From 61a33f55d2eec93a335dfecc9c9a5e85c339e00a Mon Sep 17 00:00:00 2001
From: svlandeg <sofie.vanlandeghem@gmail.com>
Date: Wed, 10 Apr 2019 16:06:09 +0200
Subject: [PATCH] little fixes

---
 spacy/kb.pxd | 12 +++++++++---
 spacy/kb.pyx | 13 +++++--------
 2 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/spacy/kb.pxd b/spacy/kb.pxd
index e34a0a9ba..e57c162fc 100644
--- a/spacy/kb.pxd
+++ b/spacy/kb.pxd
@@ -13,7 +13,7 @@ from .typedefs cimport hash_t
 # of bits we need to keep track of the answers.
 cdef struct _EntryC:
 
-    # The hash of this entry's unique ID and name in the kB
+    # The hash of this entry's unique ID/name in the kB
     hash_t entity_hash
 
     # Allows retrieval of one or more vectors.
@@ -99,7 +99,7 @@ cdef class KnowledgeBase:
     cdef inline int64_t c_add_entity(self, hash_t entity_hash, float prob,
                                      int32_t* vector_rows, int feats_row):
         """Add an entry to the knowledge base."""
-        # This is what we'll map the hash key to. It's where the entry will sit
+        # This is what we'll map the entity hash key to. It's where the entry will sit
         # in the vector of entries, so we can get it later.
         cdef int64_t new_index = self._entries.size()
         self._entries.push_back(
@@ -114,6 +114,8 @@ cdef class KnowledgeBase:
 
     cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs):
         """Connect a mention to a list of potential entities with their prior probabilities ."""
+        # This is what we'll map the alias hash key to. It's where the alias will be defined
+        # in the vector of aliases.
         cdef int64_t new_index = self._aliases_table.size()
 
         self._aliases_table.push_back(
@@ -126,12 +128,14 @@ cdef class KnowledgeBase:
 
     cdef inline _create_empty_vectors(self):
         """ 
-        Making sure the first element of each vector is a dummy,
+        Initializing the vectors and making sure the first element of each vector is a dummy,
         because the PreshMap maps pointing to indices in these vectors can not contain 0 as value
         cf. https://github.com/explosion/preshed/issues/17
         """
         cdef int32_t dummy_value = 0
         self.vocab.strings.add("")
+
+        self._entry_index = PreshMap()
         self._entries.push_back(
             _EntryC(
                 entity_hash=self.vocab.strings[""],
@@ -139,6 +143,8 @@ cdef class KnowledgeBase:
                 feats_row=dummy_value,
                 prob=dummy_value
             ))
+
+        self._alias_index = PreshMap()
         self._aliases_table.push_back(
             _AliasC(
                 entry_indices=[dummy_value],
diff --git a/spacy/kb.pyx b/spacy/kb.pyx
index 3a0a8b918..38c393355 100644
--- a/spacy/kb.pyx
+++ b/spacy/kb.pyx
@@ -1,3 +1,4 @@
+# cython: infer_types=True
 # cython: profile=True
 # coding: utf8
 from spacy.errors import Errors, Warnings, user_warning
@@ -19,7 +20,7 @@ cdef class Candidate:
     @property
     def entity_(self):
         """RETURNS (unicode): ID/name of this entity in the KB"""
-        return self.kb.vocab.strings[self.entity]
+        return self.kb.vocab.strings[self.entity_hash]
 
     @property
     def alias(self):
@@ -29,7 +30,7 @@ cdef class Candidate:
     @property
     def alias_(self):
         """RETURNS (unicode): ID of the original alias"""
-        return self.kb.vocab.strings[self.alias]
+        return self.kb.vocab.strings[self.alias_hash]
 
     @property
     def prior_prob(self):
@@ -40,8 +41,6 @@ cdef class KnowledgeBase:
 
     def __init__(self, Vocab vocab):
         self.vocab = vocab
-        self._entry_index = PreshMap()
-        self._alias_index = PreshMap()
         self.mem = Pool()
         self._create_empty_vectors()
 
@@ -56,8 +55,8 @@ cdef class KnowledgeBase:
 
     def add_entity(self, unicode entity, float prob=0.5, vectors=None, features=None):
         """
-        Add an entity to the KB.
-        Return the hash of the entity ID at the end
+        Add an entity to the KB, optionally specifying its log probability based on corpus frequency
+        Return the hash of the entity ID/name at the end
         """
         cdef hash_t entity_hash = self.vocab.strings.add(entity)
 
@@ -98,8 +97,6 @@ cdef class KnowledgeBase:
             user_warning(Warnings.W017.format(alias=alias))
             return
 
-        cdef hash_t entity_hash
-
         cdef vector[int64_t] entry_indices
         cdef vector[float] probs