itsjhuang/watsonx-docs-document-type huggingface dataset:
Language support for the product and the documentation
Language support for the product and the documentation IBM watsonx is translated into multiple languages. Supported languages The IBM watsonx user interface is translated into these languages: * Brazilian Portuguese * Simplified Chinese * Traditional Chinese * French * German * Italian * Japanese * Korean * Spanish * Swedish The documentation is automatically translated into these languages: * Brazilian Portuguese * Simplified Chinese * French * German * Italian * Japanese * Korean * Spanish IBM is not responsible for any damages or losses resulting from the use of automatically (machine) translated content. When the translated documentation is not as current as the English content, you see a message and have the option of switching to the English content. Changing languages To change the language for this documentation, scroll to the end of any documentation page, and select a language from the language selector. ![Screen capture of the language switcher](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/images/lang-switcher.png) To change the language for both the product user interface and this documentation, select a different language for your browser: * In the Google Chrome browser, you can change the language in the advanced settings. * In the Mozilla Firefox browser, you can change the language in the general settings. Learn more * [Browser support](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/browser-support.html) Parent topic:[FAQ](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/faq.html)
applyquestnode properties
applyquestnode properties You can use QUEST modeling nodes can be used to generate a QUEST model nugget. The scripting name of this model nugget is applyquestnode. For more information on scripting the modeling node itself, see [questnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/questnodeslots.html). applyquestnode properties Table 1. applyquestnode properties applyquestnode Properties Values Property description sql_generate Never <br>NoMissingValues <br>MissingValues <br>native When using data from a database, SQL code can be pushed back to the database for execution, providing superior performance for many operations. calculate_conf flag display_rule_id flag Adds a field in the scoring output that indicates the ID for the terminal node to which each record is assigned. calculate_raw_propensities flag calculate_adjusted_propensities flag
Object-oriented programming
Object-oriented programming Object-oriented programming is based on the notion of creating a model of the target problem in your programs. Object-oriented programming reduces programming errors and promotes the reuse of code. Python is an object-oriented language. Objects defined in Python have the following features: * Identity. Each object must be distinct, and this must be testable. The is and is not tests exist for this purpose. * State. Each object must be able to store state. Attributes, such as fields and instance variables, exist for this purpose. * Behavior. Each object must be able to manipulate its state. Methods exist for this purpose. Python includes the following features for supporting object-oriented programming: * Class-based object creation. Classes are templates for the creation of objects. Objects are data structures with associated behavior. * Inheritance with polymorphism. Python supports single and multiple inheritance. All Python instance methods are polymorphic and can be overridden by subclasses. * Encapsulation with data hiding. Python allows attributes to be hidden. When hidden, you can access attributes from outside the class only through methods of the class. Classes implement methods to modify the data.
coxregnode properties
coxregnode properties ![Cox node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/cox_reg_node_icon.png)The Cox regression node enables you to build a survival model for time-to-event data in the presence of censored records. The model produces a survival function that predicts the probability that the event of interest has occurred at a given time (t) for given values of the input variables. coxregnode properties Table 1. coxregnode properties coxregnode Properties Values Property description survival_time field Cox regression models require a single field containing the survival times. target field Cox regression models require a single target field, and one or more input fields. See [Common modeling node properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/modelingnodeslots_common.htmlmodelingnodeslots_common) for more information. method Enter <br>Stepwise <br>BackwardsStepwise groups field model_type MainEffects <br>Custom custom_terms [BP*Sex" "BP*Age] mode Expert <br>Simple max_iterations number p_converge 1.0E-4 <br>1.0E-5 <br>1.0E-6 <br>1.0E-7 <br>1.0E-8 <br>0 l_converge 1.0E-1 <br>1.0E-2 <br>1.0E-3 <br>1.0E-4 <br>1.0E-5 <br>0 removal_criterion LR <br>Wald <br>Conditional probability_entry number probability_removal number output_display EachStep <br>LastStep ci_enable flag ci_value 90 <br>95 <br>99 correlation flag display_baseline flag survival flag hazard flag log_minus_log flag one_minus_survival flag separate_line field value number or string If no value is specified for a field, the default option "Mean" will be used for that field.
Watson OpenScale offering plan options
Watson OpenScale offering plan options The Watson OpenScale enables responsible, transparent, and explainable AI. With Watson OpenScale you can: * Evaluate machine learning models for dimensions such as fairness, quality, or drift. * Explore transactions to gain insights about your model. Watson OpenScale legacy offering plans Important:The legacy offering plan for Watson OpenScale is available only in the Frankfurt region. In the Dallas region, the [watsonx.governance plans](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-plan-options.html) are available instead. Watson OpenScale Standard v2 plan Watson OpenScale offers a Standard v2 plan that charge users on a per model basis. There are no restrictions or limitations on payload data, feedback rows, or explanations under the Standard v2 instance. Regional limitations Watson OpenScale is not available in some regions. See [Regional availability for services and features](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/regional-datactr.html) for more details. Note:The regional availability for every service can also be found in the [IBM watsonx catalog](https://dataplatform.cloud.ibm.com/data/catalog?target=services&context=cpdaas). Quota limits To avoid performance issues and manage resources efficiently, Watson OpenScale sets the following quota limits: Asset Limit DataMart 100 per instance Service providers 100 per instance Integrated systems 100 per instance Subscriptions 100 per service provider Monitor instances 100 per subscription Every asset in Watson OpenScale has a hard limitation of 10000 instances of the asset per service instance. PostgreSQL databases for Watson OpenScale You can use a PostgreSQL database for your Watson OpenScale instance. PostgreSQL is a powerful, open source object-relational database that is highly customizable and compliant with many security standards. If your model processes personally identifiable information (PII), use a PostgreSQL database for your model. PostgreSQL is compliant with: * GDPR * HIPAA * PCI-DSS * SOC 1 Type 2 * SOC 2 Type 2 * ISO 27001 * ISO 27017 * ISO 27018 * ISO 27701 Next steps [Managing the Watson OpenScale service](https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-provision-launch.html) Parent topic:[watsonx.governance](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/aiopenscale.html)
applyfeatureselectionnode properties
applyfeatureselectionnode properties You can use Feature Selection modeling nodes to generate a Feature Selection model nugget. The scripting name of this model nugget is applyfeatureselectionnode. For more information on scripting the modeling node itself, see [featureselectionnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/featureselectionnodeslots.htmlfeatureselectionnodeslots). applyfeatureselectionnode properties Table 1. applyfeatureselectionnode properties applyfeatureselectionnode Properties Values Property description ranked_values Specifies which ranked fields are checked in the model browser. screened_values Specifies which screened fields are checked in the model browser.
Condition monitoring (SPSS Modeler)
Condition monitoring This example concerns monitoring status information from a machine and the problem of recognizing and predicting fault states. The data is created from a fictitious simulation and consists of a number of concatenated series measured over time. Each record is a snapshot report on the machine in terms of the following: * Time. An integer. * Power. An integer. * Temperature. An integer. * Pressure. 0 if normal, 1 for a momentary pressure warning. * Uptime. Time since last serviced. * Status. Normally 0, changes to an error code if an error occurs (101, 202, or 303). * Outcome. The error code that appears in this time series, or 0 if no error occurs. (These codes are available only with the benefit of hindsight.) This example uses the flow named Condition Monitoring, available in the example project . The data files are cond1n.csv and cond2n.csv. For each time series, there's a series of records from a period of normal operation followed by a period leading to the fault, as shown in the following table: Time Power Temperature Pressure Uptime Status Outcome 0 1059 259 0 404 0 0 1 1059 259 0 404 0 0 ... 51 1059 259 0 404 0 0 52 1059 259 0 404 0 0 53 1007 259 0 404 0 303 54 998 259 0 404 0 303 ... 89 839 259 0 404 0 303 90 834 259 0 404 303 303 0 965 251 0 209 0 0 1 965 251 0 209 0 0 ... 51 965 251 0 209 0 0 52 965 251 0 209 0 0 53 938 251 0 209 0 101 54 936 251 0 209 0 101 ... 208 644 251 0 209 0 101 209 640 251 0 209 101 101 The following process is common to most data mining projects: * Examine the data to determine which attributes may be relevant to the prediction or recognition of the states of interest. * Retain those attributes (if already present), or derive and add them to the data, if necessary. * Use the resultant data to train rules and neural nets. * Test the trained systems using independent test data.
Flow and SuperNode parameters
Flow and SuperNode parameters You can define parameters for use in CLEM expressions and in scripting. They are, in effect, user-defined variables that are saved and persisted with the current flow or SuperNode and can be accessed from the user interface as well as through scripting. If you save a flow, for example, any parameters you set for that flow are also saved. (This distinguishes them from local script variables, which can be used only in the script in which they are declared.) Parameters are often used in scripting to control the behavior of the script, by providing information about fields and values that don't need to be hard coded in the script. You can set flow parameters in a flow script or in a flow's properties (right-click the canvas in your flow and select Flow properties), and they're available to all nodes in the flow. They're displayed in the Parameters list in the Expression Builder. You can also set parameters for SuperNodes, in which case they're visible only to nodes encapsulated within that SuperNode. Tip: For complete details about scripting, see the [Scripting and automation](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/scripting_overview.html) guide.
Statement syntax
Statement syntax The statement syntax for Python is very simple. In general, each source line is a single statement. Except for expression and assignment statements, each statement is introduced by a keyword name, such as if or for. Blank lines or remark lines can be inserted anywhere between any statements in the code. If there's more than one statement on a line, each statement must be separated by a semicolon (;). Very long statements can continue on more than one line. In this case, the statement that is to continue on to the next line must end with a backslash (). For example: x = "A loooooooooooooooooooong string" + "another looooooooooooooooooong string" When you enclose a structure by parentheses (()), brackets ([]), or curly braces ({}), the statement can be continued on a new line after any comma, without having to insert a backslash. For example: x = (1, 2, 3, "hello", "goodbye", 4, 5, 6)
{{ document.title.text }}
Nonconsensual use ![icon for misuse risk](https://dataplatform.cloud.ibm.com/docs/content/wsj/ai-risk-atlas/images/risk-misuse.svg)Risks associated with outputMisuseAmplified Description The possibility that a model could be misused to imitate others through video (deepfakes), images, audio, or other modalities without their consent is the risk of nonconsensual use. Why is nonconsensual use a concern for foundation models? Intentionally imitating others for the purposes of deception without their consent is unethical and might be illegal. A model that has this potential must be properly governed. Otherwise, business entities could face fines, reputational harms, and other legal consequences. Example FBI Warning on Deepfakes The FBI recently warned the public of malicious actors creating synthetic, explicit content “for the purposes of harassing victims or sextortion schemes”. They noted that advancements in AI have made this content higher quality, more customizable, and more accessible than ever. Sources: [FBI, June 2023](https://www.ic3.gov/Media/Y2023/PSA230605) Example Deepfakes A deepfake is the creation of an audio or video where the people speaking are created by AI not the actual person. Sources: [CNN, January 2019](https://www.cnn.com/interactive/2019/01/business/pentagons-race-against-deepfakes/) Example Misleading Voicebot Interaction The article cited a case where a deepfake voice was used to scam a CEO out of $243,000. The CEO believed he was on the phone with his boss, the chief executive of his firm’s parent company, when he followed the orders to transfer €220,000 (approximately $243,000) to the bank account of a Hungarian supplier. Sources: [Forbes, September 2019](https://www.forbes.com/sites/jessedamiani/2019/09/03/a-voice-deepfake-was-used-to-scam-a-ceo-out-of-243000/?sh=10432a7d2241) Parent topic:[AI risk atlas](https://dataplatform.cloud.ibm.com/docs/content/wsj/ai-risk-atlas/ai-risk-atlas.html)
Concepts extraction block
Concepts extraction block The Watson Natural Language Processing Concepts block extracts general DBPedia concepts (concepts drawn from language-specific Wikipedia versions) that are directly referenced or alluded to, but not directly referenced, in the input text. Block name concepts_alchemy_<language>_stock Supported languages The Concepts block is available for the following languages. For a list of the language codes and the corresponding language, see [Language codes](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.htmllang-codes). de, en, es, fr, it, ja, ko, pt Capabilities Use this block to assign concepts from [DBPedia](https://www.dbpedia.org/) (2016 edition). The output types are based on DBPedia. Dependencies on other blocks The following block must run before you can run the Concepts extraction block: * syntax_izumo_<language>_stock Code sample import watson_nlp Load Syntax and a Concepts model for English syntax_model = watson_nlp.load('syntax_izumo_en_stock') concepts_model = watson_nlp.load('concepts_alchemy_en_stock') Run the syntax model on the input text syntax_prediction = syntax_model.run('IBM announced new advances in quantum computing') Run the concepts model on the result of syntax concepts = concepts_model.run(syntax_prediction) print(concepts) Output of the code sample: { "concepts": [ { "text": "IBM", "relevance": 0.9842190146446228, "dbpedia_resource": "http://dbpedia.org/resource/IBM" }, { "text": "Quantum_computing", "relevance": 0.9797260165214539, "dbpedia_resource": "http://dbpedia.org/resource/Quantum_computing" }, { "text": "Computing", "relevance": 0.9080164432525635, "dbpedia_resource": "http://dbpedia.org/resource/Computing" }, { "text": "Shor's_algorithm", "relevance": 0.7580527067184448, "dbpedia_resource": "http://dbpedia.org/resource/Shor's_algorithm" }, { "text": "Quantum_dot", "relevance": 0.7069802284240723, "dbpedia_resource": "http://dbpedia.org/resource/Quantum_dot" }, { "text": "Quantum_algorithm", "relevance": 0.7063655853271484, "dbpedia_resource": "http://dbpedia.org/resource/Quantum_algorithm" }, { "text": "Qubit", "relevance": 0.7063655853271484, "dbpedia_resource": "http://dbpedia.org/resource/Qubit" }, { "text": "DNA_computing", "relevance": 0.7044616341590881, "dbpedia_resource": "http://dbpedia.org/resource/DNA_computing" }, { "text": "Computation", "relevance": 0.7044616341590881, "dbpedia_resource": "http://dbpedia.org/resource/Computation" }, { "text": "Computer", "relevance": 0.7044616341590881, "dbpedia_resource": "http://dbpedia.org/resource/Computer" } ], "producer_id": { "name": "Alchemy Concepts", "version": "0.0.1" } } Parent topic:[Watson Natural Language Processing block catalog](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html)
filternode properties
filternode properties ![Filter node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/filternodeicon.png)The Filter node filters (discards) fields, renames fields, and maps fields from one import node to another. Using the default_include property. Note that setting the value of the default_include property doesn't automatically include or exclude all fields; it simply determines the default for the current selection. This is functionally equivalent to selecting the Include All Fields option in the Filter node properties. For example, suppose you run the following script: node = modeler.script.stream().create("filter", "Filter") node.setPropertyValue("default_include", False) Include these two fields in the list for f in ["Age", "Sex"]: node.setKeyedPropertyValue("include", f, True) This will cause the node to pass the fields Age and Sex and discard all others. Now suppose you run the same script again but name two different fields: node = modeler.script.stream().create("filter", "Filter") node.setPropertyValue("default_include", False) Include these two fields in the list for f in ["BP", "Na"]: node.setKeyedPropertyValue("include", f, True) This will add two more fields to the filter so that a total of four fields are passed (Age, Sex, BP, Na). In other words, resetting the value of default_include to False doesn't automatically reset all fields. Alternatively, if you now change default_include to True, either using a script or in the Filter node dialog box, this would flip the behavior so the four fields listed previously would be discarded rather than included. When in doubt, experimenting with the controls in the Filter node properties may be helpful in understanding this interaction. filternode properties Table 1. filternode properties filternode properties Data type Property description default_include flag Keyed property to specify whether the default behavior is to pass or filter fields: Note that setting this property doesn't automatically include or exclude all fields; it simply determines whether selected fields are included or excluded by default. include flag Keyed property for field inclusion and removal. new_name string
Creating a scatterplot (SPSS Modeler)
Creating a scatterplot Now let's take a look at what factors might influence Drug, the target variable. As a researcher, you know that the concentrations of sodium and potassium in the blood are important factors. Since these are both numeric values, you can create a scatterplot of sodium versus potassium, using the drug categories as a color overlay. Figure 1. Plot node ![Plot node](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_drug_scatterplot_flow.png) 1. Place a Plot node on the canvas and connect it to the drug1n.csv Data Asset node. Then double-click the Plot node to edit its properties. 2. Select Na as the X field, K as the Y field, and Drug as the Color (overlay) field. Click Save, then right-click the Plot node and select Run. A plot chart is added to the Outputs pane. The plot clearly shows a threshold above which the correct drug is always drug Y and below which the correct drug is never drug Y. This threshold is a ratio -- the ratio of sodium (Na) to potassium (K). Figure 2. Scatterplot of drug distribution ![Scatterplot of drug distribution](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_drug_scatterplot.png)
logregnode properties
logregnode properties ![Logistic node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/logisticnodeicon.png)Logistic regression is a statistical technique for classifying records based on values of input fields. It is analogous to linear regression but takes a categorical target field instead of a numeric range. logregnode properties Table 1. logregnode properties logregnode Properties Values Property description target field Logistic regression models require a single target field and one or more input fields. Frequency and weight fields are not used. See [Common modeling node properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/modelingnodeslots_common.htmlmodelingnodeslots_common) for more information. logistic_procedure BinomialMultinomial include_constant flag mode SimpleExpert method EnterStepwiseForwardsBackwardsBackwardsStepwise binomial_method EnterForwardsBackwards model_type MainEffectsFullFactorialCustom When FullFactorial is specified as the model type, stepping methods will not run, even if specified. Instead, Enter will be the method used. If the model type is set to Custom but no custom fields are specified, a main-effects model will be built. custom_terms [[BP Sex][BP][Age]] multinomial_base_category string Specifies how the reference category is determined. binomial_categorical_input string binomial_input_contrast IndicatorSimpleDifferenceHelmertRepeatedPolynomialDeviation Keyed property for categorical input that specifies how the contrast is determined. See the example for usage. binomial_input_category FirstLast Keyed property for categorical input that specifies how the reference category is determined. See the example for usage. scale NoneUserDefinedPearsonDeviance scale_value number all_probabilities flag tolerance 1.0E-51.0E-61.0E-71.0E-81.0E-91.0E-10 min_terms number use_max_terms flag max_terms number entry_criterion ScoreLR removal_criterion LRWald probability_entry number probability_removal number binomial_probability_entry number binomial_probability_removal number requirements HierarchyDiscreteHierarchyAllContainmentNone max_iterations number max_steps number p_converge 1.0E-41.0E-51.0E-61.0E-71.0E-80 l_converge 1.0E-11.0E-21.0E-31.0E-41.0E-50 delta number iteration_history flag history_steps number summary flag likelihood_ratio flag asymptotic_correlation flag goodness_fit flag parameters flag confidence_interval number asymptotic_covariance flag classification_table flag stepwise_summary flag info_criteria flag monotonicity_measures flag binomial_output_display at_each_stepat_last_step binomial_goodness_of_fit flag binomial_parameters flag binomial_iteration_history flag binomial_classification_plots flag binomial_ci_enable flag binomial_ci number binomial_residual outliersall binomial_residual_enable flag binomial_outlier_threshold number binomial_classification_cutoff number binomial_removal_criterion LRWaldConditional calculate_variable_importance flag calculate_raw_propensities flag