In [9]:
library(tidyverse)
library(MLmetrics)
In [2]:
pandas = read_tsv("~/RIDIR/Datasets/AreaInterpolateValidation/geopandas_intensive2010.tsv", col_names = c("TID", "pandas")) %>%
    arrange(desc(pandas))
head(pandas)
nrow(pandas)

Parsed with column specification:
cols(
  TID = col_character(),
  pandas = col_double()
)
TIDpandas
T4210100033219.19754
T4210100036516.92458
T4210100033116.64821
T4210100031516.09803
T4210100389815.26946
T4210100022115.17521
367
In [3]:
spark = read_tsv("~/RIDIR/Datasets/AreaInterpolateValidation/geospark_intensive2010.tsv", col_names = c("TID", "spark")) %>%
    arrange(desc(spark))
head(spark)
nrow(spark)

Parsed with column specification:
cols(
  TID = col_character(),
  spark = col_double()
)
TIDspark
T4210100033219.19754
T4210100036516.92458
T4210100033116.64821
T4210100031516.09803
T4210100389815.26946
T4210100022115.17521
367
In [4]:
table = pandas %>% left_join(spark, by = c("TID"))
head(table)
nrow(table)
TIDpandasspark
T4210100033219.19754 19.19754
T4210100036516.92458 16.92458
T4210100033116.64821 16.64821
T4210100031516.09803 16.09803
T4210100389815.26946 15.26946
T4210100022115.17521 15.17521
367
In [5]:
head(table %>% filter(is.na(spark)))
TIDpandasspark
In [6]:
table = spark %>% inner_join(pandas, by = c("TID")) %>% 
    mutate(diff = abs(pandas - spark)) %>%
    arrange(desc(diff))
head(table)
nrow(table)
TIDsparkpandasdiff
T4210100036213.816088 13.816088 5.329071e-15
T42101000293 2.022901 2.022901 4.440892e-15
T4210100033116.648214 16.648214 3.552714e-15
T4210100031614.721355 14.721355 3.552714e-15
T4210100018412.239330 12.239330 3.552714e-15
T4210100023411.637166 11.637166 3.552714e-15
367
In [7]:
ggplot(data = table, aes(x = pandas, y = spark)) + geom_point()
In [8]:
print(paste("R2:   ", R2_Score(table$spark, table$pandas)))
print(paste("MAE:  ",      MAE(table$spark, table$pandas)))
print(paste("MSE:  ",      MSE(table$spark, table$pandas)))
print(paste("RMSE: ",     RMSE(table$spark, table$pandas)))
print(paste("RAE:  ",      RAE(table$spark, table$pandas)))

[1] "R2:    1"
[1] "MAE:   3.84957596597616e-16"
[1] "MSE:   7.94549423346953e-31"
[1] "RMSE:  8.91375018354763e-16"
[1] "RAE:   1.01801250717404e-16"
In [ ]: