修复numpy.bool

examples/mxnet/scenegraph/train_freq_prior.py

@@ -73,7 +73,7 @@ for _, item in train_data.items():
             for col, row in zip(cols, rows):
                 bg_matrix[gt_classes[col], gt_classes[row]] += 1
         else:
-            all_possib = np.ones_like(iou_mat, dtype=np.bool)
+            all_possib = np.ones_like(iou_mat, dtype=bool)
             np.fill_diagonal(all_possib, 0)
             cols, rows = np.where(all_possib)
             for col, row in zip(cols, rows):

examples/pytorch/bgrl/eval_function.py

@@ -11,7 +11,7 @@ def fit_logistic_regression(X, y, data_random_seed=1, repeat=1):
     # transform targets to one-hot vector
     one_hot_encoder = OneHotEncoder(categories='auto', sparse=False)
 
-    y = one_hot_encoder.fit_transform(y.reshape(-1, 1)).astype(np.bool)
+    y = one_hot_encoder.fit_transform(y.reshape(-1, 1)).astype(bool)
 
     # normalize x
     X = normalize(X, norm='l2')
@@ -34,7 +34,7 @@ def fit_logistic_regression(X, y, data_random_seed=1, repeat=1):
 
         y_pred = clf.predict_proba(X_test)
         y_pred = np.argmax(y_pred, axis=1)
-        y_pred = one_hot_encoder.transform(y_pred.reshape(-1, 1)).astype(np.bool)
+        y_pred = one_hot_encoder.transform(y_pred.reshape(-1, 1)).astype(bool)
 
         test_acc = metrics.accuracy_score(y_test, y_pred)
         accuracies.append(test_acc)
@@ -44,7 +44,7 @@ def fit_logistic_regression(X, y, data_random_seed=1, repeat=1):
 def fit_logistic_regression_preset_splits(X, y, train_mask, val_mask, test_mask):
     # transform targets to one-hot vector
     one_hot_encoder = OneHotEncoder(categories='auto', sparse=False)
-    y = one_hot_encoder.fit_transform(y.reshape(-1, 1)).astype(np.bool)
+    y = one_hot_encoder.fit_transform(y.reshape(-1, 1)).astype(bool)
 
     # normalize x
     X = normalize(X, norm='l2')
@@ -67,13 +67,13 @@ def fit_logistic_regression_preset_splits(X, y, train_mask, val_mask, test_mask)
 
             y_pred = clf.predict_proba(X_val)
             y_pred = np.argmax(y_pred, axis=1)
-            y_pred = one_hot_encoder.transform(y_pred.reshape(-1, 1)).astype(np.bool)
+            y_pred = one_hot_encoder.transform(y_pred.reshape(-1, 1)).astype(bool)
             val_acc = metrics.accuracy_score(y_val, y_pred)
             if val_acc > best_acc:
                 best_acc = val_acc
                 y_pred = clf.predict_proba(X_test)
                 y_pred = np.argmax(y_pred, axis=1)
-                y_pred = one_hot_encoder.transform(y_pred.reshape(-1, 1)).astype(np.bool)
+                y_pred = one_hot_encoder.transform(y_pred.reshape(-1, 1)).astype(bool)
                 best_test_acc = metrics.accuracy_score(y_test, y_pred)
 
         accuracies.append(best_test_acc)

examples/pytorch/dimenet/qm9.py

@@ -152,7 +152,7 @@ class QM9(QM9Dataset):
             # calculate the distance between all atoms
             dist = np.linalg.norm(R[:, None, :] - R[None, :, :], axis=-1)
             # keep all edges that don't exceed the cutoff and delete self-loops
-            adj = sp.csr_matrix(dist <= self.cutoff) - sp.eye(n_atoms, dtype=np.bool)
+            adj = sp.csr_matrix(dist <= self.cutoff) - sp.eye(n_atoms, dtype=bool)
             adj = adj.tocoo()
             u, v = torch.tensor(adj.row), torch.tensor(adj.col)
             g = dgl_graph((u, v))

examples/pytorch/gas/dataloader.py

@@ -118,9 +118,9 @@ class GASDataset(DGLBuiltinDataset):
         train_idx = index[:int(train_size * num_edges)]
         val_idx = index[num_edges - int(val_size * num_edges):]
         test_idx = index[int(train_size * num_edges):num_edges - int(val_size * num_edges)]
-        train_mask = np.zeros(num_edges, dtype=np.bool)
-        val_mask = np.zeros(num_edges, dtype=np.bool)
-        test_mask = np.zeros(num_edges, dtype=np.bool)
+        train_mask = np.zeros(num_edges, dtype=bool)
+        val_mask = np.zeros(num_edges, dtype=bool)
+        test_mask = np.zeros(num_edges, dtype=bool)
         train_mask[train_idx] = True
         val_mask[val_idx] = True
         test_mask[test_idx] = True

examples/pytorch/grace/eval.py

@@ -33,7 +33,7 @@ def repeat(n_times):
 
 
 def prob_to_one_hot(y_pred):
-    ret = np.zeros(y_pred.shape, np.bool)
+    ret = np.zeros(y_pred.shape, bool)
     indices = np.argmax(y_pred, axis=1)
     for i in range(y_pred.shape[0]):
         ret[i][indices[i]] = True
@@ -58,7 +58,7 @@ def label_classification(embeddings, y, train_mask, test_mask, split='random', r
     Y = y.detach().cpu().numpy()
     Y = Y.reshape(-1, 1)
     onehot_encoder = OneHotEncoder(categories='auto').fit(Y)
-    Y = onehot_encoder.transform(Y).toarray().astype(np.bool)
+    Y = onehot_encoder.transform(Y).toarray().astype(bool)
 
     X = normalize(X, norm='l2')
 

examples/pytorch/graphsaint/utils.py

@@ -64,11 +64,11 @@ def load_data(args, multilabel):
     prefix = "data/{}".format(args.dataset)
     DataType = namedtuple('Dataset', ['num_classes', 'train_nid', 'g'])
 
-    adj_full = scipy.sparse.load_npz('./{}/adj_full.npz'.format(prefix)).astype(np.bool)
+    adj_full = scipy.sparse.load_npz('./{}/adj_full.npz'.format(prefix)).astype(bool)
     g = dgl.from_scipy(adj_full)
     num_nodes = g.num_nodes()
 
-    adj_train = scipy.sparse.load_npz('./{}/adj_train.npz'.format(prefix)).astype(np.bool)
+    adj_train = scipy.sparse.load_npz('./{}/adj_train.npz'.format(prefix)).astype(bool)
     train_nid = np.array(list(set(adj_train.nonzero()[0])))
 
     role = json.load(open('./{}/role.json'.format(prefix)))

examples/pytorch/model_zoo/geometric/coarsening.py

@@ -152,7 +152,7 @@ def HEM_one_level(rr, cc, vv, rid, weights):
     nnz = rr.shape[0]
     N = rr[nnz - 1] + 1
 
-    marked = np.zeros(N, np.bool)
+    marked = np.zeros(N, bool)
     rowstart = np.zeros(N, np.int32)
     rowlength = np.zeros(N, np.int32)
     cluster_id = np.zeros(N, np.int32)

examples/pytorch/pinsage/data_utils.py

@@ -9,9 +9,9 @@ import dask.dataframe as dd
 # takes.  It essentially follows the intuition of "training on the past and predict the future".
 # One can also change the threshold to make validation and test set take larger proportions.
 def train_test_split_by_time(df, timestamp, user):
-    df['train_mask'] = np.ones((len(df),), dtype=np.bool)
-    df['val_mask'] = np.zeros((len(df),), dtype=np.bool)
-    df['test_mask'] = np.zeros((len(df),), dtype=np.bool)
+    df['train_mask'] = np.ones((len(df),), dtype=bool)
+    df['val_mask'] = np.zeros((len(df),), dtype=bool)
+    df['test_mask'] = np.zeros((len(df),), dtype=bool)
     df = dd.from_pandas(df, npartitions=10)
     def train_test_split(df):
         df = df.sort_values([timestamp])

python/dgl/backend/mxnet/tensor.py

@@ -28,13 +28,13 @@ def data_type_dict():
             'int16'   : np.int16,
             'int32'   : np.int32,
             'int64'   : np.int64,
-            'bool'    : np.bool}  # mxnet does not support bool
+            'bool'    : bool}  # mxnet does not support bool
 
 def cpu():
     return mx.cpu()
 
 def tensor(data, dtype=None):
-    if dtype == np.bool:
+    if dtype == bool:
         # mxnet doesn't support bool
         dtype =  np.int32
     if isinstance(data, nd.NDArray):
@@ -47,7 +47,7 @@ def tensor(data, dtype=None):
             data = [data]
         if dtype is None:
             if isinstance(data, np.ndarray):
-                dtype = np.int32 if data.dtype == np.bool else data.dtype
+                dtype = np.int32 if data.dtype == bool else data.dtype
             elif len(data) == 0:
                 dtype = np.int64
             else:
@@ -133,7 +133,7 @@ def to_backend_ctx(dglctx):
         raise ValueError('Unsupported DGL device context:', dglctx)
 
 def astype(input, ty):
-    if ty == np.bool:
+    if ty == bool:
         ty = np.int32
     return input.astype(ty)
 

python/dgl/data/fakenews.py

@@ -151,9 +151,9 @@ class FakeNewsDataset(DGLBuiltinDataset):
         train_idx = np.load(os.path.join(self.raw_path, 'train_idx.npy'))
         val_idx = np.load(os.path.join(self.raw_path, 'val_idx.npy'))
         test_idx = np.load(os.path.join(self.raw_path, 'test_idx.npy'))
-        train_mask = np.zeros(num_graphs, dtype=np.bool)
-        val_mask = np.zeros(num_graphs, dtype=np.bool)
-        test_mask = np.zeros(num_graphs, dtype=np.bool)
+        train_mask = np.zeros(num_graphs, dtype=bool)
+        val_mask = np.zeros(num_graphs, dtype=bool)
+        test_mask = np.zeros(num_graphs, dtype=bool)
         train_mask[train_idx] = True
         val_mask[val_idx] = True
         test_mask[test_idx] = True

python/dgl/data/fraud.py

@@ -203,9 +203,9 @@ class FraudDataset(DGLBuiltinDataset):
         train_idx = index[:int(train_size * len(index))]
         val_idx = index[len(index) - int(val_size * len(index)):]
         test_idx = index[int(train_size * len(index)):len(index) - int(val_size * len(index))]
-        train_mask = np.zeros(N, dtype=np.bool)
-        val_mask = np.zeros(N, dtype=np.bool)
-        test_mask = np.zeros(N, dtype=np.bool)
+        train_mask = np.zeros(N, dtype=bool)
+        val_mask = np.zeros(N, dtype=bool)
+        test_mask = np.zeros(N, dtype=bool)
         train_mask[train_idx] = True
         val_mask[val_idx] = True
         test_mask[test_idx] = True

python/dgl/data/qm9.py

@@ -182,7 +182,7 @@ class QM9Dataset(DGLDataset):
         n_atoms = self.N[idx]
         R = self.R[self.N_cumsum[idx]:self.N_cumsum[idx + 1]]
         dist = np.linalg.norm(R[:, None, :] - R[None, :, :], axis=-1)
-        adj = sp.csr_matrix(dist <= self.cutoff) - sp.eye(n_atoms, dtype=np.bool)
+        adj = sp.csr_matrix(dist <= self.cutoff) - sp.eye(n_atoms, dtype=bool)
         adj = adj.tocoo()
         u, v = F.tensor(adj.row), F.tensor(adj.col)
         g = dgl_graph((u, v))