Initial draft

tensorly/decomposition/_cp.py

@@ -102,23 +102,7 @@ def initialize_cp(
     elif isinstance(init, (tuple, list, CPTensor)):
         # TODO: Test this
         try:
-            if normalize_factors is True:
-                warnings.warn(
-                    "It is not recommended to initialize a tensor with normalizing. Consider normalizing the tensor before using this function"
-                )
-
             kt = CPTensor(init)
-            weights, factors = kt
-
-            if tl.all(weights == 1):
-                kt = CPTensor((None, factors))
-            else:
-                weights_avg = tl.prod(weights) ** (1.0 / tl.shape(weights)[0])
-                for i in range(len(factors)):
-                    factors[i] = factors[i] * weights_avg
-                kt = CPTensor((None, factors))
-
-            return kt
         except ValueError:
             raise ValueError(
                 "If initialization method is a mapping, then it must "
@@ -242,7 +226,6 @@ def parafac(
     sparsity=None,
     l2_reg=0,
     mask=None,
-    cvg_criterion="abs_rec_error",
     fixed_modes=None,
     svd_mask_repeats=5,
     linesearch=False,
@@ -282,10 +265,6 @@ def parafac(
         the values are missing and 1 everywhere else. Note:  if tensor is
         sparse, then mask should also be sparse with a fill value of 1 (or
         True). Allows for missing values [2]_
-    cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
-       Stopping criterion for ALS, works if `tol` is not None.
-       If 'rec_error',  ALS stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
-       If 'abs_rec_error', ALS terminates when `|previous rec_error - current rec_error| < tol`.
     sparsity : float or int
         If `sparsity` is not None, we approximate tensor as a sum of low_rank_component and sparse_component, where low_rank_component = cp_to_tensor((weights, factors)). `sparsity` denotes desired fraction or number of non-zero elements in the sparse_component of the `tensor`.
     fixed_modes : list, default is None
@@ -511,14 +490,7 @@ def parafac(
                         f"iteration {iteration}, reconstruction error: {rec_error}, decrease = {rec_error_decrease}, unnormalized = {unnorml_rec_error}"
                     )
 
-                if cvg_criterion == "abs_rec_error":
-                    stop_flag = tl.abs(rec_error_decrease) < tol
-                elif cvg_criterion == "rec_error":
-                    stop_flag = rec_error_decrease < tol
-                else:
-                    raise TypeError("Unknown convergence criterion")
-
-                if stop_flag:
+                if rec_error_decrease < tol:
                     if verbose:
                         print(f"PARAFAC converged after {iteration} iterations")
                     break
@@ -796,10 +768,6 @@ class CP(DecompositionMixin):
         the values are missing and 1 everywhere else. Note:  if tensor is
         sparse, then mask should also be sparse with a fill value of 1 (or
         True). Allows for missing values [2]_
-    cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
-        Stopping criterion for ALS, works if `tol` is not None.
-        If 'rec_error',  ALS stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
-        If 'abs_rec_error', ALS terminates when `|previous rec_error - current rec_error| < tol`.
     sparsity : float or int
         If `sparsity` is not None, we approximate tensor as a sum of low_rank_component and sparse_component, where low_rank_component = cp_to_tensor((weights, factors)). `sparsity` denotes desired fraction or number of non-zero elements in the sparse_component of the `tensor`.
     fixed_modes : list, default is None
@@ -850,7 +818,6 @@ def __init__(
         sparsity=None,
         l2_reg=0,
         mask=None,
-        cvg_criterion="abs_rec_error",
         fixed_modes=None,
         svd_mask_repeats=5,
         linesearch=False,
@@ -868,7 +835,6 @@ def __init__(
         self.sparsity = sparsity
         self.l2_reg = l2_reg
         self.mask = mask
-        self.cvg_criterion = cvg_criterion
         self.fixed_modes = fixed_modes
         self.svd_mask_repeats = svd_mask_repeats
         self.linesearch = linesearch
@@ -901,7 +867,6 @@ def fit_transform(self, tensor):
             sparsity=self.sparsity,
             l2_reg=self.l2_reg,
             mask=self.mask,
-            cvg_criterion=self.cvg_criterion,
             fixed_modes=self.fixed_modes,
             svd_mask_repeats=self.svd_mask_repeats,
             linesearch=self.linesearch,