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# DatasetMapper tutorial
`DatasetMapper` is a class which holds information about a dataset. This can be
used when dataset contains categorical non-numeric features which should be
mapped to numeric features. A simple example can be
```
7,5,True,3
6,3,False,4
4,8,False,2
9,3,True,3
```
The above dataset will be represented as
```
7,5,0,3
6,3,1,4
4,8,1,2
9,3,0,3
```
Here the mappings are
- `True` mapped to `0`
- `False` mapped to `1`
**Note**: `DatasetMapper` converts non-numeric values in the order in which it
encounters them in the dataset. Therefore there is a chance that `True` might
get mapped to `0` if it encounters `True` before `False`. This `0` and `1` are
not to be confused with C++ `bool` notations. These are mapping created by
`mlpack::DatasetMapper`.
`DatasetMapper` provides an easy API to load such data and stores all the
necessary information of the dataset.
## Loading data
To use `DatasetMapper` we have to call a specific overload of the `data::Load()`
function.
```c++
using namespace mlpack;
arma::mat data;
data::DatasetInfo info;
data::Load("dataset.csv", data, info);
```
Dataset:
```
7, 5, True, 3
6, 3, False, 4
4, 8, False, 2
9, 3, True, 3
```
## Dimensionality
There are two ways to initialize a DatasetMapper object.
* The first is to initialize the object and set each property yourself.
* The second is to pass the object to `Load()` without initialization, and
mlpack will populate the object. If we use the latter option then the
dimensionality will be same as what's in the data file.
```c++
std::cout << info.Dimensionality();
```
```
4
```
## Type of each dimension
Each dimension can be of either of the two types:
- `data::Datatype::numeric`
- `data::Datatype::categorical`
The function `Type(size_t dimension)` takes an argument dimension which is the
row number for which you want to know the type
This will return an enum `data::Datatype`, which is cast to `size_t` when we
print them using `std::cout`.
- `0` represents `data::Datatype::numeric`
- `1` represents `data::Datatype::categorical`
```c++
std::cout << info.Type(0) << "\n";
std::cout << info.Type(1) << "\n";
std::cout << info.Type(2) << "\n";
std::cout << info.Type(3) << "\n";
```
This produces:
```
0
0
1
0
```
## Number of mappings
If the type of a dimension is `data::Datatype::categorical`, then during
loading, each unique token in that dimension will be mapped to an integer
starting with `0`.
`NumMappings(size_t dimension)` takes `dimension` as an argument and returns the
number of mappings in that dimension, if the dimension is numeric, or there are
no mappings, then it will return 0.
```c++
std::cout << info.NumMappings(0) << "\n";
std::cout << info.NumMappings(1) << "\n";
std::cout << info.NumMappings(2) << "\n";
std::cout << info.NumMappings(3) << "\n";
```
will print:
```
0
0
2
0
```
## Checking mappings
There are two ways to check the mappings.
- Enter the string to get mapped integer
- Enter the mapped integer to get string
### `UnmapString()`
The `UnmapString()` function has the full signature `UnmapString(int value,
size_t dimension, size_t unmappingIndex = 0UL)`.
- `value` is the integer for which you want to find the mapped value
- `dimension` is the dimension in which you want to check the mappings
```c++
std::cout << info.UnmapString(0, 2) << "\n";
std::cout << info.UnmapString(1, 2) << "\n";
```
This will print:
```
True
False
```
### `UnmapValue()`
The `UnmapValue()` function has the signature `UnmapValue(const std::string
&input, size_t dimension)`.
- `input` is the mapped value for which you want to find mapping
- `dimension` is the dimension in which you want to find the mapped value
```c++
std::cout << info.UnmapValue("True", 2) << "\n";
std::cout << info.UnmapValue("False", 2) << "\n";
```
will produce:
```
0
1
```
## Further documentation
For further documentation on `DatasetMapper` and its uses, see the comments in
the source code in `src/mlpack/core/data/`, as well as its uses in the [examples
repository](https://github.com/mlpack/examples).
|
