?> FAQ - WeAreCAS

Frequently Asked Questions

272 questions found.

The accessPersonalCaslibs action provides administrative access to all personal caslibs, which include CASUSER and CASUSERHDFS....
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This action is part of the Access Control action set, which provides actions for managing user access to resources....
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No, the syntax for CASL, Lua, Python, and R shows that the accessPersonalCaslibs action does not take any parameters....
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Personal caslibs are caslibs automatically made available to a user when a CAS session is started. The accessPersonalCaslibs action allows an administrator to gain access to all of these personal caslibs across all user sessions....
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The actionSetFromTable action restores a user-defined action set from a saved table in SAS Cloud Analytic Services (CAS)....
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The 'name' parameter is a string that specifies the name for the user-defined action set that is being restored....
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The 'table' parameter specifies the input settings for an in-memory table. The user-defined action set is constructed from the contents of this specified table....
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The actionSetInfo action from the builtins action set is used to display the build information of loaded action sets on the CAS server....
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When the 'all' parameter is set to True, the action's results will include all available action sets, not just those that are currently loaded. Using this option may cause the action to execute more slowly....
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The default value for the 'all' parameter is FALSE, meaning the action will only show information for action sets that are already loaded by default....
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The `actionSetToTable` action creates an in-memory table from a user-defined action set....
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The required parameter is `actionSet`, which specifies the name of the user-defined action set to be converted into an in-memory table....
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The `casOut` parameter specifies the settings for the output table that will be created. This includes properties such as the caslib, name of the table, whether to replace an existing table, and other table options....
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The actionstatus action is used to get the status of an action for a specific session....
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The actionstatus action requires the 'uuid' parameter, which is a string that specifies the unique identifier of the session to check....
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You can get the action status of another session by providing its session UUID to the 'uuid' parameter in the actionstatus action call....
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The addCaslib action is used to add a new caslib (CAS library), which enables access to a data source within the SAS Cloud Analytic Services (CAS) environment....
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The primary required parameter is 'name', which is a string that specifies the name for the new caslib you want to add....
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The addCaslib action does not fail if you specify an invalid path. The caslib is still created, but it will not be functional for data access until the path is corrected....
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To create a session-scoped caslib, set the 'session' parameter to TRUE. Tables loaded into this caslib will not be accessible from other sessions and the caslib will be dropped when the session ends....
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The addCaslib action supports a wide variety of data source types, including: PATH (for file system paths), DNFS, HDFS, S3, ORACLE, TERADATA, POSTGRES, MYSQL, SQLSERVER, SNOWFLAKE, BIGQUERY, REDSHIFT, and many others available through JDBC or ODBC....
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The 'activeOnAdd' parameter is set to TRUE by default, which automatically makes the new caslib the active one for the current session. You can set it to FALSE if you do not want this behavior....
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The `addCaslibSubdir` action creates a subdirectory within an existing caslib....
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The required parameter is `path`, which specifies the subdirectory to add, relative to the caslib's path....
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The `name` parameter specifies the caslib to which the subdirectory will be added. Its aliases are `lib` and `caslib`....
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The `permission` parameter specifies the host access controls for the new subdirectory. Possible values are: GROUPREAD, GROUPWRITE, GROUPWRITEPUBLICREAD, PRIVATE, PUBLICREAD, PUBLICWRITE, or an integer value. The default is based on the umask of the session process. Its alias is `perms`....
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The addFmtLib action adds a format library to the CAS session. By default, it creates a format library named _FMTLIBn, where n starts at 1 and is incremented for each subsequent library....
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Use the 'caslib' parameter to specify the caslib where the format library resides, and the 'name' parameter for the format library's table name within that caslib. Note that if you are adding a format library from a SASHDAT file, you can specify the name with or without the .sashdat file extension. ...
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The 'fmtLibName' parameter specifies the name for the format library being added. The name is restricted to a maximum of 63 characters....
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Use the 'path' parameter to provide the direct string path to a format library file. This path must be readable from the control node of the server. The 'path' parameter should not be used at the same time as the 'name' or 'caslib' parameters....
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When 'replace' is set to TRUE, it allows the action to overwrite an existing format library that has the same name. The default value is FALSE....
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If 'promote' is set to TRUE, the format library is promoted to a global scope, making it available across all sessions. The default is FALSE. Note that the CAS server can be configured to require administrative Superuser authority to promote a format library to global scope. If required, an administ...
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The 'fmtSearch' parameter specifies how the new format library is added to the session's format search order. The options are: 'APPEND' (adds it to the end of the search list, default), 'INSERT' (adds it to the beginning of the search list), 'REPLACE' (replaces the entire search list with this libra...
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The `addFormat` action is used to add a user-defined format to a format library within a CAS session. This allows for custom formatting of data values in tables....
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The `addFormat` action requires two parameters: `fmtLibName`, which specifies the name of the format library where the format will be stored, and `fmtName`, which defines the name of the new format being created....
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The `ranges` parameter is used to specify a list of value-to-label or range-to-label pairs. For example, you can define a range like '1-10="Low"' or a single value like '99="Not Applicable"'....
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Yes, by default. The `replace` parameter is set to TRUE, which means if a format with the same name already exists in the specified library, it will be replaced. You can set `replace` to FALSE to prevent overwriting....
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The `fuzz` parameter specifies a fuzz factor for matching numeric values to a range. It allows for a small tolerance, so values that are very close to a range boundary are still included. The default value is 0, meaning no fuzziness is applied....
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You can use the `locale` parameter to specify a locale for the format. This is useful for creating formats that depend on language or regional conventions, such as date or currency formats....
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A format name cannot end in a number and cannot be the same as a SAS-supplied format name. For numeric formats, the name can be up to 32 characters. For character formats, the name must start with a dollar sign ($) and can be up to 31 characters long....
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The addLayer action is used to add a layer to a Deep Learning model. It is a fundamental step in building a neural network architecture layer by layer....
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The required parameters for the addLayer action are 'layer' (which specifies the layer type and its properties), 'modelTable' (the in-memory table representing the model), and 'name' (a unique name for the layer being added)....
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You can add various types of layers, including but not limited to: 'INPUT', 'CONVOLUTION' (or 'CONVO'), 'POOLING' (or 'POOL'), 'FULLCONNECT' (or 'FC'), 'RECURRENT', 'BATCHNORM', 'RESIDUAL', and 'OUTPUT'....
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You use the 'srcLayers' parameter to specify the names of one or more source layers that will provide input to the new layer you are adding....
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Yes, you can replace an existing layer by setting the 'replace' parameter to TRUE. The new layer must have the same name as the layer you intend to replace....
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To make a new layer share weights with an existing one, you can use the 'sharingWeights' parameter, specifying the name of the layer from which to share weights....
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The 'randomCrop' parameter specifies how to crop images when they are used as input. Options include 'UNIQUE' for random offset cropping, 'RESIZETHENCROP' for resizing then cropping, and 'RANDOMRESIZED' for random scaling and aspect ratio adjustments before resizing....
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The builtins.addNode action is used to add a machine to the CAS (Cloud Analytic Services) server. This is a server management function....
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The syntax is as follows: builtins.addNode / node={"hostname-1", "hostname-2", ...}, role="WORKER" | "CONTROLLER";...
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The main parameters are 'node' and 'role'. 'node' specifies the host names of the machines to add. 'role' specifies the role for the machine, which can be either 'WORKER' or 'CONTROLLER'. The default role is 'WORKER'....
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When a machine is added with the 'CONTROLLER' role, it is configured as a backup controller for the server. It's important to note that only two controllers (one primary, one backup) are supported in a CAS server environment....
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Yes, the documentation explicitly states that the addNode action is disabled in a Kubernetes environment. This means you cannot dynamically add nodes to a CAS server that is deployed within a Kubernetes cluster using this action....
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The `addNodeStatus` action is used to list details about machines that are currently in the process of being added to the CAS server....
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The `addNodeStatus` action is part of the Session Methods Action Set....
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The CASL syntax for the action is: `session.addNodeStatus ;`...
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In Python, you can call the action using the following syntax: `results=s.session.addNodeStatus()`...
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The R syntax for the `addNodeStatus` action is: `results ...
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The Lua syntax for this action is: `results, info = s:session_addNodeStatus{}`...
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The addPrototypes action is used to add PROTO definitions and store them in a CAS table....
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The required parameters for the addPrototypes action are 'funcTable' to specify the output table and 'routineCode' to provide the PROTO definition's code....
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You can specify the output table using the 'funcTable' parameter, which is a casouttable that defines the name, caslib, and other properties of the table....
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The 'encode' parameter, which has aliases 'encrypt' and 'hide', specifies that the prototype definitions should be encoded in the saved function table....
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Yes, you can specify an existing FCMP library to load using the 'library' parameter....
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The 'bridgeFile' parameter specifies the path to the PROTO bridge file source module, which can contain code to install and handle signals if 'bridgeCatchSignals' is set to TRUE....
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The addRegion action is used to add or replace custom regions for the S3 (Simple Storage Service) environment. This allows CAS (Cloud Analytic Services) to connect to a specified server on AWS....
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The mandatory parameters are 'host' and 'name'. The 'host' parameter specifies the server that CAS connects to on AWS, and 'name' provides a unique name for the region being added or replaced....
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You can manage SSL/TLS settings with several boolean parameters. 'sslrequired=TRUE' enforces that all communications use SSL. 'sslallowed=TRUE' permits SSL communication but is ignored if 'sslrequired' is also set to TRUE. Conversely, 'nossl=TRUE' disables SSL/TLS for the data transfer....
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Yes, you can use the 'port' parameter for the HTTP port (without SSL) and the 'sslport' parameter for the HTTP port with SSL. If you do not specify a value, the default is used....
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You can use the 'region' parameter to specify the region code of the region that you are adding or replacing, such as "us-east-1"....
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The addRoutines action adds the FCMP routines and stores them in a table....
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The 'appendTable' parameter specifies whether to append the new functions to the FCMP table. Its default value is FALSE....
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The 'funcTable' parameter is required and specifies the table where the FCMP function is written. It is a common casouttable parameter....
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The 'library' parameter specifies an existing FCMP library to load....
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The 'package' parameter specifies the FCMP package name used for storing FCMP functions....
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The 'routineCode' parameter is required and is used to specify the FCMP routine's code that is saved to the table. It has an alias of 'code'....
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The 'saveTable' parameter specifies if the FCMP table should be saved. Its default value is FALSE....
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The addTable action is used to add a table by sending it from the client to the server....
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The addTable action cannot be run directly in the same way as other actions. Each client (CASL, Python, R, etc.) provides a specific function or method to transfer data to the server, which then runs the addTable action....
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The 'append' parameter, when set to TRUE, adds rows from the client-side table to an existing table on the server. The default value is FALSE....
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The 'caslib' parameter specifies the target caslib where the table will be created or appended on the server....
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When the 'replace' parameter is set to TRUE, it overwrites an existing table with the same name in the target caslib. The default value is FALSE....
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By setting the 'promote' parameter to TRUE, the table is added with a global scope, making it accessible to other sessions, provided they have the necessary permissions. The target caslib must also have a global scope....
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The 'copies' parameter specifies the number of redundant copies of the table's rows to create for fault tolerance. A value of 0 means no redundancy, while the default is 1....
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Yes, 'recLen' is a required parameter. It specifies the length, in bytes, for a single row of the table being added....
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Variables are defined using the 'vars' parameter, which is a list of objects. Each object must specify the variable's 'name', 'rType' (raw type, OBSOLETE), 'length', and 'offset' (OBSOLETE). Optional attributes include 'label', 'format', and 'type' (e.g., 'VARCHAR', 'INT64')....
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The addUserActionSetPath action adds a caslib to the user-defined action set search path....
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It belongs to the Builtins action set, which provides actions for server management....
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The required parameter is 'caslib', which specifies the caslib to be added to the search path....
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The CASL syntax is `builtins.addUserActionSetPath / caslib="string";`, where "string" is the name of the caslib....
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The 'aggregation.aggregate' action is used to perform aggregation on selected variables within a CAS table. It computes summary statistics for these variables, optionally grouping the results by other variables....
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The 'table' parameter is required. It specifies the input CAS table that you want to process and perform aggregations on....
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You can use the 'varSpecs' parameter. It is a list of specifications where each element defines an aggregation. For each specification, you can set the variable name with the 'name' subparameter and the aggregation method with the 'agg' subparameter (e.g., 'SUMMARY', 'N', 'MEAN', 'MAXIMUM')....
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Yes, you can group results by using the 'groupBy' subparameter within the main 'table' parameter. You provide a list of variable names that the action will use to group the data before performing the aggregation....
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To perform time-based aggregation, you must specify a timestamp variable using the 'id' parameter. Then, use the 'interval' parameter to define the time period for accumulation, such as 'MONTH', 'DAY', or 'HOUR'. Parameters like 'align' and 'offset' offer further control over the time intervals....
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By default, the action uses a sliding window. A jumping (or tumbling) window can be enabled by setting the 'jumpingWindow' parameter to TRUE. A jumping window contains multiple intervals and the aggregation is reset when the time range elapses, whereas a sliding window always retains the same number...
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The alJoin action is used to join a data table with an annotation table. It is part of the Active Learning action set....
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The alJoin action requires three main parameters: 'table' which specifies the primary data table, 'annotatedTable' which specifies the table containing annotation data, and 'casOut' which defines the output table for the joined results....
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You use the 'table' parameter for the main data table (aliased as 'left') and the 'annotatedTable' parameter for the annotation data (aliased as 'right' or 'annotation')....
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The 'id' parameter (aliased as 'idVar') specifies the identifier column that is used to join the data table and the annotation table....
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The 'joinType' parameter allows you to specify the join method. Supported types are: APPEND, FULL, INNER, LEFT, and RIGHT. The default join type is LEFT....
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The output is specified using the 'casOut' parameter, which is a required parameter that defines the properties of the new in-memory table that will store the joined results....
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The `alterTable` action is used to modify the metadata and structure of an in-memory table. This includes renaming the table, changing the table's label, altering column properties like names, labels, and formats, as well as dropping columns....
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To rename a table, use the `name` parameter to specify the current table and the `rename` parameter to provide the new table name....
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The top-level `drop` parameter takes a list of column names to be dropped from the table. The `drop` option within the `columns` parameter is a boolean (TRUE/FALSE) that applies to the single column specified in that column's definition block....
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To change a column's properties, use the `columns` parameter. Provide a list containing a dictionary where you specify the column's `name`, and then set the new `format` and `label` values....
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Yes, you can specify the new physical order of columns in the table's metadata by providing a list of column names to the `columnOrder` parameter....
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The `lifetime` parameter specifies the number of seconds to keep the table in memory after it is last accessed. If the table is not accessed within this duration, it is automatically dropped from memory. A value of 0 means the table will not be dropped automatically....
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You can use the `keep` parameter, providing a list of the column names that you wish to retain in the table. All other columns will be dropped....
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This parameter specifies the table redistribution policy when the number of worker pods increases on a running CAS server. It can be set to 'NOREDIST' to prevent redistribution, 'REBALANCE' to rebalance the data across the new set of workers, or 'DEFER' to let a higher-level entity decide....
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The analyzeMissingPatterns action performs a missing pattern analysis on an input data table. It is used to identify and analyze the patterns of missing values within the dataset....
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The required parameters are 'table', which specifies the input CAS table for analysis, and 'casOut', which specifies the output CAS table to store the results of the analysis....
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You can use the 'inputs' parameter to provide a list of variables to be included in the analysis. If this parameter is not specified, the action may use a default set of variables from the input table....
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The 'target' parameter allows you to specify a target variable for the analysis. This helps in understanding how missing data patterns might relate to a specific outcome variable....
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The 'distinctCountLimit' parameter (default: 10000) sets a threshold for the number of distinct values. If this limit is exceeded, the 'misraGries' parameter (default: True) determines whether to use the Misra-Gries algorithm to estimate the frequency distribution or to abort the operation....
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Yes, you can use the 'freq' parameter to specify a numeric variable that represents the frequency of each observation in the input table....
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The annCode action generates DATA step scoring code from a pre-existing artificial neural network model. This allows you to apply the trained model to new data within a SAS DATA step....
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You must use the 'modelTable' parameter to specify the input table that contains the artificial neural network model you want to use for generating the scoring code....
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The 'code' parameter is used to request the generation of the SAS score code and to specify output options for that code, such as the output CAS table name and caslib....
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You can use the 'listNode' parameter. It accepts values like 'ALL', 'HIDDEN', 'INPUT', or 'OUTPUT' to specify which types of nodes should be included in the scored output table. The default is 'HIDDEN', which is useful for autoencoding applications....
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The 'modelId' parameter specifies a variable name for the model ID that will be included in the generated DATA step scoring code. By default, if not specified, the variable name is prefixed with 'ANN_' followed by the target variable name....
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The `image.annotateImages` action annotates images in a CAS table with metadata that is also contained within the table. This allows for drawing shapes, text, or other information directly onto the images....
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The required parameters for the `image.annotateImages` action are `images`, which specifies the input image table, `annotations`, which defines the annotations to apply, and `casOut`, which specifies the output table for the annotated images....
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The `annotateImages` action supports several annotation types specified via the `annotationType` parameter. These include `LINES` for drawing lines, `POINTS` for drawing points, `PROTOBUF` for using protobuf definitions, and `SEGMENTATION` for applying color maps based on segmentation data....
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When using `annotationType="LINES"`, you can specify the color using the `r`, `g`, and `b` parameters for the red, green, and blue channels, respectively. The `thickness` parameter can be used to set the line thickness in pixels....
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The `decode` parameter and its subparameters manage image encoding and decoding. The `value` subparameter, when set to True, ensures that decoded images and their metadata are written to the output table. You can also use `encodeType` to specify the output encoding format, such as 'jpg'....
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The input table is specified using the `images` parameter. This parameter requires a `table` subparameter where you provide the `name` of the table and optionally the `caslib`....
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Yes, you can use the `copyVars` parameter to specify a list of variable names that you want to copy from the input table to the output `casOut` table....
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For annotations of type `SEGMENTATION`, you can use the `colorMap` parameter to specify a color map, such as 'JET', 'HOT', 'COOL', etc. You can also control the `transparency` of the applied color map....
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The `annScore` action scores a table using a pre-existing artificial neural network model....
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The action requires two input tables: `modelTable`, which contains the trained artificial neural network model, and `table`, which is the data table to be scored....
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Set the `assessOneRow` parameter to `True`. This will add all event probabilities as separate columns, named with the prefix `_NN_P_`, which can then be used with the `assess` action....
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When `impute` is set to `True`, observations with a non-missing target variable value will use that observed value as their predicted value. Scoring is only performed for observations where the target variable is missing....
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Use the `listNode` parameter. You can set it to `ALL` (all nodes), `HIDDEN` (only hidden nodes, useful for autoencoding), `INPUT` (only input nodes), or `OUTPUT` (only output nodes)....
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By default, observations with missing values are included in the scoring process. To exclude them, you can set the `includeMissing` parameter to `False`....
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The annTrain action is used to train an artificial neural network. It is part of the Neural Network Action Set and provides functionalities for training various network architectures....
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You can specify the network architecture using the 'arch' parameter. The available options are 'MLP' for a multilayer perceptron, 'GLIM' for a generalized linear model architecture (a two-layer perceptron with no hidden layers), and 'DIRECT' which is an extension of MLP with direct connections from ...
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The 'hiddens' parameter allows you to specify the number of hidden neurons for each hidden layer. For example, specifying hiddens={10, 5} would create a network with two hidden layers, the first having 10 neurons and the second having 5....
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The optimization algorithm can be selected via the 'algorithm' subparameter within 'nloOpts'. The supported algorithms are 'ADAM', 'HF' (Hessian-Free), 'LBFGS', and 'SGD' (Stochastic Gradient Descent)....
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You can control missing value imputation using the 'missing' parameter for input variables and 'targetMissing' for the target variable. Options include 'MEAN', 'MAX', or 'MIN' to replace missing values with the mean, maximum, or minimum value of the variable. If set to 'NONE' (the default for interv...
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Yes, you can provide a validation dataset using the 'validTable' parameter. This allows for early stopping of the training process, which can be configured through the 'validate' subparameter of the 'nloOpts' option, based on the model's performance on this validation data....
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For hidden layers, the 'acts' parameter supports 'EXP', 'IDENTITY', 'LOGISTIC', 'RECTIFIER', 'SIN', 'SOFTPLUS', and 'TANH'. For the target layer, the 'targetAct' parameter supports 'EXP', 'IDENTITY', 'LOGISTIC', 'SIN', 'SOFTMAX', and 'TANH'. The default activation function depends on the variable ty...
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You can use the 'saveState' parameter to specify an output table where the model's state, including its weights, will be saved. This allows you to resume training later or use the model for scoring with the 'annScore' action....
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The `table.append` action is used to append the rows from a source table to a target table. A key requirement is that the target table must be an in-memory table....
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The `table.append` action requires two parameters: `source`, which specifies the input table to append, and `target`, which specifies the in-memory table that the data will be appended to....
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You can use the `where` subparameter within the `source` parameter. This allows you to provide a `where-expression` to filter the rows from the source table before they are appended to the target table....
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When `singlePass` is set to True, it prevents the creation of a transient table on the server for the source data. This can be more efficient, but the ordering of the data might not be stable across repeated executions. Its default value is False....
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Yes, the `dataSourceOptions` subparameter (aliased as `options` or `dataSource`) within the `source` parameter allows you to specify a list of key-value pairs for data source-specific configurations....
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The applyCategory action categorizes text by applying a pre-existing category model, which is provided as an MCO file....
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The `model` parameter is required. It specifies the input CAS table that contains the categorization model to be applied....
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You use the `table` parameter to specify the input data table, and the `text` parameter to name the variable within that table that contains the text to be categorized....
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The `scoringAlgorithm` parameter allows you to choose between two algorithms: 'FREQUENCY' (which is the default) and 'WEIGHTED'....
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The `casOut` parameter specifies the main output data table for the categories. The `matchOut` parameter creates a table with one match per row. The `groupedMatchOut` parameter creates a table that groups all matches for a given category within each document, using the delimiter specified by `matchD...
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The `docId` parameter is used to specify the name of the variable in the input table that serves as a unique identifier for each document....
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The `applyConcept` action performs concept extraction on a given text using a concept extraction model, which is defined in a LITI (Language Interpretation for Text Information) file....
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The primary input is a CAS table specified by the `table` parameter, containing the documents to process. The `docId` and `text` parameters specify the columns for the document ID and the text content, respectively. The main output is the `casOut` table, which contains the concept match results. Oth...
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You can specify a user-defined LITI model using the `model` parameter. This parameter points to a CAS table that contains your custom model. If this parameter is not specified, the base model is used....
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The `matchType` parameter specifies the matching strategy for concepts. It can be set to 'ALL' (default) to return all matches, 'BEST' to return only the best-scoring matches, or 'LONGEST' to return the longest matches....
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To improve performance, especially when using the CLAUS_n operator, you can use a two-step process. First, run `applyConcept` with the `parseTableOut` parameter to save the pre-parsed documents to a CAS table. Then, in a subsequent run, use this table as input via the `parseTableIn` parameter to avo...
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Yes, you can use the `dropConcepts` parameter to provide a list of concept names that you want to exclude from the output tables. This is useful for filtering out predefined or intermediate concepts from the final results without removing them from the model itself....
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The `litiChunkSize` parameter specifies the size of the data chunks used for processing a document. The default is '32K'. For very large documents, using a smaller chunk size like '32K' or '64K' can improve performance and reduce memory consumption. Setting it to 'ALL' processes the entire document ...
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The arima action provides functionality for Autoregressive Integrated Moving Average (ARIMA) models, which are used for analyzing and forecasting univariate time series data....
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The autoregressive polynomials are specified using the 'p' parameter within the 'estimate' options. You can define one or more polynomial factors, each with its list of lags....
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The 'q' parameter is used to specify the moving average (MA) polynomials of the ARIMA model, similar to how the 'p' parameter specifies the autoregressive (AR) part....
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The 'method' parameter allows you to choose the estimation method. The available options are CLS (Conditional Least Squares), ML (Maximum Likelihood), and ULS (Unconditional Least Squares)....
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You can specify the differencing orders using the 'diff' parameter within the 'estimate' options. This parameter accepts a list of integers representing the differencing lags....
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The 'transform' parameter can apply a transformation to the time series. The available options are AUTO, BOXCOX (with a parameter specified by 'transformParm'), LOG (logarithmic), LOGIT (logit), SQRT (square root), and NONE (no transformation)....
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You can generate forecasts by specifying the 'forecast' option within a 'model' definition. Key parameters include 'lead' to set the number of forecast steps and 'alpha' for the confidence level....
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The `percentile.assess` action is used to assess and compare models. It provides functionalities for calculating percentiles, box plot values, and generating various model assessment statistics....
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To run the `assess` action, you must specify the `table` parameter, which defines the input data table, and the `response` parameter, which indicates the response variable for the model assessment....
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You can specify the event of interest using the `event` parameter, which takes the formatted value of the response variable that represents the event. If you don't specify this and the response is numeric, the action performs an assessment for a regression model....
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Yes. To get ROC (Receiver Operating Characteristic) data, set `includeRoc` to TRUE and specify an output table with the `rocOut` parameter. For Lift data, set `includeLift` to TRUE and use the `casOut` parameter to specify the output table. You can control the granularity of ROC calculations with `c...
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The `assess` action supports two algorithms for percentile analysis, specified via the `method` parameter: 'ITERATIVE' (the default) and 'EXACT'. The iterative method's convergence can be fine-tuned using the `maxIters` and `epsilon` parameters....
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You can save the fit statistics to a separate CAS output table by using the `fitStatOut` parameter. This is particularly useful when assessing nominal response variables where you must also specify the probability events (`pEvent`) and probability variables (`pVar`)....
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The assessBias action calculates bias metrics for predictive models....
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The 'code' parameter specifies the DATA step code that describes the model or specifies the DS2 code that is used along with an analytic store that you specify in the modelTable or modelTables parameter....
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The 'cutoff' parameter specifies the cutoff for the confusion matrix. The default value is 0.5 and the range is (0, 1)....
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The 'event' parameter specifies the formatted value of the response (target) variable that represents the event of interest....
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You can use the 'frequency' parameter to specify the variable that contains frequency values....
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The 'modelTable' parameter specifies the input table that contains the model to explain. This table must contain an analytic store or DATA step scoring code....
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The 'modelTables' parameter is used to specify the input tables that contain the model to explain when the model is composed of analytic stores and requires accompanying DS2 code specified in the 'code' parameter....
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The 'modelTableType' parameter specifies the type of scoring the model table contains. It can be 'ASTORE', 'DATASTEP', or 'NONE'. The default is 'ASTORE'....
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The 'nBins' parameter specifies the number of bins to use in lift calculations. The default is 20, and the range is from 2 to 100....
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Use the 'predictedVariables' parameter, which is a required list of variables that contain the model's predictions. The order of variables must match the order in the 'responseLevels' parameter....
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The 'referenceLevel' parameter specifies the reference level for the sensitive variable, which is used as a baseline for bias calculations....
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The 'response' parameter is used to specify the response (target) variable for the model....
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The 'responseLevels' parameter specifies the list of formatted values of the response (target) variable. The order must match the 'predictedVariables' parameter. If 'event' is not specified, the first value is assumed to be the event....
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The 'rocStep' parameter specifies the step size to use for the receiver operating characteristic (ROC) calculations. The default is 0.05....
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The 'scoredTable' parameter specifies the output table to contain the scored outputs....
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The 'selectionDepth' parameter specifies the depth to use in lift calculations. The default value is 10....
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The 'sensitiveVariable' parameter is required to specify the sensitive variable to use in bias calculations....
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The 'table' parameter is required and is used to specify the input data table for the action....
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The assumeRole action allows a user to assume a specific administrative role, such as 'ACTION', 'DATA', or 'SUPERUSER'....
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The available roles are 'ACTION' for unrestricted access to actions, 'DATA' for unrestricted access to data definitions, and 'SUPERUSER' which combines the privileges of both 'ACTION' and 'DATA' roles, plus the ability to manage roles and paths....
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The 'SUPERUSER' role provides the combined privileges of the 'ACTION' and 'DATA' roles, along with the ability to manage other roles and paths....
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The default value for the 'adminRole' parameter is 'SUPERUSER'....
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The 'ACTION' role provides unrestricted access to action sets and actions, while the 'DATA' role provides unrestricted access to caslib, table, and column definitions, including the ability to add caslibs....
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The `attribute` action is used to manage extended table attributes. This allows for adding, updating, dropping, converting, or exporting metadata associated with a table or its columns....
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The `task` parameter allows you to specify one of the following tasks: 'ADD' to add new attributes, 'CONVERT' to convert attributes, 'DROP' to remove attributes, 'EXPORT' to export attributes to an XML format, and 'UPDATE' to modify existing attributes. The default task is 'ADD'....
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You can specify attributes in three ways: 1) Using the `attributes` parameter, which takes a list of key-value pairs. 2) Using the `xml` parameter, which accepts an XML document as a string. 3) Using the `xmlPath` parameter, which specifies the path to a file containing the attributes in an XML form...
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The `name` parameter is required and specifies the name of the in-memory table to which the attributes apply. The `table` parameter (aliased as `attrTable`) specifies the name of an existing table that stores the extended attributes, which is used for tasks like ADD, UPDATE, or CONVERT....
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Yes, an attribute can be applied to a specific column. When using the `attributes` parameter, you can include the `column` parameter within an attribute's definition, providing the name of the column you want to associate the attribute with. If the `column` parameter is omitted, the attribute applie...
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The `augmentImages` action is used to augment images by creating patches from them and applying various mutations or transformations. This is a common technique in computer vision to artificially expand a training dataset....
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The required parameters are `table`, which specifies the input table containing the images, `casOut`, which defines the output table for the augmented images, and `augmentations`, which is a list specifying the cropping and mutation options....
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You can create patches by defining one or more blocks within the `augmentations` parameter. You can either specify the exact coordinates and dimensions of a patch using `x`, `y`, `width`, and `height`, or use `sweepImage` set to TRUE to automatically create patches across the entire image using a sl...
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Within each `augmentations` block, you can specify a `mutations` list. Available mutations include geometric transformations like `horizontalFlip`, `verticalFlip`, `rotateLeft`, and `rotateRight`, as well as appearance modifications such as `colorJittering`, `colorShifting`, `darken`, `lighten`, `sh...
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Yes, you can apply a random rotation by specifying `rotateLeft` or `rotateRight` with a `type` of 'RANGE'. For example, `mutations={rotateRight={type='RANGE', value={0, 90}}}` would apply a random right rotation between 0 and 90 degrees....
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You can use the `addColumns` parameter with the value `augmentAttributes`. This will add columns to your output table that provide details about the augmentation, such as `_patch_x_`, `_patch_y_`, `_patch_width_`, and `_patch_height_`....
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Yes, you can set the `useWholeImage` parameter to TRUE within an `augmentations` block. This will apply the specified mutations to the entire image instead of a cropped patch....
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The bart.bartGauss action fits Bayesian additive regression trees (BART) models to normally distributed response data....
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The 'table' parameter is required to specify the input data table for the analysis....
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The 'model' parameter is used for this. It requires a 'depVars' subparameter for the target variable and an 'effects' subparameter to list the explanatory variables....
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The 'nTree' parameter specifies the number of trees in the sum-of-trees ensemble. Its default value is 200....
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The 'nBI' parameter sets the number of burn-in iterations (default 100) which are discarded. The 'nMC' parameter sets the number of subsequent MCMC iterations that are saved for prediction (default 1000)....
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You can use the 'store' parameter to save the model in a binary table object. This object can then be used for scoring new data with the bartScore action....
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The 'missing' parameter controls this behavior. The default is 'SEPARATE', which treats missing values as a distinct category. Other options include 'NONE' (exclude observations), 'MACBIG', and 'MACSMALL' (treat as largest/smallest machine value)....
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The bart.bartProbit action is designed to fit probit Bayesian additive regression trees (BART) models specifically for binary distributed response data....
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You can use the 'nTree' parameter to specify the number of trees in the sum-of-trees ensemble. The default value is 200....
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The process involves a burn-in phase followed by the main sampling phase. The 'nBI' parameter sets the number of burn-in iterations (default 100), which are discarded. The 'nMC' parameter specifies the number of subsequent MCMC iterations that are saved for prediction (default 1000)....
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The 'missing' parameter controls this. The default is 'SEPARATE', which treats missing values as a distinct group. Other options include 'NONE' (excludes observations with missing values), 'MACBIG' (treats missing as the largest machine value), and 'MACSMALL' (treats missing as the smallest machine ...
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Yes, you can use the 'store' parameter to save the model into a binary table object. This stored model can then be used for scoring new data with the bartScore action....
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The bartScore action creates a table on the server that contains results from scoring observations by using a fitted Bayesian additive regression trees model....
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The `casOut` parameter is required to create a table on the server that contains observation-wise statistics computed from scoring a data table....
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The `restore` parameter is used to specify a binary table object from a previous model fitting....
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When `avgOnly` is set to FALSE, predictions from each MCMC sample are included in the output table in addition to the sample average predictions. By default, it is TRUE....
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Use the `copyVars` parameter to specify a list of one or more variables to be copied from the scoring table to the output table....
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The `alpha` parameter specifies the significance level for constructing all equal-tail credible limits, with a default value of 0.05....
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You can use the `pred`, `resid`, `lcl` (lower credible limit), and `ucl` (upper credible limit) parameters to name the respective output statistics columns....
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The bart.bartScoreMargin action computes predictive margins by using a fitted Bayesian additive regression trees (BART) model....
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The 'model' parameter is required and specifies a binary table object that was created from a previous model fitting, which contains the BART model to be used for scoring....
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You must use the 'table' parameter to specify the input data table that you want to compute predictive margins for....
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A predictive margin is a statistical measure. In this action, you define it using the 'margins' parameter (alias: 'scenarios'). Each margin is a scenario where you can specify variables to modify and the values they should be set to, using the 'at' subparameter....
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You can compute the difference between two predictive margins by using the 'differences' parameter (alias: 'diffs'). You need to specify the 'evtMargin' (event margin) and 'refMargin' (reference margin) by their names, which you defined in the 'margins' parameter....
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The 'alpha' parameter specifies the significance level for constructing equal-tail credible limits for the computed margins. The default value is 0.05....
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The `batchresults` action is used to change a currently running action to batch results mode....
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The `batchresults` action requires the `uuid` parameter, which is a string that specifies the UUID of the session....
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To use the `batchresults` action in CASL, you would call `session.batchresults` and provide the session's UUID. For example: `session.batchresults / uuid="your-session-uuid";`....
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Yes, the example provided in the documentation, 'Switch an Action Running in Another Session to Batch Mode,' indicates that you can use the `batchresults` action to change the mode of an action in another session by specifying its UUID....
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The biconnectedComponents action is used to calculate the biconnected components and articulation points of a graph within the Network Optimization action set....
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A biconnected component is a maximal subgraph that remains connected even if any single node is removed. It's a measure of a graph's robustness....
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An articulation point, also known as a cut vertex, is a node in a graph whose removal would increase the number of connected components, effectively splitting the graph into separate subgraphs....
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The action generates several output tables: `out` for summary information, `outLinks` for link-level component data, `outNodes` for node-level articulation point data, and `outBCTreeLinks` and `outBCTreeNodes` for the block-cut tree structure....
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Yes, you can use the `direction` parameter. It can be set to `UNDIRECTED` (which is the default) or `DIRECTED` to specify how the input graph should be treated....
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The bnet action, part of the Bayesian Net Classifier Action Set, uses Bayesian network models to classify a target variable....
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The bnet action can learn several network structures, including GENERAL (or GN), Markov Blanket (MB), NAIVE, Parent-Child (PC), and Tree-Augmented Naive (TAN) Bayesian networks....
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For interval variables, missing values can be handled in two ways: they can be ignored, or they can be imputed with the mean of the variable. This is controlled by the 'missingInt' parameter....
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For nominal variables, the bnet action provides three options for handling missing values: ignoring the observations, imputing with the mode of the variable, or treating the missing values as a distinct level. This is controlled by the 'missingNom' parameter....
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The bnet action supports several methods for independence tests, specified by the 'indepTest' parameter: CHISQUARE (chi-square statistic), GSQUARE (G-square statistic), MI (normalized mutual information), CHIGSQUARE (both chi-square and G-square), and ALL (chi-square, G-square, and mutual informatio...
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The 'maxParents' parameter specifies the maximum number of parents for each node in the network. Its default value is 5, and it can range from 1 to 16....
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The 'saveState' parameter can be used to specify an output table where the trained model is saved for future scoring purposes....
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The boxChart action, part of the Statistical Process Control (SPC) action set, is used to produce box charts for analyzing process variation....
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The 'controlStat' parameter specifies whether the control limits on the box chart are computed based on subgroup means ('MEAN') or subgroup medians ('MEDIAN'). The default value is 'MEAN'....
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Use the 'sMethod' parameter. It offers several methods: 'RMSDF' (weighted root mean square), 'RMVLUE' (minimum variance linear unbiased estimate from ranges), 'RNOWEIGHT' (unweighted estimate from ranges), 'SMVLUE' (minimum variance linear unbiased estimate from standard deviations), and 'SNOWEIGHT'...
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You can request tests for special causes using the 'primaryTests' parameter. The available tests are: Test 1 (one point beyond control limits), Test 2 (nine points in a row on one side of the center line), Test 3 (six points in a row steadily increasing or decreasing), Test 4 (fourteen points in a r...
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You can use the 'limitN' parameter to specify a nominal sample size for control limits. To include all subgroups regardless of their size, set 'allN' to True. To enable tests for special causes with varying subgroup sizes, set 'testNStd' to True....
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The 'specsTable' parameter is used to specify an input data table containing specification limits. When this table is provided, the action will compute and return process capability indices....
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The 'chartsTable' specifies the output data table for the charts summary, containing the summary statistics for each subgroup. The 'outLimitsTable' specifies the output data table for the control limits themselves....
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The boxPlot action is used to calculate quantiles, high and low whiskers, and outliers for statistical analysis, which are the fundamental components of a box plot....
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The 'table' parameter is required. It specifies the input CAS table that you want to analyze....
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You can specify the analysis variables using the 'inputs' parameter. This parameter takes a list of variables from the input table for which the box plot statistics will be calculated....
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The 'method' parameter allows you to choose between two algorithms: "ITERATIVE" and "EXACT". The default method is "ITERATIVE", which is generally faster. "EXACT" provides precise results but may be slower....
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Outlier handling is managed through several parameters. Setting 'outliers' to TRUE enables their calculation. The 'whiskerPercentile' parameter defines the range for whiskers (e.g., a value of 10 sets whiskers at the 10th and 90th percentiles). Additionally, 'nOutBins' can be used to bin the outlier...
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The 'pctlDef' parameter specifies one of five definitions for computing quantile statistics, as detailed in the UNIVARIATE procedure documentation. The default value is 6, which corresponds to an iterative process....
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The brTrain action extracts Boolean rules from text data. It is part of the Boolean Rule action set....
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The `docId` parameter specifies the variable in the input data table that contains the document ID. Its default value is "_document_"....
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The `docInfo` parameter specifies the information about the document table. It includes subparameters like `events`, `id` (which is required and specifies the document ID variable), `table` (the input data table with document info), `targets` (the target variables), and `targetType` (BINARY, MULTICL...
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The `gPositive` parameter specifies the minimum g-score for a positive term, while `gNegative` specifies the minimum g-score for a negative term to be considered for rule extraction. The default for both is 8....
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The `maxCandidates` parameter specifies the number of term candidates to be selected for each category during the rule creation process. The default value is 500....
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`maxTriesIn` specifies the k-in value for k-best search in the term ensemble process for creating rules (default 150). `maxTriesOut` specifies the k-out value for k-best search in the rule ensemble process for creating a rule set (default 50)....
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The `minSupports` parameter specifies the minimum number of documents in which a term must appear to be used for creating a rule. The default value is 3....
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The `mPositive` parameter specifies the m value for computing estimated precision for positive terms (default 2), and `mNegative` specifies the m value for computing estimated precision for negative terms (default 4)....
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The `termInfo` parameter specifies information about the terms table. It requires the `id` subparameter for the term ID, and can optionally include `label` for the term's text and `table` for the input data table containing term information....
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The `casOuts` parameter specifies the output data tables for the results. This can include `rules` (the generated rules), `ruleTerms` (the terms in each rule), and `candidateTerms` (the terms selected for rule creation)....
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