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Encoding Categorical Variables: A Deep Dive into Goal Encoding | by Juan Jose Munoz | Feb, 2024

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Information is available in completely different shapes and kinds. A type of shapes and kinds is called categorical knowledge.

This poses an issue as a result of most Machine Studying algorithms use solely numerical knowledge as enter. Nonetheless, categorical knowledge is normally not a problem to cope with, due to easy, well-defined features that remodel them into numerical values. If in case you have taken any knowledge science course, you may be aware of the one scorching encoding technique for categorical options. This technique is nice when your options have restricted classes. Nonetheless, you’ll run into some points when coping with excessive cardinal options (options with many classes)

Right here is how you need to use goal encoding to rework Categorical options into numerical values.

Photograph by Sonika Agarwal on Unsplash

Early in any knowledge science course, you might be launched to 1 scorching encoding as a key technique to cope with categorical values, and rightfully so, as this technique works very well on low cardinal options (options with restricted classes).

In a nutshell, One scorching encoding transforms every class right into a binary vector, the place the corresponding class is marked as ‘True’ or ‘1’, and all different classes are marked with ‘False’ or ‘0’.

import pandas as pd

# Pattern categorical knowledge
knowledge = {'Class': ['Red', 'Green', 'Blue', 'Red', 'Green']}

# Create a DataFrame
df = pd.DataFrame(knowledge)

# Carry out one-hot encoding
one_hot_encoded = pd.get_dummies(df['Category'])

# Show the end result
print(one_hot_encoded)

One scorching encoding output — we might enhance this by dropping one column as a result of if we all know Blue and Inexperienced, we will determine the worth of Purple. Picture by creator

Whereas this works nice for options with restricted classes (Lower than 10–20 classes), because the variety of classes will increase, the one-hot encoded vectors turn into longer and sparser, probably resulting in elevated reminiscence utilization and computational complexity, let’s have a look at an instance.

The under code makes use of Amazon Worker Entry knowledge, made publicity accessible in kaggle: https://www.kaggle.com/datasets/lucamassaron/amazon-employee-access-challenge

The information comprises eight categorical function columns indicating traits of the required useful resource, function, and workgroup of the worker at Amazon.

knowledge.data()
Column info. Picture by creator
# Show the variety of distinctive values in every column
unique_values_per_column = knowledge.nunique()

print("Variety of distinctive values in every column:")
print(unique_values_per_column)

The eight options have excessive cardinality. Picture by creator

Utilizing one scorching encoding may very well be difficult in a dataset like this as a result of excessive variety of distinct classes for every function.

#Preliminary knowledge reminiscence utilization
memory_usage = knowledge.memory_usage(deep=True)
total_memory_usage = memory_usage.sum()
print(f"nTotal reminiscence utilization of the DataFrame: {total_memory_usage / (1024 ** 2):.2f} MB")
The preliminary dataset is 11.24 MB. Picture by creator
#one-hot encoding categorical options
data_encoded = pd.get_dummies(knowledge,
columns=knowledge.select_dtypes(embody='object').columns,
drop_first=True)

data_encoded.form

After on-hot encoding, the dataset has 15 618 columns. Picture by creator
The ensuing knowledge set is extremely sparse, that means it comprises quite a lot of 0s and 1. Picture by creator
# Reminiscence utilization for the one-hot encoded dataset
memory_usage = data_encoded.memory_usage(deep=True)
total_memory_usage = memory_usage.sum()
print(f"nTotal reminiscence utilization of the DataFrame: {total_memory_usage / (1024 ** 2):.2f} MB")
Dataset reminiscence utilization elevated to 488.08 MB as a result of elevated variety of columns. Picture by creator

As you possibly can see, one-hot encoding shouldn’t be a viable answer to cope with excessive cardinal categorical options, because it considerably will increase the dimensions of the dataset.

In instances with excessive cardinal options, goal encoding is a greater possibility.

Goal encoding transforms a categorical function right into a numeric function with out including any further columns, avoiding turning the dataset into a bigger and sparser dataset.

Goal encoding works by changing every class of a categorical function into its corresponding anticipated worth. The method to calculating the anticipated worth will rely upon the worth you are attempting to foretell.

For Regression issues, the anticipated worth is solely the typical worth for that class.

For Classification issues, the anticipated worth is the conditional chance provided that class.

In each instances, we will get the outcomes by merely utilizing the ‘group_by’ perform in pandas.

#Instance of how you can calculate the anticipated worth for Goal encoding of a Binary final result
expected_values = knowledge.groupby('ROLE_TITLE')['ACTION'].value_counts(normalize=True).unstack()
expected_values
The ensuing desk signifies the chance of every `ACTION` final result by distinctive `Role_title` ID. Picture by creator

The ensuing desk signifies the chance of every “ACTION” final result by distinctive “ROLE_TITLE” id. All that’s left to do is exchange the “ROLE_TITLE” id with the values from the chance of “ACTION” being 1 within the unique dataset. (i.e as a substitute of class 117879 the dataset will present 0.889331)

Whereas this may give us an instinct of how goal encoding works, utilizing this straightforward methodology runs the danger of overfitting. Particularly for uncommon classes, as in these instances, goal encoding will primarily present the goal worth to the mannequin. Additionally, the above methodology can solely cope with seen classes, so in case your take a look at knowledge has a brand new class, it received’t be capable of deal with it.

To keep away from these errors, it’s essential make the goal encoding transformer extra sturdy.

To make goal encoding extra sturdy, you possibly can create a customized transformer class and combine it with scikit-learn in order that it may be utilized in any mannequin pipeline.

NOTE: The under code is taken from the e book “The Kaggle Guide” and may be present in Kaggle: https://www.kaggle.com/code/lucamassaron/meta-features-and-target-encoding

import numpy as np
import pandas as pd

from sklearn.base import BaseEstimator, TransformerMixin

class TargetEncode(BaseEstimator, TransformerMixin):

def __init__(self, classes='auto', okay=1, f=1,
noise_level=0, random_state=None):
if kind(classes)==str and classes!='auto':
self.classes = [categories]
else:
self.classes = classes
self.okay = okay
self.f = f
self.noise_level = noise_level
self.encodings = dict()
self.prior = None
self.random_state = random_state

def add_noise(self, collection, noise_level):
return collection * (1 + noise_level *
np.random.randn(len(collection)))

def match(self, X, y=None):
if kind(self.classes)=='auto':
self.classes = np.the place(X.dtypes == kind(object()))[0]

temp = X.loc[:, self.categories].copy()
temp['target'] = y
self.prior = np.imply(y)
for variable in self.classes:
avg = (temp.groupby(by=variable)['target']
.agg(['mean', 'count']))
# Compute smoothing
smoothing = (1 / (1 + np.exp(-(avg['count'] - self.okay) /
self.f)))
# The larger the depend the much less full_avg is accounted
self.encodings[variable] = dict(self.prior * (1 -
smoothing) + avg['mean'] * smoothing)

return self

def remodel(self, X):
Xt = X.copy()
for variable in self.classes:
Xt[variable].exchange(self.encodings[variable],
inplace=True)
unknown_value = {worth:self.prior for worth in
X[variable].distinctive()
if worth not in
self.encodings[variable].keys()}
if len(unknown_value) > 0:
Xt[variable].exchange(unknown_value, inplace=True)
Xt[variable] = Xt[variable].astype(float)
if self.noise_level > 0:
if self.random_state shouldn't be None:
np.random.seed(self.random_state)
Xt[variable] = self.add_noise(Xt[variable],
self.noise_level)
return Xt

def fit_transform(self, X, y=None):
self.match(X, y)
return self.remodel(X)

It’d look daunting at first, however let’s break down every a part of the code to grasp how you can create a strong Goal encoder.

Class Definition

class TargetEncode(BaseEstimator, TransformerMixin):

This primary step ensures that you need to use this transformer class in scikit-learn pipelines for knowledge preprocessing, function engineering, and machine studying workflows. It achieves this by inheriting the scikit-learn courses BaseEstimator and TransformerMixin.

Inheritance permits the TargetEncode class to reuse or override strategies and attributes outlined within the base courses, on this case, BaseEstimator and TransformerMixin

BaseEstimator is a base class for all scikit-learn estimators. Estimators are objects in scikit-learn with a “match” methodology for coaching on knowledge and a “predict” methodology for making predictions.

TransformerMixin is a mixin class for transformers in scikit-learn, it supplies further strategies corresponding to “fit_transform”, which mixes becoming and reworking in a single step.

Inheriting from BaseEstimator & TransformerMixin, permits TargetEncode to implement these strategies, making it appropriate with the scikit-learn API.

Defining the constructor

def __init__(self, classes='auto', okay=1, f=1, 
noise_level=0, random_state=None):
if kind(classes)==str and classes!='auto':
self.classes = [categories]
else:
self.classes = classes
self.okay = okay
self.f = f
self.noise_level = noise_level
self.encodings = dict()
self.prior = None
self.random_state = random_state

This second step defines the constructor for the “TargetEncode” class and initializes the occasion variables with default or user-specified values.

The “classes” parameter determines which columns within the enter knowledge needs to be thought of as categorical variables for goal encoding. It’s Set by default to ‘auto’ to robotically determine categorical columns throughout the becoming course of.

The parameters okay, f, and noise_level management the smoothing impact throughout goal encoding and the extent of noise added throughout transformation.

Including noise

This subsequent step is essential to keep away from overfitting.

def add_noise(self, collection, noise_level):
return collection * (1 + noise_level *
np.random.randn(len(collection)))

The “add_noise” methodology provides random noise to introduce variability and stop overfitting throughout the transformation part.

“np.random.randn(len(collection))” generates an array of random numbers from an ordinary regular distribution (imply = 0, customary deviation = 1).

Multiplying this array by “noise_level” scales the random noise primarily based on the desired noise degree.”

This step contributes to the robustness and generalization capabilities of the goal encoding course of.

Becoming the Goal encoder

This a part of the code trains the goal encoder on the offered knowledge by calculating the goal encodings for categorical columns and storing them for later use throughout transformation.

def match(self, X, y=None):
if kind(self.classes)=='auto':
self.classes = np.the place(X.dtypes == kind(object()))[0]

temp = X.loc[:, self.categories].copy()
temp['target'] = y
self.prior = np.imply(y)
for variable in self.classes:
avg = (temp.groupby(by=variable)['target']
.agg(['mean', 'count']))
# Compute smoothing
smoothing = (1 / (1 + np.exp(-(avg['count'] - self.okay) /
self.f)))
# The larger the depend the much less full_avg is accounted
self.encodings[variable] = dict(self.prior * (1 -
smoothing) + avg['mean'] * smoothing)

The smoothing time period helps stop overfitting, particularly when coping with classes with small samples.

The strategy follows the scikit-learn conference for match strategies in transformers.

It begins by checking and figuring out the specific columns and creating a short lived DataFrame, containing solely the chosen categorical columns from the enter X and the goal variable y.

The prior imply of the goal variable is calculated and saved within the prior attribute. This represents the general imply of the goal variable throughout the complete dataset.

Then, it calculates the imply and depend of the goal variable for every class utilizing the group-by methodology, as seen beforehand.

There may be a further smoothing step to stop overfitting on classes with small numbers of samples. Smoothing is calculated primarily based on the variety of samples in every class. The bigger the depend, the much less the smoothing impact.

The calculated encodings for every class within the present variable are saved within the encodings dictionary. This dictionary might be used later throughout the transformation part.

Remodeling the info

This a part of the code replaces the unique categorical values with their corresponding target-encoded values saved in self.encodings.

def remodel(self, X):
Xt = X.copy()
for variable in self.classes:
Xt[variable].exchange(self.encodings[variable],
inplace=True)
unknown_value = {worth:self.prior for worth in
X[variable].distinctive()
if worth not in
self.encodings[variable].keys()}
if len(unknown_value) > 0:
Xt[variable].exchange(unknown_value, inplace=True)
Xt[variable] = Xt[variable].astype(float)
if self.noise_level > 0:
if self.random_state shouldn't be None:
np.random.seed(self.random_state)
Xt[variable] = self.add_noise(Xt[variable],
self.noise_level)
return Xt

This step has a further robustness test to make sure the goal encoder can deal with new or unseen classes. For these new or unknown classes, it replaces them with the imply of the goal variable saved within the prior_mean variable.

Should you want extra robustness in opposition to overfitting, you possibly can arrange a noise_level better than 0 so as to add random noise to the encoded values.

The fit_transform methodology combines the performance of becoming and reworking the info by first becoming the transformer to the coaching knowledge after which remodeling it primarily based on the calculated encodings.

Now that you just perceive how the code works, let’s see it in motion.

#Instantiate TargetEncode class
te = TargetEncode(classes='ROLE_TITLE')
te.match(knowledge, knowledge['ACTION'])
te.remodel(knowledge[['ROLE_TITLE']])
Output with Goal encoded Function title. Picture by creator

The Goal encoder changed every “ROLE_TITLE” id with the chance of every class. Now, let’s do the identical for all options and test the reminiscence utilization after utilizing Goal Encoding.

y = knowledge['ACTION']
options = knowledge.drop('ACTION',axis=1)

te = TargetEncode(classes=options.columns)
te.match(options,y)
te_data = te.remodel(options)

te_data.head()

Output, Goal encoded options. Picture by creator
memory_usage = te_data.memory_usage(deep=True)
total_memory_usage = memory_usage.sum()
print(f"nTotal reminiscence utilization of the DataFrame: {total_memory_usage / (1024 ** 2):.2f} MB")
The ensuing dataset solely makes use of 2.25 MB, in comparison with 488.08 MB from the one-hot encoder. Picture by creator

Goal encoding efficiently remodeled the specific knowledge into numerical with out creating further columns or rising reminiscence utilization.

To this point now we have created our personal goal encoder class, nevertheless you don’t have to do that anymore.

In scikit-learn model 1.3 launch, someplace round June 2023, they launched the Goal Encoder class to their API. Right here is how you need to use goal encoding with Scikit Be taught

from sklearn.preprocessing import TargetEncoder

#Splitting the info
y = knowledge['ACTION']
options = knowledge.drop('ACTION',axis=1)

#Specify the goal kind
te = TargetEncoder(easy="auto",target_type='binary')
X_trans = te.fit_transform(options, y)

#Making a Dataframe
features_encoded = pd.DataFrame(X_trans, columns = options.columns)

Output from sklearn Goal Encoder transformation. Picture by creator

Word that we’re getting barely completely different outcomes from the handbook Goal encoder class due to the sleek parameter and randomness on the noise degree.

As you see, sklearn makes it straightforward to run goal encoding transformations. Nonetheless, it is very important perceive how the transformation works beneath the hood first to grasp and clarify the output.

Whereas Goal encoding is a robust encoding methodology, it’s essential to think about the particular necessities and traits of your dataset and select the encoding methodology that most closely fits your wants and the necessities of the machine studying algorithm you propose to make use of.

[1] Banachewicz, Ok. & Massaron, L. (2022). The Kaggle Guide: Information Evaluation and Machine Studying for Aggressive Information Science. Packt>

[2] Massaron, L. (2022, January). Amazon Worker Entry Problem. Retrieved February 1, 2024, from https://www.kaggle.com/datasets/lucamassaron/amazon-employee-access-challenge

[3] Massaron, L. Meta-features and goal encoding. Retrieved February 1, 2024, from https://www.kaggle.com/luca-massaron/meta-features-and-target-encoding

[4] Scikit-learn.sklearn.preprocessing.TargetEncoder. In scikit-learn: Machine studying in Python (Model 1.3). Retrieved February 1, 2024, from https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html

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