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ML之RF：kaggle比赛之利用泰坦尼克号数据集建立RF模型对每个人进行获救是否预测
2022-04-22 15:23:00 【一个处女座的程序猿】
ML之RF：kaggle比赛之利用泰坦尼克号数据集建立RF模型对每个人进行获救是否预测
目录
输出结果
实现代码
输出结果
后期更新……
实现代码
#预测模型选择的RF
import
numpy
as
np
import
pandas
as
pd
from
pandas
import
DataFrame
from
patsy
import
dmatrices
import
string
from
operator
import
itemgetter
import
json
from
sklearn.
ensemble
import
RandomForestClassifier
from
sklearn.
cross_validation
import
cross_val_score
from
sklearn.
pipeline
import
Pipeline
from
sklearn.
grid_search
import
GridSearchCV
from
sklearn.
cross_validation
import
train_test_split,
StratifiedShuffleSplit,
StratifiedKFold
from
sklearn
import
preprocessing
from
sklearn.
metrics
import
classification_report
from
sklearn.
externals
import
joblib
##Read configuration parameters
train_file
=
"train.csv"
MODEL_PATH
=
"./"
test_file
=
"test.csv"
SUBMISSION_PATH
=
"./"
seed
=
0
print(
train_file,
seed)
# 输出得分
def
report(
grid_scores,
n_top
=
3):
top_scores
=
sorted(
grid_scores,
key
=
itemgetter(
1),
reverse
=
True)[:
n_top]
for
i,
score
in
enumerate(
top_scores):
print(
"Model with rank: {0}".
format(
i
+
1))
print(
"Mean validation score: {0:.3f} (std: {1:.3f})".
format(
score.
mean_validation_score,
np.
std(
score.
cv_validation_scores)))
print(
"Parameters: {0}".
format(
score.
parameters))
print(
"")
#清理和处理数据
def
substrings_in_string(
big_string,
substrings):
for
substring
in
substrings:
if
string.
find(
big_string,
substring)
!=
-
1:
return
substring
print(
big_string)
return
np.
nan
le
=
preprocessing.
LabelEncoder()
enc
=
preprocessing.
OneHotEncoder()
def
clean_and_munge_data(
df):
#处理缺省值
df.
Fare
=
df.
Fare.
map(
lambda
x:
np.
nan
if
x
==
0
else
x)
#处理一下名字，生成Title字段
title_list
=[
'Mrs',
'Mr',
'Master',
'Miss',
'Major',
'Rev',
'Dr',
'Ms',
'Mlle',
'Col',
'Capt',
'Mme',
'Countess',
'Don',
'Jonkheer']
df[
'Title']
=
df[
'Name'].
map(
lambda
x:
substrings_in_string(
x,
title_list))
#处理特殊的称呼，全处理成mr, mrs, miss, master
def
replace_titles(
x):
title
=
x[
'Title']
if
title
in [
'Mr',
'Don',
'Major',
'Capt',
'Jonkheer',
'Rev',
'Col']:
return
'Mr'
elif
title
in [
'Master']:
return
'Master'
elif
title
in [
'Countess',
'Mme',
'Mrs']:
return
'Mrs'
elif
title
in [
'Mlle',
'Ms',
'Miss']:
return
'Miss'
elif
title
==
'Dr':
if
x[
'Sex']
==
'Male':
return
'Mr'
else:
return
'Mrs'
elif
title
==
'':
if
x[
'Sex']
==
'Male':
return
'Master'
else:
return
'Miss'
else:
return
title
df[
'Title']
=
df.
apply(
replace_titles,
axis
=
1)
#看看家族是否够大，咳咳
df[
'Family_Size']
=
df[
'SibSp']
+
df[
'Parch']
df[
'Family']
=
df[
'SibSp']
*
df[
'Parch']
df.
loc[ (
df.
Fare.
isnull())
&(
df.
Pclass
==
1),
'Fare']
=
np.
median(
df[
df[
'Pclass']
==
1][
'Fare'].
dropna())
df.
loc[ (
df.
Fare.
isnull())
&(
df.
Pclass
==
2),
'Fare']
=
np.
median(
df[
df[
'Pclass']
==
2][
'Fare'].
dropna())
df.
loc[ (
df.
Fare.
isnull())
&(
df.
Pclass
==
3),
'Fare']
=
np.
median(
df[
df[
'Pclass']
==
3][
'Fare'].
dropna())
df[
'Gender']
=
df[
'Sex'].
map( {
'female':
0,
'male':
1} ).
astype(
int)
df[
'AgeFill']
=
df[
'Age']
mean_ages
=
np.
zeros(
4)
mean_ages[
0]
=
np.
average(
df[
df[
'Title']
==
'Miss'][
'Age'].
dropna())
mean_ages[
1]
=
np.
average(
df[
df[
'Title']
==
'Mrs'][
'Age'].
dropna())
mean_ages[
2]
=
np.
average(
df[
df[
'Title']
==
'Mr'][
'Age'].
dropna())
mean_ages[
3]
=
np.
average(
df[
df[
'Title']
==
'Master'][
'Age'].
dropna())
df.
loc[ (
df.
Age.
isnull())
& (
df.
Title
==
'Miss') ,
'AgeFill']
=
mean_ages[
0]
df.
loc[ (
df.
Age.
isnull())
& (
df.
Title
==
'Mrs') ,
'AgeFill']
=
mean_ages[
1]
df.
loc[ (
df.
Age.
isnull())
& (
df.
Title
==
'Mr') ,
'AgeFill']
=
mean_ages[
2]
df.
loc[ (
df.
Age.
isnull())
& (
df.
Title
==
'Master') ,
'AgeFill']
=
mean_ages[
3]
df[
'AgeCat']
=
df[
'AgeFill']
df.
loc[ (
df.
AgeFill
<=
10) ,
'AgeCat']
=
'child'
df.
loc[ (
df.
AgeFill
>
60),
'AgeCat']
=
'aged'
df.
loc[ (
df.
AgeFill
>
10)
& (
df.
AgeFill
<=
30) ,
'AgeCat']
=
'adult'
df.
loc[ (
df.
AgeFill
>
30)
& (
df.
AgeFill
<=
60) ,
'AgeCat']
=
'senior'
df.
Embarked
=
df.
Embarked.
fillna(
'S')
df.
loc[
df.
Cabin.
isnull()
==
True,
'Cabin']
=
0.5
df.
loc[
df.
Cabin.
isnull()
==
False,
'Cabin']
=
1.5
df[
'Fare_Per_Person']
=
df[
'Fare']
/(
df[
'Family_Size']
+
1)
#Age times class
df[
'AgeClass']
=
df[
'AgeFill']
*
df[
'Pclass']
df[
'ClassFare']
=
df[
'Pclass']
*
df[
'Fare_Per_Person']
df[
'HighLow']
=
df[
'Pclass']
df.
loc[ (
df.
Fare_Per_Person
<
8) ,
'HighLow']
=
'Low'
df.
loc[ (
df.
Fare_Per_Person
>=
8) ,
'HighLow']
=
'High'
le.
fit(
df[
'Sex'] )
x_sex
=
le.
transform(
df[
'Sex'])
df[
'Sex']
=
x_sex.
astype(
np.
float)
le.
fit(
df[
'Ticket'])
x_Ticket
=
le.
transform(
df[
'Ticket'])
df[
'Ticket']
=
x_Ticket.
astype(
np.
float)
le.
fit(
df[
'Title'])
x_title
=
le.
transform(
df[
'Title'])
df[
'Title']
=
x_title.
astype(
np.
float)
le.
fit(
df[
'HighLow'])
x_hl
=
le.
transform(
df[
'HighLow'])
df[
'HighLow']
=
x_hl.
astype(
np.
float)
le.
fit(
df[
'AgeCat'])
x_age
=
le.
transform(
df[
'AgeCat'])
df[
'AgeCat']
=
x_age.
astype(
np.
float)
le.
fit(
df[
'Embarked'])
x_emb
=
le.
transform(
df[
'Embarked'])
df[
'Embarked']
=
x_emb.
astype(
np.
float)
df
=
df.
drop([
'PassengerId',
'Name',
'Age',
'Cabin'],
axis
=
1)
#remove Name,Age and PassengerId
return
df
#读取数据
traindf
=
pd.
read_csv(
train_file)
##清洗数据
df
=
clean_and_munge_data(
traindf)
########################################formula################################
formula_ml
=
'Survived~Pclass+C(Title)+Sex+C(AgeCat)+Fare_Per_Person+Fare+Family_Size'
y_train,
x_train
=
dmatrices(
formula_ml,
data
=
df,
return_type
=
'dataframe')
y_train
=
np.
asarray(
y_train).
ravel()
print(
y_train.
shape,
x_train.
shape)
##选择训练和测试集
X_train,
X_test,
Y_train,
Y_test
=
train_test_split(
x_train,
y_train,
test_size
=
0.2,
random_state
=
seed)
#初始化分类器
clf
=
RandomForestClassifier(
n_estimators
=
500,
criterion
=
'entropy',
max_depth
=
5,
min_samples_split
=
1,
min_samples_leaf
=
1,
max_features
=
'auto',
bootstrap
=
False,
oob_score
=
False,
n_jobs
=
1,
random_state
=
seed,
verbose
=
0)
###grid search找到最好的参数
param_grid
=
dict( )
##创建分类pipeline
pipeline
=
Pipeline([ (
'clf',
clf) ])
grid_search
=
GridSearchCV(
pipeline,
param_grid
=
param_grid,
verbose
=
3,
scoring
=
'accuracy',\
cv
=
StratifiedShuffleSplit(
Y_train,
n_iter
=
10,
test_size
=
0.2,
train_size
=
None,
indices
=
None, \
random_state
=
seed,
n_iterations
=
None)).
fit(
X_train,
Y_train)
# 对结果打分
print(
"Best score: %0.3f"
%
grid_search.
best_score_)
print(
grid_search.
best_estimator_)
report(
grid_search.
grid_scores_)
print(
'-----grid search end------------')
print (
'on all train set')
scores
=
cross_val_score(
grid_search.
best_estimator_,
x_train,
y_train,
cv
=
3,
scoring
=
'accuracy')
print(
scores.
mean(),
scores)
print (
'on test set')
scores
=
cross_val_score(
grid_search.
best_estimator_,
X_test,
Y_test,
cv
=
3,
scoring
=
'accuracy')
print(
scores.
mean(),
scores)
# 对结果打分
print(
classification_report(
Y_train,
grid_search.
best_estimator_.
predict(
X_train) ))
print(
'test data')
print(
classification_report(
Y_test,
grid_search.
best_estimator_.
predict(
X_test) ))
model_file
=
MODEL_PATH
+
'model-rf.pkl'
joblib.
dump(
grid_search.
best_estimator_,
model_file)
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版权声明
本文为[一个处女座的程序猿]所创，转载请带上原文链接，感谢
https://blog.51cto.com/yunyaniu/5244055
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