Python 利用SVM,KNN,随机森林进行预测

工具:Pycharm,Win10,Python3.6.4

上图是我们的数据文件,最后一列是附近有无超市的标签,1代表有,-1代表没有。可以发现数据维度比较多,我通关计算相关系数提出相关性低的特征。
data = pd.read_excel('data.xls') # print(data) column =
data.columns.tolist()[:19] # print(column) mcorr = data.corr() mcorr_data =
np.array(mcorr.标签) # print(mcorr_data) mask = np.zeros_like(mcorr,
dtype=np.bool) # 构造与mcorr同维矩阵 为bool型 mask[np.triu_indices_from(mask)] = True #
角分线右侧为True # 绘制图像 plt.figure(figsize=(24, 24)) cmap =
sns.diverging_palette(220, 10, as_cmap=True) # 返回matplotlib colormap对象 g =
sns.heatmap(mcorr, mask=mask, cmap=cmap, square=True, annot=True, fmt='0.2f') #
热力图 plt.show() #删除相关性小的特征 x = pd.DataFrame(data.drop(['风景名胜', '公共设施', '租赁成本'],
axis=1))

相关矩阵图如上,我们可以看到有些特征相关性比较低,如公共设施,风景名胜,租赁成本比较低,所以我们可以直接删除这些特征。

下面就是调用sklearn库去预测数据,我们主要使用SVM,KNN,随机森林,发现SVM和随机森林结果略优于KNN
import pandas as pd import sklearn import seaborn as sns import
matplotlib.pyplot as plt import numpy as np from sklearn.model_selection import
train_test_split plt.rcParams['font.family'] = 'SimHei' # 配置中文字体
plt.rcParams['font.size'] = 15 # 更改默认字体大小 data = pd.read_excel('data.xls') #
print(data) column = data.columns.tolist()[:19] # print(column) mcorr =
data.corr() mcorr_data = np.array(mcorr.标签) # print(mcorr_data) mask =
np.zeros_like(mcorr, dtype=np.bool) # 构造与mcorr同维矩阵 为bool型
mask[np.triu_indices_from(mask)] = True # 角分线右侧为True # 绘制图像
plt.figure(figsize=(24, 24)) cmap = sns.diverging_palette(220, 10,
as_cmap=True) # 返回matplotlib colormap对象 g = sns.heatmap(mcorr, mask=mask,
cmap=cmap, square=True, annot=True, fmt='0.2f') # 热力图 plt.show() #删除相关性小的特征 x =
pd.DataFrame(data.drop(['风景名胜', '公共设施', '租赁成本'], axis=1)) #对数据做归一化处理 x_normal =
(x - x.min()) / (x.max() - x.min()) # print(x_normal) Y = np.array(x_normal.标签)
X = np.array(pd.DataFrame(x_normal.drop(['标签'] , axis = 1),index=None)) #
print(Y) # print(X) #分割训练集和测试集 X_train,X_test,Y_train,Y_test =
train_test_split(X,Y,test_size=0.2,random_state=42) from sklearn.svm import SVC
from sklearn.metrics import precision_score from sklearn.metrics import
recall_score from sklearn.metrics import f1_score clf = SVC(kernel='rbf',
class_weight='balanced',) clf.fit(X_train, Y_train) y_predict =
clf.predict(X_test) error = 0 for i in range(len(X_test)): if
clf.predict([X_test[i]])[0] != Y_test[i]: error +=1 print( 'SVM错误率: %.4f' %
(error/float(len(X_test)))) print( 'SVM精确率: ', precision_score(Y_test,
y_predict, average='macro')) print( 'SVM召回率: ', recall_score(Y_test, y_predict,
average='macro')) print( 'F1: ', f1_score(Y_test, y_predict, average='macro'))
from sklearn.neighbors import KNeighborsClassifier as KNN knc = KNN(n_neighbors
=6,) knc.fit(X_train,Y_train) y_predict = knc.predict(X_test)
print('KNN准确率',knc.score(X_test,Y_test)) print('KNN精确率',precision_score(Y_test,
y_predict, average='macro')) print('KNN召回率',recall_score(Y_test, y_predict,
average='macro')) print('F1',f1_score(Y_test, y_predict, average='macro')) from
sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier()
rfc.fit(X_train, Y_train) y_predict = rfc.predict(X_test)
print('随机森林准确率',rfc.score(X_test, Y_test))
print('随机森林精确率',precision_score(Y_test, y_predict, average='macro'))
print('随机森林召回率',recall_score(Y_test, y_predict, average='macro'))
print('F1',f1_score(Y_test, y_predict, average='macro'))
代码中我们还计算了精确率,召回率F1。这些概念不再细讲,大家可以搜一些博客自行学习。

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