Unified Approach to Interpret Machine Learning Model: SHAP + LIME
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For companies that solve real-world problems and generate revenue from the data science products, being able to understand why a model makes a certain prediction can be as crucial as achieving high prediction accuracy in many applications. However, as data scientists pursuing higher accuracy by implementing complex algorithms such as ensemble or deep learning models, the algorithm itself becomes a blackbox and it creates the trade-off between accuracy and interpretability of a model’s output. To address this problem, a unified framework SHAP (SHapley Additive exPlanations) was developed to help users interpret the predictions of complex models. In this session, we will talk about how to apply SHAP to various modeling approaches (GLM, XGBoost, CNN) to explain how each feature contributes and extract intuitive insights from a particular prediction. This talk is intended to introduce the concept of general purpose model explainer, as well as help practitioners understand SHAP and its applications.
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Interpretable Machine Learning
Interpretable Machine Learninginovex GmbH Interpretierbarkeit von ML-Modellen hat die Zielsetzung, die Ursachen einer Prognose offenzulegen und eine daraus abgeleitete Entscheidung für einen Menschen nachvollziehbar zu erklären. Durch die Nachvollziehbarkeit von Prognosen lässt sich beispielsweise sicherstellen, dass deren Herleitung konsistent zum Domänenwissen eines Experten ist. Auch ein unfairer Bias lässt sich durch die Erklärung aussagekräftiger Beispiele identifizieren.
Prognosemodelle lassen sich grob in intrinsisch interpretierbare Modelle und nicht-interpretierbare (auch Blackbox-) Modelle unterscheiden. Intrinsisch interpretierbare Modelle sind dafür bekannt, dass sie für einen Menschen leicht nachvollziehbar sind. Ein typisches Beispiel für ein solches Modell ist der Entscheidungsbaum, dessen regelbasierter Entscheidungsprozess intuitiv und leicht zugänglich ist. Im Gegensatz dazu gelten Neuronale Netze als Blackbox-Modelle, deren Prognosen durch die komplexe Netzstruktur schwer nachvollziehbar sind.
In diesem Talk erläuterte Marcel Spitzer das Konzept von Interpretierbarkeit im Kontext von Machine Learning und stellte gängige Verfahren zur Interpretation von Modellen vor. Besonderen Fokus legte er dabei auf modellunabhängige Verfahren, die sich auch auf prognosestarke Blackbox-Modelle anwenden lassen.
Event: M3 Minds Mastering Machines
Speaker: Marcel Spitzer
Blog-Artikel: https://www.inovex.de/blog/machine-learning-interpretability/
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
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Explainable AI
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The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
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Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
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Description:
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Speaker's Bio:
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Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringDatabricks Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixDatabricks Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsDatabricks In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkDatabricks We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkDatabricks In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeDatabricks At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
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Unified Approach to Interpret Machine Learning Model: SHAP + LIME
- 1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics
- 2. Layla Yang, Databricks Unified approach to interpret machine learning model: SHAP + LIME #UnifiedDataAnalytics #SparkAISummit
- 3. Overview • What is Machine Learning Interpreter? • Why is it important? • Why is interpreting ML model difficult? • Different methodologies of model explainer • SHAP: SHapley Additive exPlanations • LIME: Local Interpretable Model-Agnostic Explanations • Application: real world use case 3#UnifiedDataAnalytics #SparkAISummit
- 4. ML interpreter: What is it? Black box 4#UnifiedDataAnalytics #SparkAISummit Accuracy and Interpretability Trade-off Input Data Output
- 5. ML interpreter: What is it? “ Interpretability is the degree to which a human can understand the cause of a decision.” -- Miller, Tim. “Explanation in artificial intelligence: Insights from the social sciences.” 5#UnifiedDataAnalytics #SparkAISummit
- 6. Why is it important? Commercial Drive: ● Many enterprises rely on machine learning models to make important decision: ○ Loan, credit card application ○ To buy / sell commodities, stocks or advertisements ○ Diagnose cancer or benign cells 6#UnifiedDataAnalytics #SparkAISummit ● Trust ○ It is easier for humans to trust a business that explains its decisions ● Legal Regulation ○ GDPR: the customer has right to obtain explanation
- 7. Why is it important? Technical Drive: Can you trust your model based on accuracy? 7#UnifiedDataAnalytics #SparkAISummit ● Understand edge cases and circumstances of failure ● Knowing the ‘why’ can help you learn more about the problem and the data ● Improve the model
- 8. Why is it important? Social and ethical aspect: ● Interpretability - a useful debugging tool for Detecting Bias ○ Sean Owen: What do Developer Salaries Tell us about the Gender Pay Gap? ○ Minority, Gender 8#UnifiedDataAnalytics #SparkAISummit ● Machine learning models pick up biases and may really harm vulnerable group of people ○ Be cautious about marketing strategy ○ Fairness: e.g. automatic approval or rejection of loan applications
- 9. Why is it difficult? ML creates functions to explicitly or implicitly combine variables (features) in sophisticated way 9#UnifiedDataAnalytics #SparkAISummit Disaggregate the final prediction to single feature contribution and untangle interaction between features are very difficult!
- 10. Why it’s difficult? Explain one model out of many good models: if they give different pictures of nature’s mechanism and lead to different conclusions - how to justify one against another and recommend to the business? 10#UnifiedDataAnalytics #SparkAISummit “ Data will often point with almost equal emphasis on several possible models. The question of which one most accurately reflects the data is difficult to resolve.” -- Leo Breiman, “Statistical Modeling: The Two Cultures”
- 11. Different methodologies ● Surrogate model ○ simple model out of fancy model (to establish relationship between input and prediction) 11#UnifiedDataAnalytics #SparkAISummit ● Tree interpreter ○ feature importance ○ it measures how often and how much a feature was used in the model ○ report relative score for feature importance ○ global picture of the effect of features
- 12. LIME (Local Interpretable Model-Agnostic Explanations) ● Model Agnostic! Approximate a black-box model by a simple linear surrogate model locally ● Learned on perturbations of the original instance in some cases faster than SHAP ● It doesn’t work out-of-the-box on all models. 12#UnifiedDataAnalytics #SparkAISummit
- 13. SHAP (SHapley Additive exPlanation) ● To unify various model explanation methods: Model-Agnostic or Model-Specific Approximations ● Based on the game theory, Shapley Values, by Scott Lundberg ● Shapley value is the average contribution of features which are predicting in different situation. 13#UnifiedDataAnalytics #SparkAISummit ● SHAP provides multiple explainers for different kind of models: ○ TreeExplainer: Support XGBoost, LightGBM, CatBoost and scikit-learn models by Tree SHAP. ○ DeepExplainer (DEEP SHAP): Support TensorFlow and Keras models by using DeepLIFT and Shapley values. ○ KernelExplainer (Kernel SHAP): Applying to any models by using LIME and Shapley values. ○ GradientExplainer: Support TensorFlow and Keras models.
- 14. SHAP: Important Benefits 14#UnifiedDataAnalytics #SparkAISummit Produce explanations at the level of individual inputs ● Traditional feature importance algorithms will tell us which features are most important across the entire population ● With individual-level SHAP values, we can pinpoint which factors are most impactful for each customer
- 15. SHAP: Important Benefits 15#UnifiedDataAnalytics #SparkAISummit Can directly relate feature values to the output, which greatly improves interpretation of the results ● SHAP can quantify the impact of a feature on the unit of the model target ● Avoid decomp matrix which involves complex transformation and calculation Read the impact of a feature in dollars!
- 16. Application SHAP explainer: CNN 16#UnifiedDataAnalytics #SparkAISummit LIME: NLP Fasttext
- 17. Real world implementation 17#UnifiedDataAnalytics #SparkAISummit Run on Single Node Distributed Implementation
- 18. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT