app.py

from flask import Blueprint
main = Blueprint('main', __name__)
 
import json
from engine import RecommendationEngine
 
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
 
from flask import Flask, request
 
@main.route("/<int:user_id>/ratings/top/<int:count>", methods=["GET"])
def top_ratings(user_id, count):
    logger.debug("User %s TOP ratings requested", user_id)
    top_ratings = recommendation_engine.get_top_ratings(user_id,count)
    return json.dumps(top_ratings)
 
@main.route("/<int:user_id>/ratings/<int:movie_id>", methods=["GET"])
def movie_ratings(user_id, movie_id):
    logger.debug("User %s rating requested for movie %s", user_id, movie_id)
    ratings = recommendation_engine.get_ratings_for_movie_ids(user_id, [movie_id])
    return json.dumps(ratings)
 
 
@main.route("/<int:user_id>/ratings", methods = ["POST"])
def add_ratings(user_id):
    # get the ratings from the Flask POST request object
    ratings_list = request.form.keys()[0].strip().split("\n")
    ratings_list = map(lambda x: x.split(","), ratings_list)
    # create a list with the format required by the negine (user_id, movie_id, rating)
    ratings = map(lambda x: (user_id, int(x[0]), float(x[1])), ratings_list)
    # add them to the model using then engine API
    recommendation_engine.add_ratings(ratings)
 
    return json.dumps(ratings)
 
 
def create_app(spark_context, dataset_path):
    global recommendation_engine 

    recommendation_engine = RecommendationEngine(spark_context, dataset_path)    
    
    app = Flask(__name__)
    app.register_blueprint(main)
    return app

engine.py

import os
from pyspark.mllib.recommendation import ALS

import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def get_counts_and_averages(ID_and_ratings_tuple):
    """Given a tuple (movieID, ratings_iterable) 
    returns (movieID, (ratings_count, ratings_avg))
    """
    nratings = len(ID_and_ratings_tuple[1])
    return ID_and_ratings_tuple[0], (nratings, float(sum(x for x in ID_and_ratings_tuple[1]))/nratings)


class RecommendationEngine:
    """A movie recommendation engine
    """

    def __count_and_average_ratings(self):
        """Updates the movies ratings counts from 
        the current data self.ratings_RDD
        """
        logger.info("Counting movie ratings...")
        movie_ID_with_ratings_RDD = self.ratings_RDD.map(lambda x: (x[1], x[2])).groupByKey()
        movie_ID_with_avg_ratings_RDD = movie_ID_with_ratings_RDD.map(get_counts_and_averages)
        self.movies_rating_counts_RDD = movie_ID_with_avg_ratings_RDD.map(lambda x: (x[0], x[1][0]))


    def __train_model(self):
        """Train the ALS model with the current dataset
        """
        logger.info("Training the ALS model...")
        self.model = ALS.train(self.ratings_RDD, self.rank, seed=self.seed,
                               iterations=self.iterations, lambda_=self.regularization_parameter)
        logger.info("ALS model built!")


    def __predict_ratings(self, user_and_movie_RDD):
        """Gets predictions for a given (userID, movieID) formatted RDD
        Returns: an RDD with format (movieTitle, movieRating, numRatings)
        """
        predicted_RDD = self.model.predictAll(user_and_movie_RDD)
        predicted_rating_RDD = predicted_RDD.map(lambda x: (x.product, x.rating))
        predicted_rating_title_and_count_RDD = \
            predicted_rating_RDD.join(self.movies_titles_RDD).join(self.movies_rating_counts_RDD)
        predicted_rating_title_and_count_RDD = \
            predicted_rating_title_and_count_RDD.map(lambda r: (r[1][0][1], r[1][0][0], r[1][1]))
        
        return predicted_rating_title_and_count_RDD
    
    def add_ratings(self, ratings):
        """Add additional movie ratings in the format (user_id, movie_id, rating)
        """
        # Convert ratings to an RDD
        new_ratings_RDD = self.sc.parallelize(ratings)
        # Add new ratings to the existing ones
        self.ratings_RDD = self.ratings_RDD.union(new_ratings_RDD)
        # Re-compute movie ratings count
        self.__count_and_average_ratings()
        # Re-train the ALS model with the new ratings
        self.__train_model()
        
        return ratings

    def get_ratings_for_movie_ids(self, user_id, movie_ids):
        """Given a user_id and a list of movie_ids, predict ratings for them 
        """
        requested_movies_RDD = self.sc.parallelize(movie_ids).map(lambda x: (user_id, x))
        # Get predicted ratings
        ratings = self.__predict_ratings(requested_movies_RDD).collect()

        return ratings
    
    def get_top_ratings(self, user_id, movies_count):
        """Recommends up to movies_count top unrated movies to user_id
        """
        # Get pairs of (userID, movieID) for user_id unrated movies
        user_unrated_movies_RDD = self.movies_RDD.filter(lambda rating: not rating[1]==user_id).map(lambda x: (user_id, x[0]))
        # Get predicted ratings
        ratings = self.__predict_ratings(user_unrated_movies_RDD).filter(lambda r: r[2]>=25).takeOrdered(movies_count, key=lambda x: -x[1])

        return ratings

    def __init__(self, sc, dataset_path):
        """Init the recommendation engine given a Spark context and a dataset path
        """

        logger.info("Starting up the Recommendation Engine: ")

        self.sc = sc

        # Load ratings data for later use
        logger.info("Loading Ratings data...")
        ratings_file_path = os.path.join(dataset_path, 'ratings.csv')
        ratings_raw_RDD = self.sc.textFile(ratings_file_path)
        ratings_raw_data_header = ratings_raw_RDD.take(1)[0]
        self.ratings_RDD = ratings_raw_RDD.filter(lambda line: line!=ratings_raw_data_header)\
            .map(lambda line: line.split(",")).map(lambda tokens: (int(tokens[0]),int(tokens[1]),float(tokens[2]))).cache()
        # Load movies data for later use
        logger.info("Loading Movies data...")
        movies_file_path = os.path.join(dataset_path, 'movies.csv')
        movies_raw_RDD = self.sc.textFile(movies_file_path)
        movies_raw_data_header = movies_raw_RDD.take(1)[0]
        self.movies_RDD = movies_raw_RDD.filter(lambda line: line!=movies_raw_data_header)\
            .map(lambda line: line.split(",")).map(lambda tokens: (int(tokens[0]),tokens[1],tokens[2])).cache()
        self.movies_titles_RDD = self.movies_RDD.map(lambda x: (int(x[0]),x[1])).cache()
        # Pre-calculate movies ratings counts
        self.__count_and_average_ratings()

        # Train the model
        self.rank = 8
        self.seed = 5L
        self.iterations = 10
        self.regularization_parameter = 0.1
        self.__train_model() 

server.py

import time, sys, cherrypy, os
from paste.translogger import TransLogger
from app import create_app
from pyspark import SparkContext, SparkConf

def init_spark_context():
    # load spark context
    conf = SparkConf().setAppName("movie_recommendation-server")
    # IMPORTANT: pass aditional Python modules to each worker
    sc = SparkContext(conf=conf, pyFiles=['engine.py', 'app.py'])
 
    return sc
 
 
def run_server(app):
 
    # Enable WSGI access logging via Paste
    app_logged = TransLogger(app)
 
    # Mount the WSGI callable object (app) on the root directory
    cherrypy.tree.graft(app_logged, '/')
 
    # Set the configuration of the web server
    cherrypy.config.update({
        'engine.autoreload.on': True,
        'log.screen': True,
        'server.socket_port': 5432,
        'server.socket_host': '0.0.0.0'
    })
 
    # Start the CherryPy WSGI web server
    cherrypy.engine.start()
    cherrypy.engine.block()
 
 
if __name__ == "__main__":
    # Init spark context and load libraries
    sc = init_spark_context()
    dataset_path = os.path.join('datasets', 'ml-latest')
    app = create_app(sc, dataset_path)
 
    # start web server
    run_server(app)

start_server.sh

~/spark-1.3.1-bin-hadoop2.6/bin/spark-submit --master spark://169.254.206.2:7077 --total-executor-cores 14 --executor-memory 6g server.py 

user_ratings.file

260,9
1,8
16,7
25,8
32,9
335,4
379,3
296,7
858,10
50,8

Hi Jose,

Thanks for the great tutorial.

I have managed to get it all running no problem apart from the add request handler.

For instance if I want to add some ratings for user 1 I enter http://0.0.0.0:5432/1/ratings

but I get Method Not Allowed, can you explain what am I doing wrong please.

Thank you

Ulrich

Hi Ulrich,
I guess the problem you are facing is, for the desired route, the method is POST, where as you might be attempting it by GET (using your browser), hence try using a REST client, ie: Postman, or cUrl for terminal.

Hi Jose,

Line #80 in engine.py should be:

user_unrated_movies_RDD = self.ratings_RDD.filter(lambda rating: not rating[1]==user_id).map(lambda x: (user_id, x[0]))