Types for Google Cloud Automl v1beta1 API¶
- class google.cloud.automl_v1beta1.types.AnnotationPayload(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageContains 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.
- classification¶
Annotation details for content or image classification.
This field is a member of oneof
detail.
- image_object_detection¶
Annotation details for image object detection.
This field is a member of oneof
detail.
- video_classification¶
Annotation details for video classification. Returned for Video Classification predictions.
This field is a member of oneof
detail.
- video_object_tracking¶
Annotation details for video object tracking.
This field is a member of oneof
detail.
- 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
- display_name¶
Output only. The value of [display_name][google.cloud.automl.v1beta1.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_namebetween any two model training.- Type
- class google.cloud.automl_v1beta1.types.AnnotationSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA 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
- 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
- class google.cloud.automl_v1beta1.types.ArrayStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe data statistics of a series of ARRAY values.
- member_stats¶
Stats of all the values of all arrays, as if they were a single long series of data. The type depends on the element type of the array.
- class google.cloud.automl_v1beta1.types.BatchPredictInputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageInput 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. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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”: “An elaborate content”, “mime_type”: “text/plain” } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf” ] } } } }
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.
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.
- gcs_source¶
The Google Cloud Storage location for the input content.
This field is a member of oneof
source.
- class google.cloud.automl_v1beta1.types.BatchPredictOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of BatchPredict operation.
- input_config¶
Output only. The input config that was given upon starting this batch predict operation.
- output_info¶
Output only. Information further describing this batch predict’s output.
- class BatchPredictOutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageFurther describes this batch predict’s output. Supplements
[BatchPredictOutputConfig][google.cloud.automl.v1beta1.BatchPredictOutputConfig].
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.
- class google.cloud.automl_v1beta1.types.BatchPredictOutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageOutput 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.jsonlwill 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 additionalerrors_1.jsonl,errors_2.jsonl,…,errors_N.jsonlfiles 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 onlycodeandmessagefields.For Image Object Detection: In the created directory files
image_object_detection_1.jsonl,image_object_detection_2.jsonl,…,image_object_detection_N.jsonlwill 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 additionalerrors_1.jsonl,errors_2.jsonl,…,errors_N.jsonlfiles 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 onlycodeandmessagefields.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.jsonlwill 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: //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.jsonlwill 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: //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.jsonlwill 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 additionalerrors_1.jsonl,errors_2.jsonl,…,errors_N.jsonlfiles 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 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.csvwill 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][google.cloud.automl.v1beta1.TablesAnnotation.score]. 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.csvwill be created (N depends on total number of failed rows). These files will have analogous format astables_*.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 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, anderrors. Thepredictionstable’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][google.cloud.automl.v1beta1.AnnotationPayload], represented as STRUCT-s, containing [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation]. The
errorstable 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: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a STRUCT, and containing onlycodeandmessage.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.
- gcs_destination¶
The Google Cloud Storage location of the directory where the output is to be written to.
This field is a member of oneof
destination.
- class google.cloud.automl_v1beta1.types.BatchPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict].
- input_config¶
Required. The input configuration for batch prediction.
- output_config¶
Required. The Configuration specifying where output predictions should be written.
- params¶
Required. 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 Tables:
feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotations. 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.
- class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.BatchPredictResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageResult 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.v1beta1.PredictionService.BatchPredict].
- metadata¶
Additional domain-specific prediction response metadata.
For Image Object Detection:
max_bounding_box_count- (int64) At most that many bounding boxes per image could have been returned.For Video Object Tracking:
max_bounding_box_count- (int64) At most that many bounding boxes per frame could have been returned.
- class MetadataEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.BigQueryDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe BigQuery location for the output content.
- class google.cloud.automl_v1beta1.types.BigQuerySource(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe BigQuery location for the input content.
- class google.cloud.automl_v1beta1.types.BoundingBoxMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageBounding 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
- mean_average_precision¶
Output only. The mean average precision, most often close to au_prc.
- Type
- 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_v1beta1.types.BoundingBoxMetricsEntry.ConfidenceMetricsEntry]
- class ConfidenceMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageMetrics for a single confidence threshold.
- confidence_threshold¶
Output only. The confidence threshold value used to compute the metrics.
- Type
- class google.cloud.automl_v1beta1.types.BoundingPoly(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA 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_v1beta1.types.NormalizedVertex]
- class google.cloud.automl_v1beta1.types.CategoryStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe data statistics of a series of CATEGORY values.
- top_category_stats¶
The statistics of the top 20 CATEGORY values, ordered by
[count][google.cloud.automl.v1beta1.CategoryStats.SingleCategoryStats.count].
- Type
MutableSequence[google.cloud.automl_v1beta1.types.CategoryStats.SingleCategoryStats]
- class google.cloud.automl_v1beta1.types.ClassificationAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageContains 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
- class google.cloud.automl_v1beta1.types.ClassificationEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel 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
- base_au_prc¶
Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated.
- Type
- au_roc¶
Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.
- Type
- 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_v1beta1.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.
- 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)[source]¶
Bases:
proto.message.MessageMetrics 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
- 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
- false_positive_rate¶
Output only. False Positive Rate for the given confidence threshold.
- Type
- 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
- 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
- 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
- f1_score_at1¶
Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
- Type
- true_positive_count¶
Output only. The number of model created labels that match a ground truth label.
- Type
- false_positive_count¶
Output only. The number of model created labels that do not match a ground truth label.
- Type
- false_negative_count¶
Output only. The number of ground truth labels that are not matched by a model created label.
- Type
- class ConfusionMatrix(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageConfusion 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.v1beta1.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.v1beta1.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 theannotation_spec_id[i]and are predicted asannotation_spec_id[j]by the model being evaluated.- Type
MutableSequence[google.cloud.automl_v1beta1.types.ClassificationEvaluationMetrics.ConfusionMatrix.Row]
- class Row(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageOutput 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_idfield or, if that one is not populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.- Type
MutableSequence[int]
- class google.cloud.automl_v1beta1.types.ClassificationType(value)[source]¶
Bases:
proto.enums.EnumType 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_v1beta1.types.ColumnSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA 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
- name¶
Output only. The resource name of the column specs. Form:
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}/columnSpecs/{column_spec_id}- Type
- data_type¶
The data type of elements stored in the column.
- display_name¶
Output only. The name of the column to show in the interface. The name can be up to 100 characters long and can consist only of ASCII Latin letters A-Z and a-z, ASCII digits 0-9, underscores(_), and forward slashes(/), and must start with a letter or a digit.
- Type
- data_stats¶
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.
Deprecated.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.ColumnSpec.CorrelatedColumn]
- etag¶
Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.
- Type
Bases:
proto.message.MessageIdentifies the table’s column, and its correlation with the column this ColumnSpec describes.
The column_spec_id of the correlated column, which belongs to the same table as the in-context column.
- Type
Correlation between this and the in-context column.
- class google.cloud.automl_v1beta1.types.CorrelationStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA 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.
- class google.cloud.automl_v1beta1.types.CreateDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.CreateDataset][google.cloud.automl.v1beta1.AutoMl.CreateDataset].
- dataset¶
Required. The dataset to create.
- class google.cloud.automl_v1beta1.types.CreateModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of CreateModel operation.
- class google.cloud.automl_v1beta1.types.CreateModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.CreateModel][google.cloud.automl.v1beta1.AutoMl.CreateModel].
- model¶
Required. The model to create.
- class google.cloud.automl_v1beta1.types.DataStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe data statistics of a series of values that share the same DataType.
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.
- class google.cloud.automl_v1beta1.types.DataType(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageIndicated the type of data that can be stored in a structured data entity (e.g. a table).
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.
- list_element_type¶
If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == [ARRAY][google.cloud.automl.v1beta1.TypeCode.ARRAY], then
list_element_typeis the type of the elements.This field is a member of oneof
details.
- struct_type¶
If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == [STRUCT][google.cloud.automl.v1beta1.TypeCode.STRUCT], then
struct_typeprovides type information for the struct’s fields.This field is a member of oneof
details.
- time_format¶
If [type_code][google.cloud.automl.v1beta1.DataType.type_code] == [TIMESTAMP][google.cloud.automl.v1beta1.TypeCode.TIMESTAMP] then
time_formatprovides the format in which that time field is expressed. The time_format must either be one of:UNIX_SECONDSUNIX_MILLISECONDSUNIX_MICROSECONDSUNIX_NANOSECONDS(for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written instrftimesyntax. If time_format is not set, then the default format as described on the type_code is used.
This field is a member of oneof
details.- Type
- type_code¶
Required. The [TypeCode][google.cloud.automl.v1beta1.TypeCode] for this type.
- class google.cloud.automl_v1beta1.types.Dataset(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA 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.
- image_classification_dataset_metadata¶
Metadata for a dataset used for image classification.
This field is a member of oneof
dataset_metadata.
- text_classification_dataset_metadata¶
Metadata for a dataset used for text classification.
This field is a member of oneof
dataset_metadata.
- image_object_detection_dataset_metadata¶
Metadata for a dataset used for image object detection.
This field is a member of oneof
dataset_metadata.
- video_classification_dataset_metadata¶
Metadata for a dataset used for video classification.
This field is a member of oneof
dataset_metadata.
- video_object_tracking_dataset_metadata¶
Metadata for a dataset used for video object tracking.
This field is a member of oneof
dataset_metadata.
- text_extraction_dataset_metadata¶
Metadata for a dataset used for text extraction.
This field is a member of oneof
dataset_metadata.
- text_sentiment_dataset_metadata¶
Metadata for a dataset used for text sentiment.
This field is a member of oneof
dataset_metadata.
- tables_dataset_metadata¶
Metadata for a dataset used for Tables.
This field is a member of oneof
dataset_metadata.
- name¶
Output only. The resource name of the dataset. Form:
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}- Type
- 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
- description¶
User-provided description of the dataset. The description can be up to 25000 characters long.
- Type
- create_time¶
Output only. Timestamp when this dataset was created.
- class google.cloud.automl_v1beta1.types.DeleteDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.DeleteDataset][google.cloud.automl.v1beta1.AutoMl.DeleteDataset].
- class google.cloud.automl_v1beta1.types.DeleteModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.DeleteModel][google.cloud.automl.v1beta1.AutoMl.DeleteModel].
- class google.cloud.automl_v1beta1.types.DeleteOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of operations that perform deletes of any entities.
- class google.cloud.automl_v1beta1.types.DeployModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of DeployModel operation.
- class google.cloud.automl_v1beta1.types.DeployModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.DeployModel][google.cloud.automl.v1beta1.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.
- class google.cloud.automl_v1beta1.types.Document(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA structured text document e.g. a PDF.
- input_config¶
An input config specifying the content of the document.
- document_text¶
The plain text version of this document.
- layout¶
Describes the layout of the document. Sorted by [page_number][].
- Type
MutableSequence[google.cloud.automl_v1beta1.types.Document.Layout]
- document_dimensions¶
The dimensions of the page in the document.
- class Layout(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDescribes the layout information of a [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the document.
- text_segment¶
Text Segment that represents a segment in [document_text][google.cloud.automl.v1beta1.Document.document_text].
- page_number¶
Page number of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the original document, starts from 1.
- Type
- bounding_poly¶
The position of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in the page. Contains exactly 4
[normalized_vertices][google.cloud.automl.v1beta1.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.v1beta1.NormalizedVertex] are relative to the page. Coordinates are based on top-left as point (0,0).
- text_segment_type¶
The type of the [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in document.
- class TextSegmentType(value)[source]¶
Bases:
proto.enums.EnumThe 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_v1beta1.types.DocumentDimensions(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageMessage that describes dimension of a document.
- unit¶
Unit of the dimension.
- class DocumentDimensionUnit(value)[source]¶
Bases:
proto.enums.EnumUnit 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_v1beta1.types.DocumentInputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageInput configuration of a [Document][google.cloud.automl.v1beta1.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.
- class google.cloud.automl_v1beta1.types.DoubleRange(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA range between two double numbers.
- class google.cloud.automl_v1beta1.types.ExamplePayload(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageExample 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.
- class google.cloud.automl_v1beta1.types.ExportDataOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of ExportData operation.
- output_info¶
Output only. Information further describing this export data’s output.
- class ExportDataOutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageFurther describes this export data’s output. Supplements [OutputConfig][google.cloud.automl.v1beta1.OutputConfig].
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.
- class google.cloud.automl_v1beta1.types.ExportDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ExportData][google.cloud.automl.v1beta1.AutoMl.ExportData].
- output_config¶
Required. The desired output location.
- class google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of EvaluatedExamples operation.
- output_info¶
Output only. Information further describing the output of this evaluated examples export.
- class ExportEvaluatedExamplesOutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageFurther describes the output of the evaluated examples export. Supplements
[ExportEvaluatedExamplesOutputConfig][google.cloud.automl.v1beta1.ExportEvaluatedExamplesOutputConfig].
- class google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesOutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageOutput 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 anevaluated_examplestable 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_<target_column>”. That last column will contain the model’s prediction result for each respective row, given as ARRAY of [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload], represented as STRUCT-s, containing [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
- class google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ExportEvaluatedExamples][google.cloud.automl.v1beta1.AutoMl.ExportEvaluatedExamples].
- name¶
Required. The resource name of the model whose evaluated examples are to be exported.
- Type
- output_config¶
Required. The desired output location and configuration.
- class google.cloud.automl_v1beta1.types.ExportModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of ExportModel operation.
- output_info¶
Output only. Information further describing the output of this model export.
- class ExportModelOutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageFurther describes the output of model export. Supplements
[ModelExportOutputConfig][google.cloud.automl.v1beta1.ModelExportOutputConfig].
- class google.cloud.automl_v1beta1.types.ExportModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.
- output_config¶
Required. The desired output location and configuration.
- class google.cloud.automl_v1beta1.types.Float64Stats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe data statistics of a series of FLOAT64 values.
- quantiles¶
Ordered from 0 to k k-quantile values of the data series of n values. The value at index i is, approximately, the i*n/k-th smallest value in the series; for i = 0 and i = k these are, respectively, the min and max values.
- Type
MutableSequence[float]
- histogram_buckets¶
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".- Type
MutableSequence[google.cloud.automl_v1beta1.types.Float64Stats.HistogramBucket]
- class HistogramBucket(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA bucket of a histogram.
- max_¶
The maximum value of the bucket, exclusive unless max =
"Infinity", in which case it’s inclusive.- Type
- class google.cloud.automl_v1beta1.types.GcrDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe GCR location where the image must be pushed to.
- output_uri¶
Required. Google Contained Registry URI of the new image, up to 2000 characters long. See
https: //cloud.google.com/container-registry/do // cs/pushing-and-pulling#pushing_an_image_to_a_registry Accepted forms:
[HOSTNAME]/[PROJECT-ID]/[IMAGE]
[HOSTNAME]/[PROJECT-ID]/[IMAGE]:[TAG]
The requesting user must have permission to push images the project.
- Type
- class google.cloud.automl_v1beta1.types.GcsDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe Google Cloud Storage location where the output is to be written to.
- class google.cloud.automl_v1beta1.types.GcsSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe Google Cloud Storage location for the input content.
- class google.cloud.automl_v1beta1.types.GetAnnotationSpecRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1beta1.AutoMl.GetAnnotationSpec].
- class google.cloud.automl_v1beta1.types.GetColumnSpecRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.GetColumnSpec][google.cloud.automl.v1beta1.AutoMl.GetColumnSpec].
- field_mask¶
Mask specifying which fields to read.
- class google.cloud.automl_v1beta1.types.GetDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.GetDataset][google.cloud.automl.v1beta1.AutoMl.GetDataset].
- class google.cloud.automl_v1beta1.types.GetModelEvaluationRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.AutoMl.GetModelEvaluation].
- class google.cloud.automl_v1beta1.types.GetModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.GetModel][google.cloud.automl.v1beta1.AutoMl.GetModel].
- class google.cloud.automl_v1beta1.types.GetTableSpecRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.GetTableSpec][google.cloud.automl.v1beta1.AutoMl.GetTableSpec].
- field_mask¶
Mask specifying which fields to read.
- class google.cloud.automl_v1beta1.types.Image(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA representation of an image. Only images up to 30MB in size are supported.
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_bytes¶
Image content represented as a stream of bytes. Note: As with all
bytesfields, protobuffers use a pure binary representation, whereas JSON representations use base64.This field is a member of oneof
data.- Type
- class google.cloud.automl_v1beta1.types.ImageClassificationDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset metadata that is specific to image classification.
- classification_type¶
Required. Type of the classification problem.
- class google.cloud.automl_v1beta1.types.ImageClassificationModelDeploymentMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel 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.v1beta1.ImageClassificationModelMetadata.node_qps]. Must be between 1 and 100, inclusive on both ends.
- Type
- class google.cloud.automl_v1beta1.types.ImageClassificationModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel metadata for image classification.
- base_model_id¶
Optional. The ID of the
basemodel. If it is specified, the new model will be created based on thebasemodel. Otherwise, the new model will be created from scratch. Thebasemodel must be in the sameprojectandlocationas the new model to create, and have the samemodel_type.- Type
- train_budget¶
Required. The train budget of creating this model, expressed in hours. The actual
train_costwill be equal or less than this value.- Type
- train_cost¶
Output only. The actual train cost of creating this model, expressed in hours. If this model is created from a
basemodel, the train cost used to create thebasemodel are not included.- Type
- stop_reason¶
Output only. The reason that this create model operation stopped, e.g.
BUDGET_REACHED,MODEL_CONVERGED.- Type
- 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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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
- 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
- class google.cloud.automl_v1beta1.types.ImageObjectDetectionAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageAnnotation details for image object detection.
- bounding_box¶
Output only. The rectangle representing the object location.
- class google.cloud.automl_v1beta1.types.ImageObjectDetectionDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset metadata specific to image object detection.
- class google.cloud.automl_v1beta1.types.ImageObjectDetectionEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel 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
- 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_v1beta1.types.BoundingBoxMetricsEntry]
- class google.cloud.automl_v1beta1.types.ImageObjectDetectionModelDeploymentMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel 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.v1beta1.ImageObjectDetectionModelMetadata.qps_per_node]. Must be between 1 and 100, inclusive on both ends.
- Type
- class google.cloud.automl_v1beta1.types.ImageObjectDetectionModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel 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.v1beta1.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.v1beta1.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.v1beta1.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
- 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
- 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
- stop_reason¶
Output only. The reason that this create model operation stopped, e.g.
BUDGET_REACHED,MODEL_CONVERGED.- Type
- train_budget_milli_node_hours¶
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_costwill 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 beMODEL_CONVERGED. Note, node_hour = actual_hour * number_of_nodes_invovled. For model typecloud-high-accuracy-1(default) andcloud-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 typemobile-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
- class google.cloud.automl_v1beta1.types.ImportDataOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of ImportData operation.
- class google.cloud.automl_v1beta1.types.ImportDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData].
- name¶
Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.
- Type
- input_config¶
Required. The desired input location and its domain specific semantics, if any.
- class google.cloud.automl_v1beta1.types.InputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageInput 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 Sample in-line JSON Lines file for entity extraction (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { “document”: { “document_text”: {“content”: “dog cat”} “layout”: [ { “text_segment”: { “start_offset”: 0, “end_offset”: 3, }, “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, }, { “text_segment”: { “start_offset”: 4, “end_offset”: 7, }, “page_number”: 1, “bounding_poly”: { “normalized_vertices”: [ {“x”: 0.4, “y”: 0.1}, {“x”: 0.4, “y”: 0.3}, {“x”: 0.8, “y”: 0.3}, {“x”: 0.8, “y”: 0.1}, ], }, “text_segment_type”: TOKEN, }
], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 1, }, "annotations": [ { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 0, "end_offset": 3}} }, { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 4, "end_offset": 7}} } ], }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 8} } } ] } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } }
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.
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.
- gcs_source¶
The Google Cloud Storage location for the input content. In ImportData, the gcs_source points to a csv with structure described in the comment.
This field is a member of oneof
source.
- bigquery_source¶
The BigQuery location for the input content.
This field is a member of oneof
source.
- params¶
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”.
- class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.ListColumnSpecsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].
- field_mask¶
Mask specifying which fields to read.
- page_size¶
Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
- Type
- page_token¶
A token identifying a page of results for the server to return. Typically obtained from the [ListColumnSpecsResponse.next_page_token][google.cloud.automl.v1beta1.ListColumnSpecsResponse.next_page_token] field of the previous [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs] call.
- Type
- class google.cloud.automl_v1beta1.types.ListColumnSpecsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageResponse message for [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].
- column_specs¶
The column specs read.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.ColumnSpec]
- class google.cloud.automl_v1beta1.types.ListDatasetsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].
- 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 hastranslation_dataset_metadata.
- Type
- page_size¶
Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
- Type
- page_token¶
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.automl.v1beta1.AutoMl.ListDatasets] call.
- Type
- class google.cloud.automl_v1beta1.types.ListDatasetsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageResponse message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].
- datasets¶
The datasets read.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.Dataset]
- class google.cloud.automl_v1beta1.types.ListModelEvaluationsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.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
- filter¶
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
- page_token¶
A token identifying a page of results for the server to return. Typically obtained via [ListModelEvaluationsResponse.next_page_token][google.cloud.automl.v1beta1.ListModelEvaluationsResponse.next_page_token] of the previous [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations] call.
- Type
- class google.cloud.automl_v1beta1.types.ListModelEvaluationsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageResponse message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].
- model_evaluation¶
List of model evaluations in the requested page.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.ModelEvaluation]
- next_page_token¶
A token to retrieve next page of results. Pass to the [ListModelEvaluationsRequest.page_token][google.cloud.automl.v1beta1.ListModelEvaluationsRequest.page_token] field of a new [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations] request to obtain that page.
- Type
- class google.cloud.automl_v1beta1.types.ListModelsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].
- 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 hasimage_classification_model_metadata.dataset_id=5–> The model was created from a dataset with ID 5.
- Type
- class google.cloud.automl_v1beta1.types.ListModelsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageResponse message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].
- model¶
List of models in the requested page.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.Model]
- class google.cloud.automl_v1beta1.types.ListTableSpecsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].
- field_mask¶
Mask specifying which fields to read.
- page_size¶
Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
- Type
- page_token¶
A token identifying a page of results for the server to return. Typically obtained from the [ListTableSpecsResponse.next_page_token][google.cloud.automl.v1beta1.ListTableSpecsResponse.next_page_token] field of the previous [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs] call.
- Type
- class google.cloud.automl_v1beta1.types.ListTableSpecsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageResponse message for [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].
- table_specs¶
The table specs read.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.TableSpec]
- class google.cloud.automl_v1beta1.types.Model(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageAPI 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.
- image_classification_model_metadata¶
Metadata for image classification models.
This field is a member of oneof
model_metadata.
- text_classification_model_metadata¶
Metadata for text classification models.
This field is a member of oneof
model_metadata.
- image_object_detection_model_metadata¶
Metadata for image object detection models.
This field is a member of oneof
model_metadata.
- video_classification_model_metadata¶
Metadata for video classification models.
This field is a member of oneof
model_metadata.
- video_object_tracking_model_metadata¶
Metadata for video object tracking models.
This field is a member of oneof
model_metadata.
- text_extraction_model_metadata¶
Metadata for text extraction models.
This field is a member of oneof
model_metadata.
- text_sentiment_model_metadata¶
Metadata for text sentiment models.
This field is a member of oneof
model_metadata.
- name¶
Output only. Resource name of the model. Format:
projects/{project_id}/locations/{location_id}/models/{model_id}- Type
- 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
- 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
- create_time¶
Output only. Timestamp when the model training finished and can be used for prediction.
- update_time¶
Output only. Timestamp when this model was last updated.
- deployment_state¶
Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed.
- class DeploymentState(value)[source]¶
Bases:
proto.enums.EnumDeployment 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 google.cloud.automl_v1beta1.types.ModelEvaluation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageEvaluation 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.
- regression_evaluation_metrics¶
Model evaluation metrics for Tables regression. Tables problem is considered a regression when the target column has FLOAT64 DataType.
This field is a member of oneof
metrics.
- translation_evaluation_metrics¶
Model evaluation metrics for translation.
This field is a member of oneof
metrics.
- image_object_detection_evaluation_metrics¶
Model evaluation metrics for image object detection.
This field is a member of oneof
metrics.
- video_object_tracking_evaluation_metrics¶
Model evaluation metrics for video object tracking.
This field is a member of oneof
metrics.
- text_sentiment_evaluation_metrics¶
Evaluation metrics for text sentiment models.
This field is a member of oneof
metrics.
- text_extraction_evaluation_metrics¶
Evaluation metrics for text extraction models.
This field is a member of oneof
metrics.
- name¶
Output only. Resource name of the model evaluation. Format:
projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}- Type
- 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.v1beta1.TablesModelMetadata.prediction_type] the [display_name][google.cloud.automl.v1beta1.ModelEvaluation.display_name] field is used.
- Type
- display_name¶
Output only. The value of [display_name][google.cloud.automl.v1beta1.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.v1beta1.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
- create_time¶
Output only. Timestamp when this model evaluation was created.
- 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.v1beta1.ModelEvaluation.annotation_spec_id].
- Type
- class google.cloud.automl_v1beta1.types.ModelExportOutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageOutput configuration for ModelExport Action.
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.
- gcs_destination¶
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.
- gcr_destination¶
The GCR location where model image is to be pushed to. This location may only be set for the following model formats:
“docker”.
The model image will be created under the given URI.
This field is a member of oneof
destination.
- 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”.
For Video Classification cloud, “tf_saved_model”.
For Video Object Tracking cloud, “tf_saved_model”.
For Video Object Tracking mobile-versatile-1: “tflite”, “edgetpu_tflite”, “tf_saved_model”, “docker”.
For Video Object Tracking mobile-coral-versatile-1: “tflite”, “edgetpu_tflite”, “docker”.
For Video Object Tracking mobile-coral-low-latency-1: “tflite”, “edgetpu_tflite”, “docker”.
For Video Object Tracking mobile-jetson-versatile-1: “tf_saved_model”, “docker”.
For Tables: “docker”.
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](https: //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
core_ml - Used for iOS mobile devices.
- Type
- 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
dockerformat:cpu_architecture- (string) “x86_64” (default).gpu_architecture- (string) “none” (default), “nvidia”.
- class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.NormalizedVertex(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA 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.
- class google.cloud.automl_v1beta1.types.OperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageMetadata 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.
- undeploy_model_details¶
Details of an UndeployModel operation.
This field is a member of oneof
details.
- export_evaluated_examples_details¶
Details of ExportEvaluatedExamples operation.
This field is a member of oneof
details.
- 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.
- update_time¶
Output only. Time when the operation was updated for the last time.
- class google.cloud.automl_v1beta1.types.OutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageFor 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 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.csvwith 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 calledprimary_tablewill be created, and filled with precisely the same data as this obtained on import.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.
- 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.
This field is a member of oneof
destination.
- class google.cloud.automl_v1beta1.types.PredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].
- payload¶
Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
- params¶
Additional domain-specific parameters, any string must be up to 25000 characters long.
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 returned in the response. Default is 100, the requested value may be limited by server.For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false.
- class ParamsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.PredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageResponse message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].
- payload¶
Prediction result. Translation and Text Sentiment will return precisely one payload.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.AnnotationPayload]
- preprocessed_input¶
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].
- metadata¶
Additional domain-specific prediction response metadata.
For Image Object Detection:
max_bounding_box_count- (int64) At most that many bounding boxes per image could have been returned.For Text Sentiment:
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 will be also positive (though the least). The sentiment_score shouldn’t be confused with “score” or “magnitude” from the previous Natural Language Sentiment Analysis API.
- class MetadataEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.RegressionEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageMetrics for regression problems.
- mean_absolute_percentage_error¶
Output only. Mean absolute percentage error. Only set if all ground truth values are are positive.
- Type
- class google.cloud.automl_v1beta1.types.Row(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA representation of a row in a relational table.
- column_spec_ids¶
The resource IDs of the column specs describing the columns of the row. If set must contain, but possibly in a different order, all input feature
[column_spec_ids][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs] of the Model this row is being passed to. Note: The below
valuesfield must match order of this field, if this field is set.- Type
MutableSequence[str]
- values¶
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.v1beta1.TablesModelMetadata.input_feature_column_specs] of the Model this row is being passed to.
- Type
MutableSequence[google.protobuf.struct_pb2.Value]
- class google.cloud.automl_v1beta1.types.StringStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe data statistics of a series of STRING values.
- top_unigram_stats¶
The statistics of the top 20 unigrams, ordered by [count][google.cloud.automl.v1beta1.StringStats.UnigramStats.count].
- Type
MutableSequence[google.cloud.automl_v1beta1.types.StringStats.UnigramStats]
- class google.cloud.automl_v1beta1.types.StructStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe data statistics of a series of STRUCT values.
- field_stats¶
Map from a field name of the struct to data stats aggregated over series of all data in that field across all the structs.
- Type
MutableMapping[str, google.cloud.automl_v1beta1.types.DataStats]
- class FieldStatsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.StructType(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageStructTypedefines the DataType-s of a [STRUCT][google.cloud.automl.v1beta1.TypeCode.STRUCT] type.- fields¶
Unordered map of struct field names to their data types. Fields cannot be added or removed via Update. Their names and data types are still mutable.
- Type
MutableMapping[str, google.cloud.automl_v1beta1.types.DataType]
- class FieldsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.TableSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA 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
- name¶
Output only. The resource name of the table spec. Form:
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}- Type
- time_column_spec_id¶
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.
- Type
- valid_row_count¶
Output only. The number of valid rows (i.e. without values that don’t match DataType-s of their columns).
- Type
- column_count¶
Output only. The number of columns of the table. That is, the number of child ColumnSpec-s.
- Type
- input_configs¶
Output only. Input configs via which data currently residing in the table had been imported.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.InputConfig]
- class google.cloud.automl_v1beta1.types.TablesAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageContains annotation details specific to Tables.
- score¶
Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher value means greater confidence in the returned value. For
[target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] of FLOAT64 data type the score is not populated.
- Type
- prediction_interval¶
Output only. Only populated when
[target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] has FLOAT64 data type. An interval in which the exactly correct target value has 95% chance to be in.
- value¶
The predicted value of the row’s
[target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]. The value depends on the column’s DataType:
CATEGORY - the predicted (with the above confidence
score) CATEGORY value.FLOAT64 - the predicted (with above
prediction_interval) FLOAT64 value.
- tables_model_column_info¶
Output only. Auxiliary information for each of the model’s
[input_feature_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.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.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.TablesModelColumnInfo]
- baseline_score¶
Output only. Stores the prediction score for the baseline example, which is defined as the example with all values set to their baseline values. This is used as part of the Sampled Shapley explanation of the model’s prediction. This field is populated only when feature importance is requested. For regression models, this holds the baseline prediction for the baseline example. For classification models, this holds the baseline prediction for the baseline example for the argmax class.
- Type
- class google.cloud.automl_v1beta1.types.TablesDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageMetadata for a dataset used for AutoML Tables.
- primary_table_spec_id¶
Output only. The table_spec_id of the primary table of this dataset.
- Type
- target_column_spec_id¶
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.
- Type
- weight_column_spec_id¶
column_spec_id of the primary table’s column that should be used as the weight column, i.e. the higher the value the more important the row will be during model training. Required type: FLOAT64. Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is ignored for training. If not set all rows are assumed to have equal weight of 1. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
- Type
- ml_use_column_spec_id¶
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,TESTamong its values, or only haveTEST,UNASSIGNEDvalues. In the latter case the rows withUNASSIGNEDvalue 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 asUNASSIGNED. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.- Type
- target_column_correlations¶
Output only. Correlations between
[TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id], and other columns of the
[TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id]. Only set if the target column is set. Mapping from other column spec id to its CorrelationStats with the target column. This field may be stale, see the stats_update_time field for for the timestamp at which these stats were last updated.
- Type
MutableMapping[str, google.cloud.automl_v1beta1.types.CorrelationStats]
- stats_update_time¶
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.
- class TargetColumnCorrelationsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.TablesModelColumnInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageAn information specific to given column and Tables Model, in context of the Model and the predictions created by it.
- column_spec_name¶
Output only. The name of the ColumnSpec describing the column. Not populated when this proto is outputted to BigQuery.
- Type
- column_display_name¶
Output only. The display name of the column (same as the display_name of its ColumnSpec).
- Type
- feature_importance¶
Output only. When given as part of a Model (always populated): Measurement of how much model predictions correctness on the TEST data depend on values in this column. A value between 0 and 1, higher means higher influence. These values are normalized - for all input feature columns of a given model they add to 1.
When given back by Predict (populated iff [feature_importance param][google.cloud.automl.v1beta1.PredictRequest.params] is set) or Batch Predict (populated iff [feature_importance][google.cloud.automl.v1beta1.PredictRequest.params] param is set): Measurement of how impactful for the prediction returned for the given row the value in this column was. Specifically, the feature importance specifies the marginal contribution that the feature made to the prediction score compared to the baseline score. These values are computed using the Sampled Shapley method.
- Type
- class google.cloud.automl_v1beta1.types.TablesModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel metadata specific to AutoML Tables.
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.
- optimization_objective_recall_value¶
Required when optimization_objective is “MAXIMIZE_PRECISION_AT_RECALL”. Must be between 0 and 1, inclusive.
This field is a member of oneof
additional_optimization_objective_config.- Type
- optimization_objective_precision_value¶
Required when optimization_objective is “MAXIMIZE_RECALL_AT_PRECISION”. Must be between 0 and 1, inclusive.
This field is a member of oneof
additional_optimization_objective_config.- Type
- target_column_spec¶
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.
- input_feature_column_specs¶
Column specs of the dataset’s primary table’s columns, on which the model is trained and which are used as the input for predictions. The
[target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] as well as, according to dataset’s state upon model creation,
[weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], and
[ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] must never be included here.
Only 3 fields are used:
name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table’s columns (except the ones listed above) are used for the training and prediction input.
display_name - Output only.
data_type - Output only.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.ColumnSpec]
- optimization_objective¶
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).
- Type
- tables_model_column_info¶
Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.TablesModelColumnInfo]
- train_budget_milli_node_hours¶
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.
- Type
- train_cost_milli_node_hours¶
Output only. The actual training cost of the 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
- class google.cloud.automl_v1beta1.types.TextClassificationDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset metadata for classification.
- classification_type¶
Required. Type of the classification problem.
- class google.cloud.automl_v1beta1.types.TextClassificationModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel metadata that is specific to text classification.
- classification_type¶
Output only. Classification type of the dataset used to train this model.
- class google.cloud.automl_v1beta1.types.TextExtractionAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageAnnotation for identifying spans of text.
- class google.cloud.automl_v1beta1.types.TextExtractionDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset metadata that is specific to text extraction
- class google.cloud.automl_v1beta1.types.TextExtractionEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel evaluation metrics for text extraction problems.
- confidence_metrics_entries¶
Output only. Metrics that have confidence thresholds. Precision-recall curve can be derived from it.
- Type
MutableSequence[google.cloud.automl_v1beta1.types.TextExtractionEvaluationMetrics.ConfidenceMetricsEntry]
- class ConfidenceMetricsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageMetrics 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
- class google.cloud.automl_v1beta1.types.TextExtractionModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel metadata that is specific to text extraction.
- class google.cloud.automl_v1beta1.types.TextSegment(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
- start_offset¶
Required. Zero-based character index of the first character of the text segment (counting characters from the beginning of the text).
- Type
- class google.cloud.automl_v1beta1.types.TextSentimentAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageContains annotation details specific to text sentiment.
- sentiment¶
Output only. The sentiment with the semantic, as given to the [AutoMl.ImportData][google.cloud.automl.v1beta1.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
- class google.cloud.automl_v1beta1.types.TextSentimentDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset 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
- class google.cloud.automl_v1beta1.types.TextSentimentEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel evaluation metrics for text sentiment problems.
- mean_absolute_error¶
Output only. Mean absolute error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- Type
- mean_squared_error¶
Output only. Mean squared error. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- Type
- linear_kappa¶
Output only. Linear weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- Type
- quadratic_kappa¶
Output only. Quadratic weighted kappa. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- Type
- confusion_matrix¶
Output only. Confusion matrix of the evaluation. Only set for the overall model evaluation, not for evaluation of a single annotation spec.
- class google.cloud.automl_v1beta1.types.TextSentimentModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel metadata that is specific to text sentiment.
- class google.cloud.automl_v1beta1.types.TextSnippet(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA representation of a text snippet.
- content¶
Required. The content of the text snippet as a string. Up to 250000 characters long.
- Type
- mime_type¶
Optional. The format of [content][google.cloud.automl.v1beta1.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.v1beta1.TextSnippet.content].
- Type
- class google.cloud.automl_v1beta1.types.TimeSegment(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageA time period inside of an example that has a time dimension (e.g. video).
- start_time_offset¶
Start of the time segment (inclusive), represented as the duration since the example start.
- end_time_offset¶
End of the time segment (exclusive), represented as the duration since the example start.
- class google.cloud.automl_v1beta1.types.TimestampStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageThe data statistics of a series of TIMESTAMP values.
- granular_stats¶
The string key is the pre-defined granularity. Currently supported: hour_of_day, day_of_week, month_of_year. Granularities finer that the granularity of timestamp data are not populated (e.g. if timestamps are at day granularity, then hour_of_day is not populated).
- Type
MutableMapping[str, google.cloud.automl_v1beta1.types.TimestampStats.GranularStats]
- class GranularStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageStats split by a defined in context granularity.
- buckets¶
A map from granularity key to example count for that key. E.g. for hour_of_day
13means 1pm, or for month_of_year5means May).
- class BucketsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class GranularStatsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)¶
Bases:
proto.message.Message
- class google.cloud.automl_v1beta1.types.TranslationAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageAnnotation details specific to translation.
- translated_content¶
Output only . The translated content.
- class google.cloud.automl_v1beta1.types.TranslationDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset metadata that is specific to translation.
- class google.cloud.automl_v1beta1.types.TranslationEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageEvaluation metrics for the dataset.
- class google.cloud.automl_v1beta1.types.TranslationModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel 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
- source_language_code¶
Output only. Inferred from the dataset. The source languge (The BCP-47 language code) that is used for training.
- Type
- class google.cloud.automl_v1beta1.types.TypeCode(value)[source]¶
Bases:
proto.enums.EnumTypeCodeis used as a part of [DataType][google.cloud.automl.v1beta1.DataType].- Values:
- TYPE_CODE_UNSPECIFIED (0):
Not specified. Should not be used.
- FLOAT64 (3):
Encoded as
number, or the strings"NaN","Infinity", or"-Infinity".- TIMESTAMP (4):
Must be between 0AD and 9999AD. Encoded as
stringaccording to [time_format][google.cloud.automl.v1beta1.DataType.time_format], or, if that format is not set, then in RFC 3339date-timeformat, wheretime-offset="Z"(e.g. 1985-04-12T23:20:50.52Z).- STRING (6):
Encoded as
string.- ARRAY (8):
Encoded as
list, where the list elements are represented according to[list_element_type][google.cloud.automl.v1beta1.DataType.list_element_type].
- STRUCT (9):
Encoded as
struct, where field values are represented according to [struct_type][google.cloud.automl.v1beta1.DataType.struct_type].- CATEGORY (10):
Values of this type are not further understood by AutoML, e.g. AutoML is unable to tell the order of values (as it could with FLOAT64), or is unable to say if one value contains another (as it could with STRING). Encoded as
string(bytes should be base64-encoded, as described in RFC 4648, section 4).
- class google.cloud.automl_v1beta1.types.UndeployModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDetails of UndeployModel operation.
- class google.cloud.automl_v1beta1.types.UndeployModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.UndeployModel][google.cloud.automl.v1beta1.AutoMl.UndeployModel].
- class google.cloud.automl_v1beta1.types.UpdateColumnSpecRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.UpdateColumnSpec][google.cloud.automl.v1beta1.AutoMl.UpdateColumnSpec]
- column_spec¶
Required. The column spec which replaces the resource on the server.
- update_mask¶
The update mask applies to the resource.
- class google.cloud.automl_v1beta1.types.UpdateDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.UpdateDataset][google.cloud.automl.v1beta1.AutoMl.UpdateDataset]
- dataset¶
Required. The dataset which replaces the resource on the server.
- update_mask¶
The update mask applies to the resource.
- class google.cloud.automl_v1beta1.types.UpdateTableSpecRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageRequest message for [AutoMl.UpdateTableSpec][google.cloud.automl.v1beta1.AutoMl.UpdateTableSpec]
- table_spec¶
Required. The table spec which replaces the resource on the server.
- update_mask¶
The update mask applies to the resource.
- class google.cloud.automl_v1beta1.types.VideoClassificationAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageContains annotation details specific to video classification.
- type_¶
Output only. Expresses the type of video classification. Possible values:
segment- Classification done on a specified by user time segment of a video. AnnotationSpec is answered to be present in that time segment, if it is present in any part of it. The video ML model evaluations are done only for this type of classification.shot- 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.1s_interval- 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.
- Type
- classification_annotation¶
Output only . The classification details of this annotation.
- time_segment¶
Output only . The time segment of the video to which the annotation applies.
- class google.cloud.automl_v1beta1.types.VideoClassificationDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset metadata specific to video classification. All Video Classification datasets are treated as multi label.
- class google.cloud.automl_v1beta1.types.VideoClassificationModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel metadata specific to video classification.
- class google.cloud.automl_v1beta1.types.VideoObjectTrackingAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageAnnotation details for video object tracking.
- instance_id¶
Optional. The instance of the object, expressed as a positive integer. Used to tell apart objects of the same type (i.e. AnnotationSpec) when multiple are present on a single example. NOTE: Instance ID prediction quality is not a part of model evaluation and is done as best effort. Especially in cases when an entity goes off-screen for a longer time (minutes), when it comes back it may be given a new instance ID.
- Type
- time_offset¶
Required. A time (frame) of a video to which this annotation pertains. Represented as the duration since the video’s start.
- bounding_box¶
Required. The rectangle representing the object location on the frame (i.e. at the time_offset of the video).
- score¶
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).
- Type
- class google.cloud.automl_v1beta1.types.VideoObjectTrackingDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageDataset metadata specific to video object tracking.
- class google.cloud.automl_v1beta1.types.VideoObjectTrackingEvaluationMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel 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).
- evaluated_frame_count¶
Output only. The number of video frames used to create this evaluation.
- Type
- evaluated_bounding_box_count¶
Output only. The total number of bounding boxes (i.e. summed over all frames) the ground truth used to create this evaluation had.
- Type
- 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_v1beta1.types.BoundingBoxMetricsEntry]
- class google.cloud.automl_v1beta1.types.VideoObjectTrackingModelMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
proto.message.MessageModel metadata specific to video object tracking.
