PySpark MLlib – Algorithms and Parameters
In our last PySpark tutorial, we discussed PySpark StorageLevel. Today, we will discuss PySpark MLlib. Moreover, we will see different algorithms and parameters of PySpark MLlib. PySpark has this machine learning API.
So, let’s start PySpark MLlib.
2. What is PySpark MLlib?
As we know, Spark offers a Machine Learning API which we call MLlib. Though, in Python as well, PySpark has this machine learning API. Also, there are different kind of algorithms in PySpark MLlib, such as:
For binary classification, various methods are available in the spark.mllib package such as multiclass classification as well as regression analysis. Moreover, in classification, some of the most popular algorithms are Naive Bayes, Random Forest, Decision Tree
An unsupervised learning problem is clustering, here we try to group subsets of entities with one another on the basis of some notion of similarity.
This algorithm supports PySpark MLlib utilities for linear algebra.
Let’s revise PySpark Spark Context
For recommender systems, collaborative filtering is commonly used. So, to fill in the missing entries of a user item association matrix is the main aim of these techniques aim.
Recently, this PySpark MLlib supports model-based collaborative filtering. By a small set of latent factors,. Here all the users and products are described, which we can use to predict missing entries. However, to learn these latent factors, spark.mllib uses the Alternating Least Squares (ALS) algorithm.
Basically, linear regression comes from the family of regression algorithms. To find relationships and dependencies between variables is the main goal of regression.
Although, PySpark MLlib package also covers other algorithms, classes, and functions.
Well to understand it better, here is the following example
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Alternating Least Squares Matrix Factorization–
def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False, seed=None):
Train a matrix factorization model given an RDD of ratings by users
for a subset of products. The rating matrix is approximated as the
product of two lower-rank matrices of a given rank (number of
features). To solve for these features, ALS is run iteratively with
a configurable level of parallelism.
RDD of `Rating` or (userID, productID, rating) tuple.
Rank of the feature matrices computed (number of features).
Number of iterations of ALS.
Number of blocks used to parallelize the computation. A value
of -1 will use an auto-configured number of blocks.
A value of True will solve least-squares with nonnegativity
Random seed for initial matrix factorization model. A value
of None will use system time as the seed.
model = callMLlibFunc(“trainALSModel”, cls._prepare(ratings), rank, iterations,
lambda_, blocks, nonnegative, seed)
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3. Parameters of PySpark MLlib
Below discussing are some main parameters of PySpark MLlib:
This is RDD of Rating or (userID, productID, rating) tuple.
It shows Rank of the feature matrices computed (number of features).
Learn PySpark SparkConf – Attributes and Applications
These are the number of iterations of ALS. (default: 5).
It is Regularization parameter. (default: 0.01).
To parallelize the computation some number of blocks used. (default: -1).
With nonnegativity constraints, a value of True will solve least-squares. (default: False).
So, this was all about PySpark MLlib. Hope you like our explanation.
Hence, we have seen all about PySpark MLlib. Moreover, in this PySpark tutorial, we discussed different algorithms and parameters for PySpark MLlib. Still, if any doubt, ask in the comment tab. Hope it helps!
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