# Notebook from singhbhupender1/DataSciencePortfolio
Path: Movie-Recommenders/Movie_recommendation_using_Spark_ALS.ipynb

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# a = [] #do not execute it!!!!!!..will crash your system
# while(1):
#     a.append('1')
# #get more ram_____no_output_____

#Overview
In this project, I will use an Alternating Least Squares (ALS) algorithm with Spark APIs to predict the ratings for the movies in [MovieLens Datasets](https://grouplens.org/datasets/movielens/latest/)

_____no_output_____#[Recommender system](https://en.wikipedia.org/wiki/Recommender_system)
A recommendation system is basically an information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. It is widely used in different internet / online business such as Amazon, Netflix, Spotify, or social media like Facebook and Youtube. By using recommender systems, those companies are able to provide better or more suited products/services/contents that are personalized to a user based on his/her historical consumer behaviors

Recommender systems typically produce a list of recommendations through collaborative filtering or through content-based filtering

This project will focus on collaborative filtering and use Alternating Least Squares (ALS) algorithm to make movie predictions_____no_output_____[Alternating Least Squares](https://endymecy.gitbooks.io/spark-ml-source-analysis/content/%E6%8E%A8%E8%8D%90/papers/Large-scale%20Parallel%20Collaborative%20Filtering%20the%20Netflix%20Prize.pdf)

ALS is one of the low rank matrix approximation algorithms for collaborative filtering. ALS decomposes user-item matrix into two low rank matrixes: user matrix and item matrix. In collaborative filtering, users and products are described by a small set of latent factors that can be used to predict missing entries. And ALS algorithm learns these latent factors by matrix factorization_____no_output_____#Data Sets
I use [MovieLens Datasets](https://grouplens.org/datasets/movielens/latest/). This dataset (ml-latest.zip) describes 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service. It contains 27753444 ratings and 1108997 tag applications across 58098 movies. These data were created by 283228 users between January 09, 1995 and September 26, 2018. This dataset was generated on September 26, 2018.

Users were selected at random for inclusion. All selected users had rated at least 1 movies. No demographic information is included. Each user is represented by an id, and no other information is provided.

The data are contained in the files genome-scores.csv, genome-tags.csv, links.csv, movies.csv, ratings.csv and tags.csv._____no_output_____#Project Content
1. Load Data
2. Spark SQL and OLAP
3. Spark ALS based approach for training model
4. ALS Model Selection and Evaluation
5. Model testing
6. Make movie recommendation to myself_____no_output_____Downloding the dataset_____no_output_____

!wget "http://files.grouplens.org/datasets/movielens/ml-latest.zip"--2020-03-07 20:15:46--  http://files.grouplens.org/datasets/movielens/ml-latest.zip
Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152
Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 277113433 (264M) [application/zip]
Saving to: ‘ml-latest.zip’

ml-latest.zip       100%[===================>] 264.28M  71.6MB/s    in 3.9s

2020-03-07 20:15:50 (67.4 MB/s) - ‘ml-latest.zip’ saved [277113433/277113433]

Installing Pyspark

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!pip install pysparkCollecting pyspark
  Downloading https://files.pythonhosted.org/packages/9a/5a/271c416c1c2185b6cb0151b29a91fff6fcaed80173c8584ff6d20e46b465/pyspark-2.4.5.tar.gz (217.8MB)
     |████████████████████████████████| 217.8MB 35kB/s
Collecting py4j==0.10.7
  Downloading https://files.pythonhosted.org/packages/e3/53/c737818eb9a7dc32a7cd4f1396e787bd94200c3997c72c1dbe028587bd76/py4j-0.10.7-py2.py3-none-any.whl (197kB)
     |████████████████████████████████| 204kB 35.9MB/s
Building wheels for collected packages: pyspark
  Building wheel for pyspark (setup.py) ... done
  Created wheel for pyspark: filename=pyspark-2.4.5-py2.py3-none-any.whl size=218257927 sha256=adfbac32ce5706980867f403b6631630f294580d9270eb00479b9184dab697b2
  Stored in directory: /root/.cache/pip/wheels/bf/db/04/61d66a5939364e756eb1c1be4ec5bdce6e04047fc7929a3c3c
Successfully built pyspark
Installing collected packages: py4j, pyspark
Successfully installed py4j-0.10.7 pyspark-2.4.5

Unzipping th file_____no_output_____

import shutil
filename = "ml-latest.zip"
#archive_format = "zip"

shutil.unpack_archive(filename)  _____no_output_____

note = check default java jdk version in the system and choose version 8 because of compatibility with spark _____no_output_____

# !java -version_____no_output_____# ! update-alternatives --config java_____no_output_____

First install Java, Spark and and run a local Spark session by just running this on Google Colab:_____no_output_____

#!apt-get install openjdk-8-jdk-headless -qq > /dev/null
!wget -q https://archive.apache.org/dist/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz
!tar -xvf spark-2.2.0-bin-hadoop2.7.tgz
!pip install -q findsparkspark-2.2.0-bin-hadoop2.7/
spark-2.2.0-bin-hadoop2.7/NOTICE
spark-2.2.0-bin-hadoop2.7/jars/
spark-2.2.0-bin-hadoop2.7/jars/parquet-common-1.8.2.jar
spark-2.2.0-bin-hadoop2.7/jars/bonecp-0.8.0.RELEASE.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-net-2.2.jar
spark-2.2.0-bin-hadoop2.7/jars/javax.servlet-api-3.1.0.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-annotations-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-hdfs-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/oro-2.0.8.jar
spark-2.2.0-bin-hadoop2.7/jars/xercesImpl-2.9.1.jar
spark-2.2.0-bin-hadoop2.7/jars/antlr-runtime-3.4.jar
spark-2.2.0-bin-hadoop2.7/jars/machinist_2.11-0.6.1.jar
spark-2.2.0-bin-hadoop2.7/jars/spire_2.11-0.13.0.jar
spark-2.2.0-bin-hadoop2.7/jars/parquet-hadoop-1.8.2.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-sketch_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/stream-2.7.0.jar
spark-2.2.0-bin-hadoop2.7/jars/kryo-shaded-3.0.3.jar
spark-2.2.0-bin-hadoop2.7/jars/metrics-jvm-3.1.2.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-mapreduce-client-common-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/RoaringBitmap-0.5.11.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-auth-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-common-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/activation-1.1.1.jar
spark-2.2.0-bin-hadoop2.7/jars/jta-1.1.jar
spark-2.2.0-bin-hadoop2.7/jars/datanucleus-core-3.2.10.jar
spark-2.2.0-bin-hadoop2.7/jars/jersey-container-servlet-2.22.2.jar
spark-2.2.0-bin-hadoop2.7/jars/jersey-guava-2.22.2.jar
spark-2.2.0-bin-hadoop2.7/jars/jets3t-0.9.3.jar
spark-2.2.0-bin-hadoop2.7/jars/jetty-util-6.1.26.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-compress-1.4.1.jar
spark-2.2.0-bin-hadoop2.7/jars/jersey-container-servlet-core-2.22.2.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-beanutils-1.7.0.jar
spark-2.2.0-bin-hadoop2.7/jars/hk2-utils-2.4.0-b34.jar
spark-2.2.0-bin-hadoop2.7/jars/parquet-format-2.3.1.jar
spark-2.2.0-bin-hadoop2.7/jars/avro-1.7.7.jar
spark-2.2.0-bin-hadoop2.7/jars/datanucleus-api-jdo-3.2.6.jar
spark-2.2.0-bin-hadoop2.7/jars/jline-2.12.1.jar
spark-2.2.0-bin-hadoop2.7/jars/metrics-core-3.1.2.jar
spark-2.2.0-bin-hadoop2.7/jars/java-xmlbuilder-1.0.jar
spark-2.2.0-bin-hadoop2.7/jars/stax-api-1.0-2.jar
spark-2.2.0-bin-hadoop2.7/jars/hk2-locator-2.4.0-b34.jar
spark-2.2.0-bin-hadoop2.7/jars/parquet-hadoop-bundle-1.6.0.jar
spark-2.2.0-bin-hadoop2.7/jars/jsp-api-2.1.jar
spark-2.2.0-bin-hadoop2.7/jars/xmlenc-0.52.jar
spark-2.2.0-bin-hadoop2.7/jars/xbean-asm5-shaded-4.4.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-core-asl-1.9.13.jar
spark-2.2.0-bin-hadoop2.7/jars/shapeless_2.11-2.3.2.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-collections-3.2.2.jar
spark-2.2.0-bin-hadoop2.7/jars/javax.inject-1.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-sql_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/json4s-jackson_2.11-3.2.11.jar
spark-2.2.0-bin-hadoop2.7/jars/json4s-ast_2.11-3.2.11.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-codec-1.10.jar
spark-2.2.0-bin-hadoop2.7/jars/leveldbjni-all-1.8.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-httpclient-3.1.jar
spark-2.2.0-bin-hadoop2.7/jars/aopalliance-repackaged-2.4.0-b34.jar
spark-2.2.0-bin-hadoop2.7/jars/hive-jdbc-1.2.1.spark2.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-yarn-server-common-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/minlog-1.3.0.jar
spark-2.2.0-bin-hadoop2.7/jars/javolution-5.5.1.jar
spark-2.2.0-bin-hadoop2.7/jars/datanucleus-rdbms-3.2.9.jar
spark-2.2.0-bin-hadoop2.7/jars/jersey-common-2.22.2.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-graphx_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/api-asn1-api-1.0.0-M20.jar
spark-2.2.0-bin-hadoop2.7/jars/apacheds-i18n-2.0.0-M15.jar
spark-2.2.0-bin-hadoop2.7/jars/validation-api-1.1.0.Final.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-dbcp-1.4.jar
spark-2.2.0-bin-hadoop2.7/jars/slf4j-log4j12-1.7.16.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-pool-1.5.4.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-network-common_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/pmml-schema-1.2.15.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-mapreduce-client-shuffle-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/joda-time-2.9.3.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-hive-thriftserver_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/spire-macros_2.11-0.13.0.jar
spark-2.2.0-bin-hadoop2.7/jars/curator-recipes-2.6.0.jar
spark-2.2.0-bin-hadoop2.7/jars/jersey-server-2.22.2.jar
spark-2.2.0-bin-hadoop2.7/jars/htrace-core-3.1.0-incubating.jar
spark-2.2.0-bin-hadoop2.7/jars/httpclient-4.5.2.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-mllib-local_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/snappy-0.2.jar
spark-2.2.0-bin-hadoop2.7/jars/breeze-macros_2.11-0.13.1.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-jaxrs-1.9.13.jar
spark-2.2.0-bin-hadoop2.7/jars/eigenbase-properties-1.1.5.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-tags_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-databind-2.6.5.jar
spark-2.2.0-bin-hadoop2.7/jars/curator-client-2.6.0.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-catalyst_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/paranamer-2.6.jar
spark-2.2.0-bin-hadoop2.7/jars/opencsv-2.3.jar
spark-2.2.0-bin-hadoop2.7/jars/json4s-core_2.11-3.2.11.jar
spark-2.2.0-bin-hadoop2.7/jars/hive-metastore-1.2.1.spark2.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-digester-1.8.jar
spark-2.2.0-bin-hadoop2.7/jars/jsr305-1.3.9.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-repl_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/jetty-6.1.26.jar
spark-2.2.0-bin-hadoop2.7/jars/pyrolite-4.13.jar
spark-2.2.0-bin-hadoop2.7/jars/log4j-1.2.17.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-yarn-server-web-proxy-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/calcite-avatica-1.2.0-incubating.jar
spark-2.2.0-bin-hadoop2.7/jars/scala-xml_2.11-1.0.2.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-network-shuffle_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/calcite-linq4j-1.2.0-incubating.jar
spark-2.2.0-bin-hadoop2.7/jars/compress-lzf-1.0.3.jar
spark-2.2.0-bin-hadoop2.7/jars/jtransforms-2.4.0.jar
spark-2.2.0-bin-hadoop2.7/jars/apache-log4j-extras-1.2.17.jar
spark-2.2.0-bin-hadoop2.7/jars/jcl-over-slf4j-1.7.16.jar
spark-2.2.0-bin-hadoop2.7/jars/guice-3.0.jar
spark-2.2.0-bin-hadoop2.7/jars/gson-2.2.4.jar
spark-2.2.0-bin-hadoop2.7/jars/httpcore-4.4.4.jar
spark-2.2.0-bin-hadoop2.7/jars/protobuf-java-2.5.0.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-mapreduce-client-core-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-yarn_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-hive_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-lang-2.6.jar
spark-2.2.0-bin-hadoop2.7/jars/stax-api-1.0.1.jar
spark-2.2.0-bin-hadoop2.7/jars/javax.annotation-api-1.2.jar
spark-2.2.0-bin-hadoop2.7/jars/netty-all-4.0.43.Final.jar
spark-2.2.0-bin-hadoop2.7/jars/curator-framework-2.6.0.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-io-2.4.jar
spark-2.2.0-bin-hadoop2.7/jars/avro-ipc-1.7.7.jar
spark-2.2.0-bin-hadoop2.7/jars/hive-beeline-1.2.1.spark2.jar
spark-2.2.0-bin-hadoop2.7/jars/mesos-1.0.0-shaded-protobuf.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-annotations-2.6.5.jar
spark-2.2.0-bin-hadoop2.7/jars/jersey-media-jaxb-2.22.2.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-yarn-common-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-mapper-asl-1.9.13.jar
spark-2.2.0-bin-hadoop2.7/jars/parquet-jackson-1.8.2.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-client-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-module-paranamer-2.6.5.jar
spark-2.2.0-bin-hadoop2.7/jars/aopalliance-1.0.jar
spark-2.2.0-bin-hadoop2.7/jars/super-csv-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/janino-3.0.0.jar
spark-2.2.0-bin-hadoop2.7/jars/antlr4-runtime-4.5.3.jar
spark-2.2.0-bin-hadoop2.7/jars/jpam-1.1.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-lang3-3.5.jar
spark-2.2.0-bin-hadoop2.7/jars/javassist-3.18.1-GA.jar
spark-2.2.0-bin-hadoop2.7/jars/bcprov-jdk15on-1.51.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-launcher_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/javax.ws.rs-api-2.0.1.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-mapreduce-client-jobclient-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/metrics-graphite-3.1.2.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-xc-1.9.13.jar
spark-2.2.0-bin-hadoop2.7/jars/lz4-1.3.0.jar
spark-2.2.0-bin-hadoop2.7/jars/core-1.1.2.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-mesos_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/antlr-2.7.7.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-yarn-client-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/mx4j-3.0.2.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-logging-1.1.3.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-beanutils-core-1.8.0.jar
spark-2.2.0-bin-hadoop2.7/jars/libfb303-0.9.3.jar
spark-2.2.0-bin-hadoop2.7/jars/libthrift-0.9.3.jar
spark-2.2.0-bin-hadoop2.7/jars/jaxb-api-2.2.2.jar
spark-2.2.0-bin-hadoop2.7/jars/hive-exec-1.2.1.spark2.jar
spark-2.2.0-bin-hadoop2.7/jars/calcite-core-1.2.0-incubating.jar
spark-2.2.0-bin-hadoop2.7/jars/parquet-encoding-1.8.2.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-mllib_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/api-util-1.0.0-M20.jar
spark-2.2.0-bin-hadoop2.7/jars/apacheds-kerberos-codec-2.0.0-M15.jar
spark-2.2.0-bin-hadoop2.7/jars/mail-1.4.7.jar
spark-2.2.0-bin-hadoop2.7/jars/stringtemplate-3.2.1.jar
spark-2.2.0-bin-hadoop2.7/jars/guice-servlet-3.0.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-crypto-1.0.0.jar
spark-2.2.0-bin-hadoop2.7/jars/JavaEWAH-0.3.2.jar
spark-2.2.0-bin-hadoop2.7/jars/metrics-json-3.1.2.jar
spark-2.2.0-bin-hadoop2.7/jars/jdo-api-3.0.1.jar
spark-2.2.0-bin-hadoop2.7/jars/scalap-2.11.8.jar
spark-2.2.0-bin-hadoop2.7/jars/hive-cli-1.2.1.spark2.jar
spark-2.2.0-bin-hadoop2.7/jars/zookeeper-3.4.6.jar
spark-2.2.0-bin-hadoop2.7/jars/jackson-module-scala_2.11-2.6.5.jar
spark-2.2.0-bin-hadoop2.7/jars/ivy-2.4.0.jar
spark-2.2.0-bin-hadoop2.7/jars/py4j-0.10.4.jar
spark-2.2.0-bin-hadoop2.7/jars/arpack_combined_all-0.1.jar
spark-2.2.0-bin-hadoop2.7/jars/spark-unsafe_2.11-2.2.0.jar
spark-2.2.0-bin-hadoop2.7/jars/macro-compat_2.11-1.1.1.jar
spark-2.2.0-bin-hadoop2.7/jars/jul-to-slf4j-1.7.16.jar
spark-2.2.0-bin-hadoop2.7/jars/pmml-model-1.2.15.jar
spark-2.2.0-bin-hadoop2.7/jars/parquet-column-1.8.2.jar
spark-2.2.0-bin-hadoop2.7/jars/javax.inject-2.4.0-b34.jar
spark-2.2.0-bin-hadoop2.7/jars/breeze_2.11-0.13.1.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-cli-1.2.jar
spark-2.2.0-bin-hadoop2.7/jars/chill-java-0.8.0.jar
spark-2.2.0-bin-hadoop2.7/jars/avro-mapred-1.7.7-hadoop2.jar
spark-2.2.0-bin-hadoop2.7/jars/snappy-java-1.1.2.6.jar
spark-2.2.0-bin-hadoop2.7/jars/base64-2.3.8.jar
spark-2.2.0-bin-hadoop2.7/jars/commons-compiler-3.0.0.jar
spark-2.2.0-bin-hadoop2.7/jars/slf4j-api-1.7.16.jar
spark-2.2.0-bin-hadoop2.7/jars/derby-10.12.1.1.jar
spark-2.2.0-bin-hadoop2.7/jars/hadoop-mapreduce-client-app-2.7.3.jar
spark-2.2.0-bin-hadoop2.7/jars/objenesis-2.1.jar
spark-2.2.0-bin-hadoop2.7/jars/jodd-core-3.5.2.jar
spark-2.2.0-bin-hadoop2.7/jars/jersey-client-2.22.2.jar
spark-2.2.0-bin-hadoop2.7/jars/ST4-4.0.4.jar
spark-2.2.0-bin-hadoop2.7/jars/univocity-parsers-2.2.1.jar
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spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/standard_scaler_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/multiclass_logistic_regression_with_elastic_net.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/fpgrowth_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/vector_assembler_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/max_abs_scaler_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/binarizer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/imputer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/bisecting_k_means_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/cross_validator.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/logistic_regression_summary_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/dataframe_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/naive_bayes_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/generalized_linear_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/decision_tree_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/count_vectorizer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/one_vs_rest_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/linear_regression_with_elastic_net.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/index_to_string_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/sql_transformer.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/correlation_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/dct_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/isotonic_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/vector_slicer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/multilayer_perceptron_classification.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/kmeans_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/pca_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/lda_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/polynomial_expansion_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/onehot_encoder_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/aft_survival_regression.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/rformula_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/logistic_regression_with_elastic_net.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/decision_tree_classification_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/estimator_transformer_param_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/tokenizer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/min_max_scaler_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/pipeline_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/random_forest_classifier_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/string_indexer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/als_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/n_gram_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/vector_indexer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/normalizer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/quantile_discretizer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/train_validation_split.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/chisq_selector_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/gaussian_mixture_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/bucketed_random_projection_lsh_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/bucketizer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/elementwise_product_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/tf_idf_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/word2vec_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/ml/chi_square_test_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/pagerank.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/hive.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/basic.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/datasource.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/streaming/
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/streaming/structured_kafka_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sql/streaming/structured_network_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/pi.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/logistic_regression.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/network_wordjoinsentiments.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/sql_network_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/queue_stream.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/network_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/kafka_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/stateful_network_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/direct_kafka_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/hdfs_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/recoverable_network_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/streaming/flume_wordcount.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/transitive_closure.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/kmeans.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/avro_inputformat.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/linear_regression_with_sgd_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/svd_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/recommendation_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/regression_metrics_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/standard_scaler_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/kernel_density_estimation_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/random_forest_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/sampled_rdds.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/fpgrowth_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/streaming_k_means_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/latent_dirichlet_allocation_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/bisecting_k_means_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/gradient_boosting_classification_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/multi_class_metrics_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/correlations_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/pca_rowmatrix_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/naive_bayes_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/decision_tree_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/ranking_metrics_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/gaussian_mixture_model.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/gradient_boosting_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/power_iteration_clustering_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/binary_classification_metrics_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/logistic_regression.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/hypothesis_testing_kolmogorov_smirnov_test_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/isotonic_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/word2vec.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/summary_statistics_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/multi_label_metrics_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/hypothesis_testing_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/kmeans.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/decision_tree_classification_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/random_rdd_generation.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/svm_with_sgd_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/streaming_linear_regression_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/stratified_sampling_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/normalizer_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/random_forest_classification_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/gaussian_mixture_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/elementwise_product_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/tf_idf_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/word2vec_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/correlations.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/mllib/k_means_example.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/parquet_inputformat.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/als.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/python/sort.py
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/
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spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaMaxAbsScalerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorIndexerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketizerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaSQLTransformerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaDocument.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaGeneralizedLinearRegressionExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaLabeledDocument.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaIndexToStringExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaImputerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaFPGrowthExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaCountVectorizerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaRFormulaExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaCorrelationExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaIsotonicRegressionExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaStandardScalerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaNormalizerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaStringIndexerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaMinMaxScalerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/hive/
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java
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spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredKafkaWordCount.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCount.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredSessionization.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java
spark-2.2.0-bin-hadoop2.7/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
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spark-2.2.0-bin-hadoop2.7/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala
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spark-2.2.0-bin-hadoop2.7/examples/src/main/scala/org/apache/spark/examples/ml/BinarizerExample.scala
spark-2.2.0-bin-hadoop2.7/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala
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spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-pyrolite.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-xmlenc.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-scala.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-SnapTree.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-AnchorJS.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-jline.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-heapq.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-cloudpickle.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-reflectasm.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-jpmml-model.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-modernizr.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-javolution.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-jbcrypt.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-sbt-launch-lib.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-postgresql.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-kryo.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-DPark.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-scopt.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-sorttable.js.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-dagre-d3.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-jquery.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-protobuf.txt
spark-2.2.0-bin-hadoop2.7/licenses/LICENSE-antlr.txt
spark-2.2.0-bin-hadoop2.7/conf/
spark-2.2.0-bin-hadoop2.7/conf/fairscheduler.xml.template
spark-2.2.0-bin-hadoop2.7/conf/metrics.properties.template
spark-2.2.0-bin-hadoop2.7/conf/spark-env.sh.template
spark-2.2.0-bin-hadoop2.7/conf/log4j.properties.template
spark-2.2.0-bin-hadoop2.7/conf/docker.properties.template
spark-2.2.0-bin-hadoop2.7/conf/slaves.template
spark-2.2.0-bin-hadoop2.7/conf/spark-defaults.conf.template
spark-2.2.0-bin-hadoop2.7/LICENSE
spark-2.2.0-bin-hadoop2.7/bin/
spark-2.2.0-bin-hadoop2.7/bin/spark-shell
spark-2.2.0-bin-hadoop2.7/bin/spark-submit.cmd
spark-2.2.0-bin-hadoop2.7/bin/spark-shell2.cmd
spark-2.2.0-bin-hadoop2.7/bin/pyspark
spark-2.2.0-bin-hadoop2.7/bin/sparkR.cmd
spark-2.2.0-bin-hadoop2.7/bin/spark-class2.cmd
spark-2.2.0-bin-hadoop2.7/bin/run-example.cmd
spark-2.2.0-bin-hadoop2.7/bin/spark-submit2.cmd
spark-2.2.0-bin-hadoop2.7/bin/spark-class
spark-2.2.0-bin-hadoop2.7/bin/spark-submit
spark-2.2.0-bin-hadoop2.7/bin/spark-sql
spark-2.2.0-bin-hadoop2.7/bin/find-spark-home
spark-2.2.0-bin-hadoop2.7/bin/run-example
spark-2.2.0-bin-hadoop2.7/bin/beeline
spark-2.2.0-bin-hadoop2.7/bin/pyspark2.cmd
spark-2.2.0-bin-hadoop2.7/bin/spark-shell.cmd
spark-2.2.0-bin-hadoop2.7/bin/spark-class.cmd
spark-2.2.0-bin-hadoop2.7/bin/pyspark.cmd
spark-2.2.0-bin-hadoop2.7/bin/sparkR
spark-2.2.0-bin-hadoop2.7/bin/beeline.cmd
spark-2.2.0-bin-hadoop2.7/bin/sparkR2.cmd
spark-2.2.0-bin-hadoop2.7/bin/load-spark-env.sh
spark-2.2.0-bin-hadoop2.7/bin/load-spark-env.cmd
spark-2.2.0-bin-hadoop2.7/yarn/
spark-2.2.0-bin-hadoop2.7/yarn/spark-2.2.0-yarn-shuffle.jar
spark-2.2.0-bin-hadoop2.7/README.md

This installs Apache Spark 2.4.4, Java 8, and Findspark, a library that makes it easy for Python to find Spark.
Second set the locations where Spark and Java are installed to let know Collab where to find it._____no_output_____using below snippet we set the environment varibles for java and spark in system_____no_output_____

import os
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
os.environ["SPARK_HOME"] = "/content/spark-2.2.0-bin-hadoop2.7"_____no_output_____import findspark
findspark.init()
from pyspark.sql import SparkSession

spark = SparkSession.builder.master("local[*]").getOrCreate()_____no_output_____import os
import time

#spark imports
from pyspark.sql import SparkSession
from pyspark.sql.functions import UserDefinedFunction, explode , desc
from pyspark.sql.types import StringType, ArrayType
from pyspark.mllib.recommendation import ALS

#ML import
import math
import numpy as np
import pandas as pd

#visualization libraries
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline_____no_output_____#spark config
spark = SparkSession \
    .builder \
    .appName('movie recommendation') \
    .config('spark.driver.maxResultsize', '20g') \
    .config("spark.driver.memory", "20g") \
    .config("spark.executor.memory", "8g") \
    .config("spark.master", "local[12]") \
    .getOrCreate()

#get spark context
sc = spark.sparkContext_____no_output_____#path config
data_path = '/content/ml-latest'_____no_output_____

#Load Data_____no_output_____

movies = spark.read.load(os.path.join(data_path, 'movies.csv'), format='csv', header=True, inferSchema=True)
ratings = spark.read.load(os.path.join(data_path, 'ratings.csv'), format='csv', header=True, inferSchema=True)
links = spark.read.load(os.path.join(data_path, 'links.csv'), format='csv', header=True, inferSchema=True)
tags = spark.read.load(os.path.join(data_path, 'tags.csv'), format='csv', header=True, inferSchema=True)_____no_output_____

###basic inspection_____no_output_____

movies.show(3)+-------+--------------------+--------------------+
|movieId|               title|              genres|
+-------+--------------------+--------------------+
|      1|    Toy Story (1995)|Adventure|Animati...|
|      2|      Jumanji (1995)|Adventure|Childre...|
|      3|Grumpier Old Men ...|      Comedy|Romance|
+-------+--------------------+--------------------+
only showing top 3 rows

ratings.show(3)+------+-------+------+----------+
|userId|movieId|rating| timestamp|
+------+-------+------+----------+
|     1|    307|   3.5|1256677221|
|     1|    481|   3.5|1256677456|
|     1|   1091|   1.5|1256677471|
+------+-------+------+----------+
only showing top 3 rows

links.show(3)+-------+------+------+
|movieId|imdbId|tmdbId|
+-------+------+------+
|      1|114709|   862|
|      2|113497|  8844|
|      3|113228| 15602|
+-------+------+------+
only showing top 3 rows

tags.show(3)+------+-------+--------+----------+
|userId|movieId|     tag| timestamp|
+------+-------+--------+----------+
|    14|    110|    epic|1443148538|
|    14|    110|Medieval|1443148532|
|    14|    260|  sci-fi|1442169410|
+------+-------+--------+----------+
only showing top 3 rows

# os.environ_____no_output_____

#Spark SQL and OLAP
Below are the questions I'd like to ask:

1. What are the ratings?
2. What is minimum number of ratings per user and minimum number of ratings per movie?
3. How many movies are rated by only one user?
4. What is the total number of users in the data sets?
5. What is the total number of movies in the data sets?
6. How many movies are rated by users? . List movies not rated yet?
7. List all movie genres
8. Find out the number of movies for each category
9. Calculate the total rating count for every movie
10. Get a count plot for each rating

What are the ratings?_____no_output_____

print('Distinct values of ratings:')
print(sorted(ratings.select('rating').distinct().rdd.map(lambda r: r[0]).collect()))Distinct values of ratings:
[0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]

What is minimum number of ratings per user and minimum number of ratings per movie?_____no_output_____

tmp1 = ratings.groupBy("userID").count().toPandas()['count'].min()
tmp2 = ratings.groupBy("movieId").count().toPandas()['count'].min()
print('For the users that rated movies and the movies that were rated:')
print('Minimum number of ratings per user is {}'.format(tmp1))
print('Minimum number of ratings per movie is {}'.format(tmp2))For the users that rated movies and the movies that were rated:
Minimum number of ratings per user is 1
Minimum number of ratings per movie is 1

How many movies are rated by only one user?_____no_output_____

tmp1 = sum(ratings.groupBy("movieId").count().toPandas()['count'] == 1)
tmp2 = ratings.select('movieId').distinct().count()
print('{} out of {} movies are rated by only one user'.format(tmp1, tmp2))10155 out of 53889 movies are rated by only one user

What is the total number of users in the data sets?_____no_output_____

tmp = ratings.select('userID').distinct().count()
print('We have a total of {} distinct users in the data sets'.format(tmp))We have a total of 283228 distinct users in the data sets

What is the total number of movies in the data sets?_____no_output_____

tmp = movies.select('movieID').distinct().count()
print('We have a total of {} distinct movies in the data sets'.format(tmp))We have a total of 58098 distinct movies in the data sets

How many movies are rated by users? List movies not rated yet?_____no_output_____

tmp1 = movies.select('movieID').distinct().count()
tmp2 = ratings.select('movieID').distinct().count()
print('We have a total of {} distinct movies that are rated by users in ratings table'.format(tmp2))
print('We have {} movies that are not rated yet'.format(tmp1-tmp2))We have a total of 53889 distinct movies that are rated by users in ratings table
We have 4209 movies that are not rated yet
# create a temp SQL table view for easier query
movies.createOrReplaceTempView("movies")
ratings.createOrReplaceTempView("ratings")
print('List movies that are not rated yet: ')
# SQL query (NOTE: WHERE ... NOT IN ... == ... LEFT JOIN ... WHERE ... IS NULL)
# Approach 1
spark.sql(
    "SELECT movieId, title "
    "FROM movies "
    "WHERE movieId NOT IN (SELECT distinct(movieId) FROM ratings)"
).show(10)
# Approach 2
# spark.sql(
#     "SELECT m.movieId, m.title "
#     "FROM movies m LEFT JOIN ratings r ON m.movieId=r.movieId "
#     "WHERE r.movieId IS NULL"
# ).show(10)List movies that are not rated yet:
+-------+--------------------+
|movieId|               title|
+-------+--------------------+
|  25817|Break of Hearts (...|
|  26361|Baby Blue Marine ...|
|  27153|Can't Be Heaven (...|
|  27433|        Bark! (2002)|
|  31945|Always a Bridesma...|
|  52696|Thousand and One ...|
|  58209|Alex in Wonder (S...|
|  60234|   Shock, The (1923)|
|  69565|Bling: A Planet R...|
|  69834|       Agency (1980)|
+-------+--------------------+
only showing top 10 rows

Find out the number of movies for each category_____no_output_____List all movie genres_____no_output_____

# define a udf for splitting the genres string
splitter = UserDefinedFunction(lambda x: x.split('|'), ArrayType(StringType()))
# query
print('All distinct genres: ')
movies.select(explode(splitter("genres")).alias("genres")).distinct().show()All distinct genres:
+------------------+
|            genres|
+------------------+
|             Crime|
|           Romance|
|          Thriller|
|         Adventure|
|             Drama|
|               War|
|       Documentary|
|           Fantasy|
|           Mystery|
|           Musical|
|         Animation|
|         Film-Noir|
|(no genres listed)|
|              IMAX|
|            Horror|
|           Western|
|            Comedy|
|          Children|
|            Action|
|            Sci-Fi|
+------------------+

print('Counts of movies per genre')
movies.select('movieID', explode(splitter("genres")).alias("genres")) \
    .groupby('genres') \
    .count() \
    .sort(desc('count')) \
    .show()Counts of movies per genre
+------------------+-----+
|            genres|count|
+------------------+-----+
|             Drama|24144|
|            Comedy|15956|
|          Thriller| 8216|
|           Romance| 7412|
|            Action| 7130|
|            Horror| 5555|
|       Documentary| 5118|
|             Crime| 5105|
|(no genres listed)| 4266|
|         Adventure| 4067|
|            Sci-Fi| 3444|
|           Mystery| 2773|
|          Children| 2749|
|         Animation| 2663|
|           Fantasy| 2637|
|               War| 1820|
|           Western| 1378|
|           Musical| 1113|
|         Film-Noir|  364|
|              IMAX|  197|
+------------------+-----+

#Spark ALS based approach for training model
1. Reload data
2. Split data into train, validation, test
3. ALS model selection and evaluation
4. Model testing

###Reload data
We will use an RDD-based API from pyspark.mllib to predict the ratings, so let's reload "ratings.csv" using sc.textFile and then convert it to the form of (user, item, rating) tuples._____no_output_____

# load data
movie_rating = sc.textFile(os.path.join(data_path, 'ratings.csv'))
# preprocess data -- only need ["userId", "movieId", "rating"]
header = movie_rating.take(1)[0]
rating_data = movie_rating \
    .filter(lambda line: line!=header) \
    .map(lambda line: line.split(",")) \
    .map(lambda tokens: (int(tokens[0]), int(tokens[1]), float(tokens[2]))) \
    .cache()
# check three rows
rating_data.take(3)_____no_output_____

###Split data
Now we split the data into training/validation/testing sets using a 6/2/2 ratio._____no_output_____

train, validation, test = rating_data.randomSplit([6, 2, 2], seed=99)
# cache data
train.cache()
validation.cache()
test.cache()_____no_output_____

###ALS model selection and evaluation
With the ALS model, we can use a grid search to find the optimal hyperparameters._____no_output_____

def train_ALS(train_data, validation_data, num_iters, reg_param, ranks):
    """
    Grid Search Function to select the best model based on RMSE of hold-out data
    """
    # initial
    min_error = float('inf')
    best_rank = -1
    best_regularization = 0
    best_model = None
    for rank in ranks:
        for reg in reg_param:
            # train ALS model
            model = ALS.train(
                ratings=train_data,    # (userID, productID, rating) tuple
                iterations=num_iters,
                rank=rank,
                lambda_=reg,           # regularization param
                seed=99)
            # make prediction
            valid_data = validation_data.map(lambda p: (p[0], p[1]))
            predictions = model.predictAll(valid_data).map(lambda r: ((r[0], r[1]), r[2]))
            # get the rating result
            ratesAndPreds = validation_data.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
            # get the RMSE
            MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
            error = math.sqrt(MSE)
            print('{} latent factors and regularization = {}: validation RMSE is {}'.format(rank, reg, error))
            if error < min_error:
                min_error = error
                best_rank = rank
                best_regularization = reg
                best_model = model
    print('\nThe best model has {} latent factors and regularization = {}'.format(best_rank, best_regularization))
    return best_model_____no_output_____# hyper-param config
num_iterations = 10
ranks = [8, 10, 12, 14, 16, 18, 20]
reg_params = [0.001, 0.01, 0.05, 0.1, 0.2]

# grid search and select best model
start_time = time.time()
final_model = train_ALS(train, validation, num_iterations, reg_params, ranks)

print ('Total Runtime: {:.2f} seconds'.format(time.time() - start_time))8 latent factors and regularization = 0.001: validation RMSE is 0.8752704094438974
8 latent factors and regularization = 0.01: validation RMSE is 0.8473267660310762
8 latent factors and regularization = 0.05: validation RMSE is 0.8166845218293678
8 latent factors and regularization = 0.1: validation RMSE is 0.818750362115683
8 latent factors and regularization = 0.2: validation RMSE is 0.8651697275197061
10 latent factors and regularization = 0.001: validation RMSE is 0.8895896379658644
10 latent factors and regularization = 0.01: validation RMSE is 0.8590780515288963
10 latent factors and regularization = 0.05: validation RMSE is 0.8201984896655033
10 latent factors and regularization = 0.1: validation RMSE is 0.8208033282670546
10 latent factors and regularization = 0.2: validation RMSE is 0.8658569799543506
12 latent factors and regularization = 0.001: validation RMSE is 0.8999339886056772
12 latent factors and regularization = 0.01: validation RMSE is 0.8605363673121076
12 latent factors and regularization = 0.05: validation RMSE is 0.8169870824986272
12 latent factors and regularization = 0.1: validation RMSE is 0.8190454116487395
12 latent factors and regularization = 0.2: validation RMSE is 0.8658247764490665
14 latent factors and regularization = 0.001: validation RMSE is 0.9084318446336416
14 latent factors and regularization = 0.01: validation RMSE is 0.8652252669571182
14 latent factors and regularization = 0.05: validation RMSE is 0.8165396108363906
14 latent factors and regularization = 0.1: validation RMSE is 0.8184916913274806
14 latent factors and regularization = 0.2: validation RMSE is 0.8659800607853257
16 latent factors and regularization = 0.001: validation RMSE is 0.9155402920482105
16 latent factors and regularization = 0.01: validation RMSE is 0.8724693720700414
16 latent factors and regularization = 0.05: validation RMSE is 0.8165319232400624
16 latent factors and regularization = 0.1: validation RMSE is 0.8200536371745892
16 latent factors and regularization = 0.2: validation RMSE is 0.8667029996038351
18 latent factors and regularization = 0.001: validation RMSE is 0.9263435281671599
18 latent factors and regularization = 0.01: validation RMSE is 0.878003179004673
18 latent factors and regularization = 0.05: validation RMSE is 0.815857962685399
18 latent factors and regularization = 0.1: validation RMSE is 0.8170500508254699
18 latent factors and regularization = 0.2: validation RMSE is 0.8658962501616806
20 latent factors and regularization = 0.001: validation RMSE is 0.9355050489640889
20 latent factors and regularization = 0.01: validation RMSE is 0.8817917263305414
20 latent factors and regularization = 0.05: validation RMSE is 0.8153633788768441
20 latent factors and regularization = 0.1: validation RMSE is 0.818618146004391
20 latent factors and regularization = 0.2: validation RMSE is 0.8664575942478149

The best model has 20 latent factors and regularization = 0.05
Total Runtime: 11163.53 seconds

###ALS model learning curve
As we increase number of iterations in training ALS, we can see how RMSE changes and whether or not model is overfitted._____no_output_____

def plot_learning_curve(arr_iters, train_data, validation_data, reg, rank):
    """
    Plot function to show learning curve of ALS
    """
    errors = []
    for num_iters in arr_iters:
        # train ALS model
        model = ALS.train(
            ratings=train_data,    # (userID, productID, rating) tuple
            iterations=num_iters,
            rank=rank,
            lambda_=reg,           # regularization param
            seed=99)
        # make prediction
        valid_data = validation_data.map(lambda p: (p[0], p[1]))
        predictions = model.predictAll(valid_data).map(lambda r: ((r[0], r[1]), r[2]))
        # get the rating result
        ratesAndPreds = validation_data.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
        # get the RMSE
        MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
        error = math.sqrt(MSE)
        # add to errors
        errors.append(error)

    # plot
    plt.figure(figsize=(12, 6))
    plt.plot(arr_iters, errors)
    plt.xlabel('number of iterations')
    plt.ylabel('RMSE')
    plt.title('ALS Learning Curve')
    plt.grid(True)
    plt.show()_____no_output_____# create an array of num_iters
iter_array = list(range(1, 11))
# create learning curve plot
plot_learning_curve(iter_array, train, validation, 0.05, 20)_____no_output_____

After 3 iterations, alternating gradient descend starts to converge at an error around 0.8

### Model testing
And finally, make a prediction and check the testing error using out-of-sample data_____no_output_____

# make prediction using test data
test_data = test.map(lambda p: (p[0], p[1]))
predictions = final_model.predictAll(test_data).map(lambda r: ((r[0], r[1]), r[2]))
# get the rating result
ratesAndPreds = test.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
# get the RMSE
MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
error = math.sqrt(MSE)
print('The out-of-sample RMSE of rating predictions is', round(error, 4))The out-of-sample RMSE of rating predictions is 0.8154

###Make movie recommendation to myself
We need to define a function that takes new user's movie rating and output top 10 recommendations_____no_output_____

def get_movieId(df_movies, fav_movie_list):
    """
    return all movieId(s) of user's favorite movies

    Parameters
    ----------
    df_movies: spark Dataframe, movies data

    fav_movie_list: list, user's list of favorite movies

    Return
    ------
    movieId_list: list of movieId(s)
    """
    movieId_list = []
    for movie in fav_movie_list:
        movieIds = df_movies \
            .filter(movies.title.like('%{}%'.format(movie))) \
            .select('movieId') \
            .rdd \
            .map(lambda r: r[0]) \
            .collect()
        movieId_list.extend(movieIds)
    return list(set(movieId_list))

def add_new_user_to_data(train_data, movieId_list, spark_context):
    """
    add new rows with new user, user's movie and ratings to
    existing train data

    Parameters
    ----------
    train_data: spark RDD, ratings data

    movieId_list: list, list of movieId(s)

    spark_context: Spark Context object

    Return
    ------
    new train data with the new user's rows
    """
    # get new user id
    new_id = train_data.map(lambda r: r[0]).max() + 1
    # get max rating
    max_rating = train_data.map(lambda r: r[2]).max()
    # create new user rdd
    user_rows = [(new_id, movieId, max_rating) for movieId in movieId_list]
    new_rdd = spark_context.parallelize(user_rows)
    # return new train data
    return train_data.union(new_rdd)

def get_inference_data(train_data, df_movies, movieId_list):
    """
    return a rdd with the userid and all movies (except ones in movieId_list)

    Parameters
    ----------
    train_data: spark RDD, ratings data

    df_movies: spark Dataframe, movies data

    movieId_list: list, list of movieId(s)

    Return
    ------
    inference data: Spark RDD
    """
    # get new user id
    new_id = train_data.map(lambda r: r[0]).max() + 1
    # return inference rdd
    return df_movies.rdd \
        .map(lambda r: r[0]) \
        .distinct() \
        .filter(lambda x: x not in movieId_list) \
        .map(lambda x: (new_id, x))

def make_recommendation(best_model_params, ratings_data, df_movies,
                        fav_movie_list, n_recommendations, spark_context):
    """
    return top n movie recommendation based on user's input list of favorite movies

    Parameters
    ----------
    best_model_params: dict, {'iterations': iter, 'rank': rank, 'lambda_': reg}

    ratings_data: spark RDD, ratings data

    df_movies: spark Dataframe, movies data

    fav_movie_list: list, user's list of favorite movies

    n_recommendations: int, top n recommendations

    spark_context: Spark Context object

    Return
    ------
    list of top n movie recommendations
    """
    # modify train data by adding new user's rows
    movieId_list = get_movieId(df_movies, fav_movie_list)
    train_data = add_new_user_to_data(ratings_data, movieId_list, spark_context)

    # train best ALS
    model = ALS.train(
        ratings=train_data,
        iterations=best_model_params.get('iterations', None),
        rank=best_model_params.get('rank', None),
        lambda_=best_model_params.get('lambda_', None),
        seed=99)

    # get inference rdd
    inference_rdd = get_inference_data(ratings_data, df_movies, movieId_list)

    # inference
    predictions = model.predictAll(inference_rdd).map(lambda r: (r[1], r[2]))

    # get top n movieId
    topn_rows = predictions.sortBy(lambda r: r[1], ascending=False).take(n_recommendations)
    topn_ids = [r[0] for r in topn_rows]

    # return movie titles
    return df_movies.filter(movies.movieId.isin(topn_ids)) \
                    .select('title') \
                    .rdd \
                    .map(lambda r: r[0]) \
                    .collect()_____no_output_____

Let's pretend I am a new user in this recommender system. I will input a handful of my all-time favorite movies into the system. And then the system should output top N movie recommendations for me to watch_____no_output_____

# my favorite movies
my_favorite_movies = ['Iron Man']

# get recommends
recommends = make_recommendation(
    best_model_params={'iterations': 10, 'rank': 20, 'lambda_': 0.05},
    ratings_data=rating_data,
    df_movies=movies,
    fav_movie_list=my_favorite_movies,
    n_recommendations=10,
    spark_context=sc)

print('Recommendations for {}:'.format(my_favorite_movies[0]))
for i, title in enumerate(recommends):
    print('{0}: {1}'.format(i+1, title))Recommendations for Iron Man:
1: Celsius 41.11: The Temperature at Which the Brain... Begins to Die (2004)
2: Pearl Jam: Immagine in Cornice - Live in Italy 2006 (2007)
3: O Pátio das Cantigas (1942)
4: Melhores do Mundo - Hermanoteu na Terra de Godah (2009)
5: The Flying Dagger (1969)
6: Liar Game: The Final Stage (2010)
7: Kizumonogatari Part 1: Tekketsu (2016)
8: Countdown (2004)
9: Heroes Above All (2017)
10: A Question of Faith (2017)

This list of movie recommendations look completely different than the list from my previous KNN model recommender. Not only it recommends movies outside of years between 2007 and 2009 periods, but also recommends movies that were less known. So this can offer users some elements of suprise so that users won't get bored by getting the same popular movies all the time.

So this list of recommendations can be blended into the previous list of recommendations from KNN model recommender_____no_output_____

_____no_output_____