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docs: add example for odt() usage to README #139

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docs: add example for odt() usage to README #139

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16 changes: 15 additions & 1 deletion README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -61,8 +61,22 @@ odatasets = list_oml_data(
number_classes = 2
)

odatasets[, c("data_id", "name")]
head(odatasets[, c("data_id", "name")])
```

To retrieve individual datasets, you can use `odt` and either manually construct a new `Task` object using `as_task()` or use it `data.table` format.

```{r}
odataset = odt(29)

# Dataset as data.table
str(odataset$data)

# Creating a new task
otask = as_task(odataset)
otask
```

## Feature Overview

* Datasets, tasks, flows, runs, and collections can be downloaded from [OpenML](https://www.openml.org) and are represented as `R6` classes.
Expand Down
118 changes: 72 additions & 46 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@

# mlr3oml

Package website: [release](https://mlr3oml.mlr-org.com/) |
Package website: [release](https://mlr3oml.mlr-org.com/) \|
[dev](https://mlr3oml.mlr-org.com/dev/)

OpenML integration to the [mlr3 ecosystem](https://mlr-org.com/).
Expand Down Expand Up @@ -75,64 +75,90 @@ odatasets = list_oml_data(
number_classes = 2
)

odatasets[, c("data_id", "name")]
head(odatasets[, c("data_id", "name")])
```

## data_id
## 1: 13
## 2: 15
## 3: 29
## 4: 49
## 5: 50
## ---
## 238: 44767
## 239: 45039
## 240: 45063
## 241: 45562
## 242: 45568
## name
## 1: breast-cancer
## 2: breast-w
## 3: credit-approval
## 4: heart-c
## 5: tic-tac-toe
## ---
## 238: Click_prediction_small_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True
## 239: compas-two-years
## 240: credit-approval
## 241: seismic-bumps
## 242: telco-customer-churn
## data_id name
## 1: 13 breast-cancer
## 2: 15 breast-w
## 3: 29 credit-approval
## 4: 49 heart-c
## 5: 50 tic-tac-toe
## 6: 51 heart-h

To retrieve individual datasets, you can use `odt` and either manually
construct a new `Task` object using `as_task()` or use it `data.table`
format.

``` r
odataset = odt(29)

# Dataset as data.table
str(odataset$data)
```

## Classes 'data.table' and 'data.frame': 690 obs. of 16 variables:
## $ A1 : Factor w/ 2 levels "b","a": 1 2 2 1 1 1 1 2 1 1 ...
## $ A2 : num 30.8 58.7 24.5 27.8 20.2 ...
## $ A3 : num 0 4.46 0.5 1.54 5.62 ...
## $ A4 : Factor w/ 4 levels "u","y","l","t": 1 1 1 1 1 1 1 1 2 2 ...
## $ A5 : Factor w/ 3 levels "g","p","gg": 1 1 1 1 1 1 1 1 2 2 ...
## $ A6 : Factor w/ 14 levels "c","d","cc","i",..: 10 9 9 10 10 7 8 3 6 10 ...
## $ A7 : Factor w/ 9 levels "v","h","bb","j",..: 1 2 2 1 1 1 2 1 2 1 ...
## $ A8 : num 1.25 3.04 1.5 3.75 1.71 ...
## $ A9 : Factor w/ 2 levels "t","f": 1 1 1 1 1 1 1 1 1 1 ...
## $ A10 : Factor w/ 2 levels "t","f": 1 1 2 1 2 2 2 2 2 2 ...
## $ A11 : int 1 6 0 5 0 0 0 0 0 0 ...
## $ A12 : Factor w/ 2 levels "t","f": 2 2 2 1 2 1 1 2 2 1 ...
## $ A13 : Factor w/ 3 levels "g","p","s": 1 1 1 1 3 1 1 1 1 1 ...
## $ A14 : int 202 43 280 100 120 360 164 80 180 52 ...
## $ A15 : int 0 560 824 3 0 0 31285 1349 314 1442 ...
## $ class: Factor w/ 2 levels "+","-": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, ".internal.selfref")=<externalptr>

``` r
# Creating a new task
otask = as_task(odataset)
otask
```

## <TaskClassif:credit-approval> (690 x 16)
## * Target: class
## * Properties: twoclass
## * Features (15):
## - fct (9): A1, A10, A12, A13, A4, A5, A6, A7, A9
## - int (3): A11, A14, A15
## - dbl (3): A2, A3, A8

## Feature Overview

- Datasets, tasks, flows, runs, and collections can be downloaded from
[OpenML](https://www.openml.org) and are represented as `R6`
classes.
- OpenML objects can be easily converted to the corresponding `mlr3`
counterpart.
- Filtering of OpenML objects can be achieved using listing functions.
- Downloaded objects can be cached by setting the `mlr3oml.cache`
option.
- Both the `arff` and `parquet` filetype for datasets are supported.
- You can upload datasets, tasks, and collections to OpenML.
- Datasets, tasks, flows, runs, and collections can be downloaded from
[OpenML](https://www.openml.org) and are represented as `R6` classes.
- OpenML objects can be easily converted to the corresponding `mlr3`
counterpart.
- Filtering of OpenML objects can be achieved using listing functions.
- Downloaded objects can be cached by setting the `mlr3oml.cache`
option.
- Both the `arff` and `parquet` filetype for datasets are supported.
- You can upload datasets, tasks, and collections to OpenML.

## Documentation

- Start by reading the [Large-Scale Benchmarking
chapter](https://mlr3book.mlr-org.com/chapters/chapter11/large-scale_benchmarking.html)
from the `mlr3` book.
- The [package website](https://mlr3oml.mlr-org.com/dev/) contains a
getting started guide.
- The OpenML [API documentation](https://www.openml.org/apis) is also
a good resource.
- Start by reading the [Large-Scale Benchmarking
chapter](https://mlr3book.mlr-org.com/chapters/chapter11/large-scale_benchmarking.html)
from the `mlr3` book.
- The [package website](https://mlr3oml.mlr-org.com/dev/) contains a
getting started guide.
- The OpenML [API documentation](https://www.openml.org/apis) is also a
good resource.

## Bugs, Questions, Feedback

*mlr3oml* is a free and open source software project that encourages
participation and feedback. If you have any issues, questions,
suggestions or feedback, please do not hesitate to open an “issue” about
it on the GitHub page\!
it on the GitHub page!

In case of problems / bugs, it is often helpful if you provide a
“minimum working example” that showcases the behaviour (but don’t
worry about this if the bug is obvious).
“minimum working example” that showcases the behaviour (but don’t worry
about this if the bug is obvious).
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