docs/dyn/bigquery_v2.models.html - platform/external/python/google-api-python-client - Gitiles

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 <h1><a href="bigquery_v2.html">BigQuery API</a> . <a href="bigquery_v2.models.html">models</a></h1>
 <h2>Instance Methods</h2>
 <p class="toc_element">
   <code><a href="#delete">delete(projectId, datasetId, modelId)</a></code></p>
 <p class="firstline">Deletes the model specified by modelId from the dataset.</p>
 <p class="toc_element">
   <code><a href="#get">get(projectId, datasetId, modelId)</a></code></p>
 <p class="firstline">Gets the specified model resource by model ID.</p>
 <p class="toc_element">
   <code><a href="#list">list(projectId, datasetId, pageToken=None, maxResults=None)</a></code></p>
 <p class="firstline">Lists all models in the specified dataset. Requires the READER dataset</p>
 <p class="toc_element">
   <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
 <p class="firstline">Retrieves the next page of results.</p>
 <p class="toc_element">
   <code><a href="#patch">patch(projectId, datasetId, modelId, body=None)</a></code></p>
 <p class="firstline">Patch specific fields in the specified model.</p>
 <h3>Method Details</h3>
 <div class="method">
     <code class="details" id="delete">delete(projectId, datasetId, modelId)</code>
   <pre>Deletes the model specified by modelId from the dataset.

 Args:
   projectId: string, Required. Project ID of the model to delete. (required)
   datasetId: string, Required. Dataset ID of the model to delete. (required)
   modelId: string, Required. Model ID of the model to delete. (required)
 </pre>
 </div>

 <div class="method">
     <code class="details" id="get">get(projectId, datasetId, modelId)</code>
   <pre>Gets the specified model resource by model ID.

 Args:
   projectId: string, Required. Project ID of the requested model. (required)
   datasetId: string, Required. Dataset ID of the requested model. (required)
   modelId: string, Required. Model ID of the requested model. (required)

 Returns:
   An object of the form:

     {
       "labels": { # 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",
       },
       "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. # Populated for clustering models.
               "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
               "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
               "clusters": [ # [Beta] Information for all clusters.
                 { # Message containing the information about one cluster.
                   "count": "A String", # Count of training data rows that were assigned to this cluster.
                   "featureValues": [ # Values of highly variant features for this cluster.
                     { # Representative value of a single feature within the cluster.
                       "featureColumn": "A String", # The feature column name.
                       "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this
                           # feature.
                       "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value.
                         "categoryCounts": [ # 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.
                           { # Represents the count of a single category within the cluster.
                             "category": "A String", # The name of category.
                             "count": "A String", # The count of training samples matching the category within the
                                 # cluster.
                           },
                         ],
                       },
                     },
                   ],
                   "centroidId": "A String", # Centroid id.
                 },
               ],
             },
             "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix
                 # factorization models.
                 # factorization 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.
             },
             "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization
                 # models.
                 # feedback_type=implicit.
               "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit
                   # recommendation models except instead of computing the rating directly,
                   # the output from evaluate is computed against a preference which is 1 or 0
                   # depending on if the rating exists or not.
               "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and
                   # then averages all the precisions across all the users.
               "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank
                   # from the predicted confidence and dividing it by the original rank.
               "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the
                   # predicted confidence by comparing it to an ideal rank measured by the
                   # original ratings.
             },
             "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, # The fraction of actual positive labels that were given a positive
                       # prediction.
                   "precision": 3.14, # The fraction of actual positive predictions that had positive actual
                       # labels.
                   "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.
                   "f1Score": 3.14, # The equally weighted average of recall and precision.
                   "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
                   "accuracy": 3.14, # The fraction of predictions given the correct label.
                 },
               ],
             },
             "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.
                       "actualLabel": "A String", # The original label of this row.
                       "entries": [ # Info describing predicted label distribution.
                         { # A single entry in the confusion matrix.
                           "predictedLabel": "A String", # The predicted label. For confidence_threshold &gt; 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.
                         },
                       ],
                     },
                   ],
                 },
               ],
             },
           },
           "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is
               # actually split.
               # data tables that were used to train the model.
             "trainingTable": { # Table reference of the training data after split.
               "projectId": "A String", # [Required] The ID of the project containing this table.
               "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
               "datasetId": "A String", # [Required] The ID of the dataset containing this table.
             },
             "evaluationTable": { # Table reference of the evaluation data after split.
               "projectId": "A String", # [Required] The ID of the project containing this table.
               "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
               "datasetId": "A String", # [Required] The ID of the dataset containing this table.
             },
           },
           "results": [ # Output of each iteration run, results.size() &lt;= max_iterations.
             { # Information about a single iteration of the training run.
               "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier
                   # refactoring if we want to use model-specific iteration results.
                 "arimaModelInfo": [ # This message is repeated because there are multiple arima models
                     # fitted in auto-arima. For non-auto-arima model, its size is one.
                   { # Arima model information.
                     "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported
                         # for one time series.
                       "A String",
                     ],
                     "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false
                         # when d is not 1.
                     "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
                       "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
                         3.14,
                       ],
                       "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
                         3.14,
                       ],
                       "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
                     },
                     "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
                       "q": "A String", # Order of the moving-average part.
                       "p": "A String", # Order of the autoregressive part.
                       "d": "A String", # Order of the differencing part.
                     },
                     "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
                       "variance": 3.14, # Variance.
                       "logLikelihood": 3.14, # Log-likelihood.
                       "aic": 3.14, # AIC.
                     },
                     "timeSeriesId": "A String", # The id to indicate different time series.
                   },
                 ],
                 "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for
                     # one time series.
                   "A String",
                 ],
               },
               "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": [ # 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.
             "itemColumn": "A String", # Item column specified for matrix factorization models.
             "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix
                 # factorization.
             "numFactors": "A String", # Num factors specified for matrix factorization models.
             "inputLabelColumns": [ # Name of input label columns in training data.
               "A String",
             ],
             "batchSize": "A String", # Batch size for dnn models.
             "distanceType": "A String", # Distance type for clustering models.
             "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm
                 # when kmeans_initialization_method is CUSTOM.
             "l2Regularization": 3.14, # L2 regularization coefficient.
             "dropout": 3.14, # Dropout probability for dnn models.
             "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
                 # less than 'min_relative_progress'. Used only for iterative training
                 # algorithms.
             "l1Regularization": 3.14, # L1 regularization coefficient.
             "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", # Number of clusters for clustering models.
             "warmStart": True or False, # Whether to train a model from the last checkpoint.
             "hiddenUnits": [ # Hidden units for dnn models.
               "A String",
             ],
             "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
             "userColumn": "A String", # User column specified for matrix factorization models.
             "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm.
             "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
             "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.
             "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
             "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent
                 # overfitting for boosted tree models.
             "labelClassWeights": { # Weights associated with each label class, for rebalancing the
                 # training data. Only applicable for classification models.
               "a_key": 3.14,
             },
             "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
             "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
                 # applicable for imported models.
             "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is
                 # specified.
             "minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
             "lossType": "A String", # Type of loss function used during training run.
           },
         },
       ],
       "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
               # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
               #   {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.
         },
       ],
       "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
               # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
               #   {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.
         },
       ],
       "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.
       "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the
           # encryption configuration of the model data while stored in BigQuery
           # storage. This field can be used with PatchModel to update encryption key
           # for an already encrypted model.
         "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key.
       },
       "modelReference": { # 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.
     }</pre>
 </div>

 <div class="method">
     <code class="details" id="list">list(projectId, datasetId, pageToken=None, maxResults=None)</code>
   <pre>Lists all models in the specified dataset. Requires the READER dataset
 role.

 Args:
   projectId: string, Required. Project ID of the models to list. (required)
   datasetId: string, Required. 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 to return in a single response page.
 Leverage the page tokens to iterate through the entire collection.

 Returns:
   An object of the form:

     {
     "nextPageToken": "A String", # A token to request the next page of results.
     "models": [ # Models in the requested dataset. Only the following fields are populated:
         # model_reference, model_type, creation_time, last_modified_time and
         # labels.
       {
           "labels": { # 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",
           },
           "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. # Populated for clustering models.
                   "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
                   "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
                   "clusters": [ # [Beta] Information for all clusters.
                     { # Message containing the information about one cluster.
                       "count": "A String", # Count of training data rows that were assigned to this cluster.
                       "featureValues": [ # Values of highly variant features for this cluster.
                         { # Representative value of a single feature within the cluster.
                           "featureColumn": "A String", # The feature column name.
                           "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this
                               # feature.
                           "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value.
                             "categoryCounts": [ # 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.
                               { # Represents the count of a single category within the cluster.
                                 "category": "A String", # The name of category.
                                 "count": "A String", # The count of training samples matching the category within the
                                     # cluster.
                               },
                             ],
                           },
                         },
                       ],
                       "centroidId": "A String", # Centroid id.
                     },
                   ],
                 },
                 "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix
                     # factorization models.
                     # factorization 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.
                 },
                 "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization
                     # models.
                     # feedback_type=implicit.
                   "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit
                       # recommendation models except instead of computing the rating directly,
                       # the output from evaluate is computed against a preference which is 1 or 0
                       # depending on if the rating exists or not.
                   "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and
                       # then averages all the precisions across all the users.
                   "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank
                       # from the predicted confidence and dividing it by the original rank.
                   "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the
                       # predicted confidence by comparing it to an ideal rank measured by the
                       # original ratings.
                 },
                 "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, # The fraction of actual positive labels that were given a positive
                           # prediction.
                       "precision": 3.14, # The fraction of actual positive predictions that had positive actual
                           # labels.
                       "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.
                       "f1Score": 3.14, # The equally weighted average of recall and precision.
                       "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
                       "accuracy": 3.14, # The fraction of predictions given the correct label.
                     },
                   ],
                 },
                 "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.
                           "actualLabel": "A String", # The original label of this row.
                           "entries": [ # Info describing predicted label distribution.
                             { # A single entry in the confusion matrix.
                               "predictedLabel": "A String", # The predicted label. For confidence_threshold &gt; 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.
                             },
                           ],
                         },
                       ],
                     },
                   ],
                 },
               },
               "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is
                   # actually split.
                   # data tables that were used to train the model.
                 "trainingTable": { # Table reference of the training data after split.
                   "projectId": "A String", # [Required] The ID of the project containing this table.
                   "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
                   "datasetId": "A String", # [Required] The ID of the dataset containing this table.
                 },
                 "evaluationTable": { # Table reference of the evaluation data after split.
                   "projectId": "A String", # [Required] The ID of the project containing this table.
                   "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
                   "datasetId": "A String", # [Required] The ID of the dataset containing this table.
                 },
               },
               "results": [ # Output of each iteration run, results.size() &lt;= max_iterations.
                 { # Information about a single iteration of the training run.
                   "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier
                       # refactoring if we want to use model-specific iteration results.
                     "arimaModelInfo": [ # This message is repeated because there are multiple arima models
                         # fitted in auto-arima. For non-auto-arima model, its size is one.
                       { # Arima model information.
                         "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported
                             # for one time series.
                           "A String",
                         ],
                         "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false
                             # when d is not 1.
                         "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
                           "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
                             3.14,
                           ],
                           "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
                             3.14,
                           ],
                           "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
                         },
                         "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
                           "q": "A String", # Order of the moving-average part.
                           "p": "A String", # Order of the autoregressive part.
                           "d": "A String", # Order of the differencing part.
                         },
                         "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
                           "variance": 3.14, # Variance.
                           "logLikelihood": 3.14, # Log-likelihood.
                           "aic": 3.14, # AIC.
                         },
                         "timeSeriesId": "A String", # The id to indicate different time series.
                       },
                     ],
                     "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for
                         # one time series.
                       "A String",
                     ],
                   },
                   "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": [ # 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.
                 "itemColumn": "A String", # Item column specified for matrix factorization models.
                 "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix
                     # factorization.
                 "numFactors": "A String", # Num factors specified for matrix factorization models.
                 "inputLabelColumns": [ # Name of input label columns in training data.
                   "A String",
                 ],
                 "batchSize": "A String", # Batch size for dnn models.
                 "distanceType": "A String", # Distance type for clustering models.
                 "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm
                     # when kmeans_initialization_method is CUSTOM.
                 "l2Regularization": 3.14, # L2 regularization coefficient.
                 "dropout": 3.14, # Dropout probability for dnn models.
                 "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
                     # less than 'min_relative_progress'. Used only for iterative training
                     # algorithms.
                 "l1Regularization": 3.14, # L1 regularization coefficient.
                 "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", # Number of clusters for clustering models.
                 "warmStart": True or False, # Whether to train a model from the last checkpoint.
                 "hiddenUnits": [ # Hidden units for dnn models.
                   "A String",
                 ],
                 "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
                 "userColumn": "A String", # User column specified for matrix factorization models.
                 "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm.
                 "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
                 "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.
                 "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
                 "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent
                     # overfitting for boosted tree models.
                 "labelClassWeights": { # Weights associated with each label class, for rebalancing the
                     # training data. Only applicable for classification models.
                   "a_key": 3.14,
                 },
                 "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
                 "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
                     # applicable for imported models.
                 "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is
                     # specified.
                 "minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
                 "lossType": "A String", # Type of loss function used during training run.
               },
             },
           ],
           "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
                   # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
                   #   {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.
             },
           ],
           "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
                   # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
                   #   {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.
             },
           ],
           "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.
           "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the
               # encryption configuration of the model data while stored in BigQuery
               # storage. This field can be used with PatchModel to update encryption key
               # for an already encrypted model.
             "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key.
           },
           "modelReference": { # 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.
         },
     ],
   }</pre>
 </div>

 <div class="method">
     <code class="details" id="list_next">list_next(previous_request, previous_response)</code>
   <pre>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.
     </pre>
 </div>

 <div class="method">
     <code class="details" id="patch">patch(projectId, datasetId, modelId, body=None)</code>
   <pre>Patch specific fields in the specified model.

 Args:
   projectId: string, Required. Project ID of the model to patch. (required)
   datasetId: string, Required. Dataset ID of the model to patch. (required)
   modelId: string, Required. Model ID of the model to patch. (required)
   body: object, The request body.
     The object takes the form of:

 {
     "labels": { # 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",
     },
     "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. # Populated for clustering models.
             "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
             "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
             "clusters": [ # [Beta] Information for all clusters.
               { # Message containing the information about one cluster.
                 "count": "A String", # Count of training data rows that were assigned to this cluster.
                 "featureValues": [ # Values of highly variant features for this cluster.
                   { # Representative value of a single feature within the cluster.
                     "featureColumn": "A String", # The feature column name.
                     "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this
                         # feature.
                     "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value.
                       "categoryCounts": [ # 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.
                         { # Represents the count of a single category within the cluster.
                           "category": "A String", # The name of category.
                           "count": "A String", # The count of training samples matching the category within the
                               # cluster.
                         },
                       ],
                     },
                   },
                 ],
                 "centroidId": "A String", # Centroid id.
               },
             ],
           },
           "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix
               # factorization models.
               # factorization 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.
           },
           "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization
               # models.
               # feedback_type=implicit.
             "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit
                 # recommendation models except instead of computing the rating directly,
                 # the output from evaluate is computed against a preference which is 1 or 0
                 # depending on if the rating exists or not.
             "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and
                 # then averages all the precisions across all the users.
             "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank
                 # from the predicted confidence and dividing it by the original rank.
             "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the
                 # predicted confidence by comparing it to an ideal rank measured by the
                 # original ratings.
           },
           "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, # The fraction of actual positive labels that were given a positive
                     # prediction.
                 "precision": 3.14, # The fraction of actual positive predictions that had positive actual
                     # labels.
                 "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.
                 "f1Score": 3.14, # The equally weighted average of recall and precision.
                 "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
                 "accuracy": 3.14, # The fraction of predictions given the correct label.
               },
             ],
           },
           "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.
                     "actualLabel": "A String", # The original label of this row.
                     "entries": [ # Info describing predicted label distribution.
                       { # A single entry in the confusion matrix.
                         "predictedLabel": "A String", # The predicted label. For confidence_threshold &gt; 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.
                       },
                     ],
                   },
                 ],
               },
             ],
           },
         },
         "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is
             # actually split.
             # data tables that were used to train the model.
           "trainingTable": { # Table reference of the training data after split.
             "projectId": "A String", # [Required] The ID of the project containing this table.
             "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
             "datasetId": "A String", # [Required] The ID of the dataset containing this table.
           },
           "evaluationTable": { # Table reference of the evaluation data after split.
             "projectId": "A String", # [Required] The ID of the project containing this table.
             "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
             "datasetId": "A String", # [Required] The ID of the dataset containing this table.
           },
         },
         "results": [ # Output of each iteration run, results.size() &lt;= max_iterations.
           { # Information about a single iteration of the training run.
             "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier
                 # refactoring if we want to use model-specific iteration results.
               "arimaModelInfo": [ # This message is repeated because there are multiple arima models
                   # fitted in auto-arima. For non-auto-arima model, its size is one.
                 { # Arima model information.
                   "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported
                       # for one time series.
                     "A String",
                   ],
                   "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false
                       # when d is not 1.
                   "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
                     "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
                       3.14,
                     ],
                     "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
                       3.14,
                     ],
                     "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
                   },
                   "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
                     "q": "A String", # Order of the moving-average part.
                     "p": "A String", # Order of the autoregressive part.
                     "d": "A String", # Order of the differencing part.
                   },
                   "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
                     "variance": 3.14, # Variance.
                     "logLikelihood": 3.14, # Log-likelihood.
                     "aic": 3.14, # AIC.
                   },
                   "timeSeriesId": "A String", # The id to indicate different time series.
                 },
               ],
               "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for
                   # one time series.
                 "A String",
               ],
             },
             "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": [ # 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.
           "itemColumn": "A String", # Item column specified for matrix factorization models.
           "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix
               # factorization.
           "numFactors": "A String", # Num factors specified for matrix factorization models.
           "inputLabelColumns": [ # Name of input label columns in training data.
             "A String",
           ],
           "batchSize": "A String", # Batch size for dnn models.
           "distanceType": "A String", # Distance type for clustering models.
           "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm
               # when kmeans_initialization_method is CUSTOM.
           "l2Regularization": 3.14, # L2 regularization coefficient.
           "dropout": 3.14, # Dropout probability for dnn models.
           "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
               # less than 'min_relative_progress'. Used only for iterative training
               # algorithms.
           "l1Regularization": 3.14, # L1 regularization coefficient.
           "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", # Number of clusters for clustering models.
           "warmStart": True or False, # Whether to train a model from the last checkpoint.
           "hiddenUnits": [ # Hidden units for dnn models.
             "A String",
           ],
           "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
           "userColumn": "A String", # User column specified for matrix factorization models.
           "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm.
           "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
           "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.
           "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
           "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent
               # overfitting for boosted tree models.
           "labelClassWeights": { # Weights associated with each label class, for rebalancing the
               # training data. Only applicable for classification models.
             "a_key": 3.14,
           },
           "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
           "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
               # applicable for imported models.
           "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is
               # specified.
           "minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
           "lossType": "A String", # Type of loss function used during training run.
         },
       },
     ],
     "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
             # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
             #   {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.
       },
     ],
     "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
             # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
             #   {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.
       },
     ],
     "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.
     "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the
         # encryption configuration of the model data while stored in BigQuery
         # storage. This field can be used with PatchModel to update encryption key
         # for an already encrypted model.
       "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key.
     },
     "modelReference": { # 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:

     {
       "labels": { # 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",
       },
       "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. # Populated for clustering models.
               "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
               "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
               "clusters": [ # [Beta] Information for all clusters.
                 { # Message containing the information about one cluster.
                   "count": "A String", # Count of training data rows that were assigned to this cluster.
                   "featureValues": [ # Values of highly variant features for this cluster.
                     { # Representative value of a single feature within the cluster.
                       "featureColumn": "A String", # The feature column name.
                       "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this
                           # feature.
                       "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value.
                         "categoryCounts": [ # 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.
                           { # Represents the count of a single category within the cluster.
                             "category": "A String", # The name of category.
                             "count": "A String", # The count of training samples matching the category within the
                                 # cluster.
                           },
                         ],
                       },
                     },
                   ],
                   "centroidId": "A String", # Centroid id.
                 },
               ],
             },
             "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix
                 # factorization models.
                 # factorization 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.
             },
             "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization
                 # models.
                 # feedback_type=implicit.
               "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit
                   # recommendation models except instead of computing the rating directly,
                   # the output from evaluate is computed against a preference which is 1 or 0
                   # depending on if the rating exists or not.
               "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and
                   # then averages all the precisions across all the users.
               "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank
                   # from the predicted confidence and dividing it by the original rank.
               "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the
                   # predicted confidence by comparing it to an ideal rank measured by the
                   # original ratings.
             },
             "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, # The fraction of actual positive labels that were given a positive
                       # prediction.
                   "precision": 3.14, # The fraction of actual positive predictions that had positive actual
                       # labels.
                   "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.
                   "f1Score": 3.14, # The equally weighted average of recall and precision.
                   "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
                   "accuracy": 3.14, # The fraction of predictions given the correct label.
                 },
               ],
             },
             "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.
                       "actualLabel": "A String", # The original label of this row.
                       "entries": [ # Info describing predicted label distribution.
                         { # A single entry in the confusion matrix.
                           "predictedLabel": "A String", # The predicted label. For confidence_threshold &gt; 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.
                         },
                       ],
                     },
                   ],
                 },
               ],
             },
           },
           "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is
               # actually split.
               # data tables that were used to train the model.
             "trainingTable": { # Table reference of the training data after split.
               "projectId": "A String", # [Required] The ID of the project containing this table.
               "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
               "datasetId": "A String", # [Required] The ID of the dataset containing this table.
             },
             "evaluationTable": { # Table reference of the evaluation data after split.
               "projectId": "A String", # [Required] The ID of the project containing this table.
               "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
               "datasetId": "A String", # [Required] The ID of the dataset containing this table.
             },
           },
           "results": [ # Output of each iteration run, results.size() &lt;= max_iterations.
             { # Information about a single iteration of the training run.
               "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier
                   # refactoring if we want to use model-specific iteration results.
                 "arimaModelInfo": [ # This message is repeated because there are multiple arima models
                     # fitted in auto-arima. For non-auto-arima model, its size is one.
                   { # Arima model information.
                     "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported
                         # for one time series.
                       "A String",
                     ],
                     "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false
                         # when d is not 1.
                     "arimaCoefficients": { # Arima coefficients. # Arima coefficients.
                       "movingAverageCoefficients": [ # Moving-average coefficients, an array of double.
                         3.14,
                       ],
                       "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double.
                         3.14,
                       ],
                       "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array.
                     },
                     "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order.
                       "q": "A String", # Order of the moving-average part.
                       "p": "A String", # Order of the autoregressive part.
                       "d": "A String", # Order of the differencing part.
                     },
                     "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics.
                       "variance": 3.14, # Variance.
                       "logLikelihood": 3.14, # Log-likelihood.
                       "aic": 3.14, # AIC.
                     },
                     "timeSeriesId": "A String", # The id to indicate different time series.
                   },
                 ],
                 "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for
                     # one time series.
                   "A String",
                 ],
               },
               "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": [ # 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.
             "itemColumn": "A String", # Item column specified for matrix factorization models.
             "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix
                 # factorization.
             "numFactors": "A String", # Num factors specified for matrix factorization models.
             "inputLabelColumns": [ # Name of input label columns in training data.
               "A String",
             ],
             "batchSize": "A String", # Batch size for dnn models.
             "distanceType": "A String", # Distance type for clustering models.
             "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm
                 # when kmeans_initialization_method is CUSTOM.
             "l2Regularization": 3.14, # L2 regularization coefficient.
             "dropout": 3.14, # Dropout probability for dnn models.
             "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
                 # less than 'min_relative_progress'. Used only for iterative training
                 # algorithms.
             "l1Regularization": 3.14, # L1 regularization coefficient.
             "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", # Number of clusters for clustering models.
             "warmStart": True or False, # Whether to train a model from the last checkpoint.
             "hiddenUnits": [ # Hidden units for dnn models.
               "A String",
             ],
             "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models.
             "userColumn": "A String", # User column specified for matrix factorization models.
             "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm.
             "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
             "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.
             "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
             "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent
                 # overfitting for boosted tree models.
             "labelClassWeights": { # Weights associated with each label class, for rebalancing the
                 # training data. Only applicable for classification models.
               "a_key": 3.14,
             },
             "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
             "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
                 # applicable for imported models.
             "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is
                 # specified.
             "minSplitLoss": 3.14, # Minimum split loss for boosted tree models.
             "lossType": "A String", # Type of loss function used during training run.
           },
         },
       ],
       "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
               # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
               #   {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.
         },
       ],
       "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&lt;STRING&gt;: {type_kind="ARRAY", array_element_type="STRING"}
               # STRUCT&lt;x STRING, y ARRAY&lt;DATE&gt;&gt;:
               #   {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.
         },
       ],
       "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.
       "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the
           # encryption configuration of the model data while stored in BigQuery
           # storage. This field can be used with PatchModel to update encryption key
           # for an already encrypted model.
         "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key.
       },
       "modelReference": { # 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.
     }</pre>
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