> ## Documentation Index > Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # Log > Call arize.log to publish the features, predicted label, actual observed label, and SHAP for a single data point to Arize for monitoring, analysis, and explainability. Use the [initialized Arize client](/ax/machine-learning/machine-learning/api-reference-ml/java-sdk/constructor) to call `arize.log()` with a predicted label, predicted actual, their feature inputs, their shap values, and a corresponding prediction id. Once records are sent to Arize's platform, you'll be able to visualize and analyze data holistically or within aggregated slices. `arize.log()` returns a `Response`. You can await on the `Response` to complete to ensure successful delivery of records. When logging a prediction for the first time for a new model, we classify the model in the Arize platform based on the data type of the prediction. For more information on model schema discovery, visit here: ## API ```swift theme={null} Response log( final String modelId, final String modelVersion, final String predictionId, final Map features, final Map tags, final Map embeddingFeatures, final T predictionLabel, final T actualLabel, final Map shapValues, long predictionTimestamp) throws IllegalArgumentExceptionArgument, IOException ``` ### API Arguments | | | | | | ----------------------- | --------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | | Argument | Type(s) | Description | Required | | **modelId** | String | Costumer provided unique identifier for a given model. | Y | | **modelVersion** | String | Used to group together a subset of predictions and actuals for a given model\_id. If null is passes, a prediction will only be associated with a model id with no version. | Y | | **predictionId** | String | Unique string identifier for specific label. Must match a previously sent Prediction record. | Y | | **features** | Map\Where value can be oneOf: String, int, long, short, double, float, boolean, List\ | Map containing human readable and debuggable model features. Map keys must be `String` and values one of: `String, int, long, short, double, float, boolean, List` | Optional | | **embeddingFeatures** | Map\ | Map containing human readable and debuggable model embedding features. Map keys must be `String` and values `Embedding` | | | **tags** | Map\ Where value can be oneOf: String, int, long, short, double, float, boolean, List\ | Map containing human readable and debuggable model features. Map keys must be `String` and values one of: `String, int, long, short, double, float, boolean, List` | Optional | | **predictionLabel** | String, boolean, int, long, short, float, double | The predicted label for a given model input (associated via predictionId). | Optional | | **actualLabel** | String, boolean, int, long, short, float, double | The actual label for a given model input (associated via predictionId) | Optional | | **shapValues** | Map\ | Map keys must be `String` and match the names of the features used in the prediction; values must be `Double`. | Optional | | **predictionTimestamp** | long | If `long` representing Unix epoch time in seconds, set overwrite the timestamp for prediction.If `null`, default to current timestamp.**Important**: Future and Historical predictions are supported up to 1 year from current wall clock time. | Optional | ## Sample Code (Categorical) ```java theme={null} import com.arize.ArizeClient; import com.arize.Response; import com.arize.types.Embedding; Map features = new HashMap<>(); features.put("key", "value"); Map embeddingFeatures = new HashMap<>(); embeddingFeatures.put( "embedding_feature_key", new Embedding( Arrays.asList(1.0, 0.5), Arrays.asList("test", "token", "array"), "https://example.com/image.jpg")); Map tags = new HashMap<>(); tags.put("tag_key", "tag_value"); ArizeClient arize = new ArizeClient(System.getenv("ARIZE_API_KEY"), System.getenv("ARIZE_SPACE_KEY")); Response asyncResponse = arize.log("exampleModelId", "v1", UUID.randomUUID().toString(), features, embeddingFeatures, tags, "pear", null, null, 0); // This is a blocking call similar to future.get() asyncResponse.resolve(); // Check that the API call was successful switch (asyncResponse.getResponseCode()) { case OK: // TODO: Success! System.out.println("Success!!!"); break; case AUTHENTICATION_ERROR: // TODO: Check to make sure your Arize API KEY and Space key are correct break; case BAD_REQUEST: // TODO: Malformed request System.out.println("Failure Reason: " + asyncResponse.getResponseBody()); case NOT_FOUND: // TODO: API endpoint not found, client is likely malconfigured, make sure you // are not overwriting Arize's endpoint URI break; case UNEXPECTED_FAILURE: // TODO: Unexpected failure, check for a reason on response body System.out.println("Failure Reason: " + asyncResponse.getResponseBody()); break; } // Don't forget to shutdown the client with your application shutdown hook. arize.close(); ``` ## Sample Code - Score Categorical ```java theme={null} import com.arize.ArizeClient; import com.arize.Response; import com.arize.types.Embedding; Map features = new HashMap<>(); features.put("key", "value"); Map embeddingFeatures = new HashMap<>(); embeddingFeatures.put( "embedding_feature_key", new Embedding( Arrays.asList(1.0, 0.5), Arrays.asList("test", "token", "array"), "https://example.com/image.jpg")); Map tags = new HashMap<>(); tags.put("tag_key", "tag_value"); ArizeClient arize = new ArizeClient(System.getenv("ARIZE_API_KEY"), System.getenv("ARIZE_SPACE_KEY")); // Score Categorical Label ArizeClient.ScoredCategorical scoreLabel = new ArizeClient.ScoredCategorical("Categorical Label", 20.21); Response asyncResponse = client.log("modelId", "modelVersion", "predictionId", features, embeddingFeatures, tags, scoreLabel, null, null, 0); // This is a blocking call similar to future.get() asyncResponse.resolve(); // Check that the API call was successful switch (asyncResponse.getResponseCode()) { case OK: // TODO: Success! System.out.println("Success!!!"); break; case AUTHENTICATION_ERROR: // TODO: Check to make sure your Arize API KEY and Space key are correct break; case BAD_REQUEST: // TODO: Malformed request System.out.println("Failure Reason: " + asyncResponse.getResponseBody()); case NOT_FOUND: // TODO: API endpoint not found, client is likely malconfigured, make sure you // are not overwriting Arize's endpoint URI break; case UNEXPECTED_FAILURE: // TODO: Unexpected failure, check for a reason on response body System.out.println("Failure Reason: " + asyncResponse.getResponseBody()); break; } // Don't forget to shutdown the client with your application shutdown hook. arize.close(); ``` ## Sample Code - Sending in Numeric Sequences for Ranking Models The following code snippet highlights the use of the `ScoredCategorical` constructor for including a numeric sequence in the `actualScoreLabel` ```java theme={null} import com.arize.ArizeClient.ScoredCategorical; Map features = new HashMap<>(); features.put("key", "value"); // Score Categorical Label ScoredCategorical predictionScoreLabel = new ScoredCategorical("Categorical Label", 20.21); ScoredCategorical actualScoreLabel = new ScoredCategorical("relevant", 4.13, Arrays.asList(0.12, 0.23, 0.34)) Response response = client.log("modelId", "modelVersion", "predictionId", features, predictionScoreLabel, actualScoreLabel, null, 0); // This is a blocking call similar to future.get() asyncResponse.resolve(); ``` Questions? Email us at [support@arize.com](mailto::support@arize.com) or [Slack us](https://arize-ai.slack.com/) in the #arize-support channel