---
title: "Introduction to precrec"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Introduction to precrec}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

The `precrec` package provides accurate computations of ROC and 
Precision-Recall curves.

## 1. Basic functions

The `evalmod` function calculates ROC and Precision-Recall curves and 
returns an S3 object.
```{r}
library(precrec)

# Load a test dataset
data(P10N10)

# Calculate ROC and Precision-Recall curves
sscurves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)
```

### S3 generics
The R language specifies S3 objects and S3 generic functions as part of the most basic object-oriented system in R. The `precrec` package provides eight S3 generics for the S3 object 
created by the `evalmod` function.

S3 generic       Package   Description 
---------------  --------  ------------------------------------------------------------------
         print   base      Print the calculation results and the summary of the test data
 as.data.frame   base      Convert a precrec object to a data frame
          plot   graphics  Plot performance evaluation measures
      autoplot   ggplot2   Plot performance evaluation measures with ggplot2
       fortify   ggplot2   Prepare a data frame for ggplot2
           auc   precrec   Make a data frame with AUC scores
          part   precrec   Set partial curves and calculate AUC scores
          pauc   precrec   Make a data frame with pAUC scores
          
#### Example of the plot function
The `plot` function outputs ROC and Precision-Recall curves
```{r, fig.width=7, fig.show='hold'}
# Show ROC and Precision-Recall plots
plot(sscurves)

# Show a Precision-Recall plot
plot(sscurves, "PRC")
```


#### Example of the autoplot function
The `autoplot` function outputs ROC and Precision-Recall curves by using the 
`ggplot2` package.
```{r, fig.width=7, fig.show='hold'}
# The ggplot2 package is required 
library(ggplot2)

# Show ROC and Precision-Recall plots
autoplot(sscurves)

# Show a Precision-Recall plot
autoplot(sscurves, "PRC")
```


Reduced supporting points make the plotting speed faster for large data sets. 

```{r, fig.show = 'hide', results = 'hold'}

# 5 data sets with 50000 positives and 50000 negatives
samp1 <- create_sim_samples(5, 50000, 50000)

# Calculate curves
eval1 <- evalmod(scores = samp1$scores, labels = samp1$labels)

# Reduced supporting points
system.time(autoplot(eval1))

# Full supporting points
system.time(autoplot(eval1, reduce_points = FALSE))
```

#### Example of the auc function
The `auc` function outputs a data frame with the AUC (Area Under the Curve)
scores.
```{r}
# Get a data frame with AUC scores
aucs <- auc(sscurves)

# Use knitr::kable to display the result in a table format
knitr::kable(aucs)

# Get AUCs of Precision-Recall
aucs_prc <- subset(aucs, curvetypes == "PRC")
knitr::kable(aucs_prc)

```

#### Example of the as.data.frame function
The `as.data.frame` function converts a precrec object to a data frame.
```{r}
# Convert sscurves to a data frame
sscurves.df <- as.data.frame(sscurves)

# Use knitr::kable to display the result in a table format
knitr::kable(head(sscurves.df))

```


## 2. Data preparation
The `precrec` package provides four functions for data preparation.

Function           Description 
------------------ -----------------------------------------------------------
       join_scores Join scores of multiple models into a list
       join_labels Join observed labels of multiple test datasets into a list
            mmdata Reformat input data for performance evaluation calculation
create_sim_samples Create random samples for simulations

### Example of the join_scores function
The `join_scores` function combines multiple score datasets.
```{r}
s1 <- c(1, 2, 3, 4)
s2 <- c(5, 6, 7, 8)
s3 <- matrix(1:8, 4, 2)

# Join two score vectors
scores1 <- join_scores(s1, s2)

# Join two vectors and a matrix
scores2 <- join_scores(s1, s2, s3)
```

### Example of the join_labels function
The `join_labels` function combines multiple score datasets.
```{r}
l1 <- c(1, 0, 1, 1)
l2 <- c(1, 0, 1, 1)
l3 <- c(1, 0, 1, 0)

# Join two label vectors
labels1 <- join_labels(l1, l2)
labels2 <- join_labels(l1, l3)
```

### Example of the mmdata function
The `mmdata` function makes an input dataset for the `evalmod` function.
```{r}
# Create an input dataset with two score vectors and one label vector
msmdat <- mmdata(scores1, labels1)

# Specify dataset IDs
smmdat <- mmdata(scores1, labels2, dsids = c(1, 2))

# Specify model names and dataset IDs
mmmdat <- mmdata(scores1, labels2, modnames = c("mod1", "mod2"), dsids = c(1, 2))
```

### Example of the create_sim_samples function
The `create_sim_samples` function is useful to make a random sample dataset 
with different performance levels.

Level name  Description 
----------- ---------------------
     random Random
    poor_er Poor early retrieval
    good_er Good early retrieval
      excel Excellent
       perf Perfect
        all All of the above

```{r}
# A dataset with 10 positives and 10 negatives for the random performance level
samps1 <- create_sim_samples(1, 10, 10, "random")

#  A dataset for five different performance levels
samps2 <- create_sim_samples(1, 10, 10, "all")

# A dataset with 20 samples for the good early retrieval performance level
samps3 <- create_sim_samples(20, 10, 10, "good_er")

# A dataset with 20 samples for five different performance levels
samps4 <- create_sim_samples(20, 10, 10, "all")
```

## 3. Multiple models
The `evalmod` function calculate performance evaluation for multiple models
when multiple model names are specified with the `mmdata` or the `evalmod` 
function.

### Data preparation
There are several ways to create a dataset with the `mmdata` function 
for multiple models.
```{r}
# Use a list with multiple score vectors and a list with a single label vector
msmdat1 <- mmdata(scores1, labels1)

# Explicitly specify model names
msmdat2 <- mmdata(scores1, labels1, modnames = c("mod1", "mod2"))

# Use a sample dataset created by the create_sim_samples function
msmdat3 <- mmdata(samps2[["scores"]], samps2[["labels"]], modnames = samps2[["modnames"]])
```

### ROC and Precision-Recall calculations
The `evalmod` function automatically detects multiple models.
```{r}
# Calculate ROC and Precision-Recall curves for multiple models
mscurves <- evalmod(msmdat3)
```

### S3 generics
All the S3 generics are effective for the S3 object generated by this approach.
```{r, fig.width=7, fig.show='hold'}
# Show ROC and Precision-Recall curves with the ggplot2 package
autoplot(mscurves)
```

#### Example of the as.data.frame function
The `as.data.frame` function also works with this object.
```{r}
# Convert mscurves to a data frame
mscurves.df <- as.data.frame(mscurves)

# Use knitr::kable to display the result in a table format
knitr::kable(head(mscurves.df))

```

## 4. Multiple test sets
The `evalmod` function calculate performance evaluation for multiple 
test datasets when different test dataset IDs are specified with the `mmdata` 
or the `evalmod` function.

### Data preparation
There are several ways to create a dataset with the `mmdata` function 
for multiple test datasets.
```{r}
# Specify test dataset IDs names
smmdat1 <- mmdata(scores1, labels2, dsids = c(1,2))

# Use a sample dataset created by the create_sim_samples function
smmdat2 <- mmdata(samps3[["scores"]], samps3[["labels"]], dsids = samps3[["dsids"]])
```

### ROC and Precision-Recall calculations
The `evalmod` function automatically detects multiple test datasets.
```{r}
# Calculate curves for multiple test datasets and keep all the curves
smcurves <- evalmod(smmdat2, raw_curves = TRUE)
```

### S3 generics
All the S3 generics are effective for the S3 object generated by this approach.
```{r, fig.width=7, fig.show='hold'}
# Show an average Precision-Recall curve with the 95% confidence bounds
autoplot(smcurves, "PRC", show_cb = TRUE)

# Show raw Precision-Recall curves
autoplot(smcurves, "PRC", show_cb = FALSE)
```

#### Example of the as.data.frame function
The `as.data.frame` function also works with this object.
```{r}
# Convert smcurves to a data frame
smcurves.df <- as.data.frame(smcurves)

# Use knitr::kable to display the result in a table format
knitr::kable(head(smcurves.df))

```

## 5. Multiple models and multiple test sets
The `evalmod` function calculates performance evaluation for multiple models and
multiple test datasets when different model names and test dataset IDs are specified with the `mmdata` or the `evalmod` function.

### Data preparation
There are several ways to create a dataset with the `mmdata` function 
for multiple models and multiple datasets.
```{r}
# Specify model names and test dataset IDs names
mmmdat1 <- mmdata(scores1, labels2, modnames= c("mod1", "mod2"), dsids = c(1, 2))

# Use a sample dataset created by the create_sim_samples function
mmmdat2 <- mmdata(samps4[["scores"]], samps4[["labels"]], 
                  modnames = samps4[["modnames"]], dsids = samps4[["dsids"]])
```

### ROC and Precision-Recall calculations
The `evalmod` function automatically detects multiple models and multiple test datasets.
```{r}
# Calculate curves for multiple models and multiple test datasets
mmcurves <- evalmod(mmmdat2)
```

### S3 generics
All the S3 generics are effective for the S3 object generated by this approach.
```{r, fig.width=7, fig.show='hold'}
# Show average Precision-Recall curves
autoplot(mmcurves, "PRC")

# Show average Precision-Recall curves with the 95% confidence bounds
autoplot(mmcurves, "PRC", show_cb = TRUE)
```

#### Example of the as.data.frame function
The `as.data.frame` function also works with this object.
```{r}
# Convert smcurves to a data frame
mmcurves.df <- as.data.frame(mmcurves)

# Use knitr::kable to display the result in a table format
knitr::kable(head(mmcurves.df))

```

## 6. Confidence interval bands
The `evalmod` function automatically calculates confidence bands when a model contains multiple 
test sets in provided dataset. Confidence intervals are calculated for additional supporting points, 
which are specified by the 'x_bins' option of the `evalmod` function.

### Example of confidence bands when x_bins is 2
The dataset `smmdat2` contains 20 samples for a single model/classifier.
```{r, fig.width=7, fig.show='hold'}
# Show all curves
smcurves_all <- evalmod(smmdat2, raw_curves = TRUE)
autoplot(smcurves_all)
```

Additional supporting points are calculated for `x = (0, 0.5, 1.0)` when `x_bins` is set to 2.
```{r, fig.width=7, fig.show='hold'}
# x_bins: 2
smcurves_xb2 <- evalmod(smmdat2, x_bins = 2)
autoplot(smcurves_xb2)
```

### Example of confidence bands when x_bins is 10
Additional supporting points are calculated for `x = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)` when `x_bins` is set to 10.
```{r, fig.width=7, fig.show='hold'}
# x_bins: 10
smcurves_xb10 <- evalmod(smmdat2, x_bins = 10)
autoplot(smcurves_xb10)
```

### Example of the alpha value
The `evalmod` function accepts the `cb_alpha` option to specify the alpha value of the point-wise confidence bounds calculation. For instance, 95\% confidence bands are calculated when `cb_alpha` is 0.05.
```{r, fig.width=7, fig.show='hold'}
# cb_alpha: 0.1 for 90% confidence band
smcurves_cb1 <- evalmod(smmdat2, x_bins = 10, cb_alpha = 0.1)
autoplot(smcurves_cb1)

# cb_alpha: 0.01 for 99% confidence band
smcurves_cb2 <- evalmod(smmdat2, x_bins = 10, cb_alpha = 0.01)
autoplot(smcurves_cb2)
```

## 7. Cross validation
The `format_nfold` function takes a data frame with scores, label and n-fold columns 
and convert it to a list for `evalmod` and `mmdata`.

#### Example of a data frame with 5-fold data
```{r}
# Load data
data(M2N50F5)

# Use knitr::kable to display the result in a table format
knitr::kable(head(M2N50F5))
```

#### Example of the format_nfold function with 5-fold datasets
```{r, fig.width=7, fig.show='hold'}
# Convert data frame to list
nfold_list1 = format_nfold(nfold_df = M2N50F5,  score_cols = c(1, 2), 
                           lab_col = 3, fold_col = 4)

# Use column names
nfold_list2 = format_nfold(nfold_df = M2N50F5, score_cols = c("score1", "score2"),
                           lab_col = "label", fold_col = "fold")

# Use the result for evalmod
cvcurves <- evalmod(scores = nfold_list2$scores, labels = nfold_list2$labels,
                    modnames = rep(c("m1", "m2"), each = 5), dsids = rep(1:5, 2))
autoplot(cvcurves)
```

### evalmod and mmdata with cross validation datasets
Both `evalmod` and `mmdata` function can directly take the arguments of the `format_nfold` function.

#### Example of evalmod and mmdata with 5-fold data
```{r, fig.width=7, fig.show='hold'}
# mmdata
cvcurves2 <- mmdata(nfold_df = M2N50F5, score_cols = c(1, 2), lab_col = 3, fold_col = 4, 
                    modnames = c("m1", "m2"), dsids = 1:5)

# evalmod
cvcurves3 <- evalmod(nfold_df = M2N50F5, score_cols = c(1, 2), lab_col = 3, fold_col = 4, 
                     modnames = c("m1", "m2"), dsids = 1:5)
autoplot(cvcurves3)
```

## 8. Basic performance measures
The `evalmod` function also calculates basic evaluation measures - error, 
accuracy, specificity, sensitivity, and precision.

Measure     Description 
----------- --------------------------
      error Error rate
   accuracy Accuracy
specificity Specificity, TNR, 1 - FPR
sensitivity Sensitivity, TPR, Recall
  precision Precision, PPV
        mcc Matthews correlation coefficient
     fscore F-score

### Basic measure calculations
The `mode = "basic"` option makes the `evalmod` function calculate the basic 
evaluation measures instead of performing ROC and Precision-Recall calculations.
```{r}
# Calculate basic evaluation measures
mmpoins <- evalmod(mmmdat2, mode = "basic")
```

### S3 generics
All the S3 generics except for `auc`, `part` and `pauc` are effective 
for the S3 object generated by this approach.
```{r, fig.width=7, fig.show='hold'}
# Show normalized ranks vs. error rate and accuracy
autoplot(mmpoins, c("error", "accuracy"))

# Show normalized ranks vs. specificity, sensitivity, and precision
autoplot(mmpoins, c("specificity", "sensitivity", "precision"))

# Show normalized ranks vs. Matthews correlation coefficient and F-score
autoplot(mmpoins, c("mcc", "fscore"))
```

#### Normalized ranks and predicted scores
In addition to the basic measures, the `autoplot` function can plot normalized 
ranks vs. scores and labels.
```{r, fig.width=7, fig.show='hold'}
# Show normalized ranks vs. scores and labels
autoplot(mmpoins, c("score", "label"))
```

#### Example of the as.data.frame function
The `as.data.frame` function also works for the precrec objects of the basic
measures.
```{r}
# Convert mmpoins to a data frame
mmpoins.df <- as.data.frame(mmpoins)

# Use knitr::kable to display the result in a table format
knitr::kable(head(mmpoins.df))

```

## 9. Partial AUCs
The `part` function calculates partial AUCs and standardized partial AUCs of both 
ROC and precision-recall curves. Standardized pAUCs (spAUCs) are standardized to the score range between 0 and 1.

### partial AUC calculations
It requires an S3 object produced by the `evalmod` function and uses `xlim` and
`ylim` to specify the partial area of your choice. The `pauc` function outputs 
a data frame with the pAUC scores.

```{r}
# Calculate ROC and Precision-Recall curves
curves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)

# Calculate partial AUCs
curves.part <- part(curves, xlim = c(0.0, 0.25))

# Retrieve a dataframe of pAUCs
paucs.df <- pauc(curves.part)

# Use knitr::kable to display the result in a table format
knitr::kable(paucs.df)
```

### S3 generics
All the S3 generics are effective for the S3 object generated by this approach.
```{r, fig.width=7, fig.show='hold'}
# Show ROC and Precision-Recall curves
autoplot(curves.part)
```

## 10. Fast AUC (ROC) calculation
The area under the ROC curve can be calculated from the U statistic, which is the test
statistic of the Mann–Whitney U test.

### AUC calculation with the U statistic
The `evalmod` function calculates AUCs with the U statistic when mode = 'aucroc'.

```{r}
# Calculate AUC (ROC)
aucs <- evalmod(scores = P10N10$scores, labels = P10N10$labels, mode = 'aucroc')

# Convert to data.frame
aucs.df <- as.data.frame(aucs)

# Use knitr::kable to display the result in a table format
knitr::kable(aucs.df)
```

## 11. Balanced and imbalanced datasets
It is easy to simulate various scenarios, such as balanced vs. imbalanced 
datasets, by using the `evalmod` and `create_sim_samples` functions.

### Data preparation
```{r}
# Balanced dataset
samps5 <- create_sim_samples(100, 100, 100, "all")
simmdat1 <- mmdata(samps5[["scores"]], samps5[["labels"]], 
                   modnames = samps5[["modnames"]], dsids = samps5[["dsids"]])

# Imbalanced dataset
samps6 <- create_sim_samples(100, 25, 100, "all")
simmdat2 <- mmdata(samps6[["scores"]], samps6[["labels"]], 
                   modnames = samps6[["modnames"]], dsids = samps6[["dsids"]])

```

### ROC and Precision-Recall calculations
The `evalmod` function automatically detects multiple models and multiple test datasets.
```{r}
# Balanced dataset
simcurves1 <- evalmod(simmdat1)

# Imbalanced dataset
simcurves2 <- evalmod(simmdat2)
```

### Balanced vs. imbalanced datasets
ROC plots are unchanged between balanced and imbalanced datasets, whereas
Precision-Recall plots show a clear difference between them. See our 
[article](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432) or [website](https://classeval.wordpress.com) for potential pitfalls by
using ROC plots with imbalanced datasets.
```{r, fig.width=7, fig.show='hold'}
# Balanced dataset
autoplot(simcurves1)

# Imbalanced dataset
autoplot(simcurves2)
```

## 12. Citation
*Precrec: fast and accurate precision-recall and ROC curve calculations in R*

Takaya Saito; Marc Rehmsmeier

Bioinformatics 2017; 33 (1): 145-147.

doi: [10.1093/bioinformatics/btw570](https://doi.org/10.1093/bioinformatics/btw570)

## 13. External links
- [Classifier evaluation with imbalanced datasets](https://classeval.wordpress.com/) - our web site that contains several pages with useful tips for performance evaluation on binary classifiers.

- [The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432) -  our paper that summarized potential pitfalls of ROC plots with imbalanced datasets and advantages of using precision-recall plots instead.

