delete(projectId, datasetId, modelId)
Deletes the model specified by modelId from the dataset.
get(projectId, datasetId, modelId)
Gets the specified model resource by model ID.
list(projectId, datasetId, pageToken=None, maxResults=None)
Lists all models in the specified dataset. Requires the READER dataset
list_next(previous_request, previous_response)
Retrieves the next page of results.
patch(projectId, datasetId, modelId, body)
Patch specific fields in the specified model.
delete(projectId, datasetId, modelId)
Deletes the model specified by modelId from the dataset. Args: projectId: string, Project ID of the model to delete. (required) datasetId: string, Dataset ID of the model to delete. (required) modelId: string, Model ID of the model to delete. (required)
get(projectId, datasetId, modelId)
Gets the specified model resource by model ID.
Args:
projectId: string, Project ID of the requested model. (required)
datasetId: string, Dataset ID of the requested model. (required)
modelId: string, Model ID of the requested model. (required)
Returns:
An object of the form:
{
"labelColumns": [ # Output only. Label columns that were used to train this model.
# The output of the model will have a "predicted_" prefix to these columns.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"description": "A String", # [Optional] A user-friendly description of this model.
"trainingRuns": [ # Output only. Information for all training runs in increasing order of
# start_time.
{ # Information about a single training query run for the model.
"evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
# end of training.
# data or just the eval data based on whether eval data was used during
# training. These are not present for imported models.
"clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
},
"regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
"meanSquaredLogError": 3.14, # Mean squared log error.
"meanAbsoluteError": 3.14, # Mean absolute error.
"meanSquaredError": 3.14, # Mean squared error.
"medianAbsoluteError": 3.14, # Median absolute error.
"rSquared": 3.14, # R^2 score.
},
"binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
"negativeLabel": "A String", # Label representing the negative class.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"positiveLabel": "A String", # Label representing the positive class.
"binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
{ # Confusion matrix for binary classification models.
"truePositives": "A String", # Number of true samples predicted as true.
"recall": 3.14, # Aggregate recall.
"precision": 3.14, # Aggregate precision.
"falseNegatives": "A String", # Number of false samples predicted as false.
"trueNegatives": "A String", # Number of true samples predicted as false.
"falsePositives": "A String", # Number of false samples predicted as true.
"positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
},
],
},
"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 # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"confusionMatrixList": [ # Confusion matrix at different thresholds.
{ # Confusion matrix for multi-class classification models.
"confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
# confusion matrix.
"rows": [ # One row per actual label.
{ # A single row in the confusion matrix.
"entries": [ # Info describing predicted label distribution.
{ # A single entry in the confusion matrix.
"predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
# also add an entry indicating the number of items under the
# confidence threshold.
"itemCount": "A String", # Number of items being predicted as this label.
},
],
"actualLabel": "A String", # The original label of this row.
},
],
},
],
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
{ # Information about a single iteration of the training run.
"index": 42, # Index of the iteration, 0 based.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"learnRate": 3.14, # Learn rate used for this iteration.
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
"clusterInfos": [ # [Beta] Information about top clusters for clustering models.
{ # Information about a single cluster for clustering model.
"centroidId": "A String", # Centroid id.
"clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
"clusterRadius": 3.14, # Cluster radius, the average distance from centroid
# to each point assigned to the cluster.
},
],
},
],
"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.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"inputLabelColumns": [ # Name of input label columns in training data.
"A String",
],
"maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
# training algorithms.
"earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
# any more (compared to min_relative_progress). Used only for iterative
# training algorithms.
"initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
# strategy.
"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
"numClusters": "A String", # [Beta] Number of clusters for clustering models.
"l1Regularization": 3.14, # L1 regularization coefficient.
"dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
"distanceType": "A String", # [Beta] Distance type for clustering models.
"warmStart": True or False, # Whether to train a model from the last checkpoint.
"labelClassWeights": { # Weights associated with each label class, for rebalancing the
# training data. Only applicable for classification models.
"a_key": 3.14,
},
"lossType": "A String", # Type of loss function used during training run.
"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.
"l2Regularization": 3.14, # L2 regularization coefficient.
"modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
# applicable for imported models.
"learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
"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.
"learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
},
},
],
"featureColumns": [ # Output only. Input feature columns that were used to train this model.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"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.
"a_key": "A String",
},
"creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
# epoch.
"modelType": "A String", # Output only. Type of the model resource.
"modelReference": { # Id path of a model. # Required. Unique identifier for this model.
"projectId": "A String", # [Required] The ID of the project containing this model.
"datasetId": "A String", # [Required] The ID of the dataset containing this model.
"modelId": "A String", # [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.
},
"etag": "A String", # Output only. A hash of this resource.
"location": "A String", # Output only. The geographic location where the model resides. This value
# is inherited from the dataset.
"friendlyName": "A String", # [Optional] A descriptive name for this model.
"expirationTime": "A String", # [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.
"lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
# since the epoch.
}
list(projectId, datasetId, pageToken=None, maxResults=None)
Lists all models in the specified dataset. Requires the READER dataset
role.
Args:
projectId: string, Project ID of the models to list. (required)
datasetId: string, Dataset ID of the models to list. (required)
pageToken: string, Page token, returned by a previous call to request the next page of
results
maxResults: integer, The maximum number of results per page.
Returns:
An object of the form:
{
"models": [ # Models in the requested dataset. Only the following fields are populated:
# model_reference, model_type, creation_time, last_modified_time and
# labels.
{
"labelColumns": [ # Output only. Label columns that were used to train this model.
# The output of the model will have a "predicted_" prefix to these columns.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"description": "A String", # [Optional] A user-friendly description of this model.
"trainingRuns": [ # Output only. Information for all training runs in increasing order of
# start_time.
{ # Information about a single training query run for the model.
"evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
# end of training.
# data or just the eval data based on whether eval data was used during
# training. These are not present for imported models.
"clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
},
"regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
"meanSquaredLogError": 3.14, # Mean squared log error.
"meanAbsoluteError": 3.14, # Mean absolute error.
"meanSquaredError": 3.14, # Mean squared error.
"medianAbsoluteError": 3.14, # Median absolute error.
"rSquared": 3.14, # R^2 score.
},
"binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
"negativeLabel": "A String", # Label representing the negative class.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"positiveLabel": "A String", # Label representing the positive class.
"binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
{ # Confusion matrix for binary classification models.
"truePositives": "A String", # Number of true samples predicted as true.
"recall": 3.14, # Aggregate recall.
"precision": 3.14, # Aggregate precision.
"falseNegatives": "A String", # Number of false samples predicted as false.
"trueNegatives": "A String", # Number of true samples predicted as false.
"falsePositives": "A String", # Number of false samples predicted as true.
"positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
},
],
},
"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 # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"confusionMatrixList": [ # Confusion matrix at different thresholds.
{ # Confusion matrix for multi-class classification models.
"confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
# confusion matrix.
"rows": [ # One row per actual label.
{ # A single row in the confusion matrix.
"entries": [ # Info describing predicted label distribution.
{ # A single entry in the confusion matrix.
"predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
# also add an entry indicating the number of items under the
# confidence threshold.
"itemCount": "A String", # Number of items being predicted as this label.
},
],
"actualLabel": "A String", # The original label of this row.
},
],
},
],
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
{ # Information about a single iteration of the training run.
"index": 42, # Index of the iteration, 0 based.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"learnRate": 3.14, # Learn rate used for this iteration.
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
"clusterInfos": [ # [Beta] Information about top clusters for clustering models.
{ # Information about a single cluster for clustering model.
"centroidId": "A String", # Centroid id.
"clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
"clusterRadius": 3.14, # Cluster radius, the average distance from centroid
# to each point assigned to the cluster.
},
],
},
],
"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.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"inputLabelColumns": [ # Name of input label columns in training data.
"A String",
],
"maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
# training algorithms.
"earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
# any more (compared to min_relative_progress). Used only for iterative
# training algorithms.
"initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
# strategy.
"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
"numClusters": "A String", # [Beta] Number of clusters for clustering models.
"l1Regularization": 3.14, # L1 regularization coefficient.
"dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
"distanceType": "A String", # [Beta] Distance type for clustering models.
"warmStart": True or False, # Whether to train a model from the last checkpoint.
"labelClassWeights": { # Weights associated with each label class, for rebalancing the
# training data. Only applicable for classification models.
"a_key": 3.14,
},
"lossType": "A String", # Type of loss function used during training run.
"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.
"l2Regularization": 3.14, # L2 regularization coefficient.
"modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
# applicable for imported models.
"learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
"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.
"learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
},
},
],
"featureColumns": [ # Output only. Input feature columns that were used to train this model.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"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.
"a_key": "A String",
},
"creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
# epoch.
"modelType": "A String", # Output only. Type of the model resource.
"modelReference": { # Id path of a model. # Required. Unique identifier for this model.
"projectId": "A String", # [Required] The ID of the project containing this model.
"datasetId": "A String", # [Required] The ID of the dataset containing this model.
"modelId": "A String", # [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.
},
"etag": "A String", # Output only. A hash of this resource.
"location": "A String", # Output only. The geographic location where the model resides. This value
# is inherited from the dataset.
"friendlyName": "A String", # [Optional] A descriptive name for this model.
"expirationTime": "A String", # [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.
"lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
# since the epoch.
},
],
"nextPageToken": "A String", # A token to request the next page of results.
}
list_next(previous_request, previous_response)
Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.
patch(projectId, datasetId, modelId, body)
Patch specific fields in the specified model.
Args:
projectId: string, Project ID of the model to patch. (required)
datasetId: string, Dataset ID of the model to patch. (required)
modelId: string, Model ID of the model to patch. (required)
body: object, The request body. (required)
The object takes the form of:
{
"labelColumns": [ # Output only. Label columns that were used to train this model.
# The output of the model will have a "predicted_" prefix to these columns.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"description": "A String", # [Optional] A user-friendly description of this model.
"trainingRuns": [ # Output only. Information for all training runs in increasing order of
# start_time.
{ # Information about a single training query run for the model.
"evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
# end of training.
# data or just the eval data based on whether eval data was used during
# training. These are not present for imported models.
"clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
},
"regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
"meanSquaredLogError": 3.14, # Mean squared log error.
"meanAbsoluteError": 3.14, # Mean absolute error.
"meanSquaredError": 3.14, # Mean squared error.
"medianAbsoluteError": 3.14, # Median absolute error.
"rSquared": 3.14, # R^2 score.
},
"binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
"negativeLabel": "A String", # Label representing the negative class.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"positiveLabel": "A String", # Label representing the positive class.
"binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
{ # Confusion matrix for binary classification models.
"truePositives": "A String", # Number of true samples predicted as true.
"recall": 3.14, # Aggregate recall.
"precision": 3.14, # Aggregate precision.
"falseNegatives": "A String", # Number of false samples predicted as false.
"trueNegatives": "A String", # Number of true samples predicted as false.
"falsePositives": "A String", # Number of false samples predicted as true.
"positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
},
],
},
"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 # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"confusionMatrixList": [ # Confusion matrix at different thresholds.
{ # Confusion matrix for multi-class classification models.
"confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
# confusion matrix.
"rows": [ # One row per actual label.
{ # A single row in the confusion matrix.
"entries": [ # Info describing predicted label distribution.
{ # A single entry in the confusion matrix.
"predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
# also add an entry indicating the number of items under the
# confidence threshold.
"itemCount": "A String", # Number of items being predicted as this label.
},
],
"actualLabel": "A String", # The original label of this row.
},
],
},
],
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
{ # Information about a single iteration of the training run.
"index": 42, # Index of the iteration, 0 based.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"learnRate": 3.14, # Learn rate used for this iteration.
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
"clusterInfos": [ # [Beta] Information about top clusters for clustering models.
{ # Information about a single cluster for clustering model.
"centroidId": "A String", # Centroid id.
"clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
"clusterRadius": 3.14, # Cluster radius, the average distance from centroid
# to each point assigned to the cluster.
},
],
},
],
"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.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"inputLabelColumns": [ # Name of input label columns in training data.
"A String",
],
"maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
# training algorithms.
"earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
# any more (compared to min_relative_progress). Used only for iterative
# training algorithms.
"initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
# strategy.
"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
"numClusters": "A String", # [Beta] Number of clusters for clustering models.
"l1Regularization": 3.14, # L1 regularization coefficient.
"dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
"distanceType": "A String", # [Beta] Distance type for clustering models.
"warmStart": True or False, # Whether to train a model from the last checkpoint.
"labelClassWeights": { # Weights associated with each label class, for rebalancing the
# training data. Only applicable for classification models.
"a_key": 3.14,
},
"lossType": "A String", # Type of loss function used during training run.
"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.
"l2Regularization": 3.14, # L2 regularization coefficient.
"modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
# applicable for imported models.
"learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
"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.
"learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
},
},
],
"featureColumns": [ # Output only. Input feature columns that were used to train this model.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"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.
"a_key": "A String",
},
"creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
# epoch.
"modelType": "A String", # Output only. Type of the model resource.
"modelReference": { # Id path of a model. # Required. Unique identifier for this model.
"projectId": "A String", # [Required] The ID of the project containing this model.
"datasetId": "A String", # [Required] The ID of the dataset containing this model.
"modelId": "A String", # [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.
},
"etag": "A String", # Output only. A hash of this resource.
"location": "A String", # Output only. The geographic location where the model resides. This value
# is inherited from the dataset.
"friendlyName": "A String", # [Optional] A descriptive name for this model.
"expirationTime": "A String", # [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.
"lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
# since the epoch.
}
Returns:
An object of the form:
{
"labelColumns": [ # Output only. Label columns that were used to train this model.
# The output of the model will have a "predicted_" prefix to these columns.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"description": "A String", # [Optional] A user-friendly description of this model.
"trainingRuns": [ # Output only. Information for all training runs in increasing order of
# start_time.
{ # Information about a single training query run for the model.
"evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
# end of training.
# data or just the eval data based on whether eval data was used during
# training. These are not present for imported models.
"clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
"meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
"daviesBouldinIndex": 3.14, # Davies-Bouldin index.
},
"regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
"meanSquaredLogError": 3.14, # Mean squared log error.
"meanAbsoluteError": 3.14, # Mean absolute error.
"meanSquaredError": 3.14, # Mean squared error.
"medianAbsoluteError": 3.14, # Median absolute error.
"rSquared": 3.14, # R^2 score.
},
"binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
"negativeLabel": "A String", # Label representing the negative class.
"aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"positiveLabel": "A String", # Label representing the positive class.
"binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
{ # Confusion matrix for binary classification models.
"truePositives": "A String", # Number of true samples predicted as true.
"recall": 3.14, # Aggregate recall.
"precision": 3.14, # Aggregate precision.
"falseNegatives": "A String", # Number of false samples predicted as false.
"trueNegatives": "A String", # Number of true samples predicted as false.
"falsePositives": "A String", # Number of false samples predicted as true.
"positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
},
],
},
"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 # Aggregate classification metrics.
# 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.
"recall": 3.14, # Recall is the fraction of actual positive labels that were given a
# positive prediction. For multiclass this is a macro-averaged metric.
"precision": 3.14, # 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.
"logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
"threshold": 3.14, # 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.
"accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
# multiclass this is a micro-averaged metric.
"f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
# this is a macro-averaged metric.
"rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
# metric.
},
"confusionMatrixList": [ # Confusion matrix at different thresholds.
{ # Confusion matrix for multi-class classification models.
"confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
# confusion matrix.
"rows": [ # One row per actual label.
{ # A single row in the confusion matrix.
"entries": [ # Info describing predicted label distribution.
{ # A single entry in the confusion matrix.
"predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
# also add an entry indicating the number of items under the
# confidence threshold.
"itemCount": "A String", # Number of items being predicted as this label.
},
],
"actualLabel": "A String", # The original label of this row.
},
],
},
],
},
},
"results": [ # Output of each iteration run, results.size() <= max_iterations.
{ # Information about a single iteration of the training run.
"index": 42, # Index of the iteration, 0 based.
"evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
"durationMs": "A String", # Time taken to run the iteration in milliseconds.
"learnRate": 3.14, # Learn rate used for this iteration.
"trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
"clusterInfos": [ # [Beta] Information about top clusters for clustering models.
{ # Information about a single cluster for clustering model.
"centroidId": "A String", # Centroid id.
"clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
"clusterRadius": 3.14, # Cluster radius, the average distance from centroid
# to each point assigned to the cluster.
},
],
},
],
"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.
"optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
"inputLabelColumns": [ # Name of input label columns in training data.
"A String",
],
"maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
# training algorithms.
"earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
# any more (compared to min_relative_progress). Used only for iterative
# training algorithms.
"initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
# strategy.
"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
"numClusters": "A String", # [Beta] Number of clusters for clustering models.
"l1Regularization": 3.14, # L1 regularization coefficient.
"dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
"distanceType": "A String", # [Beta] Distance type for clustering models.
"warmStart": True or False, # Whether to train a model from the last checkpoint.
"labelClassWeights": { # Weights associated with each label class, for rebalancing the
# training data. Only applicable for classification models.
"a_key": 3.14,
},
"lossType": "A String", # Type of loss function used during training run.
"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.
"l2Regularization": 3.14, # L2 regularization coefficient.
"modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
# applicable for imported models.
"learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
"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.
"learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
},
},
],
"featureColumns": [ # Output only. Input feature columns that were used to train this model.
{ # A field or a column.
"type": { # The type of a variable, e.g., a function argument. # 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).
# Examples:
# INT64: {type_kind="INT64"}
# ARRAY: {type_kind="ARRAY", array_element_type="STRING"}
# STRUCT>:
# {type_kind="STRUCT",
# struct_type={fields=[
# {name="x", type={type_kind="STRING"}},
# {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
# ]}}
"structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
"fields": [
# Object with schema name: StandardSqlField
],
},
"arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
"typeKind": "A String", # Required. The top level type of this field.
# Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
},
"name": "A String", # Optional. The name of this field. Can be absent for struct fields.
},
],
"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.
"a_key": "A String",
},
"creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
# epoch.
"modelType": "A String", # Output only. Type of the model resource.
"modelReference": { # Id path of a model. # Required. Unique identifier for this model.
"projectId": "A String", # [Required] The ID of the project containing this model.
"datasetId": "A String", # [Required] The ID of the dataset containing this model.
"modelId": "A String", # [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.
},
"etag": "A String", # Output only. A hash of this resource.
"location": "A String", # Output only. The geographic location where the model resides. This value
# is inherited from the dataset.
"friendlyName": "A String", # [Optional] A descriptive name for this model.
"expirationTime": "A String", # [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.
"lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
# since the epoch.
}