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import numpy as np
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import pandas as pd
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from sklearn import datasets
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from sklearn.neighbors import KNeighborsClassifier#估计器
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from sklearn.ensemble import RandomForestClassifier#估计器
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#管道
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from sklearn.model_selection import train_test_split#数据训练集、测试集划分
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from sklearn.preprocessing import StandardScaler#预处理器、转化器
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from sklearn.pipeline import make_pipeline
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from sklearn.pipeline import Pipeline#管道
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from sklearn.metrics import accuracy_score#准确性计算
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#交叉验证
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from sklearn.datasets import make_regression
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from sklearn.model_selection import cross_validate
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from sklearn.linear_model import LinearRegression
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#自动参数搜索
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from sklearn.datasets import fetch_california_housing
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from sklearn.model_selection import RandomizedSearchCV
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from scipy.stats import randint
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from sklearn.ensemble import RandomForestRegressor
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import matplotlib.pyplot as plt #python可视化库
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import seaborn as sns
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from sklearn.model_selection import cross_val_score
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from matplotlib.colors import ListedColormap
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from sklearn import neighbors
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import operator
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def run_KNN(X,X_train,y_train,K): #需要预测的数据集,训练集,训练集,K个最近
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dataSize = X_train.shape[0]
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y_predict = []
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for x in X:
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diff = np.tile(x,(dataSize,1)) - X_train # 把X扩大然后矩阵相减
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squaredDist = np.sum(diff**2,axis=1) # axis = 1计算每一行的和
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distance = squaredDist ** 0.5
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# 对距离递增排序获取最前面K个样本的种类并统计各种类出现次数
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nearIds = distance.argsort() # 按值排序，得到对应下标数组
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classesCount = {}
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for i in range(K):
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y = y_train[nearIds[i]] # 得到对应的种类
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classesCount[y] = classesCount.get(y,0)+1 # 0为设置默认值
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# print(classesCount)
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# 对字典按值进行递减排序
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sortClassesCount = sorted(classesCount.items(),key=operator.itemgetter(1),reverse=True)
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# 获取对象第二个元素 逆序
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y_predict.append(sortClassesCount[0][0]) # 预测种类为出现次数最多的那一类
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return y_predict
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sns.set_style("whitegrid")
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filename = 'iris\iris.data'
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data= pd.read_csv(filename,usecols=[0,1,2,3],header=None,names=["sepal length","sepal width","petal length","petal width"])
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target = pd.read_csv(filename,usecols=[4],header=None,names=["type"])
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test=pd.read_csv(filename,header=None,names=["sepal length","sepal width","petal length","petal width","type"])
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pipe=make_pipeline(
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StandardScaler(),#预处理器/转化器（特征缩放）
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KNeighborsClassifier() #估计器
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)
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x = data
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y = target
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#划分鸢尾花数据集
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x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)
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#求取k值
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k_range = range(1, 31)
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k_error = []
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index=0
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min=1
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#循环，取k=1到k=31，查看误差效果
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for k in k_range:
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pipe.fit(x_train,np.ravel(y_train))
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Pipeline(steps=[('standardscaler', StandardScaler()),('kneighborsclassfier', KNeighborsClassifier(n_neighbors=k))])
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#10折交叉验证
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scores = cross_val_score(KNeighborsClassifier(n_neighbors=k), x_train, y_train, cv=10, scoring='accuracy')
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k_error.append(1 - scores.mean())
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if k==1:
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index=k;
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elif k>1 and k_error[k-1]<min:
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index=k;
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min=k_error[k-1]
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#画图，x轴为k值，y值为误差值
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plt.plot(k_range, k_error)
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plt.xlabel('Value of K for KNN')
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plt.ylabel('Error')
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plt.show()
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print("最小误差为：",min)
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print("最小k值为：",index)
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# print(type(iris_x[0]))
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# print(iris_x[:2])
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# print(iris_y[:2])
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# print(x_test)
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# print(y_test)
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pipe.fit(x_test,np.ravel(y_test))#格式转化
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knn=Pipeline(steps=[('standardscaler', StandardScaler()),('kneighborsclassfier', KNeighborsClassifier(n_neighbors=index))])
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ans=pipe.predict(x_test)
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#ans1=run_KNN(test,x_train,y_train,index)
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print(type(y_test))
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y_test=y_test.values
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for i in range(0,len(ans)):
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print(" ",y_test[i][0],ans[i])
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print("scikit-learn knn模型预估准确度为：",accuracy_score(pipe.predict(x_test),y_test))#准确度
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#print("knn模型预估准确度为：",accuracy_score(ans1,y_test))#准确度
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# kn=KNeighborsClassifier()
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# ra=RandomForestClassifier(random_state=0)
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# # print(ra.predict(x_test))
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# # print(y_test)
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# StandardScaler().fit(iris_x).transform(iris_x)
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# ra.fit(x_train,y_train)
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# print(ra.predict(x_test))
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# print(y_test)
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#线性回归交叉验证
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X, y = make_regression(n_samples=1000, random_state=0)
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lr = LinearRegression()
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result = cross_validate(lr, X, y) #
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print(result['test_score']) # r_squared score is high because dataset is easy
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print("1.查看数据集直方图")
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print("2.查看数据集波形图")
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print("3.查看数据集特征关系图")
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print("4.查看数据集箱形图")
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print("0.退出")
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while(1):
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a=input()
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if a=='1':
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test.hist(bins=15)#绘制测试集各类花瓣直方图
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plt.show()
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elif a=='2':
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test.plot.area(stacked=False)#波形图
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plt.show()
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elif a=='3':
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sns.pairplot(test,hue="type",height=3)
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plt.show()
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elif a=='4':
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fig,axes=plt.subplots(2,2,figsize=(10,8))
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sns.boxplot(y=test["sepal length"],x=test["type"],ax=axes[0,0])
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sns.boxplot(y=test["sepal width"],x=test["type"],ax=axes[0,1])
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sns.boxplot(y=test["petal length"],x=test["type"],ax=axes[1,0])
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sns.boxplot(y=test["petal width"],x=test["type"],ax=axes[1,1])
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plt.show()
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elif a=='0':
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break
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#特征两两关系图
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# data.plot(kind="kde")#KDE图
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# sns.heatmap(data.corr(),annot=True,cmap="YlGnBu")
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#
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# print(data.describe())
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# #自动参数搜索
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# x,y=fetch_california_housing(return_X_y=True);
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# x_train,x_test,y_train,y_test=train_test_split(iris_x,iris_y,test_size=0.3)
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# param_distributions = {'n_estimators': randint(1, 5),
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# 'max_depth': randint(5, 10)}
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# search = RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0),
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# n_iter=5,
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# param_distributions=param_distributions,
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# random_state=0)
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# search.fit(x_train, y_train)
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# RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0), n_iter=5,
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# param_distributions={'max_depth': ...,
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# 'n_estimators': ...},
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# random_state=0)
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# search.best_params_
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# {'max_depth': 9, 'n_estimators': 4}
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# print(search.score(x_test, y_test))
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# 使用KNN预测数据类别
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