library(readr)
library(dplyr)

Results for SVR linear using a reduced set of features

#RESULTS %>% arrange(cells)
#readr::write_csv(RESULTS,path = "./devcon_challenge.svr_results_feat_less.csv")
readr::read_csv("./devcon_challenge.svr_results_feat_less.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.svr_results_feat_less.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
NA

Results for SVR linear using a full set of features

#RESULTS %>% arrange(cells)
#readr::write_csv(RESULTS,path = "./devcon_challenge.svr_full_results.csv")
readr::read_csv("./devcon_challenge.svr_full_results.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.svr_full_results.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
NA
NA

Results for SVR Radial using a full set of features

#readr::write_csv(RESULTS,path = "./devcon_challenge.svr_radial_results.csv")
readr::read_csv("./devcon_challenge.svr_radial_results.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 


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

Results for SVR Radial using a reduced set of features

readr::write_csv(RESULTS,path = "./devcon_challenge.svr_radial_results_less_feat.csv")
readr::read_csv("./devcon_challenge.svr_radial_results_less_feat.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
Parsed with column specification:
cols(
  datasets = col_character(),
  cells = col_character(),
  pearson = col_double(),
  spearman = col_double()
)

Results for liblinear using a full set of features (11 – L2-regularized L2-loss support vector regression (primal))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_11_results.csv")
readr::read_csv("./devcon_challenge.liblinear_11_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))

readr::read_csv("./devcon_challenge.liblinear_11_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear using a full set of features ( 12 - L2-regularized L2-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results.csv")
readr::read_csv("./devcon_challenge.liblinear_12_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_12_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear using a full set of features (13 L2-regularized L1-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_13_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
NA

Results for liblinear using a reduced set of features (11 – L2-regularized L2-loss support vector regression (primal))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_less_feat_results_11.csv")
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_11.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_11.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear using a reduced set of features ( 12 - L2-regularized L2-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_less_feat_results_12.csv")
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_12.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
Parsed with column specification:
cols(
  datasets = col_character(),
  cells = col_character(),
  pearson = col_double(),
  spearman = col_double()
)
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_12.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
Parsed with column specification:
cols(
  datasets = col_character(),
  cells = col_character(),
  pearson = col_double(),
  spearman = col_double()
)

Results for liblinear 13 using a reduced set of features (13 L2-regularized L1-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_less_feat_results_13.csv")

readr::read_csv("./devcon_challenge.liblinear_less_feat_results_13.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_13.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
NA
NA

Results for rf using a full set of features

#readr::write_csv(RESULTS,path = "./devcon_challenge.rf_results.csv")
readr::read_csv("./devcon_challenge.rf_results.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

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

Results for liblinear using a full set of features (noscale) ( 12 - L2-regularized L2-loss support vector regression (dual))


#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results_noscale.csv")
readr::read_csv("./devcon_challenge.liblinear_12_results_noscale.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_12_results_noscale.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear using (noscale) 1000 features ( 12 - L2-regularized L2-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results_noscale_rf_selected_features.csv")

readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear using a full set of features (noscale) 1500 features ( 12 - L2-regularized L2-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results_noscale_rf_selected_features_1500.csv")
readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear (noscale) 1500 features. (13 L2-regularized L1-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1500.csv")

readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))  %>% summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear features (noscale) 1000 features. (13 L2-regularized L1-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1000.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))  
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))  %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear (noscale) 2000 features. (13 L2-regularized L1-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_2000.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_2000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_2000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for liblinear using (noscale) 250 features. (13 L2-regularized L1-loss support vector regression (dual))

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_250.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet using 5000 features

readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_5000.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_5000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_5000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet using 250 features

readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_250.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet using 500 features

#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_500.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet using 1000 features

#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1000.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet using 1500 features

#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1500.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

Results for glmnet using 2500 features

#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_2500.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_2500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_2500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
---
title: "DEVCON Challenge results"
output: 
  html_notebook: 
    code_folding: hide
    toc: yes
---
```{r}
library(readr)
library(dplyr)
```

# Results for SVR linear using a reduced set of features
```{r message=FALSE, warning=FALSE}
#RESULTS %>% arrange(cells)
#readr::write_csv(RESULTS,path = "./devcon_challenge.svr_results_feat_less.csv")
readr::read_csv("./devcon_challenge.svr_results_feat_less.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.svr_results_feat_less.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

```

# Results for SVR linear using a full set of features

```{r message=FALSE, warning=FALSE}
#RESULTS %>% arrange(cells)
#readr::write_csv(RESULTS,path = "./devcon_challenge.svr_full_results.csv")
readr::read_csv("./devcon_challenge.svr_full_results.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.svr_full_results.csv")  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))


```



# Results for SVR Radial using a full set of features

```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.svr_radial_results.csv")
readr::read_csv("./devcon_challenge.svr_radial_results.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 


readr::read_csv("./devcon_challenge.svr_radial_results.csv") %>% 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 using a reduced set of features

```{r}
readr::write_csv(RESULTS,path = "./devcon_challenge.svr_radial_results_less_feat.csv")
readr::read_csv("./devcon_challenge.svr_radial_results_less_feat.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```




# Results for liblinear  using a full set of  features (11 – L2-regularized L2-loss support vector regression (primal))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_11_results.csv")
readr::read_csv("./devcon_challenge.liblinear_11_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))

readr::read_csv("./devcon_challenge.liblinear_11_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```
# Results for liblinear using a full set of  features ( 12 - L2-regularized L2-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results.csv")
readr::read_csv("./devcon_challenge.liblinear_12_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_12_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear using a full set of  features (13 L2-regularized L1-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_13_results.csv")  %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

```


# Results for liblinear  using a reduced set of  features (11 – L2-regularized L2-loss support vector regression (primal))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_less_feat_results_11.csv")
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_11.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_11.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear  using a reduced set of  features ( 12 - L2-regularized L2-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_less_feat_results_12.csv")
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_12.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_12.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear 13 using a reduced set of  features (13 L2-regularized L1-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_less_feat_results_13.csv")

readr::read_csv("./devcon_challenge.liblinear_less_feat_results_13.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_less_feat_results_13.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))


```

# Results for rf  using a full set of  features
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.rf_results.csv")
readr::read_csv("./devcon_challenge.rf_results.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

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

# Results for liblinear using a full set of  features (noscale) ( 12 - L2-regularized L2-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}

#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results_noscale.csv")
readr::read_csv("./devcon_challenge.liblinear_12_results_noscale.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_12_results_noscale.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for liblinear  using  (noscale) 1000 features ( 12 - L2-regularized L2-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results_noscale_rf_selected_features.csv")

readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```



# Results for liblinear using a full set of  features (noscale) 1500 features ( 12 - L2-regularized L2-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_12_results_noscale_rf_selected_features_1500.csv")
readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_12_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear  (noscale) 1500 features. (13 L2-regularized L1-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1500.csv")

readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 

readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))  %>% summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear features (noscale) 1000 features. (13 L2-regularized L1-loss support vector regression (dual))
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1000.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))  
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3"))  %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for liblinear  (noscale) 2000 features. (13 L2-regularized L1-loss support vector regression (dual))

```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_2000.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_2000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_2000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```
# Results for liblinear using (noscale) 250 features. (13 L2-regularized L1-loss support vector regression (dual))


```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_250.csv")
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.liblinear_13_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for glmnet using 5000 features
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_5000.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_5000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_5000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for glmnet using 250 features
```{r message=FALSE, warning=FALSE}
readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_250.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_250.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```


# Results for glmnet using 500 features
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_500.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for glmnet using 1000 features
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1000.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1000.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for glmnet using 1500 features
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1500.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_1500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

# Results for glmnet using 2500 features
```{r message=FALSE, warning=FALSE}
#readr::write_csv(RESULTS,path = "./devcon_challenge.gmlnet_results_noscale_rf_selected_features_2500.csv")
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_2500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) 
readr::read_csv("./devcon_challenge.gmlnet_results_noscale_rf_selected_features_2500.csv") %>% filter(!is.na(pearson)) %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))
```

