![PMML in Python
JPMML package is created and maintained by Villu Rasmussen.
From https://stackoverflow.com/questions/33221331/export-python-scikit-learn-models-into-pmml
pip install git+https://github.com/jpmml/sklearn2pmml.git
Example of how export a classifier tree to PMML. First grow the tree:
# example tree & viz from http://scikit-learn.org/stable/modules/tree.html
from sklearn import datasets, tree
iris = datasets.load_iris()
clf = tree.DecisionTreeClassifier()
clf = clf.fit(iris.data, iris.target)
SkLearn2PMML conversion takes 2 arguments: an estimator (our clf) and a mapper for preprocessing.
Our mapper is pretty basic, since no transformations.
from sklearn_pandas import DataFrameMapper
default_mapper = DataFrameMapper([(i, None) for i in iris.feature_names + ['Species']])
from sklearn2pmml import sklearn2pmml
sklearn2pmml(estimator=clf, mapper=default_mapper, pmml=“C:/workspace/IrisClassificationTree.pmml")](https://image.slidesharecdn.com/ibmmeetupwatsonstudionov1-181105232632/85/IBM-Meetup-on-November-1-2018-Machine-Learning-made-easy-with-Watson-Studio-27-320.jpg)
IBM Meetup on November 1, 2018: Machine Learning made easy with Watson Studio
- 1. Machine Learning made easy with Watson Studio Hosted by IBM Chicago Nov 1st, 2018
- 2. Presenters 22018 IBM Corporation Ahsan Rehman Data Scientist & Developer Advocate IBM Analytics Masters of Science in Analytics rehmana@us.ibm.com IBM Chicago Svetlana Levitan Developer Advocate and PMML Release Manager Center for Open Data and AI Technologies (CODAIT) IBM Digital Business Group slevitan@us.ibm.com Adam Massachi Offering Manager for Watson Studio IBM Analytics Degrees in Math and Philosophy adam.massachi@ibm.com
- 3. Special Speaker 32018 IBM Corporation IBM Chicago Jing Shyr Chief Statistician, IBM Fellow - Analytics Subject matter expert for statistical analysis and data mining, working knowledge of applying predictive analytics to business problems
- 4. Machine Learning Deep Learning and AI are everywhere 42018 IBM Corporation facial recognition unlocks your phone fraud detection protects your credit recommendations help you shop faster what other users like you buy what gets bought together speech recognition lets you go hands-free business processes automate human tasks e.g. train based on human decisions autonomous vehicles detect pedestrians machine vision detects cancer early spam detection unclogs your Inbox The future is now
- 5. How does machine learning work? A machine learning model is trained to recognize patterns in historical data data data data data 1 data data The model is then used with new data and asked to predict or classify it. 2 If patterns in the new data match the training data then the model makes accurate predictions 3 prediction or classification ??? Machine learning requires TONS OF DATA 5IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation need good data
- 6. 6IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation Enterprises generate TONS OF DATA Data Data that requires governance Which must be cleaned and shaped for training …then models must be designed …that must be hosted and monitored …and trained on high performance compute SPSS To select an optimal model... need good data
- 7. Tools & Infrastructure • Need an environment that enables quick experiments and real outcomes • Discrete tools present barriers to productivity Data Governance • If the data isn’t secure, self-service isn’t a reality • Challenge understanding data lineage and getting to a system of truth Skills • Data Science skills are in low supply and high demand • Nurturing new data professionals is challenging Data Access • Data resides in silos & difficult to access • Unstructured and external data wasn’t considered 7 2018 IBM Corporation Why are enterprises struggling to capture the value of AI?
- 8. Watson Studio: Accelerating value from AI for EnterprisesWatson Studio with Knowledge Catalog accelerates the machine and deep learning workflows required to infuse AI into your business to drive innovation. It provides a suite of tools for data scientists/engineers, app developers, domain experts to collaboratively connect, work with and analyze data, and build, train, and deploy models. AI Requires Teamwork 82018 IBM Corporation • AI is not magic • AI is algorithms + data + team
- 9. Her Job: Builds AI application that meet the requirements of the business. What she does: • Starts PoCs which includes gathering content, dialog building and model training • Focus is on app building for the team or company to use. Will handle ML Ops as needed Sometimes known as: Front-end, back-end, full stack, mobile or low-code developer Tanya Domain Expert Her Job: To transfer knowledge to Watson for a successful user experience. What she does: • Range of domain knowledge and uses that to teach Watson and develop a custom models • As Tanya gains more experience she optimizes her knowledge to teach Watson to design better end-user experiences. Sometimes known as: Subject matter expert, content strategist. His Job: Transform data into knowledge for solving business problems. What he does: •Runs experiments to build custom models that solve business problems. •Use techniques such as Machine Learning or Deep Learning and works with Tanya to validate success of trained models. Data Science and AI Teams Need to be enabled for Productivity and Collaboration Sometimes known as: ML/DL engineer, Modeler, Data Miner Ed Data Engineer His Job: Architects how data is organized and ensures operability What he does: • Builds data infrastructure and ETL pipelines. Works with Spark, Hadoop, and HDFS. • Works with data scientist to transform research models into production quality systems. Sometimes known as: Data infrastructure engineer Mike Data Scientist Deb The Developer 9
- 10. Watson Studio Supporting the end-to-end ML workflow Prepare Data for Analysis Analyze Data Build and Train ML/DL Models Deploy Models on Public or Private Cloud Monitor, Analyze and Manage Model Deployments Search and Find Relevant Data Connect & Access Data on Cloud(s) or On-Premise Catalog Data
- 11. Watson Studio Tools for supporting the end-to-end AI workflow Model Lifecycle Management Machine Learning Runtimes Deep Learning Runtimes Authoring Tools Cloud Infrastructure as a Service • Most popular open source frameworks • IBM best-in-class frameworks • Create, collaborate, deploy, and monitor • Best of breed open source & IBM tools • Code (R, Python or Scala) and no-code/visual modeling tools • Fully managed service • Container-based resource management • Elastic pay as you go cpu/gpu power
- 12. Demo 12IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation Try it at https://www.ibm.com/cloud/watson-studio
- 13. 13 Get Data Prepare Data Build Models Deploy Models Monitor and Update End To End Machine Learning Workflow
- 14. Model Deployment Challenges 14© 2018 IBM Corporation
- 15. DMG to the rescue! 15 Data Mining Group dmg.org Founded in late 1990’s by Professor Robert Grossman
- 16. What is PMML? Predictive Model Markup Language • An Open Standard for XML Representation • Developed by DMG • Over 30 vendors and organizations • PMML 4.3, 4.4 Release manager: Svetlana Levitan dmg.org/pmml
- 17. Main Components of PMML Header Data Dictionary Transformation Dictionary Model(s)
- 18. PMML under the hood 18© 2018 IBM Corporation • Application name and version • Timestamp and copyrightHeader • Field names and labels • Data type and measurement level • Valid and missing values Data Dictionary • Define Function • Derived Fields Transformation Dictionary • Mining Schema • Specific model contents Model(s)
- 19. Transformations in PMML • NormContinuous: piece-wise linear transform • NormDiscrete: map a categorical field to a set of dummy fields • Discretize: binning • MapValues: map one or more categorical fields into another categorical one • Functions: built-in and user-defined • Aggregation
- 20. PMML Models o Association Rules Model o Clustering Model o General Regression o Naïve Bayes o Nearest Neighbor Model o Neural Network o Regression o Tree Model o Mining Model: composition or ensemble (or both) of models o Baseline Model o Bayesian Network o Gaussian Process o Ruleset o Scorecard o Sequence Model o Support Vector Machine o Time Series
- 21. An example PMML – Data Dictionary, Transformations 21
- 22. Example PMML – Neural Network MiningSchema and inputs 22 Predictors
- 23. Example PMML - Neural Network hidden layer and outputs 23 Hidden layer neuron Output Layer Neurons Connecting target to the neurons
- 24. <Node id=“0"> <True/> <Node id=“1" score="Iris-setosa" recordCount="50.0"> <SimplePredicate field="petal_length" operator="lessOrEqual“ value=“2.6"/> <ScoreDistribution value="Iris-setosa" recordCount="50.0"/> <ScoreDistribution value="Iris-versicolor" recordCount="0.0"/> <ScoreDistribution value="Iris-virginica" recordCount="0.0"/> </Node> <Node id=“2"> <SimplePredicate field="petal_length" operator="greaterThan“ value=“2.6"/> Example PMML for a Tree Model
- 25. PMML Powered From http://dmg.org/pmml/prod ucts.html: Alpine Data Angoss BigML Equifax Experian FICO Fiserv Frontline Solvers GDS Link IBM (Includes SPSS) JPMML KNIME KXEN Liga Data Microsoft MicroStrategy NG Data Open Data Opera Pega Pervasive Data Rush Predixion Software Rapid I R Salford Systems (Minitab) SAND SAS Software AG (incl. Zementis) Spark Sparkling Logic Teradata TIBCO WEKA
- 26. Benefits of PMML Allows seamless deployment and model exchange Transparency: human and machine- readable Fosters best practices in model building and deployment
- 27. PMML in Python JPMML package is created and maintained by Villu Rasmussen. From https://stackoverflow.com/questions/33221331/export-python-scikit-learn-models-into-pmml pip install git+https://github.com/jpmml/sklearn2pmml.git Example of how export a classifier tree to PMML. First grow the tree: # example tree & viz from http://scikit-learn.org/stable/modules/tree.html from sklearn import datasets, tree iris = datasets.load_iris() clf = tree.DecisionTreeClassifier() clf = clf.fit(iris.data, iris.target) SkLearn2PMML conversion takes 2 arguments: an estimator (our clf) and a mapper for preprocessing. Our mapper is pretty basic, since no transformations. from sklearn_pandas import DataFrameMapper default_mapper = DataFrameMapper([(i, None) for i in iris.feature_names + ['Species']]) from sklearn2pmml import sklearn2pmml sklearn2pmml(estimator=clf, mapper=default_mapper, pmml=“C:/workspace/IrisClassificationTree.pmml")
- 28. PMML in R R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. R package “pmml” https://cran.r-project.org/package=pmml Depends on XML package Supports a number of R models Maintained by Dmitriy Bolotov and Tridivesh Jena from Software AG
- 29. Create PMML in R (using R Studio) >library(XML); >library(pmml); > data(iris); Build and save a linear regression model predicting Sepal length: > irisLR<-lm(Sepal.Length~.,iris) >saveXML( pmml(irisLR), "IrisLR.xml" ) Build and save a decision tree (C&RT) model predicting class: > irisTree <- rpart( Species~., iris ) > saveXML( pmml( irisTree ), "IrisTree.xml" )
- 30. Deploy a model or a pipeline into Watson Machine Learning 30 In Project view “+ New Watson Machine Learning model”, Give a name, select “From File”
- 31. Scoring PMML in Watson Machine Learning 31
- 32. 32 Other model deployment formats PFA from DMG – an emerging format, JSON-based, big potential Pickle in Python – binary serialization of Scikit Learn models MLeap - open format, not a standard, uses protobuf. Works for Spark ML models Open Neural Network Exchange (ONNX) from Microsoft and Facebook • binary (protobuf) format for deep learning models • describes computation graph (including operators) • supported by most deep learning frameworks (TF still in progress) • now adding support for traditional ML Neural Network Exchange Format (NNEF) by Khronos Group • Dual format (weights in binary file) • Allows user-defined functions • Only neural network models Docker containers
- 33. ONNX use pattern ONNX IR Spec .onnxFrontend Models in different frameworks Tools Netron visualizer Net Drawer visualizer Checker Shape Inferencer Graph Optimizer Opset Version Converter Backend Models in different frameworks Training Inference Export Import Run 33
Editor's Notes
- #5: AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning. A convergence of algorithmic advances, data proliferation, and tremendous increases in computing power and storage has propelled AI from hype to reality. Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.
- #17: dmg.org: PMML is the leading standard for statistical and data mining models and supported by over 20 vendors and organizations. With PMML, it is easy to develop a model on one system using one application and deploy the model on another system using another application, simply by transmitting an XML configuration file.
- #18: PMML can describe the entire data processing pipeline, including data preparation, one or several models, post-processing of model predictions.
- #28: Villu creates many Mantis issues for PMML, helps to improve it. Here we build a tree model for classifying the iris flowers based on its dimensions, then export PMML.