ontolearn.data_struct

Data structures.

Classes

PrepareBatchOfPrediction	An abstract class representing a Dataset.
PrepareBatchOfTraining	An abstract class representing a Dataset.
Experience	A class to model experiences for Replay Memory.
NCESBaseDataLoader
NCESDataLoader	An abstract class representing a Dataset.
NCESDataLoaderInference	An abstract class representing a Dataset.
CLIPDataLoader	An abstract class representing a Dataset.
CLIPDataLoaderInference	An abstract class representing a Dataset.

Module Contents

class ontolearn.data_struct.PrepareBatchOfPrediction(current_state: torch.FloatTensor, next_state_batch: torch.FloatTensor, p: torch.FloatTensor, n: torch.FloatTensor)

Bases: torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement __getitems__(), for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

X

__len__()

__getitem__(idx)

get_all()

class ontolearn.data_struct.PrepareBatchOfTraining(current_state_batch: torch.Tensor, next_state_batch: torch.Tensor, p: torch.Tensor, n: torch.Tensor, q: torch.Tensor)

Bases: torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement __getitems__(), for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

S

S_Prime

y

Negatives

X

__len__()

__getitem__(idx)

class ontolearn.data_struct.Experience(maxlen: int)

A class to model experiences for Replay Memory.

current_states

next_states

rewards

__len__()

append(e)

Append. :param e: A tuple of s_i, s_j and reward, where s_i and s_j represent refining s_i and reaching s_j.

retrieve()

clear()

class ontolearn.data_struct.NCESBaseDataLoader(vocab, inv_vocab)

vocab

inv_vocab

vocab_df

static decompose(concept_name: str) → list

get_labels(target)

class ontolearn.data_struct.NCESDataLoader(data: list, embeddings, vocab, inv_vocab, shuffle_examples, max_length, example_sizes=None, sorted_examples=True)

Bases: NCESBaseDataLoader, torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement __getitems__(), for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

data_raw

embeddings

max_length

shuffle_examples

example_sizes

sorted_examples

__len__()

__getitem__(idx)

class ontolearn.data_struct.NCESDataLoaderInference(data: list, embeddings, vocab, inv_vocab, shuffle_examples, sorted_examples=True)

Bases: NCESBaseDataLoader, torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement __getitems__(), for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

data_raw

embeddings

shuffle_examples

sorted_examples

__len__()

__getitem__(idx)

class ontolearn.data_struct.CLIPDataLoader(data: list, embeddings, shuffle_examples, example_sizes: list = None, k=5, sorted_examples=True)

Bases: torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement __getitems__(), for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

data_raw

embeddings

shuffle_examples

example_sizes

k

sorted_examples

__len__()

__getitem__(idx)

class ontolearn.data_struct.CLIPDataLoaderInference(data: list, embeddings, shuffle_examples, sorted_examples=True)

Bases: torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Subclasses could also optionally implement __getitems__(), for speedup batched samples loading. This method accepts list of indices of samples of batch and returns list of samples.

Note

DataLoader by default constructs an index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

data_raw

embeddings

shuffle_examples

sorted_examples

__len__()

__getitem__(idx)