Add methods for Models API#7562
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tswast merged 12 commits intogoogleapis:masterfrom Apr 4, 2019
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* Generate protos for Models API. The Models API is a component of the bigquery_v2 interface. It is not available as a gRPC API, but it does provide protocol buffers. This commit adds those protocol buffers to the client so that they can be used to avoid much manual work to create resource classes that can be serialized to/from JSON.
* Fixes BQ Models API generated code for Python 2.7 * Format quoted strings as plain text.
* Add handwritten model API classes. These classes provide the top-level classes for the Model API. The protocol buffer objects are used for all sub-objects. The pattern for mutable properties follows the same as with Table and Dataset: a `_properties` dictionary contains the property values in the REST API format. * Address review comments in Model. Use setdefault for labels. Simplify setter logic for expires. * Combine error messages for too many and too few parts in model/table ID.
* Regen BigQuery Model API classes.
This commit is auto-generated by:
SYNTHTOOL_GOOGLEAPIS=$HOME/src/googleapis-private python -m synthtool
* Update test for Model to new representation of TrainingRun.startTime.
* Add client methods for Models API. * Adds hack to workaround milliseconds format for model.trainingRun.startTime. * Adds code samples for Models API, which double as system tests.
Even if the Models API itself is considered GA, I'd like to mark the client methods as Beta for a time to give us some more ability to make breaking changes until we're sure of the public interface we want to expose.
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Shouldn't be too much to review, as all these commits were reviewed in the private version. Looks like there are a few conflicts now, though. I'll see what I can do about those. |
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The Models API adds the following methods to the BigQuery client:
get_modelupdate_modeldelete_modellist_modelsfor managing models created by BigQuery ML queries. Also added are models resources, which provide additional statistics about the models resource such as details about the training run.