Source code for category_encoders.leave_one_out

"""Leave one out coding"""
import numpy as np
import pandas as pd
import category_encoders.utils as util
from sklearn.utils.random import check_random_state

__author__ = 'hbghhy'

[docs]class LeaveOneOutEncoder(util.BaseEncoder, util.SupervisedTransformerMixin): """Leave one out coding for categorical features. This is very similar to target encoding but excludes the current row's target when calculating the mean target for a level to reduce the effect of outliers. 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_missing: str options are 'error', 'return_nan' and 'value', defaults to 'value', which returns the target mean. handle_unknown: str options are 'error', 'return_nan' and 'value', defaults to 'value', which returns the target mean. sigma: float adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched). Sigma gives the standard deviation (spread or "width") of the normal distribution. The optimal value is commonly between 0.05 and 0.6. The default is to not add noise, but that leads to significantly suboptimal results. Example ------- >>> from category_encoders import * >>> 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 = LeaveOneOutEncoder(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 7 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 1460 non-null float64 6 CentralAir 1460 non-null float64 dtypes: float64(6), object(1) memory usage: 80.0+ KB None References ---------- .. [1] Originally by Owen Zhang (reference broken), another short explanation at: """ prefit_ordinal = False encoding_relation = util.EncodingRelation.ONE_TO_ONE def __init__(self, verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', random_state=None, sigma=None): super().__init__(verbose=verbose, cols=cols, drop_invariant=drop_invariant, return_df=return_df, handle_unknown=handle_unknown, handle_missing=handle_missing) self.mapping = None self._mean = None self.random_state = random_state self.sigma = sigma def _fit(self, X, y, **kwargs): y = y.astype(float) categories = self.fit_leave_one_out( X, y, cols=self.cols ) self.mapping = categories def _transform(self, X, y=None): if y is not None: y = y.astype(float) X = self.transform_leave_one_out( X, y, mapping=self.mapping ) return X def _more_tags(self): tags = super()._more_tags() tags["predict_depends_on_y"] = True return tags def fit_leave_one_out(self, X_in, y, cols=None): X = X_in.copy(deep=True) if cols is None: cols = X.columns self._mean = y.mean() return {col: self.fit_column_map(X[col], y) for col in cols} def fit_column_map(self, series, y): category = pd.Categorical(series) categories = category.categories codes = codes[codes == -1] = len(categories) categories = np.append(categories, np.nan) return_map = pd.Series({code: category for code, category in enumerate(categories)}) result = y.groupby(codes).agg(['sum', 'count']) return result.rename(return_map)
[docs] def transform_leave_one_out(self, X, y, mapping=None): """ Leave one out encoding uses a single column of floats to represent the means of the target variables. """ random_state_ = check_random_state(self.random_state) for col, colmap in mapping.items(): level_notunique = colmap['count'] > 1 unique_train = colmap.index unseen_values = pd.Series([x for x in X[col].unique() if x not in unique_train], dtype=unique_train.dtype) is_nan = X[col].isna() is_unknown_value = X[col].isin(unseen_values.dropna().astype(object)) if X[col] == 'category': # Pandas 0.24 tries hard to preserve categorical data type index_dtype = X[col].dtype.categories.dtype X[col] = X[col].astype(index_dtype) if self.handle_unknown == 'error' and is_unknown_value.any(): raise ValueError('Columns to be encoded can not contain new values') if y is None: # Replace level with its mean target; if level occurs only once, use global mean level_means = (colmap['sum'] / colmap['count']).where(level_notunique, self._mean) X[col] = X[col].map(level_means) else: # Replace level with its mean target, calculated excluding this row's target # The y (target) mean for this level is normally just the sum/count; # excluding this row's y, it's (sum - y) / (count - 1) level_means = (X[col].map(colmap['sum']) - y) / (X[col].map(colmap['count']) - 1) # The 'where' fills in singleton levels (count = 1 -> div by 0) with the global mean X[col] = level_means.where(X[col].map(colmap['count'][level_notunique]).notna(), self._mean) if self.handle_unknown == 'value': X.loc[is_unknown_value, col] = self._mean elif self.handle_unknown == 'return_nan': X.loc[is_unknown_value, col] = np.nan if self.handle_missing == 'value': X.loc[is_nan & unseen_values.isna().any(), col] = self._mean elif self.handle_missing == 'return_nan': X.loc[is_nan, col] = np.nan if self.sigma is not None and y is not None: X[col] = X[col] * random_state_.normal(1., self.sigma, X[col].shape[0]) return X