## SingleStateFeaturizer Objects

class SingleStateFeaturizer()

Base class for mechanisms to transform the conversations state into ML formats.

Subclasses of SingleStateFeaturizer decide how the bot will transform the conversation state to a format which a classifier can read: feature vector.

#### prepare_from_domain

```python
prepare_from_domain(domain: Domain) -> None
```
Helper method to init based on domain.

#### encode

```python
encode(state: Dict[Text, float]) -> np.ndarray
```
Encode user input.

#### action_as_one_hot

```python
@staticmethod
action_as_one_hot(action: Text, domain: Domain) -> np.ndarray
```
Encode system action as one-hot vector.

#### create_encoded_all_actions

```python
create_encoded_all_actions(domain: Domain) -> np.ndarray
```
Create matrix with all actions from domain encoded in rows.

## BinarySingleStateFeaturizer Objects

class BinarySingleStateFeaturizer(SingleStateFeaturizer)

Assumes all features are binary.

All features should be either on or off, denoting them with 1 or 0.

#### __init__

```python
__init__() -> None
```
Declares instant variables.

#### prepare_from_domain

```python
prepare_from_domain(domain: Domain) -> None
```
Use Domain to prepare featurizer.

#### encode

```python
encode(state: Dict[Text, float]) -> np.ndarray
```
Returns a binary vector indicating which features are active.

Given a dictionary of states (e.g. 'intent_greet', 'prev_action_listen',...) return a binary vector indicating which features of `self.input_features` are in the bag. NB it's a regular double precision float array type.

For example with two active features out of five possible features this would return a vector like `[0 0 1 0 1]`

If intent features are given with a probability, for example with two active features and two uncertain intents out of five possible features this would return a vector like `[0.3, 0.7, 1.0, 0, 1.0]`.

If this is just a padding vector we set all values to `-1`. padding vectors are specified by a `None` or `[None]` value for states.

#### create_encoded_all_actions

```python
create_encoded_all_actions(domain: Domain) -> np.ndarray
```
Create matrix with all actions from domain encoded in rows as bag of words.

## LabelTokenizerSingleStateFeaturizer Objects

class LabelTokenizerSingleStateFeaturizer(SingleStateFeaturizer)

Creates bag-of-words feature vectors.

User intents and bot action names are split into tokens and used to create bag-of-words feature vectors.

**Arguments**:

- `split_symbol` - The symbol that separates words in intents and action names.
- `use_shared_vocab` - The flag that specifies if to create the same vocabulary for user intents and bot actions.

#### __init__

```python
__init__(use_shared_vocab: bool = False, split_symbol: Text = "_") -> None
```
inits vocabulary for label bag of words representation

#### prepare_from_domain

```python
prepare_from_domain(domain: Domain) -> None
```
Creates internal vocabularies for user intents and bot actions.

#### encode

```python
encode(state: Dict[Text, float]) -> np.ndarray
```
Returns a binary vector indicating which tokens are present.

#### create_encoded_all_actions

```python
create_encoded_all_actions(domain: Domain) -> np.ndarray
```
Create matrix with all actions from domain encoded in rows as bag of words.

## TrackerFeaturizer Objects

class TrackerFeaturizer()

Base class for actual tracker featurizers.

#### training_states_and_actions

```python
training_states_and_actions(trackers: List[DialogueStateTracker], domain: Domain) -> Tuple[List[List[Dict]], List[List[Text]]]
```
Transforms list of trackers to lists of states and actions.

#### featurize_trackers

```python
featurize_trackers(trackers: List[DialogueStateTracker], domain: Domain) -> DialogueTrainingData
```
Create training data.

#### prediction_states

```python
prediction_states(trackers: List[DialogueStateTracker], domain: Domain) -> List[List[Dict[Text, float]]]
```
Transforms list of trackers to lists of states for prediction.

#### create_X

```python
create_X(trackers: List[DialogueStateTracker], domain: Domain) -> np.ndarray
```
Create X for prediction.

#### load

```python
@staticmethod
load(path) -> Optional["TrackerFeaturizer"]
```
Loads the featurizer from file.

## FullDialogueTrackerFeaturizer Objects

class FullDialogueTrackerFeaturizer(TrackerFeaturizer)

Creates full dialogue training data for time distributed architectures.

Creates training data that uses each time output for prediction.
Training data is padded up to the length of the longest dialogue with -1.

#### training_states_and_actions

Training data is padded up to the length of the longest dialogue with -1.

#### prediction_states