Cloud AutoML V1 Client - Class BatchPredictInputConfig (2.0.1)

Reference documentation and code samples for the Cloud AutoML V1 Client class BatchPredictInputConfig.

Input configuration for BatchPredict Action.

The format of input depends on the ML problem of the model used for prediction. As input source the gcs_source is expected, unless specified otherwise. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

AutoML Vision

Classification
One or more CSV files where each line is a single column: GCS_FILE_PATH The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png
Object Detection
One or more CSV files where each line is a single column: GCS_FILE_PATH The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png

AutoML Video Intelligence

Classification
One or more CSV files where each line is a single column: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf
Object Tracking
One or more CSV files where each line is a single column: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf

AutoML Natural Language

Classification
One or more CSV files where each line is a single column: GCS_FILE_PATH GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be no larger than 10MB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf gs://folder/text3.tif
Sentiment Analysis
One or more CSV files where each line is a single column: GCS_FILE_PATH GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be no larger than 128kB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf gs://folder/text3.tif
Entity Extraction
One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict]. Each JSONL file contains a per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in JSON representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique. Each document JSONL file contains, per line, a proto that wraps a Document proto with input_config set. Each document cannot exceed 2MB in size. Supported document extensions: .PDF, .TIF, .TIFF Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed. Sample inline JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".): { "id": "my_first_id", "text_snippet": { "content": "dog car cat"}, "text_features": [ { "text_segment": {"start_offset": 4, "end_offset": 6}, "structural_type": PARAGRAPH, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": { "content": "Extended sample content", "mime_type": "text/plain" } } Sample document JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".): { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } }

AutoML Tables

See Preparing your training data for more information. You can use either gcs_source or bigquery_source. For gcs_source: CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows, i.e. the CSV lines, will be attempted. Sample rows from a CSV file:

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

For bigquery_source: The URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows of the table will be attempted.

Input field definitions: GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, "gs://folder/video.avi". TIME_SEGMENT_START : (TIME_OFFSET) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END : (TIME_OFFSET) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video). TIME_OFFSET : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed, and it means the end of the example. Errors: If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.

Generated from protobuf message google.cloud.automl.v1.BatchPredictInputConfig

Namespace

Google \ Cloud \ AutoMl \ V1

Methods

__construct

Constructor.

Parameters
Name Description
data array

Optional. Data for populating the Message object.
↳ gcs_source Google\Cloud\AutoMl\V1\GcsSource

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

getGcsSource

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

Returns
Type Description
Google\Cloud\AutoMl\V1\GcsSource|null

hasGcsSource

setGcsSource

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

Parameter
Name Description
var Google\Cloud\AutoMl\V1\GcsSource
Returns
Type Description
$this

getSource

Returns
Type Description
string