06-1_PCA 실습_ 타이타닉 데이터 셋 활용

이분의 사이트에서 PCA부분을 보고도 해보았다.

https://hmkim312.github.io/posts/%ED%83%80%EC%9D%B4%ED%83%80%EB%8B%89_%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%A1%9C_%ED%95%B4%EB%B3%B4%EB%8A%94_PCA%EC%99%80_kNN/

 

 

 

 

In [149]:

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

df= pd.read_csv('titanic.csv')
df

 

Out[149]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

 

 

• survival - 생존여부 ( 0 = No, 1 = Yes )
• pclass - 티켓 등급 ( 1 = 1st, 2 = 2nd, 3 = 3rd )
• sex - 성별
• Age - 나이
• sibsp - 타이타닉에 승선한 형제자매 또는 배우자의 수
• parch - 타이타닉에 승선한 부모 - 자식의 수
• ticket - 티켓 번호
• fare - 승선 요금
• cabin - 선실 번호
• embarked - 승선 항구 이름 ( C = Cherbourg, Q = Queenstown, S = Southampton )

 

In [99]:

df.info()

 

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 10 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   survived  891 non-null    int64  
 1   pclass    891 non-null    int64  
 2   sex       891 non-null    object 
 3   age       891 non-null    float64
 4   sibsp     891 non-null    int64  
 5   parch     891 non-null    int64  
 6   fare      891 non-null    float64
 7   embarked  891 non-null    object 
 8   class     891 non-null    object 
 9   alone     891 non-null    bool   
dtypes: bool(1), float64(2), int64(4), object(3)
memory usage: 63.6+ KB

 

 

전처리¶

 

In [150]:

df['age']= df['age'].fillna(df['age'].median())
df['embarked']= df['embarked'].fillna('S')
df.drop(['deck','embark_town'],axis=1,inplace=True)
df.drop(['who','alive'],axis=1,inplace=True)
df.drop(['adult_male'],axis=1,inplace=True)
df['new_sex']=df['sex'].replace(['female','male'],[0,1])

 

In [151]:

def get_group(v):
    if v == 'S':
        group = 3
    elif v == 'C':
        group = 2
    else:
        group = 1
    return group


df["new_embarked"] = df["embarked"].apply(lambda v: get_group(v))
def class_group(v):
    if v == 'Third':
        group = 3
    elif v == 'Second':
        group = 2
    else:
        group = 1
    return group


df["new_class"] = df["class"].apply(lambda v: class_group(v))


def alone_group(v):
    if v == True:
        group = 1
    else:
        group = 0
    return group


df["new_alone"] = df["alone"].apply(lambda v: alone_group(v))

 

In [152]:

df.drop(['sex','class','embarked','alone','fare','new_class','new_embarked'],axis=1, inplace=True)

 

In [154]:

df['age']=df['age'].astype('int64')

 

 

• 전처리 끝

 

In [155]:

df.head()

 

Out[155]:

	survived	pclass	age	sibsp	parch	new_sex	new_alone
0	0	3	22	1	0	1	0
1	1	1	38	1	0	0	0
2	1	3	26	0	0	0	1
3	1	1	35	1	0	0	0
4	0	3	35	0	0	1	1

 

 

PCA 시작¶

 

In [156]:

#속성과 클래스로 데이터 분류
y_target = df['survived']
X_features =df.drop(['survived'],axis=1)

y_target.value_counts()

 

Out[156]:

0    549
1    342
Name: survived, dtype: int64

 

In [157]:

import seaborn as sns
%matplotlib inline

corr = X_features.corr()
plt.figure(figsize=(14,14)) 
sns.heatmap(corr, annot=True, fmt = '.1g')  # fmt - 정밀도

 

Out[157]:

<AxesSubplot:>

 

 

In [173]:

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
col=['pclass','age','sibsp','parch','new_sex','new_alone']
scaler = StandardScaler()
df_cols_scaled = scaler.fit_transform(X_features[col])
pca = PCA(n_components=2)
pca.fit(df_cols_scaled)

print('PCA Component별 변동성', pca.explained_variance_ratio_)

 

 

PCA Component별 변동성 [0.37952548 0.22703546]

 

In [174]:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
rcf  = RandomForestClassifier(n_estimators=6,random_state=156)

#원본
scores = cross_val_score(rcf, X_features, y_target, scoring='accuracy',cv=3)
print('CV=3의 경우 개별 FOLD세트별 정확도',scores)

print('평균 정확도:{0:.4f}'.format(np.mean(scores)))

 

 

CV=3의 경우 개별 FOLD세트별 정확도 [0.77104377 0.80808081 0.79461279]
평균 정확도:0.7912

 

In [183]:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score

rcf  = RandomForestClassifier(n_estimators=6,random_state=156)


# PCA 부분
scaler = StandardScaler()
df_scaler= scaler.fit_transform(X_features)
pca = PCA(n_components=5)
df_pca = pca.fit_transform(df_scaler)
# 여기 까지



scores_df = cross_val_score(rcf, df_pca, y_target, scoring='accuracy',cv=3)
print('CV=3의 경우의 PCA 변환된  개별 FOLD세트별 정확도',scores_df)

print('PCA 변환 데이터 셋 평균 정확도:{0:.4f}'.format(np.mean(scores_df)))

 

 

CV=3의 경우의 PCA 변환된  개별 FOLD세트별 정확도 [0.76430976 0.8013468  0.77777778]
PCA 변환 데이터 셋 평균 정확도:0.7811

 

In [184]:

df

 

Out[184]:

	survived	pclass	age	sibsp	parch	new_sex	new_alone
0	0	3	22	1	0	1	0
1	1	1	38	1	0	0	0
2	1	3	26	0	0	0	1
3	1	1	35	1	0	0	0
4	0	3	35	0	0	1	1
...	...	...	...	...	...	...	...
886	0	2	27	0	0	1	1
887	1	1	19	0	0	0	1
888	0	3	28	1	2	0	0
889	1	1	26	0	0	1	1
890	0	3	32	0	0	1	1

891 rows × 7 columns

 

In [185]:

from sklearn.model_selection import train_test_split

X = df[['pclass', 'age', 'sibsp', 'parch', 'new_sex', 'new_alone',]].astype('float')

y = df['survived']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 13)

 

In [186]:

from sklearn.decomposition import PCA

def get_pca_data(ss_data, n_components = 2):
    pca = PCA(n_components = n_components)
    pca.fit(ss_data)
    
    return pca.transform(ss_data), pca

 

In [187]:

def get_pd_from_pca(pca_data, col_num):
    cols = ['pca_'+str(n) for n in range(col_num)]
    return pd.DataFrame(pca_data, columns = cols)

 

In [188]:

import numpy as np

def print_variance_ratio(pca, only_sum = False):
    if only_sum == False:
        print('variance_ratio :', pca.explained_variance_ratio_)
    print('sum of variance_ratio: ', np.sum(pca.explained_variance_ratio_))

 

In [204]:

pca_data, pca = get_pca_data(X, n_components=2)
print_variance_ratio(pca)

 

 

variance_ratio : [0.98346262 0.00828615]
sum of variance_ratio:  0.9917487772208002

 

In [201]:

pca

 

Out[201]:

PCA(n_components=2)

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In [200]:

import seaborn as sns

pca_columns = ['pca_1', 'pca_2']
pca_pd = pd.DataFrame(pca_data, columns=pca_columns)
pca_pd['survived'] = y_train

sns.pairplot(pca_pd, hue='survived', height=5,
             x_vars=['pca_1'], y_vars=['pca_2'])

plt.show()