DGCN-PyTorch/utils.py

import torch
import pickle
import os

def f1_score(y_pred, y_true):
    tp = torch.sum((y_pred == 0) & (y_true == 0), dtype=torch.float64)
    fp = torch.sum((y_pred == 0) & (y_true != 0), dtype=torch.float64)
    fn = torch.sum((y_pred != 0) & (y_true == 0), dtype=torch.float64)
    precision = tp / (tp + fp)
    recall = tp / (tp + fn)
    f1 = 2 * (precision * recall) / (precision + recall)

    return f1, precision, recall

if __name__ == "__main__":

    max_f1 = 0
    fname = None
    epoch = None
    each_split = []

    for bin_fname in os.listdir('results_edge_classification_bitcoin_otc/'):
        bin_file = open("results_edge_classification_bitcoin_otc/" + bin_fname, "rb")
        data = pickle.load(bin_file)
        assert data.shape[1] == 12

        for i in range(len(data)):
            if data[i,2] == 1 or data[i,4] == 1 or data[i,6] == 1:
                continue
            mean_f1 = (data[i, 2] + data[i, 4] + data[i, 6] ) / 3
            if mean_f1 > max_f1:
                max_f1 = mean_f1
                fname = bin_fname
                epoch = i
                each_split = [data[i, 2], data[i, 4], data[i, 6]]

    print(fname, epoch, each_split,  str(max_f1))