Demonstrate listing model evaluations.
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For detailed documentation that includes this code sample, see the following:
Code sample
Java
import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.ListModelEvaluationsRequest;
import com.google.cloud.automl.v1beta1.ModelEvaluation;
import com.google.cloud.automl.v1beta1.ModelName;
import java.io.IOException;
class ListModelEvaluations {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "YOUR_PROJECT_ID";
String modelId = "YOUR_MODEL_ID";
listModelEvaluations(projectId, modelId);
}
// List model evaluations
static void listModelEvaluations(String projectId, String modelId) throws IOException {
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// Get the full path of the model.
ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
ListModelEvaluationsRequest modelEvaluationsrequest =
ListModelEvaluationsRequest.newBuilder().setParent(modelFullId.toString()).build();
// List all the model evaluations in the model by applying filter.
System.out.println("List of model evaluations:");
for (ModelEvaluation modelEvaluation :
client.listModelEvaluations(modelEvaluationsrequest).iterateAll()) {
System.out.format("Model Evaluation Name: %s%n", modelEvaluation.getName());
System.out.format("Model Annotation Spec Id: %s", modelEvaluation.getAnnotationSpecId());
System.out.println("Create Time:");
System.out.format("\tseconds: %s%n", modelEvaluation.getCreateTime().getSeconds());
System.out.format("\tnanos: %s", modelEvaluation.getCreateTime().getNanos() / 1e9);
System.out.format(
"Evalution Example Count: %d%n", modelEvaluation.getEvaluatedExampleCount());
System.out.format(
"Tables Model Evaluation Metrics: %s%n",
modelEvaluation.getClassificationEvaluationMetrics());
}
}
}
}Node.js
const automl = require('@google-cloud/automl');
const math = require('mathjs');
const client = new automl.v1beta1.AutoMlClient();
/**
* Demonstrates using the AutoML client to list model evaluations.
* TODO(developer): Uncomment the following lines before running the sample.
*/
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL4704590352927948800";
// const filter = '[FILTER_EXPRESSIONS]' e.g., "tablesModelMetadata:*";
// Get the full path of the model.
const modelFullId = client.modelPath(projectId, computeRegion, modelId);
// List all the model evaluations in the model by applying filter.
client
.listModelEvaluations({parent: modelFullId, filter: filter})
.then(responses => {
const element = responses[0];
console.log('List of model evaluations:');
for (let i = 0; i < element.length; i++) {
const classMetrics = element[i].classificationEvaluationMetrics;
const regressionMetrics = element[i].regressionEvaluationMetrics;
const evaluationId = element[i].name.split('/')[7].split('`')[0];
console.log(`Model evaluation name: ${element[i].name}`);
console.log(`Model evaluation Id: ${evaluationId}`);
console.log(
`Model evaluation annotation spec Id: ${element[i].annotationSpecId}`
);
console.log(`Model evaluation display name: ${element[i].displayName}`);
console.log(
`Model evaluation example count: ${element[i].evaluatedExampleCount}`
);
if (classMetrics) {
const confidenceMetricsEntries = classMetrics.confidenceMetricsEntry;
console.log('Table classification evaluation metrics:');
console.log(`\tModel auPrc: ${math.round(classMetrics.auPrc, 6)}`);
console.log(`\tModel auRoc: ${math.round(classMetrics.auRoc, 6)}`);
console.log(
`\tModel log loss: ${math.round(classMetrics.logLoss, 6)}`
);
if (confidenceMetricsEntries.length > 0) {
console.log('\tConfidence metrics entries:');
for (const confidenceMetricsEntry of confidenceMetricsEntries) {
console.log(
`\t\tModel confidence threshold: ${math.round(
confidenceMetricsEntry.confidenceThreshold,
6
)}`
);
console.log(
`\t\tModel position threshold: ${math.round(
confidenceMetricsEntry.positionThreshold,
4
)}`
);
console.log(
`\t\tModel recall: ${math.round(
confidenceMetricsEntry.recall * 100,
2
)} %`
);
console.log(
`\t\tModel precision: ${math.round(
confidenceMetricsEntry.precision * 100,
2
)} %`
);
console.log(
`\t\tModel false positive rate: ${confidenceMetricsEntry.falsePositiveRate}`
);
console.log(
`\t\tModel f1 score: ${math.round(
confidenceMetricsEntry.f1Score * 100,
2
)} %`
);
console.log(
`\t\tModel recall@1: ${math.round(
confidenceMetricsEntry.recallAt1 * 100,
2
)} %`
);
console.log(
`\t\tModel precision@1: ${math.round(
confidenceMetricsEntry.precisionAt1 * 100,
2
)} %`
);
console.log(
`\t\tModel false positive rate@1: ${confidenceMetricsEntry.falsePositiveRateAt1}`
);
console.log(
`\t\tModel f1 score@1: ${math.round(
confidenceMetricsEntry.f1ScoreAt1 * 100,
2
)} %`
);
console.log(
`\t\tModel true positive count: ${confidenceMetricsEntry.truePositiveCount}`
);
console.log(
`\t\tModel false positive count: ${confidenceMetricsEntry.falsePositiveCount}`
);
console.log(
`\t\tModel false negative count: ${confidenceMetricsEntry.falseNegativeCount}`
);
console.log(
`\t\tModel true negative count: ${confidenceMetricsEntry.trueNegativeCount}`
);
console.log('\n');
}
}
console.log(
`\tModel annotation spec Id: ${classMetrics.annotationSpecId}`
);
} else if (regressionMetrics) {
console.log('Table regression evaluation metrics:');
console.log(
`\tModel root mean squared error: ${regressionMetrics.rootMeanSquaredError}`
);
console.log(
`\tModel mean absolute error: ${regressionMetrics.meanAbsoluteError}`
);
console.log(
`\tModel mean absolute percentage error: ${regressionMetrics.meanAbsolutePercentageError}`
);
console.log(`\tModel rSquared: ${regressionMetrics.rSquared}`);
}
console.log('\n');
}
})
.catch(err => {
console.error(err);
});Python
# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'
# filter = 'filter expression here'
from google.cloud import automl_v1beta1 as automl
client = automl.TablesClient(project=project_id, region=compute_region)
# List all the model evaluations in the model by applying filter.
response = client.list_model_evaluations(
model_display_name=model_display_name, filter=filter
)
print("List of model evaluations:")
for evaluation in response:
print("Model evaluation name: {}".format(evaluation.name))
print("Model evaluation id: {}".format(evaluation.name.split("/")[-1]))
print(
"Model evaluation example count: {}".format(
evaluation.evaluated_example_count
)
)
print("Model evaluation time: {}".format(evaluation.create_time))
print("\n")What's next
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