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	* chore: add cython-linter dev dependency * fix: lexeme.pyx * fix: morphology.pxd * fix: tokenizer.pxd * fix: vocab.pxd * fix: morphology.pxd (line length) * ci: add cython-lint * ci: fix cython-lint call * Fix kb/candidate.pyx. * Fix kb/kb.pyx. * Fix kb/kb_in_memory.pyx. * Fix kb. * Fix training/ partially. * Fix training/. Ignore trailing whitespaces and too long lines. * Fix ml/. * Fix matcher/. * Fix pipeline/. * Fix tokens/. * Fix build errors. Fix vocab.pyx. * Fix cython-lint install and run. * Fix lexeme.pyx, parts_of_speech.pxd, vectors.pyx. Temporarily disable cython-lint execution. * Fix attrs.pyx, lexeme.pyx, symbols.pxd, isort issues. * Make cython-lint install conditional. Fix tokenizer.pyx. * Fix remaining files. Reenable cython-lint check. * Readded parentheses. * Fix test_build_dependencies(). * Add explanatory comment to cython-lint execution. --------- Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
		
			
				
	
	
		
			179 lines
		
	
	
		
			6.8 KiB
		
	
	
	
		
			Cython
		
	
	
	
	
	
			
		
		
	
	
			179 lines
		
	
	
		
			6.8 KiB
		
	
	
	
		
			Cython
		
	
	
	
	
	
"""Knowledge-base for entity or concept linking."""
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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 libcpp.vector cimport vector
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from preshed.maps cimport PreshMap
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from ..structs cimport AliasC, KBEntryC
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from ..typedefs cimport hash_t
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from .kb cimport KnowledgeBase
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ctypedef vector[KBEntryC] entry_vec
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ctypedef vector[AliasC] alias_vec
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ctypedef vector[float] float_vec
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ctypedef vector[float_vec] float_matrix
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cdef class InMemoryLookupKB(KnowledgeBase):
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    # This maps 64bit keys (hash of unique entity string)
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    # to 64bit values (position of the _KBEntryC 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 float_matrix _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_vector(self, vector[float] entity_vector) nogil:
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        """Add an entity vector to the vectors table."""
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        cdef int64_t new_index = self._vectors_table.size()
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        self._vectors_table.push_back(entity_vector)
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        return new_index
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    cdef inline int64_t c_add_entity(
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        self,
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        hash_t entity_hash,
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        float freq,
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        int32_t vector_index,
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        int feats_row
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    ) 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
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        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.
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        # https://github.com/cython/cython/issues/1642
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        cdef KBEntryC entry
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        entry.entity_hash = entity_hash
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        entry.vector_index = vector_index
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        entry.feats_row = feats_row
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        entry.freq = freq
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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(
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        self,
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        hash_t alias_hash,
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        vector[int64_t] entry_indices,
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        vector[float] probs
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    ) nogil:
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        """Connect a mention to a list of potential entities with their prior
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        probabilities. After calling this method, make sure to update also the
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        _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
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        # defined 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
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        dummy, because the PreshMap maps pointing to indices in these vectors can not
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        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 KBEntryC entry
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        entry.entity_hash = dummy_hash
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        entry.vector_index = dummy_value
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        entry.feats_row = dummy_value
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        entry.freq = 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 set_entities(self, entity_list, freq_list, vector_list)
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cdef class Writer:
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    cdef FILE* _fp
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    cdef int write_header(
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        self, int64_t nr_entries, int64_t entity_vector_length
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    ) except -1
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    cdef int write_vector_element(self, float element) except -1
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    cdef int write_entry(
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        self, hash_t entry_hash, float entry_freq, int32_t vector_index
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    ) 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(
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        self, hash_t alias_hash, int64_t candidate_length
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    ) 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(
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        self, int64_t* nr_entries, int64_t* entity_vector_length
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    ) except -1
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    cdef int read_vector_element(self, float* element) except -1
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    cdef int read_entry(
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        self, hash_t* entity_hash, float* freq, int32_t* vector_index
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    ) 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(
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        self, hash_t* alias_hash, int64_t* candidate_length
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    ) 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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