원핫 인코딩 실습¶

학습 목표¶

01 pd.get_dummy를 이용한 원핫 인코딩 실습
02 성인 인구조사 소득 데이터 셋(adult.data)을 활용한 onehot encoding 실습

목차

01. 원핫 인코딩 실습
02. adult.data 셋을 활용한 onehot encoding 실습

01. 원핫 인코딩 실습

목차로 이동하기

  • 데이터 셋을 불러와 원핫 인코딩 실습
  • hello world 원핫 인코딩 실습
In [1]:
import mglearn
import pandas as pd
import os
In [2]:
demo_df = pd.DataFrame({"Product":['양말', '여우', '양말', '상자']})
display(demo_df)
Product
0 양말
1 여우
2 양말
3 상자
In [3]:
onehot = pd.get_dummies(demo_df)
onehot
Out[3]:
Product_상자 Product_양말 Product_여우
0 0 1 0
1 0 0 1
2 0 1 0
3 1 0 0
In [4]:
df = pd.concat([demo_df, onehot], axis=1)
df
Out[4]:
Product Product_상자 Product_양말 Product_여우
0 양말 0 1 0
1 여우 0 0 1
2 양말 0 1 0
3 상자 1 0 0

02. adult.data 셋을 활용한 onehot encoding 실습

목차로 이동하기

In [5]:
path = os.path.join(mglearn.datasets.DATA_PATH, 'adult.data')
print(path)

C:\Users\totofriend\anaconda3\lib\site-packages\mglearn\data\adult.data
In [6]:
data = pd.read_csv(path,
               header=None, 
               index_col=False,
               names=['age', 'workclass', 'fnlwgt', 'education', 
                      'education-num', 'marital-status', 'occupation', 'relationship', 
                      'race', 'gender', 'capital-gain', 'capital-loss', 
                      'hours-per-week', 'native-country', 'income'])
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32561 entries, 0 to 32560
Data columns (total 15 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   age             32561 non-null  int64 
 1   workclass       32561 non-null  object
 2   fnlwgt          32561 non-null  int64 
 3   education       32561 non-null  object
 4   education-num   32561 non-null  int64 
 5   marital-status  32561 non-null  object
 6   occupation      32561 non-null  object
 7   relationship    32561 non-null  object
 8   race            32561 non-null  object
 9   gender          32561 non-null  object
 10  capital-gain    32561 non-null  int64 
 11  capital-loss    32561 non-null  int64 
 12  hours-per-week  32561 non-null  int64 
 13  native-country  32561 non-null  object
 14  income          32561 non-null  object
dtypes: int64(6), object(9)
memory usage: 3.7+ MB
In [8]:
data.columns
Out[8]:
Index(['age', 'workclass', 'fnlwgt', 'education', 'education-num',
       'marital-status', 'occupation', 'relationship', 'race', 'gender',
       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
       'income'],
      dtype='object')

일부 변수 선택 후, 진행¶

In [9]:
sel = ['age', 'workclass','education','gender','hours-per-week',
       'occupation','income']
data = data[sel]
data.head()
Out[9]:
age workclass education gender hours-per-week occupation income
0 39 State-gov Bachelors Male 40 Adm-clerical <=50K
1 50 Self-emp-not-inc Bachelors Male 13 Exec-managerial <=50K
2 38 Private HS-grad Male 40 Handlers-cleaners <=50K
3 53 Private 11th Male 40 Handlers-cleaners <=50K
4 28 Private Bachelors Female 40 Prof-specialty <=50K

의미 있는 범주형 데이터 있는지 확인¶

In [10]:
print(data.gender.value_counts())

 Male      21790
 Female    10771
Name: gender, dtype: int64

pandas에서 get_dummies 함수를 이용하여 인코딩¶

In [11]:
print("원본 특성 :\n", list(data.columns), "\n")
data_dummies = pd.get_dummies(data)
print("get_dummies 후 특성 : \n", list(data_dummies.columns))

원본 특성 :
 ['age', 'workclass', 'education', 'gender', 'hours-per-week', 'occupation', 'income'] 

get_dummies 후 특성 : 
 ['age', 'hours-per-week', 'workclass_ ?', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Never-worked', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_ 10th', 'education_ 11th', 'education_ 12th', 'education_ 1st-4th', 'education_ 5th-6th', 'education_ 7th-8th', 'education_ 9th', 'education_ Assoc-acdm', 'education_ Assoc-voc', 'education_ Bachelors', 'education_ Doctorate', 'education_ HS-grad', 'education_ Masters', 'education_ Preschool', 'education_ Prof-school', 'education_ Some-college', 'gender_ Female', 'gender_ Male', 'occupation_ ?', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'income_ <=50K', 'income_ >50K']
  • age와 hours-per-week는 그대로이지만 범주형 특성은 새로운 특성으로 확장

특성을 포함한 열 'age'~'occupation_ Transport-moving' 모두 추출¶

In [12]:
features = data_dummies.loc[:, "age":"occupation_ Transport-moving"]
X = features.values
y = data_dummies['income_ >50K'].values
In [13]:
print("X.shape : {}, y.shape : {}".format(X.shape, y.shape))

X.shape : (32561, 44), y.shape : (32561,)

실습 1¶

  • 로지스틱 모델을 만들어보기
    • (1) 데이터를 나누어준다.
    • (2) 모델을 만든다.
    • (3) 모델을 학습한다.(학습 데이터를 이용해서)
    • (4) score를 확인(테스트 데이터를 이용해서)

로지스틱 모델 사용해 보기¶

In [14]:
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
logreg = LogisticRegression()
logreg.fit(X_train, y_train)

C:\Users\totofriend\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
Out[14]:
LogisticRegression()
In [15]:
print("학습용 점수 {:.2f}".format(logreg.score(X_train, y_train)))
print("테스트 점수 {:.2f}".format(logreg.score(X_test, y_test)))

학습용 점수 0.81
테스트 점수 0.81
In [16]:
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier

랜덤 포레스트를 활용한 모델 구축¶

In [19]:
model = RandomForestClassifier().fit(X_train, y_train)

print("학습용 점수 {:.2f}".format(model.score(X_train, y_train)))
print("테스트 점수 {:.2f}".format(model.score(X_test, y_test)))

학습용 점수 0.94
테스트 점수 0.79

knn모델을 활용한 모델 구축¶

In [18]:
model = KNeighborsClassifier().fit(X_train, y_train)

print("학습용 점수 {:.2f}".format(model.score(X_train, y_train)))
print("테스트 점수 {:.2f}".format(model.score(X_test, y_test)))

학습용 점수 0.84
테스트 점수 0.78