Regen all docs. (#700)
* Stop recursing if discovery == {}
* Generate docs with 'make docs'.
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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)</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, Project ID of the model to delete. (required)
+ datasetId: string, Dataset ID of the model to delete. (required)
+ modelId: string, Model ID of the model to delete. (required)
+</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, Project ID of the requested model. (required)
+ datasetId: string, Dataset ID of the requested model. (required)
+ modelId: string, Model ID of the requested model. (required)
+
+Returns:
+ An object of the form:
+
+ {
+ "labelColumns": [ # Output only. Label columns that were used to train this model.
+ # The output of the model will have a "predicted_" prefix to these columns.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "description": "A String", # [Optional] A user-friendly description of this model.
+ "trainingRuns": [ # Output only. Information for all training runs in increasing order of
+ # start_time.
+ { # Information about a single training query run for the model.
+ "evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
+ # end of training.
+ # data or just the eval data based on whether eval data was used during
+ # training. These are not present for imported models.
+ "clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
+ "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
+ "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
+ },
+ "regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
+ "meanSquaredLogError": 3.14, # Mean squared log error.
+ "meanAbsoluteError": 3.14, # Mean absolute error.
+ "meanSquaredError": 3.14, # Mean squared error.
+ "medianAbsoluteError": 3.14, # Median absolute error.
+ "rSquared": 3.14, # R^2 score.
+ },
+ "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
+ "negativeLabel": "A String", # Label representing the negative class.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "positiveLabel": "A String", # Label representing the positive class.
+ "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
+ { # Confusion matrix for binary classification models.
+ "truePositives": "A String", # Number of true samples predicted as true.
+ "recall": 3.14, # Aggregate recall.
+ "precision": 3.14, # Aggregate precision.
+ "falseNegatives": "A String", # Number of false samples predicted as false.
+ "trueNegatives": "A String", # Number of true samples predicted as false.
+ "falsePositives": "A String", # Number of false samples predicted as true.
+ "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
+ },
+ ],
+ },
+ "multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "confusionMatrixList": [ # Confusion matrix at different thresholds.
+ { # Confusion matrix for multi-class classification models.
+ "confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
+ # confusion matrix.
+ "rows": [ # One row per actual label.
+ { # A single row in the confusion matrix.
+ "entries": [ # Info describing predicted label distribution.
+ { # A single entry in the confusion matrix.
+ "predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
+ # also add an entry indicating the number of items under the
+ # confidence threshold.
+ "itemCount": "A String", # Number of items being predicted as this label.
+ },
+ ],
+ "actualLabel": "A String", # The original label of this row.
+ },
+ ],
+ },
+ ],
+ },
+ },
+ "results": [ # Output of each iteration run, results.size() <= max_iterations.
+ { # Information about a single iteration of the training run.
+ "index": 42, # Index of the iteration, 0 based.
+ "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
+ "durationMs": "A String", # Time taken to run the iteration in milliseconds.
+ "learnRate": 3.14, # Learn rate used for this iteration.
+ "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
+ "clusterInfos": [ # [Beta] Information about top clusters for clustering models.
+ { # Information about a single cluster for clustering model.
+ "centroidId": "A String", # Centroid id.
+ "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
+ "clusterRadius": 3.14, # Cluster radius, the average distance from centroid
+ # to each point assigned to the cluster.
+ },
+ ],
+ },
+ ],
+ "startTime": "A String", # The start time of this training run.
+ "trainingOptions": { # Options that were used for this training run, includes
+ # user specified and default options that were used.
+ "optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
+ "inputLabelColumns": [ # Name of input label columns in training data.
+ "A String",
+ ],
+ "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
+ # training algorithms.
+ "earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
+ # any more (compared to min_relative_progress). Used only for iterative
+ # training algorithms.
+ "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
+ # strategy.
+ "dataSplitColumn": "A String", # The column to split data with. This column won't be used as a
+ # feature.
+ # 1. When data_split_method is CUSTOM, the corresponding column should
+ # be boolean. The rows with true value tag are eval data, and the false
+ # are training data.
+ # 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION
+ # rows (from smallest to largest) in the corresponding column are used
+ # as training data, and the rest are eval data. It respects the order
+ # in Orderable data types:
+ # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
+ "numClusters": "A String", # [Beta] Number of clusters for clustering models.
+ "l1Regularization": 3.14, # L1 regularization coefficient.
+ "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
+ "distanceType": "A String", # [Beta] Distance type for clustering models.
+ "warmStart": True or False, # Whether to train a model from the last checkpoint.
+ "labelClassWeights": { # Weights associated with each label class, for rebalancing the
+ # training data. Only applicable for classification models.
+ "a_key": 3.14,
+ },
+ "lossType": "A String", # Type of loss function used during training run.
+ "dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest
+ # of data will be used as training data. The format should be double.
+ # Accurate to two decimal places.
+ # Default value is 0.2.
+ "l2Regularization": 3.14, # L2 regularization coefficient.
+ "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
+ # applicable for imported models.
+ "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
+ "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
+ # less than 'min_relative_progress'. Used only for iterative training
+ # algorithms.
+ "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
+ },
+ },
+ ],
+ "featureColumns": [ # Output only. Input feature columns that were used to train this model.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "labels": { # [Optional] The labels associated with this model. You can use these to
+ # organize and group your models. Label keys and values can be no longer
+ # than 63 characters, can only contain lowercase letters, numeric
+ # characters, underscores and dashes. International characters are allowed.
+ # Label values are optional. Label keys must start with a letter and each
+ # label in the list must have a different key.
+ "a_key": "A String",
+ },
+ "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
+ # epoch.
+ "modelType": "A String", # Output only. Type of the model resource.
+ "modelReference": { # Id path of a model. # Required. Unique identifier for this model.
+ "projectId": "A String", # [Required] The ID of the project containing this model.
+ "datasetId": "A String", # [Required] The ID of the dataset containing this model.
+ "modelId": "A String", # [Required] The ID of the model. The ID must contain only
+ # letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum
+ # length is 1,024 characters.
+ },
+ "etag": "A String", # Output only. A hash of this resource.
+ "location": "A String", # Output only. The geographic location where the model resides. This value
+ # is inherited from the dataset.
+ "friendlyName": "A String", # [Optional] A descriptive name for this model.
+ "expirationTime": "A String", # [Optional] The time when this model expires, in milliseconds since the
+ # epoch. If not present, the model will persist indefinitely. Expired models
+ # will be deleted and their storage reclaimed. The defaultTableExpirationMs
+ # property of the encapsulating dataset can be used to set a default
+ # expirationTime on newly created models.
+ "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
+ # since the epoch.
+ }</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, Project ID of the models to list. (required)
+ datasetId: string, Dataset ID of the models to list. (required)
+ pageToken: string, Page token, returned by a previous call to request the next page of
+results
+ maxResults: integer, The maximum number of results per page.
+
+Returns:
+ An object of the form:
+
+ {
+ "models": [ # Models in the requested dataset. Only the following fields are populated:
+ # model_reference, model_type, creation_time, last_modified_time and
+ # labels.
+ {
+ "labelColumns": [ # Output only. Label columns that were used to train this model.
+ # The output of the model will have a "predicted_" prefix to these columns.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "description": "A String", # [Optional] A user-friendly description of this model.
+ "trainingRuns": [ # Output only. Information for all training runs in increasing order of
+ # start_time.
+ { # Information about a single training query run for the model.
+ "evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
+ # end of training.
+ # data or just the eval data based on whether eval data was used during
+ # training. These are not present for imported models.
+ "clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
+ "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
+ "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
+ },
+ "regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
+ "meanSquaredLogError": 3.14, # Mean squared log error.
+ "meanAbsoluteError": 3.14, # Mean absolute error.
+ "meanSquaredError": 3.14, # Mean squared error.
+ "medianAbsoluteError": 3.14, # Median absolute error.
+ "rSquared": 3.14, # R^2 score.
+ },
+ "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
+ "negativeLabel": "A String", # Label representing the negative class.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "positiveLabel": "A String", # Label representing the positive class.
+ "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
+ { # Confusion matrix for binary classification models.
+ "truePositives": "A String", # Number of true samples predicted as true.
+ "recall": 3.14, # Aggregate recall.
+ "precision": 3.14, # Aggregate precision.
+ "falseNegatives": "A String", # Number of false samples predicted as false.
+ "trueNegatives": "A String", # Number of true samples predicted as false.
+ "falsePositives": "A String", # Number of false samples predicted as true.
+ "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
+ },
+ ],
+ },
+ "multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "confusionMatrixList": [ # Confusion matrix at different thresholds.
+ { # Confusion matrix for multi-class classification models.
+ "confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
+ # confusion matrix.
+ "rows": [ # One row per actual label.
+ { # A single row in the confusion matrix.
+ "entries": [ # Info describing predicted label distribution.
+ { # A single entry in the confusion matrix.
+ "predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
+ # also add an entry indicating the number of items under the
+ # confidence threshold.
+ "itemCount": "A String", # Number of items being predicted as this label.
+ },
+ ],
+ "actualLabel": "A String", # The original label of this row.
+ },
+ ],
+ },
+ ],
+ },
+ },
+ "results": [ # Output of each iteration run, results.size() <= max_iterations.
+ { # Information about a single iteration of the training run.
+ "index": 42, # Index of the iteration, 0 based.
+ "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
+ "durationMs": "A String", # Time taken to run the iteration in milliseconds.
+ "learnRate": 3.14, # Learn rate used for this iteration.
+ "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
+ "clusterInfos": [ # [Beta] Information about top clusters for clustering models.
+ { # Information about a single cluster for clustering model.
+ "centroidId": "A String", # Centroid id.
+ "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
+ "clusterRadius": 3.14, # Cluster radius, the average distance from centroid
+ # to each point assigned to the cluster.
+ },
+ ],
+ },
+ ],
+ "startTime": "A String", # The start time of this training run.
+ "trainingOptions": { # Options that were used for this training run, includes
+ # user specified and default options that were used.
+ "optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
+ "inputLabelColumns": [ # Name of input label columns in training data.
+ "A String",
+ ],
+ "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
+ # training algorithms.
+ "earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
+ # any more (compared to min_relative_progress). Used only for iterative
+ # training algorithms.
+ "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
+ # strategy.
+ "dataSplitColumn": "A String", # The column to split data with. This column won't be used as a
+ # feature.
+ # 1. When data_split_method is CUSTOM, the corresponding column should
+ # be boolean. The rows with true value tag are eval data, and the false
+ # are training data.
+ # 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION
+ # rows (from smallest to largest) in the corresponding column are used
+ # as training data, and the rest are eval data. It respects the order
+ # in Orderable data types:
+ # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
+ "numClusters": "A String", # [Beta] Number of clusters for clustering models.
+ "l1Regularization": 3.14, # L1 regularization coefficient.
+ "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
+ "distanceType": "A String", # [Beta] Distance type for clustering models.
+ "warmStart": True or False, # Whether to train a model from the last checkpoint.
+ "labelClassWeights": { # Weights associated with each label class, for rebalancing the
+ # training data. Only applicable for classification models.
+ "a_key": 3.14,
+ },
+ "lossType": "A String", # Type of loss function used during training run.
+ "dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest
+ # of data will be used as training data. The format should be double.
+ # Accurate to two decimal places.
+ # Default value is 0.2.
+ "l2Regularization": 3.14, # L2 regularization coefficient.
+ "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
+ # applicable for imported models.
+ "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
+ "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
+ # less than 'min_relative_progress'. Used only for iterative training
+ # algorithms.
+ "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
+ },
+ },
+ ],
+ "featureColumns": [ # Output only. Input feature columns that were used to train this model.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "labels": { # [Optional] The labels associated with this model. You can use these to
+ # organize and group your models. Label keys and values can be no longer
+ # than 63 characters, can only contain lowercase letters, numeric
+ # characters, underscores and dashes. International characters are allowed.
+ # Label values are optional. Label keys must start with a letter and each
+ # label in the list must have a different key.
+ "a_key": "A String",
+ },
+ "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
+ # epoch.
+ "modelType": "A String", # Output only. Type of the model resource.
+ "modelReference": { # Id path of a model. # Required. Unique identifier for this model.
+ "projectId": "A String", # [Required] The ID of the project containing this model.
+ "datasetId": "A String", # [Required] The ID of the dataset containing this model.
+ "modelId": "A String", # [Required] The ID of the model. The ID must contain only
+ # letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum
+ # length is 1,024 characters.
+ },
+ "etag": "A String", # Output only. A hash of this resource.
+ "location": "A String", # Output only. The geographic location where the model resides. This value
+ # is inherited from the dataset.
+ "friendlyName": "A String", # [Optional] A descriptive name for this model.
+ "expirationTime": "A String", # [Optional] The time when this model expires, in milliseconds since the
+ # epoch. If not present, the model will persist indefinitely. Expired models
+ # will be deleted and their storage reclaimed. The defaultTableExpirationMs
+ # property of the encapsulating dataset can be used to set a default
+ # expirationTime on newly created models.
+ "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
+ # since the epoch.
+ },
+ ],
+ "nextPageToken": "A String", # A token to request the next page of results.
+ }</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)</code>
+ <pre>Patch specific fields in the specified model.
+
+Args:
+ projectId: string, Project ID of the model to patch. (required)
+ datasetId: string, Dataset ID of the model to patch. (required)
+ modelId: string, Model ID of the model to patch. (required)
+ body: object, The request body. (required)
+ The object takes the form of:
+
+{
+ "labelColumns": [ # Output only. Label columns that were used to train this model.
+ # The output of the model will have a "predicted_" prefix to these columns.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "description": "A String", # [Optional] A user-friendly description of this model.
+ "trainingRuns": [ # Output only. Information for all training runs in increasing order of
+ # start_time.
+ { # Information about a single training query run for the model.
+ "evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
+ # end of training.
+ # data or just the eval data based on whether eval data was used during
+ # training. These are not present for imported models.
+ "clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
+ "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
+ "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
+ },
+ "regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
+ "meanSquaredLogError": 3.14, # Mean squared log error.
+ "meanAbsoluteError": 3.14, # Mean absolute error.
+ "meanSquaredError": 3.14, # Mean squared error.
+ "medianAbsoluteError": 3.14, # Median absolute error.
+ "rSquared": 3.14, # R^2 score.
+ },
+ "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
+ "negativeLabel": "A String", # Label representing the negative class.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "positiveLabel": "A String", # Label representing the positive class.
+ "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
+ { # Confusion matrix for binary classification models.
+ "truePositives": "A String", # Number of true samples predicted as true.
+ "recall": 3.14, # Aggregate recall.
+ "precision": 3.14, # Aggregate precision.
+ "falseNegatives": "A String", # Number of false samples predicted as false.
+ "trueNegatives": "A String", # Number of true samples predicted as false.
+ "falsePositives": "A String", # Number of false samples predicted as true.
+ "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
+ },
+ ],
+ },
+ "multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "confusionMatrixList": [ # Confusion matrix at different thresholds.
+ { # Confusion matrix for multi-class classification models.
+ "confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
+ # confusion matrix.
+ "rows": [ # One row per actual label.
+ { # A single row in the confusion matrix.
+ "entries": [ # Info describing predicted label distribution.
+ { # A single entry in the confusion matrix.
+ "predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
+ # also add an entry indicating the number of items under the
+ # confidence threshold.
+ "itemCount": "A String", # Number of items being predicted as this label.
+ },
+ ],
+ "actualLabel": "A String", # The original label of this row.
+ },
+ ],
+ },
+ ],
+ },
+ },
+ "results": [ # Output of each iteration run, results.size() <= max_iterations.
+ { # Information about a single iteration of the training run.
+ "index": 42, # Index of the iteration, 0 based.
+ "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
+ "durationMs": "A String", # Time taken to run the iteration in milliseconds.
+ "learnRate": 3.14, # Learn rate used for this iteration.
+ "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
+ "clusterInfos": [ # [Beta] Information about top clusters for clustering models.
+ { # Information about a single cluster for clustering model.
+ "centroidId": "A String", # Centroid id.
+ "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
+ "clusterRadius": 3.14, # Cluster radius, the average distance from centroid
+ # to each point assigned to the cluster.
+ },
+ ],
+ },
+ ],
+ "startTime": "A String", # The start time of this training run.
+ "trainingOptions": { # Options that were used for this training run, includes
+ # user specified and default options that were used.
+ "optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
+ "inputLabelColumns": [ # Name of input label columns in training data.
+ "A String",
+ ],
+ "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
+ # training algorithms.
+ "earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
+ # any more (compared to min_relative_progress). Used only for iterative
+ # training algorithms.
+ "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
+ # strategy.
+ "dataSplitColumn": "A String", # The column to split data with. This column won't be used as a
+ # feature.
+ # 1. When data_split_method is CUSTOM, the corresponding column should
+ # be boolean. The rows with true value tag are eval data, and the false
+ # are training data.
+ # 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION
+ # rows (from smallest to largest) in the corresponding column are used
+ # as training data, and the rest are eval data. It respects the order
+ # in Orderable data types:
+ # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
+ "numClusters": "A String", # [Beta] Number of clusters for clustering models.
+ "l1Regularization": 3.14, # L1 regularization coefficient.
+ "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
+ "distanceType": "A String", # [Beta] Distance type for clustering models.
+ "warmStart": True or False, # Whether to train a model from the last checkpoint.
+ "labelClassWeights": { # Weights associated with each label class, for rebalancing the
+ # training data. Only applicable for classification models.
+ "a_key": 3.14,
+ },
+ "lossType": "A String", # Type of loss function used during training run.
+ "dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest
+ # of data will be used as training data. The format should be double.
+ # Accurate to two decimal places.
+ # Default value is 0.2.
+ "l2Regularization": 3.14, # L2 regularization coefficient.
+ "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
+ # applicable for imported models.
+ "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
+ "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
+ # less than 'min_relative_progress'. Used only for iterative training
+ # algorithms.
+ "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
+ },
+ },
+ ],
+ "featureColumns": [ # Output only. Input feature columns that were used to train this model.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "labels": { # [Optional] The labels associated with this model. You can use these to
+ # organize and group your models. Label keys and values can be no longer
+ # than 63 characters, can only contain lowercase letters, numeric
+ # characters, underscores and dashes. International characters are allowed.
+ # Label values are optional. Label keys must start with a letter and each
+ # label in the list must have a different key.
+ "a_key": "A String",
+ },
+ "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
+ # epoch.
+ "modelType": "A String", # Output only. Type of the model resource.
+ "modelReference": { # Id path of a model. # Required. Unique identifier for this model.
+ "projectId": "A String", # [Required] The ID of the project containing this model.
+ "datasetId": "A String", # [Required] The ID of the dataset containing this model.
+ "modelId": "A String", # [Required] The ID of the model. The ID must contain only
+ # letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum
+ # length is 1,024 characters.
+ },
+ "etag": "A String", # Output only. A hash of this resource.
+ "location": "A String", # Output only. The geographic location where the model resides. This value
+ # is inherited from the dataset.
+ "friendlyName": "A String", # [Optional] A descriptive name for this model.
+ "expirationTime": "A String", # [Optional] The time when this model expires, in milliseconds since the
+ # epoch. If not present, the model will persist indefinitely. Expired models
+ # will be deleted and their storage reclaimed. The defaultTableExpirationMs
+ # property of the encapsulating dataset can be used to set a default
+ # expirationTime on newly created models.
+ "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
+ # since the epoch.
+ }
+
+
+Returns:
+ An object of the form:
+
+ {
+ "labelColumns": [ # Output only. Label columns that were used to train this model.
+ # The output of the model will have a "predicted_" prefix to these columns.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "description": "A String", # [Optional] A user-friendly description of this model.
+ "trainingRuns": [ # Output only. Information for all training runs in increasing order of
+ # start_time.
+ { # Information about a single training query run for the model.
+ "evaluationMetrics": { # Evaluation metrics of a model. These are either computed on all training # The evaluation metrics over training/eval data that were computed at the
+ # end of training.
+ # data or just the eval data based on whether eval data was used during
+ # training. These are not present for imported models.
+ "clusteringMetrics": { # Evaluation metrics for clustering models. # [Beta] Populated for clustering models.
+ "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid.
+ "daviesBouldinIndex": 3.14, # Davies-Bouldin index.
+ },
+ "regressionMetrics": { # Evaluation metrics for regression models. # Populated for regression models.
+ "meanSquaredLogError": 3.14, # Mean squared log error.
+ "meanAbsoluteError": 3.14, # Mean absolute error.
+ "meanSquaredError": 3.14, # Mean squared error.
+ "medianAbsoluteError": 3.14, # Median absolute error.
+ "rSquared": 3.14, # R^2 score.
+ },
+ "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models.
+ "negativeLabel": "A String", # Label representing the negative class.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "positiveLabel": "A String", # Label representing the positive class.
+ "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds.
+ { # Confusion matrix for binary classification models.
+ "truePositives": "A String", # Number of true samples predicted as true.
+ "recall": 3.14, # Aggregate recall.
+ "precision": 3.14, # Aggregate precision.
+ "falseNegatives": "A String", # Number of false samples predicted as false.
+ "trueNegatives": "A String", # Number of true samples predicted as false.
+ "falsePositives": "A String", # Number of false samples predicted as true.
+ "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric.
+ },
+ ],
+ },
+ "multiClassClassificationMetrics": { # Evaluation metrics for multi-class classification/classifier models. # Populated for multi-class classification/classifier models.
+ "aggregateClassificationMetrics": { # Aggregate metrics for classification/classifier models. For multi-class # Aggregate classification metrics.
+ # models, the metrics are either macro-averaged or micro-averaged. When
+ # macro-averaged, the metrics are calculated for each label and then an
+ # unweighted average is taken of those values. When micro-averaged, the
+ # metric is calculated globally by counting the total number of correctly
+ # predicted rows.
+ "recall": 3.14, # Recall is the fraction of actual positive labels that were given a
+ # positive prediction. For multiclass this is a macro-averaged metric.
+ "precision": 3.14, # Precision is the fraction of actual positive predictions that had
+ # positive actual labels. For multiclass this is a macro-averaged
+ # metric treating each class as a binary classifier.
+ "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric.
+ "threshold": 3.14, # Threshold at which the metrics are computed. For binary
+ # classification models this is the positive class threshold.
+ # For multi-class classfication models this is the confidence
+ # threshold.
+ "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For
+ # multiclass this is a micro-averaged metric.
+ "f1Score": 3.14, # The F1 score is an average of recall and precision. For multiclass
+ # this is a macro-averaged metric.
+ "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged
+ # metric.
+ },
+ "confusionMatrixList": [ # Confusion matrix at different thresholds.
+ { # Confusion matrix for multi-class classification models.
+ "confidenceThreshold": 3.14, # Confidence threshold used when computing the entries of the
+ # confusion matrix.
+ "rows": [ # One row per actual label.
+ { # A single row in the confusion matrix.
+ "entries": [ # Info describing predicted label distribution.
+ { # A single entry in the confusion matrix.
+ "predictedLabel": "A String", # The predicted label. For confidence_threshold > 0, we will
+ # also add an entry indicating the number of items under the
+ # confidence threshold.
+ "itemCount": "A String", # Number of items being predicted as this label.
+ },
+ ],
+ "actualLabel": "A String", # The original label of this row.
+ },
+ ],
+ },
+ ],
+ },
+ },
+ "results": [ # Output of each iteration run, results.size() <= max_iterations.
+ { # Information about a single iteration of the training run.
+ "index": 42, # Index of the iteration, 0 based.
+ "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration.
+ "durationMs": "A String", # Time taken to run the iteration in milliseconds.
+ "learnRate": 3.14, # Learn rate used for this iteration.
+ "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration.
+ "clusterInfos": [ # [Beta] Information about top clusters for clustering models.
+ { # Information about a single cluster for clustering model.
+ "centroidId": "A String", # Centroid id.
+ "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster.
+ "clusterRadius": 3.14, # Cluster radius, the average distance from centroid
+ # to each point assigned to the cluster.
+ },
+ ],
+ },
+ ],
+ "startTime": "A String", # The start time of this training run.
+ "trainingOptions": { # Options that were used for this training run, includes
+ # user specified and default options that were used.
+ "optimizationStrategy": "A String", # Optimization strategy for training linear regression models.
+ "inputLabelColumns": [ # Name of input label columns in training data.
+ "A String",
+ ],
+ "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative
+ # training algorithms.
+ "earlyStop": True or False, # Whether to stop early when the loss doesn't improve significantly
+ # any more (compared to min_relative_progress). Used only for iterative
+ # training algorithms.
+ "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate
+ # strategy.
+ "dataSplitColumn": "A String", # The column to split data with. This column won't be used as a
+ # feature.
+ # 1. When data_split_method is CUSTOM, the corresponding column should
+ # be boolean. The rows with true value tag are eval data, and the false
+ # are training data.
+ # 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION
+ # rows (from smallest to largest) in the corresponding column are used
+ # as training data, and the rest are eval data. It respects the order
+ # in Orderable data types:
+ # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
+ "numClusters": "A String", # [Beta] Number of clusters for clustering models.
+ "l1Regularization": 3.14, # L1 regularization coefficient.
+ "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM.
+ "distanceType": "A String", # [Beta] Distance type for clustering models.
+ "warmStart": True or False, # Whether to train a model from the last checkpoint.
+ "labelClassWeights": { # Weights associated with each label class, for rebalancing the
+ # training data. Only applicable for classification models.
+ "a_key": 3.14,
+ },
+ "lossType": "A String", # Type of loss function used during training run.
+ "dataSplitEvalFraction": 3.14, # The fraction of evaluation data over the whole input data. The rest
+ # of data will be used as training data. The format should be double.
+ # Accurate to two decimal places.
+ # Default value is 0.2.
+ "l2Regularization": 3.14, # L2 regularization coefficient.
+ "modelUri": "A String", # [Beta] Google Cloud Storage URI from which the model was imported. Only
+ # applicable for imported models.
+ "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration.
+ "minRelativeProgress": 3.14, # When early_stop is true, stops training when accuracy improvement is
+ # less than 'min_relative_progress'. Used only for iterative training
+ # algorithms.
+ "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms.
+ },
+ },
+ ],
+ "featureColumns": [ # Output only. Input feature columns that were used to train this model.
+ { # A field or a column.
+ "type": { # The type of a variable, e.g., a function argument. # Optional. The type of this parameter. Absent if not explicitly
+ # specified (e.g., CREATE FUNCTION statement can omit the return type;
+ # in this case the output parameter does not have this "type" field).
+ # Examples:
+ # INT64: {type_kind="INT64"}
+ # ARRAY<STRING>: {type_kind="ARRAY", array_element_type="STRING"}
+ # STRUCT<x STRING, y ARRAY<DATE>>:
+ # {type_kind="STRUCT",
+ # struct_type={fields=[
+ # {name="x", type={type_kind="STRING"}},
+ # {name="y", type={type_kind="ARRAY", array_element_type="DATE"}}
+ # ]}}
+ "structType": { # The fields of this struct, in order, if type_kind = "STRUCT".
+ "fields": [
+ # Object with schema name: StandardSqlField
+ ],
+ },
+ "arrayElementType": # Object with schema name: StandardSqlDataType # The type of the array's elements, if type_kind = "ARRAY".
+ "typeKind": "A String", # Required. The top level type of this field.
+ # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY").
+ },
+ "name": "A String", # Optional. The name of this field. Can be absent for struct fields.
+ },
+ ],
+ "labels": { # [Optional] The labels associated with this model. You can use these to
+ # organize and group your models. Label keys and values can be no longer
+ # than 63 characters, can only contain lowercase letters, numeric
+ # characters, underscores and dashes. International characters are allowed.
+ # Label values are optional. Label keys must start with a letter and each
+ # label in the list must have a different key.
+ "a_key": "A String",
+ },
+ "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the
+ # epoch.
+ "modelType": "A String", # Output only. Type of the model resource.
+ "modelReference": { # Id path of a model. # Required. Unique identifier for this model.
+ "projectId": "A String", # [Required] The ID of the project containing this model.
+ "datasetId": "A String", # [Required] The ID of the dataset containing this model.
+ "modelId": "A String", # [Required] The ID of the model. The ID must contain only
+ # letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum
+ # length is 1,024 characters.
+ },
+ "etag": "A String", # Output only. A hash of this resource.
+ "location": "A String", # Output only. The geographic location where the model resides. This value
+ # is inherited from the dataset.
+ "friendlyName": "A String", # [Optional] A descriptive name for this model.
+ "expirationTime": "A String", # [Optional] The time when this model expires, in milliseconds since the
+ # epoch. If not present, the model will persist indefinitely. Expired models
+ # will be deleted and their storage reclaimed. The defaultTableExpirationMs
+ # property of the encapsulating dataset can be used to set a default
+ # expirationTime on newly created models.
+ "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs
+ # since the epoch.
+ }</pre>
+</div>
+
+</body></html>
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