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# Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
schema neuralnet {
document neuralnet {
field uniqueRCount type double {
indexing: attribute
}
field pinned type int {
indexing: attribute
}
field createdAt type long {
indexing: attribute
}
field updatedAt type long {
indexing: attribute
}
field uvCount type int {
indexing: attribute
}
field dvCount type int {
indexing: attribute
}
field aVoteCount type int {
indexing: attribute
}
field rCount type int {
indexing: attribute
}
field uniqueRACount type int {
indexing: attribute
}
field rTo type string {
indexing: attribute
}
field markedAsAAt type long {
indexing: attribute
}
field normalizedTextScore type float {
indexing: attribute
}
field t type float {
indexing: attribute
}
field relevance type float {
indexing: attribute
}
field normalizedCS type float {
indexing: attribute
}
field laAt type long {
indexing: attribute
}
field hsScore type double {
indexing: attribute
}
}
rank-profile defaultRankProfile inherits default {
constants {
maxSignedSixtyFourBitInteger: 9223372036854775807
}
function log10_1p(x) {
expression: log10(x+1)
}
function textScoreToUse() {
expression: if(isNan(attribute(normalizedTextScore)) == 1, 0, attribute(normalizedTextScore))
}
function rCountToUse() {
expression: if(isNan(attribute(rCount)) == 1, 0, if(attribute(rCount) < 0, 0, attribute(rCount)))
}
function uniqueRCountToUse() {
expression: if(isNan(attribute(uniqueRCount)) == 1, 0, if(attribute(uniqueRACount) < 0, 0, attribute(uniqueRACount)))
}
function uvCountToUse() {
expression: if(isNan(attribute(uvCount)) == 1, 0, if(attribute(uvCount) < 0, 0, attribute(uvCount)))
}
function dvCountToUse() {
expression: if(isNan(attribute(dvCount)) == 1, 0, if(attribute(dvCount) < 0, 0, attribute(dvCount)))
}
function aVoteCountToUse() {
expression: if(isNan(attribute(aVoteCount)) == 1, 0, if(attribute(aVoteCount) < 0, 0, attribute(aVoteCount)))
}
function totalPR() {
expression: uniqueRCountToUse + query(voteToRRatio) * (uvCountToUse - dvCountToUse) - aVoteCountToUse
}
function totalvote() {
expression: query(reportaweight) * aVoteCountToUse + dvCountToUse + query(rweight) * uniqueRCountToUse + uvCountToUse
}
function phat() {
expression: if (totalvote == 0, 0, ( query(rweight) * uniqueRCountToUse + uvCountToUse) / totalvote)
}
function nCScoreToUse() {
expression: if (totalPR > 0, log10(totalPR), 0)
}
function hsScoreToUse() {
expression: attribute(hsScore)
}
function tScoreToUse() {
expression: if (isNan(attribute(t)) == 1, 0.6, attribute(t))
}
function relevanceScoreToUse() {
expression: if (isNan(attribute(relevance)) == 1, 0.254, attribute(relevance))
}
function freshnessToUse() {
expression: if (freshness(createdAt).logscale < 0.01, 0.01, freshness(createdAt).logscale)
}
function rankedAt() {
expression: now
}
function createdAtToUse() {
expression: if(isNan(attribute(createdAt)) == 1, rankedAt, attribute(createdAt))
}
function laAtToUse() {
expression: if(isNan(attribute(laAt)) == 1, attribute(createdAt), attribute(laAt))
}
function markedAsAAtToUse() {
expression: if(isNan(attribute(markedAsAAt)) == 1, maxSignedSixtyFourBitInteger, attribute(markedAsAAt))
}
function tdToUse() {
expression: pow(2, 0 - ((rankedAt - createdAtToUse) / query(decay)))
}
function commentOverallScore() {
expression: query(textweight) * textScoreToUse + query(communityweight) * nCScoreToUse
}
function pinScore() {
expression: if(isNan(attribute(pinned)) == 1, 0, query(pinweight) * attribute(pinned))
}
function freshnessRank() {
expression: nativeRank + freshness(createdAt)
}
first-phase {
expression: nativeRank
}
}
rank-profile neuralNetworkProfile inherits defaultRankProfile {
function nn_input() {
expression {
concat(log10_1p(aVoteCountToUse),
concat(log10_1p(dvCountToUse),
concat(log10_1p(uniqueRCountToUse),
concat(log10_1p(uvCountToUse),
concat(phat,
concat(log10_1p(totalvote),
concat(hsScoreToUse,
concat(tdToUse,
tScoreToUse, x), x), x), x), x), x), x), x)
}
}
function get_model_weights(field) {
expression: if(query(field) == 0, constant(field), query(field))
}
function layer_0() {
expression: elu(xw_plus_b(nn_input, get_model_weights(W_0), get_model_weights(b_0), x))
}
function layer_1() {
expression: elu(xw_plus_b(layer_0, get_model_weights(W_1), get_model_weights(b_1), hidden))
}
function layer_out() {
expression: sum(xw_plus_b(layer_1, get_model_weights(W_out), get_model_weights(b_out), out))
}
first-phase {
expression: freshnessRank
}
second-phase {
expression: layer_out
rerank-count: 2000
}
}
constant W_0 {
file: neural-network-201805/W_0.json
type: tensor(x[9],hidden[9])
}
constant b_0 {
file: neural-network-201805/b_0.json
type: tensor(hidden[9])
}
constant W_1 {
file: neural-network-201805/W_1.json
type: tensor(hidden[9],out[9])
}
constant b_1 {
file: neural-network-201805/b_1.json
type: tensor(out[9])
}
constant W_out {
file: neural-network-201805/W_out.json
type: tensor(out[9])
}
constant b_out {
file: neural-network-201805/b_out.json
type: tensor(out[1])
}
}
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