Close httplib2 connections.
Retrieves a specific evaluation.
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Retrieves a set of evaluations for a given processor version.
Retrieves the next page of results.
close()
Close httplib2 connections.
get(name, x__xgafv=None)
Retrieves a specific evaluation.
Args:
name: string, Required. The resource name of the Evaluation to get. `projects/{project}/locations/{location}/processors/{processor}/processorVersions/{processorVersion}/evaluations/{evaluation}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # An evaluation of a ProcessorVersion's performance.
"allEntitiesMetrics": { # Metrics across multiple confidence levels. # Metrics for all the entities in aggregate.
"auprc": 3.14, # The calculated area under the precision recall curve (AUPRC), computed by integrating over all confidence thresholds.
"auprcExact": 3.14, # The AUPRC for metrics with fuzzy matching disabled, i.e., exact matching only.
"confidenceLevelMetrics": [ # Metrics across confidence levels with fuzzy matching enabled.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"confidenceLevelMetricsExact": [ # Metrics across confidence levels with only exact matching.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"estimatedCalibrationError": 3.14, # The Estimated Calibration Error (ECE) of the confidence of the predicted entities.
"estimatedCalibrationErrorExact": 3.14, # The ECE for the predicted entities with fuzzy matching disabled, i.e., exact matching only.
"metricsType": "A String", # The metrics type for the label.
},
"createTime": "A String", # The time that the evaluation was created.
"documentCounters": { # Evaluation counters for the documents that were used. # Counters for the documents used in the evaluation.
"evaluatedDocumentsCount": 42, # How many documents were used in the evaluation.
"failedDocumentsCount": 42, # How many documents were not included in the evaluation as Document AI failed to process them.
"inputDocumentsCount": 42, # How many documents were sent for evaluation.
"invalidDocumentsCount": 42, # How many documents were not included in the evaluation as they didn't pass validation.
},
"entityMetrics": { # Metrics across confidence levels, for different entities.
"a_key": { # Metrics across multiple confidence levels.
"auprc": 3.14, # The calculated area under the precision recall curve (AUPRC), computed by integrating over all confidence thresholds.
"auprcExact": 3.14, # The AUPRC for metrics with fuzzy matching disabled, i.e., exact matching only.
"confidenceLevelMetrics": [ # Metrics across confidence levels with fuzzy matching enabled.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"confidenceLevelMetricsExact": [ # Metrics across confidence levels with only exact matching.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"estimatedCalibrationError": 3.14, # The Estimated Calibration Error (ECE) of the confidence of the predicted entities.
"estimatedCalibrationErrorExact": 3.14, # The ECE for the predicted entities with fuzzy matching disabled, i.e., exact matching only.
"metricsType": "A String", # The metrics type for the label.
},
},
"kmsKeyName": "A String", # The KMS key name used for encryption.
"kmsKeyVersionName": "A String", # The KMS key version with which data is encrypted.
"name": "A String", # The resource name of the evaluation. Format: `projects/{project}/locations/{location}/processors/{processor}/processorVersions/{processor_version}/evaluations/{evaluation}`
}
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Retrieves a set of evaluations for a given processor version.
Args:
parent: string, Required. The resource name of the ProcessorVersion to list evaluations for. `projects/{project}/locations/{location}/processors/{processor}/processorVersions/{processorVersion}` (required)
pageSize: integer, The standard list page size. If unspecified, at most `5` evaluations are returned. The maximum value is `100`. Values above `100` are coerced to `100`.
pageToken: string, A page token, received from a previous `ListEvaluations` call. Provide this to retrieve the subsequent page.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The response from `ListEvaluations`.
"evaluations": [ # The evaluations requested.
{ # An evaluation of a ProcessorVersion's performance.
"allEntitiesMetrics": { # Metrics across multiple confidence levels. # Metrics for all the entities in aggregate.
"auprc": 3.14, # The calculated area under the precision recall curve (AUPRC), computed by integrating over all confidence thresholds.
"auprcExact": 3.14, # The AUPRC for metrics with fuzzy matching disabled, i.e., exact matching only.
"confidenceLevelMetrics": [ # Metrics across confidence levels with fuzzy matching enabled.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"confidenceLevelMetricsExact": [ # Metrics across confidence levels with only exact matching.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"estimatedCalibrationError": 3.14, # The Estimated Calibration Error (ECE) of the confidence of the predicted entities.
"estimatedCalibrationErrorExact": 3.14, # The ECE for the predicted entities with fuzzy matching disabled, i.e., exact matching only.
"metricsType": "A String", # The metrics type for the label.
},
"createTime": "A String", # The time that the evaluation was created.
"documentCounters": { # Evaluation counters for the documents that were used. # Counters for the documents used in the evaluation.
"evaluatedDocumentsCount": 42, # How many documents were used in the evaluation.
"failedDocumentsCount": 42, # How many documents were not included in the evaluation as Document AI failed to process them.
"inputDocumentsCount": 42, # How many documents were sent for evaluation.
"invalidDocumentsCount": 42, # How many documents were not included in the evaluation as they didn't pass validation.
},
"entityMetrics": { # Metrics across confidence levels, for different entities.
"a_key": { # Metrics across multiple confidence levels.
"auprc": 3.14, # The calculated area under the precision recall curve (AUPRC), computed by integrating over all confidence thresholds.
"auprcExact": 3.14, # The AUPRC for metrics with fuzzy matching disabled, i.e., exact matching only.
"confidenceLevelMetrics": [ # Metrics across confidence levels with fuzzy matching enabled.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"confidenceLevelMetricsExact": [ # Metrics across confidence levels with only exact matching.
{ # Evaluations metrics, at a specific confidence level.
"confidenceLevel": 3.14, # The confidence level.
"metrics": { # Evaluation metrics, either in aggregate or about a specific entity. # The metrics at the specific confidence level.
"f1Score": 3.14, # The calculated f1 score.
"falseNegativesCount": 42, # The amount of false negatives.
"falsePositivesCount": 42, # The amount of false positives.
"groundTruthDocumentCount": 42, # The amount of documents with a ground truth occurrence.
"groundTruthOccurrencesCount": 42, # The amount of occurrences in ground truth documents.
"precision": 3.14, # The calculated precision.
"predictedDocumentCount": 42, # The amount of documents with a predicted occurrence.
"predictedOccurrencesCount": 42, # The amount of occurrences in predicted documents.
"recall": 3.14, # The calculated recall.
"totalDocumentsCount": 42, # The amount of documents that had an occurrence of this label.
"truePositivesCount": 42, # The amount of true positives.
},
},
],
"estimatedCalibrationError": 3.14, # The Estimated Calibration Error (ECE) of the confidence of the predicted entities.
"estimatedCalibrationErrorExact": 3.14, # The ECE for the predicted entities with fuzzy matching disabled, i.e., exact matching only.
"metricsType": "A String", # The metrics type for the label.
},
},
"kmsKeyName": "A String", # The KMS key name used for encryption.
"kmsKeyVersionName": "A String", # The KMS key version with which data is encrypted.
"name": "A String", # The resource name of the evaluation. Format: `projects/{project}/locations/{location}/processors/{processor}/processorVersions/{processor_version}/evaluations/{evaluation}`
},
],
"nextPageToken": "A String", # A token, which can be sent as `page_token` to retrieve the next page. If this field is omitted, there are no subsequent pages.
}
list_next()
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.