pcaStandardization.ipynb

Kernel: Unknown Kernel

import numpy as np
try:
    import pandas as pd
except ModuleNotFoundError:
    %pip install -qq pandas
    import pandas as pd
import matplotlib.pyplot as plt
try:
    from sklearn.decomposition import PCA
except ModuleNotFoundError:
    %pip install -qq scikit-learn
    from sklearn.decomposition import PCA
from pathlib import Path

import os
import scipy.io

# Get Data
data_dir = Path('.').absolute().parent / 'data' / 'heightWeight'
data = scipy.io.loadmat(data_dir / "heightWeight.mat")['heightWeightData']
data = pd.DataFrame(data).rename(columns = {0:'gender', 1: 'height', 2: 'weight'})

# Function for plotting categorical scatter plot with 1D PCA line
def make_pca_plot(data):

    pca = PCA(1)
    X_reconstr = pca.inverse_transform(pca.fit_transform(data[['height','weight']].values))
    X_reconstr = np.sort(X_reconstr,axis=0)
    
    fig, ax = plt.subplots(figsize=(6, 6))

    for i, (name, group) in enumerate(data.groupby('gender')):
        color = 'red' if i==1 else 'blue'
        marker = 'o' if i==1 else 'x'
        fc = 'none' if i==1 else 'blue'
        ax.scatter(x=group['height'], y=group['weight'], color=color, marker=marker, facecolor=fc, s=100)
        ax.set_ylabel('weight')
        ax.set_xlabel('height')
        ax.plot(X_reconstr[:,0], X_reconstr[:,1], color='black',linewidth=2)
        
    return fig, ax

# Save figure function
figdir = "figures"
def savefig(fname): 
    plt.savefig(os.path.join(figdir, fname))
    
# Create and save figures
fig, ax = make_pca_plot(data)
ax.set_title('heightWeightPCA')
savefig('heightWeightPCA.pdf')

data_std = (data - data.mean())/data.std()
fig, ax = make_pca_plot(data_std)
ax.set_title('heightWeightPCAstnd')
savefig('heightWeightPCAstnd.pdf')