[ML] 2-1 훈련 세트와 테스트 세트

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fish_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, 
                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, 
                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8, 
                10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]
fish_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, 
                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, 
                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7, 
                7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]

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fish_data = [[l, w] for l, w in zip(fish_length, fish_weight)]
fish_target = [1]*35 + [0]*14

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print(fish_data)
print(fish_target)

[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0], [29.7, 450.0], [29.7, 500.0], [30.0, 390.0], [30.0, 450.0], [30.7, 500.0], [31.0, 475.0], [31.0, 500.0], [31.5, 500.0], [32.0, 340.0], [32.0, 600.0], [32.0, 600.0], [33.0, 700.0], [33.0, 700.0], [33.5, 610.0], [33.5, 650.0], [34.0, 575.0], [34.0, 685.0], [34.5, 620.0], [35.0, 680.0], [35.0, 700.0], [35.0, 725.0], [35.0, 720.0], [36.0, 714.0], [36.0, 850.0], [37.0, 1000.0], [38.5, 920.0], [38.5, 955.0], [39.5, 925.0], [41.0, 975.0], [41.0, 950.0], [9.8, 6.7], [10.5, 7.5], [10.6, 7.0], [11.0, 9.7], [11.2, 9.8], [11.3, 8.7], [11.8, 10.0], [11.8, 9.9], [12.0, 9.8], [12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]
[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

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from sklearn.neighbors import KNeighborsClassifier
kn = KNeighborsClassifier()

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print(fish_data[4])
print(fish_data[0:5])
print(fish_data[:5])
print(fish_data[44:])

[29.0, 430.0]
[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0]]
[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0]]
[[12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]

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train_input = fish_data[:35]
train_target = fish_target[:35]

test_input = fish_data[35:]
test_target = fish_target[35:]

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kn = kn.fit(train_input, train_target)
kn.score(test_input, test_target)

Out[ ]:

0.0

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import numpy as np

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fish_data = [[25.4, 242.0], [26.3, 290.0], [14.3, 19.7], [15.0, 19.9]]
fish_target = [1, 1, 0, 0]

input_arr = np.array(fish_data)
target_arr = np.array(fish_target)

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print(fish_data)

[[25.4, 242.0], [26.3, 290.0], [14.3, 19.7], [15.0, 19.9]]

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print(input_arr.shape)

(4, 2)

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np.random.seed(42)
index = np.arange(49)
np.random.shuffle(index)

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print(index)

Out[ ]:

array([13, 45, 47, 44, 17, 27, 26, 25, 31, 19, 12,  4, 34,  8,  3,  6, 40,
       41, 46, 15,  9, 16, 24, 33, 30,  0, 43, 32,  5, 29, 11, 36,  1, 21,
        2, 37, 35, 23, 39, 10, 22, 18, 48, 20,  7, 42, 14, 28, 38])

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print(input_arr[[1, 3]])

Out[ ]:

array([[ 26.3, 290. ],
       [ 29. , 363. ]])

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train_input = input_arr[index[:35]]
train_target = target_arr[index[:35]]

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print(input_arr[13], train_input[0])

[ 32. 340.] [ 32. 340.]

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test_input = input_arr[index[35:]]
test_target = target_arr[index[35:]]

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import matplotlib.pyplot as plt
plt.scatter(train_input[:,0], train_input[:,1])
plt.scatter(test_input[:,0], test_input[:,1])
plt.xlabel('length')
plt.ylabel('weight')
plt.show()

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kn = kn.fit(train_input, train_target)

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kn.score(test_input, test_target)

Out[ ]:

1.0

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kn.predict(test_input)

Out[ ]:

array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])

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새 섹션¶

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test_target

Out[ ]:

array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])

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