Rocky LucaHermes

## label_informativeness.py
from scipy.stats import entropy
import numpy as np

def label_informativeness(edge_index, labels):
    '''
    Computes the label informativeness metric proposed in
    Platonov et al. (2022)
    Characterizing Graph Datasets for Node Classification:
    Beyond Homophily-Heterophily Dichotomy
    https://arxiv.org/abs/2209.06177

## run_sweep_with_seeds.py
'''
Wandb Sweep: Multiple seeds same config/parameters

Script to run wandb sweeps with a number of different seeds:
Given some seeds, this sweep controller samples a model configuration and starts one run per seed with the exact same setup.
I made this for a model that had a high variance to get somewhat reliable values for every configuration.
In order to group the runs with the different seeds in the wandb webapp, an additional value 'sweep_step_id' is passed
that is the same for each configuration.

To execute, run
	from scipy.stats import entropy
	import numpy as np

	def label_informativeness(edge_index, labels):
	'''
	Computes the label informativeness metric proposed in
	Platonov et al. (2022)
	Characterizing Graph Datasets for Node Classification:
	Beyond Homophily-Heterophily Dichotomy
	https://arxiv.org/abs/2209.06177
	'''
	Wandb Sweep: Multiple seeds same config/parameters

	Script to run wandb sweeps with a number of different seeds:
	Given some seeds, this sweep controller samples a model configuration and starts one run per seed with the exact same setup.
	I made this for a model that had a high variance to get somewhat reliable values for every configuration.
	In order to group the runs with the different seeds in the wandb webapp, an additional value 'sweep_step_id' is passed
	that is the same for each configuration.

	To execute, run