SpamClassifier

Configures the spam classifier model, training parameters, and auto-learning settings.

This object can be configured from the WebUI under Settings › Spam Filter › Classifier

Fields

`model`

Type: SpamClassifierModel · required

The spam classifier model to use.

`learnHamFromCard`

Type: Boolean · default: true

Whether to automatically learn ham messages from senders in the user’s address book.

`learnSpamFromRblHits`

Type: UnsignedInt · default: 2 · max: 100

Number of DNSBL servers that list the sender to auto-learn as spam

`learnSpamFromTraps`

Type: Boolean · default: true

Train as spam messages sent to spam trap addresses

`holdSamplesFor`

Type: Duration · default: "180d"

Duration to hold training samples for

`minHamSamples`

Type: UnsignedInt · default: 100 · max: 10000 · min: 1

Minimum number of ham samples required for training

`minSpamSamples`

Type: UnsignedInt · default: 100 · max: 10000 · min: 1

Minimum number of spam samples required for training

`reservoirCapacity`

Type: UnsignedInt · default: 1024 · max: 100000 · min: 100

The capacity of the training sample reservoir

`trainFrequency`

Type: Duration? · default: "12h"

Frequency to train the spam classifier

`learnHamFromReply`

Type: Boolean · default: true

Whether to automatically learn ham messages that are replies to messages sent by the recipient.

JMAP API

The SpamClassifier singleton is available via the urn:stalwart:jmap capability.

`x:SpamClassifier/get`

This is a standard Foo/get method as defined in RFC 8620, Section 5.1.

For singletons, the ids argument should be the literal singleton (or null to return the single instance).

This method requires the sysSpamClassifierGet permission.

curl -X POST https://mail.example.com/api \
  -H 'Authorization: Bearer $TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{
      "methodCalls": [
        [
          "x:SpamClassifier/get",
          {
            "ids": [
              "singleton"
            ]
          },
          "c1"
        ]
      ],
      "using": [
        "urn:ietf:params:jmap:core",
        "urn:stalwart:jmap"
      ]
    }'

`x:SpamClassifier/set`

This is a standard Foo/set method as defined in RFC 8620, Section 5.3.

For singletons, only the update argument with id singleton is accepted; create and destroy arguments are rejected.

This method requires the sysSpamClassifierUpdate permission.

curl -X POST https://mail.example.com/api \
  -H 'Authorization: Bearer $TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{
      "methodCalls": [
        [
          "x:SpamClassifier/set",
          {
            "update": {
              "singleton": {
                "model": {
                  "@type": "FtrlFh"
                }
              }
            }
          },
          "c1"
        ]
      ],
      "using": [
        "urn:ietf:params:jmap:core",
        "urn:stalwart:jmap"
      ]
    }'

CLI

stalwart-cli wraps the same JMAP calls. See the CLI reference for installation, authentication, and general usage.

Fetch

stalwart-cli get SpamClassifier

Update

stalwart-cli update SpamClassifier --field model='{"@type":"FtrlFh"}'

Nested types

SpamClassifierModel

Defines the model used for spam classification.

FtrlFh: FTRL-Proximal + Feature Hashing. Carries the fields of SpamClassifierFtrlFh.
FtrlCcfh: FTRL-Proximal + Cuckoo Feature Hashing. Carries the fields of SpamClassifierFtrlCcfh.
Disabled: Disabled. No additional fields.

SpamClassifierFtrlFh

Defines a spam classifier model using FTRL-Proximal optimization with feature hashing.

`parameters`

Type: FtrlParameters · default: {"numFeatures":"20"}

Hyperparameters for the FTRL-FH model

`featureL2Normalize`

Type: Boolean · default: true

Whether to L2-normalize feature values in the spam classifier

`featureLogScale`

Type: Boolean · default: true

Whether to apply sublinear scaling to feature values in the spam classifier

FtrlParameters

Hyperparameters for the FTRL-Proximal optimization algorithm.

`alpha`

Type: Float · default: 2 · min: 0

The alpha parameter for the FTRL-Proximal algorithm

`beta`

Type: Float · default: 1 · min: 0

The beta parameter for the FTRL-Proximal algorithm

`numFeatures`

Type: ModelSize · default: "20"

The number of parameters (2^n)

`l1Ratio`

Type: Float · default: 0.001 · min: 0

The L1 regularization parameter for the FTRL-Proximal algorithm

`l2Ratio`

Type: Float · default: 0.0001 · min: 0

The L2 regularization parameter for the FTRL-Proximal algorithm

SpamClassifierFtrlCcfh

Defines a spam classifier model using FTRL-Proximal optimization with cuckoo feature hashing.

`indicatorParameters`

Type: FtrlParameters · default: {"numFeatures":"18"}

Hyperparameters for the indicator features in the FTRL-CCFH model