itsjhuang/watsonx-docs-document-type huggingface dataset:
Deleting deployment spaces
Deleting deployment spaces Delete existing deployment spaces that you don't require anymore. Important:Before you delete a deployment space, you must delete all the deployments that are associated with it. Only a project admin can delete a deployment space. For more information, see [Deployment space collaborator roles and permissions](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/collaborator-permissions-wml.html). To remove a deployment space, follow these steps: 1. From the navigation menu, click Deployments. 2. In the deployments list, click the Spaces tab and find the deployment space that you want to delete. 3. Hover over the deployment space, select the menu (![Menu icon](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/open-close-icon.png)) icon, and click Delete. 4. In the confirmation dialog box, click Delete. Learn more To learn more about how to clean up a deployment space and delete it programmatically, refer to: * [Notebook on managing machine learning artifacts](https://dataplatform.cloud.ibm.com/exchange/public/entry/view/e5e78be14e2260ccb4bcf8181d093d7b) * [Notebook on managing spaces](https://dataplatform.cloud.ibm.com/exchange/public/entry/view/e5e78be14e2260ccb4bcf8181d0967e3) Parent topic:[Deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-spaces_local.html)
Radar charts
Radar charts Radar charts compare multiple quantitative variables and are useful for visualizing which variables have similar values, or if outliers exist among the variables. Radar charts consists of a sequence of spokes, with each spoke representing a single variable. Radar Charts are also useful for determining which variables are scoring high or low within a data set.
Troubleshooting Data Refinery
Troubleshooting Data Refinery Use this information to resolve questions about using Data Refinery. * [Cannot refine data from an Excel data asset](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/ts_index.html?context=cdpaas&locale=endr-excel) * [Data Refinery flow job fails with a large data asset](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/ts_index.html?context=cdpaas&locale=enbigdata-dr) Cannot refine data from an Excel data asset The Data Refinery flow might fail if it cannot read the data. Confirm the format of the Excel file. By default, the first line of the file is treated as the header. You can change this setting in the Flow settings ![settings icon](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/images/settings.png). Go to the Source data sets tab and click the overflow menu (![overflow menu](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/images/actions.png)) next to the data source, and select Edit format. You can also specify the first line property, which designates which row is the first row in the data set to be read. Changing these properties affects how the data is displayed in Data Refinery as well as the Data Refinery job run and flow output. Data Refinery flow job fails with a large data asset If your Data Refinery flow job fails with a large data asset, try these troubleshooting tips to fix the problem: * Instead of using a project data asset as the target of the Data Refinery flow (default), use Cloud storage. For example, IBM Cloud Object Storage, Amazon S3, or Google Cloud Storage. * Select a Spark & R environment for the Data Refinery flow job or create a new Spark & R environment template.
Mining for concepts and categories (SPSS Modeler)
Mining for concepts and categories The Text Mining node uses linguistic and frequency techniques to extract key concepts from the text and create categories with these concepts and other data. Use the node to explore the text data contents or to produce either a concept model nugget or category model nugget. ![Text Mining node](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/ta_textmining.png)When you run this node, an internal linguistic extraction engine extracts and organizes the concepts, patterns, and categories by using natural language processing methods. Two build modes are available in the Text Mining node's properties: * The Generate directly (concept model nugget) mode automatically produces a concept or category model nugget when you run the node. * The Build interactively (category model nugget) is a more hands-on, exploratory approach. You can use this mode to not only extract concepts, create categories, and refine your linguistic resources, but also run text link analysis and explore clusters. This build mode launches the Text Analytics Workbench. And you can use the Text Mining node to generate one of two text mining model nuggets: * Concept model nuggets uncover and extract important concepts from your structured or unstructured text data. * Category model nuggets score and assign documents and records to categories, which are made up of the extracted concepts (and patterns). The extracted concepts and patterns and the categories from your model nuggets can all be combined with existing structured data, such as demographics, to yield better and more-focused decisions. For example, if customers frequently list login issues as the primary impediment to completing online account management tasks, you might want to incorporate "login issues" into your models.
Managing payload data for watsonx.governance
Managing payload data for watsonx.governance You must provide payload data to configure drift v2 and generative AI quality evaluations in watsonx.governance. Payload data contains all of your model transactions. You can log payload data with watsonx.governance to enable evaluations. To log payload data, watsonx.governance must receive scoring requests. Logging payload data When you send a scoring request, watsonx.governance processes your model transactions to enable model evaluations. watsonx.governance scores the data and stores it as records in a payload logging table within the watsonx.governance data mart. The payload logging table contains the following columns when you evaluate prompt templates: * Required columns: * Prompt variable(s): Contains the values for the variables that are created for prompt templates * generated_text: Contains the output that's generated by the foundation model * Optional columns: * input_token_count: Contains the number of tokens in the input text * generated_token_count: Contains the number of tokens in the generated text * prediction_probability: Contains the aggregate value of log probabilities of generated tokens that represent the winning output The table can also include timestamp and ID columns to store your data as scoring records. You can view your payload logging table by accessing the database that you specified for the data mart or by using the [Watson OpenScale Python SDK](https://client-docs.aiopenscale.cloud.ibm.com/html/index.html) as shown in the following example: ![Python SDK sample output of payload logging table](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/images/wos-ntbok.png) Sending payload data If you are using IBM Watson Machine Learning as your machine learning provider, watsonx.governance automatically logs payload data when your model is scored. After you configure evaluations, you can also use a payload logging endpoint to send scoring requests to run on-demand evaluations. For production models, you can also upload payload data with a CSV file to send scoring requests. For more information see, [Sending model transactions](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-send-model-transactions.html). Parent topic:[Managing data for model evaluations in Watson OpenScale](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-manage-data.html)
Building the flow (SPSS Modeler)
Building the flow 1. Add a Data Asset node that points to property_values_train.csv. 2. Add a Type node, and select taxable_value as the target field (Role = Target). Other fields will be used as predictors. Figure 1. Setting the measurement level and role ![Setting the role](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_autocont_build_target.png) 3. Attach an Auto Numeric node, and select Correlation as the metric used to rank models (under BASICS in the node properties). 4. Set the Number of models to use to 3. This means that the three best models will be built when you run the node. Figure 2. Auto Numeric node BASICS ![Setting BASIC options](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_autocont_build_basics.png) 5. Under EXPERT, leave the default settings in place. The node will estimate a single model for each algorithm, for a total of six models. (Alternatively, you can modify these settings to compare multiple variants for each model type.) Because you set Number of models to use to 3 under BASICS, the node will calculate the accuracy of the six algorithms and build a single model nugget containing the three most accurate. Figure 3. Auto Numeric node EXPERT options ![Setting EXPERT options](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_autocont_build_expert.png) 6. Under ENSEMBLE, leave the default settings in place. Since this is a continuous target, the ensemble score is generated by averaging the scores for the individual models.![ENSEMBLE options](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_autocont_build_ensemble.png)
svmnode properties
svmnode properties ![SVM node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/svm_icon.png)The Support Vector Machine (SVM) node enables you to classify data into one of two groups without overfitting. SVM works well with wide data sets, such as those with a very large number of input fields. svmnode properties Table 1. svmnode properties svmnode Properties Values Property description all_probabilities flag stopping_criteria 1.0E-1 <br>1.0E-2 <br>1.0E-3 <br>1.0E-4 <br>1.0E-5 <br>1.0E-6 Determines when to stop the optimization algorithm. regularization number Also known as the C parameter. precision number Used only if measurement level of target field is Continuous. kernel RBF <br>Polynomial <br>Sigmoid <br>Linear Type of kernel function used for the transformation. RBF is the default. rbf_gamma number Used only if kernel is RBF. gamma number Used only if kernel is Polynomial or Sigmoid. bias number degree number Used only if kernel is Polynomial. calculate_variable_importance flag calculate_raw_propensities flag calculate_adjusted_propensities flag adjusted_propensity_partition Test <br>Validation
Text Mining model nuggets (SPSS Modeler)
Text Mining model nuggets You can run a Text Mining node to automatically generate a concept model nugget using the Generate directly option in the node settings. Or you can use a more hands-on, exploratory approach using the Build interactively mode to generate category model nuggets from within the Text Analytics Workbench.
Setting algorithm properties
Setting algorithm properties For the Auto Classifier, Auto Numeric, and Auto Cluster nodes, you can set properties for specific algorithms used by the node by using the general form: autonode.setKeyedPropertyValue(<algorithm>, <property>, <value>) For example: node.setKeyedPropertyValue("neuralnetwork", "method", "MultilayerPerceptron") Algorithm names for the Auto Classifier node are cart, chaid, quest, c50, logreg, decisionlist, bayesnet, discriminant, svm and knn. Algorithm names for the Auto Numeric node are cart, chaid, neuralnetwork, genlin, svm, regression, linear and knn. Algorithm names for the Auto Cluster node are twostep, k-means, and kohonen. Property names are standard as documented for each algorithm node. Algorithm properties that contain periods or other punctuation must be wrapped in single quotes. For example: node.setKeyedPropertyValue("logreg", "tolerance", "1.0E-5") Multiple values can also be assigned for a property. For example: node.setKeyedPropertyValue("decisionlist", "search_direction", ["Up", "Down"]) To enable or disable the use of a specific algorithm: node.setPropertyValue("chaid", True) Note: In cases where certain algorithm options aren't available in the Auto Classifier node, or when only a single value can be specified rather than a range of values, the same limits apply with scripting as when accessing the node in the standard manner.
Set up your system
Set up your system Before you can use IBM Federated Learning, ensure that you have the required hardware, software, and dependencies. Core requirements by role Each entity that participates in a Federated Learning experiment must meet the requirements for their role. Admin software requirements Designate an admin for the Federated Learning experiment. The admin must have: * Access to the platform with Watson Studio and Watson Machine Learning enabled. You must [create a Watson Machine Learning service instance](https://cloud.ibm.com/catalog/services/machine-learning). * A [project](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/projects.html) for assembling the global model. You must [associate the Watson Machine Learning service instance with your project](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/assoc-services.html). Party hardware and software requirements Each party must have a system that meets these minimum requirements. Note: Remote parties participating in the same Federated Learning experiment can use different hardware specs and architectures, as long as they each meet the minimum requirement. Supported architectures * x86 64-bit * PPC * Mac M-series * 4 GB memory or greater Supported environments * Linux * Mac OS/Unix * Windows Software dependencies * A supported [Python version and a machine learning framework](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fl-frames.html). * The Watson Machine Learning Python client. 1. If you are using Linux, run pip install 'ibm-watson-machine-learning[fl-rt22.2-py3.10]'. 2. If you are using Mac OS with M-series CPU and Conda, download the installation script and then run ./install_fl_rt22.2_macos.sh <name for new conda environment>. Network requirements An outbound connection from the remote party to aggregator is required. Parties can use firewalls that restrict internal connections with each other. Data sources requirements Data must comply with these requirements. * Data must be in a directory or storage repository that is accessible to the party that uses them. * Each data source for a federate model must have the same features. IBM Federated Learning supports horizontal federated learning only. * Data must be in a readable format, but the formats can vary by data source. Suggested formats include: * Hive * Excel * CSV * XML * Database Parent topic:[Creating a Federated Learning experiment](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fl-start.html)
Troubleshooting Watson Studio on IBM Cloud
Troubleshooting Watson Studio on IBM Cloud You can use the following techniques to work around problems you might encounter with Watson Studio on IBM Cloud. Project limit exceeded Symptoms When you create a project, the following error occurs: The number of projects created by the authenticated user exceeds the designated limit. Possible Causes The number of projects an authenticated user can create per data center (region) is 100. The limit applies only to projects that a user creates. Projects for which the user is listed as a collaborator are not included in this limit. Possible Resolutions Although most customers do not reach this limit, possible resolutions include: * Delete projects. * Any authenticated user can request a project limit increase by contacting [IBM Cloud Support](https://www.ibm.com/cloud/support), provided that an adequate justification is specified. Blank screen when loading Symptoms A blank screen appears when you open Watson Studio. Possible Causes A cached version is loading. Possible Resolutions 1. Clear the browser cache and cookies and re-open Watson Studio. 2. Try a different type of browser. For example, switch from Firefox to Chrome. 3. If the blank screen still occurs, [open a support case](https://cloud.ibm.com/unifiedsupport/supportcenter), generate a .har file, compress it, and upload the compressed har file to the support case.
Time Series node (SPSS Modeler)
Time Series node The Time Series node can be used with data in either a local or distributed environment. With this node, you can choose to estimate and build exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), or multivariate ARIMA (or transfer function) models for time series, and produce forecasts based on the time series data. Exponential smoothing is a method of forecasting that uses weighted values of previous series observations to predict future values. As such, exponential smoothing is not based on a theoretical understanding of the data. It forecasts one point at a time, adjusting its forecasts as new data come in. The technique is useful for forecasting series that exhibit trend, seasonality, or both. You can choose from various exponential smoothing models that differ in their treatment of trend and seasonality. ARIMA models provide more sophisticated methods for modeling trend and seasonal components than do exponential smoothing models, and, in particular, they allow the added benefit of including independent (predictor) variables in the model. This involves explicitly specifying autoregressive and moving average orders as well as the degree of differencing. You can include predictor variables and define transfer functions for any or all of them, as well as specify automatic detection of outliers or an explicit set of outliers. Note: In practical terms, ARIMA models are most useful if you want to include predictors that might help to explain the behavior of the series that is being forecast, such as the number of catalogs that are mailed or the number of hits to a company web page. Exponential smoothing models describe the behavior of the time series without attempting to understand why it behaves as it does. For example, a series that historically peaks every 12 months will probably continue to do so even if you don't know why. An Expert Modeler option is also available, which attempts to automatically identify and estimate the best-fitting ARIMA or exponential smoothing model for one or more target variables, thus eliminating the need to identify an appropriate model through trial and error. If in doubt, use the Expert Modeler option. If predictor variables are specified, the Expert Modeler selects those variables that have a statistically significant relationship with the dependent series for inclusion in ARIMA models. Model variables are transformed where appropriate using differencing and/or a square root or natural log transformation. By default, the Expert Modeler considers all exponential smoothing models and all ARIMA models and picks the best model among them for each target field. You can, however, limit the Expert Modeler only to pick the best of the exponential smoothing models or only to pick the best of the ARIMA models. You can also specify automatic detection of outliers.
Specifying values for a flag (SPSS Modeler)
Specifying values for a flag Use flag fields to display data that has two distinct values. The storage types for flags can be string, integer, real number, or date/time. True. Specify a flag value for the field when the condition is met. False. Specify a flag value for the field when the condition is not met. Labels. Specify labels for each value in the flag field. These labels appear in a variety of locations, such as graphs, tables, output, and model browsers.
Creating a class instance
Creating a class instance You can use classes to hold class (or shared) attributes or to create class instances. To create an instance of a class, you call the class as if it were a function. For example, consider the following class: class MyClass: pass Here, the pass statement is used because a statement is required to complete the class, but no action is required programmatically. The following statement creates an instance of the class MyClass: x = MyClass()
Structured properties
Structured properties There are two ways in which scripting uses structured properties for increased clarity when parsing: * To give structure to the names of properties for complex nodes, such as Type, Filter, or Balance nodes. * To provide a format for specifying multiple properties at once.
Defining a class
Defining a class Within a Python class, you can define both variables and methods. Unlike in Java, in Python you can define any number of public classes per source file (or module). Therefore, you can think of a module in Python as similar to a package in Java. In Python, classes are defined using the class statement. The class statement has the following form: class name (superclasses): statement or class name (superclasses): assignment . . function . . When you define a class, you have the option to provide zero or more assignment statements. These create class attributes that are shared by all instances of the class. You can also provide zero or more function definitions. These function definitions create methods. The superclasses list is optional. The class name should be unique in the same scope, that is within a module, function, or class. You can define multiple variables to reference the same class.
Managing feedback data for watsonx.governance
Managing feedback data for watsonx.governance You must provide feedback data to watsonx.governance to enable you to configure quality and generative AI quality evaluations and determine any changes in your model predictions. When you provide feedback data to watsonx.governance, you can regularly evaluate the accuracy of your model predictions. Feedback logging watsonx.governance stores the feedback data that you provide as records in a feedback logging table. The feedback logging table contains the following columns when you evaluate prompt templates: * Required columns: * Prompt variable(s): Contains the values for the variables that are created for prompt templates * reference_output: Contains the ground truth value * Optional columns: * _original_prediction: Contains the output that's generated by the foundation model Uploading feedback data You can use a feedback logging endpoint to upload data for quality evaluations. You can also upload feedback data with a CSV file. For more information, see [Sending model transactions](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-send-model-transactions.html). Learn more [Sending model transactions](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-send-model-transactions.html) Parent topic:[Managing data for model evaluations in Watson OpenScale](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-manage-data.html)
Examples (SPSS Modeler)
Examples This section provides Python for Spark scripting examples.
multiplotnode properties
multiplotnode properties ![Multiplot node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/multiplotnodeicon.png)The Multiplot node creates a plot that displays multiple Y fields over a single X field. The Y fields are plotted as colored lines; each is equivalent to a Plot node with Style set to Line and X Mode set to Sort. Multiplots are useful when you want to explore the fluctuation of several variables over time. multiplotnode properties Table 1. multiplotnode properties multiplotnode properties Data type Property description x_field field y_fields list panel_field field animation_field field normalize flag use_overlay_expr flag overlay_expression string records_limit number if_over_limit PlotBinsPlotSamplePlotAll x_label_auto flag x_label string y_label_auto flag y_label string use_grid flag graph_background color Standard graph colors are described at the beginning of this section. page_background color Standard graph colors are described at the beginning of this section.