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https://github.com/hydrospheredata/hydro-stat

hydrosphere python

Last synced: 28 days ago
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Host: GitHub
URL: https://github.com/hydrospheredata/hydro-stat
Owner: Hydrospheredata
Created: 2020-03-23T13:42:49.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-07-05T09:56:50.000Z (over 1 year ago)
Last Synced: 2024-04-14T07:52:52.577Z (9 months ago)
Topics: hydrosphere, python
Language: Python
Size: 64.4 MB
Stars: 0
Watchers: 5
Forks: 1
Open Issues: 6
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

Detecting domain drifts between training and production data using featurewise statistics.

## Environment variables to configure service while deploying
Addresses to other services:
* `HTTP_UI_ADDRESS` - http address of hydro-serving cluster, used to create `hydrosdk.Cluster(HS_CLUSTER_ADDRESS)`

AWS/Minio parameters:
* `AWS_ACCESS_KEY_ID`
* `AWS_SECRET_ACCESS_KEY`
* `S3_ENDPOINT`, if no s3 endpoint is present, data will be downloaded with boto3

Flask server parameters:
* `APPLICATION_ROOT` - prefix of all routes specified in [hydro_auto_od_openapi.yaml](hydro-auto-od-openapi.yaml), not used right now
* `DEBUG`

## API
[GET] /metrics?model_version_id=123

3. The JSON response will contain the following:

* overall_probability_drift
* per_feature_report:
* drift-probability.
* histogram:
* bins
* training
* deployment
* statistics: a list of statistics for each feature.
* change_probability
* deployment
* training
*warnings:
* final_decision
* report:
* drift_probability_per_feature
* message

**Example**:
Here is an example of the resulting json string to show the difference between continuous and discrete feature responses:
```json
{
"overall_probability_drift": 1.0,
"per_feature_report": {
"Class": {
"drift-probability": 1.0,
"histogram": {
"bins": [
10101500.0,
14353520.0,
18605540.0,
22857560.0,
27109580.0,
31361600.0,
35613620.0,
39865640.0,
44117660.0,
48369680.0,
52621700.0,
56873720.0,
61125740.0,
65377760.0,
69629780.0,
73881800.0,
78133820.0,
82385840.0,
86637860.0,
90889880.0,
95141900.0
],
"deployment": [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.968305977835799e-07,
4.617996712812843e-09,
8.545267421572484e-09,
6.973043469489479e-10,
0.0,
9.551753884621436e-09,
1.7366825244766249e-09,
4.848238714163912e-09,
3.058929446521328e-09,
5.196890887638385e-10,
4.775876942310718e-09
],
"training": [
5.787626079676267e-08,
6.052075464085208e-10,
5.940243634857547e-09,
1.60379999798258e-08,
2.5063486432787653e-08,
4.696936827561781e-09,
3.9272707087596405e-09,
4.237768493438795e-08,
7.808493017249066e-09,
7.084875298717141e-08,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0
]
},
"statistics": {
"mean": {
"change_probability": 1.0,
"deployment": 54260379.42714889,
"training": 33558627.974042684
},
"median": {
"change_probability": 1.0,
"deployment": 50591900.0,
"training": 40171900.0
},
"std": {
"change_probability": 1.0,
"deployment": 9453600.881636694,
"training": 15655558.710062902
}
}
},
"Class Name": {
"drift-probability": 0.0,
"histogram": {
"bins": [
"Table grape purees",
"Live animals",
"Fresh fruit purees",
"Food Beverage and Tobacco Products",
"Livestock",
"Regina grape purees",
"Mink",
"Live Plant and Animal Material and Accessories and Supplies"
],
"deployment": [
1,
0,
1,
1,
0,
1,
0,
0
],
"training": [
0,
1,
0,
0,
1,
0,
1,
1
]
},
"statistics": {
"entropy": {
"change_probability": 0.0,
"deployment": 2.0,
"training": 2.0
},
"unique values": {
"change_probability": 0.0,
"deployment": 4,
"training": 4
}
}
},
},
"warnings": {
"final_decision": "there is a change",
"report": [
{
"drift_probability_per_feature": 1.0,
"message": "the feature \"Segment\" has changed."
},
{
"drift_probability_per_feature": 1.0,
"message": "the feature \"Family\" has changed."
},
{
"drift_probability_per_feature": 1.0,
"message": "the feature \"Class\" has changed."
},
{
"drift_probability_per_feature": 1.0,
"message": "the feature \"Commodity\" has changed."
}
]
}
}
```