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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,2 @@ | ||
| include ampligraph/latent_features/prime_number_list.txt | ||
| include ampligraph/logger.conf | ||
|
|
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| Original file line number | Diff line number | Diff line change |
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| import abc | ||
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| class AmpligraphDatasetAdapter(abc.ABC): | ||
| """Abstract class for dataset adapters | ||
| Developers can design in similar format to adapt data from different sources to feed to ampligraph. | ||
| """ | ||
| def __init__(self): | ||
| """Initialize the class variables | ||
| """ | ||
| self.dataset = {} | ||
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| # relation to idx mappings | ||
| self.rel_to_idx = {} | ||
| # entities to idx mappings | ||
| self.ent_to_idx = {} | ||
| # Mapped status of each dataset | ||
| self.mapped_status = {} | ||
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| def use_mappings(self, rel_to_idx, ent_to_idx): | ||
| """Use an existing mapping with the datasource. | ||
| """ | ||
| self.rel_to_idx = rel_to_idx | ||
| self.ent_to_idx = ent_to_idx | ||
| # set the mapped status to false, since we are changing the dictionary | ||
| for key in self.dataset.keys(): | ||
| self.mapped_status[key] = False | ||
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| def generate_mappings(self, use_all=False): | ||
| """Generate mappings from either train set or use all dataset to generate mappings | ||
| Parameters | ||
| ---------- | ||
| use_all : boolean | ||
| If True, it generates mapping from all the data. If False, it only uses training set to generate mappings | ||
| Returns | ||
| ------- | ||
| rel_to_idx : dictionary | ||
| Relation to idx mapping dictionary | ||
| ent_to_idx : dictionary | ||
| entity to idx mapping dictionary | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def get_size(self, dataset_type="train"): | ||
| """Returns the size of the specified dataset | ||
| Parameters | ||
| ---------- | ||
| dataset_type : string | ||
| type of the dataset | ||
| Returns | ||
| ------- | ||
| size : int | ||
| size of the specified dataset | ||
| """ | ||
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| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def set_data(self, dataset, dataset_type=None, mapped_status=False): | ||
| """set the dataset based on the type | ||
| Parameters | ||
| ---------- | ||
| dataset : nd-array or dictionary | ||
| dataset of triples | ||
| dataset_type : string | ||
| if the dataset parameter is an nd- array then this indicates the type of the data being based | ||
| mapped_status : bool | ||
| indicates whether the data has already been mapped to the indices | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def map_data(self, remap=False): | ||
| """map the data to the mappings of ent_to_idx and rel_to_idx | ||
| Parameters | ||
| ---------- | ||
| remap : boolean | ||
| remap the data, if already mapped. One would do this if the dictionary is updated. | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def set_filter(self, filter_triples): | ||
| """set's the filter that need to be used while generating evaluation batch | ||
| Parameters | ||
| ---------- | ||
| filter_triples : nd-array | ||
| triples that would be used as filter | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def get_next_train_batch(self, batch_size=1, dataset_type="train"): | ||
| """Generator that returns the next batch of data. | ||
| Parameters | ||
| ---------- | ||
| batch_size : int | ||
| data size that needs to be returned | ||
| dataset_type: string | ||
| indicates which dataset to use | ||
| Returns | ||
| ------- | ||
| batch_output : nd-array | ||
| yields a batch of triples from the dataset type specified | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def get_next_eval_batch(self, batch_size=1, dataset_type="test"): | ||
| """Generator that returns the next batch of data. | ||
| Parameters | ||
| ---------- | ||
| batch_size : int | ||
| data size that needs to be returned | ||
| dataset_type: string | ||
| indicates which dataset to use | ||
| Returns | ||
| ------- | ||
| batch_output : nd-array | ||
| yields a batch of triples from the dataset type specified | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def get_next_batch_with_filter(self, batch_size=1, dataset_type="test"): | ||
| """Generator that returns the next batch of data along with the filter. | ||
| Parameters | ||
| ---------- | ||
| batch_size : int | ||
| data size that needs to be returned | ||
| dataset_type: string | ||
| indicates which dataset to use | ||
| Returns | ||
| ------- | ||
| batch_output : nd-array [n,3] | ||
| yields a batch of triples from the dataset type specified | ||
| participating_objects : nd-array [n,1] | ||
| all objects that were involved in the s-p-? relation | ||
| participating_subjects : nd-array [n,1] | ||
| all subjects that were involved in the ?-p-o relation | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') | ||
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| def cleanup(self): | ||
| """Cleans up the internal state | ||
| """ | ||
| raise NotImplementedError('Abstract Method not implemented!') |
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