Kaggle实战——泰坦尼克生存预测大赛
2018-01-10 09:06
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5 rows × 98 columns
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import csv
import numpy as np
csv_file_object = csv.reader(open('D:/In/kaggle/Titanic/train.csv', 'rt'))
data=[]
for row in csv_file_object:
data.append(row)
#data = np.array(data)
print (data[0])
print (np.array(data)[0])['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'] ['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch' 'Ticket' 'Fare' 'Cabin' 'Embarked']
In [ ]:
'''
#数据处理
import numpy as np
import pandas as pd
#绘图
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
#各种模型、数据处理方法
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.metrics import precision_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve
import warnings
warnings.filterwarnings('ignore')
'''In [2]:
print (data[:3]) #list是一维的，array是二维的 print (np.array(data)[:3]) print (np.array(data)[:15,5]) #print (data[0:15,5]) data=np.array(data) type(data) #data此时为一个二维的数组
[['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], ['1', '0', '3', 'Braund, Mr. Owen Harris', 'male', '22', '1', '0', 'A/5 21171', '7.25', '', 'S'], ['2', '1', '1', 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)', 'female', '38', '1', '0', 'PC 17599', '71.2833', 'C85', 'C']] [['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch' 'Ticket' 'Fare' 'Cabin' 'Embarked'] ['1' '0' '3' 'Braund, Mr. Owen Harris' 'male' '22' '1' '0' 'A/5 21171' '7.25' '' 'S'] ['2' '1' '1' 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)' 'female' '38' '1' '0' 'PC 17599' '71.2833' 'C85' 'C']] ['Age' '22' '38' '26' '35' '35' '' '54' '2' '27' '14' '4' '58' '20' '39']
Out[2]:
numpy.ndarray
In [3]:
print(data[1:6,5]) print(data[1:6,5].astype(int)) print(data[1:6,5].astype(int).mean())
['22' '38' '26' '35' '35'] [22 38 26 35 35] 31.2
In [1]:
import pandas as pd
%matplotlib inline
df=pd.read_csv('D:/In/kaggle/Titanic/train.csv')
df_test=pd.read_csv('D:/In/kaggle/Titanic/test.csv')
print(df.info())
print(df[['Age','Sex','Pclass']][:10])
print(df[df['Age']>60][['Survived','Pclass','Sex','Age']])
df[df['Age'].isnull()][:10] #只显示年龄为空的数据<class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): PassengerId 891 non-null int64 Survived 891 non-null int64 Pclass 891 non-null int64 Name 891 non-null object Sex 891 non-null object Age 714 non-null float64 SibSp 891 non-null int64 Parch 891 non-null int64 Ticket 891 non-null object Fare 891 non-null float64 Cabin 204 non-null object Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.6+ KB None Age Sex Pclass 0 22.0 male 3 1 38.0 female 1 2 26.0 female 3 3 35.0 female 1 4 35.0 male 3 5 NaN male 3 6 54.0 male 1 7 2.0 male 3 8 27.0 female 3 9 14.0 female 2 Survived Pclass Sex Age 33 0 2 male 66.0 54 0 1 male 65.0 96 0 1 male 71.0 116 0 3 male 70.5 170 0 1 male 61.0 252 0 1 male 62.0 275 1 1 female 63.0 280 0 3 male 65.0 326 0 3 male 61.0 438 0 1 male 64.0 456 0 1 male 65.0 483 1 3 female 63.0 493 0 1 male 71.0 545 0 1 male 64.0 555 0 1 male 62.0 570 1 2 male 62.0 625 0 1 male 61.0 630 1 1 male 80.0 672 0 2 male 70.0 745 0 1 male 70.0 829 1 1 female 62.0 851 0 3 male 74.0
Out[1]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 6 | 0 | 3 | Moran, Mr. James | male | NaN | 0 | 0 | 330877 | 8.4583 | NaN | Q |
| 17 | 18 | 1 | 2 | Williams, Mr. Charles Eugene | male | NaN | 0 | 0 | 244373 | 13.0000 | NaN | S |
| 19 | 20 | 1 | 3 | Masselmani, Mrs. Fatima | female | NaN | 0 | 0 | 2649 | 7.2250 | NaN | C |
| 26 | 27 | 0 | 3 | Emir, Mr. Farred Chehab | male | NaN | 0 | 0 | 2631 | 7.2250 | NaN | C |
| 28 | 29 | 1 | 3 | O'Dwyer, Miss. Ellen "Nellie" | female | NaN | 0 | 0 | 330959 | 7.8792 | NaN | Q |
| 29 | 30 | 0 | 3 | Todoroff, Mr. Lalio | male | NaN | 0 | 0 | 349216 | 7.8958 | NaN | S |
| 31 | 32 | 1 | 1 | Spencer, Mrs. William Augustus (Marie Eugenie) | female | NaN | 1 | 0 | PC 17569 | 146.5208 | B78 | C |
| 32 | 33 | 1 | 3 | Glynn, Miss. Mary Agatha | female | NaN | 0 | 0 | 335677 | 7.7500 | NaN | Q |
| 36 | 37 | 1 | 3 | Mamee, Mr. Hanna | male | NaN | 0 | 0 | 2677 | 7.2292 | NaN | C |
| 42 | 43 | 0 | 3 | Kraeff, Mr. Theodor | male | NaN | 0 | 0 | 349253 | 7.8958 | NaN | C |
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(context="paper", font="monospace")
sns.set(style="white")
f, ax = plt.subplots(figsize=(10,6))
train_corr = df.drop('PassengerId',axis=1).corr()
sns.heatmap(train_corr, ax=ax, vmax=.9, square=True)
ax.set_xticklabels(train_corr.index, size=15)
ax.set_yticklabels(train_corr.columns[::1], size=15)
ax.set_title('train feature corr', fontsize=20)Out[2]:
<matplotlib.text.Text at 0x18f56089128>
In [5]:
for i in range(1,4): print (i, len (df[ (df['Sex']=='male')&(df['Pclass'] == i) ]) ) #输出不同等级仓中男士的数量
1 122 2 108 3 347
In [6]:
import pylab as p df['Age'].dropna().hist(range=(0,100),bins=19,alpha=0.8),p.show() #bins代表直方柱的个数 alpha控制颜色深浅
Out[6]:
(<matplotlib.axes._subplots.AxesSubplot at 0x1d7d360a550>, None)
In [7]:
from scipy import stats
fig,axes=plt.subplots(2,1,figsize=(8,6))
sns.set_style('darkgrid')#设置风格主题
sns.distplot(df.Age.fillna(-20),rug=True,color='b',ax=axes[0])#rug强度(齿)
ax0=axes[0]
ax0.set_xlabel('')
ax1=axes[1]
ax1.set_title('age survived distribution')
k1=sns.distplot(df[df.Survived==0].Age.fillna(-20),hist=False,color='r',ax=ax1,label='dead')#罹难的年龄分布
k2=sns.distplot(df[df.Survived==1].Age.fillna(-20),hist=False,color='g',ax=ax1,label='alive')#存活的年龄分布
ax1.set_xlabel('')#x坐标轴名字
ax1.legend(fontsize=16)#小朋友和中青年比较容易存活Out[7]:
<matplotlib.legend.Legend at 0x227fa3b1320>
In [8]:
f,ax=plt.subplots(figsize=(8,3))
ax.set_title('Sex Age dist',size=20)
sns.distplot(df[df.Sex=='female'].dropna().Age,hist=False,color='pink',label='female')
sns.distplot(df[df.Sex=='male'].dropna().Age,hist=False,color='blue',label='male')
ax.legend(fontsize=15)#训练集中的男女年龄分布 男性中老年较多 女性较年轻Out[8]:
<matplotlib.legend.Legend at 0x227fa2ded68>
In [16]:
f,ax=plt.subplots(figsize=(8,3))
plt.ylim(0.0,0.03)
ax.set_title('Pclass Age dist',size=20)
sns.distplot(df[df.Pclass==1].dropna().Age,hist=False,color='pink',label='P1')
sns.distplot(df[df.Pclass==2].dropna().Age,hist=False,color='blue',label='P2')
sns.distplot(df[df.Pclass==3].dropna().Age,hist=False,color='green',label='P3')
ax.legend(fontsize=15)#不同仓级年龄分布Out[16]:
<matplotlib.legend.Legend at 0x227fba1cac8>
In [35]:
y_dead=df[df.Survived==0].groupby('Pclass')['Survived'].count()
y_alive=df[df.Survived==1].groupby('Pclass')['Survived'].count()
pos=[1,2,3]#横轴id
ax=plt.figure(figsize=(8,4)).add_subplot(111)
ax.bar(pos,y_dead,color='r',alpha=0.6,label='dead')
ax.bar(pos,y_alive,color='g',bottom=y_dead,alpha=0.6,label='alive')
ax.legend(fontsize=16,loc='best')
ax.set_xticks(pos)
ax.set_xticklabels(['Pclass%d'%(i) for i in range(1,4)],size=15)#x坐标轴信息
ax.set_title('Pclass Survived count',size=20)#不同仓级存活情况Out[35]:
<matplotlib.text.Text at 0x227fd256400>
In [29]:
pos=range(0,6)
age_list=[]
for Pclass_ in range(1,4):
for Survived_ in range(0,2):
age_list.append(df[(df.Pclass==Pclass_)&(df.Survived==Survived_)].Age.values)
#三个仓级的存亡年龄
fig,axes=plt.subplots(3,1,figsize=(10,6))
sns.set_style('darkgrid')#设置风格主题
#plt.ylim(0.0,0.06)
#print(axes)
print(len(age_list))
i_Pclass=1
for ax in axes:
if i_Pclass==1:
ax.set_ylim(0.0, 0.03)#设置y轴范围
sns.distplot(age_list[i_Pclass*2-2],hist=False,ax=ax,label='Pclass:%d,survived:0'%(i_Pclass),color='r')
sns.distplot(age_list[i_Pclass*2-1],hist=False,ax=ax,label='Pclass:%d,survived:1'%(i_Pclass),color='g')
i_Pclass+=1
ax.set_xlabel('age',size=15)
ax.legend(fontsize=15)6
D:\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py:454: RuntimeWarning: invalid value encountered in greater X = X[np.logical_and(X>clip[0], X<clip[1])] # won't work for two columns. D:\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py:454: RuntimeWarning: invalid value encountered in less X = X[np.logical_and(X>clip[0], X<clip[1])] # won't work for two columns.
In [33]:
#性别
print(df.Sex.value_counts())
print('******************************')
print(df.groupby('Sex')['Survived'].mean())#男女存活率male 577 female 314 Name: Sex, dtype: int64 ****************************** Sex female 0.742038 male 0.188908 Name: Survived, dtype: float64
In [36]:
ax=plt.figure(figsize=(10,4)).add_subplot(111)
sns.violinplot(x='Sex',y='Age',hue='Survived',data=df.dropna(),split=True)#小提琴图
ax.set_xlabel('Sex',size=20)
ax.set_xticklabels(['Female','male'],size=18)
ax.set_ylabel('Age',size=20)
ax.legend(fontsize=25,loc='best')#男女存亡年龄分布Out[36]:
<matplotlib.legend.Legend at 0x18f568b1f60>
In [42]:
label=[]
for sex_i in ['female','male']:
for pclass_i in range(1,4):
label.append('sex:%s,Pclass:%d'%(sex_i,pclass_i))
pos=range(6)
fig=plt.figure(figsize=(16,4))
ax=fig.add_subplot(111)
ax.bar(pos,df[df['Survived']==0].groupby(['Sex','Pclass'])['Survived'].count().values,
color='r',
alpha=0.5,
align='center',
tick_label=label,
label='dead')
ax.bar(pos,
df[df['Survived']==1].groupby(['Sex','Pclass'])['Survived'].count().values,
bottom=df[df['Survived']==0].groupby(['Sex','Pclass'])['Survived'].count().values,
color='g',
alpha=0.5,
align='center',
tick_label=label,
label='alive')
ax.tick_params(labelsize=15)
ax.set_title('sex_pclass_survived',size=30)
ax.legend(fontsize=15,loc='best')#相同性别情况下，仓级越高越容易存活Out[42]:
<matplotlib.legend.Legend at 0x18f56eb40f0>
In [69]:
#Fare费用
fig=plt.figure(figsize=(8,6))
ax=plt.subplot2grid((2,2),(0,0),colspan=2)#角标
#fig,ax=plt.subplots(1,1,figsize=(8,6))
ax.tick_params(labelsize=15)
ax.set_title('Fare dist',size=20)
ax.set_ylabel('dist',size=20)
sns.kdeplot(df.Fare,ax=ax)
sns.distplot(df.Fare,hist=True,ax=ax)
ax.legend(fontsize=15)
pos=range(0,400,50)
ax.set_xticks(pos)
ax.set_xlim([0,200])
ax.set_xlabel('')
#fig,ax1=plt.subplots(1,1,figsize=(8,6))
ax1=plt.subplot2grid((2,2),(1,0),colspan=2)
ax1.set_title('Fare Pclass dist',size=20)
for i in range(1,4):
sns.kdeplot(df[df.Pclass==i].Fare,ax=ax1,label='Pclass %d'%(i))#不同仓级的票价分布
ax1.set_xlim([0,200])
ax1.set_ylim([0,0.15])
ax1.legend(fontsize=15)#船票价分布
plt.tight_layout()#间距松紧In [70]:
fig=plt.figure(figsize=(8,3))
ax1=fig.add_subplot(111)
sns.kdeplot(df[df.Survived==0].Fare,ax=ax1,label='dead',color='r')
sns.kdeplot(df[df.Survived==1].Fare,ax=ax1,label='alive',color='g')
#sns.distplot(df[df.Survived==0].Fare,ax=ax1,color='r')
#sns.distplot(df[df.Survived==1].Fare,ax=ax1,color='g')
ax1.set_xlim([0,300])
ax1.legend(fontsize=15)
ax1.set_title('Fare survived',size=20)
ax1.set_xlabel('Fare',size=15)#存亡票价分布Out[70]:
<matplotlib.text.Text at 0x18f5b446400>
In [73]:
fig=plt.figure(figsize=(8,4))
ax1=fig.add_subplot(211)
sns.countplot(df.SibSp)#计数
ax1.set_title('SibSp',size=20)
ax2=fig.add_subplot(212,sharex=ax1)
sns.countplot(df.Parch)
ax2.set_title('Parch',size=20)#表亲和直亲
#plt.tight_layout()Out[73]:
<matplotlib.text.Text at 0x18f5ba0a2e8>
In [76]:
fig=plt.figure(figsize=(10,6))
ax1=fig.add_subplot(311)
df.groupby('SibSp')['Survived'].mean().plot(kind='bar',ax=ax1)#存活率
ax1.set_title('Sibsp Survived Rate',size=16)
ax1.set_xlabel('')
ax2=fig.add_subplot(312)
df.groupby('Parch')['Survived'].mean().plot(kind='bar',ax=ax2)
ax2.set_title('Parch Survived Rate',size=16)
ax2.set_xlabel('')
ax3=fig.add_subplot(313)
df.groupby(df.SibSp+df.Parch)['Survived'].mean().plot(kind='bar',ax=ax3)
ax3.set_title('Parch+Sibsp Survived Rate',size=16)
#plt.tight_layout()Out[76]:
<matplotlib.text.Text at 0x18f5cf92d30>
In [85]:
#上船地点
plt.style.use('ggplot')#美化
ax=plt.figure(figsize=(8,3)).add_subplot(111)
pos=[1,2,3]
y1=df[df.Survived==0].groupby('Embarked')['Survived'].count().sort_index().values#确保存亡的一一对应
print(y1)
y2=df[df.Survived==1].groupby('Embarked')['Survived'].count().sort_index().values
ax.bar(pos,y1,color='r',alpha=0.4,align='center',label='dead')
ax.bar(pos,y2,color='g',alpha=0.4,align='center',label='alive',bottom=y1)
ax.set_xticks(pos)
ax.set_xticklabels(['C','Q','S'])
ax.legend(fontsize=15,loc='best')
ax.set_title('Embarked survived count',size=18)[ 75 47 427]
Out[85]:
<matplotlib.text.Text at 0x18f5d3b8e10>
In [94]:
#C地存活概率较高
#不同的上船地点
ax=plt.figure(figsize=(8,3)).add_subplot(111)
ax.set_xlim([-20,80])
ax.set_ylim([0.0,0.03])
sns.kdeplot(df[df.Embarked=='C'].Age.fillna(-10),ax=ax,label='C',color='r')
sns.kdeplot(df[df.Embarked=='Q'].Age.fillna(-10),ax=ax,label='Q',color='b')
sns.kdeplot(df[df.Embarked=='S'].Age.fillna(-10),ax=ax,label='S',color='g')
ax.legend(fontsize=18)
ax.set_title('Embarked Age Dist',size=18)
#plt.tight_layout()
#Q上岸的年龄缺失比较多
#C和S上岸的年龄分布较相似，但是C的分布更扁平小孩和老人的占比更高Out[94]:
<matplotlib.text.Text at 0x18f5da920b8>
In [103]:
#不同仓位不同地点
y1=df[df.Survived==0].groupby(['Embarked','Pclass'])['Survived'].count().reset_index()['Survived'].values
print(y1)
y2=df[df.Survived==1].groupby(['Embarked','Pclass'])['Survived'].count().reset_index()['Survived'].values
ax=plt.figure(figsize=(8,3)).add_subplot(111)
pos=range(9)
ax.bar(pos,y1,align='center',alpha=0.5,color='r',label='dead')
ax.bar(pos,y2,align='center',bottom=y1,alpha=0.5,color='g',label='alive')
ax.set_xticks(pos)
xticklabels=[]
for embarked_val in ['C','Q','S']:
for pclass_val in range(1,4):
xticklabels.append('%s/%d'%(embarked_val,pclass_val))
ax.set_xticklabels(xticklabels,size=15)
ax.legend(fontsize=15,loc='best')#C地的存活率似乎更高[ 26 8 41 1 1 45 53 88 286]
Out[103]:
<matplotlib.legend.Legend at 0x18f5b53ecf8>
In [123]:
#Cabin船舱号 print(df['Cabin'].isnull().value_counts()) df.groupby(df['Cabin'].isnull())['Survived'].mean() #船舱号为空的存活率低，可以作为一个特征
True 687 False 204 Name: Cabin, dtype: int64
Out[123]:
Cabin False 0.666667 True 0.299854 Name: Survived, dtype: float64
In [148]:
print(df[df['PassengerId']==28]['Cabin']) print(len(df.loc[27,'Cabin'])) df[df.Cabin.apply(lambda x:len(x) if (x is not np.nan) else 0)>4].head(10)#返回Cabin大于4个字符的(有多个船舱的)
27 C23 C25 C27 Name: Cabin, dtype: object 11
Out[148]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | 28 | 0 | 1 | Fortune, Mr. Charles Alexander | male | 19.00 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S |
| 75 | 76 | 0 | 3 | Moen, Mr. Sigurd Hansen | male | 25.00 | 0 | 0 | 348123 | 7.6500 | F G73 | S |
| 88 | 89 | 1 | 1 | Fortune, Miss. Mabel Helen | female | 23.00 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S |
| 97 | 98 | 1 | 1 | Greenfield, Mr. William Bertram | male | 23.00 | 0 | 1 | PC 17759 | 63.3583 | D10 D12 | C |
| 118 | 119 | 0 | 1 | Baxter, Mr. Quigg Edmond | male | 24.00 | 0 | 1 | PC 17558 | 247.5208 | B58 B60 | C |
| 128 | 129 | 1 | 3 | Peter, Miss. Anna | female | NaN | 1 | 1 | 2668 | 22.3583 | F E69 | C |
| 297 | 298 | 0 | 1 | Allison, Miss. Helen Loraine | female | 2.00 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S |
| 299 | 300 | 1 | 1 | Baxter, Mrs. James (Helene DeLaudeniere Chaput) | female | 50.00 | 0 | 1 | PC 17558 | 247.5208 | B58 B60 | C |
| 305 | 306 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.92 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S |
| 311 | 312 | 1 | 1 | Ryerson, Miss. Emily Borie | female | 18.00 | 2 | 2 | PC 17608 | 262.3750 | B57 B59 B63 B66 | C |
#不同船舱的存亡统计
df['Cabin_Zone']=df.Cabin.fillna('0').str.split(' ').apply(lambda x: x[0][0])
df.groupby(by='Cabin_Zone')['Survived'].agg(['mean','count'])
#不同船舱的存亡率不一样Out[149]:
| mean | count | |
|---|---|---|
| Cabin_Zone | ||
| 0 | 0.299854 | 687 |
| A | 0.466667 | 15 |
| B | 0.744681 | 47 |
| C | 0.593220 | 59 |
| D | 0.757576 | 33 |
| E | 0.750000 | 32 |
| F | 0.615385 | 13 |
| G | 0.500000 | 4 |
| T | 0.000000 | 1 |
#船票Ticket print(df.Ticket.head()) print(len(df.Ticket.unique()))#船票有重复的 df[df.Ticket=='110152']
0 A/5 21171 1 PC 17599 2 STON/O2. 3101282 3 113803 4 373450 Name: Ticket, dtype: object 681
Out[155]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Cabin_Zone | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 257 | 258 | 1 | 1 | Cherry, Miss. Gladys | female | 30.0 | 0 | 0 | 110152 | 86.5 | B77 | S | B |
| 504 | 505 | 1 | 1 | Maioni, Miss. Roberta | female | 16.0 | 0 | 0 | 110152 | 86.5 | B79 | S | B |
| 759 | 760 | 1 | 1 | Rothes, the Countess. of (Lucy Noel Martha Dye... | female | 33.0 | 0 | 0 | 110152 | 86.5 | B77 | S | B |
#船票有重复的
print(df[df.Cabin=='B77'])
#有些船票有英文，有些则没有，使用正则！！！！！！
import re
def find_e_word(x):
pattern=re.compile('[a-z]|[A-Z]')
try:
re.search(pattern,x).group()
return 1
except:
return 0
df['Ticket_e']=df.Ticket.apply(lambda x: find_e_word(x))
df.groupby('Ticket_e')['Survived'].mean()
#存活率没区别PassengerId Survived Pclass \ 257 258 1 1 759 760 1 1 Name Sex Age SibSp \ 257 Cherry, Miss. Gladys female 30.0 0 759 Rothes, the Countess. of (Lucy Noel Martha Dye... female 33.0 0 Parch Ticket Fare Cabin Embarked Cabin_Zone Ticket_e 257 0 110152 86.5 B77 S B 0 759 0 110152 86.5 B77 S B 0
Out[165]:
Ticket_e
0 0.3842661 0.382609
Name: Survived, dtype: float64
In [174]:
#名字Name
print(df.Name.apply(lambda x: x.split(',')[1].split('.')[0]).value_counts())
df.Name.apply(lambda x: x.split(',')[1].split('.')[1]).value_counts()[:8]Mr 517
Miss 182
Mrs 125
Master 40
Dr 7
Rev 6Major 2
Col 2
Mlle 2
Capt 1
the Countess 1
Don 1
Ms 1
Lady 1
Mme 1
Jonkheer 1
Sir 1
Name: Name, dtype: int64
Out[174]:
John 9
James 7
Mary 6William 6Ivan 4
Bertha 4
William Henry 4
William John 4
Name: Name, dtype: int64
In [ ]:
#--------------------------------
In [183]:
df.head()
Out[183]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Cabin_Zone | Ticket_e | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 0 | 1 |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | C | 1 |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 0 | 1 |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | C | 0 |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | 0 | 0 |
#查看数据缺失情况 print(df.isnull().sum()) df_test.isnull().sum()
PassengerId 0 Survived 0 Pclass 0 Name 0 Sex 0 Age 177 SibSp 0 Parch 0 Ticket 0 Fare 0 Cabin 687 Embarked 2 Cabin_Zone 0 Ticket_e 0 dtype: int64
Out[177]:
PassengerId 0
Pclass 0
Name 0
Sex 0
Age 86SibSp 0
Parch 0
Ticket 0
Fare 1
Cabin 327
Embarked 0
dtype: int64
In [179]:
df[df['Embarked'].isnull()]
Out[179]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Cabin_Zone | Ticket_e | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | 62 | 1 | 1 | Icard, Miss. Amelie | female | 38.0 | 0 | 0 | 113572 | 80.0 | B28 | NaN | B | 0 |
| 829 | 830 | 1 | 1 | Stone, Mrs. George Nelson (Martha Evelyn) | female | 62.0 | 0 | 0 | 113572 | 80.0 | B28 | NaN | B | 0 |
print(df['Embarked'].value_counts())
print(df[df['Pclass']==1].Embarked.value_counts())
df.Embarked.fillna('S',inplace=True)
#上船地点填充S 644 C 168 Q 77 Name: Embarked, dtype: int64
Out[181]:
S 127 C 85 Q 2 Name: Embarked, dtype: int64
In [234]:
#Cabin缺失值的处理，方法一
df['Cabin_e']=df['Cabin'].isnull().map({True:0,False:1})
df_test['Cabin_e']=df_test['Cabin'].isnull().map({True:0,False:1})
#df=df.drop(['Cabin_e'],axis=1)
#df['Cabin_e']=df['Cabin'].isnull().map(lambda x:0 if x is True else 1)方法二!!!!!!
#df_test['Cabin_e']
#df.head()#df=df.drop(['Cabin_e'],axis=1)
"""方法三
import re
def Cabin_isnull(x):
pattern=re.compile("\d$")
try:
re.search(pattern,x).group()
return 1
except:
return 0
df['Cabin_e']=df['Cabin'].apply(lambda x: Cabin_isnull(x))
df.head()"""
df_test.head()
Out[234]:
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Cabin_e | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q | 0 |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S | 0 |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q | 0 |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S | 0 |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S | 0 |
df['Gender']=5
df['Gender']=df['Sex'].map(lambda x:x[0].upper())
print(df.head())
df['Gender']=df['Sex'].map({'male':1,'female':0}) #并行化执行
df.head()
PassengerId Survived Pclass \ 0 1 0 3 1 2 1 1 2 3 1 3 3 4 1 1 4 5 0 3 Name Sex Age SibSp \ 0 Braund, Mr. Owen Harris male 22.0 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 2 Heikkinen, Miss. Laina female 26.0 0 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 4 Allen, Mr. William Henry male 35.0 0 Parch Ticket Fare Cabin Embarked Gender 0 0 A/5 21171 7.2500 NaN S M 1 0 PC 17599 71.2833 C85 C F 2 0 STON/O2. 3101282 7.9250 NaN S F 3 0 113803 53.1000 C123 S F 4 0 373450 8.0500 NaN S M
Out[8]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Gender | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 1 |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 0 |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 0 |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | 0 |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | 1 |
#Age年龄离散化，以5岁为一个周期
def age_map(x):
if x<10:
return '10-'
if x<60:
return '%d-%d'%(x//5*5,x//5*5+5)
elif x>=60:
return '60+'
else:
return 'Null'
df['Age_map']=df['Age'].apply(lambda x: age_map(x))
df_test['Age_map']=df_test['Age'].apply(lambda x:age_map(x))
df.groupby('Age_map')['Survived'].agg(['count','mean'])#不同年龄层的存亡情况Out[236]:
| count | mean | |
|---|---|---|
| Age_map | ||
| 10- | 62 | 0.612903 |
| 10-15 | 16 | 0.437500 |
| 15-20 | 86 | 0.395349 |
| 20-25 | 114 | 0.342105 |
| 25-30 | 106 | 0.358491 |
| 30-35 | 95 | 0.421053 |
| 35-40 | 72 | 0.458333 |
| 40-45 | 48 | 0.375000 |
| 45-50 | 41 | 0.390244 |
| 50-55 | 32 | 0.437500 |
| 55-60 | 16 | 0.375000 |
| 60+ | 26 | 0.269231 |
| Null | 177 | 0.293785 |
#test中的Fare缺失 print(df_test[df_test['Fare'].isnull()]) df_test.loc[df_test.Fare.isnull(),'Fare']=df_test[(df_test['Pclass']==3)&(df_test['Embarked']=='S')&(df_test['Sex']=='male')].dropna().Fare.mean() df_test[df_test['PassengerId']==1044]
Empty DataFrame Columns: [PassengerId, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked, Cabin_e, Age_map] Index: []
Out[247]:
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Cabin_e | Age_map | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 152 | 1044 | 3 | Storey, Mr. Thomas | male | 60.5 | 0 | 0 | 3701 | 7.65 | NaN | S | 0 | 60+ |
#数据归一化以加速模型收敛，Fare分布太宽 import sklearn.preprocessing as preprocessing scaler=preprocessing.StandardScaler() fare_scale_param=scaler.fit(df['Fare'].values.reshape(-1,1)) df['Fare_e']=fare_scale_param.transform(df['Fare'].values.reshape(-1,1)) df_test['Fare_e']=fare_scale_param.transform(df_test['Fare'].values.reshape(-1,1)) print(df.head()) df_test.head()
PassengerId Survived Pclass \ 0 1 0 3 1 2 1 1 2 3 1 3 3 4 1 1 4 5 0 3 Name Sex Age SibSp \ 0 Braund, Mr. Owen Harris male 22.0 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 2 Heikkinen, Miss. Laina female 26.0 0 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 4 Allen, Mr. William Henry male 35.0 0 Parch Ticket Fare Cabin Embarked Cabin_Zone Ticket_e \ 0 0 A/5 21171 7.2500 NaN S 0 1 1 0 PC 17599 71.2833 C85 C C 1 2 0 STON/O2. 3101282 7.9250 NaN S 0 1 3 0 113803 53.1000 C123 S C 0 4 0 373450 8.0500 NaN S 0 0 Cabin_e Age_map Fare_e 0 0 20-25 -0.502445 1 1 35-40 0.786845 2 0 25-30 -0.488854 3 1 35-40 0.420730 4 0 35-40 -0.486337
Out[266]:
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Cabin_e | Age_map | Fare_e | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q | 0 | 30-35 | -0.490783 |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S | 0 | 45-50 | -0.507479 |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q | 0 | 60+ | -0.453367 |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S | 0 | 25-30 | -0.474005 |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S | 0 | 20-25 | -0.401017 |
#部分变量哑编码，onehot独热码 df_x = pd.concat([df[['SibSp','Parch','Fare']], pd.get_dummies(df[['Pclass','Sex','Cabin','Embarked','Age_map']])],axis=1)#按照列黏连 df_y = df.Survived df_test_x = pd.concat([df_test[['SibSp','Parch','Fare']], pd.get_dummies(df_test[['Pclass', 'Sex','Cabin','Embarked', 'Age_map']])],axis=1) print(df_x.head()) df_test_x.head()
SibSp Parch Fare Pclass Sex_female Sex_male Cabin_A10 Cabin_A14 \ 0 1 0 7.2500 3 0 1 0 0 1 1 0 71.2833 1 1 0 0 0 2 0 0 7.9250 3 1 0 0 0 3 1 0 53.1000 1 1 0 0 0 4 0 0 8.0500 3 0 1 0 0 Cabin_A16 Cabin_A19 ... Age_map_20-25 Age_map_25-30 \ 0 0 0 ... 1 0 1 0 0 ... 0 0 2 0 0 ... 0 1 3 0 0 ... 0 0 4 0 0 ... 0 0 Age_map_30-35 Age_map_35-40 Age_map_40-45 Age_map_45-50 Age_map_50-55 \ 0 0 0 0 0 0 1 0 1 0 0 0 2 0 0 0 0 0 3 0 1 0 0 0 4 0 1 0 0 0 Age_map_55-60 Age_map_60+ Age_map_Null 0 0 0 0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 [5 rows x 169 columns]
Out[267]:
| SibSp | Parch | Fare | Pclass | Sex_female | Sex_male | Cabin_A11 | Cabin_A18 | Cabin_A21 | Cabin_A29 | ... | Age_map_20-25 | Age_map_25-30 | Age_map_30-35 | Age_map_35-40 | Age_map_40-45 | Age_map_45-50 | Age_map_50-55 | Age_map_55-60 | Age_map_60+ | Age_map_Null | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 7.8292 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 0 | 7.0000 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 9.6875 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 0 | 0 | 8.6625 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 1 | 12.2875 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
In [ ]:
#缺失年龄填补
In [9]:
M=np.random.randn(2,3) #i代表性别 j代表仓等级 for i in range(2): for j in range(3): M[i][j]=df[(df['Pclass']==j+1) & (df['Gender']==i)]['Age'].median() #取中位数 M
Out[9]:
array([[ 35. , 28. , 21.5], [ 40. , 30. , 25. ]])
In [10]:
df['AgeFill']=df['Age'] print(len(df[df['Age'].isnull()][['Gender','Pclass','Age','AgeFill']])) print(df[df['Age'].isnull()][['Gender','Pclass','Age','AgeFill']].head()) for i in range(2): for j in range(3): df.loc[df[(df['Pclass']==j+1) & (df['Gender']==i) & (df['Age'].isnull())].index,['AgeFill']]=M[i][j] #.index取索引值 df[df['Age'].isnull()][['Gender','Pclass','Age','AgeFill']].head()
177 Gender Pclass Age AgeFill 5 1 3 NaN NaN 17 1 2 NaN NaN 19 0 3 NaN NaN 26 1 3 NaN NaN 28 0 3 NaN NaN
Out[10]:
| Gender | Pclass | Age | AgeFill | |
|---|---|---|---|---|
| 5 | 1 | 3 | NaN | 25.0 |
| 17 | 1 | 2 | NaN | 30.0 |
| 19 | 0 | 3 | NaN | 21.5 |
| 26 | 1 | 3 | NaN | 25.0 |
| 28 | 0 | 3 | NaN | 21.5 |
#--------------特征工程 df['familysize']=df['SibSp']+df['Parch'] df['Pclass*AgeFill']=df['Pclass']*df['AgeFill']
In [12]:
for i in df.dtypes: print (i)
int64 int64 int64 object object float64 int64 int64 object float64 object object int64 float64 int64 float64
In [13]:
#df=df.drop(['Pclass*Age'],axis=1) 丢弃一列 print(df.dtypes) df.dtypes[df.dtypes.map(lambda x: x=='object')]
PassengerId int64 Survived int64 Pclass int64 Name object Sex object Age float64 SibSp int64 Parch int64 Ticket object Fare float64 Cabin object Embarked object Gender int64 AgeFill float64 familysize int64 Pclass*AgeFill float64 dtype: object
Out[13]:
Name object Sex object Ticket object Cabin object Embarked object dtype: object
In [14]:
df=df.drop(['Name','Sex','Age','Ticket','Cabin','Embarked'],axis=1)
df.head()
Out[14]:
| PassengerId | Survived | Pclass | SibSp | Parch | Fare | Gender | AgeFill | familysize | Pclass*AgeFill | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | 1 | 0 | 7.2500 | 1 | 22.0 | 1 | 66.0 |
| 1 | 2 | 1 | 1 | 1 | 0 | 71.2833 | 0 | 38.0 | 1 | 38.0 |
| 2 | 3 | 1 | 3 | 0 | 0 | 7.9250 | 0 | 26.0 | 0 | 78.0 |
| 3 | 4 | 1 | 1 | 1 | 0 | 53.1000 | 0 | 35.0 | 1 | 35.0 |
| 4 | 5 | 0 | 3 | 0 | 0 | 8.0500 | 1 | 35.0 | 0 | 105.0 |
from sklearn.cross_validation import train_test_split from sklearn.linear_model.logistic import LogisticRegression from sklearn import preprocessing import seaborn as sns feature_names=['Pclass','SibSp','Parch','Fare','Gender','AgeFill','familysize','Pclass*AgeFill'] X=df[feature_names] Y=df['Survived'] X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=0) #7：3拆分 lr_model=LogisticRegression() lr_model.fit(X_train,y_train) y_pred_score=lr_model.predict_proba(X_test) y_pred_score[:10]
D:\Anaconda3\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20. "This module will be removed in 0.20.", DeprecationWarning)
Out[15]:
array([[ 0.857523 , 0.142477 ], [ 0.86057444, 0.13942556], [ 0.9224654 , 0.0775346 ], [ 0.10549417, 0.89450583], [ 0.41466724, 0.58533276], [ 0.59148554, 0.40851446], [ 0.0714005 , 0.9285995 ], [ 0.06661603, 0.93338397], [ 0.6030931 , 0.3969069 ], [ 0.28987151, 0.71012849]])
In [16]:
#from sklearn.metrics import roc_curve
import sklearn
import matplotlib.pyplot as plt
fpr,tpr,thresholds=sklearn.metrics.roc_curve(y_test,y_pred_score[:,1])#注意阈值
roc_auc=sklearn.metrics.auc(fpr,tpr)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr,tpr,'b',label='AUC = %0.2f'%roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.0])
plt.ylim([-0.1,1.01])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()D:\Anaconda3\lib\site-packages\matplotlib\legend.py:326: UserWarning: Unrecognized location "lower right". Falling back on "best"; valid locations are best upper right upper left lower left lower right right center left center right lower center upper center center six.iterkeys(self.codes))))
In [17]:
df=df.dropna() #有空值存在的所有行 train_data=df.values train_data
Out[17]:
array([[ 1. , 0. , 3. , ..., 22. , 1. , 66. ], [ 2. , 1. , 1. , ..., 38. , 1. , 38. ], [ 3. , 1. , 3. , ..., 26. , 0. , 78. ], ..., [ 889. , 0. , 3. , ..., 21.5, 3. , 64.5], [ 890. , 1. , 1. , ..., 26. , 0. , 26. ], [ 891. , 0. , 3. , ..., 32. , 0. , 96. ]])
In [18]:
#使用网格搜索最佳模型参数!!!!!! X_train,X_test,y_train,y_test
from sklearn.model_selection import GridSearchCV
base_line_model = LogisticRegression()
param = {'penalty':['l1','l2'],
'C':[0.1, 0.5, 1.0,5.0]}
grd = GridSearchCV(estimator=base_line_model, param_grid=param, cv=5, n_jobs=3)
grd.fit(X_train,y_train)
grd.best_estimator_Out[18]:
LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1, penalty='l1', random_state=None, solver='liblinear', tol=0.0001, verbose=0, warm_start=False)
In [27]:
from sklearn.model_selection import learning_curve from sklearn.utils import shuffle X_train, y_train = shuffle(X_train, y_train) def plot_learning_curve(clf, title, X, y, ylim=None, cv=None, n_jobs=3, train_sizes=np.linspace(.05, 1., 5)): train_sizes, train_scores, test_scores = learning_curve( clf, X, y, train_sizes=train_sizes) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) ax = plt.figure().add_subplot(111) ax.set_title(title) if ylim is not None: ax.ylim(*ylim) ax.set_xlabel(u"train_num_of_samples") ax.set_ylabel(u"score") ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="b") ax.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="r") ax.plot(train_sizes, train_scores_mean, 'o-', color="b", label=u"train score") ax.plot(train_sizes, test_scores_mean, 'o-', color="r", label=u"testCV score") ax.legend(loc="best") midpoint = ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] - test_scores_std[-1])) / 2 diff = (train_scores_mean[-1] + train_scores_std[-1]) - (test_scores_mean[-1] - test_scores_std[-1]) return midpoint, diff plot_learning_curve(grd, u"learning_rate", X_train, y_train)
D:\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:597: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5. % (min_groups, self.n_splits)), Warning)
Out[27]:
(0.79962671354141879, 0.069011468383558872)
In [28]:
plt.show()
In [44]:
#from sklearn.metrics import roc_curve
import sklearn
import matplotlib.pyplot as plt
fpr,tpr,thresholds=sklearn.metrics.roc_curve(y_test,grd.predict_proba(X_test)[:,1],pos_label=1)#grd可以预测
roc_auc=sklearn.metrics.auc(fpr,tpr)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr,tpr,'b',label='AUC = %0.2f'%roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.0])
plt.ylim([-0.1,1.01])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
D:\Anaconda3\lib\site-packages\matplotlib\legend.py:326: UserWarning: Unrecognized location "lower right". Falling back on "best"; valid locations are best upper right upper left lower left lower right right center left center right lower center upper center center six.iterkeys(self.codes))))
In [45]:
#使用grd模型生成预测结果 并存入CSV
#df_test=pd.read_csv('D:/In/kaggle/Titanic/test.csv')
gender_submission = pd.DataFrame({'PassengerId':X_test.index,'Survived':grd.predict(X_test)})
gender_submission.to_csv('C://Users//zhangshuai_lc//submission_first.csv', index=None)
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