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package com.twitter.product_mixer.shared_library.observer
import com.twitter.finagle.stats.Counter
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.product_mixer.shared_library.observer.Observer.ArrowObserver
import com.twitter.product_mixer.shared_library.observer.Observer.FunctionObserver
import com.twitter.product_mixer.shared_library.observer.Observer.FutureObserver
import com.twitter.product_mixer.shared_library.observer.Observer.Observer
import com.twitter.product_mixer.shared_library.observer.Observer.StitchObserver
import com.twitter.stitch.Arrow
import com.twitter.stitch.Stitch
import com.twitter.util.Future
import com.twitter.util.Try
/**
* Helper functions to observe requests, successes, failures, cancellations, exceptions, latency,
* and result counts. Supports native functions and asynchronous operations.
*/
object ResultsObserver {
val Total = "total"
val Found = "found"
val NotFound = "not_found"
/**
* Helper function to observe a stitch and result counts
*
* @see [[StitchResultsObserver]]
*/
def stitchResults[T](
size: T => Int,
statsReceiver: StatsReceiver,
scopes: String*
): StitchResultsObserver[T] = {
new StitchResultsObserver[T](size, statsReceiver, scopes)
}
/**
* Helper function to observe a stitch and traversable (e.g. Seq, Set) result counts
*
* @see [[StitchResultsObserver]]
*/
def stitchResults[T <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): StitchResultsObserver[T] = {
new StitchResultsObserver[T](_.size, statsReceiver, scopes)
}
/**
* Helper function to observe an arrow and result counts
*
* @see [[ArrowResultsObserver]]
*/
def arrowResults[In, Out](
size: Out => Int,
statsReceiver: StatsReceiver,
scopes: String*
): ArrowResultsObserver[In, Out] = {
new ArrowResultsObserver[In, Out](size, statsReceiver, scopes)
}
/**
* Helper function to observe an arrow and traversable (e.g. Seq, Set) result counts
*
* @see [[ArrowResultsObserver]]
*/
def arrowResults[In, Out <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): ArrowResultsObserver[In, Out] = {
new ArrowResultsObserver[In, Out](_.size, statsReceiver, scopes)
}
/**
* Helper function to observe an arrow and result counts
*
* @see [[TransformingArrowResultsObserver]]
*/
def transformingArrowResults[In, Out, Transformed](
transformer: Out => Try[Transformed],
size: Transformed => Int,
statsReceiver: StatsReceiver,
scopes: String*
): TransformingArrowResultsObserver[In, Out, Transformed] = {
new TransformingArrowResultsObserver[In, Out, Transformed](
transformer,
size,
statsReceiver,
scopes)
}
/**
* Helper function to observe an arrow and traversable (e.g. Seq, Set) result counts
*
* @see [[TransformingArrowResultsObserver]]
*/
def transformingArrowResults[In, Out, Transformed <: TraversableOnce[_]](
transformer: Out => Try[Transformed],
statsReceiver: StatsReceiver,
scopes: String*
): TransformingArrowResultsObserver[In, Out, Transformed] = {
new TransformingArrowResultsObserver[In, Out, Transformed](
transformer,
_.size,
statsReceiver,
scopes)
}
/**
* Helper function to observe a future and result counts
*
* @see [[FutureResultsObserver]]
*/
def futureResults[T](
size: T => Int,
statsReceiver: StatsReceiver,
scopes: String*
): FutureResultsObserver[T] = {
new FutureResultsObserver[T](size, statsReceiver, scopes)
}
/**
* Helper function to observe a future and traversable (e.g. Seq, Set) result counts
*
* @see [[FutureResultsObserver]]
*/
def futureResults[T <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): FutureResultsObserver[T] = {
new FutureResultsObserver[T](_.size, statsReceiver, scopes)
}
/**
* Helper function to observe a function and result counts
*
* @see [[FunctionResultsObserver]]
*/
def functionResults[T](
size: T => Int,
statsReceiver: StatsReceiver,
scopes: String*
): FunctionResultsObserver[T] = {
new FunctionResultsObserver[T](size, statsReceiver, scopes)
}
/**
* Helper function to observe a function and traversable (e.g. Seq, Set) result counts
*
* @see [[FunctionResultsObserver]]
*/
def functionResults[T <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): FunctionResultsObserver[T] = {
new FunctionResultsObserver[T](_.size, statsReceiver, scopes)
}
/** [[StitchObserver]] that also records result size */
class StitchResultsObserver[T](
override val size: T => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends StitchObserver[T](statsReceiver, scopes)
with ResultsObserver[T] {
override def apply(stitch: => Stitch[T]): Stitch[T] =
super
.apply(stitch)
.onSuccess(observeResults)
}
/** [[ArrowObserver]] that also records result size */
class ArrowResultsObserver[In, Out](
override val size: Out => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends ArrowObserver[In, Out](statsReceiver, scopes)
with ResultsObserver[Out] {
override def apply(arrow: Arrow[In, Out]): Arrow[In, Out] =
super
.apply(arrow)
.onSuccess(observeResults)
}
/**
* [[TransformingArrowResultsObserver]] functions like an [[ArrowObserver]] except
* that it transforms the result using [[transformer]] before recording stats.
*
* The original non-transformed result is then returned.
*/
class TransformingArrowResultsObserver[In, Out, Transformed](
val transformer: Out => Try[Transformed],
override val size: Transformed => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends Observer[Transformed]
with ResultsObserver[Transformed] {
/**
* Returns a new Arrow that records stats on the result after applying [[transformer]] when it's run.
* The original, non-transformed, result of the Arrow is passed through.
*
* @note the provided Arrow must contain the parts that need to be timed.
* Using this on just the result of the computation the latency stat
* will be incorrect.
*/
def apply(arrow: Arrow[In, Out]): Arrow[In, Out] = {
Arrow
.time(arrow)
.map {
case (response, stitchRunDuration) =>
observe(response.flatMap(transformer), stitchRunDuration)
.onSuccess(observeResults)
response
}.lowerFromTry
}
}
/** [[FutureObserver]] that also records result size */
class FutureResultsObserver[T](
override val size: T => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends FutureObserver[T](statsReceiver, scopes)
with ResultsObserver[T] {
override def apply(future: => Future[T]): Future[T] =
super
.apply(future)
.onSuccess(observeResults)
}
/** [[FunctionObserver]] that also records result size */
class FunctionResultsObserver[T](
override val size: T => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends FunctionObserver[T](statsReceiver, scopes)
with ResultsObserver[T] {
override def apply(f: => T): T = observeResults(super.apply(f))
}
/** [[ResultsObserver]] provides methods for recording stats for the result size */
trait ResultsObserver[T] {
protected val statsReceiver: StatsReceiver
/** Scopes that prefix all stats */
protected val scopes: Seq[String]
protected val totalCounter: Counter = statsReceiver.counter(scopes :+ Total: _*)
protected val foundCounter: Counter = statsReceiver.counter(scopes :+ Found: _*)
protected val notFoundCounter: Counter = statsReceiver.counter(scopes :+ NotFound: _*)
/** given a [[T]] returns it's size. */
protected val size: T => Int
/** Records the size of the `results` using [[size]] and return the original value. */
protected def observeResults(results: T): T = {
val resultsSize = size(results)
observeResultsWithSize(results, resultsSize)
}
/**
* Records the `resultsSize` and returns the `results`
*
* This is useful if the size is already available and is expensive to calculate.
*/
protected def observeResultsWithSize(results: T, resultsSize: Int): T = {
if (resultsSize > 0) {
totalCounter.incr(resultsSize)
foundCounter.incr()
} else {
notFoundCounter.incr()
}
results
}
}
}

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package com.twitter.product_mixer.shared_library.observer
import com.twitter.finagle.stats.Stat
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.product_mixer.shared_library.observer.Observer.ArrowObserver
import com.twitter.product_mixer.shared_library.observer.Observer.FunctionObserver
import com.twitter.product_mixer.shared_library.observer.Observer.FutureObserver
import com.twitter.product_mixer.shared_library.observer.Observer.Observer
import com.twitter.product_mixer.shared_library.observer.Observer.StitchObserver
import com.twitter.product_mixer.shared_library.observer.ResultsObserver.ResultsObserver
import com.twitter.stitch.Arrow
import com.twitter.stitch.Stitch
import com.twitter.util.Future
import com.twitter.util.Try
/**
* Helper functions to observe requests, successes, failures, cancellations, exceptions, latency,
* and result counts and time-series stats. Supports native functions and asynchronous operations.
*
* Note that since time-series stats are expensive to compute (relative to counters), prefer
* [[ResultsObserver]] unless a time-series stat is needed.
*/
object ResultsStatsObserver {
val Size = "size"
/**
* Helper function to observe a stitch and result counts and time-series stats
*/
def stitchResultsStats[T](
size: T => Int,
statsReceiver: StatsReceiver,
scopes: String*
): StitchResultsStatsObserver[T] = {
new StitchResultsStatsObserver[T](size, statsReceiver, scopes)
}
/**
* Helper function to observe a stitch and traversable (e.g. Seq, Set) result counts and
* time-series stats
*/
def stitchResultsStats[T <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): StitchResultsStatsObserver[T] = {
new StitchResultsStatsObserver[T](_.size, statsReceiver, scopes)
}
/**
* Helper function to observe an arrow and result counts and time-series stats
*/
def arrowResultsStats[T, U](
size: U => Int,
statsReceiver: StatsReceiver,
scopes: String*
): ArrowResultsStatsObserver[T, U] = {
new ArrowResultsStatsObserver[T, U](size, statsReceiver, scopes)
}
/**
* Helper function to observe an arrow and traversable (e.g. Seq, Set) result counts and
* * time-series stats
*/
def arrowResultsStats[T, U <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): ArrowResultsStatsObserver[T, U] = {
new ArrowResultsStatsObserver[T, U](_.size, statsReceiver, scopes)
}
/**
* Helper function to observe an arrow and result counts
*
* @see [[TransformingArrowResultsStatsObserver]]
*/
def transformingArrowResultsStats[In, Out, Transformed](
transformer: Out => Try[Transformed],
size: Transformed => Int,
statsReceiver: StatsReceiver,
scopes: String*
): TransformingArrowResultsStatsObserver[In, Out, Transformed] = {
new TransformingArrowResultsStatsObserver[In, Out, Transformed](
transformer,
size,
statsReceiver,
scopes)
}
/**
* Helper function to observe an arrow and traversable (e.g. Seq, Set) result counts
*
* @see [[TransformingArrowResultsStatsObserver]]
*/
def transformingArrowResultsStats[In, Out, Transformed <: TraversableOnce[_]](
transformer: Out => Try[Transformed],
statsReceiver: StatsReceiver,
scopes: String*
): TransformingArrowResultsStatsObserver[In, Out, Transformed] = {
new TransformingArrowResultsStatsObserver[In, Out, Transformed](
transformer,
_.size,
statsReceiver,
scopes)
}
/**
* Helper function to observe a future and result counts and time-series stats
*/
def futureResultsStats[T](
size: T => Int,
statsReceiver: StatsReceiver,
scopes: String*
): FutureResultsStatsObserver[T] = {
new FutureResultsStatsObserver[T](size, statsReceiver, scopes)
}
/**
* Helper function to observe a future and traversable (e.g. Seq, Set) result counts and
* time-series stats
*/
def futureResultsStats[T <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): FutureResultsStatsObserver[T] = {
new FutureResultsStatsObserver[T](_.size, statsReceiver, scopes)
}
/**
* Helper function observe a function and result counts and time-series stats
*/
def functionResultsStats[T](
size: T => Int,
statsReceiver: StatsReceiver,
scopes: String*
): FunctionResultsStatsObserver[T] = {
new FunctionResultsStatsObserver[T](size, statsReceiver, scopes)
}
/**
* Helper function observe a function and traversable (e.g. Seq, Set) result counts and
* time-series stats
*/
def functionResultsStats[T <: TraversableOnce[_]](
statsReceiver: StatsReceiver,
scopes: String*
): FunctionResultsStatsObserver[T] = {
new FunctionResultsStatsObserver[T](_.size, statsReceiver, scopes)
}
class StitchResultsStatsObserver[T](
override val size: T => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends StitchObserver[T](statsReceiver, scopes)
with ResultsStatsObserver[T] {
override def apply(stitch: => Stitch[T]): Stitch[T] =
super
.apply(stitch)
.onSuccess(observeResults)
}
class ArrowResultsStatsObserver[T, U](
override val size: U => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends ArrowObserver[T, U](statsReceiver, scopes)
with ResultsStatsObserver[U] {
override def apply(arrow: Arrow[T, U]): Arrow[T, U] =
super
.apply(arrow)
.onSuccess(observeResults)
}
/**
* [[TransformingArrowResultsStatsObserver]] functions like an [[ArrowObserver]] except
* that it transforms the result using [[transformer]] before recording stats.
*
* The original non-transformed result is then returned.
*/
class TransformingArrowResultsStatsObserver[In, Out, Transformed](
val transformer: Out => Try[Transformed],
override val size: Transformed => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends Observer[Transformed]
with ResultsStatsObserver[Transformed] {
/**
* Returns a new Arrow that records stats on the result after applying [[transformer]] when it's run.
* The original, non-transformed, result of the Arrow is passed through.
*
* @note the provided Arrow must contain the parts that need to be timed.
* Using this on just the result of the computation the latency stat
* will be incorrect.
*/
def apply(arrow: Arrow[In, Out]): Arrow[In, Out] = {
Arrow
.time(arrow)
.map {
case (response, stitchRunDuration) =>
observe(response.flatMap(transformer), stitchRunDuration)
.onSuccess(observeResults)
response
}.lowerFromTry
}
}
class FutureResultsStatsObserver[T](
override val size: T => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends FutureObserver[T](statsReceiver, scopes)
with ResultsStatsObserver[T] {
override def apply(future: => Future[T]): Future[T] =
super
.apply(future)
.onSuccess(observeResults)
}
class FunctionResultsStatsObserver[T](
override val size: T => Int,
override val statsReceiver: StatsReceiver,
override val scopes: Seq[String])
extends FunctionObserver[T](statsReceiver, scopes)
with ResultsStatsObserver[T] {
override def apply(f: => T): T = {
observeResults(super.apply(f))
}
}
trait ResultsStatsObserver[T] extends ResultsObserver[T] {
private val sizeStat: Stat = statsReceiver.stat(scopes :+ Size: _*)
protected override def observeResults(results: T): T = {
val resultsSize = size(results)
sizeStat.add(resultsSize)
observeResultsWithSize(results, resultsSize)
}
}
}

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product-mixer/shared-library/src/main/scala/com/twitter/product_mixer/shared_library/thrift_client/BUILD
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scala_library(
sources = ["*.scala"],
compiler_option_sets = ["fatal_warnings"],
strict_deps = True,
tags = ["bazel-compatible"],
dependencies = [
"finagle/finagle-core/src/main",
"finagle/finagle-thriftmux/src/main/scala",
"finatra-internal/mtls-http/src/main/scala",
"finatra-internal/mtls-thriftmux/src/main/scala",
"util/util-core",
],
exports = [
"finagle/finagle-core/src/main",
"finagle/finagle-thriftmux/src/main/scala",
"finatra-internal/mtls-http/src/main/scala",
"finatra-internal/mtls-thriftmux/src/main/scala",
"util/util-core",
],
)

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product-mixer/shared-library/src/main/scala/com/twitter/product_mixer/shared_library/thrift_client/FinagleThriftClientBuilder.scala
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package com.twitter.product_mixer.shared_library.thrift_client
import com.twitter.conversions.DurationOps._
import com.twitter.finagle.ThriftMux
import com.twitter.finagle.mtls.authentication.ServiceIdentifier
import com.twitter.finagle.mtls.client.MtlsStackClient._
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.finagle.thrift.ClientId
import com.twitter.finagle.thrift.service.Filterable
import com.twitter.finagle.thrift.service.MethodPerEndpointBuilder
import com.twitter.finagle.thrift.service.ServicePerEndpointBuilder
import com.twitter.finagle.thriftmux.MethodBuilder
import com.twitter.util.Duration
import org.apache.thrift.protocol.TProtocolFactory
sealed trait Idempotency
case object NonIdempotent extends Idempotency
case class Idempotent(maxExtraLoadPercent: Double) extends Idempotency
object FinagleThriftClientBuilder {
/**
* Library to build a Finagle Thrift method per endpoint client is a less error-prone way when
* compared to the builders in Finagle. This is achieved by requiring values for fields that should
* always be set in practice. For example, request timeouts in Finagle are unbounded when not
* explicitly set, and this method requires that timeout durations are passed into the method and
* set on the Finagle builder.
*
* Usage of
* [[com.twitter.inject.thrift.modules.ThriftMethodBuilderClientModule]] is almost always preferred,
* and the Product Mixer component library [[com.twitter.product_mixer.component_library.module]]
* package contains numerous examples. However, if multiple versions of a client are needed e.g.
* for different timeout settings, this method is useful to easily provide multiple variants.
*
* @example
* {{{
* final val SampleServiceClientName = "SampleServiceClient"
* @Provides
* @Singleton
* @Named(SampleServiceClientName)
* def provideSampleServiceClient(
* serviceIdentifier: ServiceIdentifier,
* clientId: ClientId,
* statsReceiver: StatsReceiver,
* ): SampleService.MethodPerEndpoint =
* buildFinagleMethodPerEndpoint[SampleService.ServicePerEndpoint, SampleService.MethodPerEndpoint](
* serviceIdentifier = serviceIdentifier,
* clientId = clientId,
* dest = "/s/sample/sample",
* label = "sample",
* statsReceiver = statsReceiver,
* idempotency = Idempotent(5.percent),
* timeoutPerRequest = 200.milliseconds,
* timeoutTotal = 400.milliseconds
* )
* }}}
* @param serviceIdentifier Service ID used to S2S Auth
* @param clientId Client ID
* @param dest Destination as a Wily path e.g. "/s/sample/sample"
* @param label Label of the client
* @param statsReceiver Stats
* @param idempotency Idempotency semantics of the client
* @param timeoutPerRequest Thrift client timeout per request. The Finagle default is
* unbounded which is almost never optimal.
* @param timeoutTotal Thrift client total timeout. The Finagle default is
* unbounded which is almost never optimal.
* If the client is set as idempotent, which adds a
* [[com.twitter.finagle.client.BackupRequestFilter]],
* be sure to leave enough room for the backup request. A
* reasonable (albeit usually too large) starting point is to
* make the total timeout 2x relative to the per request timeout.
* If the client is set as non-idempotent, the total timeout and
* the per request timeout should be the same, as there will be
* no backup requests.
* @param connectTimeout Thrift client transport connect timeout. The Finagle default
* of one second is reasonable but we lower this to match
* acquisitionTimeout for consistency.
* @param acquisitionTimeout Thrift client session acquisition timeout. The Finagle default
* is unbounded which is almost never optimal.
* @param protocolFactoryOverride Override the default protocol factory
* e.g. [[org.apache.thrift.protocol.TCompactProtocol.Factory]]
* @param servicePerEndpointBuilder implicit service per endpoint builder
* @param methodPerEndpointBuilder implicit method per endpoint builder
*
* @see [[https://twitter.github.io/finagle/guide/MethodBuilder.html user guide]]
* @see [[https://twitter.github.io/finagle/guide/MethodBuilder.html#idempotency user guide]]
* @return method per endpoint Finagle Thrift Client
*/
def buildFinagleMethodPerEndpoint[
ServicePerEndpoint <: Filterable[ServicePerEndpoint],
MethodPerEndpoint
](
serviceIdentifier: ServiceIdentifier,
clientId: ClientId,
dest: String,
label: String,
statsReceiver: StatsReceiver,
idempotency: Idempotency,
timeoutPerRequest: Duration,
timeoutTotal: Duration,
connectTimeout: Duration = 500.milliseconds,
acquisitionTimeout: Duration = 500.milliseconds,
protocolFactoryOverride: Option[TProtocolFactory] = None,
)(
implicit servicePerEndpointBuilder: ServicePerEndpointBuilder[ServicePerEndpoint],
methodPerEndpointBuilder: MethodPerEndpointBuilder[ServicePerEndpoint, MethodPerEndpoint]
): MethodPerEndpoint = {
val service: ServicePerEndpoint = buildFinagleServicePerEndpoint(
serviceIdentifier = serviceIdentifier,
clientId = clientId,
dest = dest,
label = label,
statsReceiver = statsReceiver,
idempotency = idempotency,
timeoutPerRequest = timeoutPerRequest,
timeoutTotal = timeoutTotal,
connectTimeout = connectTimeout,
acquisitionTimeout = acquisitionTimeout,
protocolFactoryOverride = protocolFactoryOverride
)
ThriftMux.Client.methodPerEndpoint(service)
}
/**
* Build a Finagle Thrift service per endpoint client.
*
* @note [[buildFinagleMethodPerEndpoint]] should be preferred over the service per endpoint variant
*
* @param serviceIdentifier Service ID used to S2S Auth
* @param clientId Client ID
* @param dest Destination as a Wily path e.g. "/s/sample/sample"
* @param label Label of the client
* @param statsReceiver Stats
* @param idempotency Idempotency semantics of the client
* @param timeoutPerRequest Thrift client timeout per request. The Finagle default is
* unbounded which is almost never optimal.
* @param timeoutTotal Thrift client total timeout. The Finagle default is
* unbounded which is almost never optimal.
* If the client is set as idempotent, which adds a
* [[com.twitter.finagle.client.BackupRequestFilter]],
* be sure to leave enough room for the backup request. A
* reasonable (albeit usually too large) starting point is to
* make the total timeout 2x relative to the per request timeout.
* If the client is set as non-idempotent, the total timeout and
* the per request timeout should be the same, as there will be
* no backup requests.
* @param connectTimeout Thrift client transport connect timeout. The Finagle default
* of one second is reasonable but we lower this to match
* acquisitionTimeout for consistency.
* @param acquisitionTimeout Thrift client session acquisition timeout. The Finagle default
* is unbounded which is almost never optimal.
* @param protocolFactoryOverride Override the default protocol factory
* e.g. [[org.apache.thrift.protocol.TCompactProtocol.Factory]]
*
* @return service per endpoint Finagle Thrift Client
*/
def buildFinagleServicePerEndpoint[ServicePerEndpoint <: Filterable[ServicePerEndpoint]](
serviceIdentifier: ServiceIdentifier,
clientId: ClientId,
dest: String,
label: String,
statsReceiver: StatsReceiver,
idempotency: Idempotency,
timeoutPerRequest: Duration,
timeoutTotal: Duration,
connectTimeout: Duration = 500.milliseconds,
acquisitionTimeout: Duration = 500.milliseconds,
protocolFactoryOverride: Option[TProtocolFactory] = None,
)(
implicit servicePerEndpointBuilder: ServicePerEndpointBuilder[ServicePerEndpoint]
): ServicePerEndpoint = {
val thriftMux: ThriftMux.Client = ThriftMux.client
.withMutualTls(serviceIdentifier)
.withClientId(clientId)
.withLabel(label)
.withStatsReceiver(statsReceiver)
.withTransport.connectTimeout(connectTimeout)
.withSession.acquisitionTimeout(acquisitionTimeout)
val protocolThriftMux: ThriftMux.Client = protocolFactoryOverride
.map { protocolFactory =>
thriftMux.withProtocolFactory(protocolFactory)
}.getOrElse(thriftMux)
val methodBuilder: MethodBuilder = protocolThriftMux
.methodBuilder(dest)
.withTimeoutPerRequest(timeoutPerRequest)
.withTimeoutTotal(timeoutTotal)
val idempotencyMethodBuilder: MethodBuilder = idempotency match {
case NonIdempotent => methodBuilder.nonIdempotent
case Idempotent(maxExtraLoad) => methodBuilder.idempotent(maxExtraLoad = maxExtraLoad)
}
idempotencyMethodBuilder.servicePerEndpoint[ServicePerEndpoint]
}
}

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pushservice/BUILD.bazel
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alias(
name = "frigate-pushservice",
target = ":frigate-pushservice_lib",
)
target(
name = "frigate-pushservice_lib",
dependencies = [
"frigate/frigate-pushservice-opensource/src/main/scala/com/twitter/frigate/pushservice",
],
)
jvm_binary(
name = "bin",
basename = "frigate-pushservice",
main = "com.twitter.frigate.pushservice.PushServiceMain",
runtime_platform = "java11",
tags = ["bazel-compatible"],
dependencies = [
"3rdparty/jvm/ch/qos/logback:logback-classic",
"finatra/inject/inject-logback/src/main/scala",
"frigate/frigate-pushservice-opensource/src/main/scala/com/twitter/frigate/pushservice",
"loglens/loglens-logback/src/main/scala/com/twitter/loglens/logback",
"twitter-server/logback-classic/src/main/scala",
],
excludes = [
exclude("com.twitter.translations", "translations-twitter"),
exclude("org.apache.hadoop", "hadoop-aws"),
exclude("org.tensorflow"),
scala_exclude("com.twitter", "ckoia-scala"),
],
)
jvm_app(
name = "bundle",
basename = "frigate-pushservice-package-dist",
archive = "zip",
binary = ":bin",
tags = ["bazel-compatible"],
)
python3_library(
name = "mr_model_constants",
sources = [
"config/deepbird/constants.py",
],
tags = ["bazel-compatible"],
)

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# Pushservice
Pushservice is the main push recommendation service at Twitter used to generate recommendation-based notifications for users. It currently powers two functionalities:
- RefreshForPushHandler: This handler determines whether to send a recommendation push to a user based on their ID. It generates the best push recommendation item and coordinates with downstream services to deliver it
- SendHandler: This handler determines and manage whether send the push to users based on the given target user details and the provided push recommendation item
## Overview
### RefreshForPushHandler
RefreshForPushHandler follows these steps:
- Building Target and checking eligibility
- Builds a target user object based on the given user ID
- Performs target-level filterings to determine if the target is eligible for a recommendation push
- Fetch Candidates
- Retrieves a list of potential candidates for the push by querying various candidate sources using the target
- Candidate Hydration
- Hydrates the candidate details with batch calls to different downstream services
- Pre-rank Filtering, also called Light Filtering
- Filters the hydrated candidates with lightweight RPC calls
- Rank
- Perform feature hydration for candidates and target user
- Performs light ranking on candidates
- Performs heavy ranking on candidates
- Take Step, also called Heavy Filtering
- Takes the top-ranked candidates one by one and applies heavy filtering until one candidate passes all filter steps
- Send
- Calls the appropriate downstream service to deliver the eligible candidate as a push and in-app notification to the target user
### SendHandler
SendHandler follows these steps:
- Building Target
- Builds a target user object based on the given user ID
- Candidate Hydration
- Hydrates the candidate details with batch calls to different downstream services
- Feature Hydration
- Perform feature hydration for candidates and target user
- Take Step, also called Heavy Filtering
- Perform filterings and validation checking for the given candidate
- Send
- Calls the appropriate downstream service to deliver the given candidate as a push and/or in-app notification to the target user

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python37_binary(
name = "update_warm_start_checkpoint",
source = "update_warm_start_checkpoint.py",
tags = ["no-mypy"],
dependencies = [
":deep_norm_lib",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:update_warm_start_checkpoint",
],
)
python3_library(
name = "params_lib",
sources = ["params.py"],
tags = ["no-mypy"],
dependencies = [
"3rdparty/python/pydantic:default",
"src/python/twitter/deepbird/projects/magic_recs/v11/lib:params_lib",
],
)
python3_library(
name = "features_lib",
sources = ["features.py"],
tags = ["no-mypy"],
dependencies = [
":params_lib",
"src/python/twitter/deepbird/projects/magic_recs/libs",
"twml:twml-nodeps",
],
)
python3_library(
name = "model_pools_lib",
sources = ["model_pools.py"],
tags = ["no-mypy"],
dependencies = [
":features_lib",
":params_lib",
"src/python/twitter/deepbird/projects/magic_recs/v11/lib:model_lib",
],
)
python3_library(
name = "graph_lib",
sources = ["graph.py"],
tags = ["no-mypy"],
dependencies = [
":params_lib",
"src/python/twitter/deepbird/projects/magic_recs/libs",
],
)
python3_library(
name = "run_args_lib",
sources = ["run_args.py"],
tags = ["no-mypy"],
dependencies = [
":features_lib",
":params_lib",
"twml:twml-nodeps",
],
)
python3_library(
name = "deep_norm_lib",
sources = ["deep_norm.py"],
tags = ["no-mypy"],
dependencies = [
":features_lib",
":graph_lib",
":model_pools_lib",
":params_lib",
":run_args_lib",
"src/python/twitter/deepbird/projects/magic_recs/libs",
"src/python/twitter/deepbird/util/data",
"twml:twml-nodeps",
],
)
python3_library(
name = "eval_lib",
sources = ["eval.py"],
tags = ["no-mypy"],
dependencies = [
":features_lib",
":graph_lib",
":model_pools_lib",
":params_lib",
":run_args_lib",
"src/python/twitter/deepbird/projects/magic_recs/libs",
"twml:twml-nodeps",
],
)
python37_binary(
name = "deep_norm",
source = "deep_norm.py",
dependencies = [
":deep_norm_lib",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:deep_norm",
"twml",
],
)
python37_binary(
name = "eval",
source = "eval.py",
dependencies = [
":eval_lib",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:eval",
"twml",
],
)
python3_library(
name = "mlwf_libs",
tags = ["no-mypy"],
dependencies = [
":deep_norm_lib",
"twml",
],
)
python37_binary(
name = "train_model",
source = "deep_norm.py",
dependencies = [
":deep_norm_lib",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:train_model",
],
)
python37_binary(
name = "train_model_local",
source = "deep_norm.py",
dependencies = [
":deep_norm_lib",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:train_model_local",
"twml",
],
)
python37_binary(
name = "eval_model_local",
source = "eval.py",
dependencies = [
":eval_lib",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:eval_model_local",
"twml",
],
)
python37_binary(
name = "eval_model",
source = "eval.py",
dependencies = [
":eval_lib",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:eval_model",
],
)
python37_binary(
name = "mlwf_model",
source = "deep_norm.py",
dependencies = [
":mlwf_libs",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/heavy_ranking:mlwf_model",
],
)

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# Notification Heavy Ranker Model
## Model Context
There are 4 major components of Twitter notifications recommendation system: 1) candidate generation 2) light ranking 3) heavy ranking & 4) quality control. This notification heavy ranker model is the core ranking model for the personalised notifications recommendation. It's a multi-task learning model to predict the probabilities that the target users will open and engage with the sent notifications.
## Directory Structure
- BUILD: this file defines python library dependencies
- deep_norm.py: this file contains how to set up continuous training, model evaluation and model exporting for the notification heavy ranker model
- eval.py: the main python entry file to set up the overall model evaluation pipeline
- features.py: this file contains importing feature list and support functions for feature engineering
- graph.py: this file defines how to build the tensorflow graph with specified model architecture, loss function and training configuration
- model_pools.py: this file defines the available model types for the heavy ranker
- params.py: this file defines hyper-parameters used in the notification heavy ranker
- run_args.py: this file defines command line parameters to run model training & evaluation
- update_warm_start_checkpoint.py: this file contains the support to modify checkpoints of the given saved heavy ranker model
- lib/BUILD: this file defines python library dependencies for tensorflow model architecture
- lib/layers.py: this file defines different type of convolution layers to be used in the heavy ranker model
- lib/model.py: this file defines the module containing ClemNet, the heavy ranker model type
- lib/params.py: this file defines parameters used in the heavy ranker model

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"""
Training job for the heavy ranker of the push notification service.
"""
from datetime import datetime
import json
import os
import twml
from ..libs.metric_fn_utils import flip_disliked_labels, get_metric_fn
from ..libs.model_utils import read_config
from ..libs.warm_start_utils import get_feature_list_for_heavy_ranking, warm_start_checkpoint
from .features import get_feature_config
from .model_pools import ALL_MODELS
from .params import load_graph_params
from .run_args import get_training_arg_parser
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import logging
def main() -> None:
args, _ = get_training_arg_parser().parse_known_args()
logging.info(f"Parsed args: {args}")
params = load_graph_params(args)
logging.info(f"Loaded graph params: {params}")
param_file = os.path.join(args.save_dir, "params.json")
logging.info(f"Saving graph params to: {param_file}")
with tf.io.gfile.GFile(param_file, mode="w") as file:
json.dump(params.json(), file, ensure_ascii=False, indent=4)
logging.info(f"Get Feature Config: {args.feature_list}")
feature_list = read_config(args.feature_list).items()
feature_config = get_feature_config(
data_spec_path=args.data_spec,
params=params,
feature_list_provided=feature_list,
)
feature_list_path = args.feature_list
warm_start_from = args.warm_start_from
if args.warm_start_base_dir:
logging.info(f"Get warm started model from: {args.warm_start_base_dir}.")
continuous_binary_feat_list_save_path = os.path.join(
args.warm_start_base_dir, "continuous_binary_feat_list.json"
)
warm_start_folder = os.path.join(args.warm_start_base_dir, "best_checkpoint")
job_name = os.path.basename(args.save_dir)
ws_output_ckpt_folder = os.path.join(args.warm_start_base_dir, f"warm_start_for_{job_name}")
if tf.io.gfile.exists(ws_output_ckpt_folder):
tf.io.gfile.rmtree(ws_output_ckpt_folder)
tf.io.gfile.mkdir(ws_output_ckpt_folder)
warm_start_from = warm_start_checkpoint(
warm_start_folder,
continuous_binary_feat_list_save_path,
feature_list_path,
args.data_spec,
ws_output_ckpt_folder,
)
logging.info(f"Created warm_start_from_ckpt {warm_start_from}.")
logging.info("Build Trainer.")
metric_fn = get_metric_fn("OONC_Engagement" if len(params.tasks) == 2 else "OONC", False)
trainer = twml.trainers.DataRecordTrainer(
name="magic_recs",
params=args,
build_graph_fn=lambda *args: ALL_MODELS[params.model.name](params=params)(*args),
save_dir=args.save_dir,
run_config=None,
feature_config=feature_config,
metric_fn=flip_disliked_labels(metric_fn),
warm_start_from=warm_start_from,
)
logging.info("Build train and eval input functions.")
train_input_fn = trainer.get_train_input_fn(shuffle=True)
eval_input_fn = trainer.get_eval_input_fn(repeat=False, shuffle=False)
learn = trainer.learn
if args.distributed or args.num_workers is not None:
learn = trainer.train_and_evaluate
if not args.directly_export_best:
logging.info("Starting training")
start = datetime.now()
learn(
early_stop_minimize=False,
early_stop_metric="pr_auc_unweighted_OONC",
early_stop_patience=args.early_stop_patience,
early_stop_tolerance=args.early_stop_tolerance,
eval_input_fn=eval_input_fn,
train_input_fn=train_input_fn,
)
logging.info(f"Total training time: {datetime.now() - start}")
else:
logging.info("Directly exporting the model")
if not args.export_dir:
args.export_dir = os.path.join(args.save_dir, "exported_models")
logging.info(f"Exporting the model to {args.export_dir}.")
start = datetime.now()
twml.contrib.export.export_fn.export_all_models(
trainer=trainer,
export_dir=args.export_dir,
parse_fn=feature_config.get_parse_fn(),
serving_input_receiver_fn=feature_config.get_serving_input_receiver_fn(),
export_output_fn=twml.export_output_fns.batch_prediction_continuous_output_fn,
)
logging.info(f"Total model export time: {datetime.now() - start}")
logging.info(f"The MLP directory is: {args.save_dir}")
continuous_binary_feat_list_save_path = os.path.join(
args.save_dir, "continuous_binary_feat_list.json"
)
logging.info(
f"Saving the list of continuous and binary features to {continuous_binary_feat_list_save_path}."
)
continuous_binary_feat_list = get_feature_list_for_heavy_ranking(
feature_list_path, args.data_spec
)
twml.util.write_file(
continuous_binary_feat_list_save_path, continuous_binary_feat_list, encode="json"
)
if __name__ == "__main__":
main()
logging.info("Done.")

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"""
Evaluation job for the heavy ranker of the push notification service.
"""
from datetime import datetime
import twml
from ..libs.metric_fn_utils import get_metric_fn
from ..libs.model_utils import read_config
from .features import get_feature_config
from .model_pools import ALL_MODELS
from .params import load_graph_params
from .run_args import get_eval_arg_parser
from tensorflow.compat.v1 import logging
def main():
args, _ = get_eval_arg_parser().parse_known_args()
logging.info(f"Parsed args: {args}")
params = load_graph_params(args)
logging.info(f"Loaded graph params: {params}")
logging.info(f"Get Feature Config: {args.feature_list}")
feature_list = read_config(args.feature_list).items()
feature_config = get_feature_config(
data_spec_path=args.data_spec,
params=params,
feature_list_provided=feature_list,
)
logging.info("Build DataRecordTrainer.")
metric_fn = get_metric_fn("OONC_Engagement" if len(params.tasks) == 2 else "OONC", False)
trainer = twml.trainers.DataRecordTrainer(
name="magic_recs",
params=args,
build_graph_fn=lambda *args: ALL_MODELS[params.model.name](params=params)(*args),
save_dir=args.save_dir,
run_config=None,
feature_config=feature_config,
metric_fn=metric_fn,
)
logging.info("Run the evaluation.")
start = datetime.now()
trainer._estimator.evaluate(
input_fn=trainer.get_eval_input_fn(repeat=False, shuffle=False),
steps=None if (args.eval_steps is not None and args.eval_steps < 0) else args.eval_steps,
checkpoint_path=args.eval_checkpoint,
)
logging.info(f"Evaluating time: {datetime.now() - start}.")
if __name__ == "__main__":
main()
logging.info("Job done.")

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import os
from typing import Dict
from twitter.deepbird.projects.magic_recs.libs.model_utils import filter_nans_and_infs
import twml
from twml.layers import full_sparse, sparse_max_norm
from .params import FeaturesParams, GraphParams, SparseFeaturesParams
import tensorflow as tf
from tensorflow import Tensor
import tensorflow.compat.v1 as tf1
FEAT_CONFIG_DEFAULT_VAL = 0
DEFAULT_FEATURE_LIST_PATH = "./feature_list_default.yaml"
FEATURE_LIST_DEFAULT_PATH = os.path.join(
os.path.dirname(os.path.realpath(__file__)), DEFAULT_FEATURE_LIST_PATH
)
def get_feature_config(data_spec_path=None, feature_list_provided=[], params: GraphParams = None):
a_string_feat_list = [feat for feat, feat_type in feature_list_provided if feat_type != "S"]
builder = twml.contrib.feature_config.FeatureConfigBuilder(
data_spec_path=data_spec_path, debug=False
)
builder = builder.extract_feature_group(
feature_regexes=a_string_feat_list,
group_name="continuous_features",
default_value=FEAT_CONFIG_DEFAULT_VAL,
type_filter=["CONTINUOUS"],
)
builder = builder.extract_feature_group(
feature_regexes=a_string_feat_list,
group_name="binary_features",
type_filter=["BINARY"],
)
if params.model.features.sparse_features:
builder = builder.extract_features_as_hashed_sparse(
feature_regexes=a_string_feat_list,
hash_space_size_bits=params.model.features.sparse_features.bits,
type_filter=["DISCRETE", "STRING", "SPARSE_BINARY"],
output_tensor_name="sparse_not_continuous",
)
builder = builder.extract_features_as_hashed_sparse(
feature_regexes=[feat for feat, feat_type in feature_list_provided if feat_type == "S"],
hash_space_size_bits=params.model.features.sparse_features.bits,
type_filter=["SPARSE_CONTINUOUS"],
output_tensor_name="sparse_continuous",
)
builder = builder.add_labels([task.label for task in params.tasks] + ["label.ntabDislike"])
if params.weight:
builder = builder.define_weight(params.weight)
return builder.build()
def dense_features(features: Dict[str, Tensor], training: bool) -> Tensor:
"""
Performs feature transformations on the raw dense features (continuous and binary).
"""
with tf.name_scope("dense_features"):
x = filter_nans_and_infs(features["continuous_features"])
x = tf.sign(x) * tf.math.log(tf.abs(x) + 1)
x = tf1.layers.batch_normalization(
x, momentum=0.9999, training=training, renorm=training, axis=1
)
x = tf.clip_by_value(x, -5, 5)
transformed_continous_features = tf.where(tf.math.is_nan(x), tf.zeros_like(x), x)
binary_features = filter_nans_and_infs(features["binary_features"])
binary_features = tf.dtypes.cast(binary_features, tf.float32)
output = tf.concat([transformed_continous_features, binary_features], axis=1)
return output
def sparse_features(
features: Dict[str, Tensor], training: bool, params: SparseFeaturesParams
) -> Tensor:
"""
Performs feature transformations on the raw sparse features.
"""
with tf.name_scope("sparse_features"):
with tf.name_scope("sparse_not_continuous"):
sparse_not_continuous = full_sparse(
inputs=features["sparse_not_continuous"],
output_size=params.embedding_size,
use_sparse_grads=training,
use_binary_values=False,
)
with tf.name_scope("sparse_continuous"):
shape_enforced_input = twml.util.limit_sparse_tensor_size(
sparse_tf=features["sparse_continuous"], input_size_bits=params.bits, mask_indices=False
)
normalized_continuous_sparse = sparse_max_norm(
inputs=shape_enforced_input, is_training=training
)
sparse_continuous = full_sparse(
inputs=normalized_continuous_sparse,
output_size=params.embedding_size,
use_sparse_grads=training,
use_binary_values=False,
)
output = tf.concat([sparse_not_continuous, sparse_continuous], axis=1)
return output
def get_features(features: Dict[str, Tensor], training: bool, params: FeaturesParams) -> Tensor:
"""
Performs feature transformations on the dense and sparse features and combine the resulting
tensors into a single one.
"""
with tf.name_scope("features"):
x = dense_features(features, training)
tf1.logging.info(f"Dense features: {x.shape}")
if params.sparse_features:
x = tf.concat([x, sparse_features(features, training, params.sparse_features)], axis=1)
return x

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"""
Graph class defining methods to obtain key quantities such as:
* the logits
* the probabilities
* the final score
* the loss function
* the training operator
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Dict
from twitter.deepbird.hparam import HParams
import twml
from ..libs.model_utils import generate_disliked_mask
from .params import GraphParams
import tensorflow as tf
import tensorflow.compat.v1 as tf1
class Graph(ABC):
def __init__(self, params: GraphParams):
self.params = params
@abstractmethod
def get_logits(self, features: Dict[str, tf.Tensor], mode: tf.estimator.ModeKeys) -> tf.Tensor:
pass
def get_probabilities(self, logits: tf.Tensor) -> tf.Tensor:
return tf.math.cumprod(tf.nn.sigmoid(logits), axis=1, name="probabilities")
def get_task_weights(self, labels: tf.Tensor) -> tf.Tensor:
oonc_label = tf.reshape(labels[:, 0], shape=(-1, 1))
task_weights = tf.concat([tf.ones_like(oonc_label), oonc_label], axis=1)
n_labels = len(self.params.tasks)
task_weights = tf.reshape(task_weights[:, 0:n_labels], shape=(-1, n_labels))
return task_weights
def get_loss(self, labels: tf.Tensor, logits: tf.Tensor, **kwargs: Any) -> tf.Tensor:
with tf.name_scope("weights"):
disliked_mask = generate_disliked_mask(labels)
labels = tf.reshape(labels[:, 0:2], shape=[-1, 2])
labels = labels * tf.cast(tf.logical_not(disliked_mask), dtype=labels.dtype)
with tf.name_scope("task_weight"):
task_weights = self.get_task_weights(labels)
with tf.name_scope("batch_size"):
batch_size = tf.cast(tf.shape(labels)[0], dtype=tf.float32, name="batch_size")
weights = task_weights / batch_size
with tf.name_scope("loss"):
loss = tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits) * weights,
)
return loss
def get_score(self, probabilities: tf.Tensor) -> tf.Tensor:
with tf.name_scope("score_weight"):
score_weights = tf.constant([task.score_weight for task in self.params.tasks])
score_weights = score_weights / tf.reduce_sum(score_weights, axis=0)
with tf.name_scope("score"):
score = tf.reshape(tf.reduce_sum(probabilities * score_weights, axis=1), shape=[-1, 1])
return score
def get_train_op(self, loss: tf.Tensor, twml_params) -> Any:
with tf.name_scope("optimizer"):
learning_rate = twml_params.learning_rate
optimizer = tf1.train.GradientDescentOptimizer(learning_rate=learning_rate)
update_ops = set(tf1.get_collection(tf1.GraphKeys.UPDATE_OPS))
with tf.control_dependencies(update_ops):
train_op = twml.optimizers.optimize_loss(
loss=loss,
variables=tf1.trainable_variables(),
global_step=tf1.train.get_global_step(),
optimizer=optimizer,
learning_rate=None,
)
return train_op
def __call__(
self,
features: Dict[str, tf.Tensor],
labels: tf.Tensor,
mode: tf.estimator.ModeKeys,
params: HParams,
config=None,
) -> Dict[str, tf.Tensor]:
training = mode == tf.estimator.ModeKeys.TRAIN
logits = self.get_logits(features=features, training=training)
probabilities = self.get_probabilities(logits=logits)
score = None
loss = None
train_op = None
if mode == tf.estimator.ModeKeys.PREDICT:
score = self.get_score(probabilities=probabilities)
output = {"loss": loss, "train_op": train_op, "prediction": score}
elif mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
loss = self.get_loss(labels=labels, logits=logits)
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = self.get_train_op(loss=loss, twml_params=params)
output = {"loss": loss, "train_op": train_op, "output": probabilities}
else:
raise ValueError(
f"""
Invalid mode. Possible values are: {tf.estimator.ModeKeys.PREDICT}, {tf.estimator.ModeKeys.TRAIN}, and {tf.estimator.ModeKeys.EVAL}
. Passed: {mode}
"""
)
return output

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python3_library(
name = "params_lib",
sources = [
"params.py",
],
tags = [
"bazel-compatible",
"no-mypy",
],
dependencies = [
"3rdparty/python/pydantic:default",
],
)
python3_library(
name = "layers_lib",
sources = [
"layers.py",
],
tags = [
"bazel-compatible",
"no-mypy",
],
dependencies = [
],
)
python3_library(
name = "model_lib",
sources = [
"model.py",
],
tags = [
"bazel-compatible",
"no-mypy",
],
dependencies = [
":layers_lib",
":params_lib",
"3rdparty/python/absl-py:default",
],
)

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"""
Different type of convolution layers to be used in the ClemNet.
"""
from typing import Any
import tensorflow as tf
class KerasConv1D(tf.keras.layers.Layer):
"""
Basic Conv1D layer in a wrapper to be compatible with ClemNet.
"""
def __init__(
self,
kernel_size: int,
filters: int,
strides: int,
padding: str,
use_bias: bool = True,
kernel_initializer: str = "glorot_uniform",
bias_initializer: str = "zeros",
**kwargs: Any,
):
super(KerasConv1D, self).__init__(**kwargs)
self.kernel_size = kernel_size
self.filters = filters
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.strides = strides
self.padding = padding
def build(self, input_shape: tf.TensorShape) -> None:
assert (
len(input_shape) == 3
), f"Tensor shape must be of length 3. Passed tensor of shape {input_shape}."
self.features = input_shape[1]
self.w = tf.keras.layers.Conv1D(
kernel_size=self.kernel_size,
filters=self.filters,
strides=self.strides,
padding=self.padding,
use_bias=self.use_bias,
kernel_initializer=self.kernel_initializer,
bias_initializer=self.bias_initializer,
name=self.name,
)
def call(self, inputs: tf.Tensor, **kwargs: Any) -> tf.Tensor:
return self.w(inputs)
class ChannelWiseDense(tf.keras.layers.Layer):
"""
Dense layer is applied to each channel separately. This is more memory and computationally
efficient than flattening the channels and performing single dense layers over it which is the
default behavior in tf1.
"""
def __init__(
self,
output_size: int,
use_bias: bool,
kernel_initializer: str = "uniform_glorot",
bias_initializer: str = "zeros",
**kwargs: Any,
):
super(ChannelWiseDense, self).__init__(**kwargs)
self.output_size = output_size
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
def build(self, input_shape: tf.TensorShape) -> None:
assert (
len(input_shape) == 3
), f"Tensor shape must be of length 3. Passed tensor of shape {input_shape}."
input_size = input_shape[1]
channels = input_shape[2]
self.kernel = self.add_weight(
name="kernel",
shape=(channels, input_size, self.output_size),
initializer=self.kernel_initializer,
trainable=True,
)
self.bias = self.add_weight(
name="bias",
shape=(channels, self.output_size),
initializer=self.bias_initializer,
trainable=self.use_bias,
)
def call(self, inputs: tf.Tensor, **kwargs: Any) -> tf.Tensor:
x = inputs
transposed_x = tf.transpose(x, perm=[2, 0, 1])
transposed_residual = (
tf.transpose(tf.matmul(transposed_x, self.kernel), perm=[1, 0, 2]) + self.bias
)
output = tf.transpose(transposed_residual, perm=[0, 2, 1])
return output
class ResidualLayer(tf.keras.layers.Layer):
"""
Layer implementing a 3D-residual connection.
"""
def build(self, input_shape: tf.TensorShape) -> None:
assert (
len(input_shape) == 3
), f"Tensor shape must be of length 3. Passed tensor of shape {input_shape}."
def call(self, inputs: tf.Tensor, residual: tf.Tensor, **kwargs: Any) -> tf.Tensor:
shortcut = tf.keras.layers.Conv1D(
filters=int(residual.shape[2]), strides=1, kernel_size=1, padding="SAME", use_bias=False
)(inputs)
output = tf.add(shortcut, residual)
return output

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"""
Module containing ClemNet.
"""
from typing import Any
from .layers import ChannelWiseDense, KerasConv1D, ResidualLayer
from .params import BlockParams, ClemNetParams
import tensorflow as tf
import tensorflow.compat.v1 as tf1
class Block2(tf.keras.layers.Layer):
"""
Possible ClemNet block. Architecture is as follow:
Optional(DenseLayer + BN + Act)
Optional(ConvLayer + BN + Act)
Optional(Residual Layer)
"""
def __init__(self, params: BlockParams, **kwargs: Any):
super(Block2, self).__init__(**kwargs)
self.params = params
def build(self, input_shape: tf.TensorShape) -> None:
assert (
len(input_shape) == 3
), f"Tensor shape must be of length 3. Passed tensor of shape {input_shape}."
def call(self, inputs: tf.Tensor, training: bool) -> tf.Tensor:
x = inputs
if self.params.dense:
x = ChannelWiseDense(**self.params.dense.dict())(inputs=x, training=training)
x = tf1.layers.batch_normalization(x, momentum=0.9999, training=training, axis=1)
x = tf.keras.layers.Activation(self.params.activation)(x)
if self.params.conv:
x = KerasConv1D(**self.params.conv.dict())(inputs=x, training=training)
x = tf1.layers.batch_normalization(x, momentum=0.9999, training=training, axis=1)
x = tf.keras.layers.Activation(self.params.activation)(x)
if self.params.residual:
x = ResidualLayer()(inputs=inputs, residual=x)
return x
class ClemNet(tf.keras.layers.Layer):
"""
A residual network stacking residual blocks composed of dense layers and convolutions.
"""
def __init__(self, params: ClemNetParams, **kwargs: Any):
super(ClemNet, self).__init__(**kwargs)
self.params = params
def build(self, input_shape: tf.TensorShape) -> None:
assert len(input_shape) in (
2,
3,
), f"Tensor shape must be of length 3. Passed tensor of shape {input_shape}."
def call(self, inputs: tf.Tensor, training: bool) -> tf.Tensor:
if len(inputs.shape) < 3:
inputs = tf.expand_dims(inputs, axis=-1)
x = inputs
for block_params in self.params.blocks:
x = Block2(block_params)(inputs=x, training=training)
x = tf.keras.layers.Flatten(name="flattened")(x)
if self.params.top:
x = tf.keras.layers.Dense(units=self.params.top.n_labels, name="logits")(x)
return x

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"""
Parameters used in ClemNet.
"""
from typing import List, Optional
from pydantic import BaseModel, Extra, Field, PositiveInt
# checkstyle: noqa
class ExtendedBaseModel(BaseModel):
class Config:
extra = Extra.forbid
class DenseParams(ExtendedBaseModel):
name: Optional[str]
bias_initializer: str = "zeros"
kernel_initializer: str = "glorot_uniform"
output_size: PositiveInt
use_bias: bool = Field(True)
class ConvParams(ExtendedBaseModel):
name: Optional[str]
bias_initializer: str = "zeros"
filters: PositiveInt
kernel_initializer: str = "glorot_uniform"
kernel_size: PositiveInt
padding: str = "SAME"
strides: PositiveInt = 1
use_bias: bool = Field(True)
class BlockParams(ExtendedBaseModel):
activation: Optional[str]
conv: Optional[ConvParams]
dense: Optional[DenseParams]
residual: Optional[bool]
class TopLayerParams(ExtendedBaseModel):
n_labels: PositiveInt
class ClemNetParams(ExtendedBaseModel):
blocks: List[BlockParams] = []
top: Optional[TopLayerParams]

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"""
Candidate architectures for each task's.
"""
from __future__ import annotations
from typing import Dict
from .features import get_features
from .graph import Graph
from .lib.model import ClemNet
from .params import ModelTypeEnum
import tensorflow as tf
class MagicRecsClemNet(Graph):
def get_logits(self, features: Dict[str, tf.Tensor], training: bool) -> tf.Tensor:
with tf.name_scope("logits"):
inputs = get_features(features=features, training=training, params=self.params.model.features)
with tf.name_scope("OONC_logits"):
model = ClemNet(params=self.params.model.architecture)
oonc_logit = model(inputs=inputs, training=training)
with tf.name_scope("EngagementGivenOONC_logits"):
model = ClemNet(params=self.params.model.architecture)
eng_logits = model(inputs=inputs, training=training)
return tf.concat([oonc_logit, eng_logits], axis=1)
ALL_MODELS = {ModelTypeEnum.clemnet: MagicRecsClemNet}

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import enum
import json
from typing import List, Optional
from .lib.params import BlockParams, ClemNetParams, ConvParams, DenseParams, TopLayerParams
from pydantic import BaseModel, Extra, NonNegativeFloat
import tensorflow.compat.v1 as tf
# checkstyle: noqa
class ExtendedBaseModel(BaseModel):
class Config:
extra = Extra.forbid
class SparseFeaturesParams(ExtendedBaseModel):
bits: int
embedding_size: int
class FeaturesParams(ExtendedBaseModel):
sparse_features: Optional[SparseFeaturesParams]
class ModelTypeEnum(str, enum.Enum):
clemnet: str = "clemnet"
class ModelParams(ExtendedBaseModel):
name: ModelTypeEnum
features: FeaturesParams
architecture: ClemNetParams
class TaskNameEnum(str, enum.Enum):
oonc: str = "OONC"
engagement: str = "Engagement"
class Task(ExtendedBaseModel):
name: TaskNameEnum
label: str
score_weight: NonNegativeFloat
DEFAULT_TASKS = [
Task(name=TaskNameEnum.oonc, label="label", score_weight=0.9),
Task(name=TaskNameEnum.engagement, label="label.engagement", score_weight=0.1),
]
class GraphParams(ExtendedBaseModel):
tasks: List[Task] = DEFAULT_TASKS
model: ModelParams
weight: Optional[str]
DEFAULT_ARCHITECTURE_PARAMS = ClemNetParams(
blocks=[
BlockParams(
activation="relu",
conv=ConvParams(kernel_size=3, filters=5),
dense=DenseParams(output_size=output_size),
residual=False,
)
for output_size in [1024, 512, 256, 128]
],
top=TopLayerParams(n_labels=1),
)
DEFAULT_GRAPH_PARAMS = GraphParams(
model=ModelParams(
name=ModelTypeEnum.clemnet,
architecture=DEFAULT_ARCHITECTURE_PARAMS,
features=FeaturesParams(sparse_features=SparseFeaturesParams(bits=18, embedding_size=50)),
),
)
def load_graph_params(args) -> GraphParams:
params = DEFAULT_GRAPH_PARAMS
if args.param_file:
with tf.io.gfile.GFile(args.param_file, mode="r+") as file:
params = GraphParams.parse_obj(json.load(file))
return params

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from twml.trainers import DataRecordTrainer
from .features import FEATURE_LIST_DEFAULT_PATH
def get_training_arg_parser():
parser = DataRecordTrainer.add_parser_arguments()
parser.add_argument(
"--feature_list",
default=FEATURE_LIST_DEFAULT_PATH,
type=str,
help="Which features to use for training",
)
parser.add_argument(
"--param_file",
default=None,
type=str,
help="Path to JSON file containing the graph parameters. If None, model will load default parameters.",
)
parser.add_argument(
"--directly_export_best",
default=False,
action="store_true",
help="whether to directly_export best_checkpoint",
)
parser.add_argument(
"--warm_start_from", default=None, type=str, help="model dir to warm start from"
)
parser.add_argument(
"--warm_start_base_dir",
default=None,
type=str,
help="latest ckpt in this folder will be used to ",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
help="Which type of model to train.",
)
return parser
def get_eval_arg_parser():
parser = get_training_arg_parser()
parser.add_argument(
"--eval_checkpoint",
default=None,
type=str,
help="Which checkpoint to use for evaluation",
)
return parser

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"""
Model for modifying the checkpoints of the magic recs cnn Model with addition, deletion, and reordering
of continuous and binary features.
"""
import os
from twitter.deepbird.projects.magic_recs.libs.get_feat_config import FEATURE_LIST_DEFAULT_PATH
from twitter.deepbird.projects.magic_recs.libs.warm_start_utils_v11 import (
get_feature_list_for_heavy_ranking,
mkdirp,
rename_dir,
rmdir,
warm_start_checkpoint,
)
import twml
from twml.trainers import DataRecordTrainer
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import logging
def get_arg_parser():
parser = DataRecordTrainer.add_parser_arguments()
parser.add_argument(
"--model_type",
default="deepnorm_gbdt_inputdrop2_rescale",
type=str,
help="specify the model type to use.",
)
parser.add_argument(
"--model_trainer_name",
default="None",
type=str,
help="deprecated, added here just for api compatibility.",
)
parser.add_argument(
"--warm_start_base_dir",
default="none",
type=str,
help="latest ckpt in this folder will be used.",
)
parser.add_argument(
"--output_checkpoint_dir",
default="none",
type=str,
help="Output folder for warm started ckpt. If none, it will move warm_start_base_dir to backup, and overwrite it",
)
parser.add_argument(
"--feature_list",
default="none",
type=str,
help="Which features to use for training",
)
parser.add_argument(
"--old_feature_list",
default="none",
type=str,
help="Which features to use for training",
)
return parser
def get_params(args=None):
parser = get_arg_parser()
if args is None:
return parser.parse_args()
else:
return parser.parse_args(args)
def _main():
opt = get_params()
logging.info("parse is: ")
logging.info(opt)
if opt.feature_list == "none":
feature_list_path = FEATURE_LIST_DEFAULT_PATH
else:
feature_list_path = opt.feature_list
if opt.warm_start_base_dir != "none" and tf.io.gfile.exists(opt.warm_start_base_dir):
if opt.output_checkpoint_dir == "none" or opt.output_checkpoint_dir == opt.warm_start_base_dir:
_warm_start_base_dir = os.path.normpath(opt.warm_start_base_dir) + "_backup_warm_start"
_output_folder_dir = opt.warm_start_base_dir
rename_dir(opt.warm_start_base_dir, _warm_start_base_dir)
tf.logging.info(f"moved {opt.warm_start_base_dir} to {_warm_start_base_dir}")
else:
_warm_start_base_dir = opt.warm_start_base_dir
_output_folder_dir = opt.output_checkpoint_dir
continuous_binary_feat_list_save_path = os.path.join(
_warm_start_base_dir, "continuous_binary_feat_list.json"
)
if opt.old_feature_list != "none":
tf.logging.info("getting old continuous_binary_feat_list")
continuous_binary_feat_list = get_feature_list_for_heavy_ranking(
opt.old_feature_list, opt.data_spec
)
rmdir(continuous_binary_feat_list_save_path)
twml.util.write_file(
continuous_binary_feat_list_save_path, continuous_binary_feat_list, encode="json"
)
tf.logging.info(f"Finish writting files to {continuous_binary_feat_list_save_path}")
warm_start_folder = os.path.join(_warm_start_base_dir, "best_checkpoint")
if not tf.io.gfile.exists(warm_start_folder):
warm_start_folder = _warm_start_base_dir
rmdir(_output_folder_dir)
mkdirp(_output_folder_dir)
new_ckpt = warm_start_checkpoint(
warm_start_folder,
continuous_binary_feat_list_save_path,
feature_list_path,
opt.data_spec,
_output_folder_dir,
opt.model_type,
)
logging.info(f"Created new ckpt {new_ckpt} from {warm_start_folder}")
tf.logging.info("getting new continuous_binary_feat_list")
new_continuous_binary_feat_list_save_path = os.path.join(
_output_folder_dir, "continuous_binary_feat_list.json"
)
continuous_binary_feat_list = get_feature_list_for_heavy_ranking(
feature_list_path, opt.data_spec
)
rmdir(new_continuous_binary_feat_list_save_path)
twml.util.write_file(
new_continuous_binary_feat_list_save_path, continuous_binary_feat_list, encode="json"
)
tf.logging.info(f"Finish writting files to {new_continuous_binary_feat_list_save_path}")
if __name__ == "__main__":
_main()

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python3_library(
name = "libs",
sources = ["*.py"],
tags = [
"bazel-compatible",
"no-mypy",
],
dependencies = [
"cortex/recsys/src/python/twitter/cortex/recsys/utils",
"magicpony/common/file_access/src/python/twitter/magicpony/common/file_access",
"src/python/twitter/cortex/ml/embeddings/deepbird",
"src/python/twitter/cortex/ml/embeddings/deepbird/grouped_metrics",
"src/python/twitter/deepbird/util/data",
"twml:twml-nodeps",
],
)

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# pylint: disable=no-member, arguments-differ, attribute-defined-outside-init, unused-argument
"""
Implementing Full Sparse Layer, allow specify use_binary_value in call() to
overide default action.
"""
from twml.layers import FullSparse as defaultFullSparse
from twml.layers.full_sparse import sparse_dense_matmul
import tensorflow.compat.v1 as tf
class FullSparse(defaultFullSparse):
def call(self, inputs, use_binary_values=None, **kwargs): # pylint: disable=unused-argument
"""The logic of the layer lives here.
Arguments:
inputs:
A SparseTensor or a list of SparseTensors.
If `inputs` is a list, all tensors must have same `dense_shape`.
Returns:
- If `inputs` is `SparseTensor`, then returns `bias + inputs * dense_b`.
- If `inputs` is a `list[SparseTensor`, then returns
`bias + add_n([sp_a * dense_b for sp_a in inputs])`.
"""
if use_binary_values is not None:
default_use_binary_values = use_binary_values
else:
default_use_binary_values = self.use_binary_values
if isinstance(default_use_binary_values, (list, tuple)):
raise ValueError(
"use_binary_values can not be %s when inputs is %s"
% (type(default_use_binary_values), type(inputs))
)
outputs = sparse_dense_matmul(
inputs,
self.weight,
self.use_sparse_grads,
default_use_binary_values,
name="sparse_mm",
partition_axis=self.partition_axis,
num_partitions=self.num_partitions,
compress_ids=self._use_compression,
cast_indices_dtype=self._cast_indices_dtype,
)
if self.bias is not None:
outputs = tf.nn.bias_add(outputs, self.bias)
if self.activation is not None:
return self.activation(outputs) # pylint: disable=not-callable
return outputs

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import os
from twitter.deepbird.projects.magic_recs.libs.metric_fn_utils import USER_AGE_FEATURE_NAME
from twitter.deepbird.projects.magic_recs.libs.model_utils import read_config
from twml.contrib import feature_config as contrib_feature_config
# checkstyle: noqa
FEAT_CONFIG_DEFAULT_VAL = -1.23456789
DEFAULT_INPUT_SIZE_BITS = 18
DEFAULT_FEATURE_LIST_PATH = "./feature_list_default.yaml"
FEATURE_LIST_DEFAULT_PATH = os.path.join(
os.path.dirname(os.path.realpath(__file__)), DEFAULT_FEATURE_LIST_PATH
)
DEFAULT_FEATURE_LIST_LIGHT_RANKING_PATH = "./feature_list_light_ranking.yaml"
FEATURE_LIST_DEFAULT_LIGHT_RANKING_PATH = os.path.join(
os.path.dirname(os.path.realpath(__file__)), DEFAULT_FEATURE_LIST_LIGHT_RANKING_PATH
)
FEATURE_LIST_DEFAULT = read_config(FEATURE_LIST_DEFAULT_PATH).items()
FEATURE_LIST_LIGHT_RANKING_DEFAULT = read_config(FEATURE_LIST_DEFAULT_LIGHT_RANKING_PATH).items()
LABELS = ["label"]
LABELS_MTL = {"OONC": ["label"], "OONC_Engagement": ["label", "label.engagement"]}
LABELS_LR = {
"Sent": ["label.sent"],
"HeavyRankPosition": ["meta.ranking.is_top3"],
"HeavyRankProbability": ["meta.ranking.weighted_oonc_model_score"],
}
def _get_new_feature_config_base(
data_spec_path,
labels,
add_sparse_continous=True,
add_gbdt=True,
add_user_id=False,
add_timestamp=False,
add_user_age=False,
feature_list_provided=[],
opt=None,
run_light_ranking_group_metrics_in_bq=False,
):
"""
Getter of the feature config based on specification.
Args:
data_spec_path: A string indicating the path of the data_spec.json file, which could be
either a local path or a hdfs path.
labels: A list of strings indicating the name of the label in the data spec.
add_sparse_continous: A bool indicating if sparse_continuous feature needs to be included.
add_gbdt: A bool indicating if gbdt feature needs to be included.
add_user_id: A bool indicating if user_id feature needs to be included.
add_timestamp: A bool indicating if timestamp feature needs to be included. This will be useful
for sequential models and meta learning models.
add_user_age: A bool indicating if the user age feature needs to be included.
feature_list_provided: A list of features thats need to be included. If not specified, will use
FEATURE_LIST_DEFAULT by default.
opt: A namespace of arguments indicating the hyparameters.
run_light_ranking_group_metrics_in_bq: A bool indicating if heavy ranker score info needs to be included to compute group metrics in BigQuery.
Returns:
A twml feature config object.
"""
input_size_bits = DEFAULT_INPUT_SIZE_BITS if opt is None else opt.input_size_bits
feature_list = feature_list_provided if feature_list_provided != [] else FEATURE_LIST_DEFAULT
a_string_feat_list = [f[0] for f in feature_list if f[1] != "S"]
builder = contrib_feature_config.FeatureConfigBuilder(data_spec_path=data_spec_path)
builder = builder.extract_feature_group(
feature_regexes=a_string_feat_list,
group_name="continuous",
default_value=FEAT_CONFIG_DEFAULT_VAL,
type_filter=["CONTINUOUS"],
)
builder = builder.extract_features_as_hashed_sparse(
feature_regexes=a_string_feat_list,
output_tensor_name="sparse_no_continuous",
hash_space_size_bits=input_size_bits,
type_filter=["BINARY", "DISCRETE", "STRING", "SPARSE_BINARY"],
)
if add_gbdt:
builder = builder.extract_features_as_hashed_sparse(
feature_regexes=["ads\..*"],
output_tensor_name="gbdt_sparse",
hash_space_size_bits=input_size_bits,
)
if add_sparse_continous:
s_string_feat_list = [f[0] for f in feature_list if f[1] == "S"]
builder = builder.extract_features_as_hashed_sparse(
feature_regexes=s_string_feat_list,
output_tensor_name="sparse_continuous",
hash_space_size_bits=input_size_bits,
type_filter=["SPARSE_CONTINUOUS"],
)
if add_user_id:
builder = builder.extract_feature("meta.user_id")
if add_timestamp:
builder = builder.extract_feature("meta.timestamp")
if add_user_age:
builder = builder.extract_feature(USER_AGE_FEATURE_NAME)
if run_light_ranking_group_metrics_in_bq:
builder = builder.extract_feature("meta.trace_id")
builder = builder.extract_feature("meta.ranking.weighted_oonc_model_score")
builder = builder.add_labels(labels).define_weight("meta.weight")
return builder.build()
def get_feature_config_with_sparse_continuous(
data_spec_path,
feature_list_provided=[],
opt=None,
add_user_id=False,
add_timestamp=False,
add_user_age=False,
):
task_name = opt.task_name if getattr(opt, "task_name", None) is not None else "OONC"
if task_name not in LABELS_MTL:
raise ValueError("Invalid Task Name !")
return _get_new_feature_config_base(
data_spec_path=data_spec_path,
labels=LABELS_MTL[task_name],
add_sparse_continous=True,
add_user_id=add_user_id,
add_timestamp=add_timestamp,
add_user_age=add_user_age,
feature_list_provided=feature_list_provided,
opt=opt,
)
def get_feature_config_light_ranking(
data_spec_path,
feature_list_provided=[],
opt=None,
add_user_id=True,
add_timestamp=False,
add_user_age=False,
add_gbdt=False,
run_light_ranking_group_metrics_in_bq=False,
):
task_name = opt.task_name if getattr(opt, "task_name", None) is not None else "HeavyRankPosition"
if task_name not in LABELS_LR:
raise ValueError("Invalid Task Name !")
if not feature_list_provided:
feature_list_provided = FEATURE_LIST_LIGHT_RANKING_DEFAULT
return _get_new_feature_config_base(
data_spec_path=data_spec_path,
labels=LABELS_LR[task_name],
add_sparse_continous=False,
add_gbdt=add_gbdt,
add_user_id=add_user_id,
add_timestamp=add_timestamp,
add_user_age=add_user_age,
feature_list_provided=feature_list_provided,
opt=opt,
run_light_ranking_group_metrics_in_bq=run_light_ranking_group_metrics_in_bq,
)

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"""
Utilties that aid in building the magic recs graph.
"""
import re
import tensorflow.compat.v1 as tf
def get_trainable_variables(all_trainable_variables, trainable_regexes):
"""Returns a subset of trainable variables for training.
Given a collection of trainable variables, this will return all those that match the given regexes.
Will also log those variables.
Args:
all_trainable_variables (a collection of trainable tf.Variable): The variables to search through.
trainable_regexes (a collection of regexes): Variables that match any regex will be included.
Returns a list of tf.Variable
"""
if trainable_regexes is None or len(trainable_regexes) == 0:
tf.logging.info("No trainable regexes found. Not using get_trainable_variables behavior.")
return None
assert any(
tf.is_tensor(var) for var in all_trainable_variables
), f"Non TF variable found: {all_trainable_variables}"
trainable_variables = list(
filter(
lambda var: any(re.match(regex, var.name, re.IGNORECASE) for regex in trainable_regexes),
all_trainable_variables,
)
)
tf.logging.info(f"Using filtered trainable variables: {trainable_variables}")
assert (
trainable_variables
), "Did not find trainable variables after filtering after filtering from {} number of vars originaly. All vars: {} and train regexes: {}".format(
len(all_trainable_variables), all_trainable_variables, trainable_regexes
)
return trainable_variables

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import os
import time
from twitter.cortex.ml.embeddings.deepbird.grouped_metrics.computation import (
write_grouped_metrics_to_mldash,
)
from twitter.cortex.ml.embeddings.deepbird.grouped_metrics.configuration import (
ClassificationGroupedMetricsConfiguration,
NDCGGroupedMetricsConfiguration,
)
import twml
from .light_ranking_metrics import (
CGRGroupedMetricsConfiguration,
ExpectedLossGroupedMetricsConfiguration,
RecallGroupedMetricsConfiguration,
)
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import logging
# checkstyle: noqa
def run_group_metrics(trainer, data_dir, model_path, parse_fn, group_feature_name="meta.user_id"):
start_time = time.time()
logging.info("Evaluating with group metrics.")
metrics = write_grouped_metrics_to_mldash(
trainer=trainer,
data_dir=data_dir,
model_path=model_path,
group_fn=lambda datarecord: str(
datarecord.discreteFeatures[twml.feature_id(group_feature_name)[0]]
),
parse_fn=parse_fn,
metric_configurations=[
ClassificationGroupedMetricsConfiguration(),
NDCGGroupedMetricsConfiguration(k=[5, 10, 20]),
],
total_records_to_read=1000000000,
shuffle=False,
mldash_metrics_name="grouped_metrics",
)
end_time = time.time()
logging.info(f"Evaluated Group Metics: {metrics}.")
logging.info(f"Group metrics evaluation time {end_time - start_time}.")
def run_group_metrics_light_ranking(
trainer, data_dir, model_path, parse_fn, group_feature_name="meta.trace_id"
):
start_time = time.time()
logging.info("Evaluating with group metrics.")
metrics = write_grouped_metrics_to_mldash(
trainer=trainer,
data_dir=data_dir,
model_path=model_path,
group_fn=lambda datarecord: str(
datarecord.discreteFeatures[twml.feature_id(group_feature_name)[0]]
),
parse_fn=parse_fn,
metric_configurations=[
CGRGroupedMetricsConfiguration(lightNs=[50, 100, 200], heavyKs=[1, 3, 10, 20, 50]),
RecallGroupedMetricsConfiguration(n=[50, 100, 200], k=[1, 3, 10, 20, 50]),
ExpectedLossGroupedMetricsConfiguration(lightNs=[50, 100, 200]),
],
total_records_to_read=10000000,
num_batches_to_load=50,
batch_size=1024,
shuffle=False,
mldash_metrics_name="grouped_metrics_for_light_ranking",
)
end_time = time.time()
logging.info(f"Evaluated Group Metics for Light Ranking: {metrics}.")
logging.info(f"Group metrics evaluation time {end_time - start_time}.")
def run_group_metrics_light_ranking_in_bq(trainer, params, checkpoint_path):
logging.info("getting Test Predictions for Light Ranking Group Metrics in BigQuery !!!")
eval_input_fn = trainer.get_eval_input_fn(repeat=False, shuffle=False)
info_pool = []
for result in trainer.estimator.predict(
eval_input_fn, checkpoint_path=checkpoint_path, yield_single_examples=False
):
traceID = result["trace_id"]
pred = result["prediction"]
label = result["target"]
info = np.concatenate([traceID, pred, label], axis=1)
info_pool.append(info)
info_pool = np.concatenate(info_pool)
locname = "/tmp/000/"
if not os.path.exists(locname):
os.makedirs(locname)
locfile = locname + params.pred_file_name
columns = ["trace_id", "model_prediction", "meta__ranking__weighted_oonc_model_score"]
np.savetxt(locfile, info_pool, delimiter=",", header=",".join(columns))
tf.io.gfile.copy(locfile, params.pred_file_path + params.pred_file_name, overwrite=True)
if os.path.isfile(locfile):
os.remove(locfile)
logging.info("Done Prediction for Light Ranking Group Metrics in BigQuery.")

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import numpy as np
from tensorflow.keras import backend as K
class VarianceScaling(object):
"""Initializer capable of adapting its scale to the shape of weights.
With `distribution="normal"`, samples are drawn from a truncated normal
distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:
- number of input units in the weight tensor, if mode = "fan_in"
- number of output units, if mode = "fan_out"
- average of the numbers of input and output units, if mode = "fan_avg"
With `distribution="uniform"`,
samples are drawn from a uniform distribution
within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
# Arguments
scale: Scaling factor (positive float).
mode: One of "fan_in", "fan_out", "fan_avg".
distribution: Random distribution to use. One of "normal", "uniform".
seed: A Python integer. Used to seed the random generator.
# Raises
ValueError: In case of an invalid value for the "scale", mode" or
"distribution" arguments."""
def __init__(
self,
scale=1.0,
mode="fan_in",
distribution="normal",
seed=None,
fan_in=None,
fan_out=None,
):
self.fan_in = fan_in
self.fan_out = fan_out
if scale <= 0.0:
raise ValueError("`scale` must be a positive float. Got:", scale)
mode = mode.lower()
if mode not in {"fan_in", "fan_out", "fan_avg"}:
raise ValueError(
"Invalid `mode` argument: " 'expected on of {"fan_in", "fan_out", "fan_avg"} ' "but got",
mode,
)
distribution = distribution.lower()
if distribution not in {"normal", "uniform"}:
raise ValueError(
"Invalid `distribution` argument: " 'expected one of {"normal", "uniform"} ' "but got",
distribution,
)
self.scale = scale
self.mode = mode
self.distribution = distribution
self.seed = seed
def __call__(self, shape, dtype=None, partition_info=None):
fan_in = shape[-2] if self.fan_in is None else self.fan_in
fan_out = shape[-1] if self.fan_out is None else self.fan_out
scale = self.scale
if self.mode == "fan_in":
scale /= max(1.0, fan_in)
elif self.mode == "fan_out":
scale /= max(1.0, fan_out)
else:
scale /= max(1.0, float(fan_in + fan_out) / 2)
if self.distribution == "normal":
stddev = np.sqrt(scale) / 0.87962566103423978
return K.truncated_normal(shape, 0.0, stddev, dtype=dtype, seed=self.seed)
else:
limit = np.sqrt(3.0 * scale)
return K.random_uniform(shape, -limit, limit, dtype=dtype, seed=self.seed)
def get_config(self):
return {
"scale": self.scale,
"mode": self.mode,
"distribution": self.distribution,
"seed": self.seed,
}
def customized_glorot_uniform(seed=None, fan_in=None, fan_out=None):
"""Glorot uniform initializer, also called Xavier uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(6 / (fan_in + fan_out))`
where `fan_in` is the number of input units in the weight tensor
and `fan_out` is the number of output units in the weight tensor.
# Arguments
seed: A Python integer. Used to seed the random generator.
# Returns
An initializer."""
return VarianceScaling(
scale=1.0,
mode="fan_avg",
distribution="uniform",
seed=seed,
fan_in=fan_in,
fan_out=fan_out,
)
def customized_glorot_norm(seed=None, fan_in=None, fan_out=None):
"""Glorot norm initializer, also called Xavier uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(6 / (fan_in + fan_out))`
where `fan_in` is the number of input units in the weight tensor
and `fan_out` is the number of output units in the weight tensor.
# Arguments
seed: A Python integer. Used to seed the random generator.
# Returns
An initializer."""
return VarianceScaling(
scale=1.0,
mode="fan_avg",
distribution="normal",
seed=seed,
fan_in=fan_in,
fan_out=fan_out,
)

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from functools import partial
from twitter.cortex.ml.embeddings.deepbird.grouped_metrics.configuration import (
GroupedMetricsConfiguration,
)
from twitter.cortex.ml.embeddings.deepbird.grouped_metrics.helpers import (
extract_prediction_from_prediction_record,
)
# checkstyle: noqa
def score_loss_at_n(labels, predictions, lightN):
"""
Compute the absolute ScoreLoss ranking metric
Args:
labels (list) : A list of label values (HeavyRanking Reference)
predictions (list): A list of prediction values (LightRanking Predictions)
lightN (int): size of the list at which of Initial candidates to compute ScoreLoss. (LightRanking)
"""
assert len(labels) == len(predictions)
if lightN <= 0:
return None
labels_with_predictions = zip(labels, predictions)
labels_with_sorted_predictions = sorted(
labels_with_predictions, key=lambda x: x[1], reverse=True
)[:lightN]
labels_top1_light = max([label for label, _ in labels_with_sorted_predictions])
labels_top1_heavy = max(labels)
return labels_top1_heavy - labels_top1_light
def cgr_at_nk(labels, predictions, lightN, heavyK):
"""
Compute Cumulative Gain Ratio (CGR) ranking metric
Args:
labels (list) : A list of label values (HeavyRanking Reference)
predictions (list): A list of prediction values (LightRanking Predictions)
lightN (int): size of the list at which of Initial candidates to compute CGR. (LightRanking)
heavyK (int): size of the list at which of Refined candidates to compute CGR. (HeavyRanking)
"""
assert len(labels) == len(predictions)
if (not lightN) or (not heavyK):
out = None
elif lightN <= 0 or heavyK <= 0:
out = None
else:
labels_with_predictions = zip(labels, predictions)
labels_with_sorted_predictions = sorted(
labels_with_predictions, key=lambda x: x[1], reverse=True
)[:lightN]
labels_topN_light = [label for label, _ in labels_with_sorted_predictions]
if lightN <= heavyK:
cg_light = sum(labels_topN_light)
else:
labels_topK_heavy_from_light = sorted(labels_topN_light, reverse=True)[:heavyK]
cg_light = sum(labels_topK_heavy_from_light)
ideal_ordering = sorted(labels, reverse=True)
cg_heavy = sum(ideal_ordering[: min(lightN, heavyK)])
out = 0.0
if cg_heavy != 0:
out = max(cg_light / cg_heavy, 0)
return out
def _get_weight(w, atK):
if not w:
return 1.0
elif len(w) <= atK:
return 0.0
else:
return w[atK]
def recall_at_nk(labels, predictions, n=None, k=None, w=None):
"""
Recall at N-K ranking metric
Args:
labels (list): A list of label values
predictions (list): A list of prediction values
n (int): size of the list at which of predictions to compute recall. (Light Ranking Predictions)
The default is None in which case the length of the provided predictions is used as L
k (int): size of the list at which of labels to compute recall. (Heavy Ranking Predictions)
The default is None in which case the length of the provided labels is used as L
w (list): weight vector sorted by labels
"""
assert len(labels) == len(predictions)
if not any(labels):
out = None
else:
safe_n = len(predictions) if not n else min(len(predictions), n)
safe_k = len(labels) if not k else min(len(labels), k)
labels_with_predictions = zip(labels, predictions)
sorted_labels_with_predictions = sorted(
labels_with_predictions, key=lambda x: x[0], reverse=True
)
order_sorted_labels_predictions = zip(range(len(labels)), *zip(*sorted_labels_with_predictions))
order_with_predictions = [
(order, pred) for order, label, pred in order_sorted_labels_predictions
]
order_with_sorted_predictions = sorted(order_with_predictions, key=lambda x: x[1], reverse=True)
pred_sorted_order_at_n = [order for order, _ in order_with_sorted_predictions][:safe_n]
intersection_weight = [
_get_weight(w, order) if order < safe_k else 0 for order in pred_sorted_order_at_n
]
intersection_score = sum(intersection_weight)
full_score = sum(w) if w else float(safe_k)
out = 0.0
if full_score != 0:
out = intersection_score / full_score
return out
class ExpectedLossGroupedMetricsConfiguration(GroupedMetricsConfiguration):
"""
This is the Expected Loss Grouped metric computation configuration.
"""
def __init__(self, lightNs=[]):
"""
Args:
lightNs (list): size of the list at which of Initial candidates to compute Expected Loss. (LightRanking)
"""
self.lightNs = lightNs
@property
def name(self):
return "ExpectedLoss"
@property
def metrics_dict(self):
metrics_to_compute = {}
for lightN in self.lightNs:
metric_name = "ExpectedLoss_atLight_" + str(lightN)
metrics_to_compute[metric_name] = partial(score_loss_at_n, lightN=lightN)
return metrics_to_compute
def extract_label(self, prec, drec, drec_label):
return drec_label
def extract_prediction(self, prec, drec, drec_label):
return extract_prediction_from_prediction_record(prec)
class CGRGroupedMetricsConfiguration(GroupedMetricsConfiguration):
"""
This is the Cumulative Gain Ratio (CGR) Grouped metric computation configuration.
CGR at the max length of each session is the default.
CGR at additional positions can be computed by specifying a list of 'n's and 'k's
"""
def __init__(self, lightNs=[], heavyKs=[]):
"""
Args:
lightNs (list): size of the list at which of Initial candidates to compute CGR. (LightRanking)
heavyK (int): size of the list at which of Refined candidates to compute CGR. (HeavyRanking)
"""
self.lightNs = lightNs
self.heavyKs = heavyKs
@property
def name(self):
return "cgr"
@property
def metrics_dict(self):
metrics_to_compute = {}
for lightN in self.lightNs:
for heavyK in self.heavyKs:
metric_name = "cgr_atLight_" + str(lightN) + "_atHeavy_" + str(heavyK)
metrics_to_compute[metric_name] = partial(cgr_at_nk, lightN=lightN, heavyK=heavyK)
return metrics_to_compute
def extract_label(self, prec, drec, drec_label):
return drec_label
def extract_prediction(self, prec, drec, drec_label):
return extract_prediction_from_prediction_record(prec)
class RecallGroupedMetricsConfiguration(GroupedMetricsConfiguration):
"""
This is the Recall Grouped metric computation configuration.
Recall at the max length of each session is the default.
Recall at additional positions can be computed by specifying a list of 'n's and 'k's
"""
def __init__(self, n=[], k=[], w=[]):
"""
Args:
n (list): A list of ints. List of prediction rank thresholds (for light)
k (list): A list of ints. List of label rank thresholds (for heavy)
"""
self.predN = n
self.labelK = k
self.weight = w
@property
def name(self):
return "group_recall"
@property
def metrics_dict(self):
metrics_to_compute = {"group_recall_unweighted": recall_at_nk}
if not self.weight:
metrics_to_compute["group_recall_weighted"] = partial(recall_at_nk, w=self.weight)
if self.predN and self.labelK:
for n in self.predN:
for k in self.labelK:
if n >= k:
metrics_to_compute[
"group_recall_unweighted_at_L" + str(n) + "_at_H" + str(k)
] = partial(recall_at_nk, n=n, k=k)
if self.weight:
metrics_to_compute[
"group_recall_weighted_at_L" + str(n) + "_at_H" + str(k)
] = partial(recall_at_nk, n=n, k=k, w=self.weight)
if self.labelK and not self.predN:
for k in self.labelK:
metrics_to_compute["group_recall_unweighted_at_full_at_H" + str(k)] = partial(
recall_at_nk, k=k
)
if self.weight:
metrics_to_compute["group_recall_weighted_at_full_at_H" + str(k)] = partial(
recall_at_nk, k=k, w=self.weight
)
return metrics_to_compute
def extract_label(self, prec, drec, drec_label):
return drec_label
def extract_prediction(self, prec, drec, drec_label):
return extract_prediction_from_prediction_record(prec)

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"""
Utilties for constructing a metric_fn for magic recs.
"""
from twml.contrib.metrics.metrics import (
get_dual_binary_tasks_metric_fn,
get_numeric_metric_fn,
get_partial_multi_binary_class_metric_fn,
get_single_binary_task_metric_fn,
)
from .model_utils import generate_disliked_mask
import tensorflow.compat.v1 as tf
METRIC_BOOK = {
"OONC": ["OONC"],
"OONC_Engagement": ["OONC", "Engagement"],
"Sent": ["Sent"],
"HeavyRankPosition": ["HeavyRankPosition"],
"HeavyRankProbability": ["HeavyRankProbability"],
}
USER_AGE_FEATURE_NAME = "accountAge"
NEW_USER_AGE_CUTOFF = 0
def remove_padding_and_flatten(tensor, valid_batch_size):
"""Remove the padding of the input padded tensor given the valid batch size tensor,
then flatten the output with respect to the first dimension.
Args:
tensor: A tensor of size [META_BATCH_SIZE, BATCH_SIZE, FEATURE_DIM].
valid_batch_size: A tensor of size [META_BATCH_SIZE], with each element indicating
the effective batch size of the BATCH_SIZE dimension.
Returns:
A tesnor of size [tf.reduce_sum(valid_batch_size), FEATURE_DIM].
"""
unpadded_ragged_tensor = tf.RaggedTensor.from_tensor(tensor=tensor, lengths=valid_batch_size)
return unpadded_ragged_tensor.flat_values
def safe_mask(values, mask):
"""Mask values if possible.
Boolean mask inputed values if and only if values is a tensor of the same dimension as mask (or can be broadcasted to that dimension).
Args:
values (Any or Tensor): Input tensor to mask. Dim 0 should be size N.
mask (boolean tensor): A boolean tensor of size N.
Returns Values or Values masked.
"""
if values is None:
return values
if not tf.is_tensor(values):
return values
values_shape = values.get_shape()
if not values_shape or len(values_shape) == 0:
return values
if not mask.get_shape().is_compatible_with(values_shape[0]):
return values
return tf.boolean_mask(values, mask)
def add_new_user_metrics(metric_fn):
"""Will stratify the metric_fn by adding new user metrics.
Given an input metric_fn, double every metric: One will be the orignal and the other will only include those for new users.
Args:
metric_fn (python function): Base twml metric_fn.
Returns a metric_fn with new user metrics included.
"""
def metric_fn_with_new_users(graph_output, labels, weights):
if USER_AGE_FEATURE_NAME not in graph_output:
raise ValueError(
"In order to get metrics stratified by user age, {name} feature should be added to model graph output. However, only the following output keys were found: {keys}.".format(
name=USER_AGE_FEATURE_NAME, keys=graph_output.keys()
)
)
metric_ops = metric_fn(graph_output, labels, weights)
is_new = tf.reshape(
tf.math.less_equal(
tf.cast(graph_output[USER_AGE_FEATURE_NAME], tf.int64),
tf.cast(NEW_USER_AGE_CUTOFF, tf.int64),
),
[-1],
)
labels = safe_mask(labels, is_new)
weights = safe_mask(weights, is_new)
graph_output = {key: safe_mask(values, is_new) for key, values in graph_output.items()}
new_user_metric_ops = metric_fn(graph_output, labels, weights)
new_user_metric_ops = {name + "_new_users": ops for name, ops in new_user_metric_ops.items()}
metric_ops.update(new_user_metric_ops)
return metric_ops
return metric_fn_with_new_users
def get_meta_learn_single_binary_task_metric_fn(
metrics, classnames, top_k=(5, 5, 5), use_top_k=False
):
"""Wrapper function to use the metric_fn with meta learning evaluation scheme.
Args:
metrics: A list of string representing metric names.
classnames: A list of string repsenting class names, In case of multiple binary class models,
the names for each class or label.
top_k: A tuple of int to specify top K metrics.
use_top_k: A boolean value indicating of top K of metrics is used.
Returns:
A customized metric_fn function.
"""
def get_eval_metric_ops(graph_output, labels, weights):
"""The op func of the eval_metrics. Comparing with normal version,
the difference is we flatten the output, label, and weights.
Args:
graph_output: A dict of tensors.
labels: A tensor of int32 be the value of either 0 or 1.
weights: A tensor of float32 to indicate the per record weight.
Returns:
A dict of metric names and values.
"""
metric_op_weighted = get_partial_multi_binary_class_metric_fn(
metrics, predcols=0, classes=classnames
)
classnames_unweighted = ["unweighted_" + classname for classname in classnames]
metric_op_unweighted = get_partial_multi_binary_class_metric_fn(
metrics, predcols=0, classes=classnames_unweighted
)
valid_batch_size = graph_output["valid_batch_size"]
graph_output["output"] = remove_padding_and_flatten(graph_output["output"], valid_batch_size)
labels = remove_padding_and_flatten(labels, valid_batch_size)
weights = remove_padding_and_flatten(weights, valid_batch_size)
tf.ensure_shape(graph_output["output"], [None, 1])
tf.ensure_shape(labels, [None, 1])
tf.ensure_shape(weights, [None, 1])
metrics_weighted = metric_op_weighted(graph_output, labels, weights)
metrics_unweighted = metric_op_unweighted(graph_output, labels, None)
metrics_weighted.update(metrics_unweighted)
if use_top_k:
metric_op_numeric = get_numeric_metric_fn(metrics=None, topK=top_k, predcol=0, labelcol=1)
metrics_numeric = metric_op_numeric(graph_output, labels, weights)
metrics_weighted.update(metrics_numeric)
return metrics_weighted
return get_eval_metric_ops
def get_meta_learn_dual_binary_tasks_metric_fn(
metrics, classnames, top_k=(5, 5, 5), use_top_k=False
):
"""Wrapper function to use the metric_fn with meta learning evaluation scheme.
Args:
metrics: A list of string representing metric names.
classnames: A list of string repsenting class names, In case of multiple binary class models,
the names for each class or label.
top_k: A tuple of int to specify top K metrics.
use_top_k: A boolean value indicating of top K of metrics is used.
Returns:
A customized metric_fn function.
"""
def get_eval_metric_ops(graph_output, labels, weights):
"""The op func of the eval_metrics. Comparing with normal version,
the difference is we flatten the output, label, and weights.
Args:
graph_output: A dict of tensors.
labels: A tensor of int32 be the value of either 0 or 1.
weights: A tensor of float32 to indicate the per record weight.
Returns:
A dict of metric names and values.
"""
metric_op_weighted = get_partial_multi_binary_class_metric_fn(
metrics, predcols=[0, 1], classes=classnames
)
classnames_unweighted = ["unweighted_" + classname for classname in classnames]
metric_op_unweighted = get_partial_multi_binary_class_metric_fn(
metrics, predcols=[0, 1], classes=classnames_unweighted
)
valid_batch_size = graph_output["valid_batch_size"]
graph_output["output"] = remove_padding_and_flatten(graph_output["output"], valid_batch_size)
labels = remove_padding_and_flatten(labels, valid_batch_size)
weights = remove_padding_and_flatten(weights, valid_batch_size)
tf.ensure_shape(graph_output["output"], [None, 2])
tf.ensure_shape(labels, [None, 2])
tf.ensure_shape(weights, [None, 1])
metrics_weighted = metric_op_weighted(graph_output, labels, weights)
metrics_unweighted = metric_op_unweighted(graph_output, labels, None)
metrics_weighted.update(metrics_unweighted)
if use_top_k:
metric_op_numeric = get_numeric_metric_fn(metrics=None, topK=top_k, predcol=2, labelcol=2)
metrics_numeric = metric_op_numeric(graph_output, labels, weights)
metrics_weighted.update(metrics_numeric)
return metrics_weighted
return get_eval_metric_ops
def get_metric_fn(task_name, use_stratify_metrics, use_meta_batch=False):
"""Will retrieve the metric_fn for magic recs.
Args:
task_name (string): Which task is being used for this model.
use_stratify_metrics (boolean): Should we add stratified metrics (new user metrics).
use_meta_batch (boolean): If the output/label/weights are passed in 3D shape instead of
2D shape.
Returns:
A metric_fn function to pass in twml Trainer.
"""
if task_name not in METRIC_BOOK:
raise ValueError(
"Task name of {task_name} not recognized. Unable to retrieve metrics.".format(
task_name=task_name
)
)
class_names = METRIC_BOOK[task_name]
if use_meta_batch:
get_n_binary_task_metric_fn = (
get_meta_learn_single_binary_task_metric_fn
if len(class_names) == 1
else get_meta_learn_dual_binary_tasks_metric_fn
)
else:
get_n_binary_task_metric_fn = (
get_single_binary_task_metric_fn if len(class_names) == 1 else get_dual_binary_tasks_metric_fn
)
metric_fn = get_n_binary_task_metric_fn(metrics=None, classnames=METRIC_BOOK[task_name])
if use_stratify_metrics:
metric_fn = add_new_user_metrics(metric_fn)
return metric_fn
def flip_disliked_labels(metric_fn):
"""This function returns an adapted metric_fn which flips the labels of the OONCed evaluation data to 0 if it is disliked.
Args:
metric_fn: A metric_fn function to pass in twml Trainer.
Returns:
_adapted_metric_fn: A customized metric_fn function with disliked OONC labels flipped.
"""
def _adapted_metric_fn(graph_output, labels, weights):
"""A customized metric_fn function with disliked OONC labels flipped.
Args:
graph_output: A dict of tensors.
labels: labels of training samples, which is a 2D tensor of shape batch_size x 3: [OONCs, engagements, dislikes]
weights: A tensor of float32 to indicate the per record weight.
Returns:
A dict of metric names and values.
"""
# We want to multiply the label of the observation by 0 only when it is disliked
disliked_mask = generate_disliked_mask(labels)
# Extract OONC and engagement labels only.
labels = tf.reshape(labels[:, 0:2], shape=[-1, 2])
# Labels will be set to 0 if it is disliked.
adapted_labels = labels * tf.cast(tf.logical_not(disliked_mask), dtype=labels.dtype)
return metric_fn(graph_output, adapted_labels, weights)
return _adapted_metric_fn

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from twml.trainers import DataRecordTrainer
# checkstyle: noqa
def get_arg_parser():
parser = DataRecordTrainer.add_parser_arguments()
parser.add_argument(
"--input_size_bits",
type=int,
default=18,
help="number of bits allocated to the input size",
)
parser.add_argument(
"--model_trainer_name",
default="magic_recs_mlp_calibration_MTL_OONC_Engagement",
type=str,
help="specify the model trainer name.",
)
parser.add_argument(
"--model_type",
default="deepnorm_gbdt_inputdrop2_rescale",
type=str,
help="specify the model type to use.",
)
parser.add_argument(
"--feat_config_type",
default="get_feature_config_with_sparse_continuous",
type=str,
help="specify the feature configure function to use.",
)
parser.add_argument(
"--directly_export_best",
default=False,
action="store_true",
help="whether to directly_export best_checkpoint",
)
parser.add_argument(
"--warm_start_base_dir",
default="none",
type=str,
help="latest ckpt in this folder will be used to ",
)
parser.add_argument(
"--feature_list",
default="none",
type=str,
help="Which features to use for training",
)
parser.add_argument(
"--warm_start_from", default=None, type=str, help="model dir to warm start from"
)
parser.add_argument(
"--momentum", default=0.99999, type=float, help="Momentum term for batch normalization"
)
parser.add_argument(
"--dropout",
default=0.2,
type=float,
help="input_dropout_rate to rescale output by (1 - input_dropout_rate)",
)
parser.add_argument(
"--out_layer_1_size", default=256, type=int, help="Size of MLP_branch layer 1"
)
parser.add_argument(
"--out_layer_2_size", default=128, type=int, help="Size of MLP_branch layer 2"
)
parser.add_argument("--out_layer_3_size", default=64, type=int, help="Size of MLP_branch layer 3")
parser.add_argument(
"--sparse_embedding_size", default=50, type=int, help="Dimensionality of sparse embedding layer"
)
parser.add_argument(
"--dense_embedding_size", default=128, type=int, help="Dimensionality of dense embedding layer"
)
parser.add_argument(
"--use_uam_label",
default=False,
type=str,
help="Whether to use uam_label or not",
)
parser.add_argument(
"--task_name",
default="OONC_Engagement",
type=str,
help="specify the task name to use: OONC or OONC_Engagement.",
)
parser.add_argument(
"--init_weight",
default=0.9,
type=float,
help="Initial OONC Task Weight MTL: OONC+Engagement.",
)
parser.add_argument(
"--use_engagement_weight",
default=False,
action="store_true",
help="whether to use engagement weight for base model.",
)
parser.add_argument(
"--mtl_num_extra_layers",
type=int,
default=1,
help="Number of Hidden Layers for each TaskBranch.",
)
parser.add_argument(
"--mtl_neuron_scale", type=int, default=4, help="Scaling Factor of Neurons in MTL Extra Layers."
)
parser.add_argument(
"--use_oonc_score",
default=False,
action="store_true",
help="whether to use oonc score only or combined score.",
)
parser.add_argument(
"--use_stratified_metrics",
default=False,
action="store_true",
help="Use stratified metrics: Break out new-user metrics.",
)
parser.add_argument(
"--run_group_metrics",
default=False,
action="store_true",
help="Will run evaluation metrics grouped by user.",
)
parser.add_argument(
"--use_full_scope",
default=False,
action="store_true",
help="Will add extra scope and naming to graph.",
)
parser.add_argument(
"--trainable_regexes",
default=None,
nargs="*",
help="The union of variables specified by the list of regexes will be considered trainable.",
)
parser.add_argument(
"--fine_tuning.ckpt_to_initialize_from",
dest="fine_tuning_ckpt_to_initialize_from",
type=str,
default=None,
help="Checkpoint path from which to warm start. Indicates the pre-trained model.",
)
parser.add_argument(
"--fine_tuning.warm_start_scope_regex",
dest="fine_tuning_warm_start_scope_regex",
type=str,
default=None,
help="All variables matching this will be restored.",
)
return parser
def get_params(args=None):
parser = get_arg_parser()
if args is None:
return parser.parse_args()
else:
return parser.parse_args(args)
def get_arg_parser_light_ranking():
parser = get_arg_parser()
parser.add_argument(
"--use_record_weight",
default=False,
action="store_true",
help="whether to use record weight for base model.",
)
parser.add_argument(
"--min_record_weight", default=0.0, type=float, help="Minimum record weight to use."
)
parser.add_argument(
"--smooth_weight", default=0.0, type=float, help="Factor to smooth Rank Position Weight."
)
parser.add_argument(
"--num_mlp_layers", type=int, default=3, help="Number of Hidden Layers for MLP model."
)
parser.add_argument(
"--mlp_neuron_scale", type=int, default=4, help="Scaling Factor of Neurons in MLP Layers."
)
parser.add_argument(
"--run_light_ranking_group_metrics",
default=False,
action="store_true",
help="Will run evaluation metrics grouped by user for Light Ranking.",
)
parser.add_argument(
"--use_missing_sub_branch",
default=False,
action="store_true",
help="Whether to use missing value sub-branch for Light Ranking.",
)
parser.add_argument(
"--use_gbdt_features",
default=False,
action="store_true",
help="Whether to use GBDT features for Light Ranking.",
)
parser.add_argument(
"--run_light_ranking_group_metrics_in_bq",
default=False,
action="store_true",
help="Whether to get_predictions for Light Ranking to compute group metrics in BigQuery.",
)
parser.add_argument(
"--pred_file_path",
default=None,
type=str,
help="path",
)
parser.add_argument(
"--pred_file_name",
default=None,
type=str,
help="path",
)
return parser

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import sys
import twml
from .initializer import customized_glorot_uniform
import tensorflow.compat.v1 as tf
import yaml
# checkstyle: noqa
def read_config(whitelist_yaml_file):
with tf.gfile.FastGFile(whitelist_yaml_file) as f:
try:
return yaml.safe_load(f)
except yaml.YAMLError as exc:
print(exc)
sys.exit(1)
def _sparse_feature_fixup(features, input_size_bits):
"""Rebuild a sparse tensor feature so that its dense shape attribute is present.
Arguments:
features (SparseTensor): Sparse feature tensor of shape ``(B, sparse_feature_dim)``.
input_size_bits (int): Number of columns in ``log2`` scale. Must be positive.
Returns:
SparseTensor: Rebuilt and non-faulty version of `features`."""
sparse_feature_dim = tf.constant(2**input_size_bits, dtype=tf.int64)
sparse_shape = tf.stack([features.dense_shape[0], sparse_feature_dim])
sparse_tf = tf.SparseTensor(features.indices, features.values, sparse_shape)
return sparse_tf
def self_atten_dense(input, out_dim, activation=None, use_bias=True, name=None):
def safe_concat(base, suffix):
"""Concats variables name components if base is given."""
if not base:
return base
return f"{base}:{suffix}"
input_dim = input.shape.as_list()[1]
sigmoid_out = twml.layers.FullDense(
input_dim, dtype=tf.float32, activation=tf.nn.sigmoid, name=safe_concat(name, "sigmoid_out")
)(input)
atten_input = sigmoid_out * input
mlp_out = twml.layers.FullDense(
out_dim,
dtype=tf.float32,
activation=activation,
use_bias=use_bias,
name=safe_concat(name, "mlp_out"),
)(atten_input)
return mlp_out
def get_dense_out(input, out_dim, activation, dense_type):
if dense_type == "full_dense":
out = twml.layers.FullDense(out_dim, dtype=tf.float32, activation=activation)(input)
elif dense_type == "self_atten_dense":
out = self_atten_dense(input, out_dim, activation=activation)
return out
def get_input_trans_func(bn_normalized_dense, is_training):
gw_normalized_dense = tf.expand_dims(bn_normalized_dense, -1)
group_num = bn_normalized_dense.shape.as_list()[1]
gw_normalized_dense = GroupWiseTrans(group_num, 1, 8, name="groupwise_1", activation=tf.tanh)(
gw_normalized_dense
)
gw_normalized_dense = GroupWiseTrans(group_num, 8, 4, name="groupwise_2", activation=tf.tanh)(
gw_normalized_dense
)
gw_normalized_dense = GroupWiseTrans(group_num, 4, 1, name="groupwise_3", activation=tf.tanh)(
gw_normalized_dense
)
gw_normalized_dense = tf.squeeze(gw_normalized_dense, [-1])
bn_gw_normalized_dense = tf.layers.batch_normalization(
gw_normalized_dense,
training=is_training,
renorm_momentum=0.9999,
momentum=0.9999,
renorm=is_training,
trainable=True,
)
return bn_gw_normalized_dense
def tensor_dropout(
input_tensor,
rate,
is_training,
sparse_tensor=None,
):
"""
Implements dropout layer for both dense and sparse input_tensor
Arguments:
input_tensor:
B x D dense tensor, or a sparse tensor
rate (float32):
dropout rate
is_training (bool):
training stage or not.
sparse_tensor (bool):
whether the input_tensor is sparse tensor or not. Default to be None, this value has to be passed explicitly.
rescale_sparse_dropout (bool):
Do we need to do rescaling or not.
Returns:
tensor dropped out"""
if sparse_tensor == True:
if is_training:
with tf.variable_scope("sparse_dropout"):
values = input_tensor.values
keep_mask = tf.keras.backend.random_binomial(
tf.shape(values), p=1 - rate, dtype=tf.float32, seed=None
)
keep_mask.set_shape([None])
keep_mask = tf.cast(keep_mask, tf.bool)
keep_indices = tf.boolean_mask(input_tensor.indices, keep_mask, axis=0)
keep_values = tf.boolean_mask(values, keep_mask, axis=0)
dropped_tensor = tf.SparseTensor(keep_indices, keep_values, input_tensor.dense_shape)
return dropped_tensor
else:
return input_tensor
elif sparse_tensor == False:
return tf.layers.dropout(input_tensor, rate=rate, training=is_training)
def adaptive_transformation(bn_normalized_dense, is_training, func_type="default"):
assert func_type in [
"default",
"tiny",
], f"fun_type can only be one of default and tiny, but get {func_type}"
gw_normalized_dense = tf.expand_dims(bn_normalized_dense, -1)
group_num = bn_normalized_dense.shape.as_list()[1]
if func_type == "default":
gw_normalized_dense = FastGroupWiseTrans(
group_num, 1, 8, name="groupwise_1", activation=tf.tanh, init_multiplier=8
)(gw_normalized_dense)
gw_normalized_dense = FastGroupWiseTrans(
group_num, 8, 4, name="groupwise_2", activation=tf.tanh, init_multiplier=8
)(gw_normalized_dense)
gw_normalized_dense = FastGroupWiseTrans(
group_num, 4, 1, name="groupwise_3", activation=tf.tanh, init_multiplier=8
)(gw_normalized_dense)
elif func_type == "tiny":
gw_normalized_dense = FastGroupWiseTrans(
group_num, 1, 2, name="groupwise_1", activation=tf.tanh, init_multiplier=8
)(gw_normalized_dense)
gw_normalized_dense = FastGroupWiseTrans(
group_num, 2, 1, name="groupwise_2", activation=tf.tanh, init_multiplier=8
)(gw_normalized_dense)
gw_normalized_dense = FastGroupWiseTrans(
group_num, 1, 1, name="groupwise_3", activation=tf.tanh, init_multiplier=8
)(gw_normalized_dense)
gw_normalized_dense = tf.squeeze(gw_normalized_dense, [-1])
bn_gw_normalized_dense = tf.layers.batch_normalization(
gw_normalized_dense,
training=is_training,
renorm_momentum=0.9999,
momentum=0.9999,
renorm=is_training,
trainable=True,
)
return bn_gw_normalized_dense
class FastGroupWiseTrans(object):
"""
used to apply group-wise fully connected layers to the input.
it applies a tiny, unique MLP to each individual feature."""
def __init__(self, group_num, input_dim, out_dim, name, activation=None, init_multiplier=1):
self.group_num = group_num
self.input_dim = input_dim
self.out_dim = out_dim
self.activation = activation
self.init_multiplier = init_multiplier
self.w = tf.get_variable(
name + "_group_weight",
[1, group_num, input_dim, out_dim],
initializer=customized_glorot_uniform(
fan_in=input_dim * init_multiplier, fan_out=out_dim * init_multiplier
),
trainable=True,
)
self.b = tf.get_variable(
name + "_group_bias",
[1, group_num, out_dim],
initializer=tf.constant_initializer(0.0),
trainable=True,
)
def __call__(self, input_tensor):
"""
input_tensor: batch_size x group_num x input_dim
output_tensor: batch_size x group_num x out_dim"""
input_tensor_expand = tf.expand_dims(input_tensor, axis=-1)
output_tensor = tf.add(
tf.reduce_sum(tf.multiply(input_tensor_expand, self.w), axis=-2, keepdims=False),
self.b,
)
if self.activation is not None:
output_tensor = self.activation(output_tensor)
return output_tensor
class GroupWiseTrans(object):
"""
Used to apply group fully connected layers to the input.
"""
def __init__(self, group_num, input_dim, out_dim, name, activation=None):
self.group_num = group_num
self.input_dim = input_dim
self.out_dim = out_dim
self.activation = activation
w_list, b_list = [], []
for idx in range(out_dim):
this_w = tf.get_variable(
name + f"_group_weight_{idx}",
[1, group_num, input_dim],
initializer=tf.keras.initializers.glorot_uniform(),
trainable=True,
)
this_b = tf.get_variable(
name + f"_group_bias_{idx}",
[1, group_num, 1],
initializer=tf.constant_initializer(0.0),
trainable=True,
)
w_list.append(this_w)
b_list.append(this_b)
self.w_list = w_list
self.b_list = b_list
def __call__(self, input_tensor):
"""
input_tensor: batch_size x group_num x input_dim
output_tensor: batch_size x group_num x out_dim
"""
out_tensor_list = []
for idx in range(self.out_dim):
this_res = (
tf.reduce_sum(input_tensor * self.w_list[idx], axis=-1, keepdims=True) + self.b_list[idx]
)
out_tensor_list.append(this_res)
output_tensor = tf.concat(out_tensor_list, axis=-1)
if self.activation is not None:
output_tensor = self.activation(output_tensor)
return output_tensor
def add_scalar_summary(var, name, name_scope="hist_dense_feature/"):
with tf.name_scope("summaries/"):
with tf.name_scope(name_scope):
tf.summary.scalar(name, var)
def add_histogram_summary(var, name, name_scope="hist_dense_feature/"):
with tf.name_scope("summaries/"):
with tf.name_scope(name_scope):
tf.summary.histogram(name, tf.reshape(var, [-1]))
def sparse_clip_by_value(sparse_tf, min_val, max_val):
new_vals = tf.clip_by_value(sparse_tf.values, min_val, max_val)
return tf.SparseTensor(sparse_tf.indices, new_vals, sparse_tf.dense_shape)
def check_numerics_with_msg(tensor, message="", sparse_tensor=False):
if sparse_tensor:
values = tf.debugging.check_numerics(tensor.values, message=message)
return tf.SparseTensor(tensor.indices, values, tensor.dense_shape)
else:
return tf.debugging.check_numerics(tensor, message=message)
def pad_empty_sparse_tensor(tensor):
dummy_tensor = tf.SparseTensor(
indices=[[0, 0]],
values=[0.00001],
dense_shape=tensor.dense_shape,
)
result = tf.cond(
tf.equal(tf.size(tensor.values), 0),
lambda: dummy_tensor,
lambda: tensor,
)
return result
def filter_nans_and_infs(tensor, sparse_tensor=False):
if sparse_tensor:
sparse_values = tensor.values
filtered_val = tf.where(
tf.logical_or(tf.is_nan(sparse_values), tf.is_inf(sparse_values)),
tf.zeros_like(sparse_values),
sparse_values,
)
return tf.SparseTensor(tensor.indices, filtered_val, tensor.dense_shape)
else:
return tf.where(
tf.logical_or(tf.is_nan(tensor), tf.is_inf(tensor)), tf.zeros_like(tensor), tensor
)
def generate_disliked_mask(labels):
"""Generate a disliked mask where only samples with dislike labels are set to 1 otherwise set to 0.
Args:
labels: labels of training samples, which is a 2D tensor of shape batch_size x 3: [OONCs, engagements, dislikes]
Returns:
1D tensor of shape batch_size x 1: [dislikes (booleans)]
"""
return tf.equal(tf.reshape(labels[:, 2], shape=[-1, 1]), 1)

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from collections import OrderedDict
import json
import os
from os.path import join
from twitter.magicpony.common import file_access
import twml
from .model_utils import read_config
import numpy as np
from scipy import stats
import tensorflow.compat.v1 as tf
# checkstyle: noqa
def get_model_type_to_tensors_to_change_axis():
model_type_to_tensors_to_change_axis = {
"magic_recs/model/batch_normalization/beta": ([0], "continuous"),
"magic_recs/model/batch_normalization/gamma": ([0], "continuous"),
"magic_recs/model/batch_normalization/moving_mean": ([0], "continuous"),
"magic_recs/model/batch_normalization/moving_stddev": ([0], "continuous"),
"magic_recs/model/batch_normalization/moving_variance": ([0], "continuous"),
"magic_recs/model/batch_normalization/renorm_mean": ([0], "continuous"),
"magic_recs/model/batch_normalization/renorm_stddev": ([0], "continuous"),
"magic_recs/model/logits/EngagementGivenOONC_logits/clem_net_1/block2_4/channel_wise_dense_4/kernel": (
[1],
"all",
),
"magic_recs/model/logits/OONC_logits/clem_net/block2/channel_wise_dense/kernel": ([1], "all"),
}
return model_type_to_tensors_to_change_axis
def mkdirp(dirname):
if not tf.io.gfile.exists(dirname):
tf.io.gfile.makedirs(dirname)
def rename_dir(dirname, dst):
file_access.hdfs.mv(dirname, dst)
def rmdir(dirname):
if tf.io.gfile.exists(dirname):
if tf.io.gfile.isdir(dirname):
tf.io.gfile.rmtree(dirname)
else:
tf.io.gfile.remove(dirname)
def get_var_dict(checkpoint_path):
checkpoint = tf.train.get_checkpoint_state(checkpoint_path)
var_dict = OrderedDict()
with tf.Session() as sess:
all_var_list = tf.train.list_variables(checkpoint_path)
for var_name, _ in all_var_list:
# Load the variable
var = tf.train.load_variable(checkpoint_path, var_name)
var_dict[var_name] = var
return var_dict
def get_continunous_mapping_from_feat_list(old_feature_list, new_feature_list):
"""
get var_ind for old_feature and corresponding var_ind for new_feature
"""
new_var_ind, old_var_ind = [], []
for this_new_id, this_new_name in enumerate(new_feature_list):
if this_new_name in old_feature_list:
this_old_id = old_feature_list.index(this_new_name)
new_var_ind.append(this_new_id)
old_var_ind.append(this_old_id)
return np.asarray(old_var_ind), np.asarray(new_var_ind)
def get_continuous_mapping_from_feat_dict(old_feature_dict, new_feature_dict):
"""
get var_ind for old_feature and corresponding var_ind for new_feature
"""
old_cont = old_feature_dict["continuous"]
old_bin = old_feature_dict["binary"]
new_cont = new_feature_dict["continuous"]
new_bin = new_feature_dict["binary"]
_dummy_sparse_feat = [f"sparse_feature_{_idx}" for _idx in range(100)]
cont_old_var_ind, cont_new_var_ind = get_continunous_mapping_from_feat_list(old_cont, new_cont)
all_old_var_ind, all_new_var_ind = get_continunous_mapping_from_feat_list(
old_cont + old_bin + _dummy_sparse_feat, new_cont + new_bin + _dummy_sparse_feat
)
_res = {
"continuous": (cont_old_var_ind, cont_new_var_ind),
"all": (all_old_var_ind, all_new_var_ind),
}
return _res
def warm_start_from_var_dict(
old_ckpt_path,
var_ind_dict,
output_dir,
new_len_var,
var_to_change_dict_fn=get_model_type_to_tensors_to_change_axis,
):
"""
Parameters:
old_ckpt_path (str): path to the old checkpoint path
new_var_ind (array of int): index to overlapping features in new var between old and new feature list.
old_var_ind (array of int): index to overlapping features in old var between old and new feature list.
output_dir (str): dir that used to write modified checkpoint
new_len_var ({str:int}): number of feature in the new feature list.
var_to_change_dict_fn (dict): A function to get the dictionary of format {var_name: dim_to_change}
"""
old_var_dict = get_var_dict(old_ckpt_path)
ckpt_file_name = os.path.basename(old_ckpt_path)
mkdirp(output_dir)
output_path = join(output_dir, ckpt_file_name)
tensors_to_change = var_to_change_dict_fn()
tf.compat.v1.reset_default_graph()
with tf.Session() as sess:
var_name_shape_list = tf.train.list_variables(old_ckpt_path)
count = 0
for var_name, var_shape in var_name_shape_list:
old_var = old_var_dict[var_name]
if var_name in tensors_to_change.keys():
_info_tuple = tensors_to_change[var_name]
dims_to_remove_from, var_type = _info_tuple
new_var_ind, old_var_ind = var_ind_dict[var_type]
this_shape = list(old_var.shape)
for this_dim in dims_to_remove_from:
this_shape[this_dim] = new_len_var[var_type]
stddev = np.std(old_var)
truncated_norm_generator = stats.truncnorm(-0.5, 0.5, loc=0, scale=stddev)
size = np.prod(this_shape)
new_var = truncated_norm_generator.rvs(size).reshape(this_shape)
new_var = new_var.astype(old_var.dtype)
new_var = copy_feat_based_on_mapping(
new_var, old_var, dims_to_remove_from, new_var_ind, old_var_ind
)
count = count + 1
else:
new_var = old_var
var = tf.Variable(new_var, name=var_name)
assert count == len(tensors_to_change.keys()), "not all variables are exchanged.\n"
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.save(sess, output_path)
return output_path
def copy_feat_based_on_mapping(new_array, old_array, dims_to_remove_from, new_var_ind, old_var_ind):
if dims_to_remove_from == [0, 1]:
for this_new_ind, this_old_ind in zip(new_var_ind, old_var_ind):
new_array[this_new_ind, new_var_ind] = old_array[this_old_ind, old_var_ind]
elif dims_to_remove_from == [0]:
new_array[new_var_ind] = old_array[old_var_ind]
elif dims_to_remove_from == [1]:
new_array[:, new_var_ind] = old_array[:, old_var_ind]
else:
raise RuntimeError(f"undefined dims_to_remove_from pattern: ({dims_to_remove_from})")
return new_array
def read_file(filename, decode=False):
"""
Reads contents from a file and optionally decodes it.
Arguments:
filename:
path to file where the contents will be loaded from.
Accepts HDFS and local paths.
decode:
False or 'json'. When decode='json', contents is decoded
with json.loads. When False, contents is returned as is.
"""
graph = tf.Graph()
with graph.as_default():
read = tf.read_file(filename)
with tf.Session(graph=graph) as sess:
contents = sess.run(read)
if not isinstance(contents, str):
contents = contents.decode()
if decode == "json":
contents = json.loads(contents)
return contents
def read_feat_list_from_disk(file_path):
return read_file(file_path, decode="json")
def get_feature_list_for_light_ranking(feature_list_path, data_spec_path):
feature_list = read_config(feature_list_path).items()
string_feat_list = [f[0] for f in feature_list if f[1] != "S"]
feature_config_builder = twml.contrib.feature_config.FeatureConfigBuilder(
data_spec_path=data_spec_path
)
feature_config_builder = feature_config_builder.extract_feature_group(
feature_regexes=string_feat_list,
group_name="continuous",
default_value=-1,
type_filter=["CONTINUOUS"],
)
feature_config = feature_config_builder.build()
feature_list = feature_config_builder._feature_group_extraction_configs[0].feature_map[
"CONTINUOUS"
]
return feature_list
def get_feature_list_for_heavy_ranking(feature_list_path, data_spec_path):
feature_list = read_config(feature_list_path).items()
string_feat_list = [f[0] for f in feature_list if f[1] != "S"]
feature_config_builder = twml.contrib.feature_config.FeatureConfigBuilder(
data_spec_path=data_spec_path
)
feature_config_builder = feature_config_builder.extract_feature_group(
feature_regexes=string_feat_list,
group_name="continuous",
default_value=-1,
type_filter=["CONTINUOUS"],
)
feature_config_builder = feature_config_builder.extract_feature_group(
feature_regexes=string_feat_list,
group_name="binary",
default_value=False,
type_filter=["BINARY"],
)
feature_config_builder = feature_config_builder.build()
continuous_feature_list = feature_config_builder._feature_group_extraction_configs[0].feature_map[
"CONTINUOUS"
]
binary_feature_list = feature_config_builder._feature_group_extraction_configs[1].feature_map[
"BINARY"
]
return {"continuous": continuous_feature_list, "binary": binary_feature_list}
def warm_start_checkpoint(
old_best_ckpt_folder,
old_feature_list_path,
feature_allow_list_path,
data_spec_path,
output_ckpt_folder,
*args,
):
"""
Reads old checkpoint and the old feature list, and create a new ckpt warm started from old ckpt using new features .
Arguments:
old_best_ckpt_folder:
path to the best_checkpoint_folder for old model
old_feature_list_path:
path to the json file that stores the list of continuous features used in old models.
feature_allow_list_path:
yaml file that contain the feature allow list.
data_spec_path:
path to the data_spec file
output_ckpt_folder:
folder that contains the modified ckpt.
Returns:
path to the modified ckpt."""
old_ckpt_path = tf.train.latest_checkpoint(old_best_ckpt_folder, latest_filename=None)
new_feature_dict = get_feature_list(feature_allow_list_path, data_spec_path)
old_feature_dict = read_feat_list_from_disk(old_feature_list_path)
var_ind_dict = get_continuous_mapping_from_feat_dict(new_feature_dict, old_feature_dict)
new_len_var = {
"continuous": len(new_feature_dict["continuous"]),
"all": len(new_feature_dict["continuous"] + new_feature_dict["binary"]) + 100,
}
warm_started_ckpt_path = warm_start_from_var_dict(
old_ckpt_path,
var_ind_dict,
output_dir=output_ckpt_folder,
new_len_var=new_len_var,
)
return warm_started_ckpt_path

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#":mlwf_libs",
python37_binary(
name = "eval_model",
source = "eval_model.py",
dependencies = [
":libs",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/light_ranking:eval_model",
],
)
python37_binary(
name = "train_model",
source = "deep_norm.py",
dependencies = [
":libs",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/light_ranking:train_model",
],
)
python37_binary(
name = "train_model_local",
source = "deep_norm.py",
dependencies = [
":libs",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/light_ranking:train_model_local",
"twml",
],
)
python37_binary(
name = "eval_model_local",
source = "eval_model.py",
dependencies = [
":libs",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/light_ranking:eval_model_local",
"twml",
],
)
python37_binary(
name = "mlwf_model",
source = "deep_norm.py",
dependencies = [
":mlwf_libs",
"3rdparty/python/_closures/frigate/frigate-pushservice-opensource/src/main/python/models/light_ranking:mlwf_model",
],
)
python3_library(
name = "libs",
sources = ["**/*.py"],
tags = ["no-mypy"],
dependencies = [
"src/python/twitter/deepbird/projects/magic_recs/libs",
"src/python/twitter/deepbird/util/data",
"twml:twml-nodeps",
],
)
python3_library(
name = "mlwf_libs",
sources = ["**/*.py"],
tags = ["no-mypy"],
dependencies = [
"src/python/twitter/deepbird/projects/magic_recs/libs",
"twml",
],
)

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# Notification Light Ranker Model
## Model Context
There are 4 major components of Twitter notifications recommendation system: 1) candidate generation 2) light ranking 3) heavy ranking & 4) quality control. This notification light ranker model bridges candidate generation and heavy ranking by pre-selecting highly-relevant candidates from the initial huge candidate pool. It’s a light-weight model to reduce system cost during heavy ranking without hurting user experience.
## Directory Structure
- BUILD: this file defines python library dependencies
- model_pools_mlp.py: this file defines tensorflow model architecture for the notification light ranker model
- deep_norm.py: this file contains 1) how to build the tensorflow graph with specified model architecture, loss function and training configuration. 2) how to set up the overall model training & evaluation pipeline
- eval_model.py: the main python entry file to set up the overall model evaluation pipeline

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from datetime import datetime
from functools import partial
import os
from twitter.cortex.ml.embeddings.common.helpers import decode_str_or_unicode
import twml
from twml.trainers import DataRecordTrainer
from ..libs.get_feat_config import get_feature_config_light_ranking, LABELS_LR
from ..libs.graph_utils import get_trainable_variables
from ..libs.group_metrics import (
run_group_metrics_light_ranking,
run_group_metrics_light_ranking_in_bq,
)
from ..libs.metric_fn_utils import get_metric_fn
from ..libs.model_args import get_arg_parser_light_ranking
from ..libs.model_utils import read_config
from ..libs.warm_start_utils import get_feature_list_for_light_ranking
from .model_pools_mlp import light_ranking_mlp_ngbdt
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import logging
# checkstyle: noqa
def build_graph(
features, label, mode, params, config=None, run_light_ranking_group_metrics_in_bq=False
):
is_training = mode == tf.estimator.ModeKeys.TRAIN
this_model_func = light_ranking_mlp_ngbdt
model_output = this_model_func(features, is_training, params, label)
logits = model_output["output"]
graph_output = {}
# --------------------------------------------------------
# define graph output dict
# --------------------------------------------------------
if mode == tf.estimator.ModeKeys.PREDICT:
loss = None
output_label = "prediction"
if params.task_name in LABELS_LR:
output = tf.nn.sigmoid(logits)
output = tf.clip_by_value(output, 0, 1)
if run_light_ranking_group_metrics_in_bq:
graph_output["trace_id"] = features["meta.trace_id"]
graph_output["target"] = features["meta.ranking.weighted_oonc_model_score"]
else:
raise ValueError("Invalid Task Name !")
else:
output_label = "output"
weights = tf.cast(features["weights"], dtype=tf.float32, name="RecordWeights")
if params.task_name in LABELS_LR:
if params.use_record_weight:
weights = tf.clip_by_value(
1.0 / (1.0 + weights + params.smooth_weight), params.min_record_weight, 1.0
)
loss = tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(labels=label, logits=logits) * weights
) / (tf.reduce_sum(weights))
else:
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=label, logits=logits))
output = tf.nn.sigmoid(logits)
else:
raise ValueError("Invalid Task Name !")
train_op = None
if mode == tf.estimator.ModeKeys.TRAIN:
# --------------------------------------------------------
# get train_op
# --------------------------------------------------------
optimizer = tf.train.GradientDescentOptimizer(learning_rate=params.learning_rate)
update_ops = set(tf.get_collection(tf.GraphKeys.UPDATE_OPS))
variables = get_trainable_variables(
all_trainable_variables=tf.trainable_variables(), trainable_regexes=params.trainable_regexes
)
with tf.control_dependencies(update_ops):
train_op = twml.optimizers.optimize_loss(
loss=loss,
variables=variables,
global_step=tf.train.get_global_step(),
optimizer=optimizer,
learning_rate=params.learning_rate,
learning_rate_decay_fn=twml.learning_rate_decay.get_learning_rate_decay_fn(params),
)
graph_output[output_label] = output
graph_output["loss"] = loss
graph_output["train_op"] = train_op
return graph_output
def get_params(args=None):
parser = get_arg_parser_light_ranking()
if args is None:
return parser.parse_args()
else:
return parser.parse_args(args)
def _main():
opt = get_params()
logging.info("parse is: ")
logging.info(opt)
feature_list = read_config(opt.feature_list).items()
feature_config = get_feature_config_light_ranking(
data_spec_path=opt.data_spec,
feature_list_provided=feature_list,
opt=opt,
add_gbdt=opt.use_gbdt_features,
run_light_ranking_group_metrics_in_bq=opt.run_light_ranking_group_metrics_in_bq,
)
feature_list_path = opt.feature_list
# --------------------------------------------------------
# Create Trainer
# --------------------------------------------------------
trainer = DataRecordTrainer(
name=opt.model_trainer_name,
params=opt,
build_graph_fn=build_graph,
save_dir=opt.save_dir,
run_config=None,
feature_config=feature_config,
metric_fn=get_metric_fn(opt.task_name, use_stratify_metrics=False),
)
if opt.directly_export_best:
logging.info("Directly exporting the model without training")
else:
# ----------------------------------------------------
# Model Training & Evaluation
# ----------------------------------------------------
eval_input_fn = trainer.get_eval_input_fn(repeat=False, shuffle=False)
train_input_fn = trainer.get_train_input_fn(shuffle=True)
if opt.distributed or opt.num_workers is not None:
learn = trainer.train_and_evaluate
else:
learn = trainer.learn
logging.info("Training...")
start = datetime.now()
early_stop_metric = "rce_unweighted_" + opt.task_name
learn(
early_stop_minimize=False,
early_stop_metric=early_stop_metric,
early_stop_patience=opt.early_stop_patience,
early_stop_tolerance=opt.early_stop_tolerance,
eval_input_fn=eval_input_fn,
train_input_fn=train_input_fn,
)
end = datetime.now()
logging.info("Training time: " + str(end - start))
logging.info("Exporting the models...")
# --------------------------------------------------------
# Do the model exporting
# --------------------------------------------------------
start = datetime.now()
if not opt.export_dir:
opt.export_dir = os.path.join(opt.save_dir, "exported_models")
raw_model_path = twml.contrib.export.export_fn.export_all_models(
trainer=trainer,
export_dir=opt.export_dir,
parse_fn=feature_config.get_parse_fn(),
serving_input_receiver_fn=feature_config.get_serving_input_receiver_fn(),
export_output_fn=twml.export_output_fns.batch_prediction_continuous_output_fn,
)
export_model_dir = decode_str_or_unicode(raw_model_path)
logging.info("Model export time: " + str(datetime.now() - start))
logging.info("The saved model directory is: " + opt.save_dir)
tf.logging.info("getting default continuous_feature_list")
continuous_feature_list = get_feature_list_for_light_ranking(feature_list_path, opt.data_spec)
continous_feature_list_save_path = os.path.join(opt.save_dir, "continuous_feature_list.json")
twml.util.write_file(continous_feature_list_save_path, continuous_feature_list, encode="json")
tf.logging.info(f"Finish writting files to {continous_feature_list_save_path}")
if opt.run_light_ranking_group_metrics:
# --------------------------------------------
# Run Light Ranking Group Metrics
# --------------------------------------------
run_group_metrics_light_ranking(
trainer=trainer,
data_dir=os.path.join(opt.eval_data_dir, opt.eval_start_datetime),
model_path=export_model_dir,
parse_fn=feature_config.get_parse_fn(),
)
if opt.run_light_ranking_group_metrics_in_bq:
# ----------------------------------------------------------------------------------------
# Get Light/Heavy Ranker Predictions for Light Ranking Group Metrics in BigQuery
# ----------------------------------------------------------------------------------------
trainer_pred = DataRecordTrainer(
name=opt.model_trainer_name,
params=opt,
build_graph_fn=partial(build_graph, run_light_ranking_group_metrics_in_bq=True),
save_dir=opt.save_dir + "/tmp/",
run_config=None,
feature_config=feature_config,
metric_fn=get_metric_fn(opt.task_name, use_stratify_metrics=False),
)
checkpoint_folder = os.path.join(opt.save_dir, "best_checkpoint")
checkpoint = tf.train.latest_checkpoint(checkpoint_folder, latest_filename=None)
tf.logging.info("\n\nPrediction from Checkpoint: {:}.\n\n".format(checkpoint))
run_group_metrics_light_ranking_in_bq(
trainer=trainer_pred, params=opt, checkpoint_path=checkpoint
)
tf.logging.info("Done Training & Prediction.")
if __name__ == "__main__":
_main()

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from datetime import datetime
from functools import partial
import os
from ..libs.group_metrics import (
run_group_metrics_light_ranking,
run_group_metrics_light_ranking_in_bq,
)
from ..libs.metric_fn_utils import get_metric_fn
from ..libs.model_args import get_arg_parser_light_ranking
from ..libs.model_utils import read_config
from .deep_norm import build_graph, DataRecordTrainer, get_config_func, logging
# checkstyle: noqa
if __name__ == "__main__":
parser = get_arg_parser_light_ranking()
parser.add_argument(
"--eval_checkpoint",
default=None,
type=str,
help="Which checkpoint to use for evaluation",
)
parser.add_argument(
"--saved_model_path",
default=None,
type=str,
help="Path to saved model for evaluation",
)
parser.add_argument(
"--run_binary_metrics",
default=False,
action="store_true",
help="Whether to compute the basic binary metrics for Light Ranking.",
)
opt = parser.parse_args()
logging.info("parse is: ")
logging.info(opt)
feature_list = read_config(opt.feature_list).items()
feature_config = get_config_func(opt.feat_config_type)(
data_spec_path=opt.data_spec,
feature_list_provided=feature_list,
opt=opt,
add_gbdt=opt.use_gbdt_features,
run_light_ranking_group_metrics_in_bq=opt.run_light_ranking_group_metrics_in_bq,
)
# -----------------------------------------------
# Create Trainer
# -----------------------------------------------
trainer = DataRecordTrainer(
name=opt.model_trainer_name,
params=opt,
build_graph_fn=partial(build_graph, run_light_ranking_group_metrics_in_bq=True),
save_dir=opt.save_dir,
run_config=None,
feature_config=feature_config,
metric_fn=get_metric_fn(opt.task_name, use_stratify_metrics=False),
)
# -----------------------------------------------
# Model Evaluation
# -----------------------------------------------
logging.info("Evaluating...")
start = datetime.now()
if opt.run_binary_metrics:
eval_input_fn = trainer.get_eval_input_fn(repeat=False, shuffle=False)
eval_steps = None if (opt.eval_steps is not None and opt.eval_steps < 0) else opt.eval_steps
trainer.estimator.evaluate(eval_input_fn, steps=eval_steps, checkpoint_path=opt.eval_checkpoint)
if opt.run_light_ranking_group_metrics_in_bq:
run_group_metrics_light_ranking_in_bq(
trainer=trainer, params=opt, checkpoint_path=opt.eval_checkpoint
)
if opt.run_light_ranking_group_metrics:
run_group_metrics_light_ranking(
trainer=trainer,
data_dir=os.path.join(opt.eval_data_dir, opt.eval_start_datetime),
model_path=opt.saved_model_path,
parse_fn=feature_config.get_parse_fn(),
)
end = datetime.now()
logging.info("Evaluating time: " + str(end - start))

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import warnings
from twml.contrib.layers import ZscoreNormalization
from ...libs.customized_full_sparse import FullSparse
from ...libs.get_feat_config import FEAT_CONFIG_DEFAULT_VAL as MISSING_VALUE_MARKER
from ...libs.model_utils import (
_sparse_feature_fixup,
adaptive_transformation,
filter_nans_and_infs,
get_dense_out,
tensor_dropout,
)
import tensorflow.compat.v1 as tf
# checkstyle: noqa
def light_ranking_mlp_ngbdt(features, is_training, params, label=None):
return deepnorm_light_ranking(
features,
is_training,
params,
label=label,
decay=params.momentum,
dense_emb_size=params.dense_embedding_size,
base_activation=tf.keras.layers.LeakyReLU(),
input_dropout_rate=params.dropout,
use_gbdt=False,
)
def deepnorm_light_ranking(
features,
is_training,
params,
label=None,
decay=0.99999,
dense_emb_size=128,
base_activation=None,
input_dropout_rate=None,
input_dense_type="self_atten_dense",
emb_dense_type="self_atten_dense",
mlp_dense_type="self_atten_dense",
use_gbdt=False,
):
# --------------------------------------------------------
# Initial Parameter Checking
# --------------------------------------------------------
if base_activation is None:
base_activation = tf.keras.layers.LeakyReLU()
if label is not None:
warnings.warn(
"Label is unused in deepnorm_gbdt. Stop using this argument.",
DeprecationWarning,
)
with tf.variable_scope("helper_layers"):
full_sparse_layer = FullSparse(
output_size=params.sparse_embedding_size,
activation=base_activation,
use_sparse_grads=is_training,
use_binary_values=False,
dtype=tf.float32,
)
input_normalizing_layer = ZscoreNormalization(decay=decay, name="input_normalizing_layer")
# --------------------------------------------------------
# Feature Selection & Embedding
# --------------------------------------------------------
if use_gbdt:
sparse_gbdt_features = _sparse_feature_fixup(features["gbdt_sparse"], params.input_size_bits)
if input_dropout_rate is not None:
sparse_gbdt_features = tensor_dropout(
sparse_gbdt_features, input_dropout_rate, is_training, sparse_tensor=True
)
total_embed = full_sparse_layer(sparse_gbdt_features, use_binary_values=True)
if (input_dropout_rate is not None) and is_training:
total_embed = total_embed / (1 - input_dropout_rate)
else:
with tf.variable_scope("dense_branch"):
dense_continuous_features = filter_nans_and_infs(features["continuous"])
if params.use_missing_sub_branch:
is_missing = tf.equal(dense_continuous_features, MISSING_VALUE_MARKER)
continuous_features_filled = tf.where(
is_missing,
tf.zeros_like(dense_continuous_features),
dense_continuous_features,
)
normalized_features = input_normalizing_layer(
continuous_features_filled, is_training, tf.math.logical_not(is_missing)
)
with tf.variable_scope("missing_sub_branch"):
missing_feature_embed = get_dense_out(
tf.cast(is_missing, tf.float32),
dense_emb_size,
activation=base_activation,
dense_type=input_dense_type,
)
else:
continuous_features_filled = dense_continuous_features
normalized_features = input_normalizing_layer(continuous_features_filled, is_training)
with tf.variable_scope("continuous_sub_branch"):
normalized_features = adaptive_transformation(
normalized_features, is_training, func_type="tiny"
)
if input_dropout_rate is not None:
normalized_features = tensor_dropout(
normalized_features,
input_dropout_rate,
is_training,
sparse_tensor=False,
)
filled_feature_embed = get_dense_out(
normalized_features,
dense_emb_size,
activation=base_activation,
dense_type=input_dense_type,
)
if params.use_missing_sub_branch:
dense_embed = tf.concat(
[filled_feature_embed, missing_feature_embed], axis=1, name="merge_dense_emb"
)
else:
dense_embed = filled_feature_embed
with tf.variable_scope("sparse_branch"):
sparse_discrete_features = _sparse_feature_fixup(
features["sparse_no_continuous"], params.input_size_bits
)
if input_dropout_rate is not None:
sparse_discrete_features = tensor_dropout(
sparse_discrete_features, input_dropout_rate, is_training, sparse_tensor=True
)
discrete_features_embed = full_sparse_layer(sparse_discrete_features, use_binary_values=True)
if (input_dropout_rate is not None) and is_training:
discrete_features_embed = discrete_features_embed / (1 - input_dropout_rate)
total_embed = tf.concat(
[dense_embed, discrete_features_embed],
axis=1,
name="total_embed",
)
total_embed = tf.layers.batch_normalization(
total_embed,
training=is_training,
renorm_momentum=decay,
momentum=decay,
renorm=is_training,
trainable=True,
)
# --------------------------------------------------------
# MLP Layers
# --------------------------------------------------------
with tf.variable_scope("MLP_branch"):
assert params.num_mlp_layers >= 0
embed_list = [total_embed] + [None for _ in range(params.num_mlp_layers)]
dense_types = [emb_dense_type] + [mlp_dense_type for _ in range(params.num_mlp_layers - 1)]
for xl in range(1, params.num_mlp_layers + 1):
neurons = params.mlp_neuron_scale ** (params.num_mlp_layers + 1 - xl)
embed_list[xl] = get_dense_out(
embed_list[xl - 1], neurons, activation=base_activation, dense_type=dense_types[xl - 1]
)
if params.task_name in ["Sent", "HeavyRankPosition", "HeavyRankProbability"]:
logits = get_dense_out(embed_list[-1], 1, activation=None, dense_type=mlp_dense_type)
else:
raise ValueError("Invalid Task Name !")
output_dict = {"output": logits}
return output_dict

337
pushservice/src/main/scala/com/twitter/frigate/pushservice/BUILD.bazel
View File

@ -1,337 +0,0 @@
scala_library(
sources = ["**/*.scala"],
compiler_option_sets = ["fatal_warnings"],
strict_deps = True,
tags = [
"bazel-compatible",
],
dependencies = [
"3rdparty/jvm/com/twitter/bijection:scrooge",
"3rdparty/jvm/com/twitter/storehaus:core",
"abdecider",
"abuse/detection/src/main/thrift/com/twitter/abuse/detection/scoring:thrift-scala",
"ann/src/main/scala/com/twitter/ann/common",
"ann/src/main/thrift/com/twitter/ann/common:ann-common-scala",
"audience-rewards/thrift/src/main/thrift:thrift-scala",
"communities/thrift/src/main/thrift/com/twitter/communities:thrift-scala",
"configapi/configapi-core",
"configapi/configapi-decider",
"content-mixer/thrift/src/main/thrift:thrift-scala",
"content-recommender/thrift/src/main/thrift:thrift-scala",
"copyselectionservice/server/src/main/scala/com/twitter/copyselectionservice/algorithms",
"copyselectionservice/thrift/src/main/thrift:copyselectionservice-scala",
"cortex-deepbird/thrift/src/main/thrift:thrift-java",
"cr-mixer/thrift/src/main/thrift:thrift-scala",
"cuad/projects/hashspace/thrift:thrift-scala",
"cuad/projects/tagspace/thrift/src/main/thrift:thrift-scala",
"detopic/thrift/src/main/thrift:thrift-scala",
"discovery-common/src/main/scala/com/twitter/discovery/common/configapi",
"discovery-common/src/main/scala/com/twitter/discovery/common/ddg",
"discovery-common/src/main/scala/com/twitter/discovery/common/environment",
"discovery-common/src/main/scala/com/twitter/discovery/common/fatigue",
"discovery-common/src/main/scala/com/twitter/discovery/common/nackwarmupfilter",
"discovery-common/src/main/scala/com/twitter/discovery/common/server",
"discovery-ds/src/main/thrift/com/twitter/dds/scio/searcher_aggregate_history_srp:searcher_aggregate_history_srp-scala",
"escherbird/src/scala/com/twitter/escherbird/util/metadatastitch",
"escherbird/src/scala/com/twitter/escherbird/util/uttclient",
"escherbird/src/thrift/com/twitter/escherbird/utt:strato-columns-scala",
"eventbus/client",
"eventdetection/event_context/src/main/scala/com/twitter/eventdetection/event_context/util",
"events-recos/events-recos-service/src/main/thrift:events-recos-thrift-scala",
"explore/explore-ranker/thrift/src/main/thrift:thrift-scala",
"featureswitches/featureswitches-core/src/main/scala",
"featureswitches/featureswitches-core/src/main/scala:dynmap",
"featureswitches/featureswitches-core/src/main/scala:recipient",
"featureswitches/featureswitches-core/src/main/scala:useragent",
"featureswitches/featureswitches-core/src/main/scala/com/twitter/featureswitches/v2/builder",
"finagle-internal/mtls/src/main/scala/com/twitter/finagle/mtls/authentication",
"finagle-internal/mtls/src/main/scala/com/twitter/finagle/mtls/server",
"finagle-internal/ostrich-stats",
"finagle/finagle-core/src/main",
"finagle/finagle-http/src/main/scala",
"finagle/finagle-memcached/src/main/scala",
"finagle/finagle-stats",
"finagle/finagle-thriftmux",
"finagle/finagle-tunable/src/main/scala",
"finagle/finagle-zipkin-scribe",
"finatra-internal/abdecider",
"finatra-internal/decider",
"finatra-internal/mtls-http/src/main/scala",
"finatra-internal/mtls-thriftmux/src/main/scala",
"finatra/http-client/src/main/scala",
"finatra/http-core/src/main/java/com/twitter/finatra/http",
"finatra/http-core/src/main/scala/com/twitter/finatra/http/response",
"finatra/http-server/src/main/scala/com/twitter/finatra/http",
"finatra/http-server/src/main/scala/com/twitter/finatra/http/filters",
"finatra/inject/inject-app/src/main/java/com/twitter/inject/annotations",
"finatra/inject/inject-app/src/main/scala",
"finatra/inject/inject-core/src/main/scala",
"finatra/inject/inject-server/src/main/scala",
"finatra/inject/inject-slf4j/src/main/scala/com/twitter/inject",
"finatra/inject/inject-thrift-client/src/main/scala",
"finatra/inject/inject-utils/src/main/scala",
"finatra/utils/src/main/java/com/twitter/finatra/annotations",
"fleets/fleets-proxy/thrift/src/main/thrift:fleet-scala",
"fleets/fleets-proxy/thrift/src/main/thrift/service:baseservice-scala",
"flock-client/src/main/scala",
"flock-client/src/main/thrift:thrift-scala",
"follow-recommendations-service/thrift/src/main/thrift:thrift-scala",
"frigate/frigate-common:base",
"frigate/frigate-common:config",
"frigate/frigate-common:debug",
"frigate/frigate-common:entity_graph_client",
"frigate/frigate-common:history",
"frigate/frigate-common:logger",
"frigate/frigate-common:ml-base",
"frigate/frigate-common:ml-feature",
"frigate/frigate-common:ml-prediction",
"frigate/frigate-common:ntab",
"frigate/frigate-common:predicate",
"frigate/frigate-common:rec_types",
"frigate/frigate-common:score_summary",
"frigate/frigate-common:util",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/candidate",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/experiments",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/filter",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/modules/store:semantic_core_stores",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/store",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/store/deviceinfo",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/store/interests",
"frigate/frigate-common/src/main/scala/com/twitter/frigate/common/store/strato",
"frigate/push-mixer/thrift/src/main/thrift:thrift-scala",
"geo/geo-prediction/src/main/thrift:local-viral-tweets-thrift-scala",
"geoduck/service/src/main/scala/com/twitter/geoduck/service/common/clientmodules",
"geoduck/util/country",
"gizmoduck/client/src/main/scala/com/twitter/gizmoduck/testusers/client",
"hermit/hermit-core:model-user_state",
"hermit/hermit-core:predicate",
"hermit/hermit-core:predicate-gizmoduck",
"hermit/hermit-core:predicate-scarecrow",
"hermit/hermit-core:predicate-socialgraph",
"hermit/hermit-core:predicate-tweetypie",
"hermit/hermit-core:store-labeled_push_recs",
"hermit/hermit-core:store-metastore",
"hermit/hermit-core:store-timezone",
"hermit/hermit-core:store-tweetypie",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/constants",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/model",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/store",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/store/common",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/store/gizmoduck",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/store/scarecrow",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/store/semantic_core",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/store/user_htl_session_store",
"hermit/hermit-core/src/main/scala/com/twitter/hermit/store/user_interest",
"hmli/hss/src/main/thrift/com/twitter/hss:thrift-scala",
"ibis2/service/src/main/scala/com/twitter/ibis2/lib",
"ibis2/service/src/main/thrift/com/twitter/ibis2/service:ibis2-service-scala",
"interests-service/thrift/src/main/thrift:thrift-scala",
"interests_discovery/thrift/src/main/thrift:batch-thrift-scala",
"interests_discovery/thrift/src/main/thrift:service-thrift-scala",
"kujaku/thrift/src/main/thrift:domain-scala",
"live-video-timeline/client/src/main/scala/com/twitter/livevideo/timeline/client/v2",
"live-video-timeline/domain/src/main/scala/com/twitter/livevideo/timeline/domain",
"live-video-timeline/domain/src/main/scala/com/twitter/livevideo/timeline/domain/v2",
"live-video-timeline/thrift/src/main/thrift/com/twitter/livevideo/timeline:thrift-scala",
"live-video/common/src/main/scala/com/twitter/livevideo/common/domain/v2",
"live-video/common/src/main/scala/com/twitter/livevideo/common/ids",
"notifications-platform/inbound-notifications/src/main/thrift/com/twitter/inbound_notifications:exception-scala",
"notifications-platform/inbound-notifications/src/main/thrift/com/twitter/inbound_notifications:thrift-scala",
"notifications-platform/platform-lib/src/main/thrift/com/twitter/notifications/platform:custom-notification-actions-scala",
"notifications-platform/platform-lib/src/main/thrift/com/twitter/notifications/platform:thrift-scala",
"notifications-relevance/src/scala/com/twitter/nrel/heavyranker",
"notifications-relevance/src/scala/com/twitter/nrel/hydration/base",
"notifications-relevance/src/scala/com/twitter/nrel/hydration/frigate",
"notifications-relevance/src/scala/com/twitter/nrel/hydration/push",
"notifications-relevance/src/scala/com/twitter/nrel/lightranker",
"notificationservice/common/src/main/scala/com/twitter/notificationservice/genericfeedbackstore",
"notificationservice/common/src/main/scala/com/twitter/notificationservice/model:alias",
"notificationservice/common/src/main/scala/com/twitter/notificationservice/model/service",
"notificationservice/common/src/test/scala/com/twitter/notificationservice/mocks",
"notificationservice/scribe/src/main/scala/com/twitter/notificationservice/scribe/manhattan:mh_wrapper",
"notificationservice/thrift/src/main/thrift/com/twitter/notificationservice/api:thrift-scala",
"notificationservice/thrift/src/main/thrift/com/twitter/notificationservice/badgecount-api:thrift-scala",
"notificationservice/thrift/src/main/thrift/com/twitter/notificationservice/generic_notifications:thrift-scala",
"notifinfra/ni-lib/src/main/scala/com/twitter/ni/lib/logged_out_transform",
"observability/observability-manhattan-client/src/main/scala",
"onboarding/service/src/main/scala/com/twitter/onboarding/task/service/models/external",
"onboarding/service/thrift/src/main/thrift:thrift-scala",
"people-discovery/api/thrift/src/main/thrift:thrift-scala",
"periscope/api-proxy-thrift/thrift/src/main/thrift:thrift-scala",
"product-mixer/core/src/main/scala/com/twitter/product_mixer/core/module",
"product-mixer/core/src/main/scala/com/twitter/product_mixer/core/module/stringcenter",
"product-mixer/core/src/main/thrift/com/twitter/product_mixer/core:thrift-scala",
"qig-ranker/thrift/src/main/thrift:thrift-scala",
"rux-ds/src/main/thrift/com/twitter/ruxds/jobs/user_past_aggregate:user_past_aggregate-scala",
"rux/common/src/main/scala/com/twitter/rux/common/encode",
"rux/common/thrift/src/main/thrift/rux-context:rux-context-scala",
"rux/common/thrift/src/main/thrift/strato:strato-scala",
"scribelib/marshallers/src/main/scala/com/twitter/scribelib/marshallers",
"scrooge/scrooge-core",
"scrooge/scrooge-serializer/src/main/scala",
"sensitive-ds/src/main/thrift/com/twitter/scio/nsfw_user_segmentation:nsfw_user_segmentation-scala",
"servo/decider/src/main/scala",
"servo/request/src/main/scala",
"servo/util/src/main/scala",
"src/java/com/twitter/ml/api:api-base",
"src/java/com/twitter/ml/prediction/core",
"src/scala/com/twitter/frigate/data_pipeline/common",
"src/scala/com/twitter/frigate/data_pipeline/embedding_cg:embedding_cg-test-user-ids",
"src/scala/com/twitter/frigate/data_pipeline/features_common",
"src/scala/com/twitter/frigate/news_article_recs/news_articles_metadata:thrift-scala",
"src/scala/com/twitter/frontpage/stream/util",
"src/scala/com/twitter/language/normalization",
"src/scala/com/twitter/ml/api/embedding",
"src/scala/com/twitter/ml/api/util:datarecord",
"src/scala/com/twitter/ml/featurestore/catalog/entities/core",
"src/scala/com/twitter/ml/featurestore/catalog/entities/magicrecs",
"src/scala/com/twitter/ml/featurestore/catalog/features/core:aggregate",
"src/scala/com/twitter/ml/featurestore/catalog/features/cuad:aggregate",
"src/scala/com/twitter/ml/featurestore/catalog/features/embeddings",
"src/scala/com/twitter/ml/featurestore/catalog/features/magicrecs:aggregate",
"src/scala/com/twitter/ml/featurestore/catalog/features/topic_signals:aggregate",
"src/scala/com/twitter/ml/featurestore/lib",
"src/scala/com/twitter/ml/featurestore/lib/data",
"src/scala/com/twitter/ml/featurestore/lib/dynamic",
"src/scala/com/twitter/ml/featurestore/lib/entity",
"src/scala/com/twitter/ml/featurestore/lib/online",
"src/scala/com/twitter/recommendation/interests/discovery/core/config",
"src/scala/com/twitter/recommendation/interests/discovery/core/deploy",
"src/scala/com/twitter/recommendation/interests/discovery/core/model",
"src/scala/com/twitter/recommendation/interests/discovery/popgeo/deploy",
"src/scala/com/twitter/simclusters_v2/common",
"src/scala/com/twitter/storehaus_internal/manhattan",
"src/scala/com/twitter/storehaus_internal/manhattan/config",
"src/scala/com/twitter/storehaus_internal/memcache",
"src/scala/com/twitter/storehaus_internal/memcache/config",
"src/scala/com/twitter/storehaus_internal/util",
"src/scala/com/twitter/taxi/common",
"src/scala/com/twitter/taxi/config",
"src/scala/com/twitter/taxi/deploy",
"src/scala/com/twitter/taxi/trending/common",
"src/thrift/com/twitter/ads/adserver:adserver_rpc-scala",
"src/thrift/com/twitter/clientapp/gen:clientapp-scala",
"src/thrift/com/twitter/core_workflows/user_model:user_model-scala",
"src/thrift/com/twitter/escherbird/common:constants-scala",
"src/thrift/com/twitter/escherbird/metadata:megadata-scala",
"src/thrift/com/twitter/escherbird/metadata:metadata-service-scala",
"src/thrift/com/twitter/escherbird/search:search-service-scala",
"src/thrift/com/twitter/expandodo:only-scala",
"src/thrift/com/twitter/frigate:frigate-common-thrift-scala",
"src/thrift/com/twitter/frigate:frigate-ml-thrift-scala",
"src/thrift/com/twitter/frigate:frigate-notification-thrift-scala",
"src/thrift/com/twitter/frigate:frigate-secondary-accounts-thrift-scala",
"src/thrift/com/twitter/frigate:frigate-thrift-scala",
"src/thrift/com/twitter/frigate:frigate-user-media-representation-thrift-scala",
"src/thrift/com/twitter/frigate/data_pipeline:frigate-user-history-thrift-scala",
"src/thrift/com/twitter/frigate/dau_model:frigate-dau-thrift-scala",
"src/thrift/com/twitter/frigate/magic_events:frigate-magic-events-thrift-scala",
"src/thrift/com/twitter/frigate/magic_events/scribe:thrift-scala",
"src/thrift/com/twitter/frigate/pushcap:frigate-pushcap-thrift-scala",
"src/thrift/com/twitter/frigate/pushservice:frigate-pushservice-thrift-scala",
"src/thrift/com/twitter/frigate/scribe:frigate-scribe-thrift-scala",
"src/thrift/com/twitter/frigate/subscribed_search:frigate-subscribed-search-thrift-scala",
"src/thrift/com/twitter/frigate/user_states:frigate-userstates-thrift-scala",
"src/thrift/com/twitter/geoduck:geoduck-scala",
"src/thrift/com/twitter/gizmoduck:thrift-scala",
"src/thrift/com/twitter/gizmoduck:user-thrift-scala",
"src/thrift/com/twitter/hermit:hermit-scala",
"src/thrift/com/twitter/hermit/pop_geo:hermit-pop-geo-scala",
"src/thrift/com/twitter/hermit/stp:hermit-stp-scala",
"src/thrift/com/twitter/ibis:service-scala",
"src/thrift/com/twitter/manhattan:v1-scala",
"src/thrift/com/twitter/manhattan:v2-scala",
"src/thrift/com/twitter/ml/api:data-java",
"src/thrift/com/twitter/ml/api:data-scala",
"src/thrift/com/twitter/ml/featurestore/timelines:ml-features-timelines-scala",
"src/thrift/com/twitter/ml/featurestore/timelines:ml-features-timelines-strato",
"src/thrift/com/twitter/ml/prediction_service:prediction_service-java",
"src/thrift/com/twitter/permissions_storage:thrift-scala",
"src/thrift/com/twitter/pink-floyd/thrift:thrift-scala",
"src/thrift/com/twitter/recos:recos-common-scala",
"src/thrift/com/twitter/recos/user_tweet_entity_graph:user_tweet_entity_graph-scala",
"src/thrift/com/twitter/recos/user_user_graph:user_user_graph-scala",
"src/thrift/com/twitter/relevance/feature_store:feature_store-scala",
"src/thrift/com/twitter/search:earlybird-scala",
"src/thrift/com/twitter/search/common:features-scala",
"src/thrift/com/twitter/search/query_interaction_graph:query_interaction_graph-scala",
"src/thrift/com/twitter/search/query_interaction_graph/service:qig-service-scala",
"src/thrift/com/twitter/service/metastore/gen:thrift-scala",
"src/thrift/com/twitter/service/scarecrow/gen:scarecrow-scala",
"src/thrift/com/twitter/service/scarecrow/gen:tiered-actions-scala",
"src/thrift/com/twitter/simclusters_v2:simclusters_v2-thrift-scala",
"src/thrift/com/twitter/socialgraph:thrift-scala",
"src/thrift/com/twitter/spam/rtf:safety-level-scala",
"src/thrift/com/twitter/timelinemixer:thrift-scala",
"src/thrift/com/twitter/timelinemixer/server/internal:thrift-scala",
"src/thrift/com/twitter/timelines/author_features/user_health:thrift-scala",
"src/thrift/com/twitter/timelines/real_graph:real_graph-scala",
"src/thrift/com/twitter/timelinescorer:thrift-scala",
"src/thrift/com/twitter/timelinescorer/server/internal:thrift-scala",
"src/thrift/com/twitter/timelineservice/server/internal:thrift-scala",
"src/thrift/com/twitter/timelineservice/server/suggests/logging:thrift-scala",
"src/thrift/com/twitter/trends/common:common-scala",
"src/thrift/com/twitter/trends/trip_v1:trip-tweets-thrift-scala",
"src/thrift/com/twitter/tweetypie:service-scala",
"src/thrift/com/twitter/tweetypie:tweet-scala",
"src/thrift/com/twitter/user_session_store:thrift-scala",
"src/thrift/com/twitter/wtf/candidate:wtf-candidate-scala",
"src/thrift/com/twitter/wtf/interest:interest-thrift-scala",
"src/thrift/com/twitter/wtf/scalding/common:thrift-scala",
"stitch/stitch-core",
"stitch/stitch-gizmoduck",
"stitch/stitch-socialgraph/src/main/scala",
"stitch/stitch-storehaus/src/main/scala",
"stitch/stitch-tweetypie/src/main/scala",
"storage/clients/manhattan/client/src/main/scala",
"strato/config/columns/clients:clients-strato-client",
"strato/config/columns/geo/user:user-strato-client",
"strato/config/columns/globe/curation:curation-strato-client",
"strato/config/columns/interests:interests-strato-client",
"strato/config/columns/ml/featureStore:featureStore-strato-client",
"strato/config/columns/notifications:notifications-strato-client",
"strato/config/columns/notifinfra:notifinfra-strato-client",
"strato/config/columns/periscope:periscope-strato-client",
"strato/config/columns/rux",
"strato/config/columns/rux:rux-strato-client",
"strato/config/columns/rux/open-app:open-app-strato-client",
"strato/config/columns/socialgraph/graphs:graphs-strato-client",
"strato/config/columns/socialgraph/service/soft_users:soft_users-strato-client",
"strato/config/columns/translation/service:service-strato-client",
"strato/config/columns/translation/service/platform:platform-strato-client",
"strato/config/columns/trends/trip:trip-strato-client",
"strato/config/src/thrift/com/twitter/strato/columns/frigate:logged-out-web-notifications-scala",
"strato/config/src/thrift/com/twitter/strato/columns/notifications:thrift-scala",
"strato/src/main/scala/com/twitter/strato/config",
"strato/src/main/scala/com/twitter/strato/response",
"thrift-web-forms",
"timeline-training-service/service/thrift/src/main/thrift:thrift-scala",
"timelines/src/main/scala/com/twitter/timelines/features/app",
"topic-social-proof/server/src/main/thrift:thrift-scala",
"topiclisting/topiclisting-core/src/main/scala/com/twitter/topiclisting",
"topiclisting/topiclisting-utt/src/main/scala/com/twitter/topiclisting/utt",
"trends/common/src/main/thrift/com/twitter/trends/common:thrift-scala",
"tweetypie/src/scala/com/twitter/tweetypie/tweettext",
"twitter-context/src/main/scala",
"twitter-server-internal",
"twitter-server/server/src/main/scala",
"twitter-text/lib/java/src/main/java/com/twitter/twittertext",
"twml/runtime/src/main/scala/com/twitter/deepbird/runtime/prediction_engine:prediction_engine_mkl",
"ubs/common/src/main/thrift/com/twitter/ubs:broadcast-thrift-scala",
"ubs/common/src/main/thrift/com/twitter/ubs:seller_application-thrift-scala",
"user_session_store/src/main/scala/com/twitter/user_session_store/impl/manhattan/readwrite",
"util-internal/scribe",
"util-internal/tunable/src/main/scala/com/twitter/util/tunable",
"util/util-app",
"util/util-hashing/src/main/scala",
"util/util-slf4j-api/src/main/scala",
"util/util-stats/src/main/scala",
"visibility/lib/src/main/scala/com/twitter/visibility/builder",
"visibility/lib/src/main/scala/com/twitter/visibility/interfaces/push_service",
"visibility/lib/src/main/scala/com/twitter/visibility/interfaces/spaces",
"visibility/lib/src/main/scala/com/twitter/visibility/util",
],
exports = [
"strato/config/src/thrift/com/twitter/strato/columns/frigate:logged-out-web-notifications-scala",
],
)

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package com.twitter.frigate.pushservice
import com.google.inject.Inject
import com.google.inject.Singleton
import com.twitter.finagle.mtls.authentication.ServiceIdentifier
import com.twitter.finagle.thrift.ClientId
import com.twitter.finatra.thrift.routing.ThriftWarmup
import com.twitter.util.logging.Logging
import com.twitter.inject.utils.Handler
import com.twitter.frigate.pushservice.{thriftscala => t}
import com.twitter.frigate.thriftscala.NotificationDisplayLocation
import com.twitter.util.Stopwatch
import com.twitter.scrooge.Request
import com.twitter.scrooge.Response
import com.twitter.util.Return
import com.twitter.util.Throw
import com.twitter.util.Try
/**
* Warms up the refresh request path.
* If service is running as pushservice-send then the warmup does nothing.
*
* When making the warmup refresh requests we
* - Set skipFilters to true to execute as much of the request path as possible
* - Set darkWrite to true to prevent sending a push
*/
@Singleton
class PushMixerThriftServerWarmupHandler @Inject() (
warmup: ThriftWarmup,
serviceIdentifier: ServiceIdentifier)
extends Handler
with Logging {
private val clientId = ClientId("thrift-warmup-client")
def handle(): Unit = {
val refreshServices = Set(
"frigate-pushservice",
"frigate-pushservice-canary",
"frigate-pushservice-canary-control",
"frigate-pushservice-canary-treatment"
)
val isRefresh = refreshServices.contains(serviceIdentifier.service)
if (isRefresh && !serviceIdentifier.isLocal) refreshWarmup()
}
def refreshWarmup(): Unit = {
val elapsed = Stopwatch.start()
val testIds = Seq(
1,
2,
3
)
try {
clientId.asCurrent {
testIds.foreach { id =>
val warmupReq = warmupQuery(id)
info(s"Sending warm-up request to service with query: $warmupReq")
warmup.sendRequest(
method = t.PushService.Refresh,
req = Request(t.PushService.Refresh.Args(warmupReq)))(assertWarmupResponse)
}
}
} catch {
case e: Throwable =>
error(e.getMessage, e)
}
info(s"Warm up complete. Time taken: ${elapsed().toString}")
}
private def warmupQuery(userId: Long): t.RefreshRequest = {
t.RefreshRequest(
userId = userId,
notificationDisplayLocation = NotificationDisplayLocation.PushToMobileDevice,
context = Some(
t.PushContext(
skipFilters = Some(true),
darkWrite = Some(true)
))
)
}
private def assertWarmupResponse(
result: Try[Response[t.PushService.Refresh.SuccessType]]
): Unit = {
result match {
case Return(_) => // ok
case Throw(exception) =>
warn("Error performing warm-up request.")
error(exception.getMessage, exception)
}
}
}

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package com.twitter.frigate.pushservice
import com.twitter.discovery.common.environment.modules.EnvironmentModule
import com.twitter.finagle.Filter
import com.twitter.finatra.annotations.DarkTrafficFilterType
import com.twitter.finatra.decider.modules.DeciderModule
import com.twitter.finatra.http.HttpServer
import com.twitter.finatra.http.filters.CommonFilters
import com.twitter.finatra.http.routing.HttpRouter
import com.twitter.finatra.mtls.http.{Mtls => HttpMtls}
import com.twitter.finatra.mtls.thriftmux.{Mtls => ThriftMtls}
import com.twitter.finatra.mtls.thriftmux.filters.MtlsServerSessionTrackerFilter
import com.twitter.finatra.thrift.ThriftServer
import com.twitter.finatra.thrift.filters.ExceptionMappingFilter
import com.twitter.finatra.thrift.filters.LoggingMDCFilter
import com.twitter.finatra.thrift.filters.StatsFilter
import com.twitter.finatra.thrift.filters.ThriftMDCFilter
import com.twitter.finatra.thrift.filters.TraceIdMDCFilter
import com.twitter.finatra.thrift.routing.ThriftRouter
import com.twitter.frigate.common.logger.MRLoggerGlobalVariables
import com.twitter.frigate.pushservice.controller.PushServiceController
import com.twitter.frigate.pushservice.module._
import com.twitter.inject.TwitterModule
import com.twitter.inject.annotations.Flags
import com.twitter.inject.thrift.modules.ThriftClientIdModule
import com.twitter.logging.BareFormatter
import com.twitter.logging.Level
import com.twitter.logging.LoggerFactory
import com.twitter.logging.{Logging => JLogging}
import com.twitter.logging.QueueingHandler
import com.twitter.logging.ScribeHandler
import com.twitter.product_mixer.core.module.product_mixer_flags.ProductMixerFlagModule
import com.twitter.product_mixer.core.module.ABDeciderModule
import com.twitter.product_mixer.core.module.FeatureSwitchesModule
import com.twitter.product_mixer.core.module.StratoClientModule
object PushServiceMain extends PushServiceFinatraServer
class PushServiceFinatraServer
extends ThriftServer
with ThriftMtls
with HttpServer
with HttpMtls
with JLogging {
override val name = "PushService"
override val modules: Seq[TwitterModule] = {
Seq(
ABDeciderModule,
DeciderModule,
FeatureSwitchesModule,
FilterModule,
FlagModule,
EnvironmentModule,
ThriftClientIdModule,
DeployConfigModule,
ProductMixerFlagModule,
StratoClientModule,
PushHandlerModule,
PushTargetUserBuilderModule,
PushServiceDarkTrafficModule,
LoggedOutPushTargetUserBuilderModule,
new ThriftWebFormsModule(this),
)
}
override def configureThrift(router: ThriftRouter): Unit = {
router
.filter[ExceptionMappingFilter]
.filter[LoggingMDCFilter]
.filter[TraceIdMDCFilter]
.filter[ThriftMDCFilter]
.filter[MtlsServerSessionTrackerFilter]
.filter[StatsFilter]
.filter[Filter.TypeAgnostic, DarkTrafficFilterType]
.add[PushServiceController]
}
override def configureHttp(router: HttpRouter): Unit =
router
.filter[CommonFilters]
override protected def start(): Unit = {
MRLoggerGlobalVariables.setRequiredFlags(
traceLogFlag = injector.instance[Boolean](Flags.named(FlagModule.mrLoggerIsTraceAll.name)),
nthLogFlag = injector.instance[Boolean](Flags.named(FlagModule.mrLoggerNthLog.name)),
nthLogValFlag = injector.instance[Long](Flags.named(FlagModule.mrLoggerNthVal.name))
)
}
override protected def warmup(): Unit = {
handle[PushMixerThriftServerWarmupHandler]()
}
override protected def configureLoggerFactories(): Unit = {
loggerFactories.foreach { _() }
}
override def loggerFactories: List[LoggerFactory] = {
val scribeScope = statsReceiver.scope("scribe")
List(
LoggerFactory(
level = Some(levelFlag()),
handlers = handlers
),
LoggerFactory(
node = "request_scribe",
level = Some(Level.INFO),
useParents = false,
handlers = QueueingHandler(
maxQueueSize = 10000,
handler = ScribeHandler(
category = "frigate_pushservice_log",
formatter = BareFormatter,
statsReceiver = scribeScope.scope("frigate_pushservice_log")
)
) :: Nil
),
LoggerFactory(
node = "notification_scribe",
level = Some(Level.INFO),
useParents = false,
handlers = QueueingHandler(
maxQueueSize = 10000,
handler = ScribeHandler(
category = "frigate_notifier",
formatter = BareFormatter,
statsReceiver = scribeScope.scope("frigate_notifier")
)
) :: Nil
),
LoggerFactory(
node = "push_scribe",
level = Some(Level.INFO),
useParents = false,
handlers = QueueingHandler(
maxQueueSize = 10000,
handler = ScribeHandler(
category = "test_frigate_push",
formatter = BareFormatter,
statsReceiver = scribeScope.scope("test_frigate_push")
)
) :: Nil
),
LoggerFactory(
node = "push_subsample_scribe",
level = Some(Level.INFO),
useParents = false,
handlers = QueueingHandler(
maxQueueSize = 2500,
handler = ScribeHandler(
category = "magicrecs_candidates_subsample_scribe",
maxMessagesPerTransaction = 250,
maxMessagesToBuffer = 2500,
formatter = BareFormatter,
statsReceiver = scribeScope.scope("magicrecs_candidates_subsample_scribe")
)
) :: Nil
),
LoggerFactory(
node = "mr_request_scribe",
level = Some(Level.INFO),
useParents = false,
handlers = QueueingHandler(
maxQueueSize = 2500,
handler = ScribeHandler(
category = "mr_request_scribe",
maxMessagesPerTransaction = 250,
maxMessagesToBuffer = 2500,
formatter = BareFormatter,
statsReceiver = scribeScope.scope("mr_request_scribe")
)
) :: Nil
),
LoggerFactory(
node = "high_quality_candidates_scribe",
level = Some(Level.INFO),
useParents = false,
handlers = QueueingHandler(
maxQueueSize = 2500,
handler = ScribeHandler(
category = "frigate_high_quality_candidates_log",
maxMessagesPerTransaction = 250,
maxMessagesToBuffer = 2500,
formatter = BareFormatter,
statsReceiver = scribeScope.scope("high_quality_candidates_scribe")
)
) :: Nil
),
)
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.contentrecommender.thriftscala.MetricTag
import com.twitter.cr_mixer.thriftscala.CrMixerTweetRequest
import com.twitter.cr_mixer.thriftscala.NotificationsContext
import com.twitter.cr_mixer.thriftscala.Product
import com.twitter.cr_mixer.thriftscala.ProductContext
import com.twitter.cr_mixer.thriftscala.{MetricTag => CrMixerMetricTag}
import com.twitter.finagle.stats.Stat
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base.AlgorithmScore
import com.twitter.frigate.common.base.CandidateSource
import com.twitter.frigate.common.base.CandidateSourceEligible
import com.twitter.frigate.common.base.CrMixerCandidate
import com.twitter.frigate.common.base.TopicCandidate
import com.twitter.frigate.common.base.TopicProofTweetCandidate
import com.twitter.frigate.common.base.TweetCandidate
import com.twitter.frigate.common.predicate.CommonOutNetworkTweetCandidatesSourcePredicates.filterOutInNetworkTweets
import com.twitter.frigate.common.predicate.CommonOutNetworkTweetCandidatesSourcePredicates.filterOutReplyTweet
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.frigate.pushservice.params.PushFeatureSwitchParams
import com.twitter.frigate.pushservice.params.PushParams
import com.twitter.frigate.pushservice.store.CrMixerTweetStore
import com.twitter.frigate.pushservice.store.UttEntityHydrationStore
import com.twitter.frigate.pushservice.util.AdaptorUtils
import com.twitter.frigate.pushservice.util.PushDeviceUtil
import com.twitter.frigate.pushservice.util.TopicsUtil
import com.twitter.frigate.pushservice.util.TweetWithTopicProof
import com.twitter.frigate.thriftscala.CommonRecommendationType
import com.twitter.hermit.predicate.socialgraph.RelationEdge
import com.twitter.product_mixer.core.thriftscala.ClientContext
import com.twitter.stitch.tweetypie.TweetyPie.TweetyPieResult
import com.twitter.storehaus.ReadableStore
import com.twitter.topiclisting.utt.LocalizedEntity
import com.twitter.tsp.thriftscala.TopicSocialProofRequest
import com.twitter.tsp.thriftscala.TopicSocialProofResponse
import com.twitter.util.Future
import scala.collection.Map
case class ContentRecommenderMixerAdaptor(
crMixerTweetStore: CrMixerTweetStore,
tweetyPieStore: ReadableStore[Long, TweetyPieResult],
edgeStore: ReadableStore[RelationEdge, Boolean],
topicSocialProofServiceStore: ReadableStore[TopicSocialProofRequest, TopicSocialProofResponse],
uttEntityHydrationStore: UttEntityHydrationStore,
globalStats: StatsReceiver)
extends CandidateSource[Target, RawCandidate]
with CandidateSourceEligible[Target, RawCandidate] {
override val name: String = this.getClass.getSimpleName
private[this] val stats = globalStats.scope("ContentRecommenderMixerAdaptor")
private[this] val numOfValidAuthors = stats.stat("num_of_valid_authors")
private[this] val numOutOfMaximumDropped = stats.stat("dropped_due_out_of_maximum")
private[this] val totalInputRecs = stats.counter("input_recs")
private[this] val totalOutputRecs = stats.stat("output_recs")
private[this] val totalRequests = stats.counter("total_requests")
private[this] val nonReplyTweetsCounter = stats.counter("non_reply_tweets")
private[this] val totalOutNetworkRecs = stats.counter("out_network_tweets")
private[this] val totalInNetworkRecs = stats.counter("in_network_tweets")
/**
* Builds OON raw candidates based on input OON Tweets
*/
def buildOONRawCandidates(
inputTarget: Target,
oonTweets: Seq[TweetyPieResult],
tweetScoreMap: Map[Long, Double],
tweetIdToTagsMap: Map[Long, Seq[CrMixerMetricTag]],
maxNumOfCandidates: Int
): Option[Seq[RawCandidate]] = {
val cands = oonTweets.flatMap { tweetResult =>
val tweetId = tweetResult.tweet.id
generateOONRawCandidate(
inputTarget,
tweetId,
Some(tweetResult),
tweetScoreMap,
tweetIdToTagsMap
)
}
val candidates = restrict(
maxNumOfCandidates,
cands,
numOutOfMaximumDropped,
totalOutputRecs
)
Some(candidates)
}
/**
* Builds a single RawCandidate With TopicProofTweetCandidate
*/
def buildTopicTweetRawCandidate(
inputTarget: Target,
tweetWithTopicProof: TweetWithTopicProof,
localizedEntity: LocalizedEntity,
tags: Option[Seq[MetricTag]],
): RawCandidate with TopicProofTweetCandidate = {
new RawCandidate with TopicProofTweetCandidate {
override def target: Target = inputTarget
override def topicListingSetting: Option[String] = Some(
tweetWithTopicProof.topicListingSetting)
override def tweetId: Long = tweetWithTopicProof.tweetId
override def tweetyPieResult: Option[TweetyPieResult] = Some(
tweetWithTopicProof.tweetyPieResult)
override def semanticCoreEntityId: Option[Long] = Some(tweetWithTopicProof.topicId)
override def localizedUttEntity: Option[LocalizedEntity] = Some(localizedEntity)
override def algorithmCR: Option[String] = tweetWithTopicProof.algorithmCR
override def tagsCR: Option[Seq[MetricTag]] = tags
override def isOutOfNetwork: Boolean = tweetWithTopicProof.isOON
}
}
/**
* Takes a group of TopicTweets and transforms them into RawCandidates
*/
def buildTopicTweetRawCandidates(
inputTarget: Target,
topicProofCandidates: Seq[TweetWithTopicProof],
tweetIdToTagsMap: Map[Long, Seq[CrMixerMetricTag]],
maxNumberOfCands: Int
): Future[Option[Seq[RawCandidate]]] = {
val semanticCoreEntityIds = topicProofCandidates
.map(_.topicId)
.toSet
TopicsUtil
.getLocalizedEntityMap(inputTarget, semanticCoreEntityIds, uttEntityHydrationStore)
.map { localizedEntityMap =>
val rawCandidates = topicProofCandidates.collect {
case topicSocialProof: TweetWithTopicProof
if localizedEntityMap.contains(topicSocialProof.topicId) =>
// Once we deprecate CR calls, we should replace this code to use the CrMixerMetricTag
val tags = tweetIdToTagsMap.get(topicSocialProof.tweetId).map {
_.flatMap { tag => MetricTag.get(tag.value) }
}
buildTopicTweetRawCandidate(
inputTarget,
topicSocialProof,
localizedEntityMap(topicSocialProof.topicId),
tags
)
}
val candResult = restrict(
maxNumberOfCands,
rawCandidates,
numOutOfMaximumDropped,
totalOutputRecs
)
Some(candResult)
}
}
private def generateOONRawCandidate(
inputTarget: Target,
id: Long,
result: Option[TweetyPieResult],
tweetScoreMap: Map[Long, Double],
tweetIdToTagsMap: Map[Long, Seq[CrMixerMetricTag]]
): Option[RawCandidate with TweetCandidate] = {
val tagsFromCR = tweetIdToTagsMap.get(id).map { _.flatMap { tag => MetricTag.get(tag.value) } }
val candidate = new RawCandidate with CrMixerCandidate with TopicCandidate with AlgorithmScore {
override val tweetId = id
override val target = inputTarget
override val tweetyPieResult = result
override val localizedUttEntity = None
override val semanticCoreEntityId = None
override def commonRecType =
getMediaBasedCRT(
CommonRecommendationType.TwistlyTweet,
CommonRecommendationType.TwistlyPhoto,
CommonRecommendationType.TwistlyVideo)
override def tagsCR = tagsFromCR
override def algorithmScore = tweetScoreMap.get(id)
override def algorithmCR = None
}
Some(candidate)
}
private def restrict(
maxNumToReturn: Int,
candidates: Seq[RawCandidate],
numOutOfMaximumDropped: Stat,
totalOutputRecs: Stat
): Seq[RawCandidate] = {
val newCandidates = candidates.take(maxNumToReturn)
val numDropped = candidates.length - newCandidates.length
numOutOfMaximumDropped.add(numDropped)
totalOutputRecs.add(newCandidates.size)
newCandidates
}
private def buildCrMixerRequest(
target: Target,
countryCode: Option[String],
language: Option[String],
seenTweets: Seq[Long]
): CrMixerTweetRequest = {
CrMixerTweetRequest(
clientContext = ClientContext(
userId = Some(target.targetId),
countryCode = countryCode,
languageCode = language
),
product = Product.Notifications,
productContext = Some(ProductContext.NotificationsContext(NotificationsContext())),
excludedTweetIds = Some(seenTweets)
)
}
private def selectCandidatesToSendBasedOnSettings(
isRecommendationsEligible: Boolean,
isTopicsEligible: Boolean,
oonRawCandidates: Option[Seq[RawCandidate]],
topicTweetCandidates: Option[Seq[RawCandidate]]
): Option[Seq[RawCandidate]] = {
if (isRecommendationsEligible && isTopicsEligible) {
Some(topicTweetCandidates.getOrElse(Seq.empty) ++ oonRawCandidates.getOrElse(Seq.empty))
} else if (isRecommendationsEligible) {
oonRawCandidates
} else if (isTopicsEligible) {
topicTweetCandidates
} else None
}
override def get(target: Target): Future[Option[Seq[RawCandidate]]] = {
Future
.join(
target.seenTweetIds,
target.countryCode,
target.inferredUserDeviceLanguage,
PushDeviceUtil.isTopicsEligible(target),
PushDeviceUtil.isRecommendationsEligible(target)
).flatMap {
case (seenTweets, countryCode, language, isTopicsEligible, isRecommendationsEligible) =>
val request = buildCrMixerRequest(target, countryCode, language, seenTweets)
crMixerTweetStore.getTweetRecommendations(request).flatMap {
case Some(response) =>
totalInputRecs.incr(response.tweets.size)
totalRequests.incr()
AdaptorUtils
.getTweetyPieResults(
response.tweets.map(_.tweetId).toSet,
tweetyPieStore).flatMap { tweetyPieResultMap =>
filterOutInNetworkTweets(
target,
filterOutReplyTweet(tweetyPieResultMap.toMap, nonReplyTweetsCounter),
edgeStore,
numOfValidAuthors).flatMap {
outNetworkTweetsWithId: Seq[(Long, TweetyPieResult)] =>
totalOutNetworkRecs.incr(outNetworkTweetsWithId.size)
totalInNetworkRecs.incr(response.tweets.size - outNetworkTweetsWithId.size)
val outNetworkTweets: Seq[TweetyPieResult] = outNetworkTweetsWithId.map {
case (_, tweetyPieResult) => tweetyPieResult
}
val tweetIdToTagsMap = response.tweets.map { tweet =>
tweet.tweetId -> tweet.metricTags.getOrElse(Seq.empty)
}.toMap
val tweetScoreMap = response.tweets.map { tweet =>
tweet.tweetId -> tweet.score
}.toMap
val maxNumOfCandidates =
target.params(PushFeatureSwitchParams.NumberOfMaxCrMixerCandidatesParam)
val oonRawCandidates =
buildOONRawCandidates(
target,
outNetworkTweets,
tweetScoreMap,
tweetIdToTagsMap,
maxNumOfCandidates)
TopicsUtil
.getTopicSocialProofs(
target,
outNetworkTweets,
topicSocialProofServiceStore,
edgeStore,
PushFeatureSwitchParams.TopicProofTweetCandidatesTopicScoreThreshold).flatMap {
tweetsWithTopicProof =>
buildTopicTweetRawCandidates(
target,
tweetsWithTopicProof,
tweetIdToTagsMap,
maxNumOfCandidates)
}.map { topicTweetCandidates =>
selectCandidatesToSendBasedOnSettings(
isRecommendationsEligible,
isTopicsEligible,
oonRawCandidates,
topicTweetCandidates)
}
}
}
case _ => Future.None
}
}
}
/**
* For a user to be available the following news to happen
*/
override def isCandidateSourceAvailable(target: Target): Future[Boolean] = {
Future
.join(
PushDeviceUtil.isRecommendationsEligible(target),
PushDeviceUtil.isTopicsEligible(target)
).map {
case (isRecommendationsEligible, isTopicsEligible) =>
(isRecommendationsEligible || isTopicsEligible) &&
target.params(PushParams.ContentRecommenderMixerAdaptorDecider)
}
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.finagle.stats.Stat
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base._
import com.twitter.frigate.common.candidate._
import com.twitter.frigate.common.predicate.CommonOutNetworkTweetCandidatesSourcePredicates.filterOutReplyTweet
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.frigate.pushservice.params.PushFeatureSwitchParams
import com.twitter.frigate.pushservice.params.PushParams
import com.twitter.frigate.pushservice.util.PushDeviceUtil
import com.twitter.hermit.store.tweetypie.UserTweet
import com.twitter.recos.recos_common.thriftscala.SocialProofType
import com.twitter.search.common.features.thriftscala.ThriftSearchResultFeatures
import com.twitter.stitch.tweetypie.TweetyPie.TweetyPieResult
import com.twitter.storehaus.ReadableStore
import com.twitter.timelines.configapi.Param
import com.twitter.util.Future
import com.twitter.util.Time
import scala.collection.Map
case class EarlyBirdFirstDegreeCandidateAdaptor(
earlyBirdFirstDegreeCandidates: CandidateSource[
EarlybirdCandidateSource.Query,
EarlybirdCandidate
],
tweetyPieStore: ReadableStore[Long, TweetyPieResult],
tweetyPieStoreNoVF: ReadableStore[Long, TweetyPieResult],
userTweetTweetyPieStore: ReadableStore[UserTweet, TweetyPieResult],
maxResultsParam: Param[Int],
globalStats: StatsReceiver)
extends CandidateSource[Target, RawCandidate]
with CandidateSourceEligible[Target, RawCandidate] {
type EBCandidate = EarlybirdCandidate with TweetDetails
private val stats = globalStats.scope("EarlyBirdFirstDegreeAdaptor")
private val earlyBirdCandsStat: Stat = stats.stat("early_bird_cands_dist")
private val emptyEarlyBirdCands = stats.counter("empty_early_bird_candidates")
private val seedSetEmpty = stats.counter("empty_seedset")
private val seenTweetsStat = stats.stat("filtered_by_seen_tweets")
private val emptyTweetyPieResult = stats.stat("empty_tweetypie_result")
private val nonReplyTweetsCounter = stats.counter("non_reply_tweets")
private val enableRetweets = stats.counter("enable_retweets")
private val f1withoutSocialContexts = stats.counter("f1_without_social_context")
private val userTweetTweetyPieStoreCounter = stats.counter("user_tweet_tweetypie_store")
override val name: String = earlyBirdFirstDegreeCandidates.name
private def getAllSocialContextActions(
socialProofTypes: Seq[(SocialProofType, Seq[Long])]
): Seq[SocialContextAction] = {
socialProofTypes.flatMap {
case (SocialProofType.Favorite, scIds) =>
scIds.map { scId =>
SocialContextAction(
scId,
Time.now.inMilliseconds,
socialContextActionType = Some(SocialContextActionType.Favorite)
)
}
case (SocialProofType.Retweet, scIds) =>
scIds.map { scId =>
SocialContextAction(
scId,
Time.now.inMilliseconds,
socialContextActionType = Some(SocialContextActionType.Retweet)
)
}
case (SocialProofType.Reply, scIds) =>
scIds.map { scId =>
SocialContextAction(
scId,
Time.now.inMilliseconds,
socialContextActionType = Some(SocialContextActionType.Reply)
)
}
case (SocialProofType.Tweet, scIds) =>
scIds.map { scId =>
SocialContextAction(
scId,
Time.now.inMilliseconds,
socialContextActionType = Some(SocialContextActionType.Tweet)
)
}
case _ => Nil
}
}
private def generateRetweetCandidate(
inputTarget: Target,
candidate: EBCandidate,
scIds: Seq[Long],
socialProofTypes: Seq[(SocialProofType, Seq[Long])]
): RawCandidate = {
val scActions = scIds.map { scId => SocialContextAction(scId, Time.now.inMilliseconds) }
new RawCandidate with TweetRetweetCandidate with EarlybirdTweetFeatures {
override val socialContextActions = scActions
override val socialContextAllTypeActions = getAllSocialContextActions(socialProofTypes)
override val tweetId = candidate.tweetId
override val target = inputTarget
override val tweetyPieResult = candidate.tweetyPieResult
override val features = candidate.features
}
}
private def generateF1CandidateWithoutSocialContext(
inputTarget: Target,
candidate: EBCandidate
): RawCandidate = {
f1withoutSocialContexts.incr()
new RawCandidate with F1FirstDegree with EarlybirdTweetFeatures {
override val tweetId = candidate.tweetId
override val target = inputTarget
override val tweetyPieResult = candidate.tweetyPieResult
override val features = candidate.features
}
}
private def generateEarlyBirdCandidate(
id: Long,
result: Option[TweetyPieResult],
ebFeatures: Option[ThriftSearchResultFeatures]
): EBCandidate = {
new EarlybirdCandidate with TweetDetails {
override val tweetyPieResult: Option[TweetyPieResult] = result
override val tweetId: Long = id
override val features: Option[ThriftSearchResultFeatures] = ebFeatures
}
}
private def filterOutSeenTweets(seenTweetIds: Seq[Long], inputTweetIds: Seq[Long]): Seq[Long] = {
inputTweetIds.filterNot(seenTweetIds.contains)
}
private def filterInvalidTweets(
tweetIds: Seq[Long],
target: Target
): Future[Seq[(Long, TweetyPieResult)]] = {
val resMap = {
if (target.params(PushFeatureSwitchParams.EnableF1FromProtectedTweetAuthors)) {
userTweetTweetyPieStoreCounter.incr()
val keys = tweetIds.map { tweetId =>
UserTweet(tweetId, Some(target.targetId))
}
userTweetTweetyPieStore
.multiGet(keys.toSet).map {
case (userTweet, resultFut) =>
userTweet.tweetId -> resultFut
}.toMap
} else {
(target.params(PushFeatureSwitchParams.EnableVFInTweetypie) match {
case true => tweetyPieStore
case false => tweetyPieStoreNoVF
}).multiGet(tweetIds.toSet)
}
}
Future.collect(resMap).map { tweetyPieResultMap =>
val cands = filterOutReplyTweet(tweetyPieResultMap, nonReplyTweetsCounter).collect {
case (id: Long, Some(result)) =>
id -> result
}
emptyTweetyPieResult.add(tweetyPieResultMap.size - cands.size)
cands.toSeq
}
}
private def getEBRetweetCandidates(
inputTarget: Target,
retweets: Seq[(Long, TweetyPieResult)]
): Seq[RawCandidate] = {
retweets.flatMap {
case (_, tweetypieResult) =>
tweetypieResult.tweet.coreData.flatMap { coreData =>
tweetypieResult.sourceTweet.map { sourceTweet =>
val tweetId = sourceTweet.id
val scId = coreData.userId
val socialProofTypes = Seq((SocialProofType.Retweet, Seq(scId)))
val candidate = generateEarlyBirdCandidate(
tweetId,
Some(TweetyPieResult(sourceTweet, None, None)),
None
)
generateRetweetCandidate(
inputTarget,
candidate,
Seq(scId),
socialProofTypes
)
}
}
}
}
private def getEBFirstDegreeCands(
tweets: Seq[(Long, TweetyPieResult)],
ebTweetIdMap: Map[Long, Option[ThriftSearchResultFeatures]]
): Seq[EBCandidate] = {
tweets.map {
case (id, tweetypieResult) =>
val features = ebTweetIdMap.getOrElse(id, None)
generateEarlyBirdCandidate(id, Some(tweetypieResult), features)
}
}
/**
* Returns a combination of raw candidates made of: f1 recs, topic social proof recs, sc recs and retweet candidates
*/
def buildRawCandidates(
inputTarget: Target,
firstDegreeCandidates: Seq[EBCandidate],
retweetCandidates: Seq[RawCandidate]
): Seq[RawCandidate] = {
val hydratedF1Recs =
firstDegreeCandidates.map(generateF1CandidateWithoutSocialContext(inputTarget, _))
hydratedF1Recs ++ retweetCandidates
}
override def get(inputTarget: Target): Future[Option[Seq[RawCandidate]]] = {
inputTarget.seedsWithWeight.flatMap { seedsetOpt =>
val seedsetMap = seedsetOpt.getOrElse(Map.empty)
if (seedsetMap.isEmpty) {
seedSetEmpty.incr()
Future.None
} else {
val maxResultsToReturn = inputTarget.params(maxResultsParam)
val maxTweetAge = inputTarget.params(PushFeatureSwitchParams.F1CandidateMaxTweetAgeParam)
val earlybirdQuery = EarlybirdCandidateSource.Query(
maxNumResultsToReturn = maxResultsToReturn,
seedset = seedsetMap,
maxConsecutiveResultsByTheSameUser = Some(1),
maxTweetAge = maxTweetAge,
disableTimelinesMLModel = false,
searcherId = Some(inputTarget.targetId),
isProtectTweetsEnabled =
inputTarget.params(PushFeatureSwitchParams.EnableF1FromProtectedTweetAuthors),
followedUserIds = Some(seedsetMap.keySet.toSeq)
)
Future
.join(inputTarget.seenTweetIds, earlyBirdFirstDegreeCandidates.get(earlybirdQuery))
.flatMap {
case (seenTweetIds, Some(candidates)) =>
earlyBirdCandsStat.add(candidates.size)
val ebTweetIdMap = candidates.map { cand => cand.tweetId -> cand.features }.toMap
val ebTweetIds = ebTweetIdMap.keys.toSeq
val tweetIds = filterOutSeenTweets(seenTweetIds, ebTweetIds)
seenTweetsStat.add(ebTweetIds.size - tweetIds.size)
filterInvalidTweets(tweetIds, inputTarget)
.map { validTweets =>
val (retweets, tweets) = validTweets.partition {
case (_, tweetypieResult) =>
tweetypieResult.sourceTweet.isDefined
}
val firstDegreeCandidates = getEBFirstDegreeCands(tweets, ebTweetIdMap)
val retweetCandidates = {
if (inputTarget.params(PushParams.EarlyBirdSCBasedCandidatesParam) &&
inputTarget.params(PushParams.MRTweetRetweetRecsParam)) {
enableRetweets.incr()
getEBRetweetCandidates(inputTarget, retweets)
} else Nil
}
Some(
buildRawCandidates(
inputTarget,
firstDegreeCandidates,
retweetCandidates
))
}
case _ =>
emptyEarlyBirdCands.incr()
Future.None
}
}
}
}
override def isCandidateSourceAvailable(target: Target): Future[Boolean] = {
PushDeviceUtil.isRecommendationsEligible(target)
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.explore_ranker.thriftscala.ExploreRankerProductResponse
import com.twitter.explore_ranker.thriftscala.ExploreRankerRequest
import com.twitter.explore_ranker.thriftscala.ExploreRankerResponse
import com.twitter.explore_ranker.thriftscala.ExploreRecommendation
import com.twitter.explore_ranker.thriftscala.ImmersiveRecsResponse
import com.twitter.explore_ranker.thriftscala.ImmersiveRecsResult
import com.twitter.explore_ranker.thriftscala.NotificationsVideoRecs
import com.twitter.explore_ranker.thriftscala.Product
import com.twitter.explore_ranker.thriftscala.ProductContext
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base.CandidateSource
import com.twitter.frigate.common.base.CandidateSourceEligible
import com.twitter.frigate.common.base.OutOfNetworkTweetCandidate
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.frigate.pushservice.params.PushFeatureSwitchParams
import com.twitter.frigate.pushservice.util.AdaptorUtils
import com.twitter.frigate.pushservice.util.MediaCRT
import com.twitter.frigate.pushservice.util.PushAdaptorUtil
import com.twitter.frigate.pushservice.util.PushDeviceUtil
import com.twitter.frigate.thriftscala.CommonRecommendationType
import com.twitter.product_mixer.core.thriftscala.ClientContext
import com.twitter.stitch.tweetypie.TweetyPie.TweetyPieResult
import com.twitter.storehaus.ReadableStore
import com.twitter.util.Future
case class ExploreVideoTweetCandidateAdaptor(
exploreRankerStore: ReadableStore[ExploreRankerRequest, ExploreRankerResponse],
tweetyPieStore: ReadableStore[Long, TweetyPieResult],
globalStats: StatsReceiver)
extends CandidateSource[Target, RawCandidate]
with CandidateSourceEligible[Target, RawCandidate] {
override def name: String = this.getClass.getSimpleName
private[this] val stats = globalStats.scope("ExploreVideoTweetCandidateAdaptor")
private[this] val totalInputRecs = stats.stat("input_recs")
private[this] val totalRequests = stats.counter("total_requests")
private[this] val totalEmptyResponse = stats.counter("total_empty_response")
private def buildExploreRankerRequest(
target: Target,
countryCode: Option[String],
language: Option[String],
): ExploreRankerRequest = {
ExploreRankerRequest(
clientContext = ClientContext(
userId = Some(target.targetId),
countryCode = countryCode,
languageCode = language,
),
product = Product.NotificationsVideoRecs,
productContext = Some(ProductContext.NotificationsVideoRecs(NotificationsVideoRecs())),
maxResults = Some(target.params(PushFeatureSwitchParams.MaxExploreVideoTweets))
)
}
override def get(target: Target): Future[Option[Seq[RawCandidate]]] = {
Future
.join(
target.countryCode,
target.inferredUserDeviceLanguage
).flatMap {
case (countryCode, language) =>
val request = buildExploreRankerRequest(target, countryCode, language)
exploreRankerStore.get(request).flatMap {
case Some(response) =>
val exploreResonseTweetIds = response match {
case ExploreRankerResponse(ExploreRankerProductResponse
.ImmersiveRecsResponse(ImmersiveRecsResponse(immersiveRecsResult))) =>
immersiveRecsResult.collect {
case ImmersiveRecsResult(ExploreRecommendation
.ExploreTweetRecommendation(exploreTweetRecommendation)) =>
exploreTweetRecommendation.tweetId
}
case _ =>
Seq.empty
}
totalInputRecs.add(exploreResonseTweetIds.size)
totalRequests.incr()
AdaptorUtils
.getTweetyPieResults(exploreResonseTweetIds.toSet, tweetyPieStore).map {
tweetyPieResultMap =>
val candidates = tweetyPieResultMap.values.flatten
.map(buildVideoRawCandidates(target, _))
Some(candidates.toSeq)
}
case _ =>
totalEmptyResponse.incr()
Future.None
}
case _ =>
Future.None
}
}
override def isCandidateSourceAvailable(target: Target): Future[Boolean] = {
PushDeviceUtil.isRecommendationsEligible(target).map { userRecommendationsEligible =>
userRecommendationsEligible && target.params(PushFeatureSwitchParams.EnableExploreVideoTweets)
}
}
private def buildVideoRawCandidates(
target: Target,
tweetyPieResult: TweetyPieResult
): RawCandidate with OutOfNetworkTweetCandidate = {
PushAdaptorUtil.generateOutOfNetworkTweetCandidates(
inputTarget = target,
id = tweetyPieResult.tweet.id,
mediaCRT = MediaCRT(
CommonRecommendationType.ExploreVideoTweet,
CommonRecommendationType.ExploreVideoTweet,
CommonRecommendationType.ExploreVideoTweet
),
result = Some(tweetyPieResult),
localizedEntity = None
)
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.cr_mixer.thriftscala.FrsTweetRequest
import com.twitter.cr_mixer.thriftscala.NotificationsContext
import com.twitter.cr_mixer.thriftscala.Product
import com.twitter.cr_mixer.thriftscala.ProductContext
import com.twitter.finagle.stats.Counter
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base.CandidateSource
import com.twitter.frigate.common.base.CandidateSourceEligible
import com.twitter.frigate.common.base._
import com.twitter.frigate.common.predicate.CommonOutNetworkTweetCandidatesSourcePredicates.filterOutReplyTweet
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.frigate.pushservice.params.PushFeatureSwitchParams
import com.twitter.frigate.pushservice.store.CrMixerTweetStore
import com.twitter.frigate.pushservice.store.UttEntityHydrationStore
import com.twitter.frigate.pushservice.util.MediaCRT
import com.twitter.frigate.pushservice.util.PushAdaptorUtil
import com.twitter.frigate.pushservice.util.PushDeviceUtil
import com.twitter.frigate.pushservice.util.TopicsUtil
import com.twitter.frigate.thriftscala.CommonRecommendationType
import com.twitter.hermit.constants.AlgorithmFeedbackTokens
import com.twitter.hermit.model.Algorithm.Algorithm
import com.twitter.hermit.model.Algorithm.CrowdSearchAccounts
import com.twitter.hermit.model.Algorithm.ForwardEmailBook
import com.twitter.hermit.model.Algorithm.ForwardPhoneBook
import com.twitter.hermit.model.Algorithm.ReverseEmailBookIbis
import com.twitter.hermit.model.Algorithm.ReversePhoneBook
import com.twitter.hermit.store.tweetypie.UserTweet
import com.twitter.product_mixer.core.thriftscala.ClientContext
import com.twitter.stitch.tweetypie.TweetyPie.TweetyPieResult
import com.twitter.storehaus.ReadableStore
import com.twitter.tsp.thriftscala.TopicSocialProofRequest
import com.twitter.tsp.thriftscala.TopicSocialProofResponse
import com.twitter.util.Future
object FRSAlgorithmFeedbackTokenUtil {
private val crtsByAlgoToken = Map(
getAlgorithmToken(ReverseEmailBookIbis) -> CommonRecommendationType.ReverseAddressbookTweet,
getAlgorithmToken(ReversePhoneBook) -> CommonRecommendationType.ReverseAddressbookTweet,
getAlgorithmToken(ForwardEmailBook) -> CommonRecommendationType.ForwardAddressbookTweet,
getAlgorithmToken(ForwardPhoneBook) -> CommonRecommendationType.ForwardAddressbookTweet,
getAlgorithmToken(CrowdSearchAccounts) -> CommonRecommendationType.CrowdSearchTweet
)
def getAlgorithmToken(algorithm: Algorithm): Int = {
AlgorithmFeedbackTokens.AlgorithmToFeedbackTokenMap(algorithm)
}
def getCRTForAlgoToken(algorithmToken: Int): Option[CommonRecommendationType] = {
crtsByAlgoToken.get(algorithmToken)
}
}
case class FRSTweetCandidateAdaptor(
crMixerTweetStore: CrMixerTweetStore,
tweetyPieStore: ReadableStore[Long, TweetyPieResult],
tweetyPieStoreNoVF: ReadableStore[Long, TweetyPieResult],
userTweetTweetyPieStore: ReadableStore[UserTweet, TweetyPieResult],
uttEntityHydrationStore: UttEntityHydrationStore,
topicSocialProofServiceStore: ReadableStore[TopicSocialProofRequest, TopicSocialProofResponse],
globalStats: StatsReceiver)
extends CandidateSource[Target, RawCandidate]
with CandidateSourceEligible[Target, RawCandidate] {
private val stats = globalStats.scope(this.getClass.getSimpleName)
private val crtStats = stats.scope("CandidateDistribution")
private val totalRequests = stats.counter("total_requests")
// Candidate Distribution stats
private val reverseAddressbookCounter = crtStats.counter("reverse_addressbook")
private val forwardAddressbookCounter = crtStats.counter("forward_addressbook")
private val frsTweetCounter = crtStats.counter("frs_tweet")
private val nonReplyTweetsCounter = stats.counter("non_reply_tweets")
private val crtToCounterMapping: Map[CommonRecommendationType, Counter] = Map(
CommonRecommendationType.ReverseAddressbookTweet -> reverseAddressbookCounter,
CommonRecommendationType.ForwardAddressbookTweet -> forwardAddressbookCounter,
CommonRecommendationType.FrsTweet -> frsTweetCounter
)
private val emptyTweetyPieResult = stats.stat("empty_tweetypie_result")
private[this] val numberReturnedCandidates = stats.stat("returned_candidates_from_earlybird")
private[this] val numberCandidateWithTopic: Counter = stats.counter("num_can_with_topic")
private[this] val numberCandidateWithoutTopic: Counter = stats.counter("num_can_without_topic")
private val userTweetTweetyPieStoreCounter = stats.counter("user_tweet_tweetypie_store")
override val name: String = this.getClass.getSimpleName
private def filterInvalidTweets(
tweetIds: Seq[Long],
target: Target
): Future[Map[Long, TweetyPieResult]] = {
val resMap = {
if (target.params(PushFeatureSwitchParams.EnableF1FromProtectedTweetAuthors)) {
userTweetTweetyPieStoreCounter.incr()
val keys = tweetIds.map { tweetId =>
UserTweet(tweetId, Some(target.targetId))
}
userTweetTweetyPieStore
.multiGet(keys.toSet).map {
case (userTweet, resultFut) =>
userTweet.tweetId -> resultFut
}.toMap
} else {
(if (target.params(PushFeatureSwitchParams.EnableVFInTweetypie)) {
tweetyPieStore
} else {
tweetyPieStoreNoVF
}).multiGet(tweetIds.toSet)
}
}
Future.collect(resMap).map { tweetyPieResultMap =>
// Filter out replies and generate earlybird candidates only for non-empty tweetypie result
val cands = filterOutReplyTweet(tweetyPieResultMap, nonReplyTweetsCounter).collect {
case (id: Long, Some(result)) =>
id -> result
}
emptyTweetyPieResult.add(tweetyPieResultMap.size - cands.size)
cands
}
}
private def buildRawCandidates(
target: Target,
ebCandidates: Seq[FRSTweetCandidate]
): Future[Option[Seq[RawCandidate with TweetCandidate]]] = {
val enableTopic = target.params(PushFeatureSwitchParams.EnableFrsTweetCandidatesTopicAnnotation)
val topicScoreThre =
target.params(PushFeatureSwitchParams.FrsTweetCandidatesTopicScoreThreshold)
val ebTweets = ebCandidates.map { ebCandidate =>
ebCandidate.tweetId -> ebCandidate.tweetyPieResult
}.toMap
val tweetIdLocalizedEntityMapFut = TopicsUtil.getTweetIdLocalizedEntityMap(
target,
ebTweets,
uttEntityHydrationStore,
topicSocialProofServiceStore,
enableTopic,
topicScoreThre
)
Future.join(target.deviceInfo, tweetIdLocalizedEntityMapFut).map {
case (Some(deviceInfo), tweetIdLocalizedEntityMap) =>
val candidates = ebCandidates
.map { ebCandidate =>
val crt = ebCandidate.commonRecType
crtToCounterMapping.get(crt).foreach(_.incr())
val tweetId = ebCandidate.tweetId
val localizedEntityOpt = {
if (tweetIdLocalizedEntityMap
.contains(tweetId) && tweetIdLocalizedEntityMap.contains(
tweetId) && deviceInfo.isTopicsEligible) {
tweetIdLocalizedEntityMap(tweetId)
} else {
None
}
}
PushAdaptorUtil.generateOutOfNetworkTweetCandidates(
inputTarget = target,
id = ebCandidate.tweetId,
mediaCRT = MediaCRT(
crt,
crt,
crt
),
result = ebCandidate.tweetyPieResult,
localizedEntity = localizedEntityOpt)
}.filter { candidate =>
// If user only has the topic setting enabled, filter out all non-topic cands
deviceInfo.isRecommendationsEligible || (deviceInfo.isTopicsEligible && candidate.semanticCoreEntityId.nonEmpty)
}
candidates.map { candidate =>
if (candidate.semanticCoreEntityId.nonEmpty) {
numberCandidateWithTopic.incr()
} else {
numberCandidateWithoutTopic.incr()
}
}
numberReturnedCandidates.add(candidates.length)
Some(candidates)
case _ => Some(Seq.empty)
}
}
def getTweetCandidatesFromCrMixer(
inputTarget: Target,
showAllResultsFromFrs: Boolean,
): Future[Option[Seq[RawCandidate with TweetCandidate]]] = {
Future
.join(
inputTarget.seenTweetIds,
inputTarget.pushRecItems,
inputTarget.countryCode,
inputTarget.targetLanguage).flatMap {
case (seenTweetIds, pastRecItems, countryCode, language) =>
val pastUserRecs = pastRecItems.userIds.toSeq
val request = FrsTweetRequest(
clientContext = ClientContext(
userId = Some(inputTarget.targetId),
countryCode = countryCode,
languageCode = language
),
product = Product.Notifications,
productContext = Some(ProductContext.NotificationsContext(NotificationsContext())),
excludedUserIds = Some(pastUserRecs),
excludedTweetIds = Some(seenTweetIds)
)
crMixerTweetStore.getFRSTweetCandidates(request).flatMap {
case Some(response) =>
val tweetIds = response.tweets.map(_.tweetId)
val validTweets = filterInvalidTweets(tweetIds, inputTarget)
validTweets.flatMap { tweetypieMap =>
val ebCandidates = response.tweets
.map { frsTweet =>
val candidateTweetId = frsTweet.tweetId
val resultFromTweetyPie = tweetypieMap.get(candidateTweetId)
new FRSTweetCandidate {
override val tweetId = candidateTweetId
override val features = None
override val tweetyPieResult = resultFromTweetyPie
override val feedbackToken = frsTweet.frsPrimarySource
override val commonRecType: CommonRecommendationType = feedbackToken
.flatMap(token =>
FRSAlgorithmFeedbackTokenUtil.getCRTForAlgoToken(token)).getOrElse(
CommonRecommendationType.FrsTweet)
}
}.filter { ebCandidate =>
showAllResultsFromFrs || ebCandidate.commonRecType == CommonRecommendationType.ReverseAddressbookTweet
}
numberReturnedCandidates.add(ebCandidates.length)
buildRawCandidates(
inputTarget,
ebCandidates
)
}
case _ => Future.None
}
}
}
override def get(inputTarget: Target): Future[Option[Seq[RawCandidate with TweetCandidate]]] = {
totalRequests.incr()
val enableResultsFromFrs =
inputTarget.params(PushFeatureSwitchParams.EnableResultFromFrsCandidates)
getTweetCandidatesFromCrMixer(inputTarget, enableResultsFromFrs)
}
override def isCandidateSourceAvailable(target: Target): Future[Boolean] = {
lazy val enableFrsCandidates = target.params(PushFeatureSwitchParams.EnableFrsCandidates)
PushDeviceUtil.isRecommendationsEligible(target).flatMap { isEnabledForRecosSetting =>
PushDeviceUtil.isTopicsEligible(target).map { topicSettingEnabled =>
val isEnabledForTopics =
topicSettingEnabled && target.params(
PushFeatureSwitchParams.EnableFrsTweetCandidatesTopicSetting)
(isEnabledForRecosSetting || isEnabledForTopics) && enableFrsCandidates
}
}
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base._
import com.twitter.frigate.common.candidate._
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.frigate.pushservice.params.PushParams
import com.twitter.frigate.pushservice.util.PushDeviceUtil
import com.twitter.stitch.tweetypie.TweetyPie.TweetyPieResult
import com.twitter.storehaus.ReadableStore
import com.twitter.util.Future
object GenericCandidates {
type Target =
TargetUser
with UserDetails
with TargetDecider
with TargetABDecider
with TweetImpressionHistory
with HTLVisitHistory
with MaxTweetAge
with NewUserDetails
with FrigateHistory
with TargetWithSeedUsers
}
case class GenericCandidateAdaptor(
genericCandidates: CandidateSource[GenericCandidates.Target, Candidate],
tweetyPieStore: ReadableStore[Long, TweetyPieResult],
tweetyPieStoreNoVF: ReadableStore[Long, TweetyPieResult],
stats: StatsReceiver)
extends CandidateSource[Target, RawCandidate]
with CandidateSourceEligible[Target, RawCandidate] {
override val name: String = genericCandidates.name
private def generateTweetFavCandidate(
_target: Target,
_tweetId: Long,
_socialContextActions: Seq[SocialContextAction],
socialContextActionsAllTypes: Seq[SocialContextAction],
_tweetyPieResult: Option[TweetyPieResult]
): RawCandidate = {
new RawCandidate with TweetFavoriteCandidate {
override val socialContextActions = _socialContextActions
override val socialContextAllTypeActions =
socialContextActionsAllTypes
val tweetId = _tweetId
val target = _target
val tweetyPieResult = _tweetyPieResult
}
}
private def generateTweetRetweetCandidate(
_target: Target,
_tweetId: Long,
_socialContextActions: Seq[SocialContextAction],
socialContextActionsAllTypes: Seq[SocialContextAction],
_tweetyPieResult: Option[TweetyPieResult]
): RawCandidate = {
new RawCandidate with TweetRetweetCandidate {
override val socialContextActions = _socialContextActions
override val socialContextAllTypeActions = socialContextActionsAllTypes
val tweetId = _tweetId
val target = _target
val tweetyPieResult = _tweetyPieResult
}
}
override def get(inputTarget: Target): Future[Option[Seq[RawCandidate]]] = {
genericCandidates.get(inputTarget).map { candidatesOpt =>
candidatesOpt
.map { candidates =>
val candidatesSeq =
candidates.collect {
case tweetRetweet: TweetRetweetCandidate
if inputTarget.params(PushParams.MRTweetRetweetRecsParam) =>
generateTweetRetweetCandidate(
inputTarget,
tweetRetweet.tweetId,
tweetRetweet.socialContextActions,
tweetRetweet.socialContextAllTypeActions,
tweetRetweet.tweetyPieResult)
case tweetFavorite: TweetFavoriteCandidate
if inputTarget.params(PushParams.MRTweetFavRecsParam) =>
generateTweetFavCandidate(
inputTarget,
tweetFavorite.tweetId,
tweetFavorite.socialContextActions,
tweetFavorite.socialContextAllTypeActions,
tweetFavorite.tweetyPieResult)
}
candidatesSeq.foreach { candidate =>
stats.counter(s"${candidate.commonRecType}_count").incr()
}
candidatesSeq
}
}
}
override def isCandidateSourceAvailable(target: Target): Future[Boolean] = {
PushDeviceUtil.isRecommendationsEligible(target).map { isAvailable =>
isAvailable && target.params(PushParams.GenericCandidateAdaptorDecider)
}
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.finagle.stats.Stat
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base.CandidateSource
import com.twitter.frigate.common.base.CandidateSourceEligible
import com.twitter.frigate.common.store.interests.InterestsLookupRequestWithContext
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.frigate.pushservice.params.HighQualityCandidateGroupEnum
import com.twitter.frigate.pushservice.params.HighQualityCandidateGroupEnum._
import com.twitter.frigate.pushservice.params.PushConstants.targetUserAgeFeatureName
import com.twitter.frigate.pushservice.params.PushConstants.targetUserPreferredLanguage
import com.twitter.frigate.pushservice.params.{PushFeatureSwitchParams => FS}
import com.twitter.frigate.pushservice.predicate.TargetPredicates
import com.twitter.frigate.pushservice.util.MediaCRT
import com.twitter.frigate.pushservice.util.PushAdaptorUtil
import com.twitter.frigate.pushservice.util.PushDeviceUtil
import com.twitter.frigate.pushservice.util.TopicsUtil
import com.twitter.frigate.thriftscala.CommonRecommendationType
import com.twitter.interests.thriftscala.InterestId.SemanticCore
import com.twitter.interests.thriftscala.UserInterests
import com.twitter.language.normalization.UserDisplayLanguage
import com.twitter.stitch.tweetypie.TweetyPie.TweetyPieResult
import com.twitter.storehaus.ReadableStore
import com.twitter.trends.trip_v1.trip_tweets.thriftscala.TripDomain
import com.twitter.trends.trip_v1.trip_tweets.thriftscala.TripTweet
import com.twitter.trends.trip_v1.trip_tweets.thriftscala.TripTweets
import com.twitter.util.Future
object HighQualityTweetsHelper {
def getFollowedTopics(
target: Target,
interestsWithLookupContextStore: ReadableStore[
InterestsLookupRequestWithContext,
UserInterests
],
followedTopicsStats: Stat
): Future[Seq[Long]] = {
TopicsUtil
.getTopicsFollowedByUser(target, interestsWithLookupContextStore, followedTopicsStats).map {
userInterestsOpt =>
val userInterests = userInterestsOpt.getOrElse(Seq.empty)
val extractedTopicIds = userInterests.flatMap {
_.interestId match {
case SemanticCore(semanticCore) => Some(semanticCore.id)
case _ => None
}
}
extractedTopicIds
}
}
def getTripQueries(
target: Target,
enabledGroups: Set[HighQualityCandidateGroupEnum.Value],
interestsWithLookupContextStore: ReadableStore[
InterestsLookupRequestWithContext,
UserInterests
],
sourceIds: Seq[String],
stat: Stat
): Future[Set[TripDomain]] = {
val followedTopicIdsSetFut: Future[Set[Long]] = if (enabledGroups.contains(Topic)) {
getFollowedTopics(target, interestsWithLookupContextStore, stat).map(topicIds =>
topicIds.toSet)
} else {
Future.value(Set.empty)
}
Future
.join(target.featureMap, target.inferredUserDeviceLanguage, followedTopicIdsSetFut).map {
case (
featureMap,
deviceLanguageOpt,
followedTopicIds
) =>
val ageBucketOpt = if (enabledGroups.contains(AgeBucket)) {
featureMap.categoricalFeatures.get(targetUserAgeFeatureName)
} else {
None
}
val languageOptions: Set[Option[String]] = if (enabledGroups.contains(Language)) {
val userPreferredLanguages = featureMap.sparseBinaryFeatures
.getOrElse(targetUserPreferredLanguage, Set.empty[String])
if (userPreferredLanguages.nonEmpty) {
userPreferredLanguages.map(lang => Some(UserDisplayLanguage.toTweetLanguage(lang)))
} else {
Set(deviceLanguageOpt.map(UserDisplayLanguage.toTweetLanguage))
}
} else Set(None)
val followedTopicOptions: Set[Option[Long]] = if (followedTopicIds.nonEmpty) {
followedTopicIds.map(topic => Some(topic))
} else Set(None)
val tripQueries = followedTopicOptions.flatMap { topicOption =>
languageOptions.flatMap { languageOption =>
sourceIds.map { sourceId =>
TripDomain(
sourceId = sourceId,
language = languageOption,
placeId = None,
topicId = topicOption,
gender = None,
ageBucket = ageBucketOpt
)
}
}
}
tripQueries
}
}
}
case class HighQualityTweetsAdaptor(
tripTweetCandidateStore: ReadableStore[TripDomain, TripTweets],
interestsWithLookupContextStore: ReadableStore[InterestsLookupRequestWithContext, UserInterests],
tweetyPieStore: ReadableStore[Long, TweetyPieResult],
tweetyPieStoreNoVF: ReadableStore[Long, TweetyPieResult],
globalStats: StatsReceiver)
extends CandidateSource[Target, RawCandidate]
with CandidateSourceEligible[Target, RawCandidate] {
override def name: String = this.getClass.getSimpleName
private val stats = globalStats.scope("HighQualityCandidateAdaptor")
private val followedTopicsStats = stats.stat("followed_topics")
private val missingResponseCounter = stats.counter("missing_respond_counter")
private val crtFatigueCounter = stats.counter("fatigue_by_crt")
private val fallbackRequestsCounter = stats.counter("fallback_requests")
override def isCandidateSourceAvailable(target: Target): Future[Boolean] = {
PushDeviceUtil.isRecommendationsEligible(target).map {
_ && target.params(FS.HighQualityCandidatesEnableCandidateSource)
}
}
private val highQualityCandidateFrequencyPredicate = {
TargetPredicates
.pushRecTypeFatiguePredicate(
CommonRecommendationType.TripHqTweet,
FS.HighQualityTweetsPushInterval,
FS.MaxHighQualityTweetsPushGivenInterval,
stats
)
}
private def getTripCandidatesStrato(
target: Target
): Future[Map[Long, Set[TripDomain]]] = {
val tripQueriesF: Future[Set[TripDomain]] = HighQualityTweetsHelper.getTripQueries(
target = target,
enabledGroups = target.params(FS.HighQualityCandidatesEnableGroups).toSet,
interestsWithLookupContextStore = interestsWithLookupContextStore,
sourceIds = target.params(FS.TripTweetCandidateSourceIds),
stat = followedTopicsStats
)
lazy val fallbackTripQueriesFut: Future[Set[TripDomain]] =
if (target.params(FS.HighQualityCandidatesEnableFallback))
HighQualityTweetsHelper.getTripQueries(
target = target,
enabledGroups = target.params(FS.HighQualityCandidatesFallbackEnabledGroups).toSet,
interestsWithLookupContextStore = interestsWithLookupContextStore,
sourceIds = target.params(FS.HighQualityCandidatesFallbackSourceIds),
stat = followedTopicsStats
)
else Future.value(Set.empty)
val initialTweetsFut: Future[Map[TripDomain, Seq[TripTweet]]] = tripQueriesF.flatMap {
tripQueries => getTripTweetsByDomains(tripQueries)
}
val tweetsByDomainFut: Future[Map[TripDomain, Seq[TripTweet]]] =
if (target.params(FS.HighQualityCandidatesEnableFallback)) {
initialTweetsFut.flatMap { candidates =>
val minCandidatesForFallback: Int =
target.params(FS.HighQualityCandidatesMinNumOfCandidatesToFallback)
val validCandidates = candidates.filter(_._2.size >= minCandidatesForFallback)
if (validCandidates.nonEmpty) {
Future.value(validCandidates)
} else {
fallbackTripQueriesFut.flatMap { fallbackTripDomains =>
fallbackRequestsCounter.incr(fallbackTripDomains.size)
getTripTweetsByDomains(fallbackTripDomains)
}
}
}
} else {
initialTweetsFut
}
val numOfCandidates: Int = target.params(FS.HighQualityCandidatesNumberOfCandidates)
tweetsByDomainFut.map(tweetsByDomain => reformatDomainTweetMap(tweetsByDomain, numOfCandidates))
}
private def getTripTweetsByDomains(
tripQueries: Set[TripDomain]
): Future[Map[TripDomain, Seq[TripTweet]]] = {
Future.collect(tripTweetCandidateStore.multiGet(tripQueries)).map { response =>
response
.filter(p => p._2.exists(_.tweets.nonEmpty))
.mapValues(_.map(_.tweets).getOrElse(Seq.empty))
}
}
private def reformatDomainTweetMap(
tweetsByDomain: Map[TripDomain, Seq[TripTweet]],
numOfCandidates: Int
): Map[Long, Set[TripDomain]] = tweetsByDomain
.flatMap {
case (tripDomain, tripTweets) =>
tripTweets
.sortBy(_.score)(Ordering[Double].reverse)
.take(numOfCandidates)
.map { tweet => (tweet.tweetId, tripDomain) }
}.groupBy(_._1).mapValues(_.map(_._2).toSet)
private def buildRawCandidate(
target: Target,
tweetyPieResult: TweetyPieResult,
tripDomain: Option[scala.collection.Set[TripDomain]]
): RawCandidate = {
PushAdaptorUtil.generateOutOfNetworkTweetCandidates(
inputTarget = target,
id = tweetyPieResult.tweet.id,
mediaCRT = MediaCRT(
CommonRecommendationType.TripHqTweet,
CommonRecommendationType.TripHqTweet,
CommonRecommendationType.TripHqTweet
),
result = Some(tweetyPieResult),
tripTweetDomain = tripDomain
)
}
private def getTweetyPieResults(
target: Target,
tweetToTripDomain: Map[Long, Set[TripDomain]]
): Future[Map[Long, Option[TweetyPieResult]]] = {
Future.collect((if (target.params(FS.EnableVFInTweetypie)) {
tweetyPieStore
} else {
tweetyPieStoreNoVF
}).multiGet(tweetToTripDomain.keySet))
}
override def get(target: Target): Future[Option[Seq[RawCandidate]]] = {
for {
tweetsToTripDomainMap <- getTripCandidatesStrato(target)
tweetyPieResults <- getTweetyPieResults(target, tweetsToTripDomainMap)
} yield {
val candidates = tweetyPieResults.flatMap {
case (tweetId, tweetyPieResultOpt) =>
tweetyPieResultOpt.map(buildRawCandidate(target, _, tweetsToTripDomainMap.get(tweetId)))
}
if (candidates.nonEmpty) {
highQualityCandidateFrequencyPredicate(Seq(target))
.map(_.head)
.map { isTargetFatigueEligible =>
if (isTargetFatigueEligible) Some(candidates)
else {
crtFatigueCounter.incr()
None
}
}
Some(candidates.toSeq)
} else {
missingResponseCounter.incr()
None
}
}
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base.CandidateSource
import com.twitter.frigate.common.base.CandidateSourceEligible
import com.twitter.frigate.common.base.ListPushCandidate
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.frigate.pushservice.params.PushFeatureSwitchParams
import com.twitter.frigate.pushservice.predicate.TargetPredicates
import com.twitter.frigate.pushservice.util.PushDeviceUtil
import com.twitter.frigate.thriftscala.CommonRecommendationType
import com.twitter.geoduck.service.thriftscala.LocationResponse
import com.twitter.interests_discovery.thriftscala.DisplayLocation
import com.twitter.interests_discovery.thriftscala.NonPersonalizedRecommendedLists
import com.twitter.interests_discovery.thriftscala.RecommendedListsRequest
import com.twitter.interests_discovery.thriftscala.RecommendedListsResponse
import com.twitter.storehaus.ReadableStore
import com.twitter.util.Future
case class ListsToRecommendCandidateAdaptor(
listRecommendationsStore: ReadableStore[String, NonPersonalizedRecommendedLists],
geoDuckV2Store: ReadableStore[Long, LocationResponse],
idsStore: ReadableStore[RecommendedListsRequest, RecommendedListsResponse],
globalStats: StatsReceiver)
extends CandidateSource[Target, RawCandidate]
with CandidateSourceEligible[Target, RawCandidate] {
override val name: String = this.getClass.getSimpleName
private[this] val stats = globalStats.scope(name)
private[this] val noLocationCodeCounter = stats.counter("no_location_code")
private[this] val noCandidatesCounter = stats.counter("no_candidates_for_geo")
private[this] val disablePopGeoListsCounter = stats.counter("disable_pop_geo_lists")
private[this] val disableIDSListsCounter = stats.counter("disable_ids_lists")
private def getListCandidate(
targetUser: Target,
_listId: Long
): RawCandidate with ListPushCandidate = {
new RawCandidate with ListPushCandidate {
override val listId: Long = _listId
override val commonRecType: CommonRecommendationType = CommonRecommendationType.List
override val target: Target = targetUser
}
}
private def getListsRecommendedFromHistory(
target: Target
): Future[Seq[Long]] = {
target.history.map { history =>
history.sortedHistory.flatMap {
case (_, notif) if notif.commonRecommendationType == List =>
notif.listNotification.map(_.listId)
case _ => None
}
}
}
private def getIDSListRecs(
target: Target,
historicalListIds: Seq[Long]
): Future[Seq[Long]] = {
val request = RecommendedListsRequest(
target.targetId,
DisplayLocation.ListDiscoveryPage,
Some(historicalListIds)
)
if (target.params(PushFeatureSwitchParams.EnableIDSListRecommendations)) {
idsStore.get(request).map {
case Some(response) =>
response.channels.map(_.id)
case _ => Nil
}
} else {
disableIDSListsCounter.incr()
Future.Nil
}
}
private def getPopGeoLists(
target: Target,
historicalListIds: Seq[Long]
): Future[Seq[Long]] = {
if (target.params(PushFeatureSwitchParams.EnablePopGeoListRecommendations)) {
geoDuckV2Store.get(target.targetId).flatMap {
case Some(locationResponse) if locationResponse.geohash.isDefined =>
val geoHashLength =
target.params(PushFeatureSwitchParams.ListRecommendationsGeoHashLength)
val geoHash = locationResponse.geohash.get.take(geoHashLength)
listRecommendationsStore
.get(s"geohash_$geoHash")
.map {
case Some(recommendedLists) =>
recommendedLists.recommendedListsByAlgo.flatMap { topLists =>
topLists.lists.collect {
case list if !historicalListIds.contains(list.listId) => list.listId
}
}
case _ => Nil
}
case _ =>
noLocationCodeCounter.incr()
Future.Nil
}
} else {
disablePopGeoListsCounter.incr()
Future.Nil
}
}
override def get(target: Target): Future[Option[Seq[RawCandidate]]] = {
getListsRecommendedFromHistory(target).flatMap { historicalListIds =>
Future
.join(
getPopGeoLists(target, historicalListIds),
getIDSListRecs(target, historicalListIds)
)
.map {
case (popGeoListsIds, idsListIds) =>
val candidates = (idsListIds ++ popGeoListsIds).map(getListCandidate(target, _))
Some(candidates)
case _ =>
noCandidatesCounter.incr()
None
}
}
}
private val pushCapFatiguePredicate = TargetPredicates.pushRecTypeFatiguePredicate(
CommonRecommendationType.List,
PushFeatureSwitchParams.ListRecommendationsPushInterval,
PushFeatureSwitchParams.MaxListRecommendationsPushGivenInterval,
stats,
)
override def isCandidateSourceAvailable(target: Target): Future[Boolean] = {
val isNotFatigued = pushCapFatiguePredicate.apply(Seq(target)).map(_.head)
Future
.join(
PushDeviceUtil.isRecommendationsEligible(target),
isNotFatigued
).map {
case (userRecommendationsEligible, isUnderCAP) =>
userRecommendationsEligible && isUnderCAP && target.params(
PushFeatureSwitchParams.EnableListRecommendations)
}
}
}

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package com.twitter.frigate.pushservice.adaptor
import com.twitter.finagle.stats.StatsReceiver
import com.twitter.frigate.common.base.CandidateSource
import com.twitter.frigate.common.base.CandidateSourceEligible
import com.twitter.frigate.pushservice.model.PushTypes.RawCandidate
import com.twitter.frigate.pushservice.model.PushTypes.Target
import com.twitter.geoduck.service.thriftscala.LocationResponse
import com.twitter.stitch.tweetypie.TweetyPie.TweetyPieResult
import com.twitter.storehaus.ReadableStore
import com.twitter.trends.trip_v1.trip_tweets.thriftscala.TripDomain
import com.twitter.trends.trip_v1.trip_tweets.thriftscala.TripTweets
import com.twitter.content_mixer.thriftscala.ContentMixerRequest
import com.twitter.content_mixer.thriftscala.ContentMixerResponse
import com.twitter.geoduck.common.thriftscala.Location
import com.twitter.hermit.pop_geo.thriftscala.PopTweetsInPlace
import com.twitter.recommendation.interests.discovery.core.model.InterestDomain
class LoggedOutPushCandidateSourceGenerator(
tripTweetCandidateStore: ReadableStore[TripDomain, TripTweets],
geoDuckV2Store: ReadableStore[Long, LocationResponse],
safeCachedTweetyPieStoreV2: ReadableStore[Long, TweetyPieResult],
cachedTweetyPieStoreV2NoVF: ReadableStore[Long, TweetyPieResult],
cachedTweetyPieStoreV2: ReadableStore[Long, TweetyPieResult],
contentMixerStore: ReadableStore[ContentMixerRequest, ContentMixerResponse],
softUserLocationStore: ReadableStore[Long, Location],
topTweetsByGeoStore: ReadableStore[InterestDomain[String], Map[String, List[(Long, Double)]]],
topTweetsByGeoV2VersionedStore: ReadableStore[String, PopTweetsInPlace],
)(
implicit val globalStats: StatsReceiver) {
val sources: Seq[CandidateSource[Target, RawCandidate] with CandidateSourceEligible[
Target,
RawCandidate
]] = {
Seq(
TripGeoCandidatesAdaptor(
tripTweetCandidateStore,
contentMixerStore,
safeCachedTweetyPieStoreV2,
cachedTweetyPieStoreV2NoVF,
globalStats
),
TopTweetsByGeoAdaptor(
geoDuckV2Store,
softUserLocationStore,
topTweetsByGeoStore,
topTweetsByGeoV2VersionedStore,
cachedTweetyPieStoreV2,
cachedTweetyPieStoreV2NoVF,
globalStats
)
)
}
}

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