突破R内存限制的企业级大数据挖掘利器：Microsoft R Server 快速上手

R语言是一款非常优秀的数据挖掘工具，拥有顶尖的数据处理、数据挖掘课数据可视化。是数据从业者必备的一把利器。但是其基于内存的诟病也一直被人所嫌弃，虽然这几年很多优秀的扩展包极大提升了R语言的性能，但是在面对企业级大数据挖掘面前，也会显得力不从心。

现在我们也不用担心R语言这个问题了，自从微软收购了商业版R以后，就进行了很多的整合和优化，之前只面向高校学生免费试用，现在，我们企业界的数据从业者也可以免费下载Microsoft R Server ，利用MRS处理大数据，MRS对开源R100%兼容，能充分利用CRAN 现有的10000+扩展包，实现不同数据挖掘需求。

关于Microsoft R Server的安装，陈堰平老师已经写了一篇非常详细的文档，感兴趣者可以点击以下链接，查看文档进行安装。

注意：请确保最后你都进入页面https://my.visualstudio.com，选择最新的Microsoft R Server 9.10版本下载。贴心的微软为我们提供了不同系统的安装版本，如下图所示：

我们按照堰平老师的安装步骤安装成功后，会在你的计算机出现：

说明MRS已经在你计算机安装成功。我们点击RGui，可以出现类似R相似界面：

我们也可以利用RStudio来调用MRS。操作如下：

选择MRS即可。

好了，既然我们安装好了MRS，那么接下来就用一个简单的案例来帮助大家快速上手。

如果数据集不大，可以直接导入到R或MRS中再进行处理（MRS速度优于R）。我们导入ccFraud.csv数据集，有一千万条记录。对比MRS和R导入数据的时间如下：

可见，利用MRS导数据的时间花了31秒，利用R导数据的时间花了1分钟，速度差不多提高了一倍。

如果是大数据集，MRS也提供了将数据集先保存为.xdf格式（在硬盘中），该数据对象可供大多数RevoScaleR包中的函数使用（数据处理、数据转换、数据建模等）。我们可以利用rxImport函数实现，将其outFile参数设置为你要保存的文件名即可。

> # 导入csv数据集

> readpath <- "D:/MRS/Data"

> infile <- file.path(readpath,"ccFraud.csv")

> ccFraud_xdf <- rxImport(inData = infile,

+                         outFile = "ccFraud.xdf",

+                         overwrite = TRUE)

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 1.393 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 1.440 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 1.462 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 1.462 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 1.475 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 1.408 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 1.454 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 1.381 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 1.417 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 1.429 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 1.440 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 1.425 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 1.452 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 1.456 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 1.406 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 1.379 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 1.434 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 1.409 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 1.422 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 1.384 seconds 

> 

结束后，我们利用rxGetInfo函数查看.xdf文件的数据结构（将参数getVarInfo设置为TRUE），并查看数据的前十行（numRows = 10）。

> rxGetInfo("ccFraud.xdf",getVarInfo = TRUE,numRows = 10)

File name: C:\Program Files\Microsoft\R Server\R_SERVER\library\RevoScaleR\rxLibs\x64\ccFraud.xdf 

Number of observations: 1e+07 

Number of variables: 9 

Number of blocks: 20 

Compression type: zlib 

Variable information: 

Var 1: custID, Type: integer, Low/High: (1, 1e+07)

Var 2: gender, Type: integer, Low/High: (1, 2)

Var 3: state, Type: integer, Low/High: (1, 51)

Var 4: cardholder, Type: integer, Low/High: (1, 2)

Var 5: balance, Type: integer, Low/High: (0, 41485)

Var 6: numTrans, Type: integer, Low/High: (0, 100)

Var 7: numIntlTrans, Type: integer, Low/High: (0, 60)

Var 8: creditLine, Type: integer, Low/High: (1, 75)

Var 9: fraudRisk, Type: integer, Low/High: (0, 1)

Data (10 rows starting with row 1):

   custID gender state cardholder balance numTrans numIntlTrans creditLine fraudRisk

1       1      1    35          1    3000        4           14          2         0

2       2      2     2          1       0        9            0         18         0

3       3      2     2          1       0       27            9         16         0

4       4      1    15          1       0       12            0          5         0

5       5      1    46          1       0       11           16          7         0

6       6      2    44          2    5546       21            0         13         0

7       7      1     3          1    2000       41            0          1         0

8       8      1    10          1    6016       20            3          6         0

9       9      2    32          1    2428        4           10         22         0

10     10      1    23          1       0       18           56          5         0

> 

我们也可以在保存.xdf文件时，利用stringsAsFactors，colClasses，和colInfo等参数改变变量的数据类型。比如我们利用colInfo将变量gender从数值型变为因子型，且因子水平为“F”、“M”，利用colClasses将变量fraudRisk从数值型变成因子型。

> # 改变变量的数据存储类型

> ccFraud_xdf <- rxImport(inData = infile,

+                         outFile = "ccFraud.xdf",

+                         colClasses = c(fraudRisk = "factor"),

+                         colInfo = list("gender" = list(type = "factor",

+                                                        levels = c("1","2"),

+                                                        newLevels = c("F","M"))),

+                         overwrite = TRUE)

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 1.871 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 1.832 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 1.825 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 1.808 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 2.018 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 2.061 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 2.158 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 1.917 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 1.852 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 1.795 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 1.829 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 1.793 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 1.849 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 1.806 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 1.773 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 1.813 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 1.812 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 1.850 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 1.824 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 1.828 seconds 

> 

> # 查看ccFraud_xdf的数据结构

> rxGetInfo(ccFraud_xdf,getVarInfo = TRUE,numRows = 5)

File name: C:\Program Files\Microsoft\R Server\R_SERVER\library\RevoScaleR\rxLibs\x64\ccFraud.xdf 

Number of observations: 1e+07 

Number of variables: 9 

Number of blocks: 20 

Compression type: zlib 

Variable information: 

Var 1: custID, Type: integer, Low/High: (1, 1e+07)

Var 2: gender

       2 factor levels: F M

Var 3: state, Type: integer, Low/High: (1, 51)

Var 4: cardholder, Type: integer, Low/High: (1, 2)

Var 5: balance, Type: integer, Low/High: (0, 41485)

Var 6: numTrans, Type: integer, Low/High: (0, 100)

Var 7: numIntlTrans, Type: integer, Low/High: (0, 60)

Var 8: creditLine, Type: integer, Low/High: (1, 75)

Var 9: fraudRisk

       2 factor levels: 0 1

Data (5 rows starting with row 1):

  custID gender state cardholder balance numTrans numIntlTrans creditLine fraudRisk

1      1      F    35          1    3000        4           14          2         0

2      2      M     2          1       0        9            0         18         0

3      3      M     2          1       0       27            9         16         0

4      4      F    15          1       0       12            0          5         0

5      5      F    46          1       0       11           16          7         0

> 

从数据结构可知，变量的类型已经发生改变，且gender的因子水平从1、2变成F、M。

我们也可以对.xdf文件进行描述性统计分析，通过rxSummary函数实现。

> # 利用rxSummary函数对数据进行描述性统计分析

> rxSummary(~.,ccFraud_xdf) # 对全部变量进行统计

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.069 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.072 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.078 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.079 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.080 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.081 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.081 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.080 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.077 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.080 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.080 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.085 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.080 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.078 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.082 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.084 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.082 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.079 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.086 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.078 seconds 

Computation time: 1.661 seconds.

Call:

rxSummary(formula = ~., data = ccFraud_xdf)



Summary Statistics Results for: ~.

Data: ccFraud_xdf (RxXdfData Data Source)

File name: ccFraud.xdf

Number of valid observations: 1e+07 

 

 Name         Mean         StdDev       Min Max      ValidObs MissingObs

 custID       5.000001e+06 2.886751e+06 1   10000000 1e+07    0         

 state        2.466127e+01 1.497012e+01 1         51 1e+07    0         

 cardholder   1.030004e+00 1.705991e-01 1          2 1e+07    0         

 balance      4.109920e+03 3.996847e+03 0      41485 1e+07    0         

 numTrans     2.893519e+01 2.655378e+01 0        100 1e+07    0         

 numIntlTrans 4.047190e+00 8.602970e+00 0         60 1e+07    0         

 creditLine   9.134469e+00 9.641974e+00 1         75 1e+07    0         



Category Counts for gender

Number of categories: 2

Number of valid observations: 1e+07

Number of missing observations: 0



 gender Counts 

 F      6178231

 M      3821769



Category Counts for fraudRisk

Number of categories: 2

Number of valid observations: 1e+07

Number of missing observations: 0



 fraudRisk Counts 

 0         9403986

 1          596014

> 

跟普通summary函数相似，对数值型变量返回平均值、标准差、最小值、最大值、样本个数和缺失值个数，对因子型变量则返回频数。

除了这些简单的处理外，ScaleR也包含了丰富的数据处理和算法，具体如下所示：

最后，让我们利用rxLogit函数构建Logistic回归模型（R中的glm函数也适用），并利用summary函数查看模型信息。

> # logitic回归模型

> ccFraudglm <- rxLogit(fraudRisk ~ gender + cardholder + balance + numTrans

+                       + numIntlTrans + creditLine,data = ccFraud_xdf)

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.080 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.081 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.080 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.071 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.071 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.068 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.075 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.070 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.075 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.069 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.073 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.072 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.067 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.077 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.074 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.073 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.070 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.077 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.069 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.071 seconds 



Starting values (iteration 1) time: 1.558 secs.

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.061 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.182 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.184 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.190 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.192 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.197 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.200 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.185 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.186 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.196 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.192 seconds 



Iteration 2 time: 3.866 secs.

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.070 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.189 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.205 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.194 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.199 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.194 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.199 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.194 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.187 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.190 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.184 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.201 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.208 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.216 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.191 seconds 



Iteration 3 time: 3.950 secs.

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.068 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.197 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.187 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.190 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.192 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.201 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.190 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.211 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.189 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.199 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.198 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.190 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.192 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.181 seconds 



Iteration 4 time: 3.914 secs.

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.061 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.197 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.201 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.199 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.189 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.204 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.192 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.196 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.202 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.194 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.185 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.207 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.199 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.192 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.186 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.202 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.209 seconds 



Iteration 5 time: 3.963 secs.

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.066 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.198 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.187 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.200 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.189 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.197 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.187 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.197 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.187 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.189 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.190 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.208 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.204 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.184 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.186 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.197 seconds 



Iteration 6 time: 3.903 secs.

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.061 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.192 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.198 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.198 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.204 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.196 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.201 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.197 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.181 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.193 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.198 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.194 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.199 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.192 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.201 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.198 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.201 seconds 



Iteration 7 time: 3.956 secs.

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.060 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.189 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.199 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.215 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.231 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.220 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.202 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.213 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.211 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.191 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.188 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.209 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.190 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.195 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.202 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.222 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.224 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.213 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.223 seconds 



Iteration 8 time: 4.194 secs.



Elapsed computation time: 29.316 secs.

> # 查看模型结果

> summary(ccFraudglm)

Call:

rxLogit(formula = fraudRisk ~ gender + cardholder + balance + 

    numTrans + numIntlTrans + creditLine, data = ccFraud_xdf)



Logistic Regression Results for: fraudRisk ~ gender + cardholder + balance +

    numTrans + numIntlTrans + creditLine

Data: ccFraud_xdf (RxXdfData Data Source)

File name: ccFraud.xdf

Dependent variable(s): fraudRisk

Total independent variables: 8 (Including number dropped: 1)

Number of valid observations: 1e+07

Number of missing observations: 0 

-2*LogLikelihood: 2149329.7462 (Residual deviance on 9999993 degrees of freedom)

 

Coefficients:

               Estimate Std. Error z value Pr(>|z|)    

(Intercept)  -8.888e+00  1.292e-02 -687.88 2.22e-16 ***

gender=F     -6.010e-01  3.716e-03 -161.73 2.22e-16 ***

gender=M        Dropped    Dropped Dropped  Dropped    

cardholder    4.703e-01  9.749e-03   48.24 2.22e-16 ***

balance       3.755e-04  4.558e-07  823.71 2.22e-16 ***

numTrans      4.659e-02  6.526e-05  713.86 2.22e-16 ***

numIntlTrans  2.967e-02  1.757e-04  168.83 2.22e-16 ***

creditLine    9.297e-02  1.389e-04  669.13 2.22e-16 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1



Condition number of final variance-covariance matrix: 5.5717 

Number of iterations: 8

> 

模型建好后，可以利用rxPredict函数可以对数据进行预测。

> # 利用logit模型进行预测

> rxPredict(ccFraudglm,data = ccFraud_xdf,

+           outData = ccFraud_xdf, overwrite=TRUE)

Rows Read: 500000, Total Rows Processed: 500000, Total Chunk Time: 0.116 seconds

Rows Read: 500000, Total Rows Processed: 1000000, Total Chunk Time: 0.271 seconds

Rows Read: 500000, Total Rows Processed: 1500000, Total Chunk Time: 0.284 seconds

Rows Read: 500000, Total Rows Processed: 2000000, Total Chunk Time: 0.268 seconds

Rows Read: 500000, Total Rows Processed: 2500000, Total Chunk Time: 0.270 seconds

Rows Read: 500000, Total Rows Processed: 3000000, Total Chunk Time: 0.268 seconds

Rows Read: 500000, Total Rows Processed: 3500000, Total Chunk Time: 0.297 seconds

Rows Read: 500000, Total Rows Processed: 4000000, Total Chunk Time: 0.269 seconds

Rows Read: 500000, Total Rows Processed: 4500000, Total Chunk Time: 0.271 seconds

Rows Read: 500000, Total Rows Processed: 5000000, Total Chunk Time: 0.273 seconds

Rows Read: 500000, Total Rows Processed: 5500000, Total Chunk Time: 0.302 seconds

Rows Read: 500000, Total Rows Processed: 6000000, Total Chunk Time: 0.287 seconds

Rows Read: 500000, Total Rows Processed: 6500000, Total Chunk Time: 0.270 seconds

Rows Read: 500000, Total Rows Processed: 7000000, Total Chunk Time: 0.279 seconds

Rows Read: 500000, Total Rows Processed: 7500000, Total Chunk Time: 0.291 seconds

Rows Read: 500000, Total Rows Processed: 8000000, Total Chunk Time: 0.271 seconds

Rows Read: 500000, Total Rows Processed: 8500000, Total Chunk Time: 0.284 seconds

Rows Read: 500000, Total Rows Processed: 9000000, Total Chunk Time: 0.305 seconds

Rows Read: 500000, Total Rows Processed: 9500000, Total Chunk Time: 0.280 seconds

Rows Read: 500000, Total Rows Processed: 10000000, Total Chunk Time: 0.278 seconds 

> 

> # 查看预测结果的前十条记录

> rxGetInfo(ccFraud_xdf,numRows = 10)

File name: C:\Program Files\Microsoft\R Server\R_SERVER\library\RevoScaleR\rxLibs\x64\ccFraud.xdf 

Number of observations: 1e+07 

Number of variables: 10 

Number of blocks: 20 

Compression type: zlib 

Data (10 rows starting with row 1):

   custID gender state cardholder balance numTrans numIntlTrans creditLine fraudRisk fraudRisk_Pred

1       1      F    35          1    3000        4           14          2         0   0.0008208775

2       2      M     2          1       0        9            0         18         0   0.0017884961

3       3      M     2          1       0       27            9         16         0   0.0044740019

4       4      F    15          1       0       12            0          5         0   0.0003372351

5       5      F    46          1       0       11           16          7         0   0.0006229633

6       6      M    44          2    5546       21            0         13         0   0.0246553959

7       7      F     3          1    2000       41            0          1         0   0.0018993247

8       8      F    10          1    6016       20            3          6         0   0.0055909255

9       9      M    32          1    2428        4           10         22         0   0.0068448406

10     10      F    23          1       0       18           56          5         0   0.0023438135

在数据的最后一列增加预测结果。

好了，今晚就先分享到这里，目的是让大家了解Microsoft R Server的一些基本用法。如果大家面临企业大数据难以分析建模的困境，可以下载安装MRS来尝试解决你们现实的业务问题。除了以上简单函数意外，微软也专门开发了一个MicrosoftML包，其提供了新的机器学习功能，具有更高的速度，性能和可扩展性，特别是处理大量的文本数据或高维分类数据。

感兴趣的读者可以去查看相关帮助文档。