chore: Update discovery artifacts (#1541)
## Discovery Artifact Change Summary:
feat(analyticsadmin): update the api https://github.com/googleapis/google-api-python-client/commit/c14c42a82fbd61df00b690daa328cea212441f59
feat(appengine): update the api https://github.com/googleapis/google-api-python-client/commit/22e6b63271836d2b195191c0711d3e815d7b3f29
feat(bigquery): update the api https://github.com/googleapis/google-api-python-client/commit/5325b3654e42e393911f088e9a8358aeaf733c03
feat(content): update the api https://github.com/googleapis/google-api-python-client/commit/df08fb1f3823a5edc96e6caebe24df66e943fa36
feat(dialogflow): update the api https://github.com/googleapis/google-api-python-client/commit/eaa0b250682d593572168427d92b0c3b9438a503
feat(firestore): update the api https://github.com/googleapis/google-api-python-client/commit/89ee485ce0646fb14d4f4e1d7aae095e504cf4be
feat(gkehub): update the api https://github.com/googleapis/google-api-python-client/commit/982014c5e33c29f2e0030b950b2f2ac27afa3f8f
feat(monitoring): update the api https://github.com/googleapis/google-api-python-client/commit/440201ddeeae876ab83863def611ec39649d397c
fix(oslogin): update the api https://github.com/googleapis/google-api-python-client/commit/e940d95d04a6aba60b89ece3fd630cc0ab5cde2a
feat(retail): update the api https://github.com/googleapis/google-api-python-client/commit/58f1c1ba076ed6ecc389ddf66d0c5ac609cd9d17
feat(servicenetworking): update the api https://github.com/googleapis/google-api-python-client/commit/53d51411d39049a98df6909ae16f9c5dfee4f432
diff --git a/docs/dyn/bigquery_v2.models.html b/docs/dyn/bigquery_v2.models.html
index 3679ea8..81bd48f 100644
--- a/docs/dyn/bigquery_v2.models.html
+++ b/docs/dyn/bigquery_v2.models.html
@@ -387,7 +387,12 @@
"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.
+ "boosterType": "A String", # Booster type for boosted tree models.
"cleanSpikesAndDips": True or False, # If true, clean spikes and dips in the input time series.
+ "colsampleBylevel": 3.14, # Subsample ratio of columns for each level for boosted tree models.
+ "colsampleBynode": 3.14, # Subsample ratio of columns for each node(split) for boosted tree models.
+ "colsampleBytree": 3.14, # Subsample ratio of columns when constructing each tree for boosted tree models.
+ "dartNormalizeType": "A String", # Type of normalization algorithm for boosted tree models using dart booster.
"dataFrequency": "A String", # The data frequency of a time series.
"dataSplitColumn": "A String", # The column to split data with. This column won't be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
"dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
@@ -422,6 +427,7 @@
"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.
+ "minTreeChildWeight": "A String", # Minimum sum of instance weight needed in a child for boosted tree 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.
@@ -430,6 +436,7 @@
},
"numClusters": "A String", # Number of clusters for clustering models.
"numFactors": "A String", # Num factors specified for matrix factorization models.
+ "numParallelTree": "A String", # Number of parallel trees constructed during each iteration for boosted tree models.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"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.
@@ -439,6 +446,7 @@
"A String",
],
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
+ "treeMethod": "A String", # Tree construction algorithm for boosted tree models.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.
"warmStart": True or False, # Whether to train a model from the last checkpoint.
@@ -729,7 +737,12 @@
"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.
+ "boosterType": "A String", # Booster type for boosted tree models.
"cleanSpikesAndDips": True or False, # If true, clean spikes and dips in the input time series.
+ "colsampleBylevel": 3.14, # Subsample ratio of columns for each level for boosted tree models.
+ "colsampleBynode": 3.14, # Subsample ratio of columns for each node(split) for boosted tree models.
+ "colsampleBytree": 3.14, # Subsample ratio of columns when constructing each tree for boosted tree models.
+ "dartNormalizeType": "A String", # Type of normalization algorithm for boosted tree models using dart booster.
"dataFrequency": "A String", # The data frequency of a time series.
"dataSplitColumn": "A String", # The column to split data with. This column won't be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
"dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
@@ -764,6 +777,7 @@
"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.
+ "minTreeChildWeight": "A String", # Minimum sum of instance weight needed in a child for boosted tree 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.
@@ -772,6 +786,7 @@
},
"numClusters": "A String", # Number of clusters for clustering models.
"numFactors": "A String", # Num factors specified for matrix factorization models.
+ "numParallelTree": "A String", # Number of parallel trees constructed during each iteration for boosted tree models.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"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.
@@ -781,6 +796,7 @@
"A String",
],
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
+ "treeMethod": "A String", # Tree construction algorithm for boosted tree models.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.
"warmStart": True or False, # Whether to train a model from the last checkpoint.
@@ -1084,7 +1100,12 @@
"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.
+ "boosterType": "A String", # Booster type for boosted tree models.
"cleanSpikesAndDips": True or False, # If true, clean spikes and dips in the input time series.
+ "colsampleBylevel": 3.14, # Subsample ratio of columns for each level for boosted tree models.
+ "colsampleBynode": 3.14, # Subsample ratio of columns for each node(split) for boosted tree models.
+ "colsampleBytree": 3.14, # Subsample ratio of columns when constructing each tree for boosted tree models.
+ "dartNormalizeType": "A String", # Type of normalization algorithm for boosted tree models using dart booster.
"dataFrequency": "A String", # The data frequency of a time series.
"dataSplitColumn": "A String", # The column to split data with. This column won't be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
"dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
@@ -1119,6 +1140,7 @@
"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.
+ "minTreeChildWeight": "A String", # Minimum sum of instance weight needed in a child for boosted tree 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.
@@ -1127,6 +1149,7 @@
},
"numClusters": "A String", # Number of clusters for clustering models.
"numFactors": "A String", # Num factors specified for matrix factorization models.
+ "numParallelTree": "A String", # Number of parallel trees constructed during each iteration for boosted tree models.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"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.
@@ -1136,6 +1159,7 @@
"A String",
],
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
+ "treeMethod": "A String", # Tree construction algorithm for boosted tree models.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.
"warmStart": True or False, # Whether to train a model from the last checkpoint.
@@ -1414,7 +1438,12 @@
"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.
+ "boosterType": "A String", # Booster type for boosted tree models.
"cleanSpikesAndDips": True or False, # If true, clean spikes and dips in the input time series.
+ "colsampleBylevel": 3.14, # Subsample ratio of columns for each level for boosted tree models.
+ "colsampleBynode": 3.14, # Subsample ratio of columns for each node(split) for boosted tree models.
+ "colsampleBytree": 3.14, # Subsample ratio of columns when constructing each tree for boosted tree models.
+ "dartNormalizeType": "A String", # Type of normalization algorithm for boosted tree models using dart booster.
"dataFrequency": "A String", # The data frequency of a time series.
"dataSplitColumn": "A String", # The column to split data with. This column won't be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
"dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
@@ -1449,6 +1478,7 @@
"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.
+ "minTreeChildWeight": "A String", # Minimum sum of instance weight needed in a child for boosted tree 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.
@@ -1457,6 +1487,7 @@
},
"numClusters": "A String", # Number of clusters for clustering models.
"numFactors": "A String", # Num factors specified for matrix factorization models.
+ "numParallelTree": "A String", # Number of parallel trees constructed during each iteration for boosted tree models.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"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.
@@ -1466,6 +1497,7 @@
"A String",
],
"timeSeriesTimestampColumn": "A String", # Column to be designated as time series timestamp for ARIMA model.
+ "treeMethod": "A String", # Tree construction algorithm for boosted tree models.
"userColumn": "A String", # User column specified for matrix factorization models.
"walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is specified.
"warmStart": True or False, # Whether to train a model from the last checkpoint.