From 1f210f20398131eca417ef0ed76571de2e1733b2 Mon Sep 17 00:00:00 2001 From: yoshi-automation Date: Tue, 6 Aug 2019 05:13:26 -0700 Subject: [PATCH] [CHANGE ME] Re-generated bigquery to pick up changes in the API or client library generator. --- .../google/cloud/bigquery_v2/gapic/enums.py | 19 +- .../cloud/bigquery_v2/proto/model.proto | 97 ++- .../cloud/bigquery_v2/proto/model_pb2.py | 654 ++++++++++++++++-- bigquery/synth.metadata | 10 +- 4 files changed, 695 insertions(+), 85 deletions(-) diff --git a/bigquery/google/cloud/bigquery_v2/gapic/enums.py b/bigquery/google/cloud/bigquery_v2/gapic/enums.py index e1ce20f9a130..5d95f2590785 100644 --- a/bigquery/google/cloud/bigquery_v2/gapic/enums.py +++ b/bigquery/google/cloud/bigquery_v2/gapic/enums.py @@ -91,7 +91,7 @@ class ModelType(enum.IntEnum): MODEL_TYPE_UNSPECIFIED (int) LINEAR_REGRESSION (int): Linear regression model. LOGISTIC_REGRESSION (int): Logistic regression based classification model. - KMEANS (int): [Beta] K-means clustering model. + KMEANS (int): K-means clustering model. TENSORFLOW (int): [Beta] An imported TensorFlow model. """ @@ -115,6 +115,23 @@ class OptimizationStrategy(enum.IntEnum): BATCH_GRADIENT_DESCENT = 1 NORMAL_EQUATION = 2 + class KmeansEnums(object): + class KmeansInitializationMethod(enum.IntEnum): + """ + Indicates the method used to initialize the centroids for KMeans + clustering algorithm. + + Attributes: + KMEANS_INITIALIZATION_METHOD_UNSPECIFIED (int) + RANDOM (int): Initializes the centroids randomly. + CUSTOM (int): Initializes the centroids using data specified in + kmeans\_initialization\_column. + """ + + KMEANS_INITIALIZATION_METHOD_UNSPECIFIED = 0 + RANDOM = 1 + CUSTOM = 2 + class StandardSqlDataType(object): class TypeKind(enum.IntEnum): diff --git a/bigquery/google/cloud/bigquery_v2/proto/model.proto b/bigquery/google/cloud/bigquery_v2/proto/model.proto index 2b5b6894db5b..42246e8efff2 100644 --- a/bigquery/google/cloud/bigquery_v2/proto/model.proto +++ b/bigquery/google/cloud/bigquery_v2/proto/model.proto @@ -33,6 +33,7 @@ service ModelService { option (google.api.default_host) = "bigquery.googleapis.com"; option (google.api.oauth_scopes) = "https://www.googleapis.com/auth/bigquery," + "https://www.googleapis.com/auth/bigquery.readonly," "https://www.googleapis.com/auth/cloud-platform," "https://www.googleapis.com/auth/cloud-platform.read-only"; @@ -55,7 +56,25 @@ service ModelService { } message Model { - // Evaluation metrics for regression models. + message KmeansEnums { + // Indicates the method used to initialize the centroids for KMeans + // clustering algorithm. + enum KmeansInitializationMethod { + KMEANS_INITIALIZATION_METHOD_UNSPECIFIED = 0; + + // Initializes the centroids randomly. + RANDOM = 1; + + // Initializes the centroids using data specified in + // kmeans_initialization_column. + CUSTOM = 2; + } + + + } + + // Evaluation metrics for regression and explicit feedback type matrix + // factorization models. message RegressionMetrics { // Mean absolute error. google.protobuf.DoubleValue mean_absolute_error = 1; @@ -199,11 +218,60 @@ message Model { // Evaluation metrics for clustering models. message ClusteringMetrics { + // Message containing the information about one cluster. + message Cluster { + // Representative value of a single feature within the cluster. + message FeatureValue { + // Representative value of a categorical feature. + message CategoricalValue { + // Represents the count of a single category within the cluster. + message CategoryCount { + // The name of category. + string category = 1; + + // The count of training samples matching the category within the + // cluster. + google.protobuf.Int64Value count = 2; + } + + // Counts of all categories for the categorical feature. If there are + // more than ten categories, we return top ten (by count) and return + // one more CategoryCount with category ‘_OTHER_’ and count as + // aggregate counts of remaining categories. + repeated CategoryCount category_counts = 1; + } + + // The feature column name. + string feature_column = 1; + + oneof value { + // The numerical feature value. This is the centroid value for this + // feature. + google.protobuf.DoubleValue numerical_value = 2; + + // The categorical feature value. + CategoricalValue categorical_value = 3; + } + } + + // Centroid id. + int64 centroid_id = 1; + + // Values of highly variant features for this cluster. + repeated FeatureValue feature_values = 2; + + // Count of training data rows that were assigned to this cluster. + google.protobuf.Int64Value count = 3; + } + // Davies-Bouldin index. google.protobuf.DoubleValue davies_bouldin_index = 1; // Mean of squared distances between each sample to its cluster centroid. google.protobuf.DoubleValue mean_squared_distance = 2; + + // [Beta] Information for all clusters. + repeated Cluster clusters = 3; } // Evaluation metrics of a model. These are either computed on all training @@ -211,7 +279,8 @@ message Model { // training. These are not present for imported models. message EvaluationMetrics { oneof metrics { - // Populated for regression models. + // Populated for regression models and explicit feedback type matrix + // factorization models. RegressionMetrics regression_metrics = 1; // Populated for binary classification/classifier models. @@ -220,7 +289,7 @@ message Model { // Populated for multi-class classification/classifier models. MultiClassClassificationMetrics multi_class_classification_metrics = 3; - // [Beta] Populated for clustering models. + // Populated for clustering models. ClusteringMetrics clustering_metrics = 4; } } @@ -292,10 +361,10 @@ message Model { // training data. Only applicable for classification models. map label_class_weights = 17; - // [Beta] Distance type for clustering models. + // Distance type for clustering models. DistanceType distance_type = 20; - // [Beta] Number of clusters for clustering models. + // Number of clusters for clustering models. int64 num_clusters = 21; // [Beta] Google Cloud Storage URI from which the model was imported. Only @@ -304,6 +373,13 @@ message Model { // Optimization strategy for training linear regression models. OptimizationStrategy optimization_strategy = 23; + + // The method used to initialize the centroids for kmeans algorithm. + KmeansEnums.KmeansInitializationMethod kmeans_initialization_method = 33; + + // The column used to provide the initial centroids for kmeans algorithm + // when kmeans_initialization_method is CUSTOM. + string kmeans_initialization_column = 34; } // Information about a single iteration of the training run. @@ -336,7 +412,7 @@ message Model { // Learn rate used for this iteration. double learn_rate = 7; - // [Beta] Information about top clusters for clustering models. + // Information about top clusters for clustering models. repeated ClusterInfo cluster_infos = 8; } @@ -365,7 +441,7 @@ message Model { // Logistic regression based classification model. LOGISTIC_REGRESSION = 2; - // [Beta] K-means clustering model. + // K-means clustering model. KMEANS = 3; // [Beta] An imported TensorFlow model. @@ -513,8 +589,8 @@ message PatchModelRequest { string model_id = 3; // Patched model. - // Follows patch semantics. Missing fields are not updated. To clear a field, - // explicitly set to default value. + // Follows RFC5789 patch semantics. Missing fields are not updated. + // To clear a field, explicitly set to default value. Model model = 4; } @@ -536,7 +612,8 @@ message ListModelsRequest { // Dataset ID of the models to list. string dataset_id = 2; - // The maximum number of results per page. + // The maximum number of results to return in a single response page. + // Leverage the page tokens to iterate through the entire collection. google.protobuf.UInt32Value max_results = 3; // Page token, returned by a previous call to request the next page of diff --git a/bigquery/google/cloud/bigquery_v2/proto/model_pb2.py b/bigquery/google/cloud/bigquery_v2/proto/model_pb2.py index ed82d8e4e8fd..45e6cefdf72c 100644 --- a/bigquery/google/cloud/bigquery_v2/proto/model_pb2.py +++ b/bigquery/google/cloud/bigquery_v2/proto/model_pb2.py @@ -36,7 +36,7 @@ "\n\034com.google.cloud.bigquery.v2B\nModelProtoZ@google.golang.org/genproto/googleapis/cloud/bigquery/v2;bigquery" ), serialized_pb=_b( - 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+_MODEL_KMEANSENUMS_KMEANSINITIALIZATIONMETHOD = _descriptor.EnumDescriptor( + name="KmeansInitializationMethod", + full_name="google.cloud.bigquery.v2.Model.KmeansEnums.KmeansInitializationMethod", + filename=None, + file=DESCRIPTOR, + values=[ + _descriptor.EnumValueDescriptor( + name="KMEANS_INITIALIZATION_METHOD_UNSPECIFIED", + index=0, + number=0, + serialized_options=None, + type=None, + ), + _descriptor.EnumValueDescriptor( + name="RANDOM", index=1, number=1, serialized_options=None, type=None + ), + _descriptor.EnumValueDescriptor( + name="CUSTOM", index=2, number=2, serialized_options=None, type=None + ), + ], + containing_type=None, + serialized_options=None, + serialized_start=898, + serialized_end=996, +) +_sym_db.RegisterEnumDescriptor(_MODEL_KMEANSENUMS_KMEANSINITIALIZATIONMETHOD) + _MODEL_MODELTYPE = _descriptor.EnumDescriptor( name="ModelType", full_name="google.cloud.bigquery.v2.Model.ModelType", @@ -86,8 +113,8 @@ ], containing_type=None, serialized_options=None, - serialized_start=5432, - serialized_end=5547, + serialized_start=6398, + serialized_end=6513, ) _sym_db.RegisterEnumDescriptor(_MODEL_MODELTYPE) @@ -117,8 +144,8 @@ ], containing_type=None, serialized_options=None, - serialized_start=5549, - serialized_end=5628, + serialized_start=6515, + serialized_end=6594, ) _sym_db.RegisterEnumDescriptor(_MODEL_LOSSTYPE) @@ -144,8 +171,8 @@ ], containing_type=None, serialized_options=None, - serialized_start=5630, - serialized_end=5702, + serialized_start=6596, + serialized_end=6668, ) _sym_db.RegisterEnumDescriptor(_MODEL_DISTANCETYPE) @@ -180,8 +207,8 @@ ], containing_type=None, serialized_options=None, - serialized_start=5704, - serialized_end=5826, + serialized_start=6670, + serialized_end=6792, ) _sym_db.RegisterEnumDescriptor(_MODEL_DATASPLITMETHOD) @@ -207,8 +234,8 @@ ], containing_type=None, serialized_options=None, - serialized_start=5828, - serialized_end=5915, + serialized_start=6794, + serialized_end=6881, ) _sym_db.RegisterEnumDescriptor(_MODEL_LEARNRATESTRATEGY) @@ -242,12 +269,31 @@ ], containing_type=None, serialized_options=None, - serialized_start=5917, - serialized_end=6027, + serialized_start=6883, + serialized_end=6993, ) _sym_db.RegisterEnumDescriptor(_MODEL_OPTIMIZATIONSTRATEGY) +_MODEL_KMEANSENUMS = _descriptor.Descriptor( + name="KmeansEnums", + full_name="google.cloud.bigquery.v2.Model.KmeansEnums", + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[], + extensions=[], + nested_types=[], + enum_types=[_MODEL_KMEANSENUMS_KMEANSINITIALIZATIONMETHOD], + serialized_options=None, + is_extendable=False, + syntax="proto3", + extension_ranges=[], + oneofs=[], + serialized_start=883, + serialized_end=996, +) + _MODEL_REGRESSIONMETRICS = _descriptor.Descriptor( name="RegressionMetrics", full_name="google.cloud.bigquery.v2.Model.RegressionMetrics", @@ -354,8 +400,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=884, - serialized_end=1192, + serialized_start=999, + serialized_end=1307, ) _MODEL_AGGREGATECLASSIFICATIONMETRICS = _descriptor.Descriptor( @@ -500,8 +546,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=1195, - serialized_end=1562, + serialized_start=1310, + serialized_end=1677, ) _MODEL_BINARYCLASSIFICATIONMETRICS_BINARYCONFUSIONMATRIX = _descriptor.Descriptor( @@ -682,8 +728,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=1872, - serialized_end=2364, + serialized_start=1987, + serialized_end=2479, ) _MODEL_BINARYCLASSIFICATIONMETRICS = _descriptor.Descriptor( @@ -774,8 +820,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=1565, - serialized_end=2364, + serialized_start=1680, + serialized_end=2479, ) _MODEL_MULTICLASSCLASSIFICATIONMETRICS_CONFUSIONMATRIX_ENTRY = _descriptor.Descriptor( @@ -830,8 +876,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=2799, - serialized_end=2880, + serialized_start=2914, + serialized_end=2995, ) _MODEL_MULTICLASSCLASSIFICATIONMETRICS_CONFUSIONMATRIX_ROW = _descriptor.Descriptor( @@ -886,8 +932,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=2883, - serialized_end=3014, + serialized_start=2998, + serialized_end=3129, ) _MODEL_MULTICLASSCLASSIFICATIONMETRICS_CONFUSIONMATRIX = _descriptor.Descriptor( @@ -945,8 +991,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=2621, - serialized_end=3014, + serialized_start=2736, + serialized_end=3129, ) _MODEL_MULTICLASSCLASSIFICATIONMETRICS = _descriptor.Descriptor( @@ -1001,8 +1047,260 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=2367, - serialized_end=3014, + serialized_start=2482, + serialized_end=3129, +) + +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE_CATEGORYCOUNT = _descriptor.Descriptor( + name="CategoryCount", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount", + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name="category", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount.category", + index=0, + number=1, + type=9, + cpp_type=9, + label=1, + has_default_value=False, + default_value=_b("").decode("utf-8"), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + _descriptor.FieldDescriptor( + name="count", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount.count", + index=1, + number=2, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + ], + extensions=[], + nested_types=[], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax="proto3", + extension_ranges=[], + oneofs=[], + serialized_start=3889, + serialized_end=3966, +) + +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE = _descriptor.Descriptor( + name="CategoricalValue", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue", + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name="category_counts", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.category_counts", + index=0, + number=1, + type=11, + cpp_type=10, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ) + ], + extensions=[], + nested_types=[ + _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE_CATEGORYCOUNT + ], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax="proto3", + extension_ranges=[], + oneofs=[], + serialized_start=3741, + serialized_end=3966, +) + +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE = _descriptor.Descriptor( + name="FeatureValue", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue", + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name="feature_column", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.feature_column", + index=0, + number=1, + type=9, + cpp_type=9, + label=1, + has_default_value=False, + default_value=_b("").decode("utf-8"), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + _descriptor.FieldDescriptor( + name="numerical_value", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.numerical_value", + index=1, + number=2, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + _descriptor.FieldDescriptor( + name="categorical_value", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.categorical_value", + index=2, + number=3, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + ], + extensions=[], + nested_types=[_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax="proto3", + extension_ranges=[], + oneofs=[ + _descriptor.OneofDescriptor( + name="value", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.value", + index=0, + containing_type=None, + fields=[], + ) + ], + serialized_start=3525, + serialized_end=3975, +) + +_MODEL_CLUSTERINGMETRICS_CLUSTER = _descriptor.Descriptor( + name="Cluster", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster", + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name="centroid_id", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.centroid_id", + index=0, + number=1, + type=3, + cpp_type=2, + label=1, + has_default_value=False, + default_value=0, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + _descriptor.FieldDescriptor( + name="feature_values", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.feature_values", + index=1, + number=2, + type=11, + cpp_type=10, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + _descriptor.FieldDescriptor( + name="count", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.count", + index=2, + number=3, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + ], + extensions=[], + nested_types=[_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax="proto3", + extension_ranges=[], + oneofs=[], + serialized_start=3352, + serialized_end=3975, ) _MODEL_CLUSTERINGMETRICS = _descriptor.Descriptor( @@ -1048,17 +1346,35 @@ serialized_options=None, file=DESCRIPTOR, ), + _descriptor.FieldDescriptor( + name="clusters", + full_name="google.cloud.bigquery.v2.Model.ClusteringMetrics.clusters", + index=2, + number=3, + type=11, + cpp_type=10, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), ], extensions=[], - nested_types=[], + nested_types=[_MODEL_CLUSTERINGMETRICS_CLUSTER], enum_types=[], serialized_options=None, is_extendable=False, syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=3017, - serialized_end=3157, + serialized_start=3132, + serialized_end=3975, ) _MODEL_EVALUATIONMETRICS = _descriptor.Descriptor( @@ -1157,8 +1473,8 @@ fields=[], ) ], - serialized_start=3160, - serialized_end=3565, + serialized_start=3978, + serialized_end=4383, ) _MODEL_TRAININGRUN_TRAININGOPTIONS_LABELCLASSWEIGHTSENTRY = _descriptor.Descriptor( @@ -1213,8 +1529,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=4853, - serialized_end=4909, + serialized_start=5819, + serialized_end=5875, ) _MODEL_TRAININGRUN_TRAININGOPTIONS = _descriptor.Descriptor( @@ -1566,6 +1882,42 @@ serialized_options=None, file=DESCRIPTOR, ), + _descriptor.FieldDescriptor( + name="kmeans_initialization_method", + full_name="google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.kmeans_initialization_method", + index=19, + number=33, + type=14, + cpp_type=8, + label=1, + has_default_value=False, + default_value=0, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), + _descriptor.FieldDescriptor( + name="kmeans_initialization_column", + full_name="google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.kmeans_initialization_column", + index=20, + number=34, + type=9, + cpp_type=9, + label=1, + has_default_value=False, + default_value=_b("").decode("utf-8"), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + ), ], extensions=[], nested_types=[_MODEL_TRAININGRUN_TRAININGOPTIONS_LABELCLASSWEIGHTSENTRY], @@ -1575,8 +1927,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=3876, - serialized_end=4909, + serialized_start=4694, + serialized_end=5875, ) _MODEL_TRAININGRUN_ITERATIONRESULT_CLUSTERINFO = _descriptor.Descriptor( @@ -1649,8 +2001,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=5244, - serialized_end=5383, + serialized_start=6210, + serialized_end=6349, ) _MODEL_TRAININGRUN_ITERATIONRESULT = _descriptor.Descriptor( @@ -1777,8 +2129,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=4912, - serialized_end=5383, + serialized_start=5878, + serialized_end=6349, ) _MODEL_TRAININGRUN = _descriptor.Descriptor( @@ -1872,8 +2224,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=3568, - serialized_end=5383, + serialized_start=4386, + serialized_end=6349, ) _MODEL_LABELSENTRY = _descriptor.Descriptor( @@ -1928,8 +2280,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=5385, - serialized_end=5430, + serialized_start=6351, + serialized_end=6396, ) _MODEL = _descriptor.Descriptor( @@ -2176,6 +2528,7 @@ ], extensions=[], nested_types=[ + _MODEL_KMEANSENUMS, _MODEL_REGRESSIONMETRICS, _MODEL_AGGREGATECLASSIFICATIONMETRICS, _MODEL_BINARYCLASSIFICATIONMETRICS, @@ -2199,7 +2552,7 @@ extension_ranges=[], oneofs=[], serialized_start=327, - serialized_end=6027, + serialized_end=6993, ) @@ -2273,8 +2626,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=6029, - serialized_end=6104, + serialized_start=6995, + serialized_end=7070, ) @@ -2366,8 +2719,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=6106, - serialized_end=6231, + serialized_start=7072, + serialized_end=7197, ) @@ -2441,8 +2794,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=6233, - serialized_end=6311, + serialized_start=7199, + serialized_end=7277, ) @@ -2534,8 +2887,8 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=6314, - serialized_end=6444, + serialized_start=7280, + serialized_end=7410, ) @@ -2591,10 +2944,12 @@ syntax="proto3", extension_ranges=[], oneofs=[], - serialized_start=6446, - serialized_end=6540, + serialized_start=7412, + serialized_end=7506, ) +_MODEL_KMEANSENUMS.containing_type = _MODEL +_MODEL_KMEANSENUMS_KMEANSINITIALIZATIONMETHOD.containing_type = _MODEL_KMEANSENUMS _MODEL_REGRESSIONMETRICS.fields_by_name[ "mean_absolute_error" ].message_type = google_dot_protobuf_dot_wrappers__pb2._DOUBLEVALUE @@ -2698,12 +3053,61 @@ "confusion_matrix_list" ].message_type = _MODEL_MULTICLASSCLASSIFICATIONMETRICS_CONFUSIONMATRIX _MODEL_MULTICLASSCLASSIFICATIONMETRICS.containing_type = _MODEL +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE_CATEGORYCOUNT.fields_by_name[ + "count" +].message_type = google_dot_protobuf_dot_wrappers__pb2._INT64VALUE +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE_CATEGORYCOUNT.containing_type = ( + _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE +) +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE.fields_by_name[ + "category_counts" +].message_type = ( + _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE_CATEGORYCOUNT +) +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE.containing_type = ( + _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE +) +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.fields_by_name[ + "numerical_value" +].message_type = google_dot_protobuf_dot_wrappers__pb2._DOUBLEVALUE +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.fields_by_name[ + "categorical_value" +].message_type = _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.containing_type = ( + _MODEL_CLUSTERINGMETRICS_CLUSTER +) +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.oneofs_by_name["value"].fields.append( + _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.fields_by_name["numerical_value"] +) +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.fields_by_name[ + "numerical_value" +].containing_oneof = _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.oneofs_by_name[ + "value" +] +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.oneofs_by_name["value"].fields.append( + _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.fields_by_name["categorical_value"] +) +_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.fields_by_name[ + "categorical_value" +].containing_oneof = _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE.oneofs_by_name[ + "value" +] +_MODEL_CLUSTERINGMETRICS_CLUSTER.fields_by_name[ + "feature_values" +].message_type = _MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE +_MODEL_CLUSTERINGMETRICS_CLUSTER.fields_by_name[ + "count" +].message_type = google_dot_protobuf_dot_wrappers__pb2._INT64VALUE +_MODEL_CLUSTERINGMETRICS_CLUSTER.containing_type = _MODEL_CLUSTERINGMETRICS _MODEL_CLUSTERINGMETRICS.fields_by_name[ "davies_bouldin_index" ].message_type = google_dot_protobuf_dot_wrappers__pb2._DOUBLEVALUE _MODEL_CLUSTERINGMETRICS.fields_by_name[ "mean_squared_distance" ].message_type = google_dot_protobuf_dot_wrappers__pb2._DOUBLEVALUE +_MODEL_CLUSTERINGMETRICS.fields_by_name[ + "clusters" +].message_type = _MODEL_CLUSTERINGMETRICS_CLUSTER _MODEL_CLUSTERINGMETRICS.containing_type = _MODEL _MODEL_EVALUATIONMETRICS.fields_by_name[ "regression_metrics" @@ -2778,6 +3182,9 @@ _MODEL_TRAININGRUN_TRAININGOPTIONS.fields_by_name[ "optimization_strategy" ].enum_type = _MODEL_OPTIMIZATIONSTRATEGY +_MODEL_TRAININGRUN_TRAININGOPTIONS.fields_by_name[ + "kmeans_initialization_method" +].enum_type = _MODEL_KMEANSENUMS_KMEANSINITIALIZATIONMETHOD _MODEL_TRAININGRUN_TRAININGOPTIONS.containing_type = _MODEL_TRAININGRUN _MODEL_TRAININGRUN_ITERATIONRESULT_CLUSTERINFO.fields_by_name[ "cluster_radius" @@ -2859,13 +3266,23 @@ "Model", (_message.Message,), dict( + KmeansEnums=_reflection.GeneratedProtocolMessageType( + "KmeansEnums", + (_message.Message,), + dict( + DESCRIPTOR=_MODEL_KMEANSENUMS, + __module__="google.cloud.bigquery_v2.proto.model_pb2" + # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.KmeansEnums) + ), + ), RegressionMetrics=_reflection.GeneratedProtocolMessageType( "RegressionMetrics", (_message.Message,), dict( DESCRIPTOR=_MODEL_REGRESSIONMETRICS, __module__="google.cloud.bigquery_v2.proto.model_pb2", - __doc__="""Evaluation metrics for regression models. + __doc__="""Evaluation metrics for regression and explicit feedback type matrix + factorization models. Attributes: @@ -3063,6 +3480,86 @@ "ClusteringMetrics", (_message.Message,), dict( + Cluster=_reflection.GeneratedProtocolMessageType( + "Cluster", + (_message.Message,), + dict( + FeatureValue=_reflection.GeneratedProtocolMessageType( + "FeatureValue", + (_message.Message,), + dict( + CategoricalValue=_reflection.GeneratedProtocolMessageType( + "CategoricalValue", + (_message.Message,), + dict( + CategoryCount=_reflection.GeneratedProtocolMessageType( + "CategoryCount", + (_message.Message,), + dict( + DESCRIPTOR=_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE_CATEGORYCOUNT, + __module__="google.cloud.bigquery_v2.proto.model_pb2", + __doc__="""Represents the count of a single category within the cluster. + + + Attributes: + category: + The name of category. + count: + The count of training samples matching the category within the + cluster. + """, + # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount) + ), + ), + DESCRIPTOR=_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE_CATEGORICALVALUE, + __module__="google.cloud.bigquery_v2.proto.model_pb2", + __doc__="""Representative value of a categorical feature. + + + Attributes: + category_counts: + Counts of all categories for the categorical feature. If there + are more than ten categories, we return top ten (by count) and + return one more CategoryCount with category ‘\ *OTHER*\ ’ and + count as aggregate counts of remaining categories. + """, + # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue) + ), + ), + DESCRIPTOR=_MODEL_CLUSTERINGMETRICS_CLUSTER_FEATUREVALUE, + __module__="google.cloud.bigquery_v2.proto.model_pb2", + __doc__="""Representative value of a single feature within the cluster. + + + Attributes: + feature_column: + The feature column name. + numerical_value: + The numerical feature value. This is the centroid value for + this feature. + categorical_value: + The categorical feature value. + """, + # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue) + ), + ), + DESCRIPTOR=_MODEL_CLUSTERINGMETRICS_CLUSTER, + __module__="google.cloud.bigquery_v2.proto.model_pb2", + __doc__="""Message containing the information about one cluster. + + + Attributes: + centroid_id: + Centroid id. + feature_values: + Values of highly variant features for this cluster. + count: + Count of training data rows that were assigned to this + cluster. + """, + # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster) + ), + ), DESCRIPTOR=_MODEL_CLUSTERINGMETRICS, __module__="google.cloud.bigquery_v2.proto.model_pb2", __doc__="""Evaluation metrics for clustering models. @@ -3074,6 +3571,8 @@ mean_squared_distance: Mean of squared distances between each sample to its cluster centroid. + clusters: + [Beta] Information for all clusters. """, # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.ClusteringMetrics) ), @@ -3091,13 +3590,14 @@ Attributes: regression_metrics: - Populated for regression models. + Populated for regression models and explicit feedback type + matrix factorization models. binary_classification_metrics: Populated for binary classification/classifier models. multi_class_classification_metrics: Populated for multi-class classification/classifier models. clustering_metrics: - [Beta] Populated for clustering models. + Populated for clustering models. """, # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.EvaluationMetrics) ), @@ -3177,14 +3677,20 @@ Weights associated with each label class, for rebalancing the training data. Only applicable for classification models. distance_type: - [Beta] Distance type for clustering models. + Distance type for clustering models. num_clusters: - [Beta] Number of clusters for clustering models. + Number of clusters for clustering models. model_uri: [Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models. optimization_strategy: Optimization strategy for training linear regression models. + kmeans_initialization_method: + The method used to initialize the centroids for kmeans + algorithm. + kmeans_initialization_column: + The column used to provide the initial centroids for kmeans + algorithm when kmeans\_initialization\_method is CUSTOM. """, # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions) ), @@ -3232,7 +3738,7 @@ learn_rate: Learn rate used for this iteration. cluster_infos: - [Beta] Information about top clusters for clustering models. + Information about top clusters for clustering models. """, # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.Model.TrainingRun.IterationResult) ), @@ -3321,6 +3827,7 @@ ), ) _sym_db.RegisterMessage(Model) +_sym_db.RegisterMessage(Model.KmeansEnums) _sym_db.RegisterMessage(Model.RegressionMetrics) _sym_db.RegisterMessage(Model.AggregateClassificationMetrics) _sym_db.RegisterMessage(Model.BinaryClassificationMetrics) @@ -3330,6 +3837,12 @@ _sym_db.RegisterMessage(Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry) _sym_db.RegisterMessage(Model.MultiClassClassificationMetrics.ConfusionMatrix.Row) _sym_db.RegisterMessage(Model.ClusteringMetrics) +_sym_db.RegisterMessage(Model.ClusteringMetrics.Cluster) +_sym_db.RegisterMessage(Model.ClusteringMetrics.Cluster.FeatureValue) +_sym_db.RegisterMessage(Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue) +_sym_db.RegisterMessage( + Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount +) _sym_db.RegisterMessage(Model.EvaluationMetrics) _sym_db.RegisterMessage(Model.TrainingRun) _sym_db.RegisterMessage(Model.TrainingRun.TrainingOptions) @@ -3375,8 +3888,9 @@ model_id: Model ID of the model to patch. model: - Patched model. Follows patch semantics. Missing fields are not - updated. To clear a field, explicitly set to default value. + Patched model. Follows RFC5789 patch semantics. Missing fields + are not updated. To clear a field, explicitly set to default + value. """, # @@protoc_insertion_point(class_scope:google.cloud.bigquery.v2.PatchModelRequest) ), @@ -3418,7 +3932,9 @@ dataset_id: Dataset ID of the models to list. max_results: - The maximum number of results per page. + The maximum number of results to return in a single response + page. Leverage the page tokens to iterate through the entire + collection. page_token: Page token, returned by a previous call to request the next page of results @@ -3460,10 +3976,10 @@ file=DESCRIPTOR, index=0, serialized_options=_b( - "\312A\027bigquery.googleapis.com\322A\220\001https://www.googleapis.com/auth/bigquery,https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/cloud-platform.read-only" + "\312A\027bigquery.googleapis.com\322A\302\001https://www.googleapis.com/auth/bigquery,https://www.googleapis.com/auth/bigquery.readonly,https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/cloud-platform.read-only" ), - serialized_start=6543, - serialized_end=7112, + serialized_start=7509, + serialized_end=8128, methods=[ _descriptor.MethodDescriptor( name="GetModel", diff --git a/bigquery/synth.metadata b/bigquery/synth.metadata index 44837073d6d6..b5cce0083f8e 100644 --- a/bigquery/synth.metadata +++ b/bigquery/synth.metadata @@ -1,19 +1,19 @@ { - "updateTime": "2019-06-15T12:11:48.348952Z", + "updateTime": "2019-08-06T12:13:25.893023Z", "sources": [ { "generator": { "name": "artman", - "version": "0.26.0", - "dockerImage": "googleapis/artman@sha256:6db0735b0d3beec5b887153a2a7c7411fc7bb53f73f6f389a822096bd14a3a15" + "version": "0.32.1", + "dockerImage": "googleapis/artman@sha256:a684d40ba9a4e15946f5f2ca6b4bd9fe301192f522e9de4fff622118775f309b" } }, { "git": { "name": "googleapis", "remote": "https://github.com/googleapis/googleapis.git", - "sha": "7b58b37559f6a5337c4c564518e9573d742df225", - "internalRef": "253322136" + "sha": "e699b0cba64ffddfae39633417180f1f65875896", + "internalRef": "261759677" } } ],