# TrainValSplits

## Introduction

**`TrainValSplits`** widget in Supervisely is a tool that helps with the creation of training and validation datasets. The widget allows for easy splitting of the original dataset into training and validation sets based on customizable parameters such as percentage split or based on datasets or specific tag. `TrainValSplits` helps improve the performance of machine learning models by ensuring that they are trained on diverse and representative data.

## Function signature

```python
TrainValSplits(
    project_id=None,
    project_fs=None,
    random_splits=True,
    tags_splits=True,
    datasets_splits=True,
    widget_id=None,
    collections_splits=False,
)
```

![default](https://user-images.githubusercontent.com/120389559/222371242-96bc091e-e3e4-4135-a578-727026453842.gif)

## Parameters

|      Parameters      |  Type  |                                                   Description                                                  |
| :------------------: | :----: | :------------------------------------------------------------------------------------------------------------: |
|     `project_id`     |  `int` |                                               Input `Project` ID                                               |
|     `project_fs`     |  `str` |                                      Path to input `Project` on local host                                     |
|    `random_splits`   | `bool` |                                 Shuffle data and split with defined probability                                |
|     `tags_splits`    | `bool` |                                  Images should have assigned train or val tag                                  |
|   `datasets_splits`  | `bool` |                                 Select one or several datasets for every split                                 |
|      `widget_id`     |  `str` |                                                ID of the widget                                                |
| `collections_splits` | `bool` | If True, allows to split data into train and validation sets based on selected collections within the project. |

### project\_id

Determine input `Project` ID.

**type:** `int`

**default value:** `None`

```python
splits = TrainValSplits(project_id=project_id)
```

### project\_fs

Determine path to input `Project` on local host.

**type:** `str`

**default value:** `None`

```python
project_dir = os.path.join(sly.app.get_data_dir(), project_info.name)
sly.Project.download(api, project_id, project_dir)
project_fs = sly.Project(project_dir, sly.OpenMode.READ)

splits = TrainValSplits(project_fs=project_fs)
```

![default](https://user-images.githubusercontent.com/120389559/222371242-96bc091e-e3e4-4135-a578-727026453842.gif)

### random\_splits

Shuffle data and split with defined probability.

**type:** `bool`

**default value:** `true`

```python
splits = TrainValSplits(
    project_id=project_id,
    random_splits=False,
)
```

![random\_splits](https://user-images.githubusercontent.com/120389559/222375099-fa7f0aa2-51f0-4800-9c04-5d8a64de9f73.gif)

### tags\_splits

Images should have assigned train or val tag.

**type:** `bool`

**default value:** `true`

```python
splits = TrainValSplits(
    project_id=project_id,
    tags_splits=False,
)
```

![tags\_splits](https://user-images.githubusercontent.com/120389559/222375745-ec6dc5ee-a438-427e-b4ab-b55f8c2f185b.gif)

### datasets\_splits

Select one or several datasets for every split.

**type:** `bool`

**default value:** `true`

```python
splits = TrainValSplits(
    project_id=project_id,
    datasets_splits=False,
)
```

![datasets\_splits](https://user-images.githubusercontent.com/120389559/222376683-28eb633f-1ad7-4baa-a6c5-bbd32eb7d738.gif)

### widget\_id

ID of the widget.

**type:** `str`

**default value:** `None`

### collections\_splits

Allows to split data into train and validation sets based on the provided collections.

**type:** `bool`

**default value:** `False`

## Methods and attributes

|       Attributes and Methods       | Description                                                                                                                      |
| :--------------------------------: | -------------------------------------------------------------------------------------------------------------------------------- |
|           `get_splits()`           | Return result train/val split.                                                                                                   |
|             `disable()`            | Disable widget.                                                                                                                  |
|             `enable()`             | Enable widget.                                                                                                                   |
| `set_project_id_for_collections()` | Sets the project ID and updates available collections for train and validation splits.                                           |
|    `get_train_collections_ids()`   | Returns a list of collection IDs selected for the training split.                                                                |
|     `get_val_collections_ids()`    | Returns a list of collection IDs selected for the validation split.                                                              |
|     `set_collections_splits()`     | Sets the collections (by their IDs) to be used for the training and validation splits when using the "collections" split method. |
|  `set_collections_splits_by_ids()` | Set for the specified split (train or val) sets the provided collection IDs.                                                     |

## Mini App Example

You can find this example in our Github repository:

[supervisely-ecosystem/ui-widgets-demos/controls/006\_train\_val\_splits/src/main.py](https://github.com/supervisely-ecosystem/ui-widgets-demos/blob/master/controls/006_train_val_splits/src/main.py)

### Import libraries

```python
import os

import supervisely as sly
from dotenv import load_dotenv
from supervisely.app.widgets import Card, Container, TrainValSplits, Button, Text
```

### Init API client

First, we load environment variables with credentials and init API for communicating with Supervisely Instance:

```python
load_dotenv("local.env")
load_dotenv(os.path.expanduser("~/supervisely.env"))

api = sly.Api()
```

### Prepare project\_id, download project and create project\_fs

```python
project_id = sly.env.project_id()
project_info = api.project.get_info_by_id(project_id)
project_dir = os.path.join(sly.app.get_data_dir(), project_info.name)
sly.fs.remove_dir(project_dir)
sly.Project.download(api, project_id, project_dir)
project_fs = sly.Project(project_dir, sly.OpenMode.READ)
```

### Initialize `TrainValSplits` widget

```python
splits = TrainValSplits(project_fs=project_fs)
```

### Initialize `Button` and `Text` widget we will use

```python
button = Button("Get splits")
text = Text("")
text.hide()

```

### Create app layout

Prepare a layout for app using `Card` widget with the `content` parameter and place widget that we've just created in the `Container` widget.

```python
card = Card(
    title="Train Val Splits",
    content=Container([splits, button, text], gap=5),
)
layout = Container(widgets=[card])
```

### Create app using layout

Create an app object with layout parameter.

```python
app = sly.Application(layout=layout)
```

### Add function to control widgets from python code

```python
@button.click
def get_splits():
    train_set, val_set = splits.get_splits()
    text.text = f"Train split: {len(train_set)} images, Val split: {len(val_set)} images"
    text.show()
```

![layout](https://user-images.githubusercontent.com/120389559/222379597-f31db481-297d-4e29-a086-5ba9313208ed.gif)