sklearn：决策分类树_泰坦尼克号_网格扫描 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV

# 读取数据
data = pd.read_csv("./Taitanic data/data.csv")
# 注意：标签是Survived，没哟在最后一列
data
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

891 rows × 12 columns

# 查看数据信息
# 可以看到数据类型 和 每个字段非空值数据量
# 可以看到Age、Cabin字段有数据缺失，需要专门处理
data.info()

<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: 83.7+ KB

data.head()
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

筛选特征

# 删除 Name，影响较小
# 删除 Cabin，缺失值较多
# Ticket在这里也没多大用处，也删掉
data.drop(['Name', 'Cabin', 'Ticket'], axis=1, inplace=True)

# 因为 Embarked 比其他数据多两行空值，所以删掉其为空值的两行
data = data[data['Embarked'].notna()]

data.head()
PassengerId Survived Pclass Sex Age SibSp Parch Fare Embarked
0 1 0 3 male 22.0 1 0 7.2500 S
1 2 1 1 female 38.0 1 0 71.2833 C
2 3 1 3 female 26.0 0 0 7.9250 S
3 4 1 1 female 35.0 1 0 53.1000 S
4 5 0 3 male 35.0 0 0 8.0500 S 
data['Embarked'].unique().tolist()

['S', 'C', 'Q']

# 将Sex、Embarked转换为数字类型
data['Sex'] = data['Sex'].map({'male': 0, 'female': 1})
data['Embarked'] = data['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})
data.head()

D:\Users\jayson\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
D:\Users\jayson\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until
PassengerId Survived Pclass Sex Age SibSp Parch Fare Embarked
0 1 0 3 0 22.0 1 0 7.2500 0
1 2 1 1 1 38.0 1 0 71.2833 1
2 3 1 3 1 26.0 0 0 7.9250 0
3 4 1 1 1 35.0 1 0 53.1000 0
4 5 0 3 0 35.0 0 0 8.0500 0 
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 889 entries, 0 to 890
Data columns (total 9 columns):
PassengerId    889 non-null int64
Survived       889 non-null int64
Pclass         889 non-null int64
Sex            889 non-null int64
Age            712 non-null float64
SibSp          889 non-null int64
Parch          889 non-null int64
Fare           889 non-null float64
Embarked       889 non-null int64
dtypes: float64(2), int64(7)
memory usage: 69.5 KB

# 对年龄进行填充：中值或均值，此处使用中值 填充
data.loc[data['Age'].isna(), 'Age'] = data['Age'].median()
data

D:\Users\jayson\Anaconda3\lib\site-packages\pandas\core\indexing.py:494: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self.obj[item] = s
PassengerId Survived Pclass Sex Age SibSp Parch Fare Embarked
0 1 0 3 0 22.0 1 0 7.2500 0
1 2 1 1 1 38.0 1 0 71.2833 1
2 3 1 3 1 26.0 0 0 7.9250 0
3 4 1 1 1 35.0 1 0 53.1000 0
4 5 0 3 0 35.0 0 0 8.0500 0
... ... ... ... ... ... ... ... ... ...
886 887 0 2 0 27.0 0 0 13.0000 0
887 888 1 1 1 19.0 0 0 30.0000 0
888 889 0 3 1 28.0 1 2 23.4500 0
889 890 1 1 0 26.0 0 0 30.0000 1
890 891 0 3 0 32.0 0 0 7.7500 2

889 rows × 9 columns

# 分离特征数据和 标签数据

X = data.drop('Survived', axis=1)
y = data['Survived']

X
PassengerId Pclass Sex Age SibSp Parch Fare Embarked
0 1 3 0 22.0 1 0 7.2500 0
1 2 1 1 38.0 1 0 71.2833 1
2 3 3 1 26.0 0 0 7.9250 0
3 4 1 1 35.0 1 0 53.1000 0
4 5 3 0 35.0 0 0 8.0500 0
... ... ... ... ... ... ... ... ...
886 887 2 0 27.0 0 0 13.0000 0
887 888 1 1 19.0 0 0 30.0000 0
888 889 3 1 28.0 1 2 23.4500 0
889 890 1 0 26.0 0 0 30.0000 1
890 891 3 0 32.0 0 0 7.7500 2

889 rows × 8 columns

拆分数据

from sklearn.model_selection import train_test_split

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

# 因为数据是随机拆分的，所以为了后续选择数据方便，将索引重置一下
for i in [X_train, X_test, y_train, y_test]:
    i.index = range(0, i.shape[0])

X_train
PassengerId Pclass Sex Age SibSp Parch Fare Embarked
0 294 3 1 24.0 0 0 8.8500 0
1 157 3 1 16.0 0 0 7.7333 2
2 542 3 1 9.0 4 2 31.2750 0
3 742 1 0 36.0 1 0 78.8500 0
4 220 2 0 30.0 0 0 10.5000 0
... ... ... ... ... ... ... ... ...
706 410 3 1 28.0 3 1 25.4667 0
707 821 1 1 52.0 1 1 93.5000 0
708 562 3 0 40.0 0 0 7.8958 0
709 729 2 0 25.0 1 0 26.0000 0
710 120 3 1 2.0 4 2 31.2750 0

711 rows × 8 columns

先粗略训练一下查看效果

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(random_state=2)
clf = clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
score

0.7696629213483146

通过交叉验证，画学习曲线

  • 查看训练集和测试集的效果
from sklearn.model_selection import cross_val_score

model_scores = []
cross_vscors = []
for depth in range(1, 10):
    clf = DecisionTreeClassifier(random_state=25
                                , max_depth=depth
#                                 , criterion='entropy'  # 通常认为entropy是当模型欠拟合时候使用
                                )
    clf = clf.fit(X_train, y_train)
    score_tr = clf.score(X_train, y_train)
    cross_tr = cross_val_score(clf, X, y, cv=10).mean()
    model_scores.append(score_tr)
    cross_vscors.append(cross_tr)

plt.plot(range(1, 10), model_scores, color='red', label='train')
plt.plot(range(1, 10), cross_vscors, color='green', label='test')
plt.xticks(range(1, 11))
plt.legend(loc='upper left')

<matplotlib.legend.Legend at 0x24aec8a9608>

png

网格搜索查看参数




import numpy as np

# gini_thresholds = np.linspace(0, 0.5, 50)  # 基尼系数常用取值范围
# entropy_threholds = np.linspace(0, 1, 50)

# 定义模型参数，用于传入GridSearchCV，且在实例化模型时候，不需要传入参数
parameters = {"splitter": ('best', 'random')
              , "criterion": ("gini", "entropy")
              , "min_samples_leaf": [*range(1, 50, 5)]
              , "min_impurity_decrease": [*np.linspace(0, 0.5, 20)]  # 不使用网格搜索，这个参数比较难使用
              , "max_depth": [*range(1, 10)]
             }
clf = DecisionTreeClassifier(random_state=25)
GS = GridSearchCV(clf, parameters, cv=10)
GS.fit(X_train, y_train)

D:\Users\jayson\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.
  DeprecationWarning)





GridSearchCV(cv=10, error_score='raise-deprecating',
             estimator=DecisionTreeClassifier(class_weight=None,
                                              criterion='gini', max_depth=None,
                                              max_features=None,
                                              max_leaf_nodes=None,
                                              min_impurity_decrease=0.0,
                                              min_impurity_split=None,
                                              min_samples_leaf=1,
                                              min_samples_split=2,
                                              min_weight_fraction_leaf=0.0,
                                              presort=False, random_state=25,
                                              splitter='best'),
             iid='warn', n_...
                                                   0.23684210526315788,
                                                   0.2631578947368421,
                                                   0.2894736842105263,
                                                   0.3157894736842105,
                                                   0.3421052631578947,
                                                   0.3684210526315789,
                                                   0.39473684210526316,
                                                   0.42105263157894735,
                                                   0.4473684210526315,
                                                   0.47368421052631576, 0.5],
                         'min_samples_leaf': [1, 6, 11, 16, 21, 26, 31, 36, 41,
                                              46],
                         'splitter': ('best', 'random')},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)

# 最好的参数
GS.best_params_

{'criterion': 'gini',
 'max_depth': 3,
 'min_impurity_decrease': 0.0,
 'min_samples_leaf': 1,
 'splitter': 'best'}

# 最高的得分
GS.best_score_

0.8171589310829818

# 网格搜索的缺点：输入进去的参数都会使用到，他不会自动舍弃某些参数，可能有时候舍弃某些参数的模型性能更好
posted on 2021-01-02 00:36  jaysonteng  阅读(299)  评论(0)  编辑  收藏  举报
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