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160 lines
6.3 KiB
Cython
160 lines
6.3 KiB
Cython
"""Knowledge-base for entity or concept linking."""
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from cymem.cymem cimport Pool
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from preshed.maps cimport PreshMap
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from libcpp.vector cimport vector
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from libc.stdint cimport int32_t, int64_t
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from libc.stdio cimport FILE
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from spacy.vocab cimport Vocab
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from .typedefs cimport hash_t
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from .structs cimport EntryC, AliasC
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ctypedef vector[EntryC] entry_vec
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ctypedef vector[AliasC] alias_vec
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# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
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cdef class Candidate:
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cdef readonly KnowledgeBase kb
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cdef hash_t entity_hash
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cdef float entity_freq
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cdef hash_t alias_hash
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cdef float prior_prob
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cdef class KnowledgeBase:
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cdef Pool mem
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cpdef readonly Vocab vocab
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# This maps 64bit keys (hash of unique entity string)
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# to 64bit values (position of the _EntryC struct in the _entries vector).
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# The PreshMap is pretty space efficient, as it uses open addressing. So
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# the only overhead is the vacancy rate, which is approximately 30%.
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cdef PreshMap _entry_index
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# Each entry takes 128 bits, and again we'll have a 30% or so overhead for
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# over allocation.
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# In total we end up with (N*128*1.3)+(N*128*1.3) bits for N entries.
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# Storing 1m entries would take 41.6mb under this scheme.
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cdef entry_vec _entries
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# This maps 64bit keys (hash of unique alias string)
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# to 64bit values (position of the _AliasC struct in the _aliases_table vector).
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cdef PreshMap _alias_index
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# This should map mention hashes to (entry_id, prob) tuples. The probability
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# should be P(entity | mention), which is pretty important to know.
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# We can pack both pieces of information into a 64-bit value, to keep things
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# efficient.
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cdef alias_vec _aliases_table
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# This is the part which might take more space: storing various
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# categorical features for the entries, and storing vectors for disambiguation
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# and possibly usage.
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# If each entry gets a 300-dimensional vector, for 1m entries we would need
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# 1.2gb. That gets expensive fast. What might be better is to avoid learning
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# a unique vector for every entity. We could instead have a compositional
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# model, that embeds different features of the entities into vectors. We'll
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# still want some per-entity features, like the Wikipedia text or entity
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# co-occurrence. Hopefully those vectors can be narrow, e.g. 64 dimensions.
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cdef object _vectors_table
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# It's very useful to track categorical features, at least for output, even
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# if they're not useful in the model itself. For instance, we should be
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# able to track stuff like a person's date of birth or whatever. This can
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# easily make the KB bigger, but if this isn't needed by the model, and it's
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# optional data, we can let users configure a DB as the backend for this.
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cdef object _features_table
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cdef inline int64_t c_add_entity(self, hash_t entity_hash, float prob,
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int32_t* vector_rows, int feats_row) nogil:
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"""Add an entry to the vector of entries.
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After calling this method, make sure to update also the _entry_index using the return value"""
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# This is what we'll map the entity hash key to. It's where the entry will sit
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# in the vector of entries, so we can get it later.
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cdef int64_t new_index = self._entries.size()
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# Avoid struct initializer to enable nogil, cf https://github.com/cython/cython/issues/1642
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cdef EntryC entry
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entry.entity_hash = entity_hash
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entry.vector_rows = vector_rows
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entry.feats_row = feats_row
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entry.prob = prob
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self._entries.push_back(entry)
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return new_index
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cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs) nogil:
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"""Connect a mention to a list of potential entities with their prior probabilities .
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After calling this method, make sure to update also the _alias_index using the return value"""
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# This is what we'll map the alias hash key to. It's where the alias will be defined
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# in the vector of aliases.
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cdef int64_t new_index = self._aliases_table.size()
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# Avoid struct initializer to enable nogil
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cdef AliasC alias
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alias.entry_indices = entry_indices
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alias.probs = probs
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self._aliases_table.push_back(alias)
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return new_index
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cdef inline void _create_empty_vectors(self, hash_t dummy_hash) nogil:
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"""
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Initializing the vectors and making sure the first element of each vector is a dummy,
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because the PreshMap maps pointing to indices in these vectors can not contain 0 as value
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cf. https://github.com/explosion/preshed/issues/17
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"""
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cdef int32_t dummy_value = 0
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# Avoid struct initializer to enable nogil
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cdef EntryC entry
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entry.entity_hash = dummy_hash
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entry.vector_rows = &dummy_value
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entry.feats_row = dummy_value
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entry.prob = dummy_value
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# Avoid struct initializer to enable nogil
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cdef vector[int64_t] dummy_entry_indices
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dummy_entry_indices.push_back(0)
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cdef vector[float] dummy_probs
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dummy_probs.push_back(0)
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cdef AliasC alias
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alias.entry_indices = dummy_entry_indices
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alias.probs = dummy_probs
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self._entries.push_back(entry)
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self._aliases_table.push_back(alias)
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cpdef load_bulk(self, loc)
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cdef class Writer:
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cdef FILE* _fp
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cdef int write_header(self, int64_t nr_entries) except -1
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cdef int write_entry(self, hash_t entry_hash, float entry_prob) except -1
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cdef int write_alias_length(self, int64_t alias_length) except -1
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cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1
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cdef int write_alias(self, int64_t entry_index, float prob) except -1
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cdef int _write(self, void* value, size_t size) except -1
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cdef class Reader:
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cdef FILE* _fp
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cdef int read_header(self, int64_t* nr_entries) except -1
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cdef int read_entry(self, hash_t* entity_hash, float* prob) except -1
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cdef int read_alias_length(self, int64_t* alias_length) except -1
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cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1
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cdef int read_alias(self, int64_t* entry_index, float* prob) except -1
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cdef int _read(self, void* value, size_t size) except -1
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