Instructions to use adarshcod30/margadrishti-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use adarshcod30/margadrishti-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("adarshcod30/margadrishti-models", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "window_hours": 3622, | |
| "cells": 662, | |
| "total_obstruction_m2h": 885304.5, | |
| "max_cis_seconds_per_day": 12.237083435058594, | |
| "checks": [ | |
| { | |
| "name": "congestion co-occurrence", | |
| "passed": false, | |
| "statistic": 0.546268982847154, | |
| "p_value": 0.7294, | |
| "detail": "1 congestion events in top-50 CIS cells, 0.55x exposure-matched expectation (p=0.7294)" | |
| }, | |
| { | |
| "name": "closure co-occurrence", | |
| "passed": false, | |
| "statistic": 1.4121225287855748, | |
| "p_value": 0.0848, | |
| "detail": "39 closure events in top-50 CIS cells, 1.41x exposure-matched expectation (p=0.0848)" | |
| }, | |
| { | |
| "name": "weight sensitivity", | |
| "passed": true, | |
| "statistic": 0.8522597439177156, | |
| "p_value": null, | |
| "detail": "Kendall tau 0.852 over 12 trials at +/-30%; top-50 membership overlap 92.8% (threshold tau>=0.8)" | |
| }, | |
| { | |
| "name": "width-normalisation ablation", | |
| "passed": true, | |
| "statistic": 0.76, | |
| "p_value": null, | |
| "detail": "top-50 overlap 76.0% with a flat-width index (full-ranking tau 0.727); lower means width normalisation is doing real work" | |
| } | |
| ], | |
| "n_passed": 2 | |
| } |