Auroc (#27)

* added auroc

* readme

README.md

@@ -1,7 +1,9 @@
 # modeling-and-inferring-higher-order-contagion-spread
 
+THIS IS A RAD PROJECT!
+
 Some things:
 
 * To run the unit tests, you need to pip install the package locally. Navigate to the local folder on your computer and run "pip install -e ."
 * To run the unit tests, run "pytest" in the command line.
-* The package is referenced as `lcs` (Learning complex structure) when accessing the functionality.
+* The package is referenced as `lcs` (Learning complex structure) when accessing the functionality.

lcs/measures.py

@@ -76,3 +76,10 @@ def density(A):
 
 def hamming_distance(A1, A2):
     return np.sum(np.abs(A1 - A2)) / 2
+
+
+def auroc(samples,A):
+    Q = samples.mean(axis=0)
+    A = A.flatten()
+    Q = Q.flatten()
+    return roc_auc_score(A,Q)

lcs/utilities.py

@@ -5,6 +5,7 @@
 from scipy.sparse import csr_array
 from scipy.sparse.csgraph import reverse_cuthill_mckee
 from scipy.stats import rv_discrete
+from sklearn.metrics import roc_auc_score
 
 from .contagion import *
 from .generative import erdos_renyi