Source code for category_encoders.backward_difference

"""Backward difference contrast encoding"""

import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from patsy.contrasts import Diff
import numpy as np
from category_encoders.ordinal import OrdinalEncoder
import category_encoders.utils as util

__author__ = 'willmcginnis'

[docs]class BackwardDifferenceEncoder(BaseEstimator, TransformerMixin): """Backward difference contrast coding for encoding categorical variables. 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 * >>> import pandas as pd >>> from sklearn.datasets import load_boston >>> bunch = load_boston() >>> y = >>> X = pd.DataFrame(, columns=bunch.feature_names) >>> enc = BackwardDifferenceEncoder(cols=['CHAS', 'RAD']).fit(X, y) >>> numeric_dataset = enc.transform(X) >>> print( <class 'pandas.core.frame.DataFrame'> RangeIndex: 506 entries, 0 to 505 Data columns (total 21 columns): intercept 506 non-null int64 CRIM 506 non-null float64 ZN 506 non-null float64 INDUS 506 non-null float64 CHAS_0 506 non-null float64 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 float64 RAD_1 506 non-null float64 RAD_2 506 non-null float64 RAD_3 506 non-null float64 RAD_4 506 non-null float64 RAD_5 506 non-null float64 RAD_6 506 non-null float64 RAD_7 506 non-null float64 TAX 506 non-null float64 PTRATIO 506 non-null float64 B 506 non-null float64 LSTAT 506 non-null float64 dtypes: float64(20), int64(1) memory usage: 83.1 KB None References ---------- .. [1] Contrast Coding Systems for Categorical Variables, from .. [2] Gregory Carey (2003). Coding Categorical Variables, from """ def __init__(self, verbose=0, cols=None, mapping=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value'): self.return_df = return_df self.drop_invariant = drop_invariant self.drop_cols = [] self.verbose = verbose self.mapping = mapping self.handle_unknown = handle_unknown self.handle_missing = handle_missing self.cols = cols self.ordinal_encoder = None self._dim = None self.feature_names = None
[docs] def fit(self, X, y=None, **kwargs): """Fits an ordinal encoder to produce a consistent mapping across applications and optionally finds generally invariant columns to drop consistently. 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) # first check the type 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 = ordinal_mapping = self.ordinal_encoder.category_mapping mappings_out = [] for switch in ordinal_mapping: values = switch.get('mapping') col = switch.get('col') column_mapping = self.fit_backward_difference_coding(col, values, self.handle_missing, self.handle_unknown) mappings_out.append({'col': col, 'mapping': column_mapping, }) self.mapping = mappings_out X_temp = self.transform(X, override_return_df=True) self.feature_names = X_temp.columns.tolist() # 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
[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 = self.ordinal_encoder.transform(X) if self.handle_unknown == 'error': if X[self.cols].isin([-1]).any().any(): raise ValueError('Columns to be encoded can not contain new values') X = self.backward_difference_coding(X, mapping=self.mapping) if self.drop_invariant: for col in self.drop_cols: X.drop(col, 1, inplace=True) if self.return_df or override_return_df: return X else: return X.values
@staticmethod def fit_backward_difference_coding(col, values, handle_missing, handle_unknown): if handle_missing == 'value': values = values[values > 0] values_to_encode = values.values if len(values) < 2: return pd.DataFrame(index=values_to_encode) if handle_unknown == 'indicator': values_to_encode = np.append(values_to_encode, -1) backwards_difference_matrix = Diff().code_without_intercept(values_to_encode) df = pd.DataFrame(data=backwards_difference_matrix.matrix, index=values_to_encode, columns=[str(col) + '_%d' % (i, ) for i in range(len(backwards_difference_matrix.column_suffixes))]) if handle_unknown == 'return_nan': df.loc[-1] = np.nan elif handle_unknown == 'value': df.loc[-1] = np.zeros(len(values_to_encode) - 1) if handle_missing == 'return_nan': df.loc[values.loc[np.nan]] = np.nan elif handle_missing == 'value': df.loc[-2] = np.zeros(len(values_to_encode) - 1) return df
[docs] @staticmethod def backward_difference_coding(X_in, mapping): """ """ X = X_in.copy(deep=True) cols = X.columns.values.tolist() X['intercept'] = pd.Series([1] * X.shape[0], index=X.index) for switch in 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 cols = ['intercept'] + cols return X.reindex(columns=cols)
[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