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| <h1><a href="bigquery_v2.html">BigQuery API</a> . <a href="bigquery_v2.models.html">models</a></h1> | |
| <h2>Instance Methods</h2> | |
| <p class="toc_element"> | |
| <code><a href="#delete">delete(projectId, datasetId, modelId)</a></code></p> | |
| <p class="firstline">Deletes the model specified by modelId from the dataset.</p> | |
| <p class="toc_element"> | |
| <code><a href="#get">get(projectId, datasetId, modelId)</a></code></p> | |
| <p class="firstline">Gets the specified model resource by model ID.</p> | |
| <p class="toc_element"> | |
| <code><a href="#list">list(projectId, datasetId, pageToken=None, maxResults=None)</a></code></p> | |
| <p class="firstline">Lists all models in the specified dataset. Requires the READER dataset</p> | |
| <p class="toc_element"> | |
| <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p> | |
| <p class="firstline">Retrieves the next page of results.</p> | |
| <p class="toc_element"> | |
| <code><a href="#patch">patch(projectId, datasetId, modelId, body=None)</a></code></p> | |
| <p class="firstline">Patch specific fields in the specified model.</p> | |
| <h3>Method Details</h3> | |
| <div class="method"> | |
| <code class="details" id="delete">delete(projectId, datasetId, modelId)</code> | |
| <pre>Deletes the model specified by modelId from the dataset. | |
| Args: | |
| projectId: string, Required. Project ID of the model to delete. (required) | |
| datasetId: string, Required. Dataset ID of the model to delete. (required) | |
| modelId: string, Required. Model ID of the model to delete. (required) | |
| </pre> | |
| </div> | |
| <div class="method"> | |
| <code class="details" id="get">get(projectId, datasetId, modelId)</code> | |
| <pre>Gets the specified model resource by model ID. | |
| Args: | |
| projectId: string, Required. Project ID of the requested model. (required) | |
| datasetId: string, Required. Dataset ID of the requested model. (required) | |
| modelId: string, Required. Model ID of the requested model. (required) | |
| Returns: | |
| An object of the form: | |
| { | |
| "modelType": "A String", # Output only. Type of the model resource. | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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. | |
| "trainingRuns": [ # Output only. Information for all training runs in increasing order of start_time. | |
| { # Information about a single training query run for the model. | |
| "startTime": "A String", # The start time of this training run. | |
| "results": [ # Output of each iteration run, results.size() <= max_iterations. | |
| { # Information about a single iteration of the training run. | |
| "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration. | |
| "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration. | |
| "index": 42, # Index of the iteration, 0 based. | |
| "learnRate": 3.14, # Learn rate used for this iteration. | |
| "durationMs": "A String", # Time taken to run the iteration in milliseconds. | |
| "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier | |
| # refactoring if we want to use model-specific iteration results. | |
| "arimaModelInfo": [ # This message is repeated because there are multiple arima models | |
| # fitted in auto-arima. For non-auto-arima model, its size is one. | |
| { # Arima model information. | |
| "arimaCoefficients": { # Arima coefficients. # Arima coefficients. | |
| "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double. | |
| 3.14, | |
| ], | |
| "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array. | |
| "movingAverageCoefficients": [ # Moving-average coefficients, an array of double. | |
| 3.14, | |
| ], | |
| }, | |
| "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false | |
| # when d is not 1. | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported | |
| # for one time series. | |
| "A String", | |
| ], | |
| "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order. | |
| "d": "A String", # Order of the differencing part. | |
| "p": "A String", # Order of the autoregressive part. | |
| "q": "A String", # Order of the moving-average part. | |
| }, | |
| "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics. | |
| "aic": 3.14, # AIC. | |
| "logLikelihood": 3.14, # Log-likelihood. | |
| "variance": 3.14, # Variance. | |
| }, | |
| "timeSeriesId": "A String", # The id to indicate different time series. | |
| }, | |
| ], | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for | |
| # one time series. | |
| "A String", | |
| ], | |
| }, | |
| "clusterInfos": [ # Information about top clusters for clustering models. | |
| { # Information about a single cluster for clustering model. | |
| "clusterRadius": 3.14, # Cluster radius, the average distance from centroid | |
| # to each point assigned to the cluster. | |
| "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster. | |
| "centroidId": "A String", # Centroid id. | |
| }, | |
| ], | |
| }, | |
| ], | |
| "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. | |
| "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization | |
| # models. | |
| # feedback_type=implicit. | |
| "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the | |
| # predicted confidence by comparing it to an ideal rank measured by the | |
| # original ratings. | |
| "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank | |
| # from the predicted confidence and dividing it by the original rank. | |
| "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit | |
| # recommendation models except instead of computing the rating directly, | |
| # the output from evaluate is computed against a preference which is 1 or 0 | |
| # depending on if the rating exists or not. | |
| "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and | |
| # then averages all the precisions across all the users. | |
| }, | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "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. | |
| "itemCount": "A String", # Number of items being predicted as this label. | |
| "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. | |
| }, | |
| ], | |
| "actualLabel": "A String", # The original label of this row. | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models. | |
| "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid. | |
| "daviesBouldinIndex": 3.14, # Davies-Bouldin index. | |
| "clusters": [ # [Beta] Information for all clusters. | |
| { # Message containing the information about one cluster. | |
| "centroidId": "A String", # Centroid id. | |
| "count": "A String", # Count of training data rows that were assigned to this cluster. | |
| "featureValues": [ # Values of highly variant features for this cluster. | |
| { # Representative value of a single feature within the cluster. | |
| "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this | |
| # feature. | |
| "featureColumn": "A String", # The feature column name. | |
| "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value. | |
| "categoryCounts": [ # Counts of all categories for the categorical feature. If there are | |
| # more than ten categories, we return top ten (by count) and return | |
| # one more CategoryCount with category "_OTHER_" and count as | |
| # aggregate counts of remaining categories. | |
| { # Represents the count of a single category within the cluster. | |
| "category": "A String", # The name of category. | |
| "count": "A String", # The count of training samples matching the category within the | |
| # cluster. | |
| }, | |
| ], | |
| }, | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models. | |
| "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds. | |
| { # Confusion matrix for binary classification models. | |
| "recall": 3.14, # The fraction of actual positive labels that were given a positive | |
| # prediction. | |
| "falseNegatives": "A String", # Number of false samples predicted as false. | |
| "falsePositives": "A String", # Number of false samples predicted as true. | |
| "trueNegatives": "A String", # Number of true samples predicted as false. | |
| "f1Score": 3.14, # The equally weighted average of recall and precision. | |
| "precision": 3.14, # The fraction of actual positive predictions that had positive actual | |
| # labels. | |
| "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric. | |
| "accuracy": 3.14, # The fraction of predictions given the correct label. | |
| "truePositives": "A String", # Number of true samples predicted as true. | |
| }, | |
| ], | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "negativeLabel": "A String", # Label representing the negative class. | |
| "positiveLabel": "A String", # Label representing the positive class. | |
| }, | |
| "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix | |
| # factorization models. | |
| # factorization models. | |
| "meanSquaredError": 3.14, # Mean squared error. | |
| "rSquared": 3.14, # R^2 score. | |
| "medianAbsoluteError": 3.14, # Median absolute error. | |
| "meanSquaredLogError": 3.14, # Mean squared log error. | |
| "meanAbsoluteError": 3.14, # Mean absolute error. | |
| }, | |
| }, | |
| "trainingOptions": { # Options that were used for this training run, includes | |
| # user specified and default options that were used. | |
| "dropout": 3.14, # Dropout probability for dnn models. | |
| "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms. | |
| "labelClassWeights": { # Weights associated with each label class, for rebalancing the | |
| # training data. Only applicable for classification models. | |
| "a_key": 3.14, | |
| }, | |
| "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent | |
| # overfitting for boosted tree models. | |
| "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. | |
| "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. | |
| "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate | |
| # strategy. | |
| "itemColumn": "A String", # Item column specified for matrix factorization models. | |
| "inputLabelColumns": [ # Name of input label columns in training data. | |
| "A String", | |
| ], | |
| "warmStart": True or False, # Whether to train a model from the last checkpoint. | |
| "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration. | |
| "numFactors": "A String", # Num factors specified for matrix factorization models. | |
| "lossType": "A String", # Type of loss function used during training run. | |
| "hiddenUnits": [ # Hidden units for dnn models. | |
| "A String", | |
| ], | |
| "l1Regularization": 3.14, # L1 regularization coefficient. | |
| "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm. | |
| "distanceType": "A String", # Distance type for clustering models. | |
| "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is | |
| # specified. | |
| "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix | |
| # factorization. | |
| "optimizationStrategy": "A String", # Optimization strategy for training linear regression models. | |
| "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 | |
| "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative | |
| # training algorithms. | |
| "userColumn": "A String", # User column specified for matrix factorization models. | |
| "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models. | |
| "preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. | |
| # Suppose there is a struct A with field b. | |
| # When false (default), the output feature name is A_b. | |
| # When true, the output feature name is A.b. | |
| "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. | |
| "batchSize": "A String", # Batch size for dnn models. | |
| "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm | |
| # when kmeans_initialization_method is CUSTOM. | |
| "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. | |
| "numClusters": "A String", # Number of clusters for clustering models. | |
| "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM. | |
| "minSplitLoss": 3.14, # Minimum split loss for boosted tree models. | |
| }, | |
| "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is | |
| # actually split. | |
| # data tables that were used to train the model. | |
| "trainingTable": { # Table reference of the training data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| "evaluationTable": { # Table reference of the evaluation data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| }, | |
| }, | |
| ], | |
| "modelReference": { # Required. Unique identifier for this model. | |
| "projectId": "A String", # [Required] The ID of the project containing this model. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this model. | |
| "modelId": "A String", # [Required] The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| }, | |
| "description": "A String", # Optional. A user-friendly description of this model. | |
| "etag": "A String", # Output only. A hash of this resource. | |
| "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the epoch. | |
| "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the | |
| # encryption configuration of the model data while stored in BigQuery | |
| # storage. This field can be used with PatchModel to update encryption key | |
| # for an already encrypted model. | |
| "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key. | |
| }, | |
| "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. | |
| "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs since the epoch. | |
| "labels": { # The labels associated with this model. You can use these to organize | |
| # and group your models. Label keys and values can be no longer | |
| # than 63 characters, can only contain lowercase letters, numeric | |
| # characters, underscores and dashes. International characters are allowed. | |
| # Label values are optional. Label keys must start with a letter and each | |
| # label in the list must have a different key. | |
| "a_key": "A String", | |
| }, | |
| }</pre> | |
| </div> | |
| <div class="method"> | |
| <code class="details" id="list">list(projectId, datasetId, pageToken=None, maxResults=None)</code> | |
| <pre>Lists all models in the specified dataset. Requires the READER dataset | |
| role. | |
| Args: | |
| projectId: string, Required. Project ID of the models to list. (required) | |
| datasetId: string, Required. Dataset ID of the models to list. (required) | |
| pageToken: string, Page token, returned by a previous call to request the next page of | |
| results | |
| maxResults: integer, The maximum number of results to return in a single response page. | |
| Leverage the page tokens to iterate through the entire collection. | |
| Returns: | |
| An object of the form: | |
| { | |
| "models": [ # Models in the requested dataset. Only the following fields are populated: | |
| # model_reference, model_type, creation_time, last_modified_time and | |
| # labels. | |
| { | |
| "modelType": "A String", # Output only. Type of the model resource. | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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. | |
| "trainingRuns": [ # Output only. Information for all training runs in increasing order of start_time. | |
| { # Information about a single training query run for the model. | |
| "startTime": "A String", # The start time of this training run. | |
| "results": [ # Output of each iteration run, results.size() <= max_iterations. | |
| { # Information about a single iteration of the training run. | |
| "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration. | |
| "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration. | |
| "index": 42, # Index of the iteration, 0 based. | |
| "learnRate": 3.14, # Learn rate used for this iteration. | |
| "durationMs": "A String", # Time taken to run the iteration in milliseconds. | |
| "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier | |
| # refactoring if we want to use model-specific iteration results. | |
| "arimaModelInfo": [ # This message is repeated because there are multiple arima models | |
| # fitted in auto-arima. For non-auto-arima model, its size is one. | |
| { # Arima model information. | |
| "arimaCoefficients": { # Arima coefficients. # Arima coefficients. | |
| "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double. | |
| 3.14, | |
| ], | |
| "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array. | |
| "movingAverageCoefficients": [ # Moving-average coefficients, an array of double. | |
| 3.14, | |
| ], | |
| }, | |
| "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false | |
| # when d is not 1. | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported | |
| # for one time series. | |
| "A String", | |
| ], | |
| "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order. | |
| "d": "A String", # Order of the differencing part. | |
| "p": "A String", # Order of the autoregressive part. | |
| "q": "A String", # Order of the moving-average part. | |
| }, | |
| "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics. | |
| "aic": 3.14, # AIC. | |
| "logLikelihood": 3.14, # Log-likelihood. | |
| "variance": 3.14, # Variance. | |
| }, | |
| "timeSeriesId": "A String", # The id to indicate different time series. | |
| }, | |
| ], | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for | |
| # one time series. | |
| "A String", | |
| ], | |
| }, | |
| "clusterInfos": [ # Information about top clusters for clustering models. | |
| { # Information about a single cluster for clustering model. | |
| "clusterRadius": 3.14, # Cluster radius, the average distance from centroid | |
| # to each point assigned to the cluster. | |
| "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster. | |
| "centroidId": "A String", # Centroid id. | |
| }, | |
| ], | |
| }, | |
| ], | |
| "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. | |
| "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization | |
| # models. | |
| # feedback_type=implicit. | |
| "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the | |
| # predicted confidence by comparing it to an ideal rank measured by the | |
| # original ratings. | |
| "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank | |
| # from the predicted confidence and dividing it by the original rank. | |
| "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit | |
| # recommendation models except instead of computing the rating directly, | |
| # the output from evaluate is computed against a preference which is 1 or 0 | |
| # depending on if the rating exists or not. | |
| "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and | |
| # then averages all the precisions across all the users. | |
| }, | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "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. | |
| "itemCount": "A String", # Number of items being predicted as this label. | |
| "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. | |
| }, | |
| ], | |
| "actualLabel": "A String", # The original label of this row. | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models. | |
| "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid. | |
| "daviesBouldinIndex": 3.14, # Davies-Bouldin index. | |
| "clusters": [ # [Beta] Information for all clusters. | |
| { # Message containing the information about one cluster. | |
| "centroidId": "A String", # Centroid id. | |
| "count": "A String", # Count of training data rows that were assigned to this cluster. | |
| "featureValues": [ # Values of highly variant features for this cluster. | |
| { # Representative value of a single feature within the cluster. | |
| "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this | |
| # feature. | |
| "featureColumn": "A String", # The feature column name. | |
| "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value. | |
| "categoryCounts": [ # Counts of all categories for the categorical feature. If there are | |
| # more than ten categories, we return top ten (by count) and return | |
| # one more CategoryCount with category "_OTHER_" and count as | |
| # aggregate counts of remaining categories. | |
| { # Represents the count of a single category within the cluster. | |
| "category": "A String", # The name of category. | |
| "count": "A String", # The count of training samples matching the category within the | |
| # cluster. | |
| }, | |
| ], | |
| }, | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models. | |
| "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds. | |
| { # Confusion matrix for binary classification models. | |
| "recall": 3.14, # The fraction of actual positive labels that were given a positive | |
| # prediction. | |
| "falseNegatives": "A String", # Number of false samples predicted as false. | |
| "falsePositives": "A String", # Number of false samples predicted as true. | |
| "trueNegatives": "A String", # Number of true samples predicted as false. | |
| "f1Score": 3.14, # The equally weighted average of recall and precision. | |
| "precision": 3.14, # The fraction of actual positive predictions that had positive actual | |
| # labels. | |
| "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric. | |
| "accuracy": 3.14, # The fraction of predictions given the correct label. | |
| "truePositives": "A String", # Number of true samples predicted as true. | |
| }, | |
| ], | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "negativeLabel": "A String", # Label representing the negative class. | |
| "positiveLabel": "A String", # Label representing the positive class. | |
| }, | |
| "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix | |
| # factorization models. | |
| # factorization models. | |
| "meanSquaredError": 3.14, # Mean squared error. | |
| "rSquared": 3.14, # R^2 score. | |
| "medianAbsoluteError": 3.14, # Median absolute error. | |
| "meanSquaredLogError": 3.14, # Mean squared log error. | |
| "meanAbsoluteError": 3.14, # Mean absolute error. | |
| }, | |
| }, | |
| "trainingOptions": { # Options that were used for this training run, includes | |
| # user specified and default options that were used. | |
| "dropout": 3.14, # Dropout probability for dnn models. | |
| "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms. | |
| "labelClassWeights": { # Weights associated with each label class, for rebalancing the | |
| # training data. Only applicable for classification models. | |
| "a_key": 3.14, | |
| }, | |
| "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent | |
| # overfitting for boosted tree models. | |
| "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. | |
| "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. | |
| "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate | |
| # strategy. | |
| "itemColumn": "A String", # Item column specified for matrix factorization models. | |
| "inputLabelColumns": [ # Name of input label columns in training data. | |
| "A String", | |
| ], | |
| "warmStart": True or False, # Whether to train a model from the last checkpoint. | |
| "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration. | |
| "numFactors": "A String", # Num factors specified for matrix factorization models. | |
| "lossType": "A String", # Type of loss function used during training run. | |
| "hiddenUnits": [ # Hidden units for dnn models. | |
| "A String", | |
| ], | |
| "l1Regularization": 3.14, # L1 regularization coefficient. | |
| "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm. | |
| "distanceType": "A String", # Distance type for clustering models. | |
| "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is | |
| # specified. | |
| "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix | |
| # factorization. | |
| "optimizationStrategy": "A String", # Optimization strategy for training linear regression models. | |
| "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 | |
| "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative | |
| # training algorithms. | |
| "userColumn": "A String", # User column specified for matrix factorization models. | |
| "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models. | |
| "preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. | |
| # Suppose there is a struct A with field b. | |
| # When false (default), the output feature name is A_b. | |
| # When true, the output feature name is A.b. | |
| "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. | |
| "batchSize": "A String", # Batch size for dnn models. | |
| "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm | |
| # when kmeans_initialization_method is CUSTOM. | |
| "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. | |
| "numClusters": "A String", # Number of clusters for clustering models. | |
| "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM. | |
| "minSplitLoss": 3.14, # Minimum split loss for boosted tree models. | |
| }, | |
| "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is | |
| # actually split. | |
| # data tables that were used to train the model. | |
| "trainingTable": { # Table reference of the training data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| "evaluationTable": { # Table reference of the evaluation data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| }, | |
| }, | |
| ], | |
| "modelReference": { # Required. Unique identifier for this model. | |
| "projectId": "A String", # [Required] The ID of the project containing this model. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this model. | |
| "modelId": "A String", # [Required] The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| }, | |
| "description": "A String", # Optional. A user-friendly description of this model. | |
| "etag": "A String", # Output only. A hash of this resource. | |
| "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the epoch. | |
| "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the | |
| # encryption configuration of the model data while stored in BigQuery | |
| # storage. This field can be used with PatchModel to update encryption key | |
| # for an already encrypted model. | |
| "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key. | |
| }, | |
| "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. | |
| "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs since the epoch. | |
| "labels": { # The labels associated with this model. You can use these to organize | |
| # and group your models. Label keys and values can be no longer | |
| # than 63 characters, can only contain lowercase letters, numeric | |
| # characters, underscores and dashes. International characters are allowed. | |
| # Label values are optional. Label keys must start with a letter and each | |
| # label in the list must have a different key. | |
| "a_key": "A String", | |
| }, | |
| }, | |
| ], | |
| "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=None)</code> | |
| <pre>Patch specific fields in the specified model. | |
| Args: | |
| projectId: string, Required. Project ID of the model to patch. (required) | |
| datasetId: string, Required. Dataset ID of the model to patch. (required) | |
| modelId: string, Required. Model ID of the model to patch. (required) | |
| body: object, The request body. | |
| The object takes the form of: | |
| { | |
| "modelType": "A String", # Output only. Type of the model resource. | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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. | |
| "trainingRuns": [ # Output only. Information for all training runs in increasing order of start_time. | |
| { # Information about a single training query run for the model. | |
| "startTime": "A String", # The start time of this training run. | |
| "results": [ # Output of each iteration run, results.size() <= max_iterations. | |
| { # Information about a single iteration of the training run. | |
| "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration. | |
| "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration. | |
| "index": 42, # Index of the iteration, 0 based. | |
| "learnRate": 3.14, # Learn rate used for this iteration. | |
| "durationMs": "A String", # Time taken to run the iteration in milliseconds. | |
| "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier | |
| # refactoring if we want to use model-specific iteration results. | |
| "arimaModelInfo": [ # This message is repeated because there are multiple arima models | |
| # fitted in auto-arima. For non-auto-arima model, its size is one. | |
| { # Arima model information. | |
| "arimaCoefficients": { # Arima coefficients. # Arima coefficients. | |
| "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double. | |
| 3.14, | |
| ], | |
| "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array. | |
| "movingAverageCoefficients": [ # Moving-average coefficients, an array of double. | |
| 3.14, | |
| ], | |
| }, | |
| "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false | |
| # when d is not 1. | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported | |
| # for one time series. | |
| "A String", | |
| ], | |
| "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order. | |
| "d": "A String", # Order of the differencing part. | |
| "p": "A String", # Order of the autoregressive part. | |
| "q": "A String", # Order of the moving-average part. | |
| }, | |
| "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics. | |
| "aic": 3.14, # AIC. | |
| "logLikelihood": 3.14, # Log-likelihood. | |
| "variance": 3.14, # Variance. | |
| }, | |
| "timeSeriesId": "A String", # The id to indicate different time series. | |
| }, | |
| ], | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for | |
| # one time series. | |
| "A String", | |
| ], | |
| }, | |
| "clusterInfos": [ # Information about top clusters for clustering models. | |
| { # Information about a single cluster for clustering model. | |
| "clusterRadius": 3.14, # Cluster radius, the average distance from centroid | |
| # to each point assigned to the cluster. | |
| "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster. | |
| "centroidId": "A String", # Centroid id. | |
| }, | |
| ], | |
| }, | |
| ], | |
| "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. | |
| "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization | |
| # models. | |
| # feedback_type=implicit. | |
| "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the | |
| # predicted confidence by comparing it to an ideal rank measured by the | |
| # original ratings. | |
| "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank | |
| # from the predicted confidence and dividing it by the original rank. | |
| "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit | |
| # recommendation models except instead of computing the rating directly, | |
| # the output from evaluate is computed against a preference which is 1 or 0 | |
| # depending on if the rating exists or not. | |
| "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and | |
| # then averages all the precisions across all the users. | |
| }, | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "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. | |
| "itemCount": "A String", # Number of items being predicted as this label. | |
| "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. | |
| }, | |
| ], | |
| "actualLabel": "A String", # The original label of this row. | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models. | |
| "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid. | |
| "daviesBouldinIndex": 3.14, # Davies-Bouldin index. | |
| "clusters": [ # [Beta] Information for all clusters. | |
| { # Message containing the information about one cluster. | |
| "centroidId": "A String", # Centroid id. | |
| "count": "A String", # Count of training data rows that were assigned to this cluster. | |
| "featureValues": [ # Values of highly variant features for this cluster. | |
| { # Representative value of a single feature within the cluster. | |
| "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this | |
| # feature. | |
| "featureColumn": "A String", # The feature column name. | |
| "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value. | |
| "categoryCounts": [ # Counts of all categories for the categorical feature. If there are | |
| # more than ten categories, we return top ten (by count) and return | |
| # one more CategoryCount with category "_OTHER_" and count as | |
| # aggregate counts of remaining categories. | |
| { # Represents the count of a single category within the cluster. | |
| "category": "A String", # The name of category. | |
| "count": "A String", # The count of training samples matching the category within the | |
| # cluster. | |
| }, | |
| ], | |
| }, | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models. | |
| "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds. | |
| { # Confusion matrix for binary classification models. | |
| "recall": 3.14, # The fraction of actual positive labels that were given a positive | |
| # prediction. | |
| "falseNegatives": "A String", # Number of false samples predicted as false. | |
| "falsePositives": "A String", # Number of false samples predicted as true. | |
| "trueNegatives": "A String", # Number of true samples predicted as false. | |
| "f1Score": 3.14, # The equally weighted average of recall and precision. | |
| "precision": 3.14, # The fraction of actual positive predictions that had positive actual | |
| # labels. | |
| "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric. | |
| "accuracy": 3.14, # The fraction of predictions given the correct label. | |
| "truePositives": "A String", # Number of true samples predicted as true. | |
| }, | |
| ], | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "negativeLabel": "A String", # Label representing the negative class. | |
| "positiveLabel": "A String", # Label representing the positive class. | |
| }, | |
| "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix | |
| # factorization models. | |
| # factorization models. | |
| "meanSquaredError": 3.14, # Mean squared error. | |
| "rSquared": 3.14, # R^2 score. | |
| "medianAbsoluteError": 3.14, # Median absolute error. | |
| "meanSquaredLogError": 3.14, # Mean squared log error. | |
| "meanAbsoluteError": 3.14, # Mean absolute error. | |
| }, | |
| }, | |
| "trainingOptions": { # Options that were used for this training run, includes | |
| # user specified and default options that were used. | |
| "dropout": 3.14, # Dropout probability for dnn models. | |
| "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms. | |
| "labelClassWeights": { # Weights associated with each label class, for rebalancing the | |
| # training data. Only applicable for classification models. | |
| "a_key": 3.14, | |
| }, | |
| "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent | |
| # overfitting for boosted tree models. | |
| "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. | |
| "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. | |
| "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate | |
| # strategy. | |
| "itemColumn": "A String", # Item column specified for matrix factorization models. | |
| "inputLabelColumns": [ # Name of input label columns in training data. | |
| "A String", | |
| ], | |
| "warmStart": True or False, # Whether to train a model from the last checkpoint. | |
| "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration. | |
| "numFactors": "A String", # Num factors specified for matrix factorization models. | |
| "lossType": "A String", # Type of loss function used during training run. | |
| "hiddenUnits": [ # Hidden units for dnn models. | |
| "A String", | |
| ], | |
| "l1Regularization": 3.14, # L1 regularization coefficient. | |
| "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm. | |
| "distanceType": "A String", # Distance type for clustering models. | |
| "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is | |
| # specified. | |
| "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix | |
| # factorization. | |
| "optimizationStrategy": "A String", # Optimization strategy for training linear regression models. | |
| "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 | |
| "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative | |
| # training algorithms. | |
| "userColumn": "A String", # User column specified for matrix factorization models. | |
| "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models. | |
| "preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. | |
| # Suppose there is a struct A with field b. | |
| # When false (default), the output feature name is A_b. | |
| # When true, the output feature name is A.b. | |
| "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. | |
| "batchSize": "A String", # Batch size for dnn models. | |
| "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm | |
| # when kmeans_initialization_method is CUSTOM. | |
| "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. | |
| "numClusters": "A String", # Number of clusters for clustering models. | |
| "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM. | |
| "minSplitLoss": 3.14, # Minimum split loss for boosted tree models. | |
| }, | |
| "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is | |
| # actually split. | |
| # data tables that were used to train the model. | |
| "trainingTable": { # Table reference of the training data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| "evaluationTable": { # Table reference of the evaluation data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| }, | |
| }, | |
| ], | |
| "modelReference": { # Required. Unique identifier for this model. | |
| "projectId": "A String", # [Required] The ID of the project containing this model. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this model. | |
| "modelId": "A String", # [Required] The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| }, | |
| "description": "A String", # Optional. A user-friendly description of this model. | |
| "etag": "A String", # Output only. A hash of this resource. | |
| "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the epoch. | |
| "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the | |
| # encryption configuration of the model data while stored in BigQuery | |
| # storage. This field can be used with PatchModel to update encryption key | |
| # for an already encrypted model. | |
| "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key. | |
| }, | |
| "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. | |
| "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs since the epoch. | |
| "labels": { # The labels associated with this model. You can use these to organize | |
| # and group your models. Label keys and values can be no longer | |
| # than 63 characters, can only contain lowercase letters, numeric | |
| # characters, underscores and dashes. International characters are allowed. | |
| # Label values are optional. Label keys must start with a letter and each | |
| # label in the list must have a different key. | |
| "a_key": "A String", | |
| }, | |
| } | |
| Returns: | |
| An object of the form: | |
| { | |
| "modelType": "A String", # Output only. Type of the model resource. | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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"}} | |
| # ]}} | |
| "typeKind": "A String", # Required. The top level type of this field. | |
| # Can be any standard SQL data type (e.g., "INT64", "DATE", "ARRAY"). | |
| "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". | |
| }, | |
| "name": "A String", # Optional. The name of this field. Can be absent for struct fields. | |
| }, | |
| ], | |
| "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. | |
| "trainingRuns": [ # Output only. Information for all training runs in increasing order of start_time. | |
| { # Information about a single training query run for the model. | |
| "startTime": "A String", # The start time of this training run. | |
| "results": [ # Output of each iteration run, results.size() <= max_iterations. | |
| { # Information about a single iteration of the training run. | |
| "trainingLoss": 3.14, # Loss computed on the training data at the end of iteration. | |
| "evalLoss": 3.14, # Loss computed on the eval data at the end of iteration. | |
| "index": 42, # Index of the iteration, 0 based. | |
| "learnRate": 3.14, # Learn rate used for this iteration. | |
| "durationMs": "A String", # Time taken to run the iteration in milliseconds. | |
| "arimaResult": { # (Auto-)arima fitting result. Wrap everything in ArimaResult for easier | |
| # refactoring if we want to use model-specific iteration results. | |
| "arimaModelInfo": [ # This message is repeated because there are multiple arima models | |
| # fitted in auto-arima. For non-auto-arima model, its size is one. | |
| { # Arima model information. | |
| "arimaCoefficients": { # Arima coefficients. # Arima coefficients. | |
| "autoRegressiveCoefficients": [ # Auto-regressive coefficients, an array of double. | |
| 3.14, | |
| ], | |
| "interceptCoefficient": 3.14, # Intercept coefficient, just a double not an array. | |
| "movingAverageCoefficients": [ # Moving-average coefficients, an array of double. | |
| 3.14, | |
| ], | |
| }, | |
| "hasDrift": True or False, # Whether Arima model fitted with drift or not. It is always false | |
| # when d is not 1. | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported | |
| # for one time series. | |
| "A String", | |
| ], | |
| "nonSeasonalOrder": { # Arima order, can be used for both non-seasonal and seasonal parts. # Non-seasonal order. | |
| "d": "A String", # Order of the differencing part. | |
| "p": "A String", # Order of the autoregressive part. | |
| "q": "A String", # Order of the moving-average part. | |
| }, | |
| "arimaFittingMetrics": { # ARIMA model fitting metrics. # Arima fitting metrics. | |
| "aic": 3.14, # AIC. | |
| "logLikelihood": 3.14, # Log-likelihood. | |
| "variance": 3.14, # Variance. | |
| }, | |
| "timeSeriesId": "A String", # The id to indicate different time series. | |
| }, | |
| ], | |
| "seasonalPeriods": [ # Seasonal periods. Repeated because multiple periods are supported for | |
| # one time series. | |
| "A String", | |
| ], | |
| }, | |
| "clusterInfos": [ # Information about top clusters for clustering models. | |
| { # Information about a single cluster for clustering model. | |
| "clusterRadius": 3.14, # Cluster radius, the average distance from centroid | |
| # to each point assigned to the cluster. | |
| "clusterSize": "A String", # Cluster size, the total number of points assigned to the cluster. | |
| "centroidId": "A String", # Centroid id. | |
| }, | |
| ], | |
| }, | |
| ], | |
| "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. | |
| "rankingMetrics": { # Evaluation metrics used by weighted-ALS models specified by # [Alpha] Populated for implicit feedback type matrix factorization | |
| # models. | |
| # feedback_type=implicit. | |
| "normalizedDiscountedCumulativeGain": 3.14, # A metric to determine the goodness of a ranking calculated from the | |
| # predicted confidence by comparing it to an ideal rank measured by the | |
| # original ratings. | |
| "averageRank": 3.14, # Determines the goodness of a ranking by computing the percentile rank | |
| # from the predicted confidence and dividing it by the original rank. | |
| "meanSquaredError": 3.14, # Similar to the mean squared error computed in regression and explicit | |
| # recommendation models except instead of computing the rating directly, | |
| # the output from evaluate is computed against a preference which is 1 or 0 | |
| # depending on if the rating exists or not. | |
| "meanAveragePrecision": 3.14, # Calculates a precision per user for all the items by ranking them and | |
| # then averages all the precisions across all the users. | |
| }, | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "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. | |
| "itemCount": "A String", # Number of items being predicted as this label. | |
| "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. | |
| }, | |
| ], | |
| "actualLabel": "A String", # The original label of this row. | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "clusteringMetrics": { # Evaluation metrics for clustering models. # Populated for clustering models. | |
| "meanSquaredDistance": 3.14, # Mean of squared distances between each sample to its cluster centroid. | |
| "daviesBouldinIndex": 3.14, # Davies-Bouldin index. | |
| "clusters": [ # [Beta] Information for all clusters. | |
| { # Message containing the information about one cluster. | |
| "centroidId": "A String", # Centroid id. | |
| "count": "A String", # Count of training data rows that were assigned to this cluster. | |
| "featureValues": [ # Values of highly variant features for this cluster. | |
| { # Representative value of a single feature within the cluster. | |
| "numericalValue": 3.14, # The numerical feature value. This is the centroid value for this | |
| # feature. | |
| "featureColumn": "A String", # The feature column name. | |
| "categoricalValue": { # Representative value of a categorical feature. # The categorical feature value. | |
| "categoryCounts": [ # Counts of all categories for the categorical feature. If there are | |
| # more than ten categories, we return top ten (by count) and return | |
| # one more CategoryCount with category "_OTHER_" and count as | |
| # aggregate counts of remaining categories. | |
| { # Represents the count of a single category within the cluster. | |
| "category": "A String", # The name of category. | |
| "count": "A String", # The count of training samples matching the category within the | |
| # cluster. | |
| }, | |
| ], | |
| }, | |
| }, | |
| ], | |
| }, | |
| ], | |
| }, | |
| "binaryClassificationMetrics": { # Evaluation metrics for binary classification/classifier models. # Populated for binary classification/classifier models. | |
| "binaryConfusionMatrixList": [ # Binary confusion matrix at multiple thresholds. | |
| { # Confusion matrix for binary classification models. | |
| "recall": 3.14, # The fraction of actual positive labels that were given a positive | |
| # prediction. | |
| "falseNegatives": "A String", # Number of false samples predicted as false. | |
| "falsePositives": "A String", # Number of false samples predicted as true. | |
| "trueNegatives": "A String", # Number of true samples predicted as false. | |
| "f1Score": 3.14, # The equally weighted average of recall and precision. | |
| "precision": 3.14, # The fraction of actual positive predictions that had positive actual | |
| # labels. | |
| "positiveClassThreshold": 3.14, # Threshold value used when computing each of the following metric. | |
| "accuracy": 3.14, # The fraction of predictions given the correct label. | |
| "truePositives": "A String", # Number of true samples predicted as true. | |
| }, | |
| ], | |
| "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. | |
| "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. | |
| "rocAuc": 3.14, # Area Under a ROC Curve. For multiclass this is a macro-averaged | |
| # metric. | |
| "logLoss": 3.14, # Logarithmic Loss. For multiclass this is a macro-averaged metric. | |
| "f1Score": 3.14, # The F1 score is an average of recall and precision. 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. | |
| "accuracy": 3.14, # Accuracy is the fraction of predictions given the correct label. For | |
| # multiclass this is a micro-averaged metric. | |
| "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. | |
| }, | |
| "negativeLabel": "A String", # Label representing the negative class. | |
| "positiveLabel": "A String", # Label representing the positive class. | |
| }, | |
| "regressionMetrics": { # Evaluation metrics for regression and explicit feedback type matrix # Populated for regression models and explicit feedback type matrix | |
| # factorization models. | |
| # factorization models. | |
| "meanSquaredError": 3.14, # Mean squared error. | |
| "rSquared": 3.14, # R^2 score. | |
| "medianAbsoluteError": 3.14, # Median absolute error. | |
| "meanSquaredLogError": 3.14, # Mean squared log error. | |
| "meanAbsoluteError": 3.14, # Mean absolute error. | |
| }, | |
| }, | |
| "trainingOptions": { # Options that were used for this training run, includes | |
| # user specified and default options that were used. | |
| "dropout": 3.14, # Dropout probability for dnn models. | |
| "learnRate": 3.14, # Learning rate in training. Used only for iterative training algorithms. | |
| "labelClassWeights": { # Weights associated with each label class, for rebalancing the | |
| # training data. Only applicable for classification models. | |
| "a_key": 3.14, | |
| }, | |
| "subsample": 3.14, # Subsample fraction of the training data to grow tree to prevent | |
| # overfitting for boosted tree models. | |
| "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. | |
| "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. | |
| "initialLearnRate": 3.14, # Specifies the initial learning rate for the line search learn rate | |
| # strategy. | |
| "itemColumn": "A String", # Item column specified for matrix factorization models. | |
| "inputLabelColumns": [ # Name of input label columns in training data. | |
| "A String", | |
| ], | |
| "warmStart": True or False, # Whether to train a model from the last checkpoint. | |
| "learnRateStrategy": "A String", # The strategy to determine learn rate for the current iteration. | |
| "numFactors": "A String", # Num factors specified for matrix factorization models. | |
| "lossType": "A String", # Type of loss function used during training run. | |
| "hiddenUnits": [ # Hidden units for dnn models. | |
| "A String", | |
| ], | |
| "l1Regularization": 3.14, # L1 regularization coefficient. | |
| "kmeansInitializationMethod": "A String", # The method used to initialize the centroids for kmeans algorithm. | |
| "distanceType": "A String", # Distance type for clustering models. | |
| "walsAlpha": 3.14, # Hyperparameter for matrix factoration when implicit feedback type is | |
| # specified. | |
| "feedbackType": "A String", # Feedback type that specifies which algorithm to run for matrix | |
| # factorization. | |
| "optimizationStrategy": "A String", # Optimization strategy for training linear regression models. | |
| "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 | |
| "maxIterations": "A String", # The maximum number of iterations in training. Used only for iterative | |
| # training algorithms. | |
| "userColumn": "A String", # User column specified for matrix factorization models. | |
| "maxTreeDepth": "A String", # Maximum depth of a tree for boosted tree models. | |
| "preserveInputStructs": True or False, # Whether to preserve the input structs in output feature names. | |
| # Suppose there is a struct A with field b. | |
| # When false (default), the output feature name is A_b. | |
| # When true, the output feature name is A.b. | |
| "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. | |
| "batchSize": "A String", # Batch size for dnn models. | |
| "kmeansInitializationColumn": "A String", # The column used to provide the initial centroids for kmeans algorithm | |
| # when kmeans_initialization_method is CUSTOM. | |
| "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. | |
| "numClusters": "A String", # Number of clusters for clustering models. | |
| "dataSplitMethod": "A String", # The data split type for training and evaluation, e.g. RANDOM. | |
| "minSplitLoss": 3.14, # Minimum split loss for boosted tree models. | |
| }, | |
| "dataSplitResult": { # Data split result. This contains references to the training and evaluation # Data split result of the training run. Only set when the input data is | |
| # actually split. | |
| # data tables that were used to train the model. | |
| "trainingTable": { # Table reference of the training data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| "evaluationTable": { # Table reference of the evaluation data after split. | |
| "tableId": "A String", # [Required] The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| "projectId": "A String", # [Required] The ID of the project containing this table. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this table. | |
| }, | |
| }, | |
| }, | |
| ], | |
| "modelReference": { # Required. Unique identifier for this model. | |
| "projectId": "A String", # [Required] The ID of the project containing this model. | |
| "datasetId": "A String", # [Required] The ID of the dataset containing this model. | |
| "modelId": "A String", # [Required] The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters. | |
| }, | |
| "description": "A String", # Optional. A user-friendly description of this model. | |
| "etag": "A String", # Output only. A hash of this resource. | |
| "creationTime": "A String", # Output only. The time when this model was created, in millisecs since the epoch. | |
| "encryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys). This shows the | |
| # encryption configuration of the model data while stored in BigQuery | |
| # storage. This field can be used with PatchModel to update encryption key | |
| # for an already encrypted model. | |
| "kmsKeyName": "A String", # [Optional] Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key. | |
| }, | |
| "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. | |
| "lastModifiedTime": "A String", # Output only. The time when this model was last modified, in millisecs since the epoch. | |
| "labels": { # The labels associated with this model. You can use these to organize | |
| # and group your models. Label keys and values can be no longer | |
| # than 63 characters, can only contain lowercase letters, numeric | |
| # characters, underscores and dashes. International characters are allowed. | |
| # Label values are optional. Label keys must start with a letter and each | |
| # label in the list must have a different key. | |
| "a_key": "A String", | |
| }, | |
| }</pre> | |
| </div> | |
| </body></html> |