房产估值模型训练及预测结果2
1.源数据文件下载
用于进行回归预测的源数据文件下载链接: https://pan.baidu.com/s/16-JGI-JnksC9I7I_ghvrug 密码: ey46
2.编写代码并运行
1.第1次代码修改并运行
下面一段代码与之前预测评分只有0.83的文章相比，数据源多了房屋高度的分类和房屋建筑年代的分类。根据运行结果来看，这是一个有效的分类，提高了预测准确率。
from sklearn import preprocessing
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import GradientBoostingRegressor
import pandas as pd
import math
from sklearn.model_selection import KFold
def cleanOutlier(data,column,mul=3):
data = data[data[:,column].argsort()] #得到排序后的ndarray
l = len(data)
low = int(l/4)
high = int(l/4*3)
lowValue = data[low,column]
highValue = data[high,column]
print("下四分位数为{} 上四分位数{}".format(lowValue,highValue))
if lowValue - mul * (highValue - lowValue) < data[0,column] :
delLowValue = data[0,column]
else:
delLowValue = lowValue - mul * (highValue - lowValue)
if highValue + mul * (highValue - lowValue) > data[-1,column]:
delHighValue = data[-1,column]
else:
delHighValue = highValue + mul * (highValue - lowValue)
print("删除第{}列中数值小于{}或者大于{}的部分".format(column,\
delLowValue,delHighValue))
for i in range(low):
if data[i,column] >= delLowValue:
recordLow = i
break
for i in range(len(data)-1,high,-1):
if data[i,column] <= delHighValue:
recordHigh = i
break
#打印处理异常值的相关信息
print("原矩阵共有{}行".format(len(data)),end=',')
print("保留{}到{}行".format(recordLow,recordHigh),end=',')
data = data[recordLow:recordHigh+1]
print("删除第{}列中的异常值后剩余{}行".format(column,\
recordHigh+1-recordLow))
return data
df = pd.read_excel("厦门房价数据处理结果.xlsx")
data = df.values.astype('float')
data = cleanOutlier(data,0)
x = data[:,1:]
y = data[:,0]
for i in range(len(y)):
y[i] = math.log(y[i])
kf = KFold(n_splits=5,shuffle=True)
for train_index,test_index in kf.split(x):
train_x = x[train_index]
test_x = x[test_index]
train_y = y[train_index]
test_y = y[test_index]
model_mlp = MLPRegressor(solver='lbfgs',hidden_layer_sizes=(20,20,20),random_state=1)
model_mlp.fit(train_x,train_y.ravel())
mlp_score = model_mlp.score(test_x,test_y.ravel())
print("sklearn多层感知器-回归模型得分",mlp_score)
model_gbr = GradientBoostingRegressor(learning_rate=0.1)
model_gbr.fit(train_x,train_y.ravel())
gbr_score = model_gbr.score(test_x,test_y.ravel())
print("sklearn集成-回归模型得分",gbr_score)上面一段代码的运行结果为:
sklearn多层感知器-回归模型得分 0.8372352880455567 sklearn集成-回归模型得分 0.9103113202098574 sklearn多层感知器-回归模型得分 0.8776823262293032 sklearn集成-回归模型得分 0.915195456505515 sklearn多层感知器-回归模型得分 0.8767527980991213 sklearn集成-回归模型得分 0.9155513360324288 sklearn多层感知器-回归模型得分 0.865894829352436 sklearn集成-回归模型得分 0.907927739780212 sklearn多层感知器-回归模型得分 0.8576044250407024 sklearn集成-回归模型得分 0.9156697685167987
从上面的结果看出，此次模型训练集成-回归模型优于多层感知器-回归模型
2.第2次代码修改并运行
第2次代码与第1次代码主要的不同之处是对x即输入变量做了标准化。根据运行结果来看，标准化提高了回归模型的预测准确率。
from sklearn import preprocessing
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import GradientBoostingRegressor
import pandas as pd
import math
from sklearn.model_selection import KFold
def cleanOutlier(data,column,mul=3):
data = data[data[:,column].argsort()] #得到排序后的ndarray
l = len(data)
low = int(l/4)
high = int(l/4*3)
lowValue = data[low,column]
highValue = data[high,column]
print("下四分位数为{} 上四分位数{}".format(lowValue,highValue))
if lowValue - mul * (highValue - lowValue) < data[0,column] :
delLowValue = data[0,column]
else:
delLowValue = lowValue - mul * (highValue - lowValue)
if highValue + mul * (highValue - lowValue) > data[-1,column]:
delHighValue = data[-1,column]
else:
delHighValue = highValue + mul * (highValue - lowValue)
print("删除第{}列中数值小于{}或者大于{}的部分".format(column,\
delLowValue,delHighValue))
for i in range(low):
if data[i,column] >= delLowValue:
recordLow = i
break
for i in range(len(data)-1,high,-1):
if data[i,column] <= delHighValue:
recordHigh = i
break
#打印处理异常值的相关信息
print("原矩阵共有{}行".format(len(data)),end=',')
print("保留{}到{}行".format(recordLow,recordHigh),end=',')
data = data[recordLow:recordHigh+1]
print("删除第{}列中的异常值后剩余{}行".format(column,\
recordHigh+1-recordLow))
return data
df = pd.read_excel("厦门房价数据处理结果.xlsx")
data = df.values.astype('float')
data = cleanOutlier(data,0)
x = data[:,1:]
y = data[:,0]
for i in range(len(y)):
y[i] = math.log(y[i])
kf = KFold(n_splits=5,shuffle=True)
for train_index,test_index in kf.split(x):
train_x = x[train_index]
test_x = x[test_index]
train_y = y[train_index]
test_y = y[test_index]
ss_x = preprocessing.StandardScaler()
train_x = ss_x.fit_transform(train_x)
test_x = ss_x.transform(test_x)
model_mlp = MLPRegressor(solver='lbfgs',hidden_layer_sizes=(20,20,20),random_state=1)
model_mlp.fit(train_x,train_y.ravel())
mlp_score = model_mlp.score(test_x,test_y.ravel())
print("sklearn多层感知器-回归模型得分",mlp_score)
model_gbr = GradientBoostingRegressor(learning_rate=0.1)
model_gbr.fit(train_x,train_y.ravel())
gbr_score = model_gbr.score(test_x,test_y.ravel())
print("sklearn集成-回归模型得分",gbr_score)上面一段代码的运行结果为:
sklearn多层感知器-回归模型得分 0.9420052610363624 sklearn集成-回归模型得分 0.9119298712798816 sklearn多层感知器-回归模型得分 0.9408811404890329 sklearn集成-回归模型得分 0.9119071943013952 sklearn多层感知器-回归模型得分 0.9453408533881785 sklearn集成-回归模型得分 0.9183101348039411 sklearn多层感知器-回归模型得分 0.9420925829808715 sklearn集成-回归模型得分 0.909328953608675 sklearn多层感知器-回归模型得分 0.9427244328757453 sklearn集成-回归模型得分 0.9106290975464613
从上面的结果看出，输入变量x进行标准化之后提高了多层感知器-回归模型的得分，这次训练结果多层感知器-回归模型优于集成-回归模型
3.第3次代码修改并运行
第3次代码与第2次代码主要的不同之处调整了回归模型的参数。从运行结果看出，调参使集成-回归模型得分从0.90提升到0.95,0.05的提高，是一次成功的调参。
from sklearn import preprocessing
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import GradientBoostingRegressor
import pandas as pd
import math
from sklearn.model_selection import KFold
def cleanOutlier(data,column,mul=3):
data = data[data[:,column].argsort()] #得到排序后的ndarray
l = len(data)
low = int(l/4)
high = int(l/4*3)
lowValue = data[low,column]
highValue = data[high,column]
print("下四分位数为{} 上四分位数{}".format(lowValue,highValue))
if lowValue - mul * (highValue - lowValue) < data[0,column] :
delLowValue = data[0,column]
else:
delLowValue = lowValue - mul * (highValue - lowValue)
if highValue + mul * (highValue - lowValue) > data[-1,column]:
delHighValue = data[-1,column]
else:
delHighValue = highValue + mul * (highValue - lowValue)
print("删除第{}列中数值小于{}或者大于{}的部分".format(column,\
delLowValue,delHighValue))
for i in range(low):
if data[i,column] >= delLowValue:
recordLow = i
break
for i in range(len(data)-1,high,-1):
if data[i,column] <= delHighValue:
recordHigh = i
break
#打印处理异常值的相关信息
print("原矩阵共有{}行".format(len(data)),end=',')
print("保留{}到{}行".format(recordLow,recordHigh),end=',')
data = data[recordLow:recordHigh+1]
print("删除第{}列中的异常值后剩余{}行".format(column,\
recordHigh+1-recordLow))
return data
df = pd.read_excel("厦门房价数据处理结果.xlsx")
data = df.values.astype('float')
data = cleanOutlier(data,0)
x = data[:,1:]
y = data[:,0]
for i in range(len(y)):
y[i] = math.log(y[i])
kf = KFold(n_splits=5,shuffle=True)
for train_index,test_index in kf.split(x):
train_x = x[train_index]
test_x = x[test_index]
train_y = y[train_index]
test_y = y[test_index]
ss_x = preprocessing.StandardScaler()
train_x = ss_x.fit_transform(train_x)
test_x = ss_x.transform(test_x)
model_mlp = MLPRegressor(solver='lbfgs',hidden_layer_sizes=(30,30,30),random_state=1)
model_mlp.fit(train_x,train_y.ravel())
mlp_score = model_mlp.score(test_x,test_y.ravel())
print("sklearn多层感知器-回归模型得分",mlp_score)
model_gbr = GradientBoostingRegressor(n_estimators = 1000,learning_rate=0.3)
model_gbr.fit(train_x,train_y.ravel())
gbr_score = model_gbr.score(test_x,test_y.ravel())
print("sklearn集成-回归模型得分",gbr_score)上面一段代码的运行结果为:
sklearn多层感知器-回归模型得分 0.9409698368795202 sklearn集成-回归模型得分 0.9572998845800237 sklearn多层感知器-回归模型得分 0.9415776157615223 sklearn集成-回归模型得分 0.9550918318968675 sklearn多层感知器-回归模型得分 0.9477305164540656 sklearn集成-回归模型得分 0.9601481639665502 sklearn多层感知器-回归模型得分 0.9476815842932507 sklearn集成-回归模型得分 0.9588259351144909 sklearn多层感知器-回归模型得分 0.9399513836020602 sklearn集成-回归模型得分 0.9578897231281281
两个模型的评分到0.95左右，可以算是比较准确的模型，模型训练就到此告一段落。