Replaced missing deprecated "np.mat()" with "np.asmatrix()"

I noticed several 'np.mat()' instances were still present in the package and limited fix mims-harvard#66 . As such, issue mims-harvard#65 was still present.

With this update, I've ensured all instances of 'np.mat()' have been replaced to 'np.asmatrix()'. To do so, I've the following shell command:

find * -type f -exec sed -i 's/np.mat(/np.asmatrix(/g' {} \;

This should fix several broken functions, hopefully making Nimfa compatible with numpy v.2.0.0+. Resolves issue mims-harvard#65 , building on mims-harvard#66 .

Author: feirog

nimfa/examples/gene_func_prediction.py

@@ -162,8 +162,8 @@ class information exploiting properties of Functional Catalogue hierarchy.
             if line_type == "@DATA":
                 section = 'd'
                 idxs = set(range(idx)).intersection(used_idx)
-                attr_data = np.mat(np.zeros((1e4, len(attr2idx))))
-                class_data = np.mat(np.zeros((1e4, len(class2idx))))
+                attr_data = np.asmatrix(np.zeros((1e4, len(attr2idx))))
+                class_data = np.asmatrix(np.zeros((1e4, len(class2idx))))
         elif section == 'd':
             d, _, comment = line.strip().partition("%")
             values = d.split(",")
@@ -287,10 +287,10 @@ def compute_correlations(train, test):
     # alternative, it is time consuming - can be used for partial evaluation
     """corrs = {}
     for i in xrange(test['W'].shape[0]):
-        corrs.setdefault(i, np.mat(np.zeros((train['W'].shape[0], 1))))
+        corrs.setdefault(i, np.asmatrix(np.zeros((train['W'].shape[0], 1))))
         for j in xrange(train['W'].shape[0]):
             corrs[i][j, 0] = _corr(test['W'][i, :], train['W'][j, :])"""
-    return np.mat(corrs)
+    return np.asmatrix(corrs)
 
 
 def _corr(x, y):

nimfa/examples/orl_images.py

@@ -159,7 +159,7 @@ def read():
             im = open(join(dir + str(subject + 1), str(image + 1) + ".pgm"))
             # reduce the size of the image
             im = im.resize((46, 56))
-            V[:, 10 * subject + image] = np.mat(np.asarray(im).flatten()).T
+            V[:, 10 * subject + image] = np.asmatrix(np.asarray(im).flatten()).T
     return V
 
 
@@ -174,9 +174,9 @@ def preprocess(V):
     """
     print("Data preprocessing")
     min_val = V.min(axis=0)
-    V = V - np.mat(np.ones((V.shape[0], 1))) * min_val
+    V = V - np.asmatrix(np.ones((V.shape[0], 1))) * min_val
     max_val = V.max(axis=0) + 1e-4
-    V = (255. * V) / (np.mat(np.ones((V.shape[0], 1))) * max_val) / 100.
+    V = (255. * V) / (np.asmatrix(np.ones((V.shape[0], 1))) * max_val) / 100.
     return V
 
 

nimfa/examples/synthetic.py

@@ -115,7 +115,7 @@ def run_bd(V):
     rank = 10
     bd = nimfa.Bd(V, seed="random_c", rank=rank, max_iter=12, alpha=np.zeros((V.shape[0], rank)),
                   beta=np.zeros((rank, V.shape[1])), theta=.0, k=.0, sigma=1., skip=100,
-                  stride=1, n_w=np.mat(np.zeros((rank, 1))), n_h=np.mat(np.zeros((rank, 1))),
+                  stride=1, n_w=np.asmatrix(np.zeros((rank, 1))), n_h=np.asmatrix(np.zeros((rank, 1))),
                   n_sigma=False)
     fit = bd()
     print_info(fit)

nimfa/methods/factorization/bd.py

@@ -187,10 +187,10 @@ def __init__(self, V, seed=None, W=None, H=None, rank=30, max_iter=30,
         nmf_std.Nmf_std.__init__(self, vars())
         if self.alpha is None:
             self.alpha = sp.csr_matrix((self.V.shape[0], self.rank))
-        self.alpha = self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.mat(self.alpha)
+        self.alpha = self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.asmatrix(self.alpha)
         if self.beta is None:
             self.beta = sp.csr_matrix((self.rank, self.V.shape[1]))
-        self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.mat(self.beta)
+        self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.asmatrix(self.beta)
         if self.n_w is None:
             self.n_w = np.zeros((self.rank, 1))
         if self.n_h is None:

nimfa/methods/factorization/icm.py

@@ -162,10 +162,10 @@ def __init__(self, V, seed=None, W=None, H=None, H1=None,
         nmf_std.Nmf_std.__init__(self, vars())
         if self.alpha is None:
             self.alpha = sp.rand(self.V.shape[0], self.rank, density=0.8, format='csr')
-        self.alpha= self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.mat(self.alpha)
+        self.alpha= self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.asmatrix(self.alpha)
         if self.beta is None:
             self.beta = sp.rand(self.rank, self.V.shape[1], density=0.8, format='csr')
-        self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.mat(self.beta)
+        self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.asmatrix(self.beta)
         self.tracker = mf_track.Mf_track() if self.track_factor and self.n_run > 1 \
                                               or self.track_error else None
 

nimfa/methods/factorization/lfnmf.py

@@ -157,8 +157,8 @@ def factorize(self):
         for run in range(self.n_run):
             self.W, self.H = self.seed.initialize(
                 self.V, self.rank, self.options)
-            self.Sw, self.Sb = np.mat(
-                np.zeros((1, 1))), np.mat(np.zeros((1, 1)))
+            self.Sw, self.Sb = np.asmatrix(
+                np.zeros((1, 1))), np.asmatrix(np.zeros((1, 1)))
             p_obj = c_obj = sys.float_info.max
             best_obj = c_obj if run == 0 else best_obj
             iter = 0
@@ -244,7 +244,7 @@ def update(self):
         # update within class scatter and between class
         self.Sw = sum(sum(dot(self.H[:, c2m[i][j]] - avgs[i], (self.H[:, c2m[i][j]] - avgs[i]).T)
                           for j in range(len(c2m[i]))) for i in c2m)
-        avgs_t = np.mat(np.zeros((self.rank, 1)))
+        avgs_t = np.asmatrix(np.zeros((self.rank, 1)))
         for k in avgs:
             avgs_t += avgs[k]
         avgs_t /= len(avgs)
@@ -259,7 +259,7 @@ def _encoding(self, idxH):
             c2m.setdefault(idxH[0, i], [])
             c2m[idxH[0, i]].append(i)
             # compute mean value of class idx in encoding matrix H
-            avgs.setdefault(idxH[0, i], np.mat(np.zeros((self.rank, 1))))
+            avgs.setdefault(idxH[0, i], np.asmatrix(np.zeros((self.rank, 1))))
             avgs[idxH[0, i]] += self.H[:, i]
         for k in avgs:
             avgs[k] /= len(c2m[k])

nimfa/methods/factorization/nmf.py

@@ -303,7 +303,7 @@ def conn(self):
         cons = elop(mat1, mat2, eq)
         if not hasattr(self, 'consold'):
             self.cons = cons
-            self.consold = np.mat(np.logical_not(cons))
+            self.consold = np.asmatrix(np.logical_not(cons))
         else:
             self.consold = self.cons
             self.cons = cons

nimfa/methods/factorization/pmfcc.py

@@ -133,7 +133,7 @@ def __init__(self, V, seed=None, W=None, H=None, rank=30, max_iter=30,
         self.aseeds = ["random", "fixed", "nndsvd", "random_c", "random_vcol"]
         smf.Smf.__init__(self, vars())
         if not self.Theta:
-            self.Theta = np.mat(np.zeros((self.V.shape[1], self.V.shape[1])))
+            self.Theta = np.asmatrix(np.zeros((self.V.shape[1], self.V.shape[1])))
         self.tracker = mf_track.Mf_track() if self.track_factor and self.n_run > 1 \
                                               or self.track_error else None
 

nimfa/methods/factorization/psmf.py

@@ -188,8 +188,8 @@ def factorize(self):
                 self.H = self.V.__class__(
                     (self.rank, self.V.shape[1]), dtype='d')
             else:
-                self.W = np.mat(np.zeros((self.V.shape[0], self.rank)))
-                self.H = np.mat(np.zeros((self.rank, self.V.shape[1])))
+                self.W = np.asmatrix(np.zeros((self.V.shape[0], self.rank)))
+                self.H = np.asmatrix(np.zeros((self.rank, self.V.shape[1])))
             self.s = np.zeros((self.V.shape[0], self.N), int)
             self.r = np.zeros((self.V.shape[0], 1), int)
             self.psi = np.array(std(self.V, axis=1, ddof=0))

nimfa/methods/factorization/sepnmf.py

@@ -318,7 +318,7 @@ def norm_axis(X, p=None, axis=None):
     if sp.isspmatrix(X):
         return sla.norm(X, ord=p, axis=axis)
     else:
-        n = nla.norm(np.mat(X), ord=p, axis=axis)
+        n = nla.norm(np.asmatrix(X), ord=p, axis=axis)
         if axis is None:
             return n
         else: