Source code for category_encoders.leave_one_out

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

__author__ = 'hbghhy'

[docs]class LeaveOneOutEncoder(BaseEstimator, util.TransformerWithTargetMixin): """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 load_boston >>> bunch = load_boston() >>> y = >>> X = pd.DataFrame(, columns=bunch.feature_names) >>> enc = LeaveOneOutEncoder(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 13 columns): CRIM 506 non-null float64 ZN 506 non-null float64 INDUS 506 non-null float64 CHAS 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 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(13) memory usage: 51.5 KB None References ---------- .. [1] Strategies to encode categorical variables with many categories, from """ def __init__(self, verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', random_state=None, sigma=None): self.return_df = return_df self.drop_invariant = drop_invariant self.drop_cols = [] self.verbose = verbose self.use_default_cols = cols is None # if True, even a repeated call of fit() will select string columns from X self.cols = cols self._dim = None self.mapping = None self.handle_unknown = handle_unknown self.handle_missing = handle_missing self._mean = None self.random_state = random_state self.sigma = sigma self.feature_names = None
[docs] def fit(self, X, y, **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. """ # unite the input into pandas types X = util.convert_input(X) y = util.convert_input_vector(y, X.index).astype(float) if X.shape[0] != y.shape[0]: raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".") self._dim = X.shape[1] # if columns aren't passed, just use every string column if self.use_default_cols: 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') categories = self.fit_leave_one_out( X, y, cols=self.cols ) self.mapping = categories X_temp = self.transform(X, override_return_df=True) self.feature_names = X_temp.columns.tolist() 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, y=None, override_return_df=False): """Perform the transformation to new categorical data. Parameters ---------- X : array-like, shape = [n_samples, n_features] y : array-like, shape = [n_samples] when transform by leave one out None, when transform without target information (such as transform test set) 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.') # unite the input into pandas types 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 we are encoding the training data, we have to check the target if y is not None: y = util.convert_input_vector(y, X.index).astype(float) if X.shape[0] != y.shape[0]: raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".") if not list(self.cols): return X X = self.transform_leave_one_out( X, y, 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
def fit_leave_one_out(self, X_in, y, cols=None): X = X_in.copy(deep=True) if cols is None: cols = X.columns.values 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(dict([(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_in, y, mapping=None): """ Leave one out encoding uses a single column of floats to represent the means of the target variables. """ X = X_in.copy(deep=True) 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].isnull() 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 X[col] = X[col].astype(str) 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]).notnull(), 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.isnull().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
[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