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# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
import os
import numpy as np
from matplotlib import pylab
from mpl_toolkits.mplot3d import Axes3D
from sklearn import linear_model, manifold, decomposition, datasets
logistic = linear_model.LogisticRegression()
CHART_DIR = os.path.join("..", "charts")
np.random.seed(3)
# all examples will have three classes in this file
colors = ['r', 'g', 'b']
markers = ['o', 6, '*']
def plot_demo_1():
X = np.c_[np.ones(5), 2 * np.ones(5), 10 * np.ones(5)].T
y = np.array([0, 1, 2])
fig = pylab.figure(figsize=(10, 4))
ax = fig.add_subplot(121, projection='3d')
ax.set_axis_bgcolor('white')
mds = manifold.MDS(n_components=3)
Xtrans = mds.fit_transform(X)
for cl, color, marker in zip(np.unique(y), colors, markers):
ax.scatter(
Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], Xtrans[y == cl][:, 2], c=color, marker=marker, edgecolor='black')
pylab.title("MDS on example data set in 3 dimensions")
ax.view_init(10, -15)
mds = manifold.MDS(n_components=2)
Xtrans = mds.fit_transform(X)
ax = fig.add_subplot(122)
for cl, color, marker in zip(np.unique(y), colors, markers):
ax.scatter(
Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], c=color, marker=marker, edgecolor='black')
pylab.title("MDS on example data set in 2 dimensions")
filename = "mds_demo_1.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
def plot_iris_mds():
iris = datasets.load_iris()
X = iris.data
y = iris.target
# MDS
fig = pylab.figure(figsize=(10, 4))
ax = fig.add_subplot(121, projection='3d')
ax.set_axis_bgcolor('white')
mds = manifold.MDS(n_components=3)
Xtrans = mds.fit_transform(X)
for cl, color, marker in zip(np.unique(y), colors, markers):
ax.scatter(
Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], Xtrans[y == cl][:, 2], c=color, marker=marker, edgecolor='black')
pylab.title("MDS on Iris data set in 3 dimensions")
ax.view_init(10, -15)
mds = manifold.MDS(n_components=2)
Xtrans = mds.fit_transform(X)
ax = fig.add_subplot(122)
for cl, color, marker in zip(np.unique(y), colors, markers):
ax.scatter(
Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], c=color, marker=marker, edgecolor='black')
pylab.title("MDS on Iris data set in 2 dimensions")
filename = "mds_demo_iris.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
# PCA
fig = pylab.figure(figsize=(10, 4))
ax = fig.add_subplot(121, projection='3d')
ax.set_axis_bgcolor('white')
pca = decomposition.PCA(n_components=3)
Xtrans = pca.fit(X).transform(X)
for cl, color, marker in zip(np.unique(y), colors, markers):
ax.scatter(
Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], Xtrans[y == cl][:, 2], c=color, marker=marker, edgecolor='black')
pylab.title("PCA on Iris data set in 3 dimensions")
ax.view_init(50, -35)
pca = decomposition.PCA(n_components=2)
Xtrans = pca.fit_transform(X)
ax = fig.add_subplot(122)
for cl, color, marker in zip(np.unique(y), colors, markers):
ax.scatter(
Xtrans[y == cl][:, 0], Xtrans[y == cl][:, 1], c=color, marker=marker, edgecolor='black')
pylab.title("PCA on Iris data set in 2 dimensions")
filename = "pca_demo_iris.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
if __name__ == '__main__':
plot_demo_1()
plot_iris_mds()