Frequently Asked Questions

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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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