Merge pull request #21 from KrishnaswamyLab/dev

Fix issue #20

Python/phate/mds.py

@@ -94,13 +94,15 @@ def embed_MDS(X, ndim=2, how='metric', distance_metric='euclidean', n_jobs=1, se
         Y_cmds = cmdscale_fast(X_dist, ndim)
         # Then compute Metric MDS
         Y_mmds = MDS(n_components=ndim, metric=True, max_iter=3000, eps=1e-12,
-                     dissimilarity="precomputed", random_state=seed, n_jobs=n_jobs,
-                     n_init=1).fit_transform(X_dist, init=Y_cmds)
+                     dissimilarity="precomputed", random_state=seed,
+                     n_jobs=n_jobs, n_init=1).fit_transform(X_dist,
+                                                            init=Y_cmds)
         # Nonmetric MDS from sklearn using metric MDS as an initialization
         Y = MDS(n_components=ndim, metric=False, max_iter=3000, eps=1e-12,
                 dissimilarity="precomputed", random_state=seed, n_jobs=n_jobs,
                 n_init=1).fit_transform(X_dist, init=Y_mmds)
     else:
-        raise ValueError(
-            "Allowable 'how' values for MDS: 'classic', 'metric', or 'nonmetric'. '%s' was passed." % (how))
+        raise ValueError("Allowable 'how' values for MDS: 'classic', "
+                         "'metric', or 'nonmetric'. "
+                         "'{}' was passed.".format(how))
     return Y

Python/phate/phate.py

@@ -94,7 +94,7 @@ def calculate_kernel(data, k=15, a=10, alpha_decay=True, knn_dist='euclidean',
                       random_state=random_state)
             data = pca.fit_transform(data)
             if verbose:
-                print("PCA complete in {:.2d} seconds".format(
+                print("PCA complete in {:.2f} seconds".format(
                     time.time() - start))
     if verbose:
         start = time.time()
@@ -132,7 +132,7 @@ def calculate_kernel(data, k=15, a=10, alpha_decay=True, knn_dist='euclidean',
             kernel = knn.kneighbors_graph(data, mode='connectivity')
 
     if verbose:
-        print("KNN complete in {:.2d} seconds".format(time.time() - start))
+        print("KNN complete in {:.2f} seconds".format(time.time() - start))
     kernel = kernel + kernel.T  # symmetrization
     return kernel
 
@@ -184,7 +184,7 @@ def calculate_landmark_operator(kernel, n_landmark=2000,
                                  n_components=n_svd,
                                  random_state=random_state)
         if verbose:
-            print("SVD complete in {:.2d} seconds".format(time.time() - start))
+            print("SVD complete in {:.2f} seconds".format(time.time() - start))
             start = time.time()
             print("Calculating Kmeans...")
         kmeans = MiniBatchKMeans(n_landmark,
@@ -306,8 +306,8 @@ def calculate_operator(data, k=15, a=10, alpha_decay=True, n_landmark=2000,
             kernel, n_landmark=n_landmark,
             random_state=random_state, verbose=verbose)
         if verbose:
-            print("Built graph and diffusion operator in %.2f seconds." %
-                  (time.time() - tic))
+            print("Built graph and diffusion operator in "
+                  "{:.2f} seconds.".format(time.time() - tic))
     else:
         if verbose:
             print("Using precomputed diffusion operator...")
@@ -395,8 +395,8 @@ def embed_mds(diff_op, t=30, n_components=2, diff_potential=None,
                              "'sqrt'. '%s' was passed." % (potential_method))
 
         if verbose:
-            print("Calculated diffusion potential in %.2f seconds." %
-                  (time.time() - tic))
+            print("Calculated diffusion potential in "
+                  "{:.2f} seconds.".format(time.time() - tic))
     # if diffusion potential is precomputed (i.e. 'mds' or 'mds_dist' has
     # changed on PHATE object)
     else:
@@ -405,7 +405,7 @@ def embed_mds(diff_op, t=30, n_components=2, diff_potential=None,
 
     tic = time.time()
     if verbose:
-        print("Embedding data using %s MDS..." % (mds))
+        print("Embedding data using {} MDS...".format(mds))
     if embedding is None:
         embedding = embed_MDS(diff_potential, ndim=n_components, how=mds,
                               distance_metric=mds_dist, n_jobs=n_jobs,
@@ -414,7 +414,7 @@ def embed_mds(diff_op, t=30, n_components=2, diff_potential=None,
             # return to ambient space
             embedding = landmark_transitions.dot(embedding)
         if verbose:
-            print("Embedded data in %.2f seconds." % (time.time() - tic))
+            print("Embedded data in {:.2f} seconds.".format(time.time() - tic))
     else:
         if verbose:
             print("Using precomputed embedding...")
@@ -747,8 +747,8 @@ def fit_transform(self, X, **kwargs):
         self.fit(X)
         self.transform(**kwargs)
         if self.verbose:
-            print("Finished PHATE embedding in %.2f seconds.\n" %
-                  (time.time() - start))
+            print("Finished PHATE embedding in {:.2f} seconds.\n".format(
+                time.time() - start))
         return self.embedding
 
     def von_neumann_entropy(self, t_max=100):

Python/phate/version.py

@@ -1 +1 @@
-__version__ = "0.2.2"
+__version__ = "0.2.3"

Python/setup.py

@@ -44,6 +44,7 @@
                 'big-data',
                 'dimensionality-reduction',
                 'embedding',
+                'manifold-learning',
                 'computational-biology'],
       classifiers=[
           'Development Status :: 5 - Production/Stable',