分类算法实例四：用Logistic算法和KNN算法进行鸢尾花数据分类

• 2020 年 3 月 18 日
• 笔记

import numpy as np  import matplotlib as mpl  import matplotlib.pyplot as plt  import pandas as pd  import warnings  import sklearn  from sklearn.linear_model import LogisticRegressionCV  from sklearn.linear_model.coordinate_descent import ConvergenceWarning  from sklearn.model_selection import train_test_split  from sklearn.preprocessing import StandardScaler  from sklearn.neighbors import KNeighborsClassifier  from sklearn.preprocessing import label_binarize  from sklearn import metrics    # 设置字符集，防止中文乱码  mpl.rcParams['font.sans-serif']=[u'simHei']  mpl.rcParams['axes.unicode_minus']=False  # 拦截异常  warnings.filterwarnings(action = 'ignore', category=ConvergenceWarning)    # 数据加载  path = "datas/iris.data"  names = ['sepal length', 'sepal width', 'petal length', 'petal width', 'cla']  df = pd.read_csv(path, header=None, names=names)  df['cla'].value_counts()  df.head()

def parseRecord(record):      result=[]      r = zip(names,record)      for name,v in r:          if name == 'cla':              if v == 'Iris-setosa':                  result.append(1)              elif v == 'Iris-versicolor':                  result.append(2)              elif v == 'Iris-virginica':                  result.append(3)              else:                  result.append(np.nan)          else:              result.append(float(v))      return result

# 1. 数据转换为数字以及分割  # 数据转换  datas = df.apply(lambda r: parseRecord(r), axis=1)  # 异常数据删除  datas = datas.dropna(how='any')  # 数据分割  X = datas[names[0:-1]]  Y = datas[names[-1]]  # 数据抽样（训练数据和测试数据分割）  X_train,X_test,Y_train,Y_test = train_test_split(X, Y, test_size=0.4, random_state=0)    print ("原始数据条数:%d；训练数据条数:%d；特征个数:%d；测试样本条数:%d" % (len(X), len(X_train), X_train.shape[1], X_test.shape[0]))

# 2. 数据标准化  ss = StandardScaler()  X_train = ss.fit_transform(X_train)  X_test = ss.transform(X_test)    # 3. 特征选择    # 4. 降维处理    # 5. 模型构建  lr = LogisticRegressionCV(Cs=np.logspace(-4,1,50), cv=3, fit_intercept=True, penalty='l2', solver='lbfgs', tol=0.01, multi_class='multinomial')  # solver可选：‘newton-cg’, 'lbfgs', 'liblinear', 'sag'（mini-batch），默认为liblinear  lr.fit(X_train, Y_train)

# 6. 模型效果输出  # 将正确的数据转换为矩阵形式  y_test_hot = label_binarize(Y_test,classes=(1,2,3))  # 得到预测的损失值  lr_y_score = lr.decision_function(X_test)  # 计算ROC的值  lr_fpr, lr_tpr, lr_threasholds = metrics.roc_curve(y_test_hot.ravel(),lr_y_score.ravel())  # threasholds阈值  # 计算AUC的值  lr_auc = metrics.auc(lr_fpr, lr_tpr)  print ("Logistic算法R值：", lr.score(X_train, Y_train))  print ("Logistic算法AUC值：", lr_auc)    # 7. 模型预测  lr_y_predict = lr.predict(X_test)

# KNN算法实现  # a. 模型构建  knn = KNeighborsClassifier(n_neighbors=3)  knn.fit(X_train, Y_train)    # b. 模型效果输出  # 将正确的数据转换为矩阵形式  y_test_hot = label_binarize(Y_test,classes=(1,2,3))  # 得到预测的损失值  knn_y_score = knn.predict_proba(X_test)  # 计算ROC的值  knn_fpr, knn_tpr, knn_threasholds = metrics.roc_curve(y_test_hot.ravel(),knn_y_score.ravel())  # 计算AUC的值  knn_auc = metrics.auc(knn_fpr, knn_tpr)  print ("KNN算法R值：", knn.score(X_train, Y_train))  print ("KNN算法AUC值：", knn_auc)    # c. 模型预测  knn_y_predict = knn.predict(X_test)  knn_y_score

# 画图1：ROC曲线画图  plt.figure(figsize=(8, 6), facecolor='w')  plt.plot(lr_fpr,lr_tpr,c='r',lw=2,label=u'Logistic算法,AUC=%.3f' % lr_auc)  plt.plot(knn_fpr,knn_tpr,c='g',lw=2,label=u'KNN算法,AUC=%.3f' % knn_auc)  plt.plot((0,1),(0,1),c='#a0a0a0',lw=2,ls='--')  # 设置X轴的最大和最小值  plt.xlim(-0.01, 1.02)  # 设置y轴的最大和最小值  plt.ylim(-0.01, 1.02)  plt.xticks(np.arange(0, 1.1, 0.1))  plt.yticks(np.arange(0, 1.1, 0.1))  plt.xlabel('False Positive Rate(FPR)', fontsize=16)  plt.ylabel('True Positive Rate(TPR)', fontsize=16)  plt.grid(b=True, ls=':')  plt.legend(loc='lower right', fancybox=True, framealpha=0.8, fontsize=12)  plt.title(u'鸢尾花数据Logistic和KNN算法的ROC/AUC', fontsize=18)  plt.show()

# 画图2：预测结果画图  x_test_len = range(len(X_test))  plt.figure(figsize=(12, 9), facecolor='w')  plt.ylim(0.5,3.5)  plt.plot(x_test_len, Y_test, 'ro',markersize = 6, zorder=3, label=u'真实值')  plt.plot(x_test_len, lr_y_predict, 'go', markersize = 10, zorder=2, label=u'Logis算法预测值,$R^2$=%.3f' % lr.score(X_test, Y_test))  plt.plot(x_test_len, knn_y_predict, 'yo', markersize = 16, zorder=1, label=u'KNN算法预测值,$R^2$=%.3f' % knn.score(X_test, Y_test))  plt.legend(loc = 'lower right')  plt.xlabel(u'数据编号', fontsize=18)  plt.ylabel(u'种类', fontsize=18)  plt.title(u'鸢尾花数据分类', fontsize=20)  plt.show()