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# PredictionTypeHandler

The purpose of `PredictionTypeHandler` is to describe the prediction(s) tensors for visualization and analysis purposes.

```python
@dataclass
class PredictionTypeHandler:
    name: str
    labels: List[str]
    channel_dim: int = -1
```

For more on `PredictionTypeHandler`:

<table><thead><tr><th width="158.46928201888204">Args</th><th></th></tr></thead><tbody><tr><td><code>name</code></td><td><em>(str)</em> The given name of the <strong>output,</strong> e.g. image/logits/entity</td></tr><tr><td><code>labels</code></td><td><em>(List[str])</em> an array containing the labels associated with this <strong>prediction</strong></td></tr><tr><td><code>channel_dim</code></td><td>(defaults to -1). The dimension in which the channels exists in the prediction. channel_dim should be set to 1 for channel first predictions (i.e. - C,H,W).</td></tr></tbody></table>

## Examples

#### Basic Usage

#### MNIST example

One output, which we name `classes` that has 10 channels, each a logit for the classification of a digit

```python
from code_loader.contract.datasetclasses import PredictionTypeHandler
prediction_type1 = PredictionTypeHandler(name='classes', labels=[str(i) for i in range(10)])
```

#### YOLO example

Four outputs:

* a conctatenated prediction with #channels = 4 + #classes
* Three scales, with #channels of 20,40,80

```python
prediction_type1 = PredictionTypeHandler(name='object detection', labels=["x", "y", "w", "h"] + [cl for cl in all_clss.values()], channel_dim=1)
prediction_type2 = PredictionTypeHandler(name='concatenate_20', labels=[str(i) for i in range(20)], channel_dim=-1)
prediction_type3 = PredictionTypeHandler(name='concatenate_40', labels=[str(i) for i in range(40)], channel_dim=-1)
prediction_type4 = PredictionTypeHandler(name='concatenate_80', labels=[str(i) for i in range(80)], channel_dim=-1)
```


---

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