pythonML

ML Practice 6_2

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K-means Clustering

  • Find mean of pixel value : cluster center, centroid
    1. Determine the centers of k clusters at random.
    2. Find the nearest cluster center from each sample and designate it as a sample of that cluster.
    3. Change the center of the cluster to the average value of the samples belonging to the cluster.
    4. Repeat 2~3 until there is no change in the center of the cluster.

Import Data

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!wget https://bit.ly/fruits_300_data -O fruits_300.npy
--2022-03-31 02:09:21--  https://bit.ly/fruits_300_data
Resolving bit.ly (bit.ly)... 67.199.248.10, 67.199.248.11
Connecting to bit.ly (bit.ly)|67.199.248.10|:443... connected.
HTTP request sent, awaiting response... 301 Moved Permanently
Location: https://github.com/rickiepark/hg-mldl/raw/master/fruits_300.npy [following]
--2022-03-31 02:09:21--  https://github.com/rickiepark/hg-mldl/raw/master/fruits_300.npy
Resolving github.com (github.com)... 140.82.114.3
Connecting to github.com (github.com)|140.82.114.3|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/rickiepark/hg-mldl/master/fruits_300.npy [following]
--2022-03-31 02:09:22--  https://raw.githubusercontent.com/rickiepark/hg-mldl/master/fruits_300.npy
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3000128 (2.9M) [application/octet-stream]
Saving to: ‘fruits_300.npy’

fruits_300.npy      100%[===================>]   2.86M  --.-KB/s    in 0.01s   

2022-03-31 02:09:22 (223 MB/s) - ‘fruits_300.npy’ saved [3000128/3000128]

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import numpy as np

fruits = np.load('fruits_300.npy')
print(fruits.shape)
(300, 100, 100)

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fruits_2d = fruits.reshape(-1, 100*100)
print(fruits_2d.shape)
(300, 10000)

KMeans Class

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from sklearn.cluster import KMeans
km = KMeans(n_clusters=3, random_state=42)
km.fit(fruits_2d) # no target
KMeans(n_clusters=3, random_state=42)

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print(km.labels_) # labels : [0, 1, 2]
[2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 0 2 0 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 0 0 2 2 2 2 2 2 2 2 0 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1]

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print(np.unique(km.labels_, return_counts=True))
# label 0: 111 samples / label 1: 98 samples / label 2: 91 samples
(array([0, 1, 2], dtype=int32), array([111,  98,  91]))

Images of each label

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import matplotlib.pyplot as plt

def draw_fruits(arr, ratio=1):
    n = len(arr)    # the number of sample
    rows = int(np.ceil(n/10))
    cols = n if rows < 2 else 10
    fig, axs = plt.subplots(rows, cols, 
                            figsize=(cols*ratio, rows*ratio), squeeze=False)
    for i in range(rows):
        for j in range(cols):
            if i*10 + j < n:    # n 개까지만 그립니다.
                axs[i, j].imshow(arr[i*10 + j], cmap='gray_r')
            axs[i, j].axis('off')
    plt.show()
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draw_fruits(fruits[km.labels_==0])

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draw_fruits(fruits[km.labels_==1])

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draw_fruits(fruits[km.labels_==2])

  • label 0: mostly pineapples
  • label 1: mostly bananas
  • label 2: mostly apples

Centroid

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print(km.cluster_centers_.shape)
(6, 10000)
(6, 100, 100)

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draw_fruits(km.cluster_centers_.reshape(-1, 100, 100), ratio=3)

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print(km.transform(fruits_2d[100:101])) # two-dimension array input required
[[3393.8136117  8837.37750892 5267.70439881]]

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print(km.predict(fruits_2d[100:101]))
[0]

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draw_fruits(fruits[100:101])

Finding the best K (Elbow method)

  • inertia : sum of squares of the distance between centroid and each sample
  • As K increases, inertia decreases.
  • Set the optimal K at the point where the inertia graph is bent.
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inertia = []
for k in range(2,7):
  km = KMeans(n_clusters=k, random_state=42)
  km.fit(fruits_2d)
  inertia.append(km.inertia_)

slope = []
lst = []
for idx, val in enumerate(inertia):
  if idx==0:
    slope.append(0)
    lst.append(0)
  else:
    slope.append(val - inertia[idx-1])
    lst.append(slope[idx-1]-slope[idx])

fig, ax = plt.subplots()
ax.plot(range(2,7), inertia)
ax.scatter(2+np.argmax(lst), inertia[np.argmax(lst)], marker="o", color="red")
plt.show()

Ref.) 혼자 공부하는 머신러닝+딥러닝 (박해선, 한빛미디어)

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