Source code for category_encoders.woe

"""Weight of Evidence"""
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator
from category_encoders.ordinal import OrdinalEncoder
import category_encoders.utils as util
from sklearn.utils.random import check_random_state

__author__ = 'Jan Motl'

[docs]class WOEEncoder(BaseEstimator, util.TransformerWithTargetMixin): """Weight of Evidence coding for categorical features. Supported targets: binomial. For polynomial target support, see PolynomialWrapper. 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 'return_nan', 'error' and 'value', defaults to 'value', which will assume WOE=0. handle_unknown: str options are 'return_nan', 'error' and 'value', defaults to 'value', which will assume WOE=0. randomized: bool, adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched). sigma: float standard deviation (spread or "width") of the normal distribution. regularization: float the purpose of regularization is mostly to prevent division by zero. When regularization is 0, you may encounter division by zero. Example ------- >>> from category_encoders import * >>> import pandas as pd >>> from sklearn.datasets import load_boston >>> bunch = load_boston() >>> y = > 22.5 >>> X = pd.DataFrame(, columns=bunch.feature_names) >>> enc = WOEEncoder(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] Weight of Evidence (WOE) and Information Value Explained, from """ def __init__(self, verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', random_state=None, randomized=False, sigma=0.05, regularization=1.0): self.verbose = verbose self.return_df = return_df self.drop_invariant = drop_invariant self.drop_cols = [] self.cols = cols self.ordinal_encoder = None self._dim = None self.mapping = None self.handle_unknown = handle_unknown self.handle_missing = handle_missing self._sum = None self._count = None self.random_state = random_state self.randomized = randomized self.sigma = sigma self.regularization = regularization self.feature_names = None # noinspection PyUnusedLocal
[docs] def fit(self, X, y, **kwargs): """Fit encoder according to X and binary 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] Binary target values. Returns ------- self : encoder Returns self. """ # Unite parameters into pandas types X = util.convert_input(X) y = util.convert_input_vector(y, X.index).astype(float) # The lengths must be equal 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]) + ".") # The label must be binary with values {0,1} unique = y.unique() if len(unique) != 2: raise ValueError("The target column y must be binary. But the target contains " + str(len(unique)) + " unique value(s).") if y.isnull().any(): raise ValueError("The target column y must not contain missing values.") if np.max(unique) < 1: raise ValueError("The target column y must be binary with values {0, 1}. Value 1 was not found in the target.") if np.min(unique) > 0: raise ValueError("The target column y must be binary with values {0, 1}. Value 0 was not found in the target.") 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') self.ordinal_encoder = OrdinalEncoder( verbose=self.verbose, cols=self.cols, handle_unknown='value', handle_missing='value' ) self.ordinal_encoder = X_ordinal = self.ordinal_encoder.transform(X) # Training self.mapping = self._train(X_ordinal, y) X_temp = self.transform(X, override_return_df=True) self.feature_names = X_temp.columns.tolist() # Store column names with approximately constant variance on the training data 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. When the data are used for model training, it is important to also pass the target in order to apply leave one out. 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 # Do not modify the input argument X = X.copy(deep=True) X = self.ordinal_encoder.transform(X) if self.handle_unknown == 'error': if X[self.cols].isin([-1]).any().any(): raise ValueError('Unexpected categories found in dataframe') # Loop over columns and replace nominal values with WOE X = self._score(X, y) # Postprocessing # Note: We should not even convert these columns. 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 _train(self, X, y): # Initialize the output mapping = {} # Calculate global statistics self._sum = y.sum() self._count = y.count() for switch in self.ordinal_encoder.category_mapping: col = switch.get('col') values = switch.get('mapping') # Calculate sum and count of the target for each unique value in the feature col stats = y.groupby(X[col]).agg(['sum', 'count']) # Count of x_{i,+} and x_i # Create a new column with regularized WOE. # Regularization helps to avoid division by zero. # Pre-calculate WOEs because logarithms are slow. nominator = (stats['sum'] + self.regularization) / (self._sum + 2*self.regularization) denominator = ((stats['count'] - stats['sum']) + self.regularization) / (self._count - self._sum + 2*self.regularization) woe = np.log(nominator / denominator) # Ignore unique values. This helps to prevent overfitting on id-like columns. woe[stats['count'] == 1] = 0 if self.handle_unknown == 'return_nan': woe.loc[-1] = np.nan elif self.handle_unknown == 'value': woe.loc[-1] = 0 if self.handle_missing == 'return_nan': woe.loc[values.loc[np.nan]] = np.nan elif self.handle_missing == 'value': woe.loc[-2] = 0 # Store WOE for transform() function mapping[col] = woe return mapping def _score(self, X, y): for col in self.cols: # Score the column X[col] = X[col].map(self.mapping[col]) # Randomization is meaningful only for training data -> we do it only if y is present if self.randomized and y is not None: random_state_generator = check_random_state(self.random_state) X[col] = (X[col] * random_state_generator.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("Estimator has to be fitted to return feature names.") else: return self.feature_names