update

bin/notears_nonlinear

@@ -1,10 +1,14 @@
 #!/usr/bin/env python3
 from notears import nonlinear, utils
+import torch
 import numpy as np
 import argparse
 
 
 def main(args):
+ torch.set_default_dtype(torch.double)
+ np.set_printoptions(precision=3)
+
  X = np.loadtxt(args.X_path, delimiter=',')
  n, d = X.shape
  model = nonlinear.NotearsMLP(dims=[d, args.hidden, 1], bias=True)

src/lbfgsb_scipy.py → notears/lbfgsb_scipy.py

@@ -3,7 +3,11 @@
 
 
 class LBFGSBScipy(torch.optim.Optimizer):
- """Wrap L-BFGS-B algorithm, using scipy routines."""
+ """Wrap L-BFGS-B algorithm, using scipy routines.
+ 
+ Courtesy: Arthur Mensch's gist
+ https://gist.github.com/arthurmensch/c55ac413868550f89225a0b9212aa4cd
+ """
 
  def __init__(self, params):
  defaults = dict()
@@ -81,7 +85,7 @@ def wrapped_closure(flat_params):
 
  bounds = self._gather_flat_bounds()
 
- # How can it work without getting the final solution..?
+ # Magic
  sol = sopt.minimize(wrapped_closure,
  initial_params,
  method='L-BFGS-B',

src/linear.py → notears/linear.py

@@ -84,7 +84,7 @@ def _func(w):
 
 
 if __name__ == '__main__':
- import src.utils as ut
+ import notears.utils as ut
  ut.set_random_seed(1)
 
  n, d, s0, graph_type, sem_type = 100, 20, 20, 'ER', 'gauss'

src/nonlinear.py → notears/nonlinear.py

@@ -1,5 +1,5 @@
-from src.locally_connected import LocallyConnected
-from src.lbfgsb_scipy import LBFGSBScipy
+from notears.locally_connected import LocallyConnected
+from notears.lbfgsb_scipy import LBFGSBScipy
 import torch
 import torch.nn as nn
 import numpy as np
@@ -211,9 +211,8 @@ def main():
  torch.set_default_dtype(torch.double)
  np.set_printoptions(precision=3)
 
- import src.utils as ut
+ import notears.utils as ut
  ut.set_random_seed(123)
- torch.manual_seed(123)
 
  n, d, s0, graph_type, sem_type = 200, 5, 9, 'ER', 'mim'
  B_true = ut.simulate_dag(d, s0, graph_type)

setup.py

@@ -20,6 +20,6 @@
  scripts=['bin/notears_linear',
  'bin/notears_nonlinear'],
  packages=['notears'],
- package_dir={'notears': 'src'},
+ package_dir={'notears': 'notears'},
  install_requires=['numpy', 'scipy', 'python-igraph'],
 )