com.google.api.services.language.v2.model

Class XPSConfidenceMetricsEntry

java.lang.Object

java.util.AbstractMap<String,Object>

com.google.api.client.util.GenericData

com.google.api.client.json.GenericJson

com.google.api.services.language.v2.model.XPSConfidenceMetricsEntry

All Implemented Interfaces:

Cloneable, Map<String,Object>

public final class XPSConfidenceMetricsEntry
extends com.google.api.client.json.GenericJson

ConfidenceMetricsEntry includes generic precision, recall, f1 score etc. Next tag: 16.

This is the Java data model class that specifies how to parse/serialize into the JSON that is transmitted over HTTP when working with the Cloud Natural Language API. For a detailed explanation see: https://developers.google.com/api-client-library/java/google-http-java-client/json

Author:

Google, Inc.

Nested Class Summary

Nested classes/interfaces inherited from class com.google.api.client.util.GenericData

com.google.api.client.util.GenericData.Flags

Nested classes/interfaces inherited from class java.util.AbstractMap

AbstractMap.SimpleEntry<K,V>, AbstractMap.SimpleImmutableEntry<K,V>

Nested classes/interfaces inherited from interface java.util.Map

Map.Entry<K,V>

Constructor Summary

Constructors

Constructor and Description
XPSConfidenceMetricsEntry()

Method Summary

Modifier and Type	Method and Description
XPSConfidenceMetricsEntry	clone()
Float	getConfidenceThreshold() Metrics are computed with an assumption that the model never return predictions with score lower than this value.
Float	getF1Score() The harmonic mean of recall and precision.
Float	getF1ScoreAt1() The harmonic mean of recall_at1 and precision_at1.
Long	getFalseNegativeCount() The number of ground truth labels that are not matched by a model created label.
Long	getFalsePositiveCount() The number of model created labels that do not match a ground truth label.
Float	getFalsePositiveRate() False Positive Rate for the given confidence threshold.
Float	getFalsePositiveRateAt1() The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
Integer	getPositionThreshold() Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.
Float	getPrecision() Precision for the given confidence threshold.
Float	getPrecisionAt1() The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
Float	getRecall() Recall (true positive rate) for the given confidence threshold.
Float	getRecallAt1() The recall (true positive rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
Long	getTrueNegativeCount() The number of labels that were not created by the model, but if they would, they would not match a ground truth label.
Long	getTruePositiveCount() The number of model created labels that match a ground truth label.
XPSConfidenceMetricsEntry	set(String fieldName, Object value)
XPSConfidenceMetricsEntry	setConfidenceThreshold(Float confidenceThreshold) Metrics are computed with an assumption that the model never return predictions with score lower than this value.
XPSConfidenceMetricsEntry	setF1Score(Float f1Score) The harmonic mean of recall and precision.
XPSConfidenceMetricsEntry	setF1ScoreAt1(Float f1ScoreAt1) The harmonic mean of recall_at1 and precision_at1.
XPSConfidenceMetricsEntry	setFalseNegativeCount(Long falseNegativeCount) The number of ground truth labels that are not matched by a model created label.
XPSConfidenceMetricsEntry	setFalsePositiveCount(Long falsePositiveCount) The number of model created labels that do not match a ground truth label.
XPSConfidenceMetricsEntry	setFalsePositiveRate(Float falsePositiveRate) False Positive Rate for the given confidence threshold.
XPSConfidenceMetricsEntry	setFalsePositiveRateAt1(Float falsePositiveRateAt1) The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
XPSConfidenceMetricsEntry	setPositionThreshold(Integer positionThreshold) Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.
XPSConfidenceMetricsEntry	setPrecision(Float precision) Precision for the given confidence threshold.
XPSConfidenceMetricsEntry	setPrecisionAt1(Float precisionAt1) The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
XPSConfidenceMetricsEntry	setRecall(Float recall) Recall (true positive rate) for the given confidence threshold.
XPSConfidenceMetricsEntry	setRecallAt1(Float recallAt1) The recall (true positive rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
XPSConfidenceMetricsEntry	setTrueNegativeCount(Long trueNegativeCount) The number of labels that were not created by the model, but if they would, they would not match a ground truth label.
XPSConfidenceMetricsEntry	setTruePositiveCount(Long truePositiveCount) The number of model created labels that match a ground truth label.

Methods inherited from class com.google.api.client.json.GenericJson

getFactory, setFactory, toPrettyString, toString

Methods inherited from class com.google.api.client.util.GenericData

entrySet, equals, get, getClassInfo, getUnknownKeys, hashCode, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap

clear, containsKey, containsValue, isEmpty, keySet, size, values

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.util.Map

compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll

Constructor Detail

XPSConfidenceMetricsEntry

public XPSConfidenceMetricsEntry()

Method Detail

getConfidenceThreshold

public Float getConfidenceThreshold()

Metrics are computed with an assumption that the model never return predictions with score lower than this value.

Returns:

value or null for none

setConfidenceThreshold

public XPSConfidenceMetricsEntry setConfidenceThreshold(Float confidenceThreshold)

Metrics are computed with an assumption that the model never return predictions with score lower than this value.

Parameters:

confidenceThreshold - confidenceThreshold or null for none

getF1Score

public Float getF1Score()

The harmonic mean of recall and precision.

Returns:

value or null for none

setF1Score

public XPSConfidenceMetricsEntry setF1Score(Float f1Score)

The harmonic mean of recall and precision.

Parameters:

f1Score - f1Score or null for none

getF1ScoreAt1

public Float getF1ScoreAt1()

The harmonic mean of recall_at1 and precision_at1.

Returns:

value or null for none

setF1ScoreAt1

public XPSConfidenceMetricsEntry setF1ScoreAt1(Float f1ScoreAt1)

The harmonic mean of recall_at1 and precision_at1.

Parameters:

f1ScoreAt1 - f1ScoreAt1 or null for none

getFalseNegativeCount

public Long getFalseNegativeCount()

The number of ground truth labels that are not matched by a model created label.

Returns:

value or null for none

setFalseNegativeCount

public XPSConfidenceMetricsEntry setFalseNegativeCount(Long falseNegativeCount)

The number of ground truth labels that are not matched by a model created label.

Parameters:

falseNegativeCount - falseNegativeCount or null for none

getFalsePositiveCount

public Long getFalsePositiveCount()

The number of model created labels that do not match a ground truth label.

Returns:

value or null for none

setFalsePositiveCount

public XPSConfidenceMetricsEntry setFalsePositiveCount(Long falsePositiveCount)

The number of model created labels that do not match a ground truth label.

Parameters:

falsePositiveCount - falsePositiveCount or null for none

getFalsePositiveRate

public Float getFalsePositiveRate()

False Positive Rate for the given confidence threshold.

Returns:

value or null for none

setFalsePositiveRate

public XPSConfidenceMetricsEntry setFalsePositiveRate(Float falsePositiveRate)

False Positive Rate for the given confidence threshold.

Parameters:

falsePositiveRate - falsePositiveRate or null for none

getFalsePositiveRateAt1

public Float getFalsePositiveRateAt1()

The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Returns:

value or null for none

setFalsePositiveRateAt1

public XPSConfidenceMetricsEntry setFalsePositiveRateAt1(Float falsePositiveRateAt1)

The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Parameters:

falsePositiveRateAt1 - falsePositiveRateAt1 or null for none

getPositionThreshold

public Integer getPositionThreshold()

Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.

Returns:

value or null for none

setPositionThreshold

public XPSConfidenceMetricsEntry setPositionThreshold(Integer positionThreshold)

Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.

Parameters:

positionThreshold - positionThreshold or null for none

getPrecision

public Float getPrecision()

Precision for the given confidence threshold.

Returns:

value or null for none

setPrecision

public XPSConfidenceMetricsEntry setPrecision(Float precision)

Precision for the given confidence threshold.

Parameters:

precision - precision or null for none

getPrecisionAt1

public Float getPrecisionAt1()

The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Returns:

value or null for none

setPrecisionAt1

public XPSConfidenceMetricsEntry setPrecisionAt1(Float precisionAt1)

The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Parameters:

precisionAt1 - precisionAt1 or null for none

getRecall

public Float getRecall()

Recall (true positive rate) for the given confidence threshold.

Returns:

value or null for none

setRecall

public XPSConfidenceMetricsEntry setRecall(Float recall)

Recall (true positive rate) for the given confidence threshold.

Parameters:

recall - recall or null for none

getRecallAt1

public Float getRecallAt1()

The recall (true positive rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Returns:

value or null for none

setRecallAt1

public XPSConfidenceMetricsEntry setRecallAt1(Float recallAt1)

The recall (true positive rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

Parameters:

recallAt1 - recallAt1 or null for none

getTrueNegativeCount

public Long getTrueNegativeCount()

The number of labels that were not created by the model, but if they would, they would not match a ground truth label.

Returns:

value or null for none

setTrueNegativeCount

public XPSConfidenceMetricsEntry setTrueNegativeCount(Long trueNegativeCount)

The number of labels that were not created by the model, but if they would, they would not match a ground truth label.

Parameters:

trueNegativeCount - trueNegativeCount or null for none

getTruePositiveCount

public Long getTruePositiveCount()

The number of model created labels that match a ground truth label.

Returns:

value or null for none

setTruePositiveCount

public XPSConfidenceMetricsEntry setTruePositiveCount(Long truePositiveCount)

The number of model created labels that match a ground truth label.

Parameters:

truePositiveCount - truePositiveCount or null for none

set

public XPSConfidenceMetricsEntry set(String fieldName,
                                     Object value)

Overrides:

set in class com.google.api.client.json.GenericJson

clone

public XPSConfidenceMetricsEntry clone()

Overrides:

clone in class com.google.api.client.json.GenericJson