transform .txt to .mat of network informations

Python/data/txt2mat.py

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+import numpy as np
+import scipy.io as scio
+from scipy import sparse
+import argparse
+
+
+parse = argparse.ArgumentParser()
+parse.add_argument("-g", "--graph", help="each row is a training link")
+parse.add_argument("-u", "--users", help="users/nodes list, each row is a node")
+parse.add_argument("-n", "--dimension", help="dimension of node profile")
+parse.add_argument("-f", "--profile", help="profile information of nodes, the ordering corresponding to nodes list")
+parse.add_argument("-o", "--output", help="output .mat file")
+args = parse.parse_args()
+
+
+class MatFile:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def _transform_graph(self) -> object:
+        data = np.loadtxt(args.graph, delimiter=",", dtype=np.int64)
+        net = sparse.dok_matrix((data.max() + 1, data.max() + 1))
+        for line in data:
+            net[line[0], line[1]] = 1.0
+        return net
+
+    @staticmethod
+    def _transform_feature(self) -> object:
+        users = np.loadtxt(args.users, dtype=np.int64)
+        features = np.loadtxt(args.profile, dtype=np.int64)
+        group = sparse.dok_matrix((users.max() + 1 , int(args.dimension)))
+        for index, user in enumerate(users):
+            for f_id, feature in enumerate(features[index]):
+                group[user, f_id] = feature
+        return group
+
+    def create_mat_file(self, file_path):
+        net = self._transform_graph()
+        group = self._transform_feature()
+        scio.savemat(file_path, {"net": net, "group": group})
+
+    @staticmethod
+    def load_mat_file(self, file_path):
+        data = scio.loadmat(file_path)
+        for key, value in data.items():
+            print(key)
+            print(value)
+
+if __name__ == "__main__":
+    mat_file = MatFile()
+    mat_file.create_mat_file(args.output)
+    # mat_file.load_mat_file(args.output)