Language - Custom Text Authoring APIs (2022-03-01-preview)
Models_GetTrainedModelEvaluationSummary
Get trained model evaluation summary.
Select the testing console in the region where you created your resource:
Open API testing consoleRequest URL
Request parameters
string
The project name.
string
The trained model name.
Request headers
string
Subscription key which provides access to this API. Found in your Cognitive Services accounts.
Request body
Response 200
List of all evaluation results.
{
"projectKind": "customSingleLabelClassification",
"customNamedEntityRecognitionEvaluation": {
"confusionMatrix": {},
"entities": {},
"microF1": 0.0,
"microPrecision": 0.0,
"microRecall": 0.0,
"macroF1": 0.0,
"macroPrecision": 0.0,
"macroRecall": 0.0
},
"customSingleLabelClassificationEvaluation": {
"confusionMatrix": {},
"classes": {},
"microF1": 0.0,
"microPrecision": 0.0,
"microRecall": 0.0,
"macroF1": 0.0,
"macroPrecision": 0.0,
"macroRecall": 0.0
},
"customMultiLabelClassificationEvaluation": {
"classes": {},
"microF1": 0.0,
"microPrecision": 0.0,
"microRecall": 0.0,
"macroF1": 0.0,
"macroPrecision": 0.0,
"macroRecall": 0.0
},
"evaluationOptions": {
"kind": "percentage",
"trainingSplitPercentage": 0,
"testingSplitPercentage": 0
}
}{
"required": [
"projectKind",
"customNamedEntityRecognitionEvaluation",
"customSingleLabelClassificationEvaluation",
"customMultiLabelClassificationEvaluation"
],
"type": "object",
"properties": {
"projectKind": {
"description": "Represents the model kind.",
"enum": [
"customSingleLabelClassification",
"customMultiLabelClassification",
"customNamedEntityRecognition"
],
"type": "string",
"x-ms-enum": {
"name": "ProjectKind",
"modelAsString": true
}
},
"customNamedEntityRecognitionEvaluation": {
"description": "Contains the data related to extraction evaluation.",
"required": [
"confusionMatrix",
"entities",
"microF1",
"microPrecision",
"microRecall",
"macroF1",
"macroPrecision",
"macroRecall"
],
"type": "object",
"properties": {
"confusionMatrix": {
"description": "Represents the confusion matrix between two entities (the two entities can be the same)\n the matrix is between the actual entity that was labelled and the entity that was predicted.",
"type": "object",
"additionalProperties": {
"type": "object",
"additionalProperties": {
"required": [
"normalizedValue",
"rawValue"
],
"type": "object",
"properties": {
"normalizedValue": {
"format": "float",
"description": "Represents normalized value in percentages.",
"type": "number"
},
"rawValue": {
"format": "float",
"description": "Represents raw value.",
"type": "number"
}
}
}
}
},
"entities": {
"description": "Represents the entities evaluation",
"type": "object",
"additionalProperties": {
"required": [
"f1",
"precision",
"recall",
"truePositivesCount",
"trueNegativesCount",
"falsePositivesCount",
"falseNegativesCount"
],
"type": "object",
"properties": {
"f1": {
"format": "double",
"description": "Represents the model precision",
"type": "number"
},
"precision": {
"format": "double",
"description": "Represents the model recall",
"type": "number"
},
"recall": {
"format": "double",
"description": "Represents the model F1 score",
"type": "number"
},
"truePositivesCount": {
"format": "int32",
"description": "Represents the count of true positives",
"type": "integer"
},
"trueNegativesCount": {
"format": "int32",
"description": "Represents the count of true negatives",
"type": "integer"
},
"falsePositivesCount": {
"format": "int32",
"description": "Represents the count of false positives",
"type": "integer"
},
"falseNegativesCount": {
"format": "int32",
"description": "Represents the count of false negatives",
"type": "integer"
}
}
}
},
"microF1": {
"format": "float",
"description": "Represents the micro F1",
"type": "number"
},
"microPrecision": {
"format": "float",
"description": "Represents the micro precision",
"type": "number"
},
"microRecall": {
"format": "float",
"description": "Represents the micro recall",
"type": "number"
},
"macroF1": {
"format": "float",
"description": "Represents the macro F1",
"type": "number"
},
"macroPrecision": {
"format": "float",
"description": "Represents the macro precision",
"type": "number"
},
"macroRecall": {
"format": "float",
"description": "Represents the macro recall",
"type": "number"
}
}
},
"customSingleLabelClassificationEvaluation": {
"description": "Contains the data related to single label classification evaluation.",
"required": [
"confusionMatrix",
"classes",
"microF1",
"microPrecision",
"microRecall",
"macroF1",
"macroPrecision",
"macroRecall"
],
"type": "object",
"properties": {
"confusionMatrix": {
"description": "Represents the confusion matrix between two classifiers (the two classifiers can be the same entity)\n the matrix is between the actual classifier that was labelled and the classifier that was predicted.",
"type": "object",
"additionalProperties": {
"type": "object",
"additionalProperties": {
"required": [
"normalizedValue",
"rawValue"
],
"type": "object",
"properties": {
"normalizedValue": {
"format": "float",
"description": "Represents normalized value in percentages.",
"type": "number"
},
"rawValue": {
"format": "float",
"description": "Represents raw value.",
"type": "number"
}
}
}
}
},
"classes": {
"description": "Represents the classes evaluation",
"type": "object",
"additionalProperties": {
"required": [
"f1",
"precision",
"recall",
"truePositivesCount",
"trueNegativesCount",
"falsePositivesCount",
"falseNegativesCount"
],
"type": "object",
"properties": {
"f1": {
"format": "double",
"description": "Represents the model precision",
"type": "number"
},
"precision": {
"format": "double",
"description": "Represents the model recall",
"type": "number"
},
"recall": {
"format": "double",
"description": "Represents the model F1 score",
"type": "number"
},
"truePositivesCount": {
"format": "int32",
"description": "Represents the count of true positives",
"type": "integer"
},
"trueNegativesCount": {
"format": "int32",
"description": "Represents the count of true negatives",
"type": "integer"
},
"falsePositivesCount": {
"format": "int32",
"description": "Represents the count of false positives",
"type": "integer"
},
"falseNegativesCount": {
"format": "int32",
"description": "Represents the count of false negatives",
"type": "integer"
}
}
}
},
"microF1": {
"format": "float",
"description": "Represents the micro F1",
"type": "number"
},
"microPrecision": {
"format": "float",
"description": "Represents the micro precision",
"type": "number"
},
"microRecall": {
"format": "float",
"description": "Represents the micro recall",
"type": "number"
},
"macroF1": {
"format": "float",
"description": "Represents the macro F1",
"type": "number"
},
"macroPrecision": {
"format": "float",
"description": "Represents the macro precision",
"type": "number"
},
"macroRecall": {
"format": "float",
"description": "Represents the macro recall",
"type": "number"
}
}
},
"customMultiLabelClassificationEvaluation": {
"description": "Contains the data related to multi label classification evaluation.",
"required": [
"classes",
"microF1",
"microPrecision",
"microRecall",
"macroF1",
"macroPrecision",
"macroRecall"
],
"type": "object",
"properties": {
"classes": {
"description": "Represents the classes evaluation",
"type": "object",
"additionalProperties": {
"required": [
"f1",
"precision",
"recall",
"truePositivesCount",
"trueNegativesCount",
"falsePositivesCount",
"falseNegativesCount"
],
"type": "object",
"properties": {
"f1": {
"format": "double",
"description": "Represents the model precision",
"type": "number"
},
"precision": {
"format": "double",
"description": "Represents the model recall",
"type": "number"
},
"recall": {
"format": "double",
"description": "Represents the model F1 score",
"type": "number"
},
"truePositivesCount": {
"format": "int32",
"description": "Represents the count of true positives",
"type": "integer"
},
"trueNegativesCount": {
"format": "int32",
"description": "Represents the count of true negatives",
"type": "integer"
},
"falsePositivesCount": {
"format": "int32",
"description": "Represents the count of false positives",
"type": "integer"
},
"falseNegativesCount": {
"format": "int32",
"description": "Represents the count of false negatives",
"type": "integer"
}
}
}
},
"microF1": {
"format": "float",
"description": "Represents the micro F1",
"type": "number"
},
"microPrecision": {
"format": "float",
"description": "Represents the micro precision",
"type": "number"
},
"microRecall": {
"format": "float",
"description": "Represents the micro recall",
"type": "number"
},
"macroF1": {
"format": "float",
"description": "Represents the macro F1",
"type": "number"
},
"macroPrecision": {
"format": "float",
"description": "Represents the macro precision",
"type": "number"
},
"macroRecall": {
"format": "float",
"description": "Represents the macro recall",
"type": "number"
}
}
},
"evaluationOptions": {
"type": "object",
"properties": {
"kind": {
"description": "Represents the evaluation kind. By default, the evaluation kind is set to percentage.",
"enum": [
"percentage",
"manual"
],
"type": "string",
"x-ms-enum": {
"name": "EvaluationKind",
"modelAsString": true
}
},
"trainingSplitPercentage": {
"format": "int32",
"description": "Represents the training dataset split percentage. Only needed in case the evaluation kind is percentage.",
"type": "integer"
},
"testingSplitPercentage": {
"format": "int32",
"description": "Represents the testing dataset split percentage. Only needed in case the evaluation kind is percentage.",
"type": "integer"
}
}
}
}
}Response 400
This error can be returned if the request's parameters are incorrect meaning the required parameters are missing, malformed, or too large.\r\n\r\nThis error can be returned if the request's body is incorrect meaning the JSON is missing, malformed, or too large.
Response 401
You do not have access. \r\n\r\nReasons can include:\r\n\r\n* used endpoint subscription key, instead of authoring key\r\n* invalid, malformed, or empty authoring key\r\n* authoring key doesn't match region\r\n* you are not the owner or collaborator\r\n* invalid order of API calls.
Response 403
Total monthly key quota limit exceeded.
Response 404
The project does not exist.
Response 429
Rate limit is exceeded.
Code samples
@ECHO OFF
curl -v -X GET "https://virginia.api.cognitive.microsoft.us/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview"
-H "Ocp-Apim-Subscription-Key: {subscription key}"
--data-ascii "{body}"
using System;
using System.Net.Http.Headers;
using System.Text;
using System.Net.Http;
using System.Web;
namespace CSHttpClientSample
{
static class Program
{
static void Main()
{
MakeRequest();
Console.WriteLine("Hit ENTER to exit...");
Console.ReadLine();
}
static async void MakeRequest()
{
var client = new HttpClient();
var queryString = HttpUtility.ParseQueryString(string.Empty);
// Request headers
client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "{subscription key}");
var uri = "https://virginia.api.cognitive.microsoft.us/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview&" + queryString;
var response = await client.GetAsync(uri);
}
}
} // // This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
import java.net.URI;
import org.apache.http.HttpEntity;
import org.apache.http.HttpResponse;
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.client.utils.URIBuilder;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.util.EntityUtils;
public class JavaSample
{
public static void main(String[] args)
{
HttpClient httpclient = HttpClients.createDefault();
try
{
URIBuilder builder = new URIBuilder("https://virginia.api.cognitive.microsoft.us/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview");
URI uri = builder.build();
HttpGet request = new HttpGet(uri);
request.setHeader("Ocp-Apim-Subscription-Key", "{subscription key}");
// Request body
StringEntity reqEntity = new StringEntity("{body}");
request.setEntity(reqEntity);
HttpResponse response = httpclient.execute(request);
HttpEntity entity = response.getEntity();
if (entity != null)
{
System.out.println(EntityUtils.toString(entity));
}
}
catch (Exception e)
{
System.out.println(e.getMessage());
}
}
}
<!DOCTYPE html>
<html>
<head>
<title>JSSample</title>
<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js"></script>
</head>
<body>
<script type="text/javascript">
$(function() {
var params = {
// Request parameters
};
$.ajax({
url: "https://virginia.api.cognitive.microsoft.us/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview&" + $.param(params),
beforeSend: function(xhrObj){
// Request headers
xhrObj.setRequestHeader("Ocp-Apim-Subscription-Key","{subscription key}");
},
type: "GET",
// Request body
data: "{body}",
})
.done(function(data) {
alert("success");
})
.fail(function() {
alert("error");
});
});
</script>
</body>
</html>
#import <Foundation/Foundation.h>
int main(int argc, const char * argv[])
{
NSAutoreleasePool * pool = [[NSAutoreleasePool alloc] init];
NSString* path = @"https://virginia.api.cognitive.microsoft.us/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview";
NSArray* array = @[
// Request parameters
@"entities=true",
];
NSString* string = [array componentsJoinedByString:@"&"];
path = [path stringByAppendingFormat:@"?%@", string];
NSLog(@"%@", path);
NSMutableURLRequest* _request = [NSMutableURLRequest requestWithURL:[NSURL URLWithString:path]];
[_request setHTTPMethod:@"GET"];
// Request headers
[_request setValue:@"{subscription key}" forHTTPHeaderField:@"Ocp-Apim-Subscription-Key"];
// Request body
[_request setHTTPBody:[@"{body}" dataUsingEncoding:NSUTF8StringEncoding]];
NSURLResponse *response = nil;
NSError *error = nil;
NSData* _connectionData = [NSURLConnection sendSynchronousRequest:_request returningResponse:&response error:&error];
if (nil != error)
{
NSLog(@"Error: %@", error);
}
else
{
NSError* error = nil;
NSMutableDictionary* json = nil;
NSString* dataString = [[NSString alloc] initWithData:_connectionData encoding:NSUTF8StringEncoding];
NSLog(@"%@", dataString);
if (nil != _connectionData)
{
json = [NSJSONSerialization JSONObjectWithData:_connectionData options:NSJSONReadingMutableContainers error:&error];
}
if (error || !json)
{
NSLog(@"Could not parse loaded json with error:%@", error);
}
NSLog(@"%@", json);
_connectionData = nil;
}
[pool drain];
return 0;
}
<?php
// This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
require_once 'HTTP/Request2.php';
$request = new Http_Request2('https://virginia.api.cognitive.microsoft.us/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview');
$url = $request->getUrl();
$headers = array(
// Request headers
'Ocp-Apim-Subscription-Key' => '{subscription key}',
);
$request->setHeader($headers);
$parameters = array(
// Request parameters
);
$url->setQueryVariables($parameters);
$request->setMethod(HTTP_Request2::METHOD_GET);
// Request body
$request->setBody("{body}");
try
{
$response = $request->send();
echo $response->getBody();
}
catch (HttpException $ex)
{
echo $ex;
}
?>########### Python 2.7 #############
import httplib, urllib, base64
headers = {
# Request headers
'Ocp-Apim-Subscription-Key': '{subscription key}',
}
params = urllib.urlencode({
})
try:
conn = httplib.HTTPSConnection('virginia.api.cognitive.microsoft.us')
conn.request("GET", "/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview&%s" % params, "{body}", headers)
response = conn.getresponse()
data = response.read()
print(data)
conn.close()
except Exception as e:
print("[Errno {0}] {1}".format(e.errno, e.strerror))
####################################
########### Python 3.2 #############
import http.client, urllib.request, urllib.parse, urllib.error, base64
headers = {
# Request headers
'Ocp-Apim-Subscription-Key': '{subscription key}',
}
params = urllib.parse.urlencode({
})
try:
conn = http.client.HTTPSConnection('virginia.api.cognitive.microsoft.us')
conn.request("GET", "/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview&%s" % params, "{body}", headers)
response = conn.getresponse()
data = response.read()
print(data)
conn.close()
except Exception as e:
print("[Errno {0}] {1}".format(e.errno, e.strerror))
####################################require 'net/http'
uri = URI('https://virginia.api.cognitive.microsoft.us/language/authoring/analyze-text/projects/{projectName}/models/{trainedModelLabel}/evaluation/summary-result?api-version=2022-03-01-preview')
uri.query = URI.encode_www_form({
})
request = Net::HTTP::Get.new(uri.request_uri)
# Request headers
request['Ocp-Apim-Subscription-Key'] = '{subscription key}'
# Request body
request.body = "{body}"
response = Net::HTTP.start(uri.host, uri.port, :use_ssl => uri.scheme == 'https') do |http|
http.request(request)
end
puts response.body