Committer: @aribray PiperOrigin-RevId: 373471501pull/653/head
Google APIs
4 years ago
committed by
Copybara-Service
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162641cfe5
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// Copyright 2021 Google LLC |
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// |
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// Licensed under the Apache License, Version 2.0 (the "License"); |
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// you may not use this file except in compliance with the License. |
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// You may obtain a copy of the License at |
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// |
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// http://www.apache.org/licenses/LICENSE-2.0 |
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// |
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// Unless required by applicable law or agreed to in writing, software |
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// distributed under the License is distributed on an "AS IS" BASIS, |
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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// See the License for the specific language governing permissions and |
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// limitations under the License. |
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syntax = "proto3"; |
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package google.cloud.aiplatform.v1beta1.schema.predict.prediction; |
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import "google/api/annotations.proto"; |
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option go_package = "google.golang.org/genproto/googleapis/cloud/aiplatform/v1beta1/schema/predict/prediction;prediction"; |
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option java_multiple_files = true; |
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option java_outer_classname = "TimeSeriesForecastingPredictionResultProto"; |
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option java_package = "com.google.cloud.aiplatform.v1beta1.schema.predict.prediction"; |
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// Prediction output format for Time Series Forecasting. |
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message TimeSeriesForecastingPredictionResult { |
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// The regression value. |
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float value = 1; |
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} |
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// Copyright 2021 Google LLC |
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// |
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// Licensed under the Apache License, Version 2.0 (the "License"); |
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// you may not use this file except in compliance with the License. |
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// You may obtain a copy of the License at |
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// |
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// http://www.apache.org/licenses/LICENSE-2.0 |
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// |
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// Unless required by applicable law or agreed to in writing, software |
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// distributed under the License is distributed on an "AS IS" BASIS, |
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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// See the License for the specific language governing permissions and |
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// limitations under the License. |
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syntax = "proto3"; |
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package google.cloud.aiplatform.v1beta1.schema.trainingjob.definition; |
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import "google/cloud/aiplatform/v1beta1/schema/trainingjob/definition/export_evaluated_data_items_config.proto"; |
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import "google/api/annotations.proto"; |
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option go_package = "google.golang.org/genproto/googleapis/cloud/aiplatform/v1beta1/schema/trainingjob/definition;definition"; |
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option java_multiple_files = true; |
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option java_outer_classname = "AutoMLForecastingProto"; |
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option java_package = "com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition"; |
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// A TrainingJob that trains and uploads an AutoML Forecasting Model. |
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message AutoMlForecasting { |
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// The input parameters of this TrainingJob. |
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AutoMlForecastingInputs inputs = 1; |
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// The metadata information. |
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AutoMlForecastingMetadata metadata = 2; |
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} |
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message AutoMlForecastingInputs { |
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message Transformation { |
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// Training pipeline will infer the proper transformation based on the |
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// statistic of dataset. |
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message AutoTransformation { |
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string column_name = 1; |
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} |
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// Training pipeline will perform following transformation functions. |
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// |
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// * The value converted to float32. |
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// |
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// * The z_score of the value. |
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// |
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// * log(value+1) when the value is greater than or equal to 0. Otherwise, |
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// this transformation is not applied and the value is considered a |
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// missing value. |
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// |
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// * z_score of log(value+1) when the value is greater than or equal to 0. |
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// Otherwise, this transformation is not applied and the value is |
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// considered a missing value. |
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// |
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// * A boolean value that indicates whether the value is valid. |
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message NumericTransformation { |
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string column_name = 1; |
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} |
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// Training pipeline will perform following transformation functions. |
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// |
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// * The categorical string as is--no change to case, punctuation, |
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// spelling, tense, and so on. |
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// |
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// * Convert the category name to a dictionary lookup index and generate an |
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// embedding for each index. |
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// |
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// * Categories that appear less than 5 times in the training dataset are |
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// treated as the "unknown" category. The "unknown" category gets its own |
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// special lookup index and resulting embedding. |
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message CategoricalTransformation { |
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string column_name = 1; |
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} |
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// Training pipeline will perform following transformation functions. |
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// |
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// * Apply the transformation functions for Numerical columns. |
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// |
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// * Determine the year, month, day,and weekday. Treat each value from the |
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// timestamp as a Categorical column. |
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// |
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// * Invalid numerical values (for example, values that fall outside of a |
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// typical timestamp range, or are extreme values) receive no special |
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// treatment and are not removed. |
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message TimestampTransformation { |
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string column_name = 1; |
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// The format in which that time field is expressed. The time_format must |
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// either be one of: |
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// |
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// * `unix-seconds` |
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// |
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// * `unix-milliseconds` |
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// |
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// * `unix-microseconds` |
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// |
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// * `unix-nanoseconds` |
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// |
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// (for respectively number of seconds, milliseconds, microseconds and |
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// nanoseconds since start of the Unix epoch); |
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// |
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// or be written in `strftime` syntax. |
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// |
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// If time_format is not set, then the |
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// default format is RFC 3339 `date-time` format, where |
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// `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z) |
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string time_format = 2; |
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} |
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// Training pipeline will perform following transformation functions. |
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// |
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// * The text as is--no change to case, punctuation, spelling, tense, and |
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// so on. |
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// |
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// * Convert the category name to a dictionary lookup index and generate an |
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// embedding for each index. |
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message TextTransformation { |
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string column_name = 1; |
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} |
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// The transformation that the training pipeline will apply to the input |
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// columns. |
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oneof transformation_detail { |
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AutoTransformation auto = 1; |
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NumericTransformation numeric = 2; |
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CategoricalTransformation categorical = 3; |
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TimestampTransformation timestamp = 4; |
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TextTransformation text = 5; |
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} |
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} |
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// A duration of time expressed in time granularity units. |
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message Granularity { |
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// The time granularity unit of this time period. |
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// The supported units are: |
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// |
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// * "minute" |
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// |
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// * "hour" |
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// |
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// * "day" |
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// |
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// * "week" |
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// |
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// * "month" |
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// |
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// * "year" |
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string unit = 1; |
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// The number of granularity_units between data points in the training |
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// data. If `granularity_unit` is `minute`, |
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// can be 1, 5, 10, 15, or 30. For all other values of `granularity_unit`, |
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// must be 1. |
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int64 quantity = 2; |
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} |
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// The name of the column that the model is to predict. |
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string target_column = 1; |
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// The name of the column that identifies the time series. |
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string time_series_identifier_column = 2; |
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// The name of the column that identifies time order in the time series. |
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string time_column = 3; |
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// Each transformation will apply transform function to given input column. |
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// And the result will be used for training. |
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// When creating transformation for BigQuery Struct column, the column should |
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// be flattened using "." as the delimiter. |
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repeated Transformation transformations = 4; |
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// Objective function the model is optimizing towards. The training process |
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// creates a model that optimizes the value of the objective |
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// function over the validation set. |
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// |
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// The supported optimization objectives: |
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// |
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// * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). |
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// |
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// * "minimize-mae" - Minimize mean-absolute error (MAE). |
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// |
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// * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). |
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// |
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// * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). |
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// |
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// * "minimize-wape-mae" - Minimize the combination of weighted absolute |
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// percentage error (WAPE) and mean-absolute-error (MAE). |
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// |
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// * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles |
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// defined in `quantiles`. |
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string optimization_objective = 5; |
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// Required. The train budget of creating this model, expressed in milli node |
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// hours i.e. 1,000 value in this field means 1 node hour. |
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// |
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// The training cost of the model will not exceed this budget. The final cost |
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// will be attempted to be close to the budget, though may end up being (even) |
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// noticeably smaller - at the backend's discretion. This especially may |
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// happen when further model training ceases to provide any improvements. |
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// |
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// If the budget is set to a value known to be insufficient to train a |
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// model for the given dataset, the training won't be attempted and |
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// will error. |
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// |
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// The train budget must be between 1,000 and 72,000 milli node hours, |
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// inclusive. |
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int64 train_budget_milli_node_hours = 6; |
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// Column name that should be used as the weight column. |
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// Higher values in this column give more importance to the row |
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// during model training. The column must have numeric values between 0 and |
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// 10000 inclusively; 0 means the row is ignored for training. If weight |
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// column field is not set, then all rows are assumed to have equal weight |
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// of 1. |
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string weight_column = 7; |
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// Column names that should be used as attribute columns. |
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// The value of these columns does not vary as a function of time. |
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// For example, store ID or item color. |
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repeated string time_series_attribute_columns = 19; |
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// Names of columns that are unavailable when a forecast is requested. |
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// This column contains information for the given entity (identified |
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// by the time_series_identifier_column) that is unknown before the forecast |
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// For example, actual weather on a given day. |
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repeated string unavailable_at_forecast_columns = 20; |
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// Names of columns that are available and provided when a forecast |
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// is requested. These columns |
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// contain information for the given entity (identified by the |
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// time_series_identifier_column column) that is known at forecast. |
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// For example, predicted weather for a specific day. |
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repeated string available_at_forecast_columns = 21; |
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// Expected difference in time granularity between rows in the data. |
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Granularity data_granularity = 22; |
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// The amount of time into the future for which forecasted values for the |
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// target are returned. Expressed in number of units defined by the |
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// `data_granularity` field. |
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int64 forecast_horizon = 23; |
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// The amount of time into the past training and prediction data is used |
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// for model training and prediction respectively. Expressed in number of |
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// units defined by the `data_granularity` field. |
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int64 context_window = 24; |
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// Configuration for exporting test set predictions to a BigQuery table. If |
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// this configuration is absent, then the export is not performed. |
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ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15; |
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// Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to |
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// 5 quantiles are allowed of values between 0 and 1, exclusive. Required if |
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// the value of optimization_objective is minimize-quantile-loss. Represents |
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// the percent quantiles to use for that objective. Quantiles must be unique. |
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repeated double quantiles = 16; |
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// Validation options for the data validation component. The available options |
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// are: |
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// |
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// * "fail-pipeline" - default, will validate against the validation and |
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// fail the pipeline if it fails. |
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// |
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// * "ignore-validation" - ignore the results of the validation and continue |
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string validation_options = 17; |
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} |
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// Model metadata specific to AutoML Forecasting. |
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message AutoMlForecastingMetadata { |
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// Output only. The actual training cost of the model, expressed in milli |
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// node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed |
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// to not exceed the train budget. |
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int64 train_cost_milli_node_hours = 1; |
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} |
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