| Name | Version | Summary | date |
| arize-phoenix |
8.2.1 |
AI Observability and Evaluation |
2025-02-22 00:13:17 |
| arize-phoenix-client |
1.0.1 |
LLM Observability |
2025-02-19 23:41:25 |
| effector |
0.1.0 |
A Python library for global and regional effects |
2025-02-17 11:39:23 |
| arize-phoenix-evals |
0.20.3 |
LLM Evaluations |
2025-02-13 22:33:58 |
| arize-otel |
0.7.3 |
Helper package for OTEL setup to send traces to Arize & Phoenix |
2025-01-21 02:38:57 |
| InterpreTS |
0.5.0 |
Feature extraction from time series to support the creation of interpretable and explainable predictive models. |
2025-01-08 03:48:29 |
| arize-phoenix-otel |
0.6.1 |
LLM Observability |
2024-10-17 22:55:51 |
| legrad-torch |
1.1 |
LeGrad |
2024-10-14 17:22:51 |
| xaiographs |
1.2.0 |
Python library providing Explainability & Fairness AI functionalities |
2024-10-09 10:32:34 |
| astra-logs |
4.2.8 |
AI Observability and Evaluation |
2024-06-07 14:02:04 |
| wpdm3333 |
0.1.0 |
Warsztat Praktyka Data Mining |
2024-03-12 23:16:21 |
| template-wpdm |
0.1.0 |
Warsztat Praktyka Data Mining |
2024-03-11 10:48:41 |
| milosz7wpdm |
0.1.1 |
Warsztat Praktyka Data Mining |
2024-03-06 17:28:24 |
| knac-toolkit |
1.0.2 |
Knowledge Augmented Clustering |
2024-02-29 10:37:49 |
| pgeon-xai |
1.0.1 |
pgeon (or pgeon-xai) is a Python package that produces explanations for opaque agents using Policy Graphs (PGs) |
2024-02-25 22:09:30 |
| trustyai |
0.5.0 |
Python bindings to the TrustyAI explainability library. |
2024-02-02 13:31:44 |
| wmsd |
1.0.0 |
TOPSIS ranking and interpretation using WMSD-space |
2023-11-29 22:28:22 |
| timeshap |
1.0.4 |
KernelSHAP adaptation for recurrent models. |
2023-09-13 09:24:00 |
| thermostat-datasets |
1.1.0 |
Collection of NLP model explanations and accompanying analysis tools |
2023-06-26 10:15:06 |
| fairxplainer |
0.1.0 |
A Python package for explaining bias in machine learning models |
2023-03-10 12:50:37 |