I am working with a large tabular dataset that consists of many categorical columns. I want to train a regression model (XGBoost) in this data while using as many regressors as possible.
Because of the size of data, I am using incremental training - where following sklearn API - .fit(X, y) I am not able to fit the entire matrix X into memory and therefore I am training the model in a couple of rows at the time. The problem is that in every batch, the model is expecting the same number of columns in X.
This is where it gets tricky because some variables are categorical it may be that one-hot encoding on a batch of data will same some shape (e.g. 20 columns). However, the next batch will have (26 columns) simply because in the previous batch not every unique level of the categorical feature was present. Sklearn allows for accounting for this and costume function can also be used: To keep some number of columns in matrix X.
import seaborn as sns
import numpy as np
from sklearn.preprocessing import OneHotEncoder
def one_hot_known(dataf, list_levels, col):
"""Creates a dummy coded matrix with as many columns as unique levels"""
return np.array(
[np.eye(len(list_levels))[list_levels.index(i)] for i in dataf[col]])
# Load Some Dataset with categorical variable
df_orig = sns.load_dataset('tips')
# List of unique levels - known apriori
day_level = list(df_orig['day'].unique())
# Image, we have a batch of data (subset of original data) and one categorical level (DAY) is not present here
df = df_orig.loc[lambda d: d['day'] != 'Sun']
# Missing category is filled with 0 and in next batch, if present its columns will have 1.
OneHotEncoder(categories = [day_level], sparse=False).fit_transform(np.array(df['day']).reshape(-1, 1))
#Costum function, can be used in incremental(data batches chunk fashion)
one_hot_known(df, day_level, 'day')
What I would like to do not is to utilize the TargerEncoding approach, so that we do not have matrix X with a huge number of columns. However, it still needs to be done in an Incremental fashion, just like the OneHot Encoding above.
I am writing this as a post because I know this is very useful to many people and would like to know how to utilize the same strategy for TargetEncoding.
I am aware that Deep Learning allows for Embedding layers, which represent categorical features in continuous space but I would like to apply TargetEncoding.