repo_description: A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
repo_topics: convolutional-neural-networks deep-learning deepwalk gcn gpt2 gpt3 graph-convolution graph-convolutional-network graph-neural-networks graph-representation graphsage gwnn iclr machine-learning node2vec pytorch research sklearn tensorflow transformer
code: """Parsing the parameters."""
  
  import argparse
  
  def parameter_parser():
  
      parser = argparse.ArgumentParser(description="Run GWNN.")
  
      parser.add_argument("--edge-path",
                          nargs="?",
                          default="./input/cora_edges.csv",
  	                help="Edge list csv.")
  
      parser.add_argument("--features-path",
                          nargs="?",
                          default="./input/cora_features.json",
  	                help="Feature json.")
  
      parser.add_argument("--target-path",
                          nargs="?",
                          default="./input/cora_target.csv",
  	                help="Target classes csv.")
  
      parser.add_argument("--log-path",
                          nargs="?",
                          default="./logs/cora_logs.json",
  	                help="Log json.")
  
      parser.add_argument("--epochs",
                          type=int,
                          default=200,
  	                help="Number of training epochs. Default is 200.")
  
      parser.add_argument("--filters",
                          type=int,
                          default=32,
  	                help="Filters (neurons) in convolution. Default is 32.")
  
      parser.add_argument("--approximation-order",
                          type=int,
                          default=3,
  	                help="Order of Chebyshev polynomial. Default is 3.")
  
      parser.add_argument("--test-size",
                          type=float,
                          default=0.2,
  	                help="Ratio of training samples. Default is 0.2.")
  
      parser.add_argument("--dropout",
                          type=float,
                          default=0.5,
  	                help="Dropout probability. Default is 0.5.")
  
      parser.add_argument("--seed",
                          type=int,
                          default=42,
  	                help="Random seed for sklearn pre-training. Default is 42.")
  
      parser.add_argument("--tolerance",
                          type = float,
                          default=10**-4,
  	                help="Sparsification parameter. Default is 10^-4.")
  
      parser.add_argument("--scale",
                          type=float,
                          default=1.0,
  	                help="Heat kernel scale length. Default is 1.0.")
  
      parser.add_argument("--learning-rate",
                          type=float,
                          default=0.01,
  	                help="Learning rate. Default is 0.01.")
  
      parser.add_argument("--weight-decay",
                          type=float,
                          default=10**-5,
  	                help="Adam weight decay. Default is 10^-5.")
  
      return parser.parse_args()
  