Module types (0.8.0)

API documentation for automl_v1beta1.types module.

Contains annotation information that is relevant to AutoML.

Annotation details for translation.

Annotation details for image object detection.

Annotation details for video object tracking.

Annotation details for text sentiment.

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.

A definition of an annotation spec.

Required. The name of the annotation spec to show in the interface. The name can be up to 32 characters long and must match the regexp [a-zA-Z0-9_]+. (_), and ASCII digits 0-9.

API documentation for automl_v1beta1.types.Any class.

The data statistics of a series of ARRAY values.

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.v1beta1.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:

• For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png

• For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png

• For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf

• For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf

• For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf

• For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf

• For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain 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. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given.

• For Tables: Either [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or

[bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]. GCS case: 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.v1beta1.TablesModelMetadata.input_feature_column_specs]

[display_name-s][google.cloud.automl.v1beta1.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. For FORECASTING

[prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: all columns having

[TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] type will be ignored. First three sample rows of 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"}]} BigQuery case: An 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.v1beta1.TablesModelMetadata.input_feature_column_specs]

[display_name-s][google.cloud.automl.v1beta1.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. For FORECASTING

[prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: all columns having

[TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] type will be ignored.

Definitions: GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes ("") 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 an 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.

The Google Cloud Storage location for the input content.

Details of BatchPredict operation.

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

Output configuration for BatchPredict Action.

As destination the

[gcs_destination][google.cloud.automl.v1beta1.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" : "" 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" : "" but here followed by exactly one

`google.rpc.Status <https:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__ containing onlycodeandmessage`\ fields.

• 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" : "" 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" : "" but here followed by exactly one

`google.rpc.Status <https:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__ containing onlycodeandmessage`\ fields. * 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 snippet or input text file and a list of
  zero or more AnnotationPayload protos (called annotations), which
  have classification detail populated. A single text snippet or file
  will be listed only once with all its annotations, and its
  annotations will never be split across files.
  
  If prediction for any text snippet or file 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 text snippet or input text file followed by
  exactly one
  

`google.rpc.Status <https:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__ containing onlycodeandmessage`.

• 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 snippet or input text file and a list of
  zero or more AnnotationPayload protos (called annotations), which
  have text_sentiment detail populated. A single text snippet or file
  will be listed only once with all its annotations, and its
  annotations will never be split across files.
  
  If prediction for any text snippet or file 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 text snippet or input text file followed by
  exactly one
  

`google.rpc.Status <https:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__ containing onlycodeandmessage`.

• 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" : "" (in case of inline) or the document proto (in case of document) but here followed by exactly one

`google.rpc.Status <https:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__ containing onlycodeandmessage`.

• For Tables: Output depends on whether

[gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination] or

[bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination] is set (either is allowed). GCS 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.v1beta1.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns'

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] given on input followed by M target column names in the format of

"<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.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. For REGRESSION and FORECASTING

[prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns' [display_name-s][google.cloud.automl.v1beta1.display_name] given on input followed by the predicted target column with name in the format of

"predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.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:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__ represented as a JSON string, and containing onlycodeandmessage`. BigQuery case:

[bigquery_destination][google.cloud.automl.v1beta1.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.v1beta1.ColumnSpec.display_name] followed by the target column with name in the format of

"predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.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, represented as STRUCT-s, containing 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.v1beta1.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>", and as a value has

`google.rpc.Status <https:%20//github.com/googleapis/googleapis/blob/master/google/rpc/status.proto>__ represented as a STRUCT, and containing onlycodeandmessage`.

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

Request message for PredictionService.BatchPredict.

Required. The input configuration for batch prediction.

Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. - For Text 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. - For Image 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. - For Image 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) No more than this number of bounding boxes will be produced per image. Default is 100, the requested value may be limited by server. - For Video 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. 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. The default is "false". 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. 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. The default is "false". - For Video 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) No more than this number of bounding boxes will be returned per frame. Default is 100, the requested value may be limited by server. min_bounding_box_size - (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size will be returned. Value in 0 to 1 range. Default is 0.

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

The BigQuery location for the output content.

The BigQuery location for the input content.

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

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

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.

API documentation for automl_v1beta1.types.CancelOperationRequest class.

The data statistics of a series of CATEGORY values.

Contains annotation details specific to classification.

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

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

Output only. The Log Loss metric.

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.

A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: * Tables

The data type of elements stored in the column.

Output only. Stats of the series of values in the column. This field may be stale, see the ancestor's Dataset.tables_dataset_metadata.stats_update_time field for the timestamp at which these stats were last updated.

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

A correlation statistics between two series of DataType values. The series may have differing DataType-s, but within a single series the DataType must be the same.

Request message for AutoMl.CreateDataset.

The dataset to create.

Details of CreateModel operation.

Request message for AutoMl.CreateModel.

The model to create.

The data statistics of a series of values that share the same DataType.

The statistics for FLOAT64 DataType.

The statistics for TIMESTAMP DataType.

The statistics for STRUCT DataType.

The number of distinct values.

The number of values that are valid.

Indicated the type of data that can be stored in a structured data entity (e.g. a table).

If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == ARRAY, then list_element_type is the type of the elements.

If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == TIMESTAMP then time_format provides the format in which that time field is expressed. The time_format must either be one of: * UNIX_SECONDS * UNIX_MILLISECONDS * UNIX_MICROSECONDS * UNIX_NANOSECONDS (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in strftime syntax. If time_format is not set, then the default format as described on the type_code is used.

If true, this DataType can also be NULL. In .CSV files NULL value is expressed as an empty string.

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

Metadata for a dataset used for translation.

Metadata for a dataset used for text classification.

Metadata for a dataset used for video classification.

Metadata for a dataset used for text extraction.

Metadata for a dataset used for Tables.

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.

Output only. The number of examples in the dataset.

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

Request message for AutoMl.DeleteDataset.

Request message for AutoMl.DeleteModel.

Details of operations that perform deletes of any entities.

API documentation for automl_v1beta1.types.DeleteOperationRequest class.

Details of DeployModel operation.

Request message for AutoMl.DeployModel.

Model deployment metadata specific to Image Object Detection.

Resource name of the model to deploy.

A structured text document e.g. a PDF.

The plain text version of this document.

The dimensions of the page in the document.

Message that describes dimension of a document.

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

Input configuration of a Document.

A range between two double numbers.

End of the range, exclusive.

API documentation for automl_v1beta1.types.Duration class.

Example data used for training or prediction.

Example image.

Example document.

Details of ExportData operation.

Request message for AutoMl.ExportData.

Required. The desired output location.

Details of EvaluatedExamples operation.

Output configuration for ExportEvaluatedExamples Action. Note that this call is available only for 30 days since the moment the model was evaluated. The output depends on the domain, as follows (note that only examples from the TEST set are exported):

• For Tables:

[bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name

export_evaluated_examples_<model-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 the dataset an evaluated_examples table will be created. It will have all the same columns as the

[primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id] of the [dataset][google.cloud.automl.v1beta1.Model.dataset_id] from which the model was created, as they were at the moment of model's evaluation (this includes the target column with its ground truth), followed by a column called "predicted_". That last column will contain the model's prediction result for each respective row, given as ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation.

The BigQuery location where the output is to be written to.

Request message for AutoMl.ExportEvaluatedExamples.

Required. The desired output location and configuration.

Details of ExportModel operation.

Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.

Required. The desired output location and configuration.

API documentation for automl_v1beta1.types.FieldMask class.

The data statistics of a series of FLOAT64 values.

The standard deviation of the series.

Histogram buckets of the data series. Sorted by the min value of the bucket, ascendingly, and the number of the buckets is dynamically generated. The buckets are non-overlapping and completely cover whole FLOAT64 range with min of first bucket being "-Infinity", and max of the last one being "Infinity".

The GCR location where the image must be pushed to.

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

The Google Cloud Storage location for the input content.

Request message for AutoMl.GetAnnotationSpec.

Request message for AutoMl.GetColumnSpec.

Mask specifying which fields to read.

Request message for AutoMl.GetDataset.

Request message for AutoMl.GetModelEvaluation.

Request message for AutoMl.GetModel.

API documentation for automl_v1beta1.types.GetOperationRequest class.

Request message for AutoMl.GetTableSpec.

Mask specifying which fields to read.

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

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.

Output only. HTTP URI to the thumbnail image.

Dataset metadata that is specific to image classification.

Model deployment metadata specific to Image Classification.

Model metadata for image classification.

Required. The train budget of creating this model, expressed in hours. The actual train_cost will be equal or less than this value.

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

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

Annotation details for image object detection.

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

Dataset metadata specific to image object detection.

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

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.

Model deployment metadata specific to Image Object Detection.

Model metadata specific to image object detection.

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.

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

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.

Details of ImportData operation.

Request message for AutoMl.ImportData.

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

Input configuration for 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.v1beta1.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:

• For Image Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO For 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

• For Image Object Detection: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined per line). If an image has not yet been labeled, then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. For images which are known to not contain any bounding boxes, they should be labelled explictly as "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the BOUNDING_BOX. 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,,,,,,,,, TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,

• For Video Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have 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 end has to be after the start. 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 shuold 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,,,

• For Video Object Tracking: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using one of 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,,,,,,,,,,,

• For Text Extraction: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which either imports text in-line or as documents. Any given .JSONL file must be 100MB or smaller. The in-line .JSONL file contains, per line, a proto that wraps a TextSnippet proto (in json representation) followed by one or more AnnotationPayload protos (called annotations), which have display_name and text_extraction detail populated. The given text is expected to be annotated exhaustively, for example, if you look for animals and text contains "dolphin" that is not labeled, then "dolphin" is assumed to not be an animal. Any given text snippet content must be 10KB or smaller, and also be UTF-8 NFC encoded (ASCII already is). The document .JSONL file contains, per line, a proto that wraps a Document proto. The Document proto must have either document_text or input_config set. In document_text case, the Document proto may also contain the spatial information of the document, including layout, document dimension and page number. In input_config case, only PDF documents are supported now, and each document may be up to 2MB large. Currently, annotations on documents cannot be specified at import. Three sample CSV rows: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl

• For Text Classification: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, i.e. prefixed by "gs://", it will be treated as a GCS_FILE_PATH, else if the content is enclosed within double quotes (""), it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content excluding quotes is treated as to be imported text snippet. In both cases, the text snippet/file size must be within 128kB. Maximum 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always bad service there.",RudeService VALIDATE,"Stomach ache to go.",BadFood

• For Text Sentiment: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content itself is treated as to be imported text snippet. In both cases, the text snippet must be up to 500 characters long. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 TRAIN,"I need this product so bad",3 TEST,"Thank you for this product.",4 VALIDATE,gs://folder/content.txt,2

• For Tables: Either [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or

[bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source] can be used. All inputs is concatenated into a single

[primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name] 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. 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 = A path to file on GCS, e.g. "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 an 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 = A content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). 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 huge.

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.

The Google Cloud Storage location for the input content. In ImportData, the gcs_source points to a csv with structure described in the comment.

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

• (integer) Required. The version of the algorithm that should be used for the initial inference of the schema (columns' DataTypes) of the table the data is being imported into. Allowed values: "1".

Request message for AutoMl.ListColumnSpecs.

Mask specifying which fields to read.

Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.

Response message for AutoMl.ListColumnSpecs.

A token to retrieve next page of results. Pass to [ListColumnS pecsRequest.page_token][google.cloud.automl.v1beta1.ListColum nSpecsRequest.page_token] to obtain that page.

Request message for AutoMl.ListDatasets.

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.

A token identifying a page of results for the server to return Typically obtained via [ListDatasetsResponse.next_page_token ][google.cloud.automl.v1beta1.ListDatasetsResponse.next_page _token] of the previous [AutoMl.ListDatasets][google.cloud.aut oml.v1beta1.AutoMl.ListDatasets] call.

Response message for AutoMl.ListDatasets.

A token to retrieve next page of results. Pass to [ListDataset sRequest.page_token][google.cloud.automl.v1beta1.ListDatasets Request.page_token] to obtain that page.

Request message for AutoMl.ListModelEvaluations.

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.

  A token identifying a page of results for the server to return. Typically obtained via [ListModelEvaluationsResponse.n ext_page_token][google.cloud.automl.v1beta1.ListModelEvaluat ionsResponse.next_page_token] of the previous [AutoMl.ListMo delEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEv aluations] call.

Response message for AutoMl.ListModelEvaluations.

A token to retrieve next page of results. Pass to the [ListMod elEvaluationsRequest.page_token][google.cloud.automl.v1beta1. ListModelEvaluationsRequest.page_token] field of a new [AutoM l.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.Lis tModelEvaluations] request to obtain that page.

Request message for AutoMl.ListModels.

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.

  A token identifying a page of results for the server to return Typically obtained via [ListModelsResponse.next_page_token][ google.cloud.automl.v1beta1.ListModelsResponse.next_page_tok en] of the previous [AutoMl.ListModels][google.cloud.automl.v1 beta1.AutoMl.ListModels] call.

Response message for AutoMl.ListModels.

A token to retrieve next page of results. Pass to [ListModelsR equest.page_token][google.cloud.automl.v1beta1.ListModelsRequ est.page_token] to obtain that page.

API documentation for automl_v1beta1.types.ListOperationsRequest class.

API documentation for automl_v1beta1.types.ListOperationsResponse class.

Request message for AutoMl.ListTableSpecs.

Mask specifying which fields to read.

Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.

Response message for AutoMl.ListTableSpecs.

A token to retrieve next page of results. Pass to [ListTableSp ecsRequest.page_token][google.cloud.automl.v1beta1.ListTableS pecsRequest.page_token] to obtain that page.

API documentation for automl_v1beta1.types.ListValue class.

API proto representing a trained machine learning model.

Metadata for translation models.

Metadata for text classification models.

Metadata for video classification models.

Metadata for text extraction models.

Metadata for text sentiment models.

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.

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

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

Evaluation results of a model.

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

Model evaluation metrics for translation.

Model evaluation metrics for video object tracking.

Evaluation metrics for text extraction models.

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.v1bet a1.TablesModelMetadata.prediction_type] the [display_name][g oogle.cloud.automl.v1beta1.ModelEvaluation.display_name] field is used.

Output only. Timestamp when this model evaluation was created.

Output configuration for ModelExport Action.

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.

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). Formats description: - tflite - Used for Android mobile devices. - edgetpu_tflite - Used for Edge TPU <https://cloud.google.com/edge-tpu/> devices. - tf_saved_model - A tensorflow model in SavedModel format. - tf_js - A TensorFlow.js <https://www.tensorflow.org/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](https: //cloud.google.com/vision/automl/docs/containers-gcs- quickstart) * core_ml - Used for iOS mobile devices.

Required. Horizontal coordinate.

API documentation for automl_v1beta1.types.Operation class.

API documentation for automl_v1beta1.types.OperationInfo class.

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

Details of a Delete operation.

Details of an UndeployModel operation.

Details of ImportData operation.

Details of ExportData operation.

Details of ExportEvaluatedExamples operation.

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.

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

• 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) \tTEXT_SNIPPET (in target language)

  • For Tables: Output depends on whether the dataset was imported from GCS or BigQuery. GCS case:

  [gcs_destination][google.cloud.automl.v1beta1.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.v1beta1.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.

Attributes: destination: Required. The destination of the output. gcs_destination: 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. bigquery_destination: The BigQuery location where the output is to be written to.

Request message for PredictionService.Predict.

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

Response message for PredictionService.Predict.

The preprocessed example that AutoML actually makes prediction on. Empty if AutoML does not preprocess the input example. * For Text Extraction: If the input is a .pdf file, the OCR'ed text will be provided in [document_text][google.cloud.automl. v1beta1.Document.document_text].

Metrics for regression problems.

Output only. Mean Absolute Error (MAE).

Output only. R squared.

A representation of a row in a relational table.

Required. The values of the row cells, given in the same order as the column_spec_ids, or, if not set, then in the same order as input feature [column_specs][google.cloud.automl.v1 beta1.TablesModelMetadata.input_feature_column_specs] of the Model this row is being passed to.

API documentation for automl_v1beta1.types.Status class.

The data statistics of a series of STRING values.

API documentation for automl_v1beta1.types.Struct class.

The data statistics of a series of STRUCT values.

StructType defines the DataType-s of a STRUCT type.

A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: * Tables

column_spec_id of the time column. Only used if the parent dataset's ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and those in between to VALIDATE. Required type: TIMESTAMP. If both this column and ml_use_column are not set, then ML use of all rows will be assigned by AutoML. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.

Output only. The number of valid rows (i.e. without values that don't match DataType-s of their columns).

Output only. Input configs via which data currently residing in the table had been imported.

Contains annotation details specific to Tables.

Output only. Only populated when [target_column_spec][googl e.cloud.automl.v1beta1.TablesModelMetadata.target_column_spe c] has FLOAT64 data type. An interval in which the exactly correct target value has 95% chance to be in.

Output only. Auxiliary information for each of the model's [i nput_feature_column_specs][google.cloud.automl.v1beta1.Tabl esModelMetadata.input_feature_column_specs] with respect to this particular prediction. If no other fields than [column_ spec_name][google.cloud.automl.v1beta1.TablesModelColumnInfo. column_spec_name] and [column_display_name][google.cloud. automl.v1beta1.TablesModelColumnInfo.column_display_name] would be populated, then this whole field is not.

Metadata for a dataset used for AutoML Tables.

column_spec_id of the primary table's column that should be used as the training & prediction target. This column must be non-nullable and have one of following data types (otherwise model creation will error): - CATEGORY - FLOAT64 If the type is CATEGORY , only up to 100 unique values may exist in that column across all rows. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.

column_spec_id of the primary table column which specifies a possible ML use of the row, i.e. the column will be used to split the rows into TRAIN, VALIDATE and TEST sets. Required type: STRING. This column, if set, must either have all of TRAIN, VALIDATE, TEST among its values, or only have TEST, UNASSIGNED values. In the latter case the rows with UNASSIGNED value will be assigned by AutoML. Note that if a given ml use distribution makes it impossible to create a "good" model, that call will error describing the issue. If both this column_spec_id and primary table's time_column_spec_id are not set, then all rows are treated as UNASSIGNED. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.

Output only. The most recent timestamp when target_column_correlations field and all descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns fields were last (re-)generated. Any changes that happened to the dataset afterwards are not reflected in these fields values. The regeneration happens in the background on a best effort basis.

An information specific to given column and Tables Model, in context of the Model and the predictions created by it.

Output only. The display name of the column (same as the display_name of its ColumnSpec).

Model metadata specific to AutoML Tables.

Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.

Column spec of the dataset's primary table's column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it's not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. CLASSIFICATION_BINARY: "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "MINIMIZE_LOG_LOSS" - Minimize log loss. "MAXIMIZE_AU_PRC" - Maximize the area under the precision- recall curve. "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified recall value. "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified precision value. CLASSIFICATION_MULTI_CLASS : "MINIMIZE_LOG_LOSS" (default) - Minimize log loss. REGRESSION: "MINIMIZE_RMSE" (default) - Minimize root-mean- squared error (RMSE). "MINIMIZE_MAE" - Minimize mean-absolute error (MAE). "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).

Required. 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 training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller

• at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.

  Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.

Dataset metadata for classification.

Model metadata that is specific to text classification.

Annotation for identifying spans of text.

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

Dataset metadata that is specific to text extraction

Model evaluation metrics for text extraction problems.

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

Model metadata that is specific to text extraction.

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

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

Contains annotation details specific to text sentiment.

Dataset metadata for text sentiment.

Model evaluation metrics for text sentiment problems.

Output only. Recall.

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

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

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

Model metadata that is specific to text sentiment.

A representation of a text snippet.

Optional. The format of 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.

A time period inside of an example that has a time dimension (e.g. video).

End of the time segment (exclusive), represented as the duration since the example start.

API documentation for automl_v1beta1.types.Timestamp class.

The data statistics of a series of TIMESTAMP values.

Annotation details specific to translation.

Dataset metadata that is specific to translation.

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

Evaluation metrics for the dataset.

Output only. BLEU score for base model.

Model metadata that is specific to translation.

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

Details of UndeployModel operation.

Request message for AutoMl.UndeployModel.

Request message for AutoMl.UpdateColumnSpec

The update mask applies to the resource.

Request message for AutoMl.UpdateDataset

The update mask applies to the resource.

Request message for AutoMl.UpdateTableSpec

The update mask applies to the resource.

API documentation for automl_v1beta1.types.Value class.

Contains annotation details specific to video classification.

Output only . The classification details of this annotation.

Dataset metadata specific to video classification. All Video Classification datasets are treated as multi label.

Model metadata specific to video classification.

Annotation details for video object tracking.

Required. A time (frame) of a video to which this annotation pertains. Represented as the duration since the video's start.

Output only. The confidence that this annotation is positive for the video at the time_offset, value in [0, 1], higher means higher positivity confidence. For annotations created by the user the score is 1. When user approves an annotation, the original float score is kept (and not changed to 1).

Dataset metadata specific to video object tracking.

Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).

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

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

Model metadata specific to video object tracking.

API documentation for automl_v1beta1.types.WaitOperationRequest class.