s4753993-GCN

recognition/MySolution/s47539934-GCN/Data.py

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+'''
+Author name: Arsh Upadhyaya, s47539934
+To preprocess dataset facebook.npz
+'''
+
+import numpy as np
+from sklearn import preprocessing
+import torch
+import scipy.sparse
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+import scipy.sparse as sp
+import torch.optim as optim
+
+
+def load_data(file_path):
+    '''
+    parameters: 
+    takes file path
+    returns:
+    Adjacency matrix: normalized coo matrix
+    features and labels(targets) as tensors
+    '''
+
+    data = np.load("/content/drive/MyDrive/facebook.npz")
+    edges = data['edges']
+    features = data['features']
+    labels = data['target']
+
+    features = sp.csr_matrix(features)
+
+    adj= sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0]))
+
+    #normalize
+    colsum = np.array(adj.sum(0))
+    D = np.power(colsum, -1)[0]
+    D[np.isinf(D)] = 0
+    D_inv = sp.diags(D)
+    adj_trans = D_inv.dot(adj)
+
+    #transform data type
+    indices = torch.LongTensor(np.vstack((adj_trans.tocoo().row, adj_trans.tocoo().col)))
+    values = torch.FloatTensor(adj_trans.data)
+    shape = adj_trans.shape
+
+    adj_trans = torch.sparse_coo_tensor(indices, values, shape)
+    features = torch.FloatTensor(np.array(features.todense()))
+    labels = torch.LongTensor(labels)
+
+    return adj_trans, features, labels
+