Python利用SVM进行实例建模

一、建立时间预测器
1.准备工作
我们所获得的数据集主要来源于课本配套网站，我们首先看building_event_binary.txt文件中6个字符串数据的排序：
星期、日期、时间、离开大楼的人数、进入大楼的人数、是否有活动
前5个字段组成输入数据，我们的任务是预测大楼是否举行活动。
building_event_multiclass.txt文件星期、日期、时间、离开大楼的人数、进入大楼的人数、活动类型

2.详细步骤
首先将数据全部加在X中：import numpy as np
from sklearn import preprocessing
from sklearn.svm import SVC
input_file = 'building_event_binary.txt'
#读取数据
X = []
count = 0
with open(input_file,'r') as f:
for line in f.readlines():
data = line[:-1].split(',')
X.append([data[0]] + data[2:])
X = np.array(X)下面将字符串格式转换成数值格式label_encoder = []
X_encoder = np.empty(X.shape)
for i,item in enumerate(X[0]):
if item.isdigit():
X_encoder[:,i] = X[:,i]
else:
label_encoder.append(preprocessing.LabelEncoder())
X_encoder[:,i] = label_encoder[-1].fit_transform(X[:,i])
X = X_encoder[:,:-1].astype(int)
y = X_encoder[:,-1].astype(int)用径向基函数、概率输出和类型平衡方法训练SVM分类器并进行交叉验证params = {'kernel':'rbf','probability':True}
classifier = SVC(**params)
classifier.fit(X,y)
#交叉验证
from sklearn import cross_validation
accuracy = cross_validation.cross_val_score(classifier,
X,y,scoring = 'accuracy',cv = 3)
print("Accuracy of the classifier:" + str(round(100*accuracy.mean(),2))+"%")用一个新的数据点测试SVMinput_data = ['Tuesday', '12:30:00','21','23']
input_data_encoded = [-1] * len(input_data)
count = 0
for i,item in enumerate(input_data):
if item.isdigit():
input_data_encoded[i] = int(input_data[i])
else:
input_data_encoded[i] = int(label_encoder[count].transform(input_data[i]))
count = count + 1

input_data_encoded = np.array(input_data_encoded)为特定数据点预测并打印输出结果output_class = classifier.predict(input_data_encoded)
print("Output class:", label_encoder[-1].inverse_transform(output_class)[0])结果如下：



准确率达到93%
二、估算交通流量
1.traffuc_data.txt文件包含以下字段：
星期、时间、对手球队、棒球比赛是否继续、通行汽车数量
2.代码步骤
数据加载import numpy as np
from sklearn import preprocessing
from sklearn.svm import SVR

input_file = 'traffic_data.txt'

# Reading the data
X = []
count = 0
with open(input_file, 'r') as f:
for line in f.readlines():
data = line[:-1].split(',')
X.append(data)

X = np.array(X)把数据进行编码label_encoder = []
X_encoded = np.empty(X.shape)
for i,item in enumerate(X[0]):
if item.isdigit():
X_encoded[:, i] = X[:, i]
else:
label_encoder.append(preprocessing.LabelEncoder())
X_encoded[:, i] = label_encoder[-1].fit_transform(X[:, i])

X = X_encoded[:, :-1].astype(int)
y = X_encoded[:, -1].astype(int)用径向基函数创建并训练SVM回归器，参数C指定了对错误分类的惩罚，参数epsilon指定了不使用惩罚的限制params = {'kernel': 'rbf', 'C': 10.0, 'epsilon': 0.2}
regressor = SVR(**params)
regressor.fit(X, y)用交叉验证来检查回归器的性能import sklearn.metrics as sm

y_pred = regressor.predict(X)
print("Mean asbolute error = ",round(sm.mean_absolute_error(y,y_pred),2)在一个数据点上进行测试input_data = ['Tuesday', '13:35', 'San Francisco', 'yes']
input_data_encoded = [-1] * len(input_data)
count = 0
for i,item in enumerate(input_data):
if item.isdigit():
input_data_encoded[i] = int(input_data[i])
else:
input_data_encoded[i] = int(label_encoder[count].transform(input_data[i]))
count = count + 1
input_data_encoded = np.array(input_data_encoded)
# Predict and print output for a particular datapoint
print("Predicted traffic:", int(regressor.predict(input_data_encoded)[0]))结果如下：