In [ ]:
!pip install fancyimpute
Collecting fancyimpute
Downloading fancyimpute-0.7.0.tar.gz (25 kB)
Preparing metadata (setup.py) ... done
Collecting knnimpute>=0.1.0 (from fancyimpute)
Downloading knnimpute-0.1.0.tar.gz (8.3 kB)
Preparing metadata (setup.py) ... done
Requirement already satisfied: scikit-learn>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from fancyimpute) (1.2.2)
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Collecting nose (from fancyimpute)
Downloading nose-1.3.7-py3-none-any.whl (154 kB)
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Building wheels for collected packages: fancyimpute, knnimpute
Building wheel for fancyimpute (setup.py) ... done
Created wheel for fancyimpute: filename=fancyimpute-0.7.0-py3-none-any.whl size=29881 sha256=69659b92f38c93063f7045ae34fb6555c5ad4ff7f0b8e1016e663aa766542fb9
Stored in directory: /root/.cache/pip/wheels/7b/0c/d3/ee82d1fbdcc0858d96434af108608d01703505d453720c84ed
Building wheel for knnimpute (setup.py) ... done
Created wheel for knnimpute: filename=knnimpute-0.1.0-py3-none-any.whl size=11330 sha256=965cd83d5045c712639e5e7cfa876114e89694f8f2245058d32a5d3d549edfd7
Stored in directory: /root/.cache/pip/wheels/46/06/a5/45a724630562413c374e29c08732411d496092408b3a7bf754
Successfully built fancyimpute knnimpute
Installing collected packages: nose, knnimpute, fancyimpute
Successfully installed fancyimpute-0.7.0 knnimpute-0.1.0 nose-1.3.7
In [ ]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.impute import SimpleImputer, KNNImputer
from fancyimpute import IterativeImputer결측치를 포함한 예시 데이터 만들기¶
In [ ]:
np.random.seed(0)
sample_data = np.random.normal(1,10,100)
sample_data
Out[ ]:
array([ 18.64052346, 5.00157208, 10.78737984, 23.40893199,
19.6755799 , -8.7727788 , 10.50088418, -0.51357208,
-0.03218852, 5.10598502, 2.44043571, 15.54273507,
8.61037725, 2.21675016, 5.43863233, 4.33674327,
15.94079073, -1.05158264, 4.13067702, -7.54095739,
-24.52989816, 7.53618595, 9.64436199, -6.4216502 ,
23.69754624, -13.54365675, 1.45758517, -0.8718385 ,
16.32779214, 15.6935877 , 2.54947426, 4.7816252 ,
-7.87785748, -18.80796468, -2.47912149, 2.56348969,
13.30290681, 13.02379849, -2.87326817, -2.02302751,
-9.48552965, -13.20017937, -16.06270191, 20.50775395,
-4.09652182, -3.38074302, -11.5279536 , 8.77490356,
-15.13897848, -1.1274028 , -7.95466561, 4.86902498,
-4.10805138, -10.80632184, 0.71817772, 5.28331871,
1.66517222, 4.02471898, -5.34322094, -2.62741166,
-5.72460448, -2.59553162, -7.13146282, -16.26282602,
2.77426142, -3.01780936, -15.30198347, 5.62782256,
-8.07298364, 1.51945396, 8.29090562, 2.28982911,
12.39400685, -11.3482582 , 5.02341641, -5.84810091,
-7.70797149, -4.78849665, -2.11552532, 1.56165342,
-10.65149841, 10.00826487, 5.6566244 , -14.36243686,
15.88252194, 19.95889176, 12.78779571, -0.79924836,
-9.70752622, 11.54451727, -3.03176947, 13.2244507 ,
3.08274978, 10.76639036, 4.56366397, 8.06573168,
1.10500021, 18.85870494, 2.26912093, 5.01989363])In [ ]:
df = pd.DataFrame(sample_data, columns=['feature'])
df.loc[10:30, 'feature']=np.nan
df.loc[55:60, 'feature']=np.nan
df.loc[80:85, 'feature']=np.nan
In [ ]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, marker='o')
plt.title('Original')
plt.show()
1차 선형보간법¶
- interpolate 함수로 결측치 보간
- 기본적인 선형보간 외에도 method 인자를 사용해 보간 방법 지정 가능
- (method='polynomial', order=2)는 2차 다항 보간
In [ ]:
df_linear = df.interpolate(method='linear')
2차 선형보간법¶
In [ ]:
df_quadratic = df.interpolate(method='quadratic')
평균대치법¶
- 평균은 중심에 대한 경향성을 알 수 있는 척도이지만 모든 관측치값을 반영하므로 이상치의 영향을 많이 받는다.
- 평균을 이용하기 때문에 수치형 변수만 사용 가능
- strategy 인자를 사용해 대치법 지정 가능
- mean(평균)
- median(중앙값)
- most_frequent(최빈값, 빈도수 사용하기 때문에 범주형 변수에만 사용 가능)
In [ ]:
imputer_mean = SimpleImputer(strategy = 'mean')
df_mean = imputer_mean.fit_transform(df1)
df_mean = pd.DataFrame(df_mean, columns = ['feature'])
In [ ]:
imputer_mean = SimpleImputer(strategy = 'most_frequent')
df_mean = imputer_mean.fit_transform(df)
fillna()를 이용해서 0으로 대체¶
- fillna() 안에 원하는 값 넣을 수 있음
In [ ]:
df_zero = df.fillna(0)
KNN 방법¶
- KNN이란?
- 패턴 인식에서 k-최근접 이웃 알고리즘은 분류나 회귀에 사용되는 비모수 방식
- KNNImputer를 활용하여 원하는 인접 이웃 수의 가중 또는 가중 평균을 사용하여 결측값 대치
- → 결측치를 기준으로 두 이웃(n_neighbors = 2)의 근사값으로 채워짐
In [ ]:
imputer_knn = KNNImputer(n_neighbors = 2)
df_knn = imputer_knn.fit_transform(df)
df_knn = pd.DataFrame(df_knn, columns=['feature'])
MICE 다중대치법¶
- 결측값을 회귀하는 방식으로 처리하기 때문에 수치형 변수에 자주 사용함
- 범주형 변수에는 먼저 인코딩 후 사용 가능
In [ ]:
mice_imputer = IterativeImputer()
df_mice = mice_imputer.fit_transform(df)
df_mice = pd.DataFrame(df_mice, columns=['feature'])
In [ ]:
df_quadratic[5:10]
Out[ ]:
| feature | |
|---|---|
| 5 | -8.772779 |
| 6 | 10.500884 |
| 7 | -0.513572 |
| 8 | -0.032189 |
| 9 | 5.105985 |
- 결측치를 확인하는 과정에서는 데이터의 도메인 지식이 필요하다
- → 결측치가 유의미한 데이터일 수도 있기 때문
- 결측치를 삭제하는 방법으로 처리하는 것은 리스크가 따를 수 있음
- → 데이터의 누락으로 이어질 수 있기 때문
시각화를 통해 어떤 식으로 변환하는지 확인¶
In [ ]:
fig, axs = plt.subplots(7,1,figsize=(10,12))
sns.lineplot(data= df, marker='o', ax=axs[0], legend='auto')
axs[0].set_title('Original Data')
sns.lineplot(data=df_linear , marker='o', ax=axs[1], legend='auto')
axs[1].set_title('Linear Data')
sns.lineplot(data=df_quadratic , marker='o', ax=axs[2], legend='auto')
axs[2].set_title('Quadratic Data')
sns.lineplot(data=df_mean , marker='o', ax=axs[3], legend='auto')
axs[3].set_title('Simple Mean')
sns.lineplot(data=df_zero , marker='o', ax=axs[4], legend='auto')
axs[4].set_title('Zero')
sns.lineplot(data=df_knn , marker='o', ax=axs[5], legend='auto')
axs[5].set_title('df_knn')
sns.lineplot(data=df_mice , marker='o', ax=axs[6], legend='auto')
axs[6].set_title('df_mice')
Out[ ]:
Text(0.5, 1.0, 'df_mice')
- 결측치를 보간하고 데이터 분석, 모델링 할 때 성능을 비교할 수 있음
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