데이터 전처리

넘파이로 데이터 준비하기

In [0]:
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]
In [0]:
import numpy as np
In [3]:
np.column_stack(([1,2,3], [4,5,6]))
Out[3]:
array([[1, 4],
       [2, 5],
       [3, 6]])
In [0]:
fish_data = np.column_stack((fish_length, fish_weight))
In [5]:
print(fish_data[:5])
[[ 25.4 242. ]
 [ 26.3 290. ]
 [ 26.5 340. ]
 [ 29.  363. ]
 [ 29.  430. ]]
In [6]:
print(np.ones(5))
[1. 1. 1. 1. 1.]
In [0]:
fish_target = np.concatenate((np.ones(35), np.zeros(14)))
In [8]:
print(fish_target)
[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.]

사이킷런으로 훈련 세트와 테스트 세트 나누기

In [0]:
from sklearn.model_selection import train_test_split
In [0]:
train_input, test_input, train_target, test_target = train_test_split(
    fish_data, fish_target, random_state=42)
In [11]:
print(train_input.shape, test_input.shape)
(36, 2) (13, 2)
In [12]:
print(train_target.shape, test_target.shape)
(36,) (13,)
In [13]:
print(test_target)
[1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
In [0]:
train_input, test_input, train_target, test_target = train_test_split(
    fish_data, fish_target, stratify=fish_target, random_state=42)
In [15]:
print(test_target)
[0. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]

수상한 도미 한마리

In [16]:
from sklearn.neighbors import KNeighborsClassifier

kn = KNeighborsClassifier()
kn.fit(train_input, train_target)
kn.score(test_input, test_target)
Out[16]:
1.0
In [17]:
print(kn.predict([[25, 150]]))
[0.]
In [0]:
import matplotlib.pyplot as plt
In [19]:
plt.scatter(train_input[:,0], train_input[:,1])
plt.scatter(25, 150, marker='^')
plt.xlabel('length')
plt.ylabel('weight')
plt.show()
In [0]:
distances, indexes = kn.kneighbors([[25, 150]])
In [21]:
plt.scatter(train_input[:,0], train_input[:,1])
plt.scatter(25, 150, marker='^')
plt.scatter(train_input[indexes,0], train_input[indexes,1], marker='D')
plt.xlabel('length')
plt.ylabel('weight')
plt.show()
In [22]:
print(train_input[indexes])
[[[ 25.4 242. ]
  [ 15.   19.9]
  [ 14.3  19.7]
  [ 13.   12.2]
  [ 12.2  12.2]]]
In [23]:
print(train_target[indexes])
[[1. 0. 0. 0. 0.]]
In [24]:
print(distances)
[[ 92.00086956 130.48375378 130.73859415 138.32150953 138.39320793]]

기준을 맞춰라

In [25]:
plt.scatter(train_input[:,0], train_input[:,1])
plt.scatter(25, 150, marker='^')
plt.scatter(train_input[indexes,0], train_input[indexes,1], marker='D')
plt.xlim((0, 1000))
plt.xlabel('length')
plt.ylabel('weight')
plt.show()
In [0]:
mean = np.mean(train_input, axis=0)
std = np.std(train_input, axis=0)
In [27]:
print(mean, std)
[ 27.29722222 454.09722222] [  9.98244253 323.29893931]
In [0]:
train_scaled = (train_input - mean) / std

전처리 데이터로 모델 훈련하기

In [29]:
plt.scatter(train_scaled[:,0], train_scaled[:,1])
plt.scatter(25, 150, marker='^')
plt.xlabel('length')
plt.ylabel('weight')
plt.show()
In [0]:
new = ([25, 150] - mean) / std
In [31]:
plt.scatter(train_scaled[:,0], train_scaled[:,1])
plt.scatter(new[0], new[1], marker='^')
plt.xlabel('length')
plt.ylabel('weight')
plt.show()
In [32]:
kn.fit(train_scaled, train_target)
Out[32]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,
                     weights='uniform')
In [0]:
test_scaled = (test_input - mean) / std
In [34]:
kn.score(test_scaled, test_target)
Out[34]:
1.0
In [35]:
print(kn.predict([new]))
[1.]
In [0]:
distances, indexes = kn.kneighbors([new])
In [37]:
plt.scatter(train_scaled[:,0], train_scaled[:,1])
plt.scatter(new[0], new[1], marker='^')
plt.scatter(train_scaled[indexes,0], train_scaled[indexes,1], marker='D')
plt.xlabel('length')
plt.ylabel('weight')
plt.show()