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Update docstrings and simplify most_similar

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ines 2017-11-01 00:18:08 +01:00
parent ba2e6c8c6f
commit 2ad2f09d12
1 changed files with 22 additions and 25 deletions
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47
spacy/vectors.pyx
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@ -70,17 +70,18 @@ cdef class Vectors:
@property
def size(self):
"""Return rows*dims"""
"""RETURNS (int): rows*dims"""
return self.data.shape[0] * self.data.shape[1]
@property
def is_full(self):
"""Returns True if no keys are available for new keys."""
"""RETURNS (bool): `True` if no slots are available for new keys."""
return len(self._unset) == 0
@property
def n_keys(self):
"""Returns True if no keys are available for new keys."""
"""RETURNS (int) The number of keys in the table. Note that this is the
number of all keys, not just unique vectors."""
return len(self.key2row)
def __reduce__(self):
@ -198,9 +199,10 @@ cdef class Vectors:
"""Add a key to the table. Keys can be mapped to an existing vector
by setting `row`, or a new vector can be added.
key (unicode / int): The key to add.
vector (numpy.ndarray / None): A vector to add for the key.
row (int / None): The row-number of a vector to map the key to.
key (int): The key to add.
vector (ndarray / None): A vector to add for the key.
row (int / None): The row number of a vector to map the key to.
RETURNS (int): The row the vector was added to.
"""
if row is None and key in self.key2row:
row = self.key2row[key]
@ -216,17 +218,20 @@ cdef class Vectors:
self._unset.remove(row)
return row
def most_similar(self, queries, *, return_scores=False, return_rows=False,
batch_size=1024):
'''For each of the given vectors, find the single entry most similar
def most_similar(self, queries, *, batch_size=1024):
"""For each of the given vectors, find the single entry most similar
to it, by cosine.
Queries are by vector. Results are returned as an array of keys,
or a tuple of (keys, scores) if return_scores=True. If `queries` is
large, the calculations are performed in chunks, to avoid consuming
too much memory. You can set the `batch_size` to control the size/space
trade-off during the calculations.
'''
Queries are by vector. Results are returned as a `(keys, best_rows,
scores)` tuple. If `queries` is large, the calculations are performed in
chunks, to avoid consuming too much memory. You can set the `batch_size`
to control the size/space trade-off during the calculations.
queries (ndarray): An array with one or more vectors.
batch_size (int): The batch size to use.
RETURNS (tuple): The most similar entry as a `(keys, best_rows, scores)`
tuple.
"""
xp = get_array_module(self.data)
vectors = self.data / xp.linalg.norm(self.data, axis=1, keepdims=True)
@ -244,14 +249,7 @@ cdef class Vectors:
best_rows[i:i+batch_size] = sims.argmax(axis=1)
scores[i:i+batch_size] = sims.max(axis=1)
keys = self.get_keys(best_rows)
if return_rows and return_scores:
return (keys, best_rows, scores)
elif return_rows:
return (keys, best_rows)
elif return_scores:
return (keys, scores)
else:
return keys
return (keys, best_rows, scores)
def from_glove(self, path):
"""Load GloVe vectors from a directory. Assumes binary format,
@ -261,8 +259,7 @@ cdef class Vectors:
By default GloVe outputs 64-bit vectors.
path (unicode / Path): The path to load the GloVe vectors from.
RETURNS: A StringStore object, holding the key-to-string mapping.
RETURNS: A `StringStore` object, holding the key-to-string mapping.
"""
path = util.ensure_path(path)
width = None