Adding type hints to the functions in these scripts

Data_Plotting/Plot_TSNE.py

@@ -2,28 +2,35 @@ from sklearn.manifold import TSNE
 import matplotlib.pyplot as plt
 import numpy as np
 
+
 # Latent Feature Cluster for Training Data using T-SNE
-def TSNE_reduction(latent_points: list, perplexity=30, learning_rate=20):
-    latent_dimensionality = len(latent_points[0])
+def TSNE_reduction(latent_points: np.ndarray, perplexity=30, learning_rate=20):
+    """
+    :param latent_points: [ndarray] - an array of arrays that define the points of an object in the latent space
+    :param perplexity: [int] - default perplexity = 30 " Perplexity balances the attention t-SNE gives to local and
+    global aspects of the data. It is roughly a guess of the number of close neighbors each point has...
+    a denser dataset ... requires higher perplexity value" Recommended: Perplexity(5-50)
+    :param learning_rate: [int] - default learning rate = 200 "If the learning rate is too high, the data may look
+    like a ‘ball’ with any point approximately equidistant from its nearest neighbours.
+    If the learning rate is too low, most points may look compressed in a dense cloud with few outliers."
+    Recommended: learning_rate(10-1000)
+    :return: [tuple] - the output is the x and y coordinates for the reduced latent space, a title, and an embedding
+    """
     model = TSNE(n_components=2, random_state=0, perplexity=perplexity,
-                 learning_rate=learning_rate)  # Perplexity(5-50) | learning_rate(10-1000)
-    embedding = model
-    # configuring the parameters
+                 learning_rate=learning_rate)
     # the number of components = dimension of the embedded space
-    # default perplexity = 30 " Perplexity balances the attention t-SNE gives to local and global aspects of the data.
-    # It is roughly a guess of the number of close neighbors each point has. ..a denser dataset ... requires higher perplexity value"
-    # default learning rate = 200 "If the learning rate is too high, the data may look like a ‘ball’ with any point
-    # approximately equidistant from its nearest neighbours. If the learning rate is too low,
-    # most points may look compressed in a dense cloud with few outliers."
-    tsne_data = model.fit_transform(
-        latent_points)  # When there are more data points, trainX should be the first couple hundred points so TSNE doesn't take too long
+
+    embedding = model
+
+    tsne_data = model.fit_transform(latent_points)
+    # When there are more data points, only use a couple of hundred points so TSNE doesn't take too long
     x = tsne_data[:, 0]
     y = tsne_data[:, 1]
     title = ("T-SNE of Data")
     return x, y, title, embedding
 
 
-def plot_dimensionality_reduction(x, y, label_set, title):
+def plot_dimensionality_reduction(x: list, y: list, label_set: list, title: str):
     plt.title(title)
     # Color points based on their density
     if label_set[0].dtype == float: