SAS^® Visual Data Mining and Machine Learning Features

Interactive programming in a web-based development environment

• Visual interface for the entire analytical life cycle process.
• Drag-and-drop interactive interface requires no coding, though coding is an option.
• Supports automated code creation at each node in the pipeline.
• Choose best practice templates (basic, intermediate or advanced) to get started quickly with machine learning tasks or take advantage of our automated modeling process.
• Interpretability reports such as PD, LIME, ICE, and Kernel SHAP.
• Share modeling insights via a PDF report.
• Explore data from within Model Studio and launch directly into SAS Visual Analytics.
• Edit models imported from SAS Visual Analytics in Model Studio.
• View data within each node in Model Studio.
• Run SAS^® Enterprise Miner^™ 14.3 batch code within Model Studio.
• Provides a collaborative environment for easy sharing of data, code snippets, annotations and best practices among different personas.
• Create, manage and share content and administer content permissions via SAS Drive.
• The SAS lineage viewer visually displays the relationships between decisions, models, data and decisions.

Intelligent automation with human oversight

• Public API to automate many of the manual, complex modeling steps to build machine learning models – from data wrangling, to feature engineering, to algorithm selection, to deployment.
• Automatic Feature Engineering node for automatically cleansing, transforming, and selecting features for models.
• Automatic Modeling node for automatically selecting the best model using a set of optimization and autotuning routines across multiple techniques.
• Interactively adjust the pruning and splitting of decision tree nodes.
• Automated data prep suggestions from meta learning.
• Automated pipeline generation with complete customization capability.

Natural language generation

• View results in simple language to facilitate understanding of reports, including model assessment and interpretability.

Embedded support for Python & R languages

• Embed open source code within an analysis, and call open source algorithms within Model Studio.
• The Open Source Code node in Model Studio is agnostic to Python or R versions.
• Manage Python models in a common repository within Model Studio.

Deep learning with Python (DLPy)

• Build deep learning models for image, text, audio and time-series data using Jupyter Notebook.
• High level APIs are available on GitHub for:
  • Deep neural networks for tabular data.
  • Image classification and regression.
  • Object detection.
  • RNN-based tasks – text classification, text generation and sequence labeling.
  • RNN-based time-series processing and modeling.
• Support for predefined network architectures, such as LeNet, VGG, ResNet, DenseNet, Darknet, Inception, ShuffleNet, MobileNet, YOLO, Tiny YOLO, Faster R-CNN and U-Net.
• Import and export deep learning models in the ONNX format.
• Use ONNX models to score new data sets in a variety of environments by taking advantage of Analytic Store (ASTORE)

SAS^® procedures (PROCs) & CAS actions

• A programming interface (SAS Studio) allows IT or developers to access a CAS server, load and save data directly from a CAS server, and support local and remote processing on a CAS server.
• Python, Java, R, Lua and Scala programmers or IT staff can access data and perform basic data manipulation against a CAS server, or execute CAS actions using PROC CAS.
• CAS actions support for interpretability, feature engineering and modeling.
• Integrate and add the power of SAS to other applications using REST APIs.

Highly scalable, distributed in-memory analytical processing

• Distributed, in-memory processing of complex analytical calculations on large data sets provides low-latency answers.
• Analytical tasks are chained together as a single, in-memory job without having to reload the data or write out intermediate results to disks.
• Concurrent access to the same data in memory by many users improves efficiency.
• Data and intermediate results are held in memory as long as required, reducing latency.
• Built-in workload management ensures efficient use of compute resources.
• Built-in failover management guarantees submitted jobs always finish.
• Automated I/O disk spillover for improved memory management.

Model development with modern machine learning algorithms

• Reinforcement learning:
  • Techniques include Fitted Q-Network (FQN) and Deep Q-Network (DQN). 
  • FQN can train a model over precollected data points without the need to communicate with the environment. 
    
  • Uses replay memory and target network techniques to decorrelate the non-i.i.d. data points and stabilize the training process. 
    
  • Ability to specify a custom environment for state-action pairs and rewards. 
    
• Decision forests:
  • Automated ensemble of decision trees to predict a single target.
  • Automated distribution of independent training runs.
  • Supports intelligent autotuning of model parameters.
  • Automated generation of SAS code for production scoring.
• Gradient boosting:
  • Automated iterative search for optimal partition of the data in relation to selected label variable.
  • Automated resampling of input data several times with adjusted weights based on residuals.
  • Automated generation of weighted average for final supervised model.
  • Supports binary, nominal and interval labels.
  • Ability to customize tree training with variety of options for numbers of trees to grow, splitting criteria to apply, depth of subtrees and compute resources.
  • Automated stopping criteria based on validation data scoring to avoid overfitting.
  • Automated generation of SAS code for production scoring.
  • Access lightGBM, a popular open source modeling package.
• Neural networks:
  • Automated intelligent tuning of parameter set to identify optimal model.
  • Supports modeling of count data.
  • Intelligent defaults for most neural network parameters.
  • Ability to customize neural networks architecture and weights.
  • Techniques include deep forward neural network (DNN), convolutional neural networks (CNNs), recurrent neural networks (RNNs) and autoencoders.
  • Ability to use an arbitrary number of hidden layers to support deep learning.
  • Support for different types of layers, such as convolution and pooling.
  • Automatic standardization of input and target variables.
  • Automatic selection and use of a validation data subset.
  • Automatic out-of-bag validation for early stopping to avoid overfitting.
  • Supports intelligent autotuning of model parameters.
  • Automated generation of SAS code for production scoring.
• Support vector machines:
  • Models binary target labels.
  • Supports linear and polynomial kernels for model training.
  • Ability to include continuous and categorical in/out features.
  • Automated scaling of input features.
  • Ability to apply the interior-point method and the active-set method.
  • Supports data partition for model validation.
  • Supports cross-validation for penalty selection.
  • Automated generation of SAS code for production scoring.
• Factorization machines:
  • Supports the development of recommender systems based on sparse matrices of user IDs and item ratings.
  • Ability to apply full pairwise-interaction tensor factorization.
  • Includes additional categorical and numerical input features for more accurate models.
  • Supercharge models with timestamps, demographic data and context information.
  • Supports warm restart (update models with new transactions without full retraining).
  • Automated generation of SAS score code for production scoring.
• Bayesian networks:
  • Learns different Bayesian network structures, including naive, tree-augmented naive (TAN), Bayesian network-augmented naive (BAN), parent-child Bayesian networks and Markov blanket.
  • Performs efficient variable selection through independence tests.
  • Selects the best model automatically from specified parameters.
  • Generates SAS code or an analytics store to score data.
  • Loads data from multiple nodes and performs computations in parallel.
• Dirichlet Gaussian mixture models (GMM):
  • Can execute clustering in parallel and is highly multithreaded.
  • Performs soft clustering, which provides not only the predicted cluster score but also the probability distribution over the clusters for each observation.
  • Learns the best number of clusters during the clustering process, which is supported by the Dirichlet process.
  • Uses a parallel variational Bayes (VB) method as the model inference method. This method approximates the (intractable) posterior distribution and then iteratively updates the model parameters until it reaches convergence.
• Semisupervised learning algorithm:
  • Highly distributed and multithreaded.
  • Returns the predicted labels for both the unlabeled data table and the labeled data table.
• T-distributed stochastic neighbor embedding (t-SNE):
  • Highly distributed and multithreaded.
  • Returns low-dimensional embeddings that are based on a parallel implementation of the t-SNE algorithm.
• Generative adversarial networks (GANs)
  • Techniques include StyleGANs for image data and GANs for tabular data.
  • Generate synthetic data for deep learning models.

Analytical data preparation

• Feature engineering best practice pipeline includes best transformations.
• Distributed data management routines provided via a visual front end.
• Large-scale data exploration and summarization.
• Cardinality profiling:
  • Large-scale data profiling of input data sources.
  • Intelligent recommendation for variable measurement and role.
• Sampling:
  • Supports random and stratified sampling, oversampling for rare events and indicator variables for sampled records.

Data exploration, feature engineering & dimension reduction

• T-distributed stochastic neighbor embedding (t-SNE).
• Feature binning.
• High-performance imputation of missing values in features with user-specified values, mean, pseudo median and random value of nonmissing values.
• Feature dimension reduction.
• Large-scale principal components analysis (PCA), including moving windows and robust PCA.
• Unsupervised learning with cluster analysis and mixed variable clustering.
• Segment profiles for clustering.

Integrated text analytics

• Supports 33 native languages out of the box:
  • English
  • Arabic
  • Chinese
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Farsi
  • Finnish
  • French
  • German
  • Greek
  • Hebrew
  • Hindi
  • Hungarian
  • Indonesian
  • Italian
  • Japanese
  • Kazakh
  • Korean
  • Norwegian
  • Polish
  • Portuguese
  • Romanian
  • Russian
  • Slovak
  • Slovenian
  • Spanish
  • Swedish
  • Tagalog
  • Turkish
  • Thai
  • Vietnamese
• Stop lists are automatically included and applied for all languages.
• Automated parsing, tokenization, part-of-speech tagging and lemmatization.
• Predefined concepts extract common entities such as names, dates, currency values, measurements, people, places and more.
• Automated feature extraction with machine-generated topics (singular value decomposition and latent Dirichlet allocation).
• Supports machine learning and rules-based approaches within a single project.
• Automatic rule generation with the BoolRule.
• Classify documents more accurately with deep learning (recurrent neural networks).

Model assessment

• Automatically calculates supervised learning model performance statistics.
• Produces output statistics for interval and categorical targets.
• Creates lift table for interval and categorical target.
• Creates ROC table for categorical target.
• Creates Event Classification and Nominal Classification charts for supervised learning models with a class target.

Model scoring

• Automatically generates SAS DATA step code for model scoring.
• Applies scoring logic to training, holdout data and new data.

SAS^® Viya^® in-memory engine

• CAS (SAS Cloud Analytic Services) performs processing in memory and distributes processing across nodes in a cluster.
• User requests (expressed in a procedural language) are translated into actions with the parameters needed to process in a distributed environment. The result set and messages are passed back to the procedure for further action by the user.
• Data is managed in blocks and can be loaded in memory and on demand.
• If tables exceed memory capacity, the server caches the blocks on disk. Data and intermediate results are held in memory as long as required, across jobs and user boundaries.
• Includes highly efficient node-to-node communication. An algorithm determines the optimal number of nodes for a given job.
• Communication layer supports fault tolerance and lets you remove or add nodes from a server while it is running. All components can be replicated for high availability.
• Support for legacy SAS code and direct interoperability with SAS 9.4M6 clients.
• Supports multitenancy deployment, allowing for a shared software stack to support isolated tenants in a secure manner.