chore: Update discovery artifacts (#1195)
* chore(accesscontextmanager): update the api
* chore(adexchangebuyer2): update the api
* chore(admin): update the api
* chore(alertcenter): update the api
* chore(analyticsadmin): update the api
* chore(analyticsdata): update the api
* chore(androidmanagement): update the api
* chore(apigateway): update the api
* chore(apigee): update the api
* chore(appengine): update the api
* chore(area120tables): update the api
* chore(artifactregistry): update the api
* chore(bigquery): update the api
* chore(bigqueryconnection): update the api
* chore(bigqueryreservation): update the api
* chore(billingbudgets): update the api
* chore(binaryauthorization): update the api
* chore(blogger): update the api
* chore(calendar): update the api
* chore(chat): update the api
* chore(cloudasset): update the api
* chore(cloudbuild): update the api
* chore(cloudfunctions): update the api
* chore(cloudidentity): update the api
* chore(cloudkms): update the api
* chore(cloudresourcemanager): update the api
* chore(cloudscheduler): update the api
* chore(cloudtasks): update the api
* chore(composer): update the api
* chore(compute): update the api
* chore(container): update the api
* chore(containeranalysis): update the api
* chore(content): update the api
* chore(datacatalog): update the api
* chore(dataflow): update the api
* chore(datafusion): update the api
* chore(datamigration): update the api
* chore(dataproc): update the api
* chore(deploymentmanager): update the api
* chore(dialogflow): update the api
* chore(displayvideo): update the api
* chore(dlp): update the api
* chore(dns): update the api
* chore(documentai): update the api
* chore(eventarc): update the api
* chore(file): update the api
* chore(firebaseml): update the api
* chore(games): update the api
* chore(gameservices): update the api
* chore(genomics): update the api
* chore(healthcare): update the api
* chore(homegraph): update the api
* chore(iam): update the api
* chore(iap): update the api
* chore(jobs): update the api
* chore(lifesciences): update the api
* chore(localservices): update the api
* chore(managedidentities): update the api
* chore(manufacturers): update the api
* chore(memcache): update the api
* chore(ml): update the api
* chore(monitoring): update the api
* chore(notebooks): update the api
* chore(osconfig): update the api
* chore(pagespeedonline): update the api
* chore(people): update the api
* chore(privateca): update the api
* chore(prod_tt_sasportal): update the api
* chore(pubsub): update the api
* chore(pubsublite): update the api
* chore(recommender): update the api
* chore(remotebuildexecution): update the api
* chore(reseller): update the api
* chore(run): update the api
* chore(safebrowsing): update the api
* chore(sasportal): update the api
* chore(searchconsole): update the api
* chore(secretmanager): update the api
* chore(securitycenter): update the api
* chore(serviceconsumermanagement): update the api
* chore(servicecontrol): update the api
* chore(servicenetworking): update the api
* chore(serviceusage): update the api
* chore(sheets): update the api
* chore(slides): update the api
* chore(spanner): update the api
* chore(speech): update the api
* chore(sqladmin): update the api
* chore(storage): update the api
* chore(storagetransfer): update the api
* chore(sts): update the api
* chore(tagmanager): update the api
* chore(testing): update the api
* chore(toolresults): update the api
* chore(transcoder): update the api
* chore(vectortile): update the api
* chore(videointelligence): update the api
* chore(vision): update the api
* chore(webmasters): update the api
* chore(workflowexecutions): update the api
* chore(youtube): update the api
diff --git a/docs/dyn/bigquery_v2.models.html b/docs/dyn/bigquery_v2.models.html
index 20ace78..05478a3 100644
--- a/docs/dyn/bigquery_v2.models.html
+++ b/docs/dyn/bigquery_v2.models.html
@@ -208,7 +208,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"hasDrift": [ # Whether Arima model fitted with drift or not. It is always false when d is not 1.
@@ -255,7 +255,7 @@
"positiveLabel": "A String", # Label representing the positive class.
},
"clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models.
- "clusters": [ # [Beta] Information for all clusters.
+ "clusters": [ # Information for all clusters.
{ # Message containing the information about one cluster.
"centroidId": "A String", # Centroid id.
"count": "A String", # Count of training data rows that were assigned to this cluster.
@@ -278,6 +278,9 @@
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
},
+ "dimensionalityReductionMetrics": { # Model evaluation metrics for dimensionality reduction models. # Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
+ "totalExplainedVarianceRatio": 3.14, # Total percentage of variance explained by the selected principal components.
+ },
"multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows. # Aggregate classification metrics.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
@@ -316,7 +319,7 @@
"meanSquaredError": 3.14, # Mean squared error.
"meanSquaredLogError": 3.14, # Mean squared log error.
"medianAbsoluteError": 3.14, # Median absolute error.
- "rSquared": 3.14, # R^2 score.
+ "rSquared": 3.14, # R^2 score. This corresponds to r2_score in ML.EVALUATE.
},
},
"globalExplanations": [ # Global explanations for important features of the model. For multi-class models, there is one entry for each label class. For other models, there is only one entry in the list.
@@ -358,7 +361,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
@@ -376,11 +379,19 @@
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.
"learnRate": 3.14, # Learn rate used for this iteration.
+ "principalComponentInfos": [ # The information of the principal components.
+ { # Principal component infos, used only for eigen decomposition based models, e.g., PCA. Ordered by explained_variance in the descending order.
+ "cumulativeExplainedVarianceRatio": 3.14, # The explained_variance is pre-ordered in the descending order to compute the cumulative explained variance ratio.
+ "explainedVariance": 3.14, # Explained variance by this principal component, which is simply the eigenvalue.
+ "explainedVarianceRatio": 3.14, # Explained_variance over the total explained variance.
+ "principalComponentId": "A String", # Id of the principal component.
+ },
+ ],
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
},
],
"startTime": "A String", # The start time of this training run.
- "trainingOptions": { # Options that were used for this training run, includes user specified and default options that were used.
+ "trainingOptions": { # Options used in model training. # Options that were used for this training run, includes user specified and default options that were used.
"autoArima": True or False, # Whether to enable auto ARIMA or not.
"autoArimaMaxOrder": "A String", # The max value of non-seasonal p and q.
"batchSize": "A String", # Batch size for dnn models.
@@ -417,7 +428,7 @@
"maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
"minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
"minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
- "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
+ "modelUri": "A String", # Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
"nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
"d": "A String", # Order of the differencing part.
"p": "A String", # Order of the autoregressive part.
@@ -429,7 +440,7 @@
"preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
"subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
"timeSeriesDataColumn": "A String", # Column to be designated as time series data for ARIMA model.
- "timeSeriesIdColumn": "A String", # The id column that will be used to indicate different time series to forecast in parallel.
+ "timeSeriesIdColumn": "A String", # The time series id column that was used during ARIMA model training.
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.
@@ -542,7 +553,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"hasDrift": [ # Whether Arima model fitted with drift or not. It is always false when d is not 1.
@@ -589,7 +600,7 @@
"positiveLabel": "A String", # Label representing the positive class.
},
"clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models.
- "clusters": [ # [Beta] Information for all clusters.
+ "clusters": [ # Information for all clusters.
{ # Message containing the information about one cluster.
"centroidId": "A String", # Centroid id.
"count": "A String", # Count of training data rows that were assigned to this cluster.
@@ -612,6 +623,9 @@
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
},
+ "dimensionalityReductionMetrics": { # Model evaluation metrics for dimensionality reduction models. # Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
+ "totalExplainedVarianceRatio": 3.14, # Total percentage of variance explained by the selected principal components.
+ },
"multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows. # Aggregate classification metrics.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
@@ -650,7 +664,7 @@
"meanSquaredError": 3.14, # Mean squared error.
"meanSquaredLogError": 3.14, # Mean squared log error.
"medianAbsoluteError": 3.14, # Median absolute error.
- "rSquared": 3.14, # R^2 score.
+ "rSquared": 3.14, # R^2 score. This corresponds to r2_score in ML.EVALUATE.
},
},
"globalExplanations": [ # Global explanations for important features of the model. For multi-class models, there is one entry for each label class. For other models, there is only one entry in the list.
@@ -692,7 +706,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
@@ -710,11 +724,19 @@
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.
"learnRate": 3.14, # Learn rate used for this iteration.
+ "principalComponentInfos": [ # The information of the principal components.
+ { # Principal component infos, used only for eigen decomposition based models, e.g., PCA. Ordered by explained_variance in the descending order.
+ "cumulativeExplainedVarianceRatio": 3.14, # The explained_variance is pre-ordered in the descending order to compute the cumulative explained variance ratio.
+ "explainedVariance": 3.14, # Explained variance by this principal component, which is simply the eigenvalue.
+ "explainedVarianceRatio": 3.14, # Explained_variance over the total explained variance.
+ "principalComponentId": "A String", # Id of the principal component.
+ },
+ ],
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
},
],
"startTime": "A String", # The start time of this training run.
- "trainingOptions": { # Options that were used for this training run, includes user specified and default options that were used.
+ "trainingOptions": { # Options used in model training. # Options that were used for this training run, includes user specified and default options that were used.
"autoArima": True or False, # Whether to enable auto ARIMA or not.
"autoArimaMaxOrder": "A String", # The max value of non-seasonal p and q.
"batchSize": "A String", # Batch size for dnn models.
@@ -751,7 +773,7 @@
"maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
"minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
"minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
- "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
+ "modelUri": "A String", # Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
"nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
"d": "A String", # Order of the differencing part.
"p": "A String", # Order of the autoregressive part.
@@ -763,7 +785,7 @@
"preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
"subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
"timeSeriesDataColumn": "A String", # Column to be designated as time series data for ARIMA model.
- "timeSeriesIdColumn": "A String", # The id column that will be used to indicate different time series to forecast in parallel.
+ "timeSeriesIdColumn": "A String", # The time series id column that was used during ARIMA model training.
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.
@@ -889,7 +911,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"hasDrift": [ # Whether Arima model fitted with drift or not. It is always false when d is not 1.
@@ -936,7 +958,7 @@
"positiveLabel": "A String", # Label representing the positive class.
},
"clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models.
- "clusters": [ # [Beta] Information for all clusters.
+ "clusters": [ # Information for all clusters.
{ # Message containing the information about one cluster.
"centroidId": "A String", # Centroid id.
"count": "A String", # Count of training data rows that were assigned to this cluster.
@@ -959,6 +981,9 @@
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
},
+ "dimensionalityReductionMetrics": { # Model evaluation metrics for dimensionality reduction models. # Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
+ "totalExplainedVarianceRatio": 3.14, # Total percentage of variance explained by the selected principal components.
+ },
"multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows. # Aggregate classification metrics.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
@@ -997,7 +1022,7 @@
"meanSquaredError": 3.14, # Mean squared error.
"meanSquaredLogError": 3.14, # Mean squared log error.
"medianAbsoluteError": 3.14, # Median absolute error.
- "rSquared": 3.14, # R^2 score.
+ "rSquared": 3.14, # R^2 score. This corresponds to r2_score in ML.EVALUATE.
},
},
"globalExplanations": [ # Global explanations for important features of the model. For multi-class models, there is one entry for each label class. For other models, there is only one entry in the list.
@@ -1039,7 +1064,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
@@ -1057,11 +1082,19 @@
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.
"learnRate": 3.14, # Learn rate used for this iteration.
+ "principalComponentInfos": [ # The information of the principal components.
+ { # Principal component infos, used only for eigen decomposition based models, e.g., PCA. Ordered by explained_variance in the descending order.
+ "cumulativeExplainedVarianceRatio": 3.14, # The explained_variance is pre-ordered in the descending order to compute the cumulative explained variance ratio.
+ "explainedVariance": 3.14, # Explained variance by this principal component, which is simply the eigenvalue.
+ "explainedVarianceRatio": 3.14, # Explained_variance over the total explained variance.
+ "principalComponentId": "A String", # Id of the principal component.
+ },
+ ],
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
},
],
"startTime": "A String", # The start time of this training run.
- "trainingOptions": { # Options that were used for this training run, includes user specified and default options that were used.
+ "trainingOptions": { # Options used in model training. # Options that were used for this training run, includes user specified and default options that were used.
"autoArima": True or False, # Whether to enable auto ARIMA or not.
"autoArimaMaxOrder": "A String", # The max value of non-seasonal p and q.
"batchSize": "A String", # Batch size for dnn models.
@@ -1098,7 +1131,7 @@
"maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
"minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
"minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
- "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
+ "modelUri": "A String", # Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
"nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
"d": "A String", # Order of the differencing part.
"p": "A String", # Order of the autoregressive part.
@@ -1110,7 +1143,7 @@
"preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
"subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
"timeSeriesDataColumn": "A String", # Column to be designated as time series data for ARIMA model.
- "timeSeriesIdColumn": "A String", # The id column that will be used to indicate different time series to forecast in parallel.
+ "timeSeriesIdColumn": "A String", # The time series id column that was used during ARIMA model training.
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.
@@ -1211,7 +1244,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"hasDrift": [ # Whether Arima model fitted with drift or not. It is always false when d is not 1.
@@ -1258,7 +1291,7 @@
"positiveLabel": "A String", # Label representing the positive class.
},
"clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models.
- "clusters": [ # [Beta] Information for all clusters.
+ "clusters": [ # Information for all clusters.
{ # Message containing the information about one cluster.
"centroidId": "A String", # Centroid id.
"count": "A String", # Count of training data rows that were assigned to this cluster.
@@ -1281,6 +1314,9 @@
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
},
+ "dimensionalityReductionMetrics": { # Model evaluation metrics for dimensionality reduction models. # Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
+ "totalExplainedVarianceRatio": 3.14, # Total percentage of variance explained by the selected principal components.
+ },
"multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows. # Aggregate classification metrics.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
@@ -1319,7 +1355,7 @@
"meanSquaredError": 3.14, # Mean squared error.
"meanSquaredLogError": 3.14, # Mean squared log error.
"medianAbsoluteError": 3.14, # Median absolute error.
- "rSquared": 3.14, # R^2 score.
+ "rSquared": 3.14, # R^2 score. This corresponds to r2_score in ML.EVALUATE.
},
},
"globalExplanations": [ # Global explanations for important features of the model. For multi-class models, there is one entry for each label class. For other models, there is only one entry in the list.
@@ -1361,7 +1397,7 @@
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
"A String",
],
- "timeSeriesId": "A String", # The id to indicate different time series.
+ "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.
},
],
"seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for one time series.
@@ -1379,11 +1415,19 @@
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"index": 42, # Index of the iteration, 0 based.
"learnRate": 3.14, # Learn rate used for this iteration.
+ "principalComponentInfos": [ # The information of the principal components.
+ { # Principal component infos, used only for eigen decomposition based models, e.g., PCA. Ordered by explained_variance in the descending order.
+ "cumulativeExplainedVarianceRatio": 3.14, # The explained_variance is pre-ordered in the descending order to compute the cumulative explained variance ratio.
+ "explainedVariance": 3.14, # Explained variance by this principal component, which is simply the eigenvalue.
+ "explainedVarianceRatio": 3.14, # Explained_variance over the total explained variance.
+ "principalComponentId": "A String", # Id of the principal component.
+ },
+ ],
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
},
],
"startTime": "A String", # The start time of this training run.
- "trainingOptions": { # Options that were used for this training run, includes user specified and default options that were used.
+ "trainingOptions": { # Options used in model training. # Options that were used for this training run, includes user specified and default options that were used.
"autoArima": True or False, # Whether to enable auto ARIMA or not.
"autoArimaMaxOrder": "A String", # The max value of non-seasonal p and q.
"batchSize": "A String", # Batch size for dnn models.
@@ -1420,7 +1464,7 @@
"maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
"minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
"minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
- "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
+ "modelUri": "A String", # Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
"nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
"d": "A String", # Order of the differencing part.
"p": "A String", # Order of the autoregressive part.
@@ -1432,7 +1476,7 @@
"preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
"subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
"timeSeriesDataColumn": "A String", # Column to be designated as time series data for ARIMA model.
- "timeSeriesIdColumn": "A String", # The id column that will be used to indicate different time series to forecast in parallel.
+ "timeSeriesIdColumn": "A String", # The time series id column that was used during ARIMA model training.
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.