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PCA.py

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+import pandas as pd
+import numpy as np
+import os
+import random
+from glob import glob
+from skimage.io import imread, imsave
+from skimage.transform import resize
+import matplotlib.pyplot as plt
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+
+np.random.seed(0)
+
+# data_path_base = "/ocean/projects/mch210006p/shared/HW1/Classification"
+# post_path = os.path.join(data_path_base, "post-CHF")
+# pre_path = os.path.join(data_path_base, "pre-CHF")
+# post_dirs = glob(f"{post_path}/*.jpg")
+# pre_dirs = glob(f"{pre_path}/*.jpg")
+
+# dataset = []
+
+# for dir in post_dirs:
+#     dataset.append([dir, 1])
+
+# for dir in pre_dirs:
+#     dataset.append([dir, 0])
+
+# dataset = np.asarray(dataset)
+# print(dataset.shape)
+# df = pd.DataFrame(dataset)
+# df.to_csv("dataset.csv", index=False, header=["path", "label"])
+
+n_samples = 23890
+# print(len(dataset))
+
+image_size = (240, 240)
+dataset = pd.read_csv("dataset.csv")
+idx = np.random.choice(len(dataset), n_samples)
+selected = dataset.iloc[idx]
+data = np.empty((n_samples, image_size[0]*image_size[1]))
+for i in range(n_samples):
+    path = selected.iloc[i, 0]
+    img = np.float32(imread(path)) / 255.
+    image_resized = resize(img, image_size, anti_aliasing=True)
+    data[i] = image_resized.flatten()
+print(data.shape)
+
+split_ratio = 0.2
+test_num = int(n_samples*split_ratio)
+train_num = n_samples - test_num
+x_train = data[:train_num, :]
+x_test =  data[train_num:, :]
+
+
+def fit_pca(n_components):
+    # sc = StandardScaler()
+    # sc.fit(data)
+    # data_std = sc.transform(data)
+
+    # Instantiate PCA
+    pca = PCA(n_components=n_components)
+
+    # Determine transformed features
+    train_pcs = pca.fit_transform(x_train)
+
+    ###################################### prob 1 ######################################
+    # Determine explained variance using explained_variance_ration_ attribute
+    exp_var_pca = pca.explained_variance_ratio_
+    # Cumulative sum of eigenvalues; This will be used to create step plot
+    # for visualizing the variance explained by each principal component.
+    cum_sum_eigenvalues = np.cumsum(exp_var_pca)
+
+    # Create the visualization plot
+    plt.figure()
+    plt.bar(range(0,len(exp_var_pca)), exp_var_pca, alpha=0.5, align='center', label='Individual explained variance')
+    plt.step(range(0,len(cum_sum_eigenvalues)), cum_sum_eigenvalues, where='mid',label='Cumulative explained variance')
+    plt.ylabel('Explained variance ratio')
+    plt.xlabel('Principal component index')
+    plt.legend(loc='best')
+    plt.tight_layout()
+    plt.savefig(f"res/hw3/1_{n_components}_train{train_num}_val{test_num}.jpg")
+
+    ###################################### prob 2 ######################################
+    sample = x_test[1].reshape(1, -1)
+    sample_pcs = pca.transform(sample)
+    projected_sample = pca.inverse_transform(sample_pcs)
+
+    plt.figure()
+    plt.subplot(1, 2, 1)
+    plt.imshow(sample.reshape(image_size), cmap="gray")
+    plt.title("Original")
+    plt.axis('off')
+    plt.subplot(1, 2, 2)
+    plt.imshow(projected_sample.reshape(image_size), cmap="gray")
+    plt.title("Reconstructed")
+    plt.axis('off')
+    plt.savefig(f"res/hw3/2_{n_components}_train{train_num}_val{test_num}.jpg")
+
+    ###################################### prob 3 ######################################
+    test_pcs = pca.transform(x_test)
+    projected_test = pca.inverse_transform(test_pcs)
+    err = ((projected_test - x_test) ** 2).mean()
+
+    reconstruction_err.append(err)
+
+    ###################################### prob 4 ######################################
+    n_clusters = 2
+    k_means = KMeans(n_clusters=n_clusters, random_state=0)
+    k_means.fit(train_pcs)
+    centroids = k_means.cluster_centers_
+    label = k_means.labels_
+
+    if n_components > 1:
+        plt.figure()
+        for i in range(n_clusters):
+            plt.scatter(train_pcs[label == i, 0], train_pcs[label == i, 1], edgecolor='none', c=np.random.rand(1, 3),
+                        label=f"Cluster {i + 1}")
+
+        plt.scatter(centroids[:, 0], centroids[:, 1], marker="*", s=100, c="r", label="Cluster Centroid")
+        plt.legend()
+        plt.xlabel('Principal Component 1')
+        plt.ylabel('Principal Component 2')
+        plt.title(f"K-Means Clustering Results with K={n_clusters}")
+        plt.savefig(f"res/hw3/4_{n_components}_train{train_num}_val{test_num}.jpg")
+
+n_pcomponents = [1, 2, 10, 20, 50, 100]
+
+reconstruction_err = []
+for components in n_pcomponents:
+    fit_pca(components)
+
+plt.figure()
+plt.plot(n_pcomponents, reconstruction_err)
+plt.savefig(f"res/hw3/3_train{train_num}_val{test_num}.jpg")
+
+

PCA.sh

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+#!/bin/bash
+######## --send email ########
+#SBATCH --mail-type=begin
+#SBATCH --mail-type=end
+#SBATCH --mail-user=xs018@uark.edu
+
+######## Job Name: Train_Job ########
+#SBATCH -J HW3_Job
+#SBATCH -o log/HW3_Job.o%j
+#SBATCH -e log/HW3_Job.e%j
+
+#SBATCH -p GPU-shared
+#SBATCH -N 1
+#SBATCH --export=ALL
+#SBATCH --gres=gpu:1
+#SBATCH -t 02:00:00
+
+module load AI/anaconda3-tf2.2020.11
+conda activate /jet/home/xs018/envs
+cd /jet/home/xs018/code
+
+python hw3.py