概述

数据探索见：python：乳腺癌预测之数据探索

@H_301_13@实验器材

● UCI

● python

● seaborn

@H_301_13@进群：548377875   即可获取数十套PDF哦！

@H_301_13@实验内容

数据预处理

对诊断结果进行二值话，方便适应所有的预测算法。同时采用 @H_301_13@preprocessing.scale 进行量化处理

拆分数据集

按照80/20方式，拆分训练集和测试机。 训练集按照交叉验证方式进行训练。

@H_301_13@train_x1,test_x1,train_y,test_y = train_test_split(x_value_scaled,y_values,test_size=0.2)

选择最好的预测诊断算法

本次实验分别实验了 逻辑回归，随机森林，svm，线性SVM，决策树，高斯贝叶斯，梯度迭代决策树 几种算法。并利用learning_curve来判断是否过拟合。

本次预测实验的评价标准为预测精确度。

先来定义几个常用的函数

1

来看看初步的算法筛选

总体上看逻辑回归，svm 效果是最好的。看看是不是存在过拟合的情况

并没有存在过拟合的情况。

随机森林和决策树（未调参）情况下，存在一定的过拟合情况。

2

超参数调整

从上面的实验情况看，LR和SVM是比较好的精度。现在来进一步调整下这两个算法的超参数。

@H_301_13@C : float,optional (default=1.0)

@H_301_13@kernel : string,optional (default=’rbf’)

在测试集上，有3个恶性的被判定为良性。

@H_301_13@C : float,default: 1.0 含义如SVM的C

测试集合判断的情况是一样的。

3

算法融合

采用VotingClassifier的硬分类方式。采用SVM和LR融合

可以加入不同方式的算法来进一步融合，这里选择KNN

@H_301_13@实验结语

本实验『@H_301_13@WedO实验君』和大家一个做了乳腺癌的预测，采用不同的算法融合的策略。关键点为：过拟合判断，超参数筛选，算法融合。

@H_301_13@附上jupyter notebook代码

# coding: utf-8

# In[1]:

import itertools

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn import preprocessing

from sklearn import model_selection

from sklearn.model_selection import train_test_split

from sklearn.decomposition import PCA

from sklearn.metrics import confusion_matrix,make_scorer,accuracy_score

from sklearn.model_selection import gridsearchcv,learning_curve

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC,LinearSVC

from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,VotingClassifier

from sklearn.neural_network import MLPClassifier as MLPC

get_ipython().run_line_magic('matplotlib','inline')

# In[2]:

data = pd.read_csv('f:/dm/data.csv')

col = data.columns

col

# In[3]:

data.isnull().sum()

# In[4]:

data.head()

# In[5]:

data.info()

# In[6]:

x_values = data.drop(['diagnosis','id'],axis = 1)

y_values = data['diagnosis']

# In[7]:

data.describe()

# In[8]:

def plot_Box(data,cols = 3):

size = len(data.columns)

rows = size//cols + 1

fig = plt.figure(figsize=(13,10))

cnt = 1

for col_name in data.columns:

ax = fig.add_subplot(rows,cols,cnt)

plt.Boxplot(data[col_name])

ax.set_xlabel(col_name)

cnt = cnt + 1

plt.tight_layout()

plt.show()

plot_Box(x_values.iloc[:,0:8],4)

# In[9]:

plot_Box(x_values.iloc[:,8:16],4)

# In[10]:

plot_Box(x_values.iloc[:,16:32],4)

# In[11]:

def plot_distribution(data,target_col):

sns.set_style("whitegrid")

for col_name in data.columns:

if col_name != target_col:

title=("# of %s vs %s " % (col_name,target_col))

distributionOne = sns.FacetGrid(data,hue=target_col,aspect=2.5)

distributionOne.map(plt.hist,col_name,bins=30)

distributionOne.add_legend()

distributionOne.set_axis_labels(col_name,'Count')

distributionOne.fig.suptitle(title)

distributionTwo = sns.FacetGrid(data,aspect=2.5)

distributionTwo.map(sns.kdeplot,shade= True)

distributionTwo.set(xlim=(0,data[col_name].max()))

distributionTwo.add_legend()

distributionTwo.set_axis_labels(col_name,'Proportion')

distributionTwo.fig.suptitle(title)

plot_distribution(data,'diagnosis')

# In[12]:

g = sns.heatmap(x_values.corr(),cmap="BrBG",annot=False)

# In[13]:

plot_distribution(data[( data['area_mean'] > 500 ) & (data['area_mean'] < 800)],'diagnosis')

# In[14]:

g = sns.heatmap(x_values.iloc[:,1:10].corr(),annot=False)

# In[15]:

def diagnosis_to_binary(data):

data["diagnosis"] = data["diagnosis"].astype("category")

data["diagnosis"].cat.categories = [0,1]

data["diagnosis"] = data["diagnosis"].astype("int")

diagnosis_to_binary(data)

x_values = data.drop(['diagnosis',axis = 1)

y_values = data['diagnosis']

x_value_scaled = preprocessing.scale(x_values)

x_value_scaled = pd.DataFrame(x_value_scaled,columns = x_values.columns,index=data["id"])

x_value_all = x_value_scaled

#x_value_all['diag'] = y_values.tolist()

#x_value_all.head()

# In[16]:

#x_value_scaled.groupby([u'diag']).agg({ 'compactness_mean': [np.mean]}).reset_index()

# In[17]:

variance_pct = .99 # Minimum percentage of variance we want to be described by the resulting transformed components

pca = PCA(n_components=variance_pct) # Create PCA object

x_transformed = pca.fit_transform(x_value_scaled,y_values) # Transform the initial features

x_values_scaled_PCA = pd.DataFrame(x_transformed) # Create a data frame from the PCA'd data

# In[18]:

g = sns.heatmap(x_values_scaled_PCA.corr(),annot=False)

# ## 拆分数据集合

# In[19]:

x_value_scaled.head()

# In[20]:

y_values.head()

# In[21]:

def plot_learning_curve(estimator,title,X,y,ylim=None,cv=None,

n_jobs=1,train_sizes=np.linspace(.1,1.0,5)):

"""

Plots a learning curve. http://scikit-learn.org/stable/modules/learning_curve.html

"""

plt.figure()

plt.title(title)

if ylim is not None:

plt.ylim(*ylim)

plt.xlabel("Training examples")

plt.ylabel("score")

train_sizes,train_scores,test_scores = learning_curve(

estimator,cv=cv,n_jobs=n_jobs,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)

plt.grid()

plt.fill_between(train_sizes,train_scores_mean - train_scores_std,

train_scores_mean + train_scores_std,alpha=0.1,

color="r")

plt.fill_between(train_sizes,test_scores_mean - test_scores_std,

test_scores_mean + test_scores_std,color="g")

plt.plot(train_sizes,train_scores_mean,'o-',color="r",

label="Training score")

plt.plot(train_sizes,test_scores_mean,color="g",

label="cross-validation score")

plt.legend(loc="best")

return plt

def plot_confusion_matrix(cm,classes,

normalize=False,

title='Confusion matrix',

cmap=plt.cm.Blues):

"""

http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

"""

if normalize:

cm = cm.astype('float') / cm.sum(axis=1)[:,np.newaxis]

print("Normalized confusion matrix")

else:

print('Confusion matrix,without normalization')

print(cm)

plt.imshow(cm,interpolation='nearest',cmap=cmap)

plt.title(title)

plt.colorbar()

tick_marks = np.arange(len(classes))

plt.xticks(tick_marks,rotation=45)

plt.yticks(tick_marks,classes)

fmt = '.2f' if normalize else 'd'

thresh = cm.max() / 2.

for i,j in itertools.product(range(cm.shape[0]),range(cm.shape[1])):

plt.text(j,i,format(cm[i,j],fmt),

horizontalalignment="center",

color="white" if cm[i,j] > thresh else "black")

plt.tight_layout()

plt.ylabel('True label')

plt.xlabel('Predicted label')

dict_characters = {1: 'Malignant',0: 'Benign'}

# In[22]:

def compareABunchOfDifferentModelsAccuracy(a,b,c,d):

"""

compare performance of classifiers on X_train,X_test,Y_train,Y_test

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score

http://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-score

"""

print(' Compare Multiple Classifiers: ')

print('K-Fold cross-validation Accuracy: ')

names = []

models = []

resultsAccuracy = []

models.append(('LR',LogisticRegression()))

models.append(('RF',RandomForestClassifier()))

models.append(('KNN',KNeighborsClassifier()))

models.append(('SVM',SVC()))

models.append(('LSVM',LinearSVC()))

models.append(('GNB',GaussianNB()))

models.append(('DTC',DecisionTreeClassifier()))

models.append(('GBC',GradientBoostingClassifier()))

for name,model in models:

plot_learning_curve(model,'Learning Curve For %s Classifier'% (name),a,(0.8,1.1),10)

for name,model in models:

model.fit(a,b)

kfold = model_selection.KFold(n_splits=10,random_state=7)

accuracy_results = model_selection.cross_val_score(model,cv=kfold,scoring='accuracy')

resultsAccuracy.append(accuracy_results)

names.append(name)

accuracyMessage = "%s: %f (%f)" % (name,accuracy_results.mean(),accuracy_results.std())

print(accuracyMessage)

# Boxplot

fig = plt.figure()

fig.suptitle('Algorithm Comparison: Accuracy')

ax = fig.add_subplot(111)

plt.Boxplot(resultsAccuracy)

ax.set_xticklabels(names)

ax.set_ylabel('cross-validation: Accuracy score')

plt.show()

# In[56]:

train_x1,test_size=0.2)

# In[57]:

train_x1.columns

# In[58]:

#'texture_se','texture_worst'

drop_list = []

train_x = train_x1.drop(drop_list,axis = 1)

test_x = test_x1.drop(drop_list,axis = 1)

# In[59]:

compareABunchOfDifferentModelsAccuracy(train_x,None,None)

# In[60]:

def selectParametersForSVM(a,d):

model = SVC()

parameters = {'C': [ 0.01,0.1,0.5,5.0,10,25,50,100],

'kernel': ['linear','poly','rbf','sigmoid']}

accuracy_scorer = make_scorer(accuracy_score)

grid_obj = gridsearchcv(model,parameters,scoring=accuracy_scorer)

grid_obj = grid_obj.fit(a,b)

model = grid_obj.best_estimator_

model.fit(a,b)

print('Selected Parameters for SVM: ')

print(model," ")

kfold = model_selection.KFold(n_splits=10,random_state=7)

accuracy = model_selection.cross_val_score(model,scoring='accuracy')

mean = accuracy.mean()

stdev = accuracy.std()

print('Support Vector Machine - Training set accuracy: %s (%s)' % (mean,stdev))

print('')

prediction = model.predict(c)

#print(prediction[0])

cnf_matrix = confusion_matrix(d,prediction)

np.set_printoptions(precision=2)

class_names = dict_characters

plt.figure()

plot_confusion_matrix(cnf_matrix,classes=class_names,title='Confusion matrix')

plt.figure()

plot_confusion_matrix(cnf_matrix,normalize=True,

title='Normalized confusion matrix')

plot_learning_curve(model,'Learning Curve For SVM Classifier',(0.85,10)

return prediction

# In[61]:

def selectParametersForLR(a,d):

model = LogisticRegression()

parameters = {'C': [ 0.01,100]}

accuracy_scorer = make_scorer(accuracy_score)

grid_obj = gridsearchcv(model,b)

print('Selected Parameters for LR: ')

print(model,'Learning Curve For LR Classifier',10)

return prediction

# In[62]:

prediction = selectParametersForLR(train_x,test_x,test_y)

# In[63]:

prediction = selectParametersForSVM(train_x,test_y)

x_err_data = pd.DataFrame(columns = train_x.columns)

real_ = test_y.tolist()

indexs = []

err_diag = []

k=0

for i in range(len(prediction)):

if prediction[i] != real_[i]:

x_err_data.loc[k] = test_x.iloc[i].tolist()

indexs.append(test_x.index[i])

err_diag.append(test_y.iloc[i])

k = k + 1

x_err_data.index = indexs

x_err_data["diag"] = err_diag

x_err_data

# In[64]:

data[data['id']==91594602]

# In[65]:

def selectParametersForMLPC(a,d):

"""http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.gridsearchcv.html

http://scikit-learn.org/stable/modules/grid_search.html#grid-search"""

model = MLPC()

parameters = {'verbose': [False],

'activation': ['logistic','relu'],

'max_iter': [1000,2000],'learning_rate': ['constant','adaptive']}

accuracy_scorer = make_scorer(accuracy_score)

grid_obj = gridsearchcv(model,b)

print('Selected Parameters for Multi-Layer Perceptron NN: ')

print(model)

print('')

kfold = model_selection.KFold(n_splits=10)

accuracy = model_selection.cross_val_score(model,scoring='accuracy')

mean = accuracy.mean()

stdev = accuracy.std()

print('SKlearn Multi-Layer Perceptron - Training set accuracy: %s (%s)' % (mean,stdev))

print('')

prediction = model.predict(c)

cnf_matrix = confusion_matrix(d,'Learning Curve For MLPC Classifier',1),10)

# In[66]:

selectParametersForMLPC(train_x,test_y)

# In[71]:

def runVotingClassifier(a,d):

"""http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html

http://scikit-learn.org/stable/modules/ensemble.html#voting-classifier"""

#global votingC,mean,stdev # eventually I should get rid of these global variables and use classes instead. in this case i need these variables for the submission function.

votingC = VotingClassifier(estimators=[('SVM',SVC(C=5.0,cache_size=200,class_weight=None,coef0=0.0,

decision_function_shape='ovr',degree=3,gamma='auto',kernel='rbf',

max_iter=-1,probability=False,random_state=None,shrinking=True,

tol=0.001,verbose=False)),('LR',LogisticRegression(C=0.1,dual=False,fit_intercept=True,

intercept_scaling=1,max_iter=100,multi_class='ovr',n_jobs=1,

penalty='l2',solver='liblinear',tol=0.0001,

verbose=0,warm_start=False)),('KNN',KNeighborsClassifier())],voting='hard')

votingC = votingC.fit(a,b)

kfold = model_selection.KFold(n_splits=10)

accuracy = model_selection.cross_val_score(votingC,scoring='accuracy')

meanC = accuracy.mean()

stdevC = accuracy.std()

print('Ensemble Voting Classifier - Training set accuracy: %s (%s)' % (meanC,stdevC))

print('')

#return votingC,meanC,stdevC

prediction = votingC.predict(c)

cnf_matrix = confusion_matrix(d,

title='Normalized confusion matrix')

plot_learning_curve(votingC,'Learning Curve For Ensemble Voting Classifier',10)

# In[72]:

runVotingClassifier(train_x,test_y)

总结

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