Source code for category_encoders.glmm

"""Generalized linear mixed model"""
import warnings
import re
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator
from sklearn.utils.random import check_random_state
from category_encoders.ordinal import OrdinalEncoder
import category_encoders.utils as util
import statsmodels.formula.api as smf
from statsmodels.genmod.bayes_mixed_glm import BinomialBayesMixedGLM as bgmm

__author__ = 'Jan Motl'


[docs]class GLMMEncoder(BaseEstimator, util.TransformerWithTargetMixin): """Generalized linear mixed model. Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper. This is a supervised encoder similar to TargetEncoder or MEstimateEncoder, but there are some advantages: 1) Solid statistical theory behind the technique. Mixed effects models are a mature branch of statistics. 2) No hyper-parameters to tune. The amount of shrinkage is automatically determined through the estimation process. In short, the less observations a category has and/or the more the outcome varies for a category then the higher the regularization towards "the prior" or "grand mean". 3) The technique is applicable for both continuous and binomial targets. If the target is continuous, the encoder returns regularized difference of the observation's category from the global mean. If the target is binomial, the encoder returns regularized log odds per category. In comparison to JamesSteinEstimator, this encoder utilizes generalized linear mixed models from statsmodels library. Note: This is an alpha implementation. The API of the method may change in the future. 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 encoded 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 returns 0. handle_unknown: str options are 'return_nan', 'error' and 'value', defaults to 'value', which returns 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. binomial_target: bool if True, the target must be binomial with values {0, 1} and Binomial mixed model is used. If False, the target must be continuous and Linear mixed model is used. If None (the default), a heuristic is applied to estimate the target type. Example ------- >>> from category_encoders import * >>> import pandas as pd >>> from sklearn.datasets import load_boston >>> bunch = load_boston() >>> y = bunch.target > 22.5 >>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names) >>> enc = GLMMEncoder(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 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] Data Analysis Using Regression and Multilevel/Hierarchical Models, page 253, from https://faculty.psau.edu.sa/filedownload/doc-12-pdf-a1997d0d31f84d13c1cdc44ac39a8f2c-original.pdf """ 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, binomial_target=None): 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.random_state = random_state self.randomized = randomized self.sigma = sigma self.binomial_target = binomial_target 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]) + ".") 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 = self.ordinal_encoder.fit(X) 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 the columns and replace the nominal values with the numbers 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 = {} # Estimate target type, if necessary if self.binomial_target is None: if len(y.unique()) <= 2: binomial_target = True else: binomial_target = False else: binomial_target = self.binomial_target # The estimation does not have to converge -> at least converge to the same value. np.random.seed(2001) for switch in self.ordinal_encoder.category_mapping: col = switch.get('col') values = switch.get('mapping') data = self._rename_and_merge(X, y, col) try: with warnings.catch_warnings(): warnings.filterwarnings("ignore") if binomial_target: # Classification, returns (regularized) log odds per category as stored in vc_mean # Note: md.predict() returns: output = fe_mean + vcp_mean + vc_mean[category] md = bgmm.from_formula('target ~ 1', {'a': '0 + C(feature)'}, data).fit_vb() index_names = [int(float(re.sub(r'C\(feature\)\[(\S+)\]', r'\1', index_name))) for index_name in md.model.vc_names] estimate = pd.Series(md.vc_mean, index=index_names) else: # Regression, returns (regularized) mean deviation of the observation's category from the global mean md = smf.mixedlm('target ~ 1', data, groups=data['feature']).fit() tmp = dict() for key, value in md.random_effects.items(): tmp[key] = value[0] estimate = pd.Series(tmp) except np.linalg.LinAlgError: # Singular matrix -> just return all zeros estimate = pd.Series(np.zeros(len(values)), index=values) # Ignore unique columns. This helps to prevent overfitting on id-like columns if len(X[col].unique()) == len(y): estimate[:] = 0 if self.handle_unknown == 'return_nan': estimate.loc[-1] = np.nan elif self.handle_unknown == 'value': estimate.loc[-1] = 0 if self.handle_missing == 'return_nan': estimate.loc[values.loc[np.nan]] = np.nan elif self.handle_missing == 'value': estimate.loc[-2] = 0 mapping[col] = estimate 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
def _rename_and_merge(self, X, y, col): """ Statsmodels requires: 1) unique column names 2) non-numeric columns names Solution: internally rename the columns. """ merged = pd.DataFrame() merged['feature'] = X[col] merged['target'] = y return merged