chore: Update discovery artifacts (#1587)
## Deleted keys were detected in the following stable discovery artifacts:
bigquery v2 https://github.com/googleapis/google-api-python-client/commit/faedd49d24634e1646e15cb79a983391a6938faa
container v1 https://github.com/googleapis/google-api-python-client/commit/3c92c46132922d77598527fc98780edca5978ce2
gameservices v1 https://github.com/googleapis/google-api-python-client/commit/ea5f2216765868a1d6e48995f23b74709ca5ebef
## Deleted keys were detected in the following pre-stable discovery artifacts:
container v1beta1 https://github.com/googleapis/google-api-python-client/commit/3c92c46132922d77598527fc98780edca5978ce2
gameservices v1beta https://github.com/googleapis/google-api-python-client/commit/ea5f2216765868a1d6e48995f23b74709ca5ebef
## Discovery Artifact Change Summary:
feat(adexchangebuyer2): update the api https://github.com/googleapis/google-api-python-client/commit/49432a596a9d2e0a014afb2c57cc0ceec37aa403
feat(bigquery): update the api https://github.com/googleapis/google-api-python-client/commit/faedd49d24634e1646e15cb79a983391a6938faa
feat(chromemanagement): update the api https://github.com/googleapis/google-api-python-client/commit/89fc0743cff2c64a438339003fc1c8fdf99938dd
feat(cloudbuild): update the api https://github.com/googleapis/google-api-python-client/commit/8c2010464ec7a8aa6ffe8a044ae44ea0ab199f45
feat(compute): update the api https://github.com/googleapis/google-api-python-client/commit/c2acfdbefef85f8a4696ae467bd61d05db25cf31
feat(contactcenterinsights): update the api https://github.com/googleapis/google-api-python-client/commit/04ba8ef034ac1fd69e8e641c585e762e19078806
feat(container): update the api https://github.com/googleapis/google-api-python-client/commit/3c92c46132922d77598527fc98780edca5978ce2
feat(content): update the api https://github.com/googleapis/google-api-python-client/commit/037b9e1d5c9865af22a3c70dc44542ea3ce547dc
feat(datafusion): update the api https://github.com/googleapis/google-api-python-client/commit/fc6d716e71875ea73036e576bbaafb2826e01e1d
feat(dlp): update the api https://github.com/googleapis/google-api-python-client/commit/08353cbe37fc4c0dcc1311efead553797067417e
feat(documentai): update the api https://github.com/googleapis/google-api-python-client/commit/7974abc07dceffbc9fdb5365b706ed5e1a9899fc
feat(firebaseappcheck): update the api https://github.com/googleapis/google-api-python-client/commit/13220f0704b0d5b954520307a6702efead926e5e
feat(gameservices): update the api https://github.com/googleapis/google-api-python-client/commit/ea5f2216765868a1d6e48995f23b74709ca5ebef
feat(gkehub): update the api https://github.com/googleapis/google-api-python-client/commit/f4ae68ff69c32b5708f1e5f735cb03f3184f7650
feat(healthcare): update the api https://github.com/googleapis/google-api-python-client/commit/5c430ab79811ae3565a83d57ba06e0d48560f791
feat(monitoring): update the api https://github.com/googleapis/google-api-python-client/commit/3ad9f05ae340101c6016e4ceeef52661d0c01e21
feat(networkmanagement): update the api https://github.com/googleapis/google-api-python-client/commit/992b9f851e871feb796485e6af936a3d05899e4e
feat(osconfig): update the api https://github.com/googleapis/google-api-python-client/commit/972e716c233348d6e5d686f3718607a42e7d728a
feat(oslogin): update the api https://github.com/googleapis/google-api-python-client/commit/1ba23c68428c3c07a778f25dc9fc10998022c414
feat(paymentsresellersubscription): update the api https://github.com/googleapis/google-api-python-client/commit/cc7fd94a993048d6ca7cc34e42c4536df6eeb93d
feat(recaptchaenterprise): update the api https://github.com/googleapis/google-api-python-client/commit/2da594c083639420e285d70d26483e46df9fa1a0
feat(redis): update the api https://github.com/googleapis/google-api-python-client/commit/c16b96450466eb72e8122fc8aca0ce010a113350
feat(run): update the api https://github.com/googleapis/google-api-python-client/commit/c502728bfd31e520d4f5f06cc763dc2316cbb221
feat(searchconsole): update the api https://github.com/googleapis/google-api-python-client/commit/eede698004d718213b315b3728793a967a92e87b
feat(speech): update the api https://github.com/googleapis/google-api-python-client/commit/9f0148f2f0f035b9e3c7d73dbd95a8f961ef3eb1
feat(tagmanager): update the api https://github.com/googleapis/google-api-python-client/commit/fe4351bca507192a682440fa52f50eb98cef4434
feat(vmmigration): update the api https://github.com/googleapis/google-api-python-client/commit/d8afe7c7e0a556d31dc904e8878c1bf884a375af
diff --git a/docs/dyn/bigquery_v2.models.html b/docs/dyn/bigquery_v2.models.html
index 81bd48f..f399830 100644
--- a/docs/dyn/bigquery_v2.models.html
+++ b/docs/dyn/bigquery_v2.models.html
@@ -327,53 +327,7 @@
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
- { # Information about a single iteration of the training run.
- "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
- "arimaModelInfo": [ # This message is repeated because there are multiple arima models fitted in auto-arima. For non-auto-arima model, its size is one.
- { # Arima model information.
- "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
- "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
- 3.14,
- ],
- "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
- "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
- 3.14,
- ],
- },
- "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
- "aic": 3.14, # AIC.
- "logLikelihood": 3.14, # Log-likelihood.
- "variance": 3.14, # Variance.
- },
- "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false when d is not 1.
- "hasHolidayEffect": True or False, # If true, holiday_effect is a part of time series decomposition result.
- "hasSpikesAndDips": True or False, # If true, spikes_and_dips is a part of time series decomposition result.
- "hasStepChanges": True or False, # If true, step_changes is a part of time series decomposition result.
- "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
- "d": "A String", # Order of the differencing part.
- "p": "A String", # Order of the autoregressive part.
- "q": "A String", # Order of the moving-average part.
- },
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- "timeSeriesId": "A String", # The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
- "timeSeriesIds": [ # The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
- "A String",
- ],
- },
- ],
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- },
- "clusterInfos": [ # Information about top clusters for clustering models.
- { # Information about a single cluster for clustering model.
- "centroidId": "A String", # Centroid id.
- "clusterRadius": 3.14, # Cluster radius, the average distance from centroid to each point assigned to the cluster.
- "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
- },
- ],
+ {
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.
@@ -677,53 +631,7 @@
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
- { # Information about a single iteration of the training run.
- "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
- "arimaModelInfo": [ # This message is repeated because there are multiple arima models fitted in auto-arima. For non-auto-arima model, its size is one.
- { # Arima model information.
- "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
- "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
- 3.14,
- ],
- "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
- "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
- 3.14,
- ],
- },
- "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
- "aic": 3.14, # AIC.
- "logLikelihood": 3.14, # Log-likelihood.
- "variance": 3.14, # Variance.
- },
- "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false when d is not 1.
- "hasHolidayEffect": True or False, # If true, holiday_effect is a part of time series decomposition result.
- "hasSpikesAndDips": True or False, # If true, spikes_and_dips is a part of time series decomposition result.
- "hasStepChanges": True or False, # If true, step_changes is a part of time series decomposition result.
- "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
- "d": "A String", # Order of the differencing part.
- "p": "A String", # Order of the autoregressive part.
- "q": "A String", # Order of the moving-average part.
- },
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- "timeSeriesId": "A String", # The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
- "timeSeriesIds": [ # The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
- "A String",
- ],
- },
- ],
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- },
- "clusterInfos": [ # Information about top clusters for clustering models.
- { # Information about a single cluster for clustering model.
- "centroidId": "A String", # Centroid id.
- "clusterRadius": 3.14, # Cluster radius, the average distance from centroid to each point assigned to the cluster.
- "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
- },
- ],
+ {
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.
@@ -1040,53 +948,7 @@
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
- { # Information about a single iteration of the training run.
- "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
- "arimaModelInfo": [ # This message is repeated because there are multiple arima models fitted in auto-arima. For non-auto-arima model, its size is one.
- { # Arima model information.
- "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
- "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
- 3.14,
- ],
- "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
- "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
- 3.14,
- ],
- },
- "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
- "aic": 3.14, # AIC.
- "logLikelihood": 3.14, # Log-likelihood.
- "variance": 3.14, # Variance.
- },
- "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false when d is not 1.
- "hasHolidayEffect": True or False, # If true, holiday_effect is a part of time series decomposition result.
- "hasSpikesAndDips": True or False, # If true, spikes_and_dips is a part of time series decomposition result.
- "hasStepChanges": True or False, # If true, step_changes is a part of time series decomposition result.
- "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
- "d": "A String", # Order of the differencing part.
- "p": "A String", # Order of the autoregressive part.
- "q": "A String", # Order of the moving-average part.
- },
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- "timeSeriesId": "A String", # The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
- "timeSeriesIds": [ # The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
- "A String",
- ],
- },
- ],
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- },
- "clusterInfos": [ # Information about top clusters for clustering models.
- { # Information about a single cluster for clustering model.
- "centroidId": "A String", # Centroid id.
- "clusterRadius": 3.14, # Cluster radius, the average distance from centroid to each point assigned to the cluster.
- "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
- },
- ],
+ {
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.
@@ -1378,53 +1240,7 @@
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
- { # Information about a single iteration of the training run.
- "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
- "arimaModelInfo": [ # This message is repeated because there are multiple arima models fitted in auto-arima. For non-auto-arima model, its size is one.
- { # Arima model information.
- "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
- "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
- 3.14,
- ],
- "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
- "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
- 3.14,
- ],
- },
- "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
- "aic": 3.14, # AIC.
- "logLikelihood": 3.14, # Log-likelihood.
- "variance": 3.14, # Variance.
- },
- "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false when d is not 1.
- "hasHolidayEffect": True or False, # If true, holiday_effect is a part of time series decomposition result.
- "hasSpikesAndDips": True or False, # If true, spikes_and_dips is a part of time series decomposition result.
- "hasStepChanges": True or False, # If true, step_changes is a part of time series decomposition result.
- "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
- "d": "A String", # Order of the differencing part.
- "p": "A String", # Order of the autoregressive part.
- "q": "A String", # Order of the moving-average part.
- },
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- "timeSeriesId": "A String", # The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
- "timeSeriesIds": [ # The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
- "A String",
- ],
- },
- ],
- "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
- "A String",
- ],
- },
- "clusterInfos": [ # Information about top clusters for clustering models.
- { # Information about a single cluster for clustering model.
- "centroidId": "A String", # Centroid id.
- "clusterRadius": 3.14, # Cluster radius, the average distance from centroid to each point assigned to the cluster.
- "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
- },
- ],
+ {
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.