Need new libs to be considered in the app

Data_Plotting/Plot_TSNE.py

@@ -23,15 +23,41 @@ def TSNE_reduction(latent_points, perplexity=30, learning_rate=20):
     return x, y, title, embedding
 
 
+def plot_dimensionality_reduction(x, y, label_set, title):
+    plt.title(title)
+    if label_set[0].dtype == float:
+        plt.scatter(x, y, c=label_set)
+        plt.colorbar()
+        print("using scatter")
+    else:
+        for label in set(label_set):
+            cond = np.where(np.array(label_set) == str(label))
+            plt.plot(x[cond], y[cond], marker='o', linestyle='none', label=label)
+
+        plt.legend(numpoints=1)
 ########################################################################################################################
+"""
+# Use for personal plotting
+
 import pandas as pd
 import json
 
-"""
 df = pd.read_csv('2D_Lattice.csv')
-row = 0
-box = df.iloc[row,1]
-array = np.array(json.loads(box))
+# row = 0
+# box = df.iloc[row,1]
+# array = np.array(json.loads(box))
+
+# Select a subset of the data to use
+number_samples = 10000
+perplexity = 300
+
+random_samples = sorted(np.random.randint(0,len(df), number_samples))  # Generates ordered samples
+
+df = df.iloc[random_samples]
+
+print(df)
+print(np.shape(df))
+
 
 # For plotting CSV data
 # define a function to flatten a box
@@ -42,27 +68,12 @@ def flatten_box(box_str):
 
 # apply the flatten_box function to each row of the dataframe and create a list of flattened arrays
 flattened_arrays = df['Array'].apply(flatten_box).tolist()
+avg_density = np.sum(flattened_arrays, axis=1)/(len(flattened_arrays[0]))
 
-
-x, y, title, embedding = TSNE_reduction(flattened_arrays)
-
-plt.scatter(x,y)
+x, y, title, embedding = TSNE_reduction(flattened_arrays, perplexity=perplexity)
+plot_dimensionality_reduction(x, y, avg_density, title)
 plt.title(title)
-plt.show()
+plt.savefig('TSNE_Partial_Factorial_Perplexity_' + str(perplexity) + "_Data_Samples_" + str(number_samples))
+
 """
 
-# def plot_dimensionality_reduction(x, y, label_set, title):
-#     plt.title(title)
-#     if label_set[0].dtype == float:
-#         plt.scatter(x, y, c=label_set)
-#         plt.colorbar()
-#         print("using scatter")
-#     else:
-#         for label in set(label_set):
-#             cond = np.where(np.array(label_set) == str(label))
-#             plt.plot(x[cond], y[cond], marker='o', linestyle='none', label=label)
-#
-#         plt.legend(numpoints=1)
-#
-#     plt.show()
-#     plt.close()

app.py

@@ -7,7 +7,7 @@ import streamlit as st
 
 from Data_Generation.Dataset_Generation_Functions import make_boxes
 from Data_Generation.Piecewise_Box_Functions import basic_box_array, forward_slash_array, combine_arrays, add_thickness
-from Data_Plotting.Plot_TSNE import TSNE_reduction
+from Data_Plotting.Plot_TSNE import TSNE_reduction, plot_dimensionality_reduction
 ########################################################################################################################
 # User Inputs
 image_size = st.slider('Select a value for the image size', min_value=9, max_value=16)
@@ -81,11 +81,17 @@ if st.button('Generate Dataset'):  # Generate the dataset
 
     # apply the flatten_array function to each array in the list and create a list of flattened arrays
     flattened_arrays = np.array([flatten_array(a) for a in box_arrays])
+    # calculate the average density for each array
+    avg_density = np.sum(flattened_arrays, axis=1)/(np.shape(box_arrays[0])[0]*np.shape(box_arrays[0])[1])
+
 
     # Perform the TSNE Reduction
     x, y, title, embedding = TSNE_reduction(flattened_arrays)
     plt.figure(3)
-    plt.scatter(x, y)
+    # set the color values for the plot
+    plot_dimensionality_reduction(x, y, avg_density, title)
+
+    # plt.scatter(x, y)
     plt.title(title)
     plt.figure(3)
     st.pyplot(plt.figure(3))