Let’s Explore what are discrete, categorical, and continuous variables, their identification techniques, and their importance in machine learning and statistical modeling.
Data preprocessing is a critical step in machine learning and statistical modeling. Before diving into model building, it is essential to understand and identify the types of variables present in the dataset.
Furthermore, I will provide a PySpark function to identify variable types in a PySpark DataFrame.
Types of Variables:
a. Discrete Variables: Discrete variables represent countable data, typically integers. Examples include the number of employees in a company or the number of students in a class.
b. Categorical Variables: Categorical variables represent data that can be divided into distinct categories, such as gender or eye color. They can be either nominal (no order) or ordinal (with a meaningful order).
c. Continuous Variables: Continuous variables represent data that can take any value within a given range. Examples include height, weight, and temperature.
How to Identifying Variable types?
a. Discrete Variables: Check if the variable represents countable data and has a limited number of unique values.
b. Categorical Variables: Check if the variable can be divided into distinct categories. Ordinal variables can be identified by a meaningful order among categories.
c. Continuous Variables: Check if the variable can take any value within a given range, and if the data has a continuous distribution.
Importance of Identifying Variables in Machine Learning and Statistical Modeling
a. Data preprocessing: Understanding the variable types is crucial for selecting appropriate preprocessing techniques, such as scaling or encoding.
b. Model selection: Some models are more suitable for specific types of variables, e.g., linear regression for continuous data or logistic regression for categorical data.
c. Feature engineering: Identifying variables helps in creating meaningful features that improve the model’s performance.
1. Import required libraries and initialize SparkSession
First, let’s import the necessary libraries and create a SparkSession, the entry point to use PySpark.
import findspark
findspark.init()
from pyspark import SparkFiles
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, countDistinct
from pyspark.sql.types import IntegerType, StringType, NumericType
spark = SparkSession.builder.appName("Identify Variable Types").getOrCreate()
2. Preparing the Sample Data
To demonstrate the Variable type Identification, we’ll use a sample dataset. First, let’s load the data into a DataFrame
url = "https://raw.githubusercontent.com/selva86/datasets/master/Churn_Modelling.csv"
spark.sparkContext.addFile(url)
df = spark.read.csv(SparkFiles.get("Churn_Modelling.csv"), header=True, inferSchema=True)
df.show(5, truncate=False)
+---------+----------+--------+-----------+---------+------+---+------+---------+-------------+---------+--------------+---------------+------+
|RowNumber|CustomerId|Surname |CreditScore|Geography|Gender|Age|Tenure|Balance |NumOfProducts|HasCrCard|IsActiveMember|EstimatedSalary|Exited|
+---------+----------+--------+-----------+---------+------+---+------+---------+-------------+---------+--------------+---------------+------+
|1 |15634602 |Hargrave|619 |France |Female|42 |2 |0.0 |1 |1 |1 |101348.88 |1 |
|2 |15647311 |Hill |608 |Spain |Female|41 |1 |83807.86 |1 |0 |1 |112542.58 |0 |
|3 |15619304 |Onio |502 |France |Female|42 |8 |159660.8 |3 |1 |0 |113931.57 |1 |
|4 |15701354 |Boni |699 |France |Female|39 |1 |0.0 |2 |0 |0 |93826.63 |0 |
|5 |15737888 |Mitchell|850 |Spain |Female|43 |2 |125510.82|1 |1 |1 |79084.1 |0 |
+---------+----------+--------+-----------+---------+------+---+------+---------+-------------+---------+--------------+---------------+------+
only showing top 5 rows
3. Variable type Identification Function
Let’s create a PySpark function to Identify the varaible type in a PySpark DataFrame
def identify_variable_types(df, unique_threshold=10, id_vars=[]):
"""
Identify variable types in a PySpark DataFrame.
:param df: The input PySpark DataFrame
:param unique_threshold: The maximum number of unique values for a discrete variable. Default is 10.
:id_vars: Unique keys like CustomerId, Names etc..
:return: A dictionary with variable names as keys and variable types as values.
"""
discrete_columns = []
categorical_columns = []
continuous_columns = []
Other_columns = id_vars
df = df.drop(*Other_columns)
for column in df.columns:
dtype = df.schema[column].dataType
if isinstance(dtype, StringType):
unique_count = df.agg(countDistinct(col(column)).alias("unique_count")).collect()[0]["unique_count"]
if unique_count <= unique_threshold:
categorical_columns.append(column)
else:
Other_columns.append(column)
elif isinstance(dtype, IntegerType):
unique_count = df.agg(countDistinct(col(column)).alias("unique_count")).collect()[0]["unique_count"]
if unique_count <= unique_threshold:
discrete_columns.append(column)
else:
continuous_columns.append(column)
elif isinstance(dtype, NumericType):
continuous_columns.append(column)
return discrete_columns, categorical_columns, continuous_columns, Other_columns
4. How to use the identify_variable_types function?
Before exetuing the identify_variable_types function have a detailed understading on the fuction parameters and their data types
This function will return four PySpark objects
A dictionary with variable names as keys and variable types as values
Let’s take a look at below example to better understand the execution process
# Identify variable types
discrete_columns, categorical_columns, continuous_columns, Other_columns = identify_variable_types(df, id_vars=['RowNumber', 'CustomerId'])
print("Discrete columns:", discrete_columns)
print("Categorical columns:", categorical_columns)
print("Continuous columns:", continuous_columns)
print("Other columns:", Other_columns)
Discrete columns: ['NumOfProducts', 'HasCrCard', 'IsActiveMember', 'Exited']
Categorical columns: ['Geography', 'Gender']
Continuous columns: ['CreditScore', 'Age', 'Tenure', 'Balance', 'EstimatedSalary']
Other columns: ['RowNumber', 'CustomerId', 'Surname']
Conclusion
Identifying discrete, categorical, and continuous variables is an essential step in data preprocessing for machine learning and statistical modeling. Understanding these variable types is crucial for selecting appropriate preprocessing techniques, model selection, and feature engineering.
The provided PySpark function helps to automate this process, enabling efficient and accurate identification of variable types in a PySpark DataFrame.
