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Visualise the PAP-SMEAR data using PCA

Visualise the data using Principal Component Analysis. Features in the CSV file have been precomputed.

import math
import numpy as np
import matplotlib.pyplot as plt

import skimage
from skimage import io, measure, exposure
from skimage.color import rgba2rgb, rgb2gray
from sklearn.cluster import KMeans

from sklearn.decomposition import PCA

import os
import csv
import random

%matplotlib inline

Step 1: Load the csv files that contain the generated features

features = []

with open('../images/papsmear-features-normal.csv', newline='') as csvfile:
    stored_features = csv.reader(csvfile, delimiter=',', quotechar='|')
    for row in stored_features:
        filename = row[0]
        class_label = row[1]
        v = np.array(row[2:len(row)], dtype=np.float32)
        f = [filename, class_label, v]
        features.append(f)

with open('../images/papsmear-features-displastic.csv', newline='') as csvfile:
    stored_features = csv.reader(csvfile, delimiter=',', quotechar='|')
    for row in stored_features:
        filename = row[0]
        class_label = row[1]
        v = np.array(row[2:len(row)], dtype=np.float32)
        f = [filename, class_label, v]
        features.append(f)

Step 1: Split data into test and training data

feature_length = len(features[0][2])
print(feature_length)

22

random.shuffle(features)

features_train = features[0:280]
features_test = features[280:len(features)]

data_train = np.zeros((len(features_train), feature_length))
colours_train = np.zeros(len(features_train))

for i in range(0, len(features_train)):
    data_train[i, :] = features_train[i][2]
    if (features_train[i][1] == '0'):
        colours_train[i] = 0.25
    else:
        colours_train[i] = 0.75

pca = PCA(n_components=2)

train_pca = pca.fit_transform(data_train)

train_projected = pca.transform(data_train)

plt.scatter(train_projected[:,0], train_projected[:, 1], c=colours_train, marker='o');