final class PresenceAndValencyStatistics extends GeneratedMessageV3 with PresenceAndValencyStatisticsOrBuilder
Statistics about the presence and valency of feature values. Feature values could be nested lists. A feature in tf.Examples or other "flat" datasets has values of nest level 1 -- they are lists of primitives. A nest level N (N > 1) feature value is a list of lists of nest level (N - 1). This proto can be used to describe the presence and valency of values at each level.
Protobuf type tensorflow.metadata.v0.PresenceAndValencyStatistics
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- def getMaxNumValues(): Long
Maximum length of the values.
Maximum length of the values.
uint64 max_num_values = 4;- returns
The maxNumValues.
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- def getMinNumValues(): Long
Minimum length of the values (note that nulls are not considered).
Minimum length of the values (note that nulls are not considered).
uint64 min_num_values = 3;- returns
The minNumValues.
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- def getNumMissing(): Long
Number of missing (null) values.
Number of missing (null) values.
uint64 num_missing = 2;- returns
The numMissing.
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- def getNumNonMissing(): Long
Note: missing and non-missing counts are conditioned on the upper level being non-missing (i.e. if the upper level is missing/null, all the levels nested below are by definition missing, but not counted). Number non-missing (not-null) values.
Note: missing and non-missing counts are conditioned on the upper level being non-missing (i.e. if the upper level is missing/null, all the levels nested below are by definition missing, but not counted). Number non-missing (not-null) values.
uint64 num_non_missing = 1;- returns
The numNonMissing.
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- def getTotNumValues(): Long
Total number of values.
Total number of values.
uint64 tot_num_values = 5;- returns
The totNumValues.
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