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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> |
