As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Types for Google Cloud Automl v1 API

class google.cloud.automl_v1.types.AnnotationPayload(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Contains annotation information that is relevant to AutoML.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

translation

Annotation details for translation.

This field is a member of oneof detail.

Type

google.cloud.automl_v1.types.TranslationAnnotation

classification

Annotation details for content or image classification.

This field is a member of oneof detail.

Type

google.cloud.automl_v1.types.ClassificationAnnotation

image_object_detection

Annotation details for image object detection.

This field is a member of oneof detail.

Type

google.cloud.automl_v1.types.ImageObjectDetectionAnnotation

text_extraction

Annotation details for text extraction.

This field is a member of oneof detail.

Type

google.cloud.automl_v1.types.TextExtractionAnnotation

text_sentiment

Annotation details for text sentiment.

This field is a member of oneof detail.

Type

google.cloud.automl_v1.types.TextSentimentAnnotation

annotation_spec_id

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.

Type

str

display_name

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.

Type

str

class google.cloud.automl_v1.types.AnnotationSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A definition of an annotation spec.

name

Output only. Resource name of the annotation spec. Form: ‘projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}’

Type

str

display_name

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_]+.

Type

str

example_count

Output only. The number of examples in the parent dataset labeled by the annotation spec.

Type

int

class google.cloud.automl_v1.types.BatchPredictInputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Input configuration for BatchPredict Action.

The format of input depends on the ML problem of the model used for prediction. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

AutoML Vision

Classification

One or more CSV files where each line is a single column:

GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

gs://folder/image1.jpeg
gs://folder/image2.gif
gs://folder/image3.png

Object Detection

One or more CSV files where each line is a single column:

GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

gs://folder/image1.jpeg
gs://folder/image2.gif
gs://folder/image3.png

AutoML Video Intelligence

Classification

One or more CSV files where each line is a single column:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time.

Sample rows:

gs://folder/video1.mp4,10,40
gs://folder/video1.mp4,20,60
gs://folder/vid2.mov,0,inf

Object Tracking

One or more CSV files where each line is a single column:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time.

Sample rows:

gs://folder/video1.mp4,10,40
gs://folder/video1.mp4,20,60
gs://folder/vid2.mov,0,inf

AutoML Natural Language

Classification

One or more CSV files where each line is a single column:

GCS_FILE_PATH

GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 10MB in size.

Sample rows:

gs://folder/text1.txt
gs://folder/text2.pdf
gs://folder/text3.tif

Sentiment Analysis

One or more CSV files where each line is a single column:

GCS_FILE_PATH

GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 128kB in size.

Sample rows:

gs://folder/text1.txt
gs://folder/text2.pdf
gs://folder/text3.tif

Entity Extraction

One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict].

Each JSONL file contains a per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called “id”, a TextSnippet proto (in JSON representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique.

Each document JSONL file contains, per line, a proto that wraps a Document proto with input_config set. Each document cannot exceed 2MB in size.

Supported document extensions: .PDF, .TIF, .TIFF

Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed.

Sample inline JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by “n”.):

{
   "id": "my_first_id",
   "text_snippet": { "content": "dog car cat"},
   "text_features": [
     {
       "text_segment": {"start_offset": 4, "end_offset": 6},
       "structural_type": PARAGRAPH,
       "bounding_poly": {
         "normalized_vertices": [
           {"x": 0.1, "y": 0.1},
           {"x": 0.1, "y": 0.3},
           {"x": 0.3, "y": 0.3},
           {"x": 0.3, "y": 0.1},
         ]
       },
     }
   ],
 }\n
 {
   "id": "2",
   "text_snippet": {
     "content": "Extended sample content",
     "mime_type": "text/plain"
   }
 }

Sample document JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by “n”.):

{
  "document": {
    "input_config": {
      "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
      }
    }
  }
}\n
{
  "document": {
    "input_config": {
      "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ]
      }
    }
  }
}

AutoML Tables

See Preparing your training data for more information.

You can use either [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source] or [bigquery_source][BatchPredictInputConfig.bigquery_source].

For gcs_source:

CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns.

The column names must contain the model’s [input_feature_column_specs’][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn’t matter). The columns corresponding to the model’s input feature column specs must contain values compatible with the column spec’s data types. Prediction on all the rows, i.e. the CSV lines, will be attempted.

Sample rows from a CSV file:

"First Name","Last Name","Dob","Addresses"
"John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
"Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}

For bigquery_source:

The URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller.

The column names must contain the model’s [input_feature_column_specs’][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn’t matter). The columns corresponding to the model’s input feature column specs must contain values compatible with the column spec’s data types. Prediction on all the rows of the table will be attempted.

Input field definitions:

GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, “gs://folder/video.avi”.

TIME_SEGMENT_START : (TIME_OFFSET) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video).

TIME_SEGMENT_END : (TIME_OFFSET) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video).

TIME_OFFSET : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. “inf” is allowed, and it means the end of the example.

Errors:

If any of the provided CSV files can’t be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.

gcs_source

Required. The Google Cloud Storage location for the input content.

This field is a member of oneof source.

Type

google.cloud.automl_v1.types.GcsSource

class google.cloud.automl_v1.types.BatchPredictOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of BatchPredict operation.

input_config

Output only. The input config that was given upon starting this batch predict operation.

Type

google.cloud.automl_v1.types.BatchPredictInputConfig

output_info

Output only. Information further describing this batch predict’s output.

Type

google.cloud.automl_v1.types.BatchPredictOperationMetadata.BatchPredictOutputInfo

class BatchPredictOutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Further describes this batch predict’s output. Supplements [BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig].

gcs_output_directory

The full path of the Google Cloud Storage directory created, into which the prediction output is written.

This field is a member of oneof output_location.

Type

str

class google.cloud.automl_v1.types.BatchPredictOutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Output configuration for BatchPredict Action.

As destination the [gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination] must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be “prediction–”, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.

• For Image Classification: In the created directory files image_classification_1.jsonl, image_classification_2.jsonl,…,image_classification_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image’s “ID” : “<id_value>” followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional errors_1.jsonl, errors_2.jsonl,…, errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same “ID” : “<id_value>” but here followed by exactly one `google.rpc.Status <https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>`__ containing only code and messagefields.

• For Image Object Detection: In the created directory files image_object_detection_1.jsonl, image_object_detection_2.jsonl,…,image_object_detection_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image’s “ID” : “<id_value>” followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,…, errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same “ID” : “<id_value>” but here followed by exactly one `google.rpc.Status <https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>`__ containing only code and messagefields.

• For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created.

  The format of video_classification.csv is:
  GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
  where:
  GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
      the prediction input lines (i.e. video_classification.csv has
      precisely the same number of lines as the prediction input had.)
  JSON_FILE_NAME = Name of .JSON file in the output directory, which
      contains prediction responses for the video time segment.
  STATUS = "OK" if prediction completed successfully, or an error code
      with message otherwise. If STATUS is not "OK" then the .JSON file
      for that line may not exist or be empty.
  
  Each .JSON file, assuming STATUS is "OK", will contain a list of
  AnnotationPayload protos in JSON format, which are the predictions
  for the video time segment the file is assigned to in the
  video_classification.csv. All AnnotationPayload protos will have
  video_classification field set, and will be sorted by
  video_classification.type field (note that the returned types are
  governed by `classifaction_types` parameter in
  [PredictService.BatchPredictRequest.params][]).
  

• For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,…, video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)).

  The format of video_object_tracking.csv is:
  GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
  where:
  GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
      the prediction input lines (i.e. video_object_tracking.csv has
      precisely the same number of lines as the prediction input had.)
  JSON_FILE_NAME = Name of .JSON file in the output directory, which
      contains prediction responses for the video time segment.
  STATUS = "OK" if prediction completed successfully, or an error
      code with message otherwise. If STATUS is not "OK" then the .JSON
      file for that line may not exist or be empty.
  
  Each .JSON file, assuming STATUS is "OK", will contain a list of
  AnnotationPayload protos in JSON format, which are the predictions
  for each frame of the video time segment the file is assigned to in
  video_object_tracking.csv. All AnnotationPayload protos will have
  video_object_tracking field set.
  

• For Text Classification: In the created directory files text_classification_1.jsonl, text_classification_2.jsonl,…,text_classification_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found.

  Each .JSONL file will contain, per line, a JSON representation of a
  proto that wraps input text file (or document) in
  the text snippet (or document) proto and a list of
  zero or more AnnotationPayload protos (called annotations), which
  have classification detail populated. A single text file (or
  document) will be listed only once with all its annotations, and its
  annotations will never be split across files.
  
  If prediction for any input file (or document) failed (partially or
  completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
  `errors_N.jsonl` files will be created (N depends on total number of
  failed predictions). These files will have a JSON representation of a
  proto that wraps input file followed by exactly one
  [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
  containing only `code` and `message`.
  

• For Text Sentiment: In the created directory files text_sentiment_1.jsonl, text_sentiment_2.jsonl,…,text_sentiment_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found.

  Each .JSONL file will contain, per line, a JSON representation of a
  proto that wraps input text file (or document) in
  the text snippet (or document) proto and a list of
  zero or more AnnotationPayload protos (called annotations), which
  have text_sentiment detail populated. A single text file (or
  document) will be listed only once with all its annotations, and its
  annotations will never be split across files.
  
  If prediction for any input file (or document) failed (partially or
  completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
  `errors_N.jsonl` files will be created (N depends on total number of
  failed predictions). These files will have a JSON representation of a
  proto that wraps input file followed by exactly one
  [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
  containing only `code` and `message`.
  

• For Text Extraction: In the created directory files text_extraction_1.jsonl, text_extraction_2.jsonl,…,text_extraction_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet’s “id” (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,…, errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the “id” : “<id_value>” (in case of inline) or the document proto (in case of document) but here followed by exactly one `google.rpc.Status <https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>`__ containing only code and message.

• For Tables: Output depends on whether [gcs_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.gcs_destination] or [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination] is set (either is allowed). Google Cloud Storage case: In the created directory files tables_1.csv, tables_2.csv,…, tables_N.csv will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns’ [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] given on input followed by M target column names in the format of “<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>*score” where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction [scores][google.cloud.automl.v1p1beta.TablesAnnotation.score]. For REGRESSION and FORECASTING [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns’ [display_name-s][google.cloud.automl.v1p1beta.display_name] given on input followed by the predicted target column with name in the format of “predicted<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>” Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional errors_1.csv, errors_2.csv,…, errors_N.csv will be created (N depends on total number of failed rows). These files will have analogous format as tables_*.csv, but always with a single target column having*`google.rpc.Status <https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>`__represented as a JSON string, and containing only ``code`` and ``message``. BigQuery case: [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name ``prediction_<model-display-name>_<timestamp-of-prediction-call>`` where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ “based on ISO-8601” format. In the dataset two tables will be created, ``predictions``, and ``errors``. The ``predictions`` table’s column names will be the input columns’ [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] followed by the target column with name in the format of “predicted<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>” The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of [AnnotationPayloads][google.cloud.automl.v1p1beta.AnnotationPayload], represented as STRUCT-s, containing [TablesAnnotation][google.cloud.automl.v1p1beta.TablesAnnotation]. The errors table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of “errors_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>”, and as a value has `google.rpc.Status <https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>`__ represented as a STRUCT, and containing only code and message.

gcs_destination

Required. The Google Cloud Storage location of the directory where the output is to be written to.

This field is a member of oneof destination.

Type

google.cloud.automl_v1.types.GcsDestination

class google.cloud.automl_v1.types.BatchPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].

name

Required. Name of the model requested to serve the batch prediction.

Type

str

input_config

Required. The input configuration for batch prediction.

Type

google.cloud.automl_v1.types.BatchPredictInputConfig

output_config

Required. The Configuration specifying where output predictions should be written.

Type

google.cloud.automl_v1.types.BatchPredictOutputConfig

params

Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long.

AutoML Natural Language Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5.

AutoML Vision Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5.

AutoML Vision Object Detection

score_threshold : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.

max_bounding_box_count : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server. AutoML Video Intelligence Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for a video, it will only produce results that have at least this confidence score. The default is 0.5.

segment_classification : (boolean) Set to true to request segment-level classification. AutoML Video Intelligence returns labels and their confidence scores for the entire segment of the video that user specified in the request configuration. The default is true.

shot_classification : (boolean) Set to true to request shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. The default is false.

WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality.

1s_interval_classification : (boolean) Set to true to request classification for a video at one-second intervals. AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. The default is false.

WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality.

AutoML Video Intelligence Object Tracking

score_threshold : (float) When Model detects objects on video frames, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.

max_bounding_box_count : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server.

min_bounding_box_size : (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size are returned. Value in 0 to 1 range. Default is 0.

Type

MutableMapping[str, str]

class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.BatchPredictResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Result of the Batch Predict. This message is returned in [response][google.longrunning.Operation.response] of the operation returned by the [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].

metadata

Additional domain-specific prediction response metadata.

AutoML Vision Object Detection

max_bounding_box_count : (int64) The maximum number of bounding boxes returned per image.

AutoML Video Intelligence Object Tracking

max_bounding_box_count : (int64) The maximum number of bounding boxes returned per frame.

Type

MutableMapping[str, str]

class MetadataEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.BoundingBoxMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

iou_threshold

Output only. The intersection-over-union threshold value used to compute this metrics entry.

Type

float

mean_average_precision

Output only. The mean average precision, most often close to au_prc.

Type

float

confidence_metrics_entries

Output only. Metrics for each label-match confidence_threshold from 0.05,0.10,…,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them.

Type

MutableSequence[google.cloud.automl_v1.types.BoundingBoxMetricsEntry.ConfidenceMetricsEntry]

class ConfidenceMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Metrics for a single confidence threshold.

confidence_threshold

Output only. The confidence threshold value used to compute the metrics.

Type

float

recall

Output only. Recall under the given confidence threshold.

Type

float

precision

Output only. Precision under the given confidence threshold.

Type

float

f1_score

Output only. The harmonic mean of recall and precision.

Type

float

class google.cloud.automl_v1.types.BoundingPoly(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.

normalized_vertices

Output only . The bounding polygon normalized vertices.

Type

MutableSequence[google.cloud.automl_v1.types.NormalizedVertex]

class google.cloud.automl_v1.types.ClassificationAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Contains annotation details specific to classification.

score

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.

Type

float

class google.cloud.automl_v1.types.ClassificationEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of “segment_classification” type.

au_prc

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

Type

float

au_roc

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

Type

float

log_loss

Output only. The Log Loss metric.

Type

float

confidence_metrics_entry

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.

Type

MutableSequence[google.cloud.automl_v1.types.ClassificationEvaluationMetrics.ConfidenceMetricsEntry]

confusion_matrix

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.

Type

google.cloud.automl_v1.types.ClassificationEvaluationMetrics.ConfusionMatrix

annotation_spec_id

Output only. The annotation spec ids used for this evaluation.

Type

MutableSequence[str]

class ConfidenceMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Metrics for a single confidence threshold.

confidence_threshold

Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.

Type

float

position_threshold

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.

Type

int

recall

Output only. Recall (True Positive Rate) for the given confidence threshold.

Type

float

precision

Output only. Precision for the given confidence threshold.

Type

float

false_positive_rate

Output only. False Positive Rate for the given confidence threshold.

Type

float

f1_score

Output only. The harmonic mean of recall and precision.

Type

float

recall_at1

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.

Type

float

precision_at1

Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Type

float

false_positive_rate_at1

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.

Type

float

f1_score_at1

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].

Type

float

true_positive_count

Output only. The number of model created labels that match a ground truth label.

Type

int

false_positive_count

Output only. The number of model created labels that do not match a ground truth label.

Type

int

false_negative_count

Output only. The number of ground truth labels that are not matched by a model created label.

Type

int

true_negative_count

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.

Type

int

class ConfusionMatrix(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Confusion matrix of the model running the classification.

annotation_spec_id

Output only. IDs of the annotation specs used in the confusion matrix. For Tables CLASSIFICATION [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type] only list of [annotation_spec_display_name-s][] is populated.

Type

MutableSequence[str]

display_name

Output only. Display name of the annotation specs used in the confusion matrix, as they were at the moment of the evaluation. For Tables CLASSIFICATION [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type], distinct values of the target column at the moment of the model evaluation are populated here.

Type

MutableSequence[str]

row

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.

Type

MutableSequence[google.cloud.automl_v1.types.ClassificationEvaluationMetrics.ConfusionMatrix.Row]

class Row(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Output only. A row in the confusion matrix.

example_count

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.

Type

MutableSequence[int]

class google.cloud.automl_v1.types.ClassificationType(value)

Bases: proto.enums.Enum

Type of the classification problem.

Values:

CLASSIFICATION_TYPE_UNSPECIFIED (0):

An un-set value of this enum.

MULTICLASS (1):

At most one label is allowed per example.

MULTILABEL (2):

Multiple labels are allowed for one example.

class google.cloud.automl_v1.types.CreateDatasetOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of CreateDataset operation.

class google.cloud.automl_v1.types.CreateDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].

parent

Required. The resource name of the project to create the dataset for.

Type

str

dataset

Required. The dataset to create.

Type

google.cloud.automl_v1.types.Dataset

class google.cloud.automl_v1.types.CreateModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of CreateModel operation.

class google.cloud.automl_v1.types.CreateModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].

parent

Required. Resource name of the parent project where the model is being created.

Type

str

model

Required. The model to create.

Type

google.cloud.automl_v1.types.Model

class google.cloud.automl_v1.types.Dataset(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

translation_dataset_metadata

Metadata for a dataset used for translation.

This field is a member of oneof dataset_metadata.

Type

google.cloud.automl_v1.types.TranslationDatasetMetadata

image_classification_dataset_metadata

Metadata for a dataset used for image classification.

This field is a member of oneof dataset_metadata.

Type

google.cloud.automl_v1.types.ImageClassificationDatasetMetadata

text_classification_dataset_metadata

Metadata for a dataset used for text classification.

This field is a member of oneof dataset_metadata.

Type

google.cloud.automl_v1.types.TextClassificationDatasetMetadata

image_object_detection_dataset_metadata

Metadata for a dataset used for image object detection.

This field is a member of oneof dataset_metadata.

Type

google.cloud.automl_v1.types.ImageObjectDetectionDatasetMetadata

text_extraction_dataset_metadata

Metadata for a dataset used for text extraction.

This field is a member of oneof dataset_metadata.

Type

google.cloud.automl_v1.types.TextExtractionDatasetMetadata

text_sentiment_dataset_metadata

Metadata for a dataset used for text sentiment.

This field is a member of oneof dataset_metadata.

Type

google.cloud.automl_v1.types.TextSentimentDatasetMetadata

name

Output only. The resource name of the dataset. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}

Type

str

display_name

Required. The name of the dataset to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9.

Type

str

description

User-provided description of the dataset. The description can be up to 25000 characters long.

Type

str

example_count

Output only. The number of examples in the dataset.

Type

int

create_time

Output only. Timestamp when this dataset was created.

Type

google.protobuf.timestamp_pb2.Timestamp

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type

str

labels

Optional. The labels with user-defined metadata to organize your dataset. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.

See https://goo.gl/xmQnxf for more information on and examples of labels.

Type

MutableMapping[str, str]

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.DeleteDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].

name

Required. The resource name of the dataset to delete.

Type

str

class google.cloud.automl_v1.types.DeleteModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].

name

Required. Resource name of the model being deleted.

Type

str

class google.cloud.automl_v1.types.DeleteOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of operations that perform deletes of any entities.

class google.cloud.automl_v1.types.DeployModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of DeployModel operation.

class google.cloud.automl_v1.types.DeployModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

image_object_detection_model_deployment_metadata

Model deployment metadata specific to Image Object Detection.

This field is a member of oneof model_deployment_metadata.

Type

google.cloud.automl_v1.types.ImageObjectDetectionModelDeploymentMetadata

image_classification_model_deployment_metadata

Model deployment metadata specific to Image Classification.

This field is a member of oneof model_deployment_metadata.

Type

google.cloud.automl_v1.types.ImageClassificationModelDeploymentMetadata

name

Required. Resource name of the model to deploy.

Type

str

class google.cloud.automl_v1.types.Document(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A structured text document e.g. a PDF.

input_config

An input config specifying the content of the document.

Type

google.cloud.automl_v1.types.DocumentInputConfig

document_text

The plain text version of this document.

Type

google.cloud.automl_v1.types.TextSnippet

layout

Describes the layout of the document. Sorted by [page_number][].

Type

MutableSequence[google.cloud.automl_v1.types.Document.Layout]

document_dimensions

The dimensions of the page in the document.

Type

google.cloud.automl_v1.types.DocumentDimensions

page_count

Number of pages in the document.

Type

int

class Layout(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Describes the layout information of a [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the document.

text_segment

Text Segment that represents a segment in [document_text][google.cloud.automl.v1p1beta.Document.document_text].

Type

google.cloud.automl_v1.types.TextSegment

page_number

Page number of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the original document, starts from 1.

Type

int

bounding_poly

The position of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the page. Contains exactly 4 [normalized_vertices][google.cloud.automl.v1p1beta.BoundingPoly.normalized_vertices] and they are connected by edges in the order provided, which will represent a rectangle parallel to the frame. The [NormalizedVertex-s][google.cloud.automl.v1p1beta.NormalizedVertex] are relative to the page. Coordinates are based on top-left as point (0,0).

Type

google.cloud.automl_v1.types.BoundingPoly

text_segment_type

The type of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in document.

Type

google.cloud.automl_v1.types.Document.Layout.TextSegmentType

class TextSegmentType(value)

Bases: proto.enums.Enum

The type of TextSegment in the context of the original document.

Values:

TEXT_SEGMENT_TYPE_UNSPECIFIED (0):

Should not be used.

TOKEN (1):

The text segment is a token. e.g. word.

PARAGRAPH (2):

The text segment is a paragraph.

FORM_FIELD (3):

The text segment is a form field.

FORM_FIELD_NAME (4):

The text segment is the name part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD.

FORM_FIELD_CONTENTS (5):

The text segment is the text content part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD.

TABLE (6):

The text segment is a whole table, including headers, and all rows.

TABLE_HEADER (7):

The text segment is a table’s headers. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE.

TABLE_ROW (8):

The text segment is a row in table. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE.

TABLE_CELL (9):

The text segment is a cell in table. It will be treated as child of another TABLE_ROW TextSegment if its span is subspan of another TextSegment with type TABLE_ROW.

class google.cloud.automl_v1.types.DocumentDimensions(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Message that describes dimension of a document.

unit

Unit of the dimension.

Type

google.cloud.automl_v1.types.DocumentDimensions.DocumentDimensionUnit

width

Width value of the document, works together with the unit.

Type

float

height

Height value of the document, works together with the unit.

Type

float

class DocumentDimensionUnit(value)

Bases: proto.enums.Enum

Unit of the document dimension.

Values:

DOCUMENT_DIMENSION_UNIT_UNSPECIFIED (0):

Should not be used.

INCH (1):

Document dimension is measured in inches.

CENTIMETER (2):

Document dimension is measured in centimeters.

POINT (3):

Document dimension is measured in points. 72 points = 1 inch.

class google.cloud.automl_v1.types.DocumentInputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Input configuration of a [Document][google.cloud.automl.v1.Document].

gcs_source

The Google Cloud Storage location of the document file. Only a single path should be given.

Max supported size: 512MB.

Supported extensions: .PDF.

Type

google.cloud.automl_v1.types.GcsSource

class google.cloud.automl_v1.types.ExamplePayload(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Example data used for training or prediction.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

image

Example image.

This field is a member of oneof payload.

Type

google.cloud.automl_v1.types.Image

text_snippet

Example text.

This field is a member of oneof payload.

Type

google.cloud.automl_v1.types.TextSnippet

document

Example document.

This field is a member of oneof payload.

Type

google.cloud.automl_v1.types.Document

class google.cloud.automl_v1.types.ExportDataOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of ExportData operation.

output_info

Output only. Information further describing this export data’s output.

Type

google.cloud.automl_v1.types.ExportDataOperationMetadata.ExportDataOutputInfo

class ExportDataOutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Further describes this export data’s output. Supplements [OutputConfig][google.cloud.automl.v1.OutputConfig].

gcs_output_directory

The full path of the Google Cloud Storage directory created, into which the exported data is written.

This field is a member of oneof output_location.

Type

str

class google.cloud.automl_v1.types.ExportDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].

name

Required. The resource name of the dataset.

Type

str

output_config

Required. The desired output location.

Type

google.cloud.automl_v1.types.OutputConfig

class google.cloud.automl_v1.types.ExportModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of ExportModel operation.

output_info

Output only. Information further describing the output of this model export.

Type

google.cloud.automl_v1.types.ExportModelOperationMetadata.ExportModelOutputInfo

class ExportModelOutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Further describes the output of model export. Supplements [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].

gcs_output_directory

The full path of the Google Cloud Storage directory created, into which the model will be exported.

Type

str

class google.cloud.automl_v1.types.ExportModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.

name

Required. The resource name of the model to export.

Type

str

output_config

Required. The desired output location and configuration.

Type

google.cloud.automl_v1.types.ModelExportOutputConfig

class google.cloud.automl_v1.types.GcsDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

The Google Cloud Storage location where the output is to be written to.

output_uri_prefix

Required. Google Cloud Storage URI to output directory, up to 2000 characters long. Accepted forms:

• Prefix path: gs://bucket/directory The requesting user must have write permission to the bucket. The directory is created if it doesn’t exist.

Type

str

class google.cloud.automl_v1.types.GcsSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

The Google Cloud Storage location for the input content.

input_uris

Required. Google Cloud Storage URIs to input files, up to 2000 characters long. Accepted forms:

• Full object path, e.g. gs://bucket/directory/object.csv

Type

MutableSequence[str]

class google.cloud.automl_v1.types.GetAnnotationSpecRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].

name

Required. The resource name of the annotation spec to retrieve.

Type

str

class google.cloud.automl_v1.types.GetDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].

name

Required. The resource name of the dataset to retrieve.

Type

str

class google.cloud.automl_v1.types.GetModelEvaluationRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].

name

Required. Resource name for the model evaluation.

Type

str

class google.cloud.automl_v1.types.GetModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].

name

Required. Resource name of the model.

Type

str

class google.cloud.automl_v1.types.Image(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A representation of an image. Only images up to 30MB in size are supported.

image_bytes

Image content represented as a stream of bytes. Note: As with all bytes fields, protobuffers use a pure binary representation, whereas JSON representations use base64.

This field is a member of oneof data.

Type

bytes

thumbnail_uri

Output only. HTTP URI to the thumbnail image.

Type

str

class google.cloud.automl_v1.types.ImageClassificationDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Dataset metadata that is specific to image classification.

classification_type

Required. Type of the classification problem.

Type

google.cloud.automl_v1.types.ClassificationType

class google.cloud.automl_v1.types.ImageClassificationModelDeploymentMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model deployment metadata specific to Image Classification.

node_count

Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model’s [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps]. Must be between 1 and 100, inclusive on both ends.

Type

int

class google.cloud.automl_v1.types.ImageClassificationModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata for image classification.

base_model_id

Optional. The ID of the base model. If it is specified, the new model will be created based on the base model. Otherwise, the new model will be created from scratch. The base model must be in the same project and location as the new model to create, and have the same model_type.

Type

str

train_budget_milli_node_hours

Optional. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be MODEL_CONVERGED. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type cloud(default), the train budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192, 000 which represents one day in wall time. For model type mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1, mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time.

Type

int

train_cost_milli_node_hours

Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

Type

int

stop_reason

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

Type

str

model_type

Optional. Type of the model. The available values are:

• cloud - Model to be used via prediction calls to AutoML API. This is the default value.

• mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.

• mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards.

• mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

• mobile-core-ml-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models.

• mobile-core-ml-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards.

• mobile-core-ml-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

Type

str

node_qps

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

Type

float

node_count

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.

Type

int

class google.cloud.automl_v1.types.ImageObjectDetectionAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Annotation details for image object detection.

bounding_box

Output only. The rectangle representing the object location.

Type

google.cloud.automl_v1.types.BoundingPoly

score

Output only. The confidence that this annotation is positive for the parent example, value in [0, 1], higher means higher positivity confidence.

Type

float

class google.cloud.automl_v1.types.ImageObjectDetectionDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Dataset metadata specific to image object detection.

class google.cloud.automl_v1.types.ImageObjectDetectionEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.

evaluated_bounding_box_count

Output only. The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had.

Type

int

bounding_box_metrics_entries

Output only. The bounding boxes match metrics for each Intersection-over-union threshold 0.05,0.10,…,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,…,0.95,0.96,0.97,0.98,0.99 pair.

Type

MutableSequence[google.cloud.automl_v1.types.BoundingBoxMetricsEntry]

bounding_box_mean_average_precision

Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries.

Type

float

class google.cloud.automl_v1.types.ImageObjectDetectionModelDeploymentMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model deployment metadata specific to Image Object Detection.

node_count

Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model’s [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node]. Must be between 1 and 100, inclusive on both ends.

Type

int

class google.cloud.automl_v1.types.ImageObjectDetectionModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata specific to image object detection.

model_type

Optional. Type of the model. The available values are:

• cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.

• cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.

• mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.

• mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards.

• mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

Type

str

node_count

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.

Type

int

node_qps

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

Type

float

stop_reason

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

Type

str

train_budget_milli_node_hours

Optional. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be MODEL_CONVERGED. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type cloud-high-accuracy-1(default) and cloud-low-latency-1, the train budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216, 000 which represents one day in wall time. For model type mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1, mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time.

Type

int

train_cost_milli_node_hours

Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

Type

int

class google.cloud.automl_v1.types.ImportDataOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of ImportData operation.

class google.cloud.automl_v1.types.ImportDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].

name

Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.

Type

str

input_config

Required. The desired input location and its domain specific semantics, if any.

Type

google.cloud.automl_v1.types.InputConfig

class google.cloud.automl_v1.types.InputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Input configuration for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] action.

The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an “example” file (that is, image, video etc.) with identical content (even if it had different GCS_FILE_PATH) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same ML_USE and GCS_FILE_PATH, if it is not, then these values are nondeterministically selected from the given ones.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

AutoML Vision

Classification

See Preparing your training data for more information.

CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH,LABEL,LABEL,...

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.

  • TEST - Rows in this file are used to test the model during training.

  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO.

• LABEL - A label that identifies the object in the image.

For the MULTICLASS classification type, at most one LABEL is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no LABEL.

Some sample rows:

TRAIN,gs://folder/image1.jpg,daisy
TEST,gs://folder/image2.jpg,dandelion,tulip,rose
UNASSIGNED,gs://folder/image3.jpg,daisy
UNASSIGNED,gs://folder/image4.jpg

Object Detection

See [Preparing your training data](https://cloud.google.com/vision/automl/object-detection/docs/prepare) for more information.

A CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.

  • TEST - Rows in this file are used to test the model during training.

  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled.

• LABEL - A label that identifies the object in the image specified by the BOUNDING_BOX.

• BOUNDING BOX - The vertices of an object in the example image. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX instances per image are allowed (one BOUNDING_BOX per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the “,,,,,,,” in place of the BOUNDING_BOX.

Four sample rows:

TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
TEST,gs://folder/im3.png,,,,,,,,,

AutoML Video Intelligence

Classification

See Preparing your training data for more information.

CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH

For ML_USE, do not use VALIDATE.

GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format:

GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,)

Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with “,,” in place of LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END.

Sample top level CSV file:

TRAIN,gs://folder/train_videos.csv
TEST,gs://folder/test_videos.csv
UNASSIGNED,gs://folder/other_videos.csv

Sample rows of a CSV file for a particular ML_USE:

gs://folder/video1.avi,car,120,180.000021
gs://folder/video1.avi,bike,150,180.000021
gs://folder/vid2.avi,car,0,60.5
gs://folder/vid3.avi,,,

Object Tracking

See Preparing your training data for more information.

CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH

For ML_USE, do not use VALIDATE.

GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format:

GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX

or

GCS_FILE_PATH,,,,,,,,,,

Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same INSTANCE_ID is given to it.

TIMESTAMP must be within the length of the video, the BOUNDING_BOX is assumed to be drawn on the closest video’s frame to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with “,,,,,,,,,,” in place of LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX.

Sample top level CSV file:

TRAIN,gs://folder/train_videos.csv
TEST,gs://folder/test_videos.csv
UNASSIGNED,gs://folder/other_videos.csv

Seven sample rows of a CSV file for a particular ML_USE:

gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
gs://folder/video2.avi,,,,,,,,,,,

AutoML Natural Language

Entity Extraction

See Preparing your training data for more information.

One or more CSV file(s) with each line in the following format:

ML_USE,GCS_FILE_PATH

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.

  • TEST - Rows in this file are used to test the model during training.

  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing..

• GCS_FILE_PATH - a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training.

After the training data set has been determined from the TRAIN and UNASSIGNED CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation.

For example:

TRAIN,gs://folder/file1.jsonl
VALIDATE,gs://folder/file2.jsonl
TEST,gs://folder/file3.jsonl

In-line JSONL files

In-line .JSONL files contain, per line, a JSON document that wraps a [text_snippet][google.cloud.automl.v1.TextSnippet] field followed by one or more [annotations][google.cloud.automl.v1.AnnotationPayload] fields, which have display_name and text_extraction fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (n).

The supplied text must be annotated exhaustively. For example, if you include the text “horse”, but do not label it as “animal”, then “horse” is assumed to not be an “animal”.

Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded.

For example:

{
  "text_snippet": {
    "content": "dog car cat"
  },
  "annotations": [
     {
       "display_name": "animal",
       "text_extraction": {
         "text_segment": {"start_offset": 0, "end_offset": 2}
      }
     },
     {
      "display_name": "vehicle",
       "text_extraction": {
         "text_segment": {"start_offset": 4, "end_offset": 6}
       }
     },
     {
       "display_name": "animal",
       "text_extraction": {
         "text_segment": {"start_offset": 8, "end_offset": 10}
       }
     }
 ]
}\n
{
   "text_snippet": {
     "content": "This dog is good."
   },
   "annotations": [
      {
        "display_name": "animal",
        "text_extraction": {
          "text_segment": {"start_offset": 5, "end_offset": 7}
        }
      }
   ]
}

JSONL files that reference documents

.JSONL files contain, per line, a JSON document that wraps a input_config that contains the path to a source document. Multiple JSON documents can be separated using line breaks (n).

Supported document extensions: .PDF, .TIF, .TIFF

For example:

{
  "document": {
    "input_config": {
      "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
      }
    }
  }
}\n
{
  "document": {
    "input_config": {
      "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ]
      }
    }
  }
}

In-line JSONL files with document layout information

Note: You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI or exportData.

In-line .JSONL files for documents contain, per line, a JSON document that wraps a document field that provides the textual content of the document and the layout information.

For example:

{
  "document": {
          "document_text": {
            "content": "dog car cat"
          }
          "layout": [
            {
              "text_segment": {
                "start_offset": 0,
                "end_offset": 11,
               },
               "page_number": 1,
               "bounding_poly": {
                  "normalized_vertices": [
                    {"x": 0.1, "y": 0.1},
                    {"x": 0.1, "y": 0.3},
                    {"x": 0.3, "y": 0.3},
                    {"x": 0.3, "y": 0.1},
                  ],
                },
                "text_segment_type": TOKEN,
            }
          ],
          "document_dimensions": {
            "width": 8.27,
            "height": 11.69,
            "unit": INCH,
          }
          "page_count": 3,
        },
        "annotations": [
          {
            "display_name": "animal",
            "text_extraction": {
              "text_segment": {"start_offset": 0, "end_offset": 3}
            }
          },
          {
            "display_name": "vehicle",
            "text_extraction": {
              "text_segment": {"start_offset": 4, "end_offset": 7}
            }
          },
          {
            "display_name": "animal",
            "text_extraction": {
              "text_segment": {"start_offset": 8, "end_offset": 11}
            }
          },
        ],

Classification

See Preparing your training data for more information.

One or more CSV file(s) with each line in the following format:

ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.

  • TEST - Rows in this file are used to test the model during training.

  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by “gs://”, it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (“”), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, “gs://folder/content.txt” AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size.

  For the MULTICLASS classification type, at most one LABEL is allowed.

  The ML_USE and LABEL columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

A maximum of 100 unique labels are allowed per CSV row.

Sample rows:

TRAIN,"They have bad food and very rude",RudeService,BadFood
gs://folder/content.txt,SlowService
TEST,gs://folder/document.pdf
VALIDATE,gs://folder/text_files.zip,BadFood

Sentiment Analysis

See Preparing your training data for more information.

CSV file(s) with each line in format:

ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.

  • TEST - Rows in this file are used to test the model during training.

  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by “gs://”, it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (“”), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, “gs://folder/content.txt” AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size.

  The ML_USE and SENTIMENT columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

• SENTIMENT - An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn’t be confused with “score” or “magnitude” from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large.

Sample rows:

TRAIN,"@freewrytin this is way too good for your product",2
gs://folder/content.txt,3
TEST,gs://folder/document.pdf
VALIDATE,gs://folder/text_files.zip,2

AutoML Tables

See Preparing your training data for more information.

You can use either [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source]. All input is concatenated into a single [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id]

For gcs_source:

CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns.

Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller.

First three sample rows of a CSV file:

"Id","First Name","Last Name","Dob","Addresses"
"1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
"2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}

For bigquery_source:

An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller.

An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel.

Input field definitions:

ML_USE : (“TRAIN” | “VALIDATE” | “TEST” | “UNASSIGNED”) Describes how the given example (file) should be used for model training. “UNASSIGNED” can be used when user has no preference.

GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, “gs://folder/image1.png”.

LABEL : A display name of an object on an image, video etc., e.g. “dog”. Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions.

INSTANCE_ID : A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which.

BOUNDING_BOX : (VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,) A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle.

VERTEX : (COORDINATE,COORDINATE) First coordinate is horizontal (x), the second is vertical (y).

COORDINATE : A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left.

TIME_SEGMENT_START : (TIME_OFFSET) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video).

TIME_SEGMENT_END : (TIME_OFFSET) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video).

TIME_OFFSET : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. “inf” is allowed, and it means the end of the example.

TEXT_SNIPPET : The content of a text snippet, UTF-8 encoded, enclosed within double quotes (“”).

DOCUMENT : A field that provides the textual content with document and the layout information.

Errors:

If any of the provided CSV files can’t be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.

gcs_source

The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], gcs_source points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].

This field is a member of oneof source.

Type

google.cloud.automl_v1.types.GcsSource

params

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long.

AutoML Tables

schema_inference_version : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: “1”.

Type

MutableMapping[str, str]

class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.ListDatasetsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].

parent

Required. The resource name of the project from which to list datasets.

Type

str

filter

An expression for filtering the results of the request.

• dataset_metadata - for existence of the case (e.g. image_classification_dataset_metadata:*). Some examples of using the filter are:

• translation_dataset_metadata:* –> The dataset has translation_dataset_metadata.

Type

str

page_size

Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.

Type

int

page_token

A token identifying a page of results for the server to return Typically obtained via [ListDatasetsResponse.next_page_token][google.cloud.automl.v1.ListDatasetsResponse.next_page_token] of the previous [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets] call.

Type

str

class google.cloud.automl_v1.types.ListDatasetsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].

datasets

The datasets read.

Type

MutableSequence[google.cloud.automl_v1.types.Dataset]

next_page_token

A token to retrieve next page of results. Pass to [ListDatasetsRequest.page_token][google.cloud.automl.v1.ListDatasetsRequest.page_token] to obtain that page.

Type

str

class google.cloud.automl_v1.types.ListModelEvaluationsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].

parent

Required. Resource name of the model to list the model evaluations for. If modelId is set as “-”, this will list model evaluations from across all models of the parent location.

Type

str

filter

Required. An expression for filtering the results of the request.

• annotation_spec_id - for =, != or existence. See example below for the last.

Some examples of using the filter are:

• annotation_spec_id!=4 –> The model evaluation was done for annotation spec with ID different than 4.

• NOT annotation_spec_id:* –> The model evaluation was done for aggregate of all annotation specs.

Type

str

page_size

Requested page size.

Type

int

page_token

A token identifying a page of results for the server to return. Typically obtained via [ListModelEvaluationsResponse.next_page_token][google.cloud.automl.v1.ListModelEvaluationsResponse.next_page_token] of the previous [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] call.

Type

str

class google.cloud.automl_v1.types.ListModelEvaluationsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].

model_evaluation

List of model evaluations in the requested page.

Type

MutableSequence[google.cloud.automl_v1.types.ModelEvaluation]

next_page_token

A token to retrieve next page of results. Pass to the [ListModelEvaluationsRequest.page_token][google.cloud.automl.v1.ListModelEvaluationsRequest.page_token] field of a new [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] request to obtain that page.

Type

str

class google.cloud.automl_v1.types.ListModelsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].

parent

Required. Resource name of the project, from which to list the models.

Type

str

filter

An expression for filtering the results of the request.

• model_metadata - for existence of the case (e.g. video_classification_model_metadata:*).

• dataset_id - for = or !=. Some examples of using the filter are:

• image_classification_model_metadata:* –> The model has image_classification_model_metadata.

• dataset_id=5 –> The model was created from a dataset with ID 5.

Type

str

page_size

Requested page size.

Type

int

page_token

A token identifying a page of results for the server to return Typically obtained via [ListModelsResponse.next_page_token][google.cloud.automl.v1.ListModelsResponse.next_page_token] of the previous [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels] call.

Type

str

class google.cloud.automl_v1.types.ListModelsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].

model

List of models in the requested page.

Type

MutableSequence[google.cloud.automl_v1.types.Model]

next_page_token

A token to retrieve next page of results. Pass to [ListModelsRequest.page_token][google.cloud.automl.v1.ListModelsRequest.page_token] to obtain that page.

Type

str

class google.cloud.automl_v1.types.Model(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

API proto representing a trained machine learning model.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

translation_model_metadata

Metadata for translation models.

This field is a member of oneof model_metadata.

Type

google.cloud.automl_v1.types.TranslationModelMetadata

image_classification_model_metadata

Metadata for image classification models.

This field is a member of oneof model_metadata.

Type

google.cloud.automl_v1.types.ImageClassificationModelMetadata

text_classification_model_metadata

Metadata for text classification models.

This field is a member of oneof model_metadata.

Type

google.cloud.automl_v1.types.TextClassificationModelMetadata

image_object_detection_model_metadata

Metadata for image object detection models.

This field is a member of oneof model_metadata.

Type

google.cloud.automl_v1.types.ImageObjectDetectionModelMetadata

text_extraction_model_metadata

Metadata for text extraction models.

This field is a member of oneof model_metadata.

Type

google.cloud.automl_v1.types.TextExtractionModelMetadata

text_sentiment_model_metadata

Metadata for text sentiment models.

This field is a member of oneof model_metadata.

Type

google.cloud.automl_v1.types.TextSentimentModelMetadata

name

Output only. Resource name of the model. Format: projects/{project_id}/locations/{location_id}/models/{model_id}

Type

str

display_name

Required. The name of the model to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9. It must start with a letter.

Type

str

dataset_id

Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location.

Type

str

create_time

Output only. Timestamp when the model training finished and can be used for prediction.

Type

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this model was last updated.

Type

google.protobuf.timestamp_pb2.Timestamp

deployment_state

Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed.

Type

google.cloud.automl_v1.types.Model.DeploymentState

etag

Used to perform a consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type

str

labels

Optional. The labels with user-defined metadata to organize your model. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.

See https://goo.gl/xmQnxf for more information on and examples of labels.

Type

MutableMapping[str, str]

class DeploymentState(value)

Bases: proto.enums.Enum

Deployment state of the model.

Values:

DEPLOYMENT_STATE_UNSPECIFIED (0):

Should not be used, an un-set enum has this value by default.

DEPLOYED (1):

Model is deployed.

UNDEPLOYED (2):

Model is not deployed.

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.ModelEvaluation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Evaluation results of a model.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

classification_evaluation_metrics

Model evaluation metrics for image, text, video and tables classification. Tables problem is considered a classification when the target column is CATEGORY DataType.

This field is a member of oneof metrics.

Type

google.cloud.automl_v1.types.ClassificationEvaluationMetrics

translation_evaluation_metrics

Model evaluation metrics for translation.

This field is a member of oneof metrics.

Type

google.cloud.automl_v1.types.TranslationEvaluationMetrics

image_object_detection_evaluation_metrics

Model evaluation metrics for image object detection.

This field is a member of oneof metrics.

Type

google.cloud.automl_v1.types.ImageObjectDetectionEvaluationMetrics

text_sentiment_evaluation_metrics

Evaluation metrics for text sentiment models.

This field is a member of oneof metrics.

Type

google.cloud.automl_v1.types.TextSentimentEvaluationMetrics

text_extraction_evaluation_metrics

Evaluation metrics for text extraction models.

This field is a member of oneof metrics.

Type

google.cloud.automl_v1.types.TextExtractionEvaluationMetrics

name

Output only. Resource name of the model evaluation. Format: projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}

Type

str

annotation_spec_id

Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation. For Tables annotation specs in the dataset do not exist and this ID is always not set, but for CLASSIFICATION [prediction_type-s][google.cloud.automl.v1.TablesModelMetadata.prediction_type] the [display_name][google.cloud.automl.v1.ModelEvaluation.display_name] field is used.

Type

str

display_name

Output only. The value of [display_name][google.cloud.automl.v1.AnnotationSpec.display_name] at the moment when the model was trained. Because this field returns a value at model training time, for different models trained from the same dataset, the values may differ, since display names could had been changed between the two model’s trainings. For Tables CLASSIFICATION [prediction_type-s][google.cloud.automl.v1.TablesModelMetadata.prediction_type] distinct values of the target column at the moment of the model evaluation are populated here. The display_name is empty for the overall model evaluation.

Type

str

create_time

Output only. Timestamp when this model evaluation was created.

Type

google.protobuf.timestamp_pb2.Timestamp

evaluated_example_count

Output only. The number of examples used for model evaluation, i.e. for which ground truth from time of model creation is compared against the predicted annotations created by the model. For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is the total number of all examples used for evaluation. Otherwise, this is the count of examples that according to the ground truth were annotated by the [annotation_spec_id][google.cloud.automl.v1.ModelEvaluation.annotation_spec_id].

Type

int

class google.cloud.automl_v1.types.ModelExportOutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Output configuration for ModelExport Action.

gcs_destination

Required. The Google Cloud Storage location where the model is to be written to. This location may only be set for the following model formats: “tflite”, “edgetpu_tflite”, “tf_saved_model”, “tf_js”, “core_ml”.

Under the directory given as the destination a new one with name “model-export–”, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside the model and any of its supporting files will be written.

This field is a member of oneof destination.

Type

google.cloud.automl_v1.types.GcsDestination

model_format

The format in which the model must be exported. The available, and default, formats depend on the problem and model type (if given problem and type combination doesn’t have a format listed, it means its models are not exportable):

• For Image Classification mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1: “tflite” (default), “edgetpu_tflite”, “tf_saved_model”, “tf_js”, “docker”.

• For Image Classification mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1: “core_ml” (default).

• For Image Object Detection mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1: “tflite”, “tf_saved_model”, “tf_js”. Formats description:

• tflite - Used for Android mobile devices.

• edgetpu_tflite - Used for Edge TPU devices.

• tf_saved_model - A tensorflow model in SavedModel format.

• tf_js - A TensorFlow.js model that can be used in the browser and in Node.js using JavaScript.

• docker - Used for Docker containers. Use the params field to customize the container. The container is verified to work correctly on ubuntu 16.04 operating system. See more at containers quickstart

• core_ml - Used for iOS mobile devices.

Type

str

params

Additional model-type and format specific parameters describing the requirements for the to be exported model files, any string must be up to 25000 characters long.

• For docker format: cpu_architecture - (string) “x86_64” (default). gpu_architecture - (string) “none” (default), “nvidia”.

Type

MutableMapping[str, str]

class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.NormalizedVertex(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.

x

Required. Horizontal coordinate.

Type

float

y

Required. Vertical coordinate.

Type

float

class google.cloud.automl_v1.types.OperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Metadata used across all long running operations returned by AutoML API.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

delete_details

Details of a Delete operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.DeleteOperationMetadata

deploy_model_details

Details of a DeployModel operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.DeployModelOperationMetadata

undeploy_model_details

Details of an UndeployModel operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.UndeployModelOperationMetadata

create_model_details

Details of CreateModel operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.CreateModelOperationMetadata

create_dataset_details

Details of CreateDataset operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.CreateDatasetOperationMetadata

import_data_details

Details of ImportData operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.ImportDataOperationMetadata

batch_predict_details

Details of BatchPredict operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.BatchPredictOperationMetadata

export_data_details

Details of ExportData operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.ExportDataOperationMetadata

export_model_details

Details of ExportModel operation.

This field is a member of oneof details.

Type

google.cloud.automl_v1.types.ExportModelOperationMetadata

progress_percent

Output only. Progress of operation. Range: [0, 100]. Not used currently.

Type

int

partial_failures

Output only. Partial failures encountered. E.g. single files that couldn’t be read. This field should never exceed 20 entries. Status details field will contain standard GCP error details.

Type

MutableSequence[google.rpc.status_pb2.Status]

create_time

Output only. Time when the operation was created.

Type

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the operation was updated for the last time.

Type

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.automl_v1.types.OutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

• For Translation: CSV file translation.csv, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language)

  • For Tables: Output depends on whether the dataset was imported from Google Cloud Storage or BigQuery. Google Cloud Storage case: [gcs_destination][google.cloud.automl.v1p1beta.OutputConfig.gcs_destination] must be set. Exported are CSV file(s) tables_1.csv, tables_2.csv,…,tables_N.csv with each having as header line the table’s column names, and all other lines contain values for the header columns. BigQuery case: [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name export_data_<automl-dataset-display-name>_<timestamp-of-export-call> where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ “based on ISO-8601” format. In that dataset a new table called primary_table will be created, and filled with precisely the same data as this obtained on import.

gcs_destination

Required. The Google Cloud Storage location where the output is to be written to. For Image Object Detection, Text Extraction, Video Classification and Tables, in the given directory a new directory will be created with name: export_data– where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory.

This field is a member of oneof destination.

Type

google.cloud.automl_v1.types.GcsDestination

class google.cloud.automl_v1.types.PredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].

name

Required. Name of the model requested to serve the prediction.

Type

str

payload

Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.

Type

google.cloud.automl_v1.types.ExamplePayload

params

Additional domain-specific parameters, any string must be up to 25000 characters long.

AutoML Vision Classification

score_threshold : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5.

AutoML Vision Object Detection

score_threshold : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.

max_bounding_box_count : (int64) The maximum number of bounding boxes returned. The default is 100. The number of returned bounding boxes might be limited by the server.

AutoML Tables

feature_importance : (boolean) Whether [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance] is populated in the returned list of [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation] objects. The default is false.

Type

MutableMapping[str, str]

class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.PredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Response message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].

payload

Prediction result. AutoML Translation and AutoML Natural Language Sentiment Analysis return precisely one payload.

Type

MutableSequence[google.cloud.automl_v1.types.AnnotationPayload]

preprocessed_input

The preprocessed example that AutoML actually makes prediction on. Empty if AutoML does not preprocess the input example.

For AutoML Natural Language (Classification, Entity Extraction, and Sentiment Analysis), if the input is a document, the recognized text is returned in the [document_text][google.cloud.automl.v1.Document.document_text] property.

Type

google.cloud.automl_v1.types.ExamplePayload

metadata

Additional domain-specific prediction response metadata.

AutoML Vision Object Detection

max_bounding_box_count : (int64) The maximum number of bounding boxes to return per image.

AutoML Natural Language Sentiment Analysis

sentiment_score : (float, deprecated) A value between -1 and 1, -1 maps to least positive sentiment, while 1 maps to the most positive one and the higher the score, the more positive the sentiment in the document is. Yet these values are relative to the training data, so e.g. if all data was positive then -1 is also positive (though the least). sentiment_score is not the same as “score” and “magnitude” from Sentiment Analysis in the Natural Language API.

Type

MutableMapping[str, str]

class MetadataEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.automl_v1.types.TextClassificationDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Dataset metadata for classification.

classification_type

Required. Type of the classification problem.

Type

google.cloud.automl_v1.types.ClassificationType

class google.cloud.automl_v1.types.TextClassificationModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata that is specific to text classification.

classification_type

Output only. Classification type of the dataset used to train this model.

Type

google.cloud.automl_v1.types.ClassificationType

class google.cloud.automl_v1.types.TextExtractionAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Annotation for identifying spans of text.

text_segment

An entity annotation will set this, which is the part of the original text to which the annotation pertains.

This field is a member of oneof annotation.

Type

google.cloud.automl_v1.types.TextSegment

score

Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence in correctness of the annotation.

Type

float

class google.cloud.automl_v1.types.TextExtractionDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Dataset metadata that is specific to text extraction

class google.cloud.automl_v1.types.TextExtractionEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model evaluation metrics for text extraction problems.

au_prc

Output only. The Area under precision recall curve metric.

Type

float

confidence_metrics_entries

Output only. Metrics that have confidence thresholds. Precision-recall curve can be derived from it.

Type

MutableSequence[google.cloud.automl_v1.types.TextExtractionEvaluationMetrics.ConfidenceMetricsEntry]

class ConfidenceMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Metrics for a single confidence threshold.

confidence_threshold

Output only. The confidence threshold value used to compute the metrics. Only annotations with score of at least this threshold are considered to be ones the model would return.

Type

float

recall

Output only. Recall under the given confidence threshold.

Type

float

precision

Output only. Precision under the given confidence threshold.

Type

float

f1_score

Output only. The harmonic mean of recall and precision.

Type

float

class google.cloud.automl_v1.types.TextExtractionModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata that is specific to text extraction.

class google.cloud.automl_v1.types.TextSegment(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.

content

Output only. The content of the TextSegment.

Type

str

start_offset

Required. Zero-based character index of the first character of the text segment (counting characters from the beginning of the text).

Type

int

end_offset

Required. Zero-based character index of the first character past the end of the text segment (counting character from the beginning of the text). The character at the end_offset is NOT included in the text segment.

Type

int

class google.cloud.automl_v1.types.TextSentimentAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Contains annotation details specific to text sentiment.

sentiment

Output only. The sentiment with the semantic, as given to the [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] when populating the dataset from which the model used for the prediction had been trained. The sentiment values are between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive), with higher value meaning more positive sentiment. They are completely relative, i.e. 0 means least positive sentiment and sentiment_max means the most positive from the sentiments present in the train data. Therefore e.g. if train data had only negative sentiment, then sentiment_max, would be still negative (although least negative). The sentiment shouldn’t be confused with “score” or “magnitude” from the previous Natural Language Sentiment Analysis API.

Type

int

class google.cloud.automl_v1.types.TextSentimentDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Dataset metadata for text sentiment.

sentiment_max

Required. A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentiment_max (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. sentiment_max value must be between 1 and 10 (inclusive).

Type

int

class google.cloud.automl_v1.types.TextSentimentEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model evaluation metrics for text sentiment problems.

precision

Output only. Precision.

Type

float

recall

Output only. Recall.

Type

float

f1_score

Output only. The harmonic mean of recall and precision.

Type

float

mean_absolute_error

Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

Type

float

mean_squared_error

Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

Type

float

linear_kappa

Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

Type

float

quadratic_kappa

Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

Type

float

confusion_matrix

Output only. Confusion matrix of the evaluation. Only set for the overall model evaluation, not for evaluation of a single annotation spec.

Type

google.cloud.automl_v1.types.ClassificationEvaluationMetrics.ConfusionMatrix

class google.cloud.automl_v1.types.TextSentimentModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata that is specific to text sentiment.

class google.cloud.automl_v1.types.TextSnippet(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A representation of a text snippet.

content

Required. The content of the text snippet as a string. Up to 250000 characters long.

Type

str

mime_type

Optional. The format of [content][google.cloud.automl.v1.TextSnippet.content]. Currently the only two allowed values are “text/html” and “text/plain”. If left blank, the format is automatically determined from the type of the uploaded [content][google.cloud.automl.v1.TextSnippet.content].

Type

str

content_uri

Output only. HTTP URI where you can download the content.

Type

str

class google.cloud.automl_v1.types.TranslationAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Annotation details specific to translation.

translated_content

Output only . The translated content.

Type

google.cloud.automl_v1.types.TextSnippet

class google.cloud.automl_v1.types.TranslationDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Dataset metadata that is specific to translation.

source_language_code

Required. The BCP-47 language code of the source language.

Type

str

target_language_code

Required. The BCP-47 language code of the target language.

Type

str

class google.cloud.automl_v1.types.TranslationEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Evaluation metrics for the dataset.

bleu_score

Output only. BLEU score.

Type

float

base_bleu_score

Output only. BLEU score for base model.

Type

float

class google.cloud.automl_v1.types.TranslationModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata that is specific to translation.

base_model

The resource name of the model to use as a baseline to train the custom model. If unset, we use the default base model provided by Google Translate. Format: projects/{project_id}/locations/{location_id}/models/{model_id}

Type

str

source_language_code

Output only. Inferred from the dataset. The source language (The BCP-47 language code) that is used for training.

Type

str

target_language_code

Output only. The target language (The BCP-47 language code) that is used for training.

Type

str

class google.cloud.automl_v1.types.UndeployModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Details of UndeployModel operation.

class google.cloud.automl_v1.types.UndeployModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].

name

Required. Resource name of the model to undeploy.

Type

str

class google.cloud.automl_v1.types.UpdateDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset]

dataset

Required. The dataset which replaces the resource on the server.

Type

google.cloud.automl_v1.types.Dataset

update_mask

Required. The update mask applies to the resource.

Type

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.automl_v1.types.UpdateModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Request message for [AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel]

model

Required. The model which replaces the resource on the server.

Type

google.cloud.automl_v1.types.Model

update_mask

Required. The update mask applies to the resource.

Type

google.protobuf.field_mask_pb2.FieldMask