feat: adds GetAnnotationSpec, DeployModel, UndeployModel, ExportModel (…

…#258)

.nycrc

@@ -10,7 +10,6 @@
     "**/docs",
     "**/samples",
     "**/scripts",
-    "**/src/**/v*/**/*.js",
     "**/protos",
     "**/test",
     ".jsdoc.js",

README.md

@@ -111,7 +111,6 @@ has instructions for running the samples.
 | Sample                      | Source Code                       | Try it |
 | --------------------------- | --------------------------------- | ------ |
 | Quickstart | [source code](https://github.com/googleapis/nodejs-automl/blob/master/samples/quickstart.js) | [![Open in Cloud Shell][shell_img]](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/googleapis/nodejs-automl&page=editor&open_in_editor=samples/quickstart.js,samples/README.md) |
-| Set Endpoint | [source code](https://github.com/googleapis/nodejs-automl/blob/master/samples/beta/setEndpoint.js) | [![Open in Cloud Shell][shell_img]](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/googleapis/nodejs-automl&page=editor&open_in_editor=samples/beta/setEndpoint.js,samples/README.md) |
 
 
 

protos/google/cloud/automl/v1/annotation_payload.proto

@@ -17,12 +17,16 @@ syntax = "proto3";
 
 package google.cloud.automl.v1;
 
+import "google/cloud/automl/v1/classification.proto";
+import "google/cloud/automl/v1/detection.proto";
+import "google/cloud/automl/v1/text_extraction.proto";
+import "google/cloud/automl/v1/text_sentiment.proto";
 import "google/cloud/automl/v1/translation.proto";
 import "google/protobuf/any.proto";
 import "google/api/annotations.proto";
 
-option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
 option csharp_namespace = "Google.Cloud.AutoML.V1";
+option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
 option java_multiple_files = true;
 option java_package = "com.google.cloud.automl.v1";
 option php_namespace = "Google\\Cloud\\AutoML\\V1";
@@ -35,5 +39,30 @@ message AnnotationPayload {
   oneof detail {
     // Annotation details for translation.
     TranslationAnnotation translation = 2;
+
+    // Annotation details for content or image classification.
+    ClassificationAnnotation classification = 3;
+
+    // Annotation details for image object detection.
+    ImageObjectDetectionAnnotation image_object_detection = 4;
+
+    // Annotation details for text extraction.
+    TextExtractionAnnotation text_extraction = 6;
+
+    // Annotation details for text sentiment.
+    TextSentimentAnnotation text_sentiment = 7;
   }
+
+  // Output only . The resource ID of the annotation spec that
+  // this annotation pertains to. The annotation spec comes from either an
+  // ancestor dataset, or the dataset that was used to train the model in use.
+  string annotation_spec_id = 1;
+
+  // Output only. The value of
+  // [display_name][google.cloud.automl.v1.AnnotationSpec.display_name]
+  // when the model was trained. Because this field returns a value at model
+  // training time, for different models trained using the same dataset, the
+  // returned value could be different as model owner could update the
+  // `display_name` between any two model training.
+  string display_name = 5;
 }

protos/google/cloud/automl/v1/annotation_spec.proto

@@ -0,0 +1,46 @@
+// Copyright 2019 Google LLC.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+syntax = "proto3";
+
+package google.cloud.automl.v1;
+
+import "google/api/annotations.proto";
+
+option csharp_namespace = "Google.Cloud.AutoML.V1";
+option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
+option java_multiple_files = true;
+option java_package = "com.google.cloud.automl.v1";
+option php_namespace = "Google\\Cloud\\AutoML\\V1";
+option ruby_package = "Google::Cloud::AutoML::V1";
+
+// A definition of an annotation spec.
+message AnnotationSpec {
+  // Output only. Resource name of the annotation spec.
+  // Form:
+  //
+  // 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
+  string name = 1;
+
+  // Required.
+  // The name of the annotation spec to show in the interface. The name can be
+  // up to 32 characters long and must match the regexp `[a-zA-Z0-9_]+`.
+  // (_), and ASCII digits 0-9.
+  string display_name = 2;
+
+  // Output only. The number of examples in the parent dataset
+  // labeled by the annotation spec.
+  int32 example_count = 9;
+}

protos/google/cloud/automl/v1/classification.proto

@@ -0,0 +1,172 @@
+// Copyright 2019 Google LLC.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+syntax = "proto3";
+
+package google.cloud.automl.v1;
+
+import "google/api/annotations.proto";
+
+option csharp_namespace = "Google.Cloud.AutoML.V1";
+option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
+option java_multiple_files = true;
+option java_outer_classname = "ClassificationProto";
+option java_package = "com.google.cloud.automl.v1";
+option php_namespace = "Google\\Cloud\\AutoML\\V1";
+option ruby_package = "Google::Cloud::AutoML::V1";
+
+// Type of the classification problem.
+enum ClassificationType {
+  // An un-set value of this enum.
+  CLASSIFICATION_TYPE_UNSPECIFIED = 0;
+
+  // At most one label is allowed per example.
+  MULTICLASS = 1;
+
+  // Multiple labels are allowed for one example.
+  MULTILABEL = 2;
+}
+
+// Contains annotation details specific to classification.
+message ClassificationAnnotation {
+  // Output only. A confidence estimate between 0.0 and 1.0. A higher value
+  // means greater confidence that the annotation is positive. If a user
+  // approves an annotation as negative or positive, the score value remains
+  // unchanged. If a user creates an annotation, the score is 0 for negative or
+  // 1 for positive.
+  float score = 1;
+}
+
+// Model evaluation metrics for classification problems.
+message ClassificationEvaluationMetrics {
+  // Metrics for a single confidence threshold.
+  message ConfidenceMetricsEntry {
+    // Output only. Metrics are computed with an assumption that the model
+    // never returns predictions with score lower than this value.
+    float confidence_threshold = 1;
+
+    // Output only. Metrics are computed with an assumption that the model
+    // always returns at most this many predictions (ordered by their score,
+    // descendingly), but they all still need to meet the confidence_threshold.
+    int32 position_threshold = 14;
+
+    // Output only. Recall (True Positive Rate) for the given confidence
+    // threshold.
+    float recall = 2;
+
+    // Output only. Precision for the given confidence threshold.
+    float precision = 3;
+
+    // Output only. False Positive Rate for the given confidence threshold.
+    float false_positive_rate = 8;
+
+    // Output only. The harmonic mean of recall and precision.
+    float f1_score = 4;
+
+    // Output only. The Recall (True Positive Rate) when only considering the
+    // label that has the highest prediction score and not below the confidence
+    // threshold for each example.
+    float recall_at1 = 5;
+
+    // Output only. The precision when only considering the label that has the
+    // highest prediction score and not below the confidence threshold for each
+    // example.
+    float precision_at1 = 6;
+
+    // Output only. The False Positive Rate when only considering the label that
+    // has the highest prediction score and not below the confidence threshold
+    // for each example.
+    float false_positive_rate_at1 = 9;
+
+    // Output only. The harmonic mean of
+    // [recall_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1]
+    // and
+    // [precision_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
+    float f1_score_at1 = 7;
+
+    // Output only. The number of model created labels that match a ground truth
+    // label.
+    int64 true_positive_count = 10;
+
+    // Output only. The number of model created labels that do not match a
+    // ground truth label.
+    int64 false_positive_count = 11;
+
+    // Output only. The number of ground truth labels that are not matched
+    // by a model created label.
+    int64 false_negative_count = 12;
+
+    // Output only. The number of labels that were not created by the model,
+    // but if they would, they would not match a ground truth label.
+    int64 true_negative_count = 13;
+  }
+
+  // Confusion matrix of the model running the classification.
+  message ConfusionMatrix {
+    // Output only. A row in the confusion matrix.
+    message Row {
+      // Output only. Value of the specific cell in the confusion matrix.
+      // The number of values each row has (i.e. the length of the row) is equal
+      // to the length of the `annotation_spec_id` field or, if that one is not
+      // populated, length of the
+      // [display_name][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name]
+      // field.
+      repeated int32 example_count = 1;
+    }
+
+    // Output only. IDs of the annotation specs used in the confusion matrix.
+    repeated string annotation_spec_id = 1;
+
+    // Output only. Display name of the annotation specs used in the confusion
+    // matrix, as they were at the moment of the evaluation.
+    repeated string display_name = 3;
+
+    // Output only. Rows in the confusion matrix. The number of rows is equal to
+    // the size of `annotation_spec_id`.
+    // `row[i].example_count[j]` is the number of examples that have ground
+    // truth of the `annotation_spec_id[i]` and are predicted as
+    // `annotation_spec_id[j]` by the model being evaluated.
+    repeated Row row = 2;
+  }
+
+  // Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
+  // for the overall evaluation.
+  float au_prc = 1;
+
+  // Output only. The Area Under Receiver Operating Characteristic curve metric.
+  // Micro-averaged for the overall evaluation.
+  float au_roc = 6;
+
+  // Output only. The Log Loss metric.
+  float log_loss = 7;
+
+  // Output only. Metrics for each confidence_threshold in
+  // 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
+  // position_threshold = INT32_MAX_VALUE.
+  // ROC and precision-recall curves, and other aggregated metrics are derived
+  // from them. The confidence metrics entries may also be supplied for
+  // additional values of position_threshold, but from these no aggregated
+  // metrics are computed.
+  repeated ConfidenceMetricsEntry confidence_metrics_entry = 3;
+
+  // Output only. Confusion matrix of the evaluation.
+  // Only set for MULTICLASS classification problems where number
+  // of labels is no more than 10.
+  // Only set for model level evaluation, not for evaluation per label.
+  ConfusionMatrix confusion_matrix = 4;
+
+  // Output only. The annotation spec ids used for this evaluation.
+  repeated string annotation_spec_id = 5;
+}