Awesome

SpringRank

This is a sparse numpy and scipy implementation of SpringRank.

Paper: Cate De Bacco, Dan Larremore, and Cris Moore. Science Advances.

Code: Dan Larremore, K. Hunter Wapman, Apara Venkateswaran.

Installation

pip install -r requirements.txt

Examples

Get the ranks from a directed adjacency matrix (numpy array)

from springrank import SpringRank
A = np.random.binomial(1, 0.3, size=(10, 10))
# Initialize and fit model
model = SpringRank()
model.fit(A)
# Print the ranks
print(model.ranks)

Compute the inverse temperature parameter (beta) of the ranking and matrix

print(model.get_beta())

Get the scaled ranks so that a one-rank difference means a 75% win rate

scaled_ranks = model.get_scaled_ranks(0.75)

Include or change regularization alpha (defaults to alpha=0 unless specified)

# Instantiate with regularization 
model = SpringRank(alpha=0.1)
model.fit(A)
print(model.ranks)
# Change the regularization of an existing model
model.alpha = 0.2
model.fit(A)
print(model.ranks)

Make predictions about edge directions

from springrank import SpringRank
A = np.random.binomial(1, 0.3, size=(10, 10))
# Initialize and fit model
model = SpringRank()
model.fit(A)
print("The probability that an undirected edge beween 3 and 5 points from 3 to 5 is:\n")
print(model.predict([3,5]))