【机器学习】三大树模型实战乳腺癌预测分类

公众号：尤而小屋
作者：Peter
编辑：Peter

今天给大家带来一篇新的UCI数据集建模的文章。

本文从数据的探索分析出发，经过特征工程和样本均衡性处理，使用决策树、随机森林、梯度提升树对一份女性乳腺癌的数据集进行分析和预测建模。

关键词：相关性、决策树、随机森林、降维、独热码、乳腺癌

数据集

数据是来自UCI官网，很老的一份数据，主要是用于分类问题，可以自行下载学习

https://archive.ics.uci.edu/ml/datasets/breast+cancer

导入库

import pandas as pd
import numpy as npimport matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inlineimport plotly_express as px
import plotly.graph_objects as gofrom sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifierfrom sklearn.tree import export_graphviz
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn import metrics from sklearn.model_selection import train_test_split

导入数据

数据是来自UCI官网，下载到本地可以直接读取。只是这份数据是.data文件，没有文件头，我们需要自行指定对应的文件头（网上搜索的）

# 来自ucidf = pd.read_table("breast-cancer.data",sep=",",names=["Class","age","menopause","tumor-size","inv-nodes","node-caps","deg-malig","breast","breast-quad","irradiat"])df

基本信息

In [3]:

df.dtypes   # 字段类型

Out[3]:

全部是object类型，只有一个int64类型

Class          object
age            object
menopause      object
tumor-size     object
inv-nodes      object
node-caps      object
deg-malig       int64
breast         object
breast-quad    object
irradiat       object
dtype: object

In [4]:

df.isnull().sum()  # 缺失值

Out[4]:

数据比较完整，没有缺失值

Class          0
age            0
menopause      0
tumor-size     0
inv-nodes      0
node-caps      0
deg-malig      0
breast         0
breast-quad    0
irradiat       0
dtype: int64

In [5]:

## 字段解释columns = df.columns.tolist()
columns

Out[5]:

['Class','age','menopause','tumor-size','inv-nodes','node-caps','deg-malig','breast','breast-quad','irradiat']

下面是每个字段的含义和具体的取值范围：

属性名	含义	取值范围
Class	是否复发	no-recurrence-events, recurrence-events
age	年龄	10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99
menopause	绝经情况	lt40（40岁之前绝经）, ge40（40岁之后绝经）, premeno（还未绝经）
tumor-size	肿瘤大小	0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59
inv-nodes	受侵淋巴结数	0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39
node-caps	有无结节帽	yes, no
deg-malig	恶性肿瘤程度	1, 2, 3
breast	肿块位置	left, right
breast-quad	肿块所在象限	left-up, left-low, right-up, right-low, central
irradiat	是否放疗	yes,no

去除缺失值

In [6]:

两个字段中的？就是本数据中的缺失值，我们直接选择非缺失值值的数据

df = df[(df["node-caps"] != "?") & (df["breast-quad"] != "?")]len(df)

Out[6]:

字段处理

In [7]:

from sklearn.preprocessing import LabelEncoder

年龄段-age

In [8]:

age = df["age"].value_counts().reset_index()
age.columns = ["年龄段", "人数"]age

可以看到数据中大部分的用户集中在40-59岁。对年龄段执行独热码：

df = df.join(pd.get_dummies(df["age"]))
df.drop("age", axis=1, inplace=True)
df.head()

绝经-menopause

In [11]:

menopause = df["menopause"].value_counts().reset_index()
menopause

Out[11]:

	index	menopause
0	premeno	149
1	ge40	123
2	lt40	5

In [12]:

fig = px.pie(menopause,names="index",values="menopause")fig.update_traces(textposition='inside',   textinfo='percent+label')fig.show()

df = df.join(pd.get_dummies(df["menopause"]))  # 独热码df.drop("menopause",axis=1, inplace=True)

肿瘤大小-tumor-size

In [14]:

tumor_size = df["tumor-size"].value_counts().reset_index()tumor_size

Out[14]:

	index	tumor-size
0	30-34	57
1	25-29	51
2	20-24	48
3	15-19	29
4	10-14	28
5	40-44	22
6	35-39	19
7	0-4	8
8	50-54	8
9	5-9	4
10	45-49	3

In [15]:

fig = px.bar(tumor_size, x="index",y="tumor-size",color="tumor-size",text="tumor-size")fig.show()

df = df.join(pd.get_dummies(df["tumor-size"]))df.drop("tumor-size",axis=1, inplace=True)

In [18]:

df = df.join(pd.get_dummies(df["inv-nodes"]))df.drop("inv-nodes",axis=1, inplace=True)

有无结节帽-node-caps

In [19]:

df["node-caps"].value_counts()

Out[19]:

no     221
yes     56
Name: node-caps, dtype: int64

In [20]:

df = df.join(pd.get_dummies(df["node-caps"]).rename(columns={"no":"node_capes_no", "yes":"node_capes_yes"}))df.drop("node-caps",axis=1, inplace=True)

恶性肿瘤程度-deg-malig

In [21]:

df["deg-malig"].value_counts()

Out[21]:

2    129
3     82
1     66
Name: deg-malig, dtype: int64

肿块位置-breast

In [22]:

df["breast"].value_counts()

Out[22]:

left     145
right    132
Name: breast, dtype: int64

In [23]:

df = df.join(pd.get_dummies(df["breast"]))df.drop("breast",axis=1, inplace=True)

...

是否复发-Class

这个是最终预测的因变量，我们需要将文本信息转成0-1的数值信息

In [29]:

dic = {"no-recurrence-events":0, "recurrence-events":1}
df["Class"] = df["Class"].map(dic)  # 实施转换df

复发和非复发的统计：

sns.countplot(df['Class'],label="Count")plt.show()

样本不均衡处理

In [31]:

# 样本量分布df["Class"].value_counts()

Out[31]:

0    196
1     81
Name: Class, dtype: int64

In [32]:

from imblearn.over_sampling import SMOTE

In [33]:

X = df.iloc[:,1:]
y = df.iloc[:,0]y.head()

Out[33]:

0    0
1    0
2    0
3    0
4    0
Name: Class, dtype: int64

In [34]:

groupby_df = df.groupby('Class').count()      # 输出原始数据集样本分类分布
groupby_df

model_smote = SMOTE()x_smote_resampled, y_smote_resampled = model_smote.fit_resample(X, y)
x_smoted = pd.DataFrame(x_smote_resampled, columns=df.columns.tolist()[1:])
y_smoted = pd.DataFrame(y_smote_resampled,columns=['Class'])
df_smoted = pd.concat([x_smoted, y_smoted],axis=1)

建模

数据集划分

In [38]:

X = df_smoted.iloc[:,:-1]
y = df_smoted.iloc[:,-1]from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,random_state=123)

决策树

In [39]:

dt = DecisionTreeClassifier(max_depth=5)
dt.fit(X_train, y_train)

Out[39]:

DecisionTreeClassifier(max_depth=5)

In [40]:

# 预测
y_prob = dt.predict_proba(X_test)[:,1]# 预测的概率转成0-1分类
y_pred = np.where(y_prob > 0.5, 1, 0)
dt.score(X_test, y_pred)

Out[40]:

1.0

In [41]:

# 混淆矩阵confusion_matrix(y_test, y_pred)

Out[41]:

array([[29,  8],[19, 23]])

In [42]:

# 分类得分报告
print(classification_report(y_test, y_pred))precision    recall  f1-score   support0       0.60      0.78      0.68        371       0.74      0.55      0.63        42accuracy                           0.66        79macro avg       0.67      0.67      0.66        79
weighted avg       0.68      0.66      0.65        79

In [43]:

# roc
metrics.roc_auc_score(y_test, y_pred)

Out[43]:

0.6657014157014157

In [44]:

# roc曲线from sklearn.metrics import roc_curve, auc
false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)
roc_auc = auc(false_positive_rate, true_positive_rate)import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))  # 画布
plt.title('ROC')  # 标题plt.plot(false_positive_rate,  # 绘图true_positive_rate,color='red',label = 'AUC = %0.2f' % roc_auc)plt.legend(loc = 'lower right') #  图例位置
plt.plot([0, 1], [0, 1],linestyle='--')  # 正比例直线plt.axis('tight')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()

随机森林

In [45]:

rf = RandomForestClassifier(max_depth=5)
rf.fit(X_train, y_train)

梯度提升树

In [50]:

from sklearn.ensemble import GradientBoostingClassifier

In [51]:

gbc = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=5, subsample=1,min_samples_split=2, min_samples_leaf=1, max_depth=3)gbc.fit(X_train, y_train)

Out[51]:

GradientBoostingClassifier(n_estimators=5, subsample=1)

In [55]:

# roc曲线from sklearn.metrics import roc_curve, auc
false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)
roc_auc = auc(false_positive_rate, true_positive_rate)import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))  # 画布
plt.title('ROC')  # 标题plt.plot(false_positive_rate,  # 绘图true_positive_rate,color='red',label = 'AUC = %0.2f' % roc_auc)plt.legend(loc = 'lower right') #  图例位置
plt.plot([0, 1], [0, 1],linestyle='--')  # 正比例直线plt.axis('tight')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()

PCA降维

降维过程

In [56]:

from sklearn.decomposition import PCA
pca = PCA(n_components=17)
pca.fit(X)#返回所保留的17个成分各自的方差百分比
print(pca.explained_variance_ratio_)
[0.17513053 0.12941834 0.11453698 0.07323991 0.05889187 0.056903040.04869476 0.0393374  0.03703477 0.03240863 0.03062932 0.025741370.01887462 0.0180381  0.01606983 0.01453912 0.01318003]

In [57]:

sum(pca.explained_variance_ratio_)

Out[57]:

0.9026686181152915

降维后数据

In [58]:

X_NEW = pca.transform(X)
X_NEW

Out[58]:

array([[ 1.70510215e-01,  5.39929099e-01, -1.04314303e+00, ...,-2.26541223e-01, -6.39332871e-02, -8.97923150e-02],[-9.01105403e-01,  8.01693088e-01,  5.92260258e-01, ...,9.66299251e-02,  1.40755806e-03, -2.74626972e-01],[-6.05200264e-01,  6.08455330e-01, -1.00524376e+00, ...,4.11416630e-02,  4.15705282e-02, -8.46941345e-02],...,[ 1.40652211e-02,  5.35906106e-01,  5.64150123e-02, ...,1.70834934e-01,  7.11616391e-02, -1.72250445e-01],[-4.41363597e-01,  9.11950641e-01, -4.22184256e-01, ...,-4.13385344e-02, -7.64405982e-02,  1.04686148e-01],[ 1.98533663e+00, -4.74547396e-01, -1.52557494e-01, ...,2.72194184e-02,  5.71553613e-02,  1.78074886e-01]])

In [59]:

X_NEW.shape

Out[59]:

(392, 17)

重新划分数据

In [60]:

X_train,X_test,y_train,y_test = train_test_split(X_NEW,y,test_size=0.20,random_state=123)

再用随机森林

In [61]:

rf = RandomForestClassifier(max_depth=5)
rf.fit(X_train, y_train)

Out[61]:

RandomForestClassifier(max_depth=5)

In [62]:

# 预测
y_prob = rf.predict_proba(X_test)[:,1]# 预测的概率转成0-1分类
y_pred = np.where(y_prob > 0.5, 1, 0)
rf.score(X_test, y_pred)

Out[62]:

1.0

In [63]:

# 混淆矩阵confusion_matrix(y_test, y_pred)

Out[63]:

array([[26, 11],[13, 29]])

In [64]:

# roc
metrics.roc_auc_score(y_test, y_pred)

Out[64]:

0.6965894465894465

In [65]:

# roc曲线from sklearn.metrics import roc_curve, auc
false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)
roc_auc = auc(false_positive_rate, true_positive_rate)import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.title('ROC')  plt.plot(false_positive_rate,  true_positive_rate,color='red',label = 'AUC = %0.2f' % roc_auc)plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],linestyle='--')  plt.axis('tight')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()

总结

从数据预处理和特征工程出发，建立不同的树模型表现来看，随机森林表现的最好，AUC值高达0.81，在经过对特征简单的降维之后，我们选择前17个特征，它们的重要性超过90%，再次建模，此时AUC值达到0.83。

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