minor clean-up of ngclearn.utils.viz.dim_reduce

Author: Alexander Ororbia

ngclearn/utils/viz/dim_reduce.py

@@ -3,8 +3,8 @@
 default_cmap = plt.cm.jet
 
 import numpy as np
-from sklearn.decomposition import IncrementalPCA
-from sklearn.manifold import TSNE
+from sklearn.decomposition import IncrementalPCA ## sci-kit learning dependency
+from sklearn.manifold import TSNE ## sci-kit learning dependency
 
 def extract_pca_latents(vectors): ## PCA mapping routine
     """
@@ -20,7 +20,6 @@ def extract_pca_latents(vectors): ## PCA mapping routine
     """
     batch_size = 50
     z_dim = vectors.shape[1]
-    z_2D = None
     if z_dim != 2:
         ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
         ipca.fit(vectors)
@@ -31,26 +30,25 @@ def extract_pca_latents(vectors): ## PCA mapping routine
 
 def extract_tsne_latents(vectors, perplexity=30, n_pca_comp=32, batch_size=500): ## tSNE mapping routine
     """
-    Projects collection of K vectors (stored in a matrix) to a two-dimensional (2D)
-    visualization space via the t-distributed stochastic neighbor embedding
-    algorithm (t-SNE). This algorithm also uses PCA to produce an
-    intermediate project to speed up the t-SNE final mapping step. Note that
-    if the input already has a 2D dimensionality, the original input is returned.
+    Projects collection of K vectors (stored in a matrix) to a two-dimensional (2D) visualization space via the
+    t-distributed stochastic neighbor embedding algorithm (t-SNE). This algorithm also uses PCA to produce an
+    intermediate project to speed up the t-SNE final mapping step. Note that if the input already has a 2D
+    dimensionality, the original input is returned.
 
     Args:
         vectors: a matrix/codebook of (K x D) vectors to project
 
         perplexity: the perplexity control factor for t-SNE (Default: 30)
 
-        batch_size: number of sampled embedding vectors to use per iteration 
-            of online internal PCA
+        n_pca_comp: number of PCA top components (sorted by eigen-values) to retain/extract before continuing
+            with t-SNE dimensionality reduction
+
+        batch_size: number of sampled embedding vectors to use per iteration of online internal PCA
 
     Returns:
         a matrix (K x 2) of projected vectors (to 2D space)
     """
-    #batch_size = 500 #50
     z_dim = vectors.shape[1]
-    z_2D = None
     if z_dim != 2:
         print(" > Projecting latents via iPCA...")
         n_comp = n_pca_comp #32 #10 #16 #50
@@ -69,11 +67,10 @@ def extract_tsne_latents(vectors, perplexity=30, n_pca_comp=32, batch_size=500):
         z_2D = vectors
     return z_2D
 
-def plot_latents(code_vectors, labels, plot_fname="2Dcode_plot.jpg", alpha=1., 
-                 cmap=None):
+def plot_latents(code_vectors, labels, plot_fname="2Dcode_plot.jpg", alpha=1., cmap=None):
     """
-    Produces a label-overlaid (label map to distinct colors) scatterplot for
-    visualizing two-dimensional latent codes (produced by either PCA or t-SNE).
+    Produces a label-overlaid (label map to distinct colors) scatterplot for visualizing two-dimensional latent codes
+    (produced by either PCA or t-SNE).
 
     Args:
         code_vectors: a matrix of shape (K x 2) with vectors to plot/visualize
@@ -92,8 +89,7 @@ def plot_latents(code_vectors, labels, plot_fname="2Dcode_plot.jpg", alpha=1.,
     matplotlib.use('Agg') ## temporarily go in Agg plt backend for tsne plotting
     print(" > Plotting 2D latent encodings...")
     curr_backend = plt.rcParams["backend"]
-    matplotlib.use(
-        'Agg')  ## temporarily go in Agg plt backend for tsne plotting
+    matplotlib.use('Agg')  ## temporarily go in Agg plt backend for tsne plotting
     lab = labels
     if lab.shape[1] > 1: ## extract integer class labels from a one-hot matrix
         lab = np.argmax(lab, 1)