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
File size: 1,168 Bytes
55151a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"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
} |