MonitorAI SDK
The Official Python SDK for Monitoring Machines Lab. MonitorAI is an observabiltiy framework designed for tabular ML to GenAI offering root cause analysis and decision intelligence allowing a linkup between ML and decision making for organizations that rely and use ML for solutions.
Getting started with monitorai sdk for feature drift :
- Install the package:
pip install monitorai
import pandas as pd
from monitorai import MonitorAI
from monitorai.drift.feature import CategoricalFeatureDrift
client=MonitorAI(api_key="***")
ref_df=pd.DataFrame({
"color":["red","red","blue","blue","red","yellow","yellow","red","blue"],
"social":["twitter","twitter","facebook","facebook","twitter","insta","insta","insta","insta"]})
analysis_df=pd.DataFrame({
"color":["red","red","red","blue","blue","yellow"],
"social":["twitter","twitter","facebook","insta","insta","insta"]
})
## create feature drift variable:
feature_drift=CategoricalFeatureDrift(ref_data=ref_df,analysis_data=analysis_df)
## get the bhattacharrya coefficients
bhatta_coeff_social=feature_drift.bhatta_coeff(feature="social")
bhatta_coeff_color=feature_drift.bhatta_coeff(feature="color")
## cauchy schwartz distance:
cauchy_schwartz_social=feature_drift.cauchy_schwartz_dist(feature="social")
cauchy_schwartz_color=feature_drift.cauchy_schwartz_dist(feature="color")
## euclidean distance:
euclid_social=feature_drift.euclidean_dist(feature="social")
euclid_color=feature_drift.euclidean_dist(feature="color")
## hellinger distance:
hellinger_social=feature_drift.hellinger_dist(feature="social")
hellinger_color=feature_drift.hellinger_dist(feature="color")
## jensen-shannon divergence:
js_social=feature_drift.js_divergence(feature="social")
js_color=feature_drift.js_divergence(feature="social")
## kl-divergence:
kl_social=feature_drift.kl_divergence(feature="social")
kl_color=feature_drift.kl_divergence(feature="color")
##psi:
psi_social=feature_drift.psi(feature="social")
psi_color=feature_drift.psi(feature="color")
Citing monitorai
If you use monitorai in your research, please cite it:
@software{monitorai,
title = {monitorai: A Root Cause and Decision Intelligence Kit For Machine Learning},
author = {Monitoring Machines Lab},
year = {2026},
url = {https://github.com/MasemeneMatlakanaBenny/monitorai},
doi = {10.5281/zenodo.19646175},
license = {MIT},
}
Metadata
Release files for monitor-aii 0.1.48
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| monitor_aii-0.1.48.tar.gz | 6.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| monitor_aii-0.1.48-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 18.2 kB
Release files / monitor_aii-0.1.48.tar.gz
| Download URL | monitor_aii-0.1.48.tar.gz |
|---|---|
| Size | 6.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
df86fa3111c88a38a2ca9af8900d07c308e0e0279a72dc0f315a6e485a80e127
|
|
BLAKE2b-256 checksum How to use checksums |
296eff1c393cbc58a7de0bf42bd9aef20f3a8bf985dc59632cf43eb624db61d6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.3
|
Release files / monitor_aii-0.1.48-py3-none-any.whl
| Download URL | monitor_aii-0.1.48-py3-none-any.whl |
|---|---|
| Size | 11.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
610d48966fa6e10e8f7f1f1f77ec1c7f9782a37c821171a82004d0841036e98e
|
|
BLAKE2b-256 checksum How to use checksums |
76733b5414bd70ce5b9d89dfebd60a46faf8ef63138fc3ad8611855096f0fc62
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.3
|