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docs: Include time dependency in documentation for weight, time, and target columns. #1102

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gcf-merge-on-green merged 1 commit into from
Mar 22, 2022
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docs: Include time dependency in documentation for weight, time, and target columns. #1102

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16 changes: 10 additions & 6 deletions google/cloud/aiplatform/training_jobs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4005,9 +4005,11 @@ def run(
For time series Datasets, all their data is exported to
training, to pick and choose from.
target_column (str):
Required. Name of the column that the Model is to predict values for.
Required. Name of the column that the Model is to predict values for. This
column must be unavailable at forecast.
time_column (str):
Required. Name of the column that identifies time order in the time series.
This column must be available at forecast.
time_series_identifier_column (str):
Required. Name of the column that identifies the time series.
unavailable_at_forecast_columns (List[str]):
Expand Down Expand Up @@ -4046,7 +4048,7 @@ def run(
during Model training. The column must have numeric values between 0 and
10000 inclusively, and 0 value means that the row is ignored.
If the weight column field is not set, then all rows are assumed to have
equal weight of 1.
equal weight of 1. This column must be available at forecast.
time_series_attribute_columns (List[str]):
Optional. Column names that should be used as attribute columns.
Each column is constant within a time series.
Expand Down Expand Up @@ -4078,7 +4080,7 @@ def run(
Applies only if [export_evaluated_data_items] is True and
[export_evaluated_data_items_bigquery_destination_uri] is specified.
quantiles (List[float]):
Quantiles to use for the `minizmize-quantile-loss`
Quantiles to use for the `minimize-quantile-loss`
[AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in
this case.

Expand Down Expand Up @@ -4236,9 +4238,11 @@ def _run(
For time series Datasets, all their data is exported to
training, to pick and choose from.
target_column (str):
Required. Name of the column that the Model is to predict values for.
Required. Name of the column that the Model is to predict values for. This
column must be unavailable at forecast.
time_column (str):
Required. Name of the column that identifies time order in the time series.
This column must be available at forecast.
time_series_identifier_column (str):
Required. Name of the column that identifies the time series.
unavailable_at_forecast_columns (List[str]):
Expand Down Expand Up @@ -4286,7 +4290,7 @@ def _run(
during Model training. The column must have numeric values between 0 and
10000 inclusively, and 0 value means that the row is ignored.
If the weight column field is not set, then all rows are assumed to have
equal weight of 1.
equal weight of 1. This column must be available at forecast.
time_series_attribute_columns (List[str]):
Optional. Column names that should be used as attribute columns.
Each column is constant within a time series.
Expand Down Expand Up @@ -4317,7 +4321,7 @@ def _run(
Applies only if [export_evaluated_data_items] is True and
[export_evaluated_data_items_bigquery_destination_uri] is specified.
quantiles (List[float]):
Quantiles to use for the `minizmize-quantile-loss`
Quantiles to use for the `minimize-quantile-loss`
[AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in
this case.

Expand Down