吴裕雄 python 人工智能——基于神经网络算法在智能医疗诊断中的应用探索代码简要展示

#K-NN分类

import os

import sys

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable \

    from menzhenZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        lables.append(row[5])

    return np.array(dataset),np.array(lables)

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from \

    testZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        testlables.append(row[5])

    return np.array(testdataset),np.array(testlables)

#K-NN分类

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

#归一化

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVals, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))

    return normDataSet, ranges, minVals

erace = []

accuc = []

t = []

#启动和检测模型

def datingClassTest():

    datingDataMat,datingLabels = getdata("外科","胸外科")

    normMat, ranges, minVals = autoNorm(datingDataMat)

    testdataset,testlables = gettestdata("外科","胸外科")

    testnormMat, testranges, testminVals = autoNorm(testdataset)

    errorCount = 0.0

    start = time.time()

    for j in [3,5,7,9,11,13]:

        for i in range(np.shape(testnormMat)[0]):

            classifierResult = classify0(testnormMat[i,:],normMat,datingLabels,j)

            print("the classifier came back with: %s, the real answer is: %s" % (classifierResult, testlables[i]))

            if (classifierResult != testlables[i]):

                errorCount += 1.0

        end = time.time()

        t.append(end)

        erace.append(errorCount/float(np.shape(testnormMat)[0])*100)

        accuc.append((1.0-errorCount/float(np.shape(testnormMat)[0]))*100)

        print("错误率: %.2f%%" % (errorCount/float(np.shape(testnormMat)[0])*100))

        print("准确率: %.2f%%" % ((1.0-errorCount/float(np.shape(testnormMat)[0]))*100))

        print("训练和预测一共耗时: %.2f 秒" % (end-start))

datingClassTest()

print(accuc)

print(erace)

print(t)

#探索不同的K值对算法的影响

import matplotlib.pyplot as plt

x = [3,5,7,9,11,13]

plt.plot(x,erace,c='r')

plt.plot(x,accuc,c='g')

plt.legend(['error race','accuce race'],loc=9)

plt.show()

print(accuc)

print(erace)

#决策树

import os

import sys

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

from math import log

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhenZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        temp.append(row[5])

        dataset.append(temp)

    lables = []

    lables.append("呼吸急促")

    lables.append("持续性脉搏加快")

    lables.append("畏寒")

    lables.append("血压降低")

    lables.append("咳血")

    return dataset,lables

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from testZ where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        testlables.append(row[5])

    return testdataset,testlables

#计算熵值

def calcShannonEnt(dataSet):

    numEntries = len(dataSet)

    labelCounts = {}

    for featVec in dataSet:

        currentLabel = featVec[-1]

        if currentLabel not in labelCounts.keys(): labelCounts[currentLabel] = 0

        labelCounts[currentLabel] += 1

    shannonEnt = 0.0

    for key in labelCounts:

        prob = float(labelCounts[key])/numEntries

        shannonEnt -= prob * log(prob,2)

    return shannonEnt

#按照给定特征划分数据集

def splitDataSet(dataSet, axis, value):

    retDataSet = []

    for featVec in dataSet:

        if featVec[axis] == value:

            reducedFeatVec = featVec[:axis]

            reducedFeatVec.extend(featVec[axis+1:])

            retDataSet.append(reducedFeatVec)

    return retDataSet

#选择最好的属性

def chooseBestFeatureToSplit(dataSet):

    numFeatures = len(dataSet[0]) - 1

    baseEntropy = calcShannonEnt(dataSet)

    bestInfoGain = 0.0

    bestFeature = -1

    for i in range(numFeatures):

        featList = [example[i] for example in dataSet]

        uniqueVals = set(featList)

        newEntropy = 0.0

        for value in uniqueVals:

            subDataSet = splitDataSet(dataSet, i, value)

            prob = len(subDataSet)/float(len(dataSet))

            newEntropy += prob * calcShannonEnt(subDataSet)

        infoGain = baseEntropy - newEntropy

        if (infoGain > bestInfoGain):

            bestInfoGain = infoGain

            bestFeature = i

    return bestFeature                     

#统计机制

def majorityCnt(classList):

    classCount={}

    for vote in classList:

        if vote not in classCount.keys(): classCount[vote] = 0

        classCount[vote] += 1

    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

#创建决策树

def createTree(dataSet,labels):

    classList = [example[-1] for example in dataSet]

    if classList.count(classList[0]) == len(classList):

        return classList[0]

    if len(dataSet[0]) == 1:

        return majorityCnt(classList)

    bestFeat = chooseBestFeatureToSplit(dataSet)

    bestFeatLabel = labels[bestFeat]

    myTree = {bestFeatLabel:{}}

    temp = []

    for i in labels:

        if i != labels[bestFeat]:

            temp.append(i)

    labels = temp

    featValues = [example[bestFeat] for example in dataSet]

    uniqueVals = set(featValues)

    for value in uniqueVals:

        subLabels = labels[:]

        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)

    return myTree   

#使用决策树模型分类

def classify(inputTree,featLabels,testVec):

    for i in inputTree.keys():

        firstStr = i

        break

    secondDict = inputTree[firstStr]

    featIndex = featLabels.index(firstStr)

    key = testVec[featIndex]

    valueOfFeat = secondDict[key]

    if isinstance(valueOfFeat, dict):

        classLabel = classify(valueOfFeat, featLabels, testVec)

    else: classLabel = valueOfFeat

    return classLabel

#启动和检测模型

def datingClassTest():

    dataSet,labels = getdata("外科","胸外科")

    myTree = createTree(dataSet,labels)

    testdataset,testlables = gettestdata("外科","胸外科")

    errorCount = 0.0

    start = time.time()

    for i in range(np.shape(testdataset)[0]):

        classifierResult = classify(myTree,labels,testdataset[i])

        print("the classifier came back with: %s, the real answer is: %s" % (classifierResult, testlables[i]))

        if (classifierResult != testlables[i]):

            errorCount += 1.0

    end = time.time()

    print("错误率: %.2f%%" % (errorCount/float(np.shape(testdataset)[0])*100))

    print("准确率: %.2f%%" % ((1.0-errorCount/float(np.shape(testdataset)[0]))*100))

    print("训练和预测一共耗时: %.2f 秒" % (end-start))

datingClassTest()

#选取前600条记录生成并打印决策树

dataSet,labels = getdata("外科","胸外科")

dataSet = dataSet[0:600]

labels = labels[0:600]

myTree = createTree(dataSet,labels)

print(myTree)

#比较K-NN算法与决策树算法的优劣

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

x = np.array([10,12])

y = [85.6,87.3]

plt.bar(x,y,edgecolor='yellow')

for i,j in zip(x,y):

    plt.text(i-0.2,j-0.2,'%.2f%%' % j)

plt.text(9.7,40,'K-NN right race')

plt.text(11.7,40,'Tree right race')

plt.show()

#使用神经网络探索数据集

import sys

import os

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#one-hot编码

def onehot(labels):

    n_sample = len(labels)

    n_class = max(labels) + 1

    onehot_labels = np.zeros((n_sample, n_class))

    onehot_labels[np.arange(n_sample), labels] = 1

    return onehot_labels

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhen where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        if(row[5]==3):

            lables.append(0)

        elif(row[5]==6):

            lables.append(1)

        else:

            lables.append(2)

    dataset = np.array(dataset)

    lables = np.array(lables)

    dataset = dataset.astype(np.float32)

    labless = onehot(lables)

    return dataset,labless

#获取测试数据集

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from test where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        if(row[5]==3):

            testlables.append(0)

        elif(row[5]==6):

            testlables.append(1)

        else:

            testlables.append(2)

    testdataset = np.array(testdataset)

    testlables = np.array(testlables)

    testdataset = testdataset.astype(np.float32)

    testlabless = onehot(testlables)

    return testdataset,testlabless

dataset,labless = getdata("外科","胸外科")

testdataset,testlables = gettestdata("外科","胸外科")

dataset = dataset[0:100]

labless = labless[0:100]

x_data = tf.placeholder("float32", [None, 5])

y_data = tf.placeholder("float32", [None, 3])

weight = tf.Variable(tf.ones([5, 3]))

bias = tf.Variable(tf.ones([3]))

#使用softmax激活函数

y_model = tf.nn.softmax(tf.matmul(x_data, weight) + bias)

#y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)

# loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

#使用交叉熵作为损失函数

loss = -tf.reduce_sum(y_data*tf.log(y_model))

# train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)

#使用AdamOptimizer优化器

train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

#train_step = tf.train.MomentumOptimizer(1e-4,0.9).minimize(loss)

#评估模型

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

start = time.time()

for _ in range(10):

    for i in range(int(len(dataset)/100)):

        sess.run(train_step, feed_dict={x_data:dataset[i:i+100,:], y_data:labless[i:i+100,:]})

print("模型准确率",sess.run(accuracy, feed_dict={x_data:testdataset , y_data:testlables}))

end = time.time()

print("模型训练和测试公耗时：%.2f 秒" % (end-start))

#加深一层神经网络

import sys

import os

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

conn=cx_Oracle.connect('doctor/admin@localhost:1521/tszr')

cursor = conn.cursor()

#one-hot编码

def onehot(labels):

    n_sample = len(labels)

    n_class = max(labels) + 1

    onehot_labels = np.zeros((n_sample, n_class))

    onehot_labels[np.arange(n_sample), labels] = 1

    return onehot_labels

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhen where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        if(row[5]==3):

            lables.append(0)

        elif(row[5]==6):

            lables.append(1)

        else:

            lables.append(2)

    dataset = np.array(dataset)

    lables = np.array(lables)

    dataset = dataset.astype(np.float32)

    labless = onehot(lables)

    return dataset,labless

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from test where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        if(row[5]==3):

            testlables.append(0)

        elif(row[5]==6):

            testlables.append(1)

        else:

            testlables.append(2)

    testdataset = np.array(testdataset)

    testlables = np.array(testlables)

    testdataset = testdataset.astype(np.float32)

    testlabless = onehot(testlables)

    return testdataset,testlabless

dataset,labless = getdata("外科","胸外科")

testdataset,testlables = gettestdata("外科","胸外科")

dataset = dataset[0:100]

labless = labless[0:100]

x_data = tf.placeholder("float32", [None, 5])

y_data = tf.placeholder("float32", [None, 3])

weight1 = tf.Variable(tf.ones([5, 20]))

bias1 = tf.Variable(tf.ones([20]))

y_model1 = tf.matmul(x_data, weight1) + bias1

#加深一层神经网络

weight2 = tf.Variable(tf.ones([20, 3]))

bias2 = tf.Variable(tf.ones([3]))

y_model = tf.nn.softmax(tf.matmul(y_model1, weight2) + bias2)

loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

# loss = -tf.reduce_sum(y_data*tf.log(y_model))

#train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)

train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

# train_step = tf.train.MomentumOptimizer(1e-4,0.9).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

start = time.time()

for _ in range(10):

    for i in range(int(len(dataset)/100)):

        sess.run(train_step, feed_dict={x_data:dataset[i:i+100,:], y_data:labless[i:i+100,:]})

print("模型准确率",sess.run(accuracy, feed_dict={x_data:testdataset , y_data:testlables}))

end = time.time()

print("模型训练和测试公耗时：%.2f 秒" % (end-start))

#比较决策树与神经网络的优劣

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

x = np.array([10,12])

y = [87.1,87.4]

plt.bar(x,y,edgecolor="yellow")

for i,j in zip(x,y):

    plt.text(i-0.2,j-0.2,"%.2f%%" % j)

plt.text(9.7,40,"Tree right race")

plt.text(11.7,40,"Net right race")

plt.scatter([9.7,11.7],[0.05,0.36],c="r")

plt.plot([9.7,11.7],[0.05,0.36],c="g")

plt.show()

#统计各种算法处理模型数据

K-NN算法：

当K取[3,5,7,9,11,13]时，对应的：

准确率：[85.6, 72.6, 60.0, 47.4, 34.8, 22.299999999999996]

总耗时：[1554119134.435363, 1554119136.6192698,

     1554119138.846019, 1554119141.2507513, 1554119143.4782736, 1554119145.5415804]

决策树：

准确率: 87.10%

训练和预测一共耗时: 0.05 秒

神经网络设计：

1 最小二乘法 softmax GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.16 秒

2 最小二乘法 softmax AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.19 秒

3 最小二乘法 softmax MomentumOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.18 秒

4 最小二乘法 relu GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.17 秒

5 最小二乘法 relu AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.15 秒

6 最小二乘法 relu MomentumOptimizer 模型

模型准确率 0.006

模型训练和测试公耗时：0.19 秒

7 交叉熵 softmax GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.09 秒

8 交叉熵 softmax AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.08 秒

9 交叉熵 softmax MomentumOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.06 秒

10 交叉熵 relu GradientDescentOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.08 秒

11 交叉熵 relu AdamOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.08 秒

12 交叉熵 relu MomentumOptimizer 模型

模型准确率 0.874

模型训练和测试公耗时：0.09 秒

从上面的12种神经网络设计模型中可以看出：最小二乘法 relu MomentumOptimizer 模型

的准确率只有0.006，所以这种模型的设计是失败的。

a = [0.874]*10

print(a)

#计算成功的各种神经网络模型的准确率与耗时的比值：

a = [0.874]*11

b = [0.16,0.19,0.18,0.17,0.15,0.09,0.08,0.06,0.08,0.09,0.09]

c = []

for i in range(len(a)):

    c.append(a[i]/b[i])

for i in range(len(c)):

    print("准确率与耗时的比值:%.4f" % (c[i]))

#K-NN算法

#当K取3、5、7、9、11、13时的准确率饼图分布显示

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

acc = [85.6, 72.6, 60.0, 47.4, 34.8, 22.2]

labels = ['K-3','K-5','K-7','K-9','K-11','K-13']

plt.pie(acc,labels=labels,shadow=True,startangle=90,autopct='%1.4f%%')

plt.axis('equal')

plt.title('K-NN',fontsize=25)

plt.show()

#K-NN算法耗时散点图

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

x = np.array([1,2,3,4,5,6])

z = np.array([1554119134.435363, 1554119136.6192698,1554119138.846019,

              1554119141.2507513, 1554119143.4782736, 1554119145.5415804])

plt.scatter(x,z,c='g')

plt.xticks(x+0.4,['KNN-1','KNN-2','KNN-3','KNN-4','KNN-5','KNN-6'])

plt.show()

#神经网络算法对应各种有用的模型设计耗时曲线图

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

x = np.array([1,2,3,4,5,6,7,8,9,10,11])

z = np.array([0.16,0.19,0.18,0.17,0.15,0.09,0.08,0.06,0.08,0.09,0.09])

plt.scatter(x,z,c='r')

plt.xticks(x+0.4,['NET-1','NET-2','NET-3','NET-4','NET-5',

                 'NET-6','NET-7','NET-8','NET-9','NET-10','NET-11'])

plt.show()

#K-NN、决策树以及神经网络算法对比

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

acc = [85.6, 72.6, 60.0, 47.4, 34.8, 22.2,87.10,0.874,

       87.4,87.4,87.4,87.4,87.4,87.4,87.4,87.4,87.4,87.4]

labels = ['K-3','K-5','K-7','K-9','K-11','K-13','TREE',

          'NET-1','NET-2','NET-3','NET-4','NET-5','NET-6','NET-7',

         'NET-8','NET-9','NET-10','NET-11']

explode = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0]

plt.pie(acc,labels=labels,explode=explode,shadow=True,startangle=90,autopct='%1.4f%%')

plt.axis('equal')

plt.title('K-NN AND TREE AND NET',fontsize=25)

plt.show()

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