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openapi: 3.2.0
info:
title: V1 Lytics Segment ML API
version: 1.0.0
description: "The Lytics API is a _restful_ *JSON* api that includes:\n* *Data Collection* api's for collection, and upload of custom data.\n* *Personalization api* for real-time user profile usage in personalization.\n* *Segmentation api* for lists of users, and creating/managing the segmentation rules.\n* *Catalog api* for schema information.\n* *Content api* for content recommendation, and content-classification to drive personalization.\n* *Management api* for general account management.\n## Authentication\nThe *Lytics API* supports authentication using one of the following:\nLogin to your account [Lytics App](https://activate.getlytics.com) and navigate to *Account* to find your keys.\nAfter you have acquired your token, use it to access the Lytics API.\nOur api supports two methods for authorization:\n* query string url parameter, using **access_token**\n* http **Authorization** HEADER\n\n```\n# example showing passing auth token in header\ncurl -XPOST 'https://api.lytics.io/api/segment' \\\n -H \"Authorization: pretendtoken8762\" \\\n -H 'Content-type: application/json' \\\n -d '{\"notreal\" : []}'\n\n# example as query string parameter\ncurl -XPOST 'https://api.lytics.io/api/segment?access_token=804ef78pretendtoken8762' \\\n -H 'Content-type: application/json' \\\n -d '{\"notreal\" : []}'\n\n```\n\nAdditionally, there are two types of authentication token's:\n\n* *User Auth Token* is normally just for the web admin. But may be used on the api, this is a user-specific token, and attributes actions to this user. This token expires.\n\n* *API User* is a less privileged role and does not expire. But, less history is available on actions.\n\n## IP Whitelisting\n\nFor better security, you can manage access to the Lytics API using the IP address whitelisting api_ip_whitelist setting on your account. This setting will also be applied to manage admin access to your Lytics account.\n\nProvide a CIDR value for the range of IP addresses you trust. Lytics will then ignore any unauthenticated users and/or IP addresses that fall out of the valid range. This means you can grant access to only your trusted users.\n\nWhat is CIDR?\nCIDR is a flexible allocation of IP addresses. Use an [IP address tool] (https://www.ipaddressguide.com/), to convert your IP addresses into a CIDR format, either v4 or v6.\n\n## Documentation Examples\n\nWe use [jq json command line prettifier](https://stedolan.github.io/jq/) in our examples throughout this doc.\n\n## Media Types\n\nOur API is a JSON REST API. We have data-upload api's which support\ncsv uploads as well.\n\nRequests with a message-body use plain JSON to set or update resource states.\n\n## Error States\n\nThe common [HTTP Response Status Codes](https://github.com/for-GET/know-your-http-well/blob/master/status-codes.md) are used.\n\n## Query Parameters\n\nA variety of places our api accepts query parameters that allow a list of values.\nThe documentation will often say it allows `[]string or []int` (meaning an array of strings, or integers).\nWhen this occurs, we allow a variety of formats to pass these.\n\n* `ids=1234` convert this to []string{\"123\"}\n\n* `ids=[123,456]` convert this to []string{\"123\",\"456\"}\n\n* `ids=123,456` convert this to []string{\"123\",\"456\"}\n\n* `ids=123&ids=456` convert this to []string{\"123\",\"456\"}\n\n* `ids[]=123&ids[]=456` convert this to []string{\"123\",\"456\"} Note that we alias ids[] = ids"
servers:
- url: https://api.lytics.io
tags:
- name: SegmentML
description: 'SegmentML provides a framework for building custom machine learning
models directly in Lytics. Lytics SegmentML models are self-training,
continuously-updating and real-time.
SegmentML models are built by identifying:
1. A segment of users, called the **Target Segment** who exhibit behavior for *prediction*,
2. A segment of users, called the **Source Segment** to be candidates for model *evaluation*, or scoring.
Models are built with a variety of pre-selected candidate features, which include behavioral scores and content affinities, and can additionally support any custom field available in Lytics user profiles.
Attributes concerning the SegmentML model''s setup configuration are detailed in **SegmentML Create**. Attributes concerning the model''s results are defined in **SegmentML Model Fetch**.
Generic attributes from SegmentML model GET and POST:
| field | DataType | Description |
|-------------- |------------------|---------------|
| name | string | The model''s name
| state | string | The state of the model: Either *building*, *invalid*, or *complete*
| reason | string | If the state is *invalid* the reason will denote the error
| created | string | Date and time the model was created'
paths:
/api/segmentml/{id}:
get:
responses:
'200':
description: OK
headers: {}
security:
- ApiKeyAuth: []
summary: SegmentML Model Fetch
operationId: SegmentML Model Fetch
description: 'Get a SegmentML model.
Additional atttributes from a completed SegmentML model GET response:
| field | DataType | Description |
|-----------------------|------------------|---------------|
| features: kind | string | The field is either a Lytics Segment feature (*segment*), a lql/user-field feature (*lql*), a Lytics Behavioral Score feature (*score*), or a Lytics Content Affinity feature (*content*)
| features: fieldtype | string | Field type is either *numeric* or *categorical*
| features: name | string | The name of a field
| features: importance | number | The relative importance of a field in the model
| features: correlation | number | Correlation between specific field and target
| features: impact | object | The impact object details the Lift and shows the marginal effect of a feature on the predicted outcome of the model
| mse | number | Mean-squared error value
| rsq | number | R-squared value or coefficient of determination
| false_negative | number | The number of users in the source segment who are predicted to be in the target segment.
| false_positive | number | The number of users in the target segment who are not predicted to be in the target segment.
| true_negative | number | The number of users in the source segment who are not predicted to be in the target segment.
| true_positive | number | The number of users in the target segment who are predicted to be in the target segment.
| success | []number | Number of successful predictions for a given prediction value
| failure | []number | Number of failed predictions for a given prediction value
| auc | number | Area under the ROC curve
| threshold | number | Optimal decision threshold to minimize false-positives and false-negatives
| accuracy | number | A value that represents the accuracy of the model; scale ranges from 0 (least accurate) to 10 (most accurate).
| reach | number | A value that represents the number of source users that look like target users; scale ranges from 0 (low reach) to 10 (high reach).
| model_health | number | The overall health of the model (i.e. "healthy", "unhealthy")
| msgs | number | Messages for the user about the model with levels of severity (i.e. "debug", "info", "warn", "error")
To learn more about the metrics false negative, false positive etc., check out [binary classification](https://en.wikipedia.org/wiki/Binary_classification).
```
# Curl example of getting a SegmentML model
curl -s -J -XGET "https://api.lytics.io/api/segmentml/all::smt_power" -H "Authorization: $LIOKEY"
```'
tags:
- SegmentML
parameters:
- name: account_id
in: query
description: Your Lytics account ID.
required: false
schema:
type: string
- name: id
in: path
description: ID of the SegmentML model to retrieve, of the form `SOURCE_SLUG::TARGET_SLUG`.
required: true
example: source::target
schema:
type: string
delete:
responses:
'204':
description: No Content
headers: {}
security:
- ApiKeyAuth: []
summary: SegmentML Delete
operationId: SegmentML Delete
description: 'Delete a SegmentML model.
```sh
# Curl example of deleting a SegmentML model
curl -s -J -XDELETE "https://api.lytics.io/api/segmentml/all::smt_power" -H "Authorization: $LIOKEY"
```'
tags:
- SegmentML
parameters:
- name: account_id
in: query
description: Your Lytics account ID.
required: false
schema:
type: string
- name: id
in: path
description: ID of the SegmentML model to delete, of the form `SOURCE_SLUG::TARGET_SLUG`.
required: true
example: source::target
schema:
type: string
/api/segmentml:
post:
responses:
'201':
description: Created
headers: {}
content:
application/json:
schema:
$ref: '#/components/schemas/SegmentMLModel'
examples:
response:
value:
name: test_rf
state: building
reason: ''
created: '2018-07-10T16:10:54.456003352-07:00'
conf:
source:
aid: 123
account_id: lol
id: ''
name: all
is_public: false
slug_name: ''
description: ''
author_id: ''
updated: '0001-01-01T00:00:00Z'
created: '2018-07-10T16:10:54.442558895-07:00'
invalid: false
invalid_reason: ''
deleted: false
datemath_calc: false
forward_datemath: false
save_hist: false
schedule_exit: false
tags: null
target:
aid: 123
account_id: lol
id: ''
name: goal
is_public: false
slug_name: ''
description: ''
author_id: ''
updated: '0001-01-01T00:00:00Z'
created: '2018-07-10T16:10:54.442559277-07:00'
invalid: false
invalid_reason: ''
deleted: false
datemath_calc: false
forward_datemath: false
save_hist: false
schedule_exit: false
tags: null
target_field: null
model_name: ''
additional: null
collections: null
collect: 0
use_scores: true
use_content: false
build_only: false
auto_tune: false
security:
- ApiKeyAuth: []
summary: SegmentML Create
operationId: SegmentML Create
description: "Create a new SegmentML model.\n\nModel configuration can be specified either through sending the options\nas a flat JSON object POST body or through URL parameters on the request.\n\n```sh\n# Curl example of creating a SegmentML model\ncurl -s -J -XPOST \"https://api.lytics.io/api/segmentml\" -H \"Authorization: $LIOKEY\" -d '\n{\n \"source\": \"all\",\n \"target\": \"smt_power\",\n \"use_scores\": true\n}\n'\n```"
tags:
- SegmentML
parameters:
- name: account_id
in: query
description: Your Lytics account ID.
required: false
schema:
type: string
- name: source
in: query
description: ID or slug of the source segment.
required: true
example: all
schema:
type: string
- name: target
in: query
description: ID or slug of the target segment. **Required** if target field is not supplied.
required: false
example: smt_power
schema:
type: string
- name: use_scores
in: query
description: Include raw behavioral scores as features in the model (usually very useful).
required: false
example: 'true'
schema:
type: boolean
- name: target_field
in: query
description: Slug of the target field. Cannot provide both a target and a target field, hence required if target segment is not indicated (see above).
required: false
example: LTV
schema:
type: string
- name: use_content
in: query
description: If true, include content affinities as features in the model.
required: false
example: 'false'
schema:
type: boolean
- name: aspect_collections
in: query
description: List of Segment Collections to include in the model. Possible values are "email", "web", "support", "mobile", "commerce", "behaviors", "content".
required: false
example: '["web", "mobile"]'
schema:
type: string
- name: additional_fields
in: query
description: List of additional user fields to include in the model.
required: false
example: '["age", "country"]'
schema:
type: string
- name: model_only
in: query
description: Build the model without scoring each user. This is useful during model building exercises when comparing efficiency and accuracy between models. Set true by default unless **evalonly** is selected.
required: false
example: 'true'
schema:
type: boolean
- name: eval_only
in: query
description: If true, a previously built model is used to rescore users. Only the source and target parameters are needed in the API call to to identify which model to use.
required: false
example: 'false'
schema:
type: boolean
- name: auto_tune
in: query
description: If true, enable auto-tune feature selection.
required: false
example: 'false'
schema:
type: boolean
- name: tune_model
in: query
description: If true, model tuning parameters are optimized before any models are built. Experimental.
required: false
example: 'false'
schema:
type: boolean
- name: tags
in: query
description: Includes tags to be associated with the model.
required: false
example: '["increase momentum", "mobile users"]'
schema:
type: string
- name: re_run
in: query
description: If true, re-run the model every week.
required: false
example: 'false'
schema:
type: boolean
- name: save_segments
in: query
description: "If true, this saves three different segments:\n 1) Users from source and target segments who \"look like\" users from the target segment.\n 2) Users not in the target segment.\n 3) Users from the source segment who look like users from the target segment.\n"
required: false
example: 'false'
schema:
type: boolean
- name: as_is
in: query
description: If true, do not remove any of the model features when creating the model.
required: false
example: 'false'
schema:
type: boolean
- name: num_to_train
in: query
description: The number of samples to collect from both the source and target segment for feature matrices. Defaults to 5,000.
required: false
example: '5000'
schema:
type: number
- name: cor_threshold
in: query
description: Threshold (0.0-1.0) at which to remove correlated features.
required: false
example: '0.9'
schema:
type: number
/api/segmentml/_dependencies/{modelname}:
get:
responses:
'200':
description: OK
headers: {}
content:
application/json:
schema:
$ref: '#/components/schemas/SegmentMLDependenciesModel'
examples:
response:
value:
message: success
status: 200
data:
fields:
lytics_score_frequency:
- - 1
- 0.5283225806451614
- - 4.6
- 0.5283225806451614
- - 8.2
- 0.5281612903225807
lytics_score_intensity:
- - 1
- 0.5283225806451614
- - 4.6
- 0.5283225806451614
- - 8.2
- 0.5281612903225807
security:
- ApiKeyAuth: []
summary: SegmentML Dependencies
operationId: SegmentML Dependencies
description: ''
tags:
- SegmentML
parameters:
- name: account_id
in: query
description: Your Lytics account ID.
required: false
schema:
type: string
- name: modelname
in: path
description: Name of the model to get dependencies for.
required: true
example: my_model
schema:
type: string
components:
schemas:
SegmentMLDependenciesModel:
type: object
properties:
message:
type: string
status:
type: number
data:
type: object
properties:
fields:
type: object
properties:
lytics_score_frequency:
type: array
items:
type: array
items: {}
lytics_score_intensity:
type: array
items:
type: array
items: {}
example:
message: success
status: 200
data:
fields:
lytics_score_frequency:
- - 1
- 0.5283225806451614
- - 4.6
- 0.5283225806451614
- - 8.2
- 0.5281612903225807
lytics_score_intensity:
- - 1
- 0.5283225806451614
- - 4.6
- 0.5283225806451614
- - 8.2
- 0.5281612903225807
SegmentMLModel:
type: object
properties:
name:
type: string
state:
type: string
reason:
type: string
created:
type: string
conf:
type: object
properties:
source:
type: object
properties:
aid:
type: number
account_id:
type: string
id:
type: string
name:
type: string
is_public:
type: boolean
slug_name:
type: string
description:
type: string
author_id:
type: string
updated:
type: string
created:
type: string
invalid:
type: boolean
invalid_reason:
type: string
deleted:
type: boolean
datemath_calc:
type: boolean
forward_datemath:
type: boolean
save_hist:
type: boolean
schedule_exit:
type: boolean
tags: {}
target:
type: object
properties:
aid:
type: number
account_id:
type: string
id:
type: string
name:
type: string
is_public:
type: boolean
slug_name:
type: string
description:
type: string
author_id:
type: string
updated:
type: string
created:
type: string
invalid:
type: boolean
invalid_reason:
type: string
deleted:
type: boolean
datemath_calc:
type: boolean
forward_datemath:
type: boolean
save_hist:
type: boolean
schedule_exit:
type: boolean
tags: {}
target_field: {}
model_name:
type: string
additional: {}
collections: {}
collect:
type: number
use_scores:
type: boolean
use_content:
type: boolean
build_only:
type: boolean
auto_tune:
type: boolean
example:
name: test_rf
state: building
reason: ''
created: '2018-07-10T16:10:54.456003352-07:00'
conf:
source:
aid: 123
account_id: lol
id: ''
name: all
is_public: false
slug_name: ''
description: ''
author_id: ''
updated: '0001-01-01T00:00:00Z'
created: '2018-07-10T16:10:54.442558895-07:00'
invalid: false
invalid_reason: ''
deleted: false
datemath_calc: false
forward_datemath: false
save_hist: false
schedule_exit: false
tags: null
target:
aid: 123
account_id: lol
id: ''
name: goal
is_public: false
slug_name: ''
description: ''
author_id: ''
updated: '0001-01-01T00:00:00Z'
created: '2018-07-10T16:10:54.442559277-07:00'
invalid: false
invalid_reason: ''
deleted: false
datemath_calc: false
forward_datemath: false
save_hist: false
schedule_exit: false
tags: null
target_field: null
model_name: ''
additional: null
collections: null
collect: 0
use_scores: true
use_content: false
build_only: false
auto_tune: false
securitySchemes:
ApiKeyAuth:
in: header
name: Authorization
type: apiKey
x-readme:
explorer-enabled: true
proxy-enabled: true