Results for glmnet 50 features over 20000 (RF2)

Results per dataset, person and spearman means, rnaseq means

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_50_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a0<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a0
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet 100 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_100_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a1

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet 150 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_150_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet 200 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_200_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet 250 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_250_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet 350 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_350_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
a5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear 50 features over 20000 (RF2) (13)

filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_50_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
s1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s1
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear 100 features over 20000 (RF2) (13)

filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_100_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
s2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear 150 features over 20000 (RF2) (13)

filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_150_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
s3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear 200 features over 20000 (RF2) (13)

filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_200_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
s4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear 250 features over 20000 (RF2) (13)

filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_250_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
s5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear 350 features over 20000 (RF2) (13)

filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_350_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
s6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s6
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial 50 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_50_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
r1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r1
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial 100 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_100_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
r2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial 150 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_150_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
r3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial 200 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_200_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial 250 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_250_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial 350 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_350_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r6
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial noscale 50 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_50_20000_noscale_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
x1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x1
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial noscale 100 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_100_20000_noscale_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
x2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial noscale 150 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_150_20000_noscale_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
x3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial noscale 200 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_200_20000_noscale_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
x4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial noscale 250 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_250_20000_noscale_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
x5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for svr radial noscale 350 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_350_20000_noscale_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
x6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x6
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Heatmap results per dataset

library(gridExtra)
a0$type<-"glmnet50"
a1$type<-"glmnet100"
a2$type<-"glmnet150"
a3$type<-"glmnet200"
a4$type<-"glmnet250"
a5$type<-"glmnet350"
s1$type<-"liblinear50"
s2$type<-"liblinear100"
s3$type<-"liblinear150"
s4$type<-"liblinear200" 
s5$type<-"liblinear250" 
s6$type<-"liblinear350"
x1$type<-"svr50"
x2$type<-"svr100"
x3$type<-"svr150"
x4$type<-"svr200"
x5$type<-"svr250"
x6$type<-"svr350"

heatmap <-rbind(a0,a1,a2,a3,a4,a5,
                s1,s2,s3,s4,s5,
                x1,x2,x3,x4,x5,x6) 

plot2<- heatmap  %>% group_by(type,datasets) %>% summarise(pearson=mean(pearson)) %>% arrange(pearson) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=pearson))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))
  
  
plot1<- heatmap %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% arrange(spearman) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=spearman))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))


plot3<- heatmap %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,datasets) %>% summarise(pearson=mean(pearson)) %>% arrange(pearson) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=pearson))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))
  
  
plot4<-heatmap %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% arrange(spearman) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=spearman))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))




gridExtra::grid.arrange(plot2,plot1,plot3,plot4,ncol=2)

Heatmap per dataset (Pearson)

#library(ggplotify)
par(mfrow=c(2,1))
heatmap %>% group_by(type,datasets) %>% summarise(pearson=mean(pearson)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Blues")

Heatmap per dataset (Spearman)

heatmap %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Reds")
---
title: "Deconv Final Phase partial results (Feature Selection)"
output: 
  html_notebook: 
    code_folding: hide
    toc: yes
---



# Results for glmnet 50 features over 20000 (RF2)
Results per dataset, person and spearman means, rnaseq means
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_50_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a0<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a0
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for glmnet 100 features over 20000 (RF2)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_100_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a1

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```



# Results for glmnet 150 features over 20000 (RF2)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_150_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for glmnet 200 features over 20000 (RF2)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_200_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for glmnet 250 features over 20000 (RF2)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_250_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
a4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for glmnet 350 features over 20000 (RF2)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_350_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
a5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```



# Results for liblinear 50 features over 20000 (RF2) (13)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_50_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
s1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s1
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear 100 features over 20000 (RF2) (13)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_100_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
s2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear 150 features over 20000 (RF2) (13)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_150_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
s3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear 200 features over 20000 (RF2) (13)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_200_20000_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
s4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear 250 features over 20000 (RF2) (13)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_250_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
s5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear 350 features over 20000 (RF2) (13)
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.liblinear13_results_noscale_rf_selected_features_350_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
s6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
s6
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for svr radial 50 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_50_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r1
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for svr radial 100 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_100_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```



# Results for svr radial 150 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_150_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```



# Results for svr radial 200 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_200_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```



# Results for svr radial 250 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_250_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for svr radial 350 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_noscale_rf_selected_features_350_20000_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
r6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
r6
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for svr radial noscale 50 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_rf_selected_features_50_20000_noscale_all_lines.csv"
readr::write_csv(RESULTS,path = filename)
x1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x1
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for svr radial noscale 100 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_rf_selected_features_100_20000_noscale_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
x2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x2
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for svr radial noscale 150 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_rf_selected_features_150_20000_noscale_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
x3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x3
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for svr radial noscale 200 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_rf_selected_features_200_20000_noscale_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
x4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x4
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```



# Results for svr radial noscale 250 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_rf_selected_features_250_20000_noscale_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
x5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x5
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for svr radial noscale 350 features over 20000 (RF2) 
```{r message=FALSE, warning=FALSE}
filename<-"./devcon_challenge.svr_results_rf_selected_features_350_20000_noscale_all_lines.csv"
#readr::write_csv(RESULTS,path = filename)
x6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x6
readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Heatmap results per dataset 

```{r fig.height=8, fig.width=8}
library(gridExtra)
a0$type<-"glmnet50"
a1$type<-"glmnet100"
a2$type<-"glmnet150"
a3$type<-"glmnet200"
a4$type<-"glmnet250"
a5$type<-"glmnet350"
s1$type<-"liblinear50"
s2$type<-"liblinear100"
s3$type<-"liblinear150"
s4$type<-"liblinear200" 
s5$type<-"liblinear250" 
s6$type<-"liblinear350"
x1$type<-"svr50"
x2$type<-"svr100"
x3$type<-"svr150"
x4$type<-"svr200"
x5$type<-"svr250"
x6$type<-"svr350"

heatmap <-rbind(a0,a1,a2,a3,a4,a5,
                s1,s2,s3,s4,s5,
                x1,x2,x3,x4,x5,x6) 

plot2<- heatmap  %>% group_by(type,datasets) %>% summarise(pearson=mean(pearson)) %>% arrange(pearson) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=pearson))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))
  
  
plot1<- heatmap %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% arrange(spearman) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=spearman))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))


plot3<- heatmap %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,datasets) %>% summarise(pearson=mean(pearson)) %>% arrange(pearson) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=pearson))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))
  
  
plot4<-heatmap %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% arrange(spearman) %>%
  ggplot()+
  geom_tile(aes(x=type,y=datasets,fill=spearman))+
  scale_fill_gradient(low = "red", high = "white",limits=c(0.4, 1),)+
  theme(axis.text.x=element_text(angle=45,hjust=1))

gridExtra::grid.arrange(plot2,plot1,plot3,plot4,ncol=2) 
```


# Heatmap per dataset (Pearson)
```{r fig.width=7, message=FALSE, warning=FALSE}
#library(ggplotify)
par(mfrow=c(2,1))
heatmap %>% group_by(type,datasets) %>% summarise(pearson=mean(pearson)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Blues")
```

# Heatmap per dataset (Spearman)
```{r fig.width=7, message=FALSE, warning=FALSE}
heatmap %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Reds")
```

