diff options
Diffstat (limited to 'src/algorithms.py')
| -rw-r--r-- | src/algorithms.py | 23 |
1 files changed, 11 insertions, 12 deletions
diff --git a/src/algorithms.py b/src/algorithms.py index 5b07f78..c880a7b 100644 --- a/src/algorithms.py +++ b/src/algorithms.py @@ -2,7 +2,6 @@ import numpy as np import networkx as nx import cascade_creation from collections import Counter -from itertools import izip import convex_optimization import timeout @@ -15,7 +14,7 @@ def greedy_prediction(G, cascades): G_hat = cascade_creation.InfluenceGraph(max_proba=None) G_hat.add_nodes_from(G.nodes()) for node in G_hat.nodes(): - print node + print(node) # Avoid cases where infection time is None or 0 tmp = [cascade for cascade in cascades if cascade.infection_time(node) [0]] @@ -41,14 +40,14 @@ def recovery_l1obj_l2constraint(G, cascades, floor_cstt, passed_function, G_hat = cascade_creation.InfluenceGraph(max_proba=None) G_hat.add_nodes_from(G.nodes()) for node in G_hat.nodes(): - print node + print(node) try: M, w = cascade_creation.icc_matrixvector_for_node(cascades, node) p_node, __ = passed_function(M,w, *args, **kwargs) G_hat = cascade_creation.add_edges_from_proba_vector(G=G_hat, p_node=p_node, node=node, floor_cstt=floor_cstt) except timeout.TimeoutError: - print "TimeoutError, skipping to next node" + print("TimeoutError, skipping to next node") return G_hat @@ -69,14 +68,14 @@ def correctness_measure(G, G_hat, print_values=False): f1_score = 2.* tp / (2 * tp + fp + fn) if print_values: - print "False Positives: {}".format(fp) - print "False Negatives: {}".format(fn) - print "True Positives: {}".format(tp) - print "True Negatives: {}".format(tn) - print "-------------------------------" - print "Precision: {}".format(precision) - print "Recall: {}".format(recall) - print "F1 score: {}".format(f1_score) + print("False Positives: {}".format(fp)) + print("False Negatives: {}".format(fn)) + print("True Positives: {}".format(tp)) + print("True Negatives: {}".format(tn)) + print("-------------------------------") + print("Precision: {}".format(precision)) + print("Recall: {}".format(recall)) + print("F1 score: {}".format(f1_score)) return fp, fn, tp, tn |