Introduction to Apache Hivemall v0.5.0

Introduction to Apache Hivemall v0.5.0:
Machine Learning on Hive/Spark
Makoto YUI @myui
ApacheCon North America 2018
Takashi Yamamuro @maropu
@ApacheHivemall
1). Principal Engineer,
2). Research Engineer,
1

Plan of the talk
1. Introduction to Hivemall
2. Hivemall on Spark
A quick walk-through of feature, usages, what's
new in v0.5.0, and future roadmaps
New top-k join enhancement, and a feature plan
for Supporting spark 2.3 and feature selection
2

We released the first Apache release
v0.5.0 on Mar 3rd, 2018 !
hivemall.incubator.apache.org
We plan to start voting for the 2nd Apache release (v0.5.2) in
the next month (Oct 2018).
3

What’s new in v0.5.0?
Anomaly/Change Point
Detection
Topic Modeling
(Soft Clustering)
Algorithm:
LDA, pLSA
Algorithm:
ChangeFinder, SST
Hivmall on Spark
2.0/2.1/2.1
SparkSQL/Dataframe support,
Top-k data processing
ApacheCon North America 2018 4

What is Apache Hivemall
Scalable machine learning library built
as a collection of Hive UDFs
Multi/Cross
platform VersatileScalableEase-of-use

Hivemall is easy and scalable …
ML made easy for SQL developers
Born to be parallel and scalable
Ease-of-use
Scalable
100+ lines
of code
CREATE TABLE lr_model AS
SELECT
feature, -- reducers perform model averaging in parallel
avg(weight) as weight
FROM (
SELECT logress(features,label,..) as (feature,weight)
FROM train
) t -- map-only task
GROUP BY feature; -- shuffled to reducers
This query automatically runs in parallel on Hadoop

Hivemall is a multi/cross-platform ML library
HiveQL SparkSQL/Dataframe API Pig Latin
Hivemall is Multi/Cross platform ..
Multi/Cross
platform
prediction models built by Hive can be used from Spark, and conversely,
prediction models build by Spark can be used from Hive

Hadoop HDFS
MapReduce
(MRv1)
Hivemall
Apache YARN
Apache Tez
DAG processing
Machine Learning
Query Processing
Parallel Data
Processing Framework
Resource Management
Distributed File System
Cloud Storage
SparkSQL
Apache Spark
MESOS
Hive Pig
MLlib
Hivemall’s Technology Stack
Amazon S3

Hivemall on Apache Hive

Hivemall on Apache Spark Dataframe

Hivemall on SparkSQL

Hivemall on Apache Pig

Online Prediction by Apache Streaming

Versatile
Hivemall is a Versatile library ..
ü Not only for Machine Learning
ü provides a bunch of generic utility functions
Each organization has own sets of
UDFs for data preprocessing
Don’t Repeat Yourself!
Don’t Repeat Yourself!

Hivemall generic functions
Array and Map Bit and compress String and NLP
Brickhouse UDFs are merged in v0.5.2 release.
We welcome contributing your generic UDFs to Hivemall
Geo Spatial
Top-k processing
> BASE91
> UNBASE91
> NORMALIZE_UNICODE
> SPLIT_WORDS
> IS_STOPWORD
> TOKENIZE
> TOKENIZE_JA/CN
> TF/IDF
> SINGULARIZE
> TILE
> MAP_URL
> HAVERSINE_DISTANCE
JSON
> TO_JSON
> FROM_JSON

student class score
1 b 70
2 a 80
3 a 90
4 b 50
5 a 70
6 b 60
Top-k query processing
List top-2 students for each class
SELECT * FROM (
SELECT
*,
rank() over (partition by class order by score desc)
as rank
FROM table
) t
WHERE rank <= 2
RANK over() query does not finishes in 24 hours L
where 20 million MOOCs classes and avg 1,000 students in each classes
16

student class score
1 b 70
2 a 80
3 a 90
4 b 50
5 a 70
6 b 60
Top-k query processing
List top-2 students for each class
SELECT
each_top_k(
2, class, score,
class, student
) as (rank, score, class, student)
FROM (
SELECT * FROM table
DISTRIBUTE BY class SORT BY class
) t
EACH_TOP_K finishes in 2 hours J
17

Map tiling functions

Tile(lat,lon,zoom)
= xtile(lon,zoom) + ytile(lat,zoom) * 2^n
Map tiling functions
Zoom=10
Zoom=15

List of Supported Algorithms
Classification
✓ Perceptron
✓ Passive Aggressive (PA, PA1, PA2)
✓ Confidence Weighted (CW)
✓ Adaptive Regularization of Weight
Vectors (AROW)
✓ Soft Confidence Weighted (SCW)
✓ AdaGrad+RDA
✓ Factorization Machines
✓ RandomForest Classification
Regression
✓Logistic Regression (SGD)
✓AdaGrad (logistic loss)
✓AdaDELTA (logistic loss)
✓PA Regression
✓AROW Regression
✓Factorization Machines
✓RandomForest Regression
SCW is a good first choice
Try RandomForest if SCW does not
work
Logistic regression is good for getting a
probability of a positive class
Factorization Machines is good where
features are sparse and categorical ones

Generic Classifier/Regressor
OLD Style New Style from v0.5.0

•Squared Loss
•Quantile Loss
•Epsilon Insensitive Loss
•Squared Epsilon Insensitive
Loss
•Huber Loss
Generic Classifier/Regressor
Available Loss functions
•HingeLoss
•LogLoss (synonym: logistic)
•SquaredHingeLoss
•ModifiedHuberLoss
• L1
• L2
• ElasticNet
• RDA
Other options
For Binary Classification:
For Regression:
• SGD
• AdaGrad
• AdaDelta
• ADAM
Optimizer
• Iteration support
• mini-batch
• Early stopping
Regularization

RandomForest in Hivemall
Ensemble of Decision Trees

Training of RandomForest
Good news: Sparse Vector Input (Libsvm
format) is supported since v0.5.0 in
addition Dense Vector input.

Prediction of RandomForest

Decision Tree Visualization

SELECT train_xgboost_classifier(features, label) as (model_id, model)
FROM training_data
XGBoost support in Hivemall (beta version)
SELECT rowed, AVG(predicted) as predicted
FROM (
-- predict with each model
SELECT xgboost_predict(rowid, features, model_id, model) AS (rowid, predicted)
-- join each test record with each model
FROM xgboost_models CROSS JOIN test_data_with_id
) t
GROUP BY rowid;

Supported Algorithms for Recommendation
K-Nearest Neighbor
✓ Minhash and b-Bit Minhash
(LSH variant)
✓ Similarity Search on Vector
Space
(Euclid/Cosine/Jaccard/Angular)
Matrix Completion
✓ Matrix Factorization
✓ Factorization Machines
(regression)
each_top_k function of Hivemall is useful for
recommending top-k items

Other Supported Algorithms
Feature Engineering
✓Feature Hashing
✓Feature Scaling
(normalization, z-score)
✓ Feature Binning
✓ TF-IDF vectorizer
✓ Polynomial Expansion
✓ Amplifier
NLP
✓Basic Englist text Tokenizer
✓English/Japanese/Chinese
Tokenizer
Evaluation metrics
✓AUC, nDCG, logloss, precision
recall@K, and etc

Feature Engineering – Feature Hashing

Feature Engineering – Feature Binning
Maps quantitative variables to fixed number of
bins based on quantiles/distribution
Map Ages into 3 bins

Feature Engineering – Feature Binning
33

Evaluation Metrics

Other Supported Features
Anomaly Detection
✓Local Outlier Factor (LoF)
✓ChangeFinder
Clustering / Topic models
✓Online mini-batch LDA
✓Online mini-batch PLSA
Change Point Detection
✓ChangeFinder
✓Singular Spectrum
Transformation

Efficient algorithm for finding change point and outliers from
time-series data
J. Takeuchi and K. Yamanishi, A Unifying Framework for Detecting Outliers and Change Points from Time Series, IEEE transactions on
Knowledge and Data Engineering, pp.482-492, 2006.
Anomaly/Change-point Detection by ChangeFinder

Take this…

…and do this!

Efficient algorithm for finding change point and outliers from
timeseries data
J. Takeuchi and K. Yamanishi, A Unifying Framework for Detecting Outliers and Change Points from Time Series, IEEE transactions on
Knowledge and Data Engineering, pp.482-492, 2006.

• T. Ide and K. Inoue, "Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point
Correlations", Proc. SDM, 2005T.
• T. Ide and K. Tsuda, "Change-point detection using Krylov subspace learning", Proc. SDM, 2007.
Change-point detection by Singular Spectrum Transformation

Online mini-batch LDA

Probabilistic Latent Semantic Analysis - training

Probabilistic Latent Semantic Analysis - predict

ü Spark 2.3 support
ü Merged Brickhouse UDFs
ü Field-aware Factorization Machines
ü SLIM recommendation
What’s new in the coming v0.5.2
Xia Ning and George Karypis, SLIM: Sparse Linear Methods for Top-N Recommender Systems, Proc. ICDM, 2011.
Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin, "Field-aware Factorization Machines for CTR
Prediction", Proc. RecSys. 2016.
State-of-the-art method for CTR prediction, often used algorithm in Kaggle
Very promising algorithm for top-k recommendation
44

ü Word2Vec support
ü Multi-class Logistic Regression
ü More efficient XGBoost support
ü LightGBM support
ü Gradient Boosting
ü Kafka KSQL UDF porting
Future work for v0.6 and later
PR#91
PR#116

,
• .
•
• .. . / /

- : -:
•
• 8 8 .4- .4- 8 . 4-
. 4
• :
• . 8 8. ,
4
• :
• 8 .8 .8

(Copyright©2018 NTT corp. All Rights Reserved.
-: , : 2
• - :1 . -:
• - 31 - 1:- 1 31
• 31 :
$ E F > AA
> E E F $ E 9> $DF= :D AD : $> A
9 A - A A E F $F :$ L 1 A 4 ./ $ : "
9 A - A $ A F $9D M5E F ""$9D "
F 2D = )

• ( 0 2 244 0 24 40
10 0 1 00 0 0
• )0 10 0 2
• ) E F F 1C :
• C .F5 C E * C E
• EC : 0 / C :
• : 2 C
• I
3 *0.0
FCE C C E 5 F EE ( 5 E H H

•
• / 1 5 5 *55 -51 13 5 / 1 :
5 5 53 5 A 5 3 5 A 2 .D ,

• .
• +55- /25 -2- + / + + +2-
• 2- / / /- -+ - / 5+
• .
• 2 / -2+ / / 5+
• - +25 - -+ / / +5 5
• /25 - 2- / 5- 2+ - / 5+
.

•
• / . / ++ 3 : / . /3 /
. 5 5 5 . 3 / /3 .

*Copyright©2018 NTT corp. All Rights Reserved.
• ,7 299 A 3 7 A 7 2
,-1 ).. ( 2
• - 1 :3 4 13 1 23
A A 2 A
5 1: 3 $$5 0 1 $/ /1 3$ 1 0/ 3 /::
12 1 0/ 3 /::
/1 /53 , -3
: / 53 $ /
/ 53 $ 3 /:: / * ... D 23 3 23 1 3 /

• . 3 3 3
• 4 . 3
• 1 24 1
• 4 43
2 1

• 6 . .21 6 6
• ## :2. 6 .- # 26# 2 : 5 :#-
:. :# .0 .::2 6 5 $$ /2-/
0 6 6 .1.1 1 6 6 6
0. ## :2. 6 .- # 26# 2 : 5 :#- :. :#
.0 .::2 6# $$ 26
0. ## :2. 6 .- # 26# 2 : 5 :#- :. :#
.0 .::2 6# $$ .:

2 . .
// Downloads Spark v2.3 and launches a spark-shell with Hivemall
$ 5 D C= D: >> < CD
/C E 0 E 2C C E: 5 7 > 5D C EE 7 >
D6 > . > EC 0 - D C=$C 7$ C E > 5D "$> 7 )$EC $5 "
D6 > . EC 0 $ C E 6:
C E
> 5 > 7 F5> F>> 5> - ECF "
EFC D 6E C F>> 5> - ECF "

3 . -
= . . .=> (
. 8: , ) > . , > . - : .> = . "
: B .> "
. 5> $ . "

- -.
= D ( L CF, = L D = E C O CF D
= D ( L
D EG> D, D
P 5 LM " ) O CABL )5 O CABL
P .
P 5 L CF:DGA A LM " D D
P )5 LM " O CABL
P . CF D
P
P 9 LM
L C ACF

. .-
E 6 6 6EF )
6 : * F EF : E F"DB = "# : 6F D E #
B FBD" : 6F D E #
6
B D = F=B E
: >B= " B : :" : 6F D # *** B " : 6F D # 0. . #
DB , " DB = #
6 "E= B= "E " = F $ 6 ###

. - 4
N >G>) JABG C MB>OB6M B G> B:B FBR T JABG:> GB
N >G>) AC MB>OB6M B G> B:B FBR T:BNO:> GBT
N >G>) >NOB
NLG
S . .,: MJRFA NFD JFA >GPB " RBFDEO * MBAF OBA
S 6 :M>FI:> GB O
S . : 6 :. 61 JABG:> GB
S 6 O CB>OPMB ( CB>OPMB
S 6 = MJRFA
NOMF >MDFI

• - . .
- . :
• , :
• .2 6 2 2 ) /- 2> 6 :=:C
-: : -
2 2+ :>C 6=2
C
6 ) C :> > 23 6 C 6
2 6) C :> > 23 6 C 6
) 3 6 > 23 6 2 6
2 2+ 62 C :C $" ".2 (" 6"), $" 6 ".2 (" "))
, = C6 C 6>C :6 62

•
• 3 A J KN I=D KA J$ K = E K=J K > I = I
-
J D , JK=)
D K C > + D=>K > B A IA K >$ I R )) AD$ R"
J=D= K D=>K > I "$ D=>K > R" IA K > PR"" J J I=R"
NAK D E
I C $ I C " =I IKAKA .P I R" I<=I.P J I= <=J """
N =I= I C + K "
E K=J K :P JA IC ADD -6 J DP

• : 3 : : .
• A J KN I=D KA J$ K = E K=J K 4 > I = I
>3 :1 : 3:> : . 13>> :
J D , JK=)
D K C > + D=>K > B A IA K >$ I R )) AD$ R"
J=D= K D=>K > I "$ D=>K > R" IA K > PR"" J J I=R"
NAK D E
I C $ I C " =I IKAKA .P I R" I<=I.P J I= <=J """
N =I= I C + K 4"
E K=J K 4 :P JA IC ADD -6 J DP
:> 3
- 2 : 3 1
3 2

• ::-
• .= AD= : A = A = > A A=> 5= 6 = >
- : :- -
: ) > A
: A=> +5 ( : 5A+5 :
: A A=> 6 A+5 : 5A+5 6 = >H ((( 6 A+5 6 = >H
: 5A+5 H 6 A+5 H = H
- >: A 55 A 5 - , ::

• : :
-

• : :
-
K-length
priority queue
Computes top-K rows
by using a priority queue

• : :
-
K-length
priority queue
Computes top-K rows
by using a priority queue
Only joins top-K rows

• - -
• ) 9 6 96 6 9 , /9
, 6 6 , ,
- :
) 9 ( 66 9 9

• - -
• 0 / 7 , /
7/ / 7 7 /
- :
7/

• - - *: -::
• H: K> :DD > > :K>J 2: : => E : L DK
H J :D HD: # : = EH D>J > > LK>J K
:- - :
J :D:. K H / > HD:
-- J :D D: --
L D>=1:J 2 7 H (# 8 LH ( # 8 LH )
, 0 : > :J H: K K LH ( # )
, :D7: D> : 8 LH ( # ((
0 : > :J H: K K LH ) # )
:D7: D> : 8 LH ) # )+
- -* -* : * - - *

• - 3:
3 -:1 : 1 1
! :1 : : : : : -

• : -: : :
: =: -:
• -7 1 73 1: 8 1 1-7 73
- 7 73 - - 1 8 1 1- 1 :1 1 87:
+ : : -:
Data Extraction (e.g., by SQL) Feature Selection (e.g., by scikit-learn)
Selected Features
Arun Kumar, Jeffrey Naughton, Jignesh M. Patel, and Xiaojin Zhu, To Join or Not to Join?: Thinking Twice
about Joins before Feature Selection, Proceedings of SIGMOD, 2016.

• : -: : :
: =: -:
• -7 1 4 47 1: 8 4 1 1-747
-4747 - - 1 8 1 1- 1 :1 1 487:
+ : : -:
Arun Kumar, Jeffrey Naughton, Jignesh M. Patel, and Xiaojin Zhu, To Join or Not to Join?: Thinking Twice
about Joins before Feature Selection, Proceedings of SIGMOD, 2016.
Data Extraction + Feature Selection
Join Pruning by Data Statistics

Conclusion and Takeaway
Hivemall is a multi/cross-platform ML library
providing a collection of machine learning algorithms as Hive UDFs/UDTFs
The 2nd Apache release (v0.5.2) will appear soon!
We welcome your contributions to Apache Hivemall J
HiveQL SparkSQL/Dataframe API Pig Latin

Thank you! Questions?

Introduction to Apache Hivemall v0.5.0

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à Introduction to Apache Hivemall v0.5.0

Similaire à Introduction to Apache Hivemall v0.5.0 (20)

Plus de Makoto Yui

Plus de Makoto Yui (20)

Dernier

Dernier (20)

Introduction to Apache Hivemall v0.5.0