ML - Lab 6 - ISOMAP

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn

# Read data
srdata = pd.read_csv("./srdata.txt")
print("srdata = ")
print(srdata)
print()
print("srdata summary = ")
print(srdata.describe())
print()

# Plot 3D - Initial Points
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(srdata.V1, srdata.V2, srdata.V3)
ax.set_xlabel('V1')
ax.set_ylabel('V2')
ax.set_zlabel('V3')
plt.title("Initial points (3-D)")
plt.show()




# ISOMAP - Convert 3-D data to 2-D data
from sklearn.manifold import Isomap
neigh = 4
comp = 2            # of new dimensions
isomap = Isomap(n_neighbors = neigh, n_components = comp)
isomap = isomap.fit(srdata)
transformed = pd.DataFrame(isomap.transform(srdata))
# srdata = 3D, while transformed = 2D
print()
print("2-D converted data by ISOMAP: ")
print(transformed)
print()
print()


# Plot 3-D - Initial Points, but colors given by ISOMAP
colors = [i - min(transformed.loc[:, 0].tolist()) + 1 for i in transformed.loc[:, 0].tolist()]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(srdata.V1, srdata.V2, srdata.V3, c=colors)
ax.set_xlabel('V1')
ax.set_ylabel('V2')
ax.set_zlabel('V3')
plt.title("ISOMAP-colored initial points (3-D)")
plt.show()


# New 2-D plot by ISOMAP
plt.figure()
plt.scatter(transformed.loc[:, 0], transformed.loc[:, 1], c=colors)
plt.title("ISOMAP-colored initial points (3-D)")
plt.xlabel("New var 0")
plt.ylabel("New var 1")
plt.show()