• 用管道简化工作流

• 使用k折交叉验证评估模型性能

• 使用学习和验证曲线调试算法

• 通过网格搜索进行超参数调优

• 比较不同的性能评估指标

一、用管道简化工作流

在很多机器学习算法中，我们可能需要做一系列的基本操作后才能进行建模，如：在建立逻辑回归之前，我们可能需要先对数据进行标准化，然后使用PCA将维，最后拟合逻辑回归模型并预测。那有没有什么办法可以同时进行这些操作，使得这些操作形成一个工作流呢？下面请看代码：

1. 加载基本工具库

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use("ggplot")
import warnings
warnings.filterwarnings("ignore")

2. 加载数据，并做基本预处理

# 加载数据
df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data",header=None)
# 做基本的数据预处理
from sklearn.preprocessing import LabelEncoderX = df.iloc[:,2:].values
y = df.iloc[:,1].values
le = LabelEncoder()    #将M-B等字符串编码成计算机能识别的0-1
y = le.fit_transform(y)
le.transform(['M','B'])
# 数据切分8：2
from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,stratify=y,random_state=1)

3. 把所有的操作全部封在一个管道pipeline内形成一个工作流：标准化+PCA+逻辑回归

完成以上操作，共有两种方式：

方式1：make_pipeline

# 把所有的操作全部封在一个管道pipeline内形成一个工作流：
## 标准化+PCA+逻辑回归### 方式1：make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipelinepipe_lr1 = make_pipeline(StandardScaler(),PCA(n_components=2),LogisticRegression(random_state=1))
pipe_lr1.fit(X_train,y_train)
y_pred1 = pipe_lr.predict(X_test)
print("Test Accuracy: %.3f"% pipe_lr1.score(X_test,y_test))

Test Accuracy: 0.956

方式2：Pipeline

# 把所有的操作全部封在一个管道pipeline内形成一个工作流：
## 标准化+PCA+逻辑回归### 方式2：Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipelinepipe_lr2 = Pipeline([['std',StandardScaler()],['pca',PCA(n_components=2)],['lr',LogisticRegression(random_state=1)]])
pipe_lr2.fit(X_train,y_train)
y_pred2 = pipe_lr2.predict(X_test)
print("Test Accuracy: %.3f"% pipe_lr2.score(X_test,y_test))

Test Accuracy: 0.956

二、使用k折交叉验证评估模型性能

评估方式1：k折交叉验证

# 评估方式1：k折交叉验证from sklearn.model_selection import cross_val_scorescores1 = cross_val_score(estimator=pipe_lr,X = X_train,y = y_train,cv=10,n_jobs=1)
print("CV accuracy scores:%s" % scores1)
print("CV accuracy:%.3f +/-%.3f"%(np.mean(scores1),np.std(scores1)))

评估方式2：分层k折交叉验证

# 评估方式2：分层k折交叉验证from sklearn.model_selection import StratifiedKFoldkfold = StratifiedKFold(n_splits=10,random_state=1).split(X_train,y_train)
scores2 = []
for k,(train,test) in enumerate(kfold):pipe_lr.fit(X_train[train],y_train[train])score = pipe_lr.score(X_train[test],y_train[test])scores2.append(score)print('Fold:%2d,Class dist.:%s,Acc:%.3f'%(k+1,np.bincount(y_train[train]),score))
print('\nCV accuracy :%.3f +/-%.3f'%(np.mean(scores2),np.std(scores2)))

三、 使用学习和验证曲线调试算法

如果模型过于复杂，即模型有太多的自由度或者参数，就会有过拟合的风险（高方差）；而模型过于简单，则会有欠拟合的风险(高偏差)。

下面我们用这些曲线去识别并解决方差和偏差问题：

1. 用学习曲线诊断偏差与方差

# 用学习曲线诊断偏差与方差
from sklearn.model_selection import learning_curvepipe_lr3 = make_pipeline(StandardScaler(),LogisticRegression(random_state=1,penalty='l2'))
train_sizes,train_scores,test_scores = learning_curve(estimator=pipe_lr3,X=X_train,y=y_train,train_sizes=np.linspace(0.1,1,10),cv=10,n_jobs=1)
train_mean = np.mean(train_scores,axis=1)
train_std = np.std(train_scores,axis=1)
test_mean = np.mean(test_scores,axis=1)
test_std = np.std(test_scores,axis=1)
plt.plot(train_sizes,train_mean,color='blue',marker='o',markersize=5,label='training accuracy')
plt.fill_between(train_sizes,train_mean+train_std,train_mean-train_std,alpha=0.15,color='blue')
plt.plot(train_sizes,test_mean,color='red',marker='s',markersize=5,label='validation accuracy')
plt.fill_between(train_sizes,test_mean+test_std,test_mean-test_std,alpha=0.15,color='red')
plt.xlabel("Number of training samples")
plt.ylabel("Accuracy")
plt.legend(loc='lower right')
plt.ylim([0.8,1.02])
plt.show()

2. 用验证曲线解决欠拟合和过拟合

# 用验证曲线解决欠拟合和过拟合
from sklearn.model_selection import validation_curvepipe_lr3 = make_pipeline(StandardScaler(),LogisticRegression(random_state=1,penalty='l2'))
param_range = [0.001,0.01,0.1,1.0,10.0,100.0]
train_scores,test_scores = validation_curve(estimator=pipe_lr3,X=X_train,y=y_train,param_name='logisticregression__C',param_range=param_range,cv=10,n_jobs=1)
train_mean = np.mean(train_scores,axis=1)
train_std = np.std(train_scores,axis=1)
test_mean = np.mean(test_scores,axis=1)
test_std = np.std(test_scores,axis=1)
plt.plot(param_range,train_mean,color='blue',marker='o',markersize=5,label='training accuracy')
plt.fill_between(param_range,train_mean+train_std,train_mean-train_std,alpha=0.15,color='blue')
plt.plot(param_range,test_mean,color='red',marker='s',markersize=5,label='validation accuracy')
plt.fill_between(param_range,test_mean+test_std,test_mean-test_std,alpha=0.15,color='red')
plt.xscale('log')
plt.xlabel("Parameter C")
plt.ylabel("Accuracy")
plt.legend(loc='lower right')
plt.ylim([0.8,1.02])
plt.show()

四、通过网格搜索进行超参数调优

如果只有一个参数需要调整，那么用验证曲线手动调整是一个好方法，但是随着需要调整的超参数越来越多的时候，我们能不能自动去调整呢？！！！注意对比各个算法的时间复杂度。

（注意参数与超参数的区别：参数可以通过优化算法进行优化，如逻辑回归的系数；超参数是不能用优化模型进行优化的，如正则话的系数。）

方式1：网格搜索GridSearchCV()

# 方式1：网格搜索GridSearchCV()
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
import timestart_time = time.time()
pipe_svc = make_pipeline(StandardScaler(),SVC(random_state=1))
param_range = [0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]
param_grid = [{'svc__C':param_range,'svc__kernel':['linear']},{'svc__C':param_range,'svc__gamma':param_range,'svc__kernel':['rbf']}]
gs = GridSearchCV(estimator=pipe_svc,param_grid=param_grid,scoring='accuracy',cv=10,n_jobs=-1)
gs = gs.fit(X_train,y_train)
end_time = time.time()
print("网格搜索经历时间：%.3f S" % float(end_time-start_time))
print(gs.best_score_)
print(gs.best_params_)

方式2：随机网格搜索RandomizedSearchCV()

# 方式2：随机网格搜索RandomizedSearchCV()
from sklearn.model_selection import RandomizedSearchCV
from sklearn.svm import SVC
import timestart_time = time.time()
pipe_svc = make_pipeline(StandardScaler(),SVC(random_state=1))
param_range = [0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]
param_grid = [{'svc__C':param_range,'svc__kernel':['linear']},{'svc__C':param_range,'svc__gamma':param_range,'svc__kernel':['rbf']}]
# param_grid = [{'svc__C':param_range,'svc__kernel':['linear','rbf'],'svc__gamma':param_range}]
gs = RandomizedSearchCV(estimator=pipe_svc, param_distributions=param_grid,scoring='accuracy',cv=10,n_jobs=-1)
gs = gs.fit(X_train,y_train)
end_time = time.time()
print("随机网格搜索经历时间：%.3f S" % float(end_time-start_time))
print(gs.best_score_)
print(gs.best_params_)

方式3：嵌套交叉验证

# 方式3：嵌套交叉验证
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
import timestart_time = time.time()
pipe_svc = make_pipeline(StandardScaler(),SVC(random_state=1))
param_range = [0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]
param_grid = [{'svc__C':param_range,'svc__kernel':['linear']},{'svc__C':param_range,'svc__gamma':param_range,'svc__kernel':['rbf']}]
gs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid,scoring='accuracy',cv=2,n_jobs=-1)
scores = cross_val_score(gs,X_train,y_train,scoring='accuracy',cv=5)
end_time = time.time()
print("嵌套交叉验证：%.3f S" % float(end_time-start_time))
print('CV accuracy :%.3f +/-%.3f'%(np.mean(scores),np.std(scores)))

五、比较不同的性能评估指标

有时候，准确率不是我们唯一需要考虑的评价指标，因为有时候会存在各类预测错误的代价不一样。例如：在预测一个人的肿瘤疾病的时候，如果病人A真实得肿瘤但是我们预测他是没有肿瘤，跟A真实是健康但是预测他是肿瘤，二者付出的代价很大区别（想想为什么）。所以我们需要其他更加广泛的指标：

1. 绘制混淆矩阵

# 绘制混淆矩阵
from sklearn.metrics import confusion_matrixpipe_svc.fit(X_train,y_train)
y_pred = pipe_svc.predict(X_test)
confmat = confusion_matrix(y_true=y_test,y_pred=y_pred)
fig,ax = plt.subplots(figsize=(2.5,2.5))
ax.matshow(confmat, cmap=plt.cm.Blues,alpha=0.3)
for i in range(confmat.shape[0]):for j in range(confmat.shape[1]):ax.text(x=j,y=i,s=confmat[i,j],va='center',ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.show()

2. 各种指标的计算

# 各种指标的计算
from sklearn.metrics import precision_score,recall_score,f1_scoreprint('Precision:%.3f'%precision_score(y_true=y_test,y_pred=y_pred))
print('recall_score:%.3f'%recall_score(y_true=y_test,y_pred=y_pred))
print('f1_score:%.3f'%f1_score(y_true=y_test,y_pred=y_pred))

3. 将不同的指标与GridSearch结合

# 将不同的指标与GridSearch结合
from sklearn.metrics import make_scorer,f1_score
scorer = make_scorer(f1_score,pos_label=0)
gs = GridSearchCV(estimator=pipe_svc,param_grid=param_grid,scoring=scorer,cv=10)
gs = gs.fit(X_train,y_train)
print(gs.best_score_)
print(gs.best_params_)

4. 绘制ROC曲线

# 绘制ROC曲线
from sklearn.metrics import roc_curve,auc
from sklearn.metrics import make_scorer,f1_score
scorer = make_scorer(f1_score,pos_label=0)
gs = GridSearchCV(estimator=pipe_svc,param_grid=param_grid,scoring=scorer,cv=10)
y_pred = gs.fit(X_train,y_train).decision_function(X_test)
#y_pred = gs.predict(X_test)
fpr,tpr,threshold = roc_curve(y_test, y_pred) ###计算真阳率和假阳率
roc_auc = auc(fpr,tpr) ###计算auc的值
plt.figure()
lw = 2
plt.figure(figsize=(7,5))
plt.plot(fpr, tpr, color='darkorange',lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) ###假阳率为横坐标，真阳率为纵坐标做曲线
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([-0.05, 1.0])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic ')
plt.legend(loc="lower right")
plt.show()