Types for BigQuery API Client#
-
class
google.cloud.bigquery_v2.types.
DeleteModelRequest
# Protocol buffer.
-
project_id
# Project ID of the model to delete.
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dataset_id
# Dataset ID of the model to delete.
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model_id
# Model ID of the model to delete.
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dataset_id
Field google.cloud.bigquery.v2.DeleteModelRequest.dataset_id
-
model_id
Field google.cloud.bigquery.v2.DeleteModelRequest.model_id
-
project_id
Field google.cloud.bigquery.v2.DeleteModelRequest.project_id
-
-
class
google.cloud.bigquery_v2.types.
Empty
#
-
class
google.cloud.bigquery_v2.types.
GetModelRequest
# Protocol buffer.
-
project_id
# Project ID of the requested model.
-
dataset_id
# Dataset ID of the requested model.
-
model_id
# Model ID of the requested model.
-
dataset_id
Field google.cloud.bigquery.v2.GetModelRequest.dataset_id
-
model_id
Field google.cloud.bigquery.v2.GetModelRequest.model_id
-
project_id
Field google.cloud.bigquery.v2.GetModelRequest.project_id
-
-
class
google.cloud.bigquery_v2.types.
ListModelsRequest
# Protocol buffer.
-
project_id
# Project ID of the models to list.
-
dataset_id
# Dataset ID of the models to list.
-
max_results
# 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
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dataset_id
Field google.cloud.bigquery.v2.ListModelsRequest.dataset_id
-
max_results
Field google.cloud.bigquery.v2.ListModelsRequest.max_results
-
page_token
Field google.cloud.bigquery.v2.ListModelsRequest.page_token
-
project_id
Field google.cloud.bigquery.v2.ListModelsRequest.project_id
-
-
class
google.cloud.bigquery_v2.types.
ListModelsResponse
# Protocol buffer.
-
models
# Models in the requested dataset. Only the following fields are populated: model_reference, model_type, creation_time, last_modified_time and labels.
-
next_page_token
# A token to request the next page of results.
-
models
Field google.cloud.bigquery.v2.ListModelsResponse.models
-
next_page_token
Field google.cloud.bigquery.v2.ListModelsResponse.next_page_token
-
-
class
google.cloud.bigquery_v2.types.
Model
# Protocol buffer.
-
etag
# Output only. A hash of this resource.
-
model_reference
# Required. Unique identifier for this model.
-
creation_time
# Output only. The time when this model was created, in millisecs since the epoch.
-
last_modified_time
# Output only. The time when this model was last modified, in millisecs since the epoch.
-
description
# [Optional] A user-friendly description of this model.
-
friendly_name
# [Optional] A descriptive name for this model.
-
labels
# [Optional] The labels associated with this model. You can use these to organize and group your models. Label keys and values can be no longer than 63 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter and each label in the list must have a different key.
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expiration_time
# [Optional] The time when this model expires, in milliseconds since the epoch. If not present, the model will persist indefinitely. Expired models will be deleted and their storage reclaimed. The defaultTableExpirationMs property of the encapsulating dataset can be used to set a default expirationTime on newly created models.
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location
# Output only. The geographic location where the model resides. This value is inherited from the dataset.
-
model_type
# Output only. Type of the model resource.
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training_runs
# Output only. Information for all training runs in increasing order of start_time.
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feature_columns
# Output only. Input feature columns that were used to train this model.
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label_columns
# Output only. Label columns that were used to train this model. The output of the model will have a “predicted_” prefix to these columns.
-
class
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.
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precision
# Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro- averaged metric treating each class as a binary classifier.
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recall
# Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro- averaged metric.
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accuracy
# Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
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threshold
# Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
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f1_score
# The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
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log_loss
# Logarithmic Loss. For multiclass this is a macro-averaged metric.
-
roc_auc
# Area Under a ROC Curve. For multiclass this is a macro- averaged metric.
-
accuracy
Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.accuracy
-
f1_score
Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.f1_score
-
log_loss
Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.log_loss
-
precision
Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.precision
-
recall
Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.recall
-
roc_auc
Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.roc_auc
-
threshold
Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.threshold
-
-
class
BinaryClassificationMetrics
# Evaluation metrics for binary classification/classifier models.
-
aggregate_classification_metrics
# Aggregate classification metrics.
-
binary_confusion_matrix_list
# Binary confusion matrix at multiple thresholds.
-
positive_label
# Label representing the positive class.
-
negative_label
# Label representing the negative class.
-
class
BinaryConfusionMatrix
# Confusion matrix for binary classification models.
-
positive_class_threshold
# Threshold value used when computing each of the following metric.
-
true_positives
# Number of true samples predicted as true.
-
false_positives
# Number of false samples predicted as true.
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true_negatives
# Number of true samples predicted as false.
-
false_negatives
# Number of false samples predicted as false.
-
precision
# The fraction of actual positive predictions that had positive actual labels.
-
recall
# The fraction of actual positive labels that were given a positive prediction.
-
f1_score
# The equally weighted average of recall and precision.
-
accuracy
# The fraction of predictions given the correct label.
-
accuracy
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.accuracy
-
f1_score
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.f1_score
-
false_negatives
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.false_negatives
-
false_positives
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.false_positives
-
positive_class_threshold
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.positive_class_threshold
-
precision
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.precision
-
recall
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.recall
-
true_negatives
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.true_negatives
-
true_positives
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.true_positives
-
-
aggregate_classification_metrics
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.aggregate_classification_metrics
-
binary_confusion_matrix_list
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.binary_confusion_matrix_list
-
negative_label
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.negative_label
-
positive_label
Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.positive_label
-
-
class
ClusteringMetrics
# Evaluation metrics for clustering models.
-
davies_bouldin_index
# Davies-Bouldin index.
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mean_squared_distance
# Mean of squared distances between each sample to its cluster centroid.
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clusters
# [Beta] Information for all clusters.
-
class
Cluster
# Message containing the information about one cluster.
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centroid_id
# Centroid id.
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feature_values
# Values of highly variant features for this cluster.
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count
# Count of training data rows that were assigned to this cluster.
-
class
FeatureValue
# Representative value of a single feature within the cluster.
-
feature_column
# The feature column name.
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numerical_value
# The numerical feature value. This is the centroid value for this feature.
-
categorical_value
# The categorical feature value.
-
class
CategoricalValue
# Representative value of a categorical feature.
-
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.
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class
CategoryCount
# Represents the count of a single category within the cluster.
-
category
# The name of category.
-
count
# The count of training samples matching the category within the cluster.
-
category
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount.category
-
count
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount.count
-
-
category_counts
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.category_counts
-
-
categorical_value
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.categorical_value
-
feature_column
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.feature_column
-
numerical_value
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.numerical_value
-
-
centroid_id
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.centroid_id
-
count
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.count
-
feature_values
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.feature_values
-
-
clusters
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.clusters
-
davies_bouldin_index
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.davies_bouldin_index
-
mean_squared_distance
Field google.cloud.bigquery.v2.Model.ClusteringMetrics.mean_squared_distance
-
-
class
EvaluationMetrics
# Evaluation metrics of a model. These are either computed on all training data or just the eval data based on whether eval data was used during training. These are not present for imported models.
-
regression_metrics
# 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
# Populated for clustering models.
-
binary_classification_metrics
Field google.cloud.bigquery.v2.Model.EvaluationMetrics.binary_classification_metrics
-
clustering_metrics
Field google.cloud.bigquery.v2.Model.EvaluationMetrics.clustering_metrics
-
multi_class_classification_metrics
Field google.cloud.bigquery.v2.Model.EvaluationMetrics.multi_class_classification_metrics
-
regression_metrics
Field google.cloud.bigquery.v2.Model.EvaluationMetrics.regression_metrics
-
-
class
KmeansEnums
#
-
class
LabelsEntry
# -
key
# Field google.cloud.bigquery.v2.Model.LabelsEntry.key
-
value
# Field google.cloud.bigquery.v2.Model.LabelsEntry.value
-
-
class
MultiClassClassificationMetrics
# Evaluation metrics for multi-class classification/classifier models.
-
aggregate_classification_metrics
# Aggregate classification metrics.
-
confusion_matrix_list
# Confusion matrix at different thresholds.
-
class
ConfusionMatrix
# Confusion matrix for multi-class classification models.
-
confidence_threshold
# Confidence threshold used when computing the entries of the confusion matrix.
-
rows
# One row per actual label.
-
class
Entry
# A single entry in the confusion matrix.
-
predicted_label
# The predicted label. For confidence_threshold > 0, we will also add an entry indicating the number of items under the confidence threshold.
-
item_count
# Number of items being predicted as this label.
-
item_count
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry.item_count
-
predicted_label
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry.predicted_label
-
-
class
Row
# A single row in the confusion matrix.
-
actual_label
# The original label of this row.
-
entries
# Info describing predicted label distribution.
-
actual_label
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Row.actual_label
-
entries
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Row.entries
-
-
confidence_threshold
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.confidence_threshold
-
rows
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.rows
-
-
aggregate_classification_metrics
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.aggregate_classification_metrics
-
confusion_matrix_list
Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.confusion_matrix_list
-
-
class
RegressionMetrics
# Evaluation metrics for regression and explicit feedback type matrix factorization models.
-
mean_absolute_error
# Mean absolute error.
-
mean_squared_error
# Mean squared error.
-
mean_squared_log_error
# Mean squared log error.
-
median_absolute_error
# Median absolute error.
-
r_squared
# R^2 score.
-
mean_absolute_error
Field google.cloud.bigquery.v2.Model.RegressionMetrics.mean_absolute_error
-
mean_squared_error
Field google.cloud.bigquery.v2.Model.RegressionMetrics.mean_squared_error
-
mean_squared_log_error
Field google.cloud.bigquery.v2.Model.RegressionMetrics.mean_squared_log_error
-
median_absolute_error
Field google.cloud.bigquery.v2.Model.RegressionMetrics.median_absolute_error
-
r_squared
Field google.cloud.bigquery.v2.Model.RegressionMetrics.r_squared
-
-
class
TrainingRun
# Information about a single training query run for the model.
-
training_options
# Options that were used for this training run, includes user specified and default options that were used.
-
start_time
# The start time of this training run.
-
results
# Output of each iteration run, results.size() <= max_iterations.
-
evaluation_metrics
# The evaluation metrics over training/eval data that were computed at the end of training.
-
class
IterationResult
# Information about a single iteration of the training run.
-
index
# Index of the iteration, 0 based.
-
duration_ms
# Time taken to run the iteration in milliseconds.
-
training_loss
# Loss computed on the training data at the end of iteration.
-
eval_loss
# Loss computed on the eval data at the end of iteration.
-
learn_rate
# Learn rate used for this iteration.
-
cluster_infos
# Information about top clusters for clustering models.
-
class
ClusterInfo
# Information about a single cluster for clustering model.
-
centroid_id
# Centroid id.
-
cluster_radius
# Cluster radius, the average distance from centroid to each point assigned to the cluster.
-
cluster_size
# Cluster size, the total number of points assigned to the cluster.
-
centroid_id
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.ClusterInfo.centroid_id
-
cluster_radius
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.ClusterInfo.cluster_radius
-
cluster_size
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.ClusterInfo.cluster_size
-
-
cluster_infos
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.cluster_infos
-
duration_ms
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.duration_ms
-
eval_loss
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.eval_loss
-
index
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.index
-
learn_rate
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.learn_rate
-
training_loss
Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.training_loss
-
-
class
TrainingOptions
# Protocol buffer.
-
max_iterations
# The maximum number of iterations in training. Used only for iterative training algorithms.
-
loss_type
# Type of loss function used during training run.
-
learn_rate
# Learning rate in training. Used only for iterative training algorithms.
-
l1_regularization
# L1 regularization coefficient.
-
l2_regularization
# L2 regularization coefficient.
-
min_relative_progress
# When early_stop is true, stops training when accuracy improvement is less than ‘min_relative_progress’. Used only for iterative training algorithms.
-
warm_start
# Whether to train a model from the last checkpoint.
-
early_stop
# Whether to stop early when the loss doesn’t improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
-
input_label_columns
# Name of input label columns in training data.
-
data_split_method
# The data split type for training and evaluation, e.g. RANDOM.
-
data_split_eval_fraction
# 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.
-
data_split_column
# 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
-
learn_rate_strategy
# The strategy to determine learn rate for the current iteration.
-
initial_learn_rate
# Specifies the initial learning rate for the line search learn rate strategy.
-
label_class_weights
# Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.
-
distance_type
# Distance type for clustering models.
-
num_clusters
# 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.
-
class
LabelClassWeightsEntry
# -
key
# Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.LabelClassWeightsEntry.key
-
value
# Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.LabelClassWeightsEntry.value
-
-
data_split_column
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.data_split_column
-
data_split_eval_fraction
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.data_split_eval_fraction
-
data_split_method
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.data_split_method
-
distance_type
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.distance_type
-
early_stop
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.early_stop
-
initial_learn_rate
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.initial_learn_rate
-
input_label_columns
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.input_label_columns
-
kmeans_initialization_column
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.kmeans_initialization_column
-
kmeans_initialization_method
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.kmeans_initialization_method
-
l1_regularization
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.l1_regularization
-
l2_regularization
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.l2_regularization
-
label_class_weights
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.label_class_weights
-
learn_rate
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.learn_rate
-
learn_rate_strategy
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.learn_rate_strategy
-
loss_type
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.loss_type
-
max_iterations
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.max_iterations
-
min_relative_progress
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.min_relative_progress
-
model_uri
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.model_uri
-
num_clusters
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.num_clusters
-
optimization_strategy
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.optimization_strategy
-
warm_start
Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.warm_start
-
-
evaluation_metrics
Field google.cloud.bigquery.v2.Model.TrainingRun.evaluation_metrics
-
results
Field google.cloud.bigquery.v2.Model.TrainingRun.results
-
start_time
Field google.cloud.bigquery.v2.Model.TrainingRun.start_time
-
training_options
Field google.cloud.bigquery.v2.Model.TrainingRun.training_options
-
-
creation_time
Field google.cloud.bigquery.v2.Model.creation_time
-
description
Field google.cloud.bigquery.v2.Model.description
-
etag
Field google.cloud.bigquery.v2.Model.etag
-
expiration_time
Field google.cloud.bigquery.v2.Model.expiration_time
-
feature_columns
Field google.cloud.bigquery.v2.Model.feature_columns
-
friendly_name
Field google.cloud.bigquery.v2.Model.friendly_name
-
label_columns
Field google.cloud.bigquery.v2.Model.label_columns
-
labels
Field google.cloud.bigquery.v2.Model.labels
-
last_modified_time
Field google.cloud.bigquery.v2.Model.last_modified_time
-
location
Field google.cloud.bigquery.v2.Model.location
-
model_reference
Field google.cloud.bigquery.v2.Model.model_reference
-
model_type
Field google.cloud.bigquery.v2.Model.model_type
-
training_runs
Field google.cloud.bigquery.v2.Model.training_runs
-
-
class
google.cloud.bigquery_v2.types.
ModelReference
# Id path of a model.
-
project_id
# [Required] The ID of the project containing this model.
-
dataset_id
# [Required] The ID of the dataset containing this model.
-
model_id
# [Required] The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
-
dataset_id
Field google.cloud.bigquery.v2.ModelReference.dataset_id
-
model_id
Field google.cloud.bigquery.v2.ModelReference.model_id
-
project_id
Field google.cloud.bigquery.v2.ModelReference.project_id
-
-
class
google.cloud.bigquery_v2.types.
PatchModelRequest
# Protocol buffer.
-
project_id
# Project ID of the model to patch.
-
dataset_id
# Dataset ID of the model to patch.
-
model_id
# Model ID of the model to patch.
-
model
# Patched model. Follows RFC5789 patch semantics. Missing fields are not updated. To clear a field, explicitly set to default value.
-
dataset_id
Field google.cloud.bigquery.v2.PatchModelRequest.dataset_id
-
model
Field google.cloud.bigquery.v2.PatchModelRequest.model
-
model_id
Field google.cloud.bigquery.v2.PatchModelRequest.model_id
-
project_id
Field google.cloud.bigquery.v2.PatchModelRequest.project_id
-
-
class
google.cloud.bigquery_v2.types.
StandardSqlDataType
# The type of a variable, e.g., a function argument. Examples: INT64: {type_kind=”INT64”} ARRAY: {type_kind=”ARRAY”, array_element_type=”STRING”} STRUCT<x STRING, y ARRAY>: {type_kind=”STRUCT”, struct_type={fields=[ {name=”x”, type={type_kind=”STRING”}}, {name=”y”, type={type_kind=”ARRAY”, array_element_type=”DATE”}} ]}}
-
type_kind
# Required. The top level type of this field. Can be any standard SQL data type (e.g., “INT64”, “DATE”, “ARRAY”).
-
array_element_type
# The type of the array’s elements, if type_kind = “ARRAY”.
-
struct_type
# The fields of this struct, in order, if type_kind = “STRUCT”.
-
array_element_type
Field google.cloud.bigquery.v2.StandardSqlDataType.array_element_type
-
struct_type
Field google.cloud.bigquery.v2.StandardSqlDataType.struct_type
-
type_kind
Field google.cloud.bigquery.v2.StandardSqlDataType.type_kind
-
-
class
google.cloud.bigquery_v2.types.
StandardSqlField
# A field or a column.
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name
# Optional. The name of this field. Can be absent for struct fields.
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type
# Optional. The type of this parameter. Absent if not explicitly specified (e.g., CREATE FUNCTION statement can omit the return type; in this case the output parameter does not have this “type” field).
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name
Field google.cloud.bigquery.v2.StandardSqlField.name
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type
Field google.cloud.bigquery.v2.StandardSqlField.type
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class
google.cloud.bigquery_v2.types.
StandardSqlStructType
# -
fields
# Field google.cloud.bigquery.v2.StandardSqlStructType.fields
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