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:
What Is A Model Schema
API
<T> Response log(
final String modelId,
final String modelVersion,
final String predictionId,
final Map<String, ?> features,
final Map<String, ?> tags,
final Map<String, Embedding> embeddingFeatures,
final T predictionLabel,
final T actualLabel,
final Map<String, Double> 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<String, ?>Where value can be oneOf: String, int, long, short, double, float, boolean, List<String> | 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<String> | Optional |
| embeddingFeatures | Map<String, Embedding> | Map containing human readable and debuggable model embedding features. Map keys must be String and values Embedding | |
| tags | Map<String, ?> Where value can be oneOf: String, int, long, short, double, float, boolean, List<String> | 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<String> | 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<String, Double> | 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)
import com.arize.ArizeClient;
import com.arize.Response;
import com.arize.types.Embedding;
Map<String, String> features = new HashMap<>();
features.put("key", "value");
Map<String, Embedding> 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<String, String> 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
import com.arize.ArizeClient;
import com.arize.Response;
import com.arize.types.Embedding;
Map<String, String> features = new HashMap<>();
features.put("key", "value");
Map<String, Embedding> 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<String, String> 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 theScoredCategorical constructor for including a numeric sequence in the actualScoreLabel
import com.arize.ArizeClient.ScoredCategorical;
Map<String, String> 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 or Slack us in the #arize-support channel