Removing absolute file path

Author: akashshah59

examples/dcrnn/main.py

@@ -1,7 +1,6 @@
 import time
-import os
-import numpy as np
 
+import numpy as np
 import torch.optim
 
 from torchts.nn.models.dcrnn import DCRNN
@@ -11,40 +10,53 @@
     "cl_decay_steps": 2000,
     "filter_type": "dual_random_walk",
     "horizon": 12,
-    "seq_len":12,
+    "seq_len": 12,
     "input_dim": 2,
-    "max_diffusion_step": 2, 
+    "max_diffusion_step": 2,
     "num_layers": 2,
     "output_dim": 2,
     "use_curriculum_learning": True,
 }
 
-optimizer_args = {
-        'lr':0.01
-        }
+optimizer_args = {"lr": 0.01}
 
 # Code to retrieve the graph in the form of an adjacency matrix.
 # This corresponds to the distance between 2 traffic sensors in a traffic network.
 # For other applications it can mean anything that defines the adjacency between nodes
 # eg. distance between airports of different cities when predicting
 # covid infection rate.
 
-graph_pkl_filename = "/home/akash/Desktop/multi-gpu/newest_adj_max.pkl" # Absolute path of graph expected.
+graph_pkl_filename = "<Path to graph>"
 
-_,_, adj_mx = utils.load_graph_data(graph_pkl_filename)
+_, _, adj_mx = utils.load_graph_data(graph_pkl_filename)
 
 num_units = adj_mx.shape[0]
 
-model_config['num_nodes'] = num_units
+model_config["num_nodes"] = num_units
+
+data = np.load(
+    "<Path to training *.npz file>"
+)  # Absolute path of train, test, val needed.
 
-data = np.load("/home/akash/Desktop/multi-gpu/train.npz") # Absolute path of train, test, val needed. 
 
 def run():
-    model = DCRNN(adj_mx,num_units,optimizer = torch.optim.SGD,optimizer_args = optimizer_args,**model_config)
+    model = DCRNN(
+        adj_mx,
+        num_units,
+        optimizer=torch.optim.SGD,
+        optimizer_args=optimizer_args,
+        **model_config
+    )
     start = time.time()
-    model.fit(torch.from_numpy(data['x'].astype('float32')),torch.from_numpy(data['y'].astype('float32')),max_epochs = 10,batch_size = 8)
+    model.fit(
+        torch.from_numpy(data["x"].astype("float32")),
+        torch.from_numpy(data["y"].astype("float32")),
+        max_epochs=10,
+        batch_size=8,
+    )
     end = time.time() - start
     print("Training time taken %f" % (end - start))
 
+
 if __name__ == "__main__":
     run()