Adding some warnings

nn_fac/nmf.py

@@ -7,13 +7,15 @@ Created on Tue Jun 11 15:49:25 2019
 
 import numpy as np
 import time
+import math
+import warnings
+
 import nn_fac.update_rules.nnls as nnls
 import nn_fac.update_rules.mu as mu
 import nn_fac.utils.beta_divergence as beta_div
 import nn_fac.utils.errors as err
-from nimfa.methods import seeding
-import math
 
+from nimfa.methods import seeding
 
 def nmf(data, rank, init = "random", U_0 = None, V_0 = None, n_iter_max=100, tol=1e-8,
         update_rule = "hals", beta = 2,
@@ -171,6 +173,11 @@ def nmf(data, rank, init = "random", U_0 = None, V_0 = None, n_iter_max=100, tol
     Learning the parts of objects by non-negative matrix factorization.
     Nature, 401(6755), 788-791.
     """
+    if min(data.shape) < rank:
+        min_data = min(data.shape)
+        rank = min_data
+        warnings.warn(f"The rank is too high for the input matrix. It was set to {min_data} instead.")
+
     if init.lower() == "random":
         k, n = data.shape
         if deterministic:
@@ -182,6 +189,8 @@ def nmf(data, rank, init = "random", U_0 = None, V_0 = None, n_iter_max=100, tol
             V_0 = np.random.rand(rank, n)
 
     elif init.lower() == "nndsvd":
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore") # A warning arises from the nimfa toolbox, because of the sue of np.asmatrix.
             U_0, V_0 = seeding.Nndsvd().initialize(data, rank, {'flag': 0})
         U_0 = np.array(U_0 + 1e-12)
         V_0 = np.array(V_0 + 1e-12)

nn_fac/ntf.py

@@ -8,6 +8,8 @@ Created on Tue Jun 11 16:52:21 2019
 import numpy as np
 import time
 import tensorly as tl
+import warnings
+
 from nimfa.methods import seeding
 
 import nn_fac.update_rules.nnls as nnls
@@ -193,6 +195,8 @@ def ntf(tensor, rank, init = "random", factors_0 = [], n_iter_max=100, tol=1e-8,
             if tensor.shape[mode] < rank:
                 current_factor = np.random.rand(tensor.shape[mode], rank)
             else:
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore") # A warning arises from the nimfa toolbox, because of the sue of np.asmatrix.
                     current_factor, useless_variable = seeding.Nndsvd().initialize(tl.unfold(tensor, mode), rank, {'flag': 0})
             factors.append(tl.tensor(current_factor))
 

nn_fac/parafac2.py

@@ -5,6 +5,7 @@ Created on Tue Jun 11 17:12:33 2019
 @author: amarmore
 """
 
+import warnings
 import numpy as np
 import time
 import nn_fac.update_rules.nnls as nnls
@@ -194,6 +195,8 @@ def parafac_2(tensor_slices, rank, init_with_P, init = "random", W_list_in = Non
 
     elif init.lower() == "nndsvd":
         for k in range(nb_channel):
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore") # A warning arises from the nimfa toolbox, because of the sue of np.asmatrix.
                 W_k, H = seeding.Nndsvd().initialize(tensor_slices[k], rank, {'flag': 0})
             W_list.append(W_k)
             D_list.append(np.diag(np.random.rand(rank)))