Incubating Apache Hivemall

Incubating Apache Hivemall
Research Engineer, Treasure Data
Makoto YUI @myui
@ApacheHivemall
12018/2/15 Plazma OSS day

Hivemall entered Apache Incubator
on Sept 13, 2016
Since then, we invited 3 contributors as new committers (a
committer has been voted as PPMC). Currently, we are working
toward the first Apache release (v0.5.0).
hivemall.incubator.apache.org

What’s new in v0.5.0?
3
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

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

13
Generic Classifier/Regressor
OLD Style New Style from v0.5.0

14
•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

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
> TF/IDF
> TILE
> MAP_URL

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
17

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
18

Map tiling functions

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

21
SELECT count(distinct id) FROM data
More useful functions (Sketch, NLP)
SELECT approx_count_distinct(id) FROM data
select tokenize_ja(“ ",
"normal", null, null, "https://s3.amazonaws.com/td-
hivemall/dist/kuromoji-user-dict-neologd.csv.gz");
[“ ”, "," "," "]

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

RandomForest in Hivemall
Ensemble of Decision Trees

Training of RandomForest
24
Good news: Sparse Vector Input (Libsvm
format) is supported since v0.5.0 in
addition Dense Vector!

Prediction of RandomForest

26
Decision Tree Visualization

27
Decision Tree Visualization

28
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
✓Japanese 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

42
Probabilistic Latent Semantic Analysis - training

43
Probabilistic Latent Semantic Analysis - predict

ü Word2Vec support
ü Multi-class Logistic Regression
ü Field-aware Factorization Machines
ü SLIM recommendation
ü More efficient XGBoost support
ü LightGBM support
ü DecisionTree prediction tracing
ü Gradient Boosting
Future work for v0.5.2 and later
44
PR#91
PR#116
PR#58
PR#111

ü Word2Vec support
ü Multi-class Logistic Regression
ü Field-aware Factorization Machines
ü SLIM recommendation
ü More efficient XGBoost support
ü LightGBM support
ü DecisionTree prediction tracing
ü Gradient Boosting
Future work for v0.5.2 and later
45
PR#91
PR#116
PR#58
PR#111

Conclusion and Takeaway
Hivemall is a multi/cross-platform ML library
providing a collection of machine learning algorithms as Hive UDFs/UDTFs
The first Apache release (v0.5.0) will appear soon!
We welcome your contributions to Apache Hivemall J
HiveQL SparkSQL/Dataframe API Pig Latin

Any feature request or questions?
BTW, we are hiring!

Hivemall Digdag

Machine Learning Workflow using Digdag

Feature Selection – Signal Noise Ratio

Incubating Apache Hivemall

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