PySpark - MLlib

Apache Spark offers a Machine Learning API called MLlib. PySpark has this machine learning API in Python as well. It supports different kind of algorithms, which are mentioned below −
mllib.classification − The spark.mllib package supports various methods for binary classification, multiclass classification and regression analysis. Some of the most popular algorithms in classification are Random Forest, Naive Bayes, Decision Tree, etc.
mllib.classification − The spark.mllib package supports various methods for binary classification, multiclass classification and regression analysis. Some of the most popular algorithms in classification are Random Forest, Naive Bayes, Decision Tree, etc.
mllib.clustering − Clustering is an unsupervised learning problem, whereby you aim to group subsets of entities with one another based on some notion of similarity.
mllib.clustering − Clustering is an unsupervised learning problem, whereby you aim to group subsets of entities with one another based on some notion of similarity.
mllib.fpm − Frequent pattern matching is mining frequent items, itemsets, subsequences or other substructures that are usually among the first steps to analyze a large-scale dataset. This has been an active research topic in data mining for years.
mllib.fpm − Frequent pattern matching is mining frequent items, itemsets, subsequences or other substructures that are usually among the first steps to analyze a large-scale dataset. This has been an active research topic in data mining for years.
mllib.linalg − MLlib utilities for linear algebra.
mllib.linalg − MLlib utilities for linear algebra.
mllib.recommendation − Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user item association matrix.
mllib.recommendation − Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user item association matrix.
spark.mllib − It ¬currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. spark.mllib uses the Alternating Least Squares (ALS) algorithm to learn these latent factors.
spark.mllib − It ¬currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. spark.mllib uses the Alternating Least Squares (ALS) algorithm to learn these latent factors.
mllib.regression − Linear regression belongs to the family of regression algorithms. The goal of regression is to find relationships and dependencies between variables. The interface for working with linear regression models and model summaries is similar to the logistic regression case.
mllib.regression − Linear regression belongs to the family of regression algorithms. The goal of regression is to find relationships and dependencies between variables. The interface for working with linear regression models and model summaries is similar to the logistic regression case.
There are other algorithms, classes and functions also as a part of the mllib package. As of now, let us understand a demonstration on pyspark.mllib.
The following example is of collaborative filtering using ALS algorithm to build the recommendation model and evaluate it on training data.
Dataset used − test.data
1,1,5.0
1,2,1.0
1,3,5.0
1,4,1.0
2,1,5.0
2,2,1.0
2,3,5.0
2,4,1.0
3,1,1.0
3,2,5.0
3,3,1.0
3,4,5.0
4,1,1.0
4,2,5.0
4,3,1.0
4,4,5.0

--------------------------------------recommend.py----------------------------------------
from __future__ import print_function
from pyspark import SparkContext
from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
if __name__ == "__main__":
   sc = SparkContext(appName="Pspark mllib Example")
   data = sc.textFile("test.data")
   ratings = data.map(lambda l: l.split(','))\
      .map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2])))
   
   # Build the recommendation model using Alternating Least Squares
   rank = 10
   numIterations = 10
   model = ALS.train(ratings, rank, numIterations)
   
   # Evaluate the model on training data
   testdata = ratings.map(lambda p: (p[0], p[1]))
   predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
   ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
   MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
   print("Mean Squared Error = " + str(MSE))
   
   # Save and load model
   model.save(sc, "target/tmp/myCollaborativeFilter")
   sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter")
--------------------------------------recommend.py----------------------------------------

Command − The command will be as follows −
$SPARK_HOME/bin/spark-submit recommend.py

Output − The output of the above command will be −
Mean Squared Error = 1.20536041839e-05