range(digits)], data=np.array([self.col_transform(x, digits) for x in range(1, len(values) + 1)])) if self.handle_unknown == 'return_nan': X_unique.loc[-1] = np.nan elif self.handle_unknown == 'value': X_unique.loc[-1] = 0 if self.handle_missing == 'return_nan': X_unique.loc[values.loc[np.nan]] = np.nan elif self.handle_missing == 'value': X_unique.loc[-2] = 0 mappings_out.append({'col': col, 'mapping': X_unique}) return mappings_out
[docs] def transform(self, X, override_return_df=False): """Perform the transformation to new categorical data. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- p : array, shape = [n_samples, n_numeric + N] Transformed values with encoding applied. """ if self.handle_missing == 'error': if X[self.cols].isnull().any().any(): raise ValueError('Columns to be encoded can not contain null') if self._dim is None: raise ValueError('Must train encoder before it can be used to transform data.') # first check the type X = util.convert_input(X) # then make sure that it is the right size if X.shape[1] != self._dim: raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim,)) if not list(self.cols): return X X_out = self.ordinal_encoder.transform(X) if self.handle_unknown == 'error': if X_out[self.cols].isin([-1]).any().any(): raise ValueError('Columns to be encoded can not contain new values') X_out = self.basen_encode(X_out, cols=self.cols) if self.drop_invariant: for col in self.drop_cols: X_out.drop(col, 1, inplace=True) # impute missing values only in the generated columns # generated_cols = util.get_generated_cols(X, X_out, self.cols) # X_out[generated_cols] = X_out[generated_cols].fillna(value=0.0) if self.return_df or override_return_df: return X_out else: return X_out.values
[docs] def inverse_transform(self, X_in): """ Perform the inverse transformation to encoded data. Parameters ---------- X_in : array-like, shape = [n_samples, n_features] Returns ------- p: array, the same size of X_in """ # fail fast if self._dim is None: raise ValueError('Must train encoder before it can be used to inverse_transform data') # unite the type into pandas dataframe (it makes the input size detection code easier...) and make deep copy X = util.convert_input(X_in, columns=self.feature_names, deep=True) X = self.basen_to_integer(X, self.cols, self.base) # make sure that it is the right size if X.shape[1] != self._dim: if self.drop_invariant: raise ValueError("Unexpected input dimension %d, the attribute drop_invariant should " "be False when transforming the data" % (X.shape[1],)) else: raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim,)) if not list(self.cols): return X if self.return_df else X.values for switch in self.ordinal_encoder.mapping: column_mapping = switch.get('mapping') inverse = pd.Series(data=column_mapping.index, index=column_mapping.values) X[switch.get('col')] = X[switch.get('col')].map(inverse).astype(switch.get('data_type')) if self.handle_unknown == 'return_nan' and self.handle_missing == 'return_nan': for col in self.cols: if X[switch.get('col')].isnull().any(): warnings.warn("inverse_transform is not supported because transform impute " "the unknown category nan when encode %s" % (col,)) return X if self.return_df else X.values
def calc_required_digits(self, values): # figure out how many digits we need to represent the classes present if self.base == 1: digits = len(values) + 1 else: digits = int(np.ceil(math.log(len(values), self.base))) + 1 return digits
[docs] def basen_encode(self, X_in, cols=None): """ Basen encoding encodes the integers as basen code with one column per digit. Parameters ---------- X_in: DataFrame cols: list-like, default None Column names in the DataFrame to be encoded Returns ------- dummies : DataFrame """ X = X_in.copy(deep=True) cols = X.columns.values.tolist() for switch in self.mapping: col = switch.get('col') mod = switch.get('mapping') base_df = mod.reindex(X[col]) base_df.set_index(X.index, inplace=True) X = pd.concat([base_df, X], axis=1) old_column_index = cols.index(col) cols[old_column_index: old_column_index + 1] = mod.columns return X.reindex(columns=cols)
[docs] def basen_to_integer(self, X, cols, base): """ Convert basen code as integers. Parameters ---------- X : DataFrame encoded data cols : list-like Column names in the DataFrame that be encoded base : int The base of transform Returns ------- numerical: DataFrame """ out_cols = X.columns.values.tolist() for col in cols: col_list = [col0 for col0 in out_cols if re.match(str(col)+'_\\d+', str(col0))] insert_at = out_cols.index(col_list[0]) if base == 1: value_array = np.array([int(col0.split('_')[-1]) for col0 in col_list]) else: len0 = len(col_list) value_array = np.array([base ** (len0 - 1 - i) for i in range(len0)]) X.insert(insert_at, col, np.dot(X[col_list].values, value_array.T)) X.drop(col_list, axis=1, inplace=True) out_cols = X.columns.values.tolist() return X
[docs] def col_transform(self, col, digits): """ The lambda body to transform the column values """ if col is None or float(col) < 0.0: return None else: col = self.number_to_base(int(col), self.base, digits) if len(col) == digits: return col else: return [0 for _ in range(digits - len(col))] + col
@staticmethod def number_to_base(n, b, limit): if b == 1: return [0 if n != _ else 1 for _ in range(limit)] if n == 0: return [0 for _ in range(limit)] digits = [] for _ in range(limit): digits.append(int(n % b)) n, _ = divmod(n, b) return digits[::-1]
[docs] def get_feature_names(self): """ Returns the names of all transformed / added columns. Returns ------- feature_names: list A list with all feature names transformed or added. Note: potentially dropped features are not included! """ if not isinstance(self.feature_names, list): raise ValueError('Must fit data first. Affected feature names are not known before.') else: return self.feature_names