change convergence conditions

Author: ddbourgin

numpy_ml/factorization/factors.py

@@ -140,20 +140,18 @@ def fit(self, X, W=None, H=None, n_initializations=10, verbose=False):
 
  def _fit(self, X, W, H, verbose):
  self._init_factor_matrices(X, W, H)
- prev_loss = loss = np.inf
  W, H = self.W, self.H
 
  for i in range(self.max_iter):
- prev_loss = loss
  W = self._update_factor(X, H.T)
  H = self._update_factor(X.T, W).T
 
  loss = self._loss(X, W @ H)
 
  if verbose:
- print("[Iter {}] Loss: {:.6f}".format(i + 1, loss))
+ print("[Iter {}] Loss: {:.8f}".format(i + 1, loss))
 
- if (prev_loss - loss) <= self.tol:
+ if loss <= self.tol:
  break
 
  return W, H, loss
@@ -252,8 +250,7 @@ def _loss(self, X, Xhat):
 
  def _update_H(self, X, W, H):
  """Perform the fast HALS update for H"""
- # eps = np.finfo(float).eps
- eps = 1e-16
+ eps = np.finfo(float).eps
  XtW = X.T @ W # dim: (M, K)
  WtW = W.T @ W # dim: (K, K)
 
@@ -264,7 +261,7 @@ def _update_H(self, X, W, H):
 
  def _update_W(self, X, W, H):
  """Perform the fast HALS update for W"""
- eps = 1e-16 # np.finfo(float).eps
+ eps = np.finfo(float).eps
  XHt = X @ H.T # dim: (N, K)
  HHt = H @ H.T # dim: (K, K)
 
@@ -360,16 +357,14 @@ def _fit(self, X, W, H, verbose):
  self._init_factor_matrices(X, W, H)
 
  W, H = self.W, self.H
- prev_loss = loss = np.inf
  for i in range(self.max_iter):
- prev_loss = loss
  H = self._update_H(X, W, H)
  W = self._update_W(X, W, H)
  loss = self._loss(X, W @ H)
 
  if verbose:
- print("[Iter {}] Loss: {:.4f}".format(i + 1, loss))
+ print("[Iter {}] Loss: {:.8f}".format(i + 1, loss))
 
- if (prev_loss - loss) <= self.tol:
+ if loss <= self.tol:
  break
  return W, H, loss