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This is documentation for Rasa Open Source Documentation v2.0.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (2.2.x).

Version: 2.0.x

rasa.utils.tensorflow.model_data

FeatureSignature Objects

class FeatureSignature(NamedTuple)

Stores the shape and the type (sparse vs dense) of features.

RasaModelData Objects

class RasaModelData()

Data object used for all RasaModels.

It contains all features needed to train the models.

__init__

| __init__(label_key: Optional[Text] = None, label_sub_key: Optional[Text] = None, data: Optional[Data] = None) -> None

Initializes the RasaModelData object.

Arguments:

  • label_key - the key of a label used for balancing, etc.
  • label_sub_key - the sub key of a label used for balancing, etc.
  • data - the data holding the features

get

| get(key: Text, sub_key: Optional[Text] = None) -> Union[Dict[Text, List[np.ndarray]], List[np.ndarray]]

Get the data under the given keys.

Arguments:

  • key - The key.
  • sub_key - The optional sub key.

Returns:

The requested data.

items

| items() -> ItemsView

Return the items of the data attribute.

Returns:

The items of data.

values

| values() -> ValuesView[Dict[Text, List[np.ndarray]]]

Return the values of the data attribute.

Returns:

The values of data.

keys

| keys(key: Optional[Text] = None) -> List[Text]

Return the keys of the data attribute.

Arguments:

  • key - The optional key.

Returns:

The keys of the data.

first_data_example

| first_data_example() -> Data

Return the data with just one feature example per key, sub-key.

Returns:

The simplified data.

does_feature_not_exist

| does_feature_not_exist(key: Text, sub_key: Optional[Text] = None) -> bool

Check if feature key (and sub-key) is present and features are available.

Arguments:

  • key - The key.
  • sub_key - The optional sub-key.

Returns:

True, if no features for the given keys exists, False otherwise.

is_empty

| is_empty() -> bool

Checks if data is set.

number_of_examples

| number_of_examples(data: Optional[Data] = None) -> int

Obtain number of examples in data.

Arguments:

  • data - The data.
  • Raises - A ValueError if number of examples differ for different features.

Returns:

The number of examples in data.

feature_dimension

| feature_dimension(key: Text, sub_key: Text) -> int

Get the feature dimension of the given key.

Arguments:

  • key - The key.
  • sub_key - The optional sub-key.

Returns:

The feature dimension.

add_data

| add_data(data: Data, key_prefix: Optional[Text] = None) -> None

Add incoming data to data.

Arguments:

  • data - The data to add.
  • key_prefix - Optional key prefix to use in front of the key value.

add_features

| add_features(key: Text, sub_key: Text, features: Optional[List[np.ndarray]]) -> None

Add list of features to data under specified key.

Should update number of examples.

Arguments:

  • key - The key
  • sub_key - The sub-key
  • features - The features to add.

add_lengths

| add_lengths(key: Text, sub_key: Text, from_key: Text, from_sub_key: Text) -> None

Adds np.array of lengths of sequences to data under given key.

Arguments:

  • key - The key to add the lengths to
  • sub_key - The sub-key to add the lengths to
  • from_key - The key to take the lengths from
  • from_sub_key - The sub-key to take the lengths from

split

| split(number_of_test_examples: int, random_seed: int) -> Tuple["RasaModelData", "RasaModelData"]

Create random hold out test set using stratified split.

Arguments:

  • number_of_test_examples - Number of test examples.
  • random_seed - Random seed.

Returns:

A tuple of train and test RasaModelData.

get_signature

| get_signature() -> Dict[Text, Dict[Text, List[FeatureSignature]]]

Get signature of RasaModelData.

Signature stores the shape and whether features are sparse or not for every key.

Returns:

A dictionary of key and sub-key to a list of feature signatures (same structure as the data attribute).

as_tf_dataset

| as_tf_dataset(batch_size: int, batch_strategy: Text = SEQUENCE, shuffle: bool = False) -> tf.data.Dataset

Create tf dataset.

Arguments:

  • batch_size - The batch size to use.
  • batch_strategy - The batch strategy to use.
  • shuffle - Boolean indicating whether the data should be shuffled or not.

Returns:

The tf.data.Dataset.

prepare_batch

| prepare_batch(data: Optional[Data] = None, start: Optional[int] = None, end: Optional[int] = None, tuple_sizes: Optional[Dict[Text, int]] = None) -> Tuple[Optional[np.ndarray]]

Slices model data into batch using given start and end value.

Arguments:

  • data - The data to prepare.
  • start - The start index of the batch
  • end - The end index of the batch
  • tuple_sizes - In case the feature is not present we propagate the batch with None. Tuple sizes contains the number of how many None values to add for what kind of feature.

Returns:

The features of the batch.