update demo files

demo/basic_demo.py

@@ -12,9 +12,9 @@
 # Initialize the pacmap instance
 # By default, the n_neighbors is set to 10.
 # Setting n_neighbors to "None" can enable an automatic parameter selection
-# choice shown in "parameter" section of the README file. 
+# choice shown in "parameter" section of the README file.
 # Notice that from v0.6.0 on, we rename the n_dims parameter to n_components.
-reducer = pacmap.PaCMAP(n_components=2, n_neighbors=10, MN_ratio=0.5, FP_ratio=2.0) 
+reducer = pacmap.PaCMAP(n_components=2, n_neighbors=10, MN_ratio=0.5, FP_ratio=2.0)
 
 # fit the data (The index of transformed data corresponds to the index of the original data)
 X_transformed = reducer.fit_transform(X, init="pca")

demo/demo_utils.py

@@ -1,18 +1,20 @@
 import matplotlib.pyplot as plt
 
+
 def generate_figure(embedding, labels, title):
     fig, ax = plt.subplots(1, 1, figsize=(6, 6))
     ax.scatter(embedding[:, 0], embedding[:, 1], s=0.5, c=labels, cmap='Spectral')
     ax.axis('off')
     ax.set_title(title)
     plt.savefig(f"./{title}.png")
 
+
 def generate_combined_figure(embeddings, labels, titles, theme_title):
     len_subfigs = len(embeddings)
     assert len(labels) == len_subfigs
     assert len(titles) == len_subfigs
     n_rows = (len_subfigs + 2) // 3
-    fig, axes = plt.subplots(n_rows, 3, figsize = (18, n_rows * 6))
+    fig, axes = plt.subplots(n_rows, 3, figsize=(18, n_rows * 6))
     axes = axes.flatten()
     for i in range(len_subfigs):
         ax = axes[i]

demo/specify_nn_demo.py

@@ -23,10 +23,10 @@
 
 nbrs = np.zeros((n, 20), dtype=np.int32)
 for i in range(n):
-    nbrs_ = tree.get_nns_by_item(i, 20 + 1) # The first nbr is always the point itself
+    nbrs_ = tree.get_nns_by_item(i, 20 + 1)  # The first nbr is always the point itself
     nbrs[i, :] = nbrs_[1:]
 
-scaled_dist = np.ones((n, n_neighbors)) # No scaling is needed
+scaled_dist = np.ones((n, n_neighbors))  # No scaling is needed
 
 # Type casting is needed for numba acceleration
 X = X.astype(np.float32)
@@ -37,7 +37,11 @@
 
 # initializing the pacmap instance
 # feed the pair_neighbors into the instance
-embedding = pacmap.PaCMAP(n_components=2, n_neighbors=n_neighbors, MN_ratio=0.5, FP_ratio=2.0, pair_neighbors=pair_neighbors) 
+embedding = pacmap.PaCMAP(n_components=2,
+                          n_neighbors=n_neighbors,
+                          MN_ratio=0.5,
+                          FP_ratio=2.0,
+                          pair_neighbors=pair_neighbors)
 
 # fit the data (The index of transformed data corresponds to the index of the original data)
 X_transformed = embedding.fit_transform(X, init="pca")

demo/transform_demo.py

@@ -9,7 +9,7 @@
 import pacmap
 import numpy as np
 from sklearn.model_selection import StratifiedKFold
-from demo_utils import *
+import demo_utils
 
 
 # MNIST
@@ -25,7 +25,7 @@
         X_train, X_test = mnist[train_index], mnist[test_index]
         y_train, y_test = labels[train_index], labels[test_index]
         break
-    
+
     # Initialize the instance
     reducer = pacmap.PaCMAP(n_components=2, n_neighbors=10, MN_ratio=0.5, FP_ratio=2.0, random_state=20, save_tree=False)
 
@@ -41,4 +41,4 @@
     embeddings = [embedding, embedding_test, embedding_combined]
     labelset = [y_train, y_test, y]
     titles = ['Training', 'Test', 'Combined']
-    generate_combined_figure(embeddings, labelset, titles, f'mnist_transform_{n}')
+    demo_utils.generate_combined_figure(embeddings, labelset, titles, f'mnist_transform_{n}')