Merge branch 'master' into spacy.io

.github/contributors/bintay.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
+[ExplosionAI GmbH](https://explosion.ai/legal). The term
+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
+object code, patch, tool, sample, graphic, specification, manual,
+documentation, or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and
+registrations, in your contribution:
+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
+    * you agree that you will not assert any moral rights in your contribution
+    against us, our licensees or transferees;
+
+    * you agree that we may register a copyright in your contribution and
+    exercise all ownership rights associated with it; and
+
+    * you agree that neither of us has any duty to consult with, obtain the
+    consent of, pay or render an accounting to the other for any use or
+    distribution of your contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable,
+non-exclusive, worldwide, no-charge, royalty-free license to:
+
+    * make, have made, use, sell, offer to sell, import, and otherwise transfer
+    your contribution in whole or in part, alone or in combination with or
+    included in any product, work or materials arising out of the project to
+    which your contribution was submitted, and
+
+    * at our option, to sublicense these same rights to third parties through
+    multiple levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective
+on the date you first submitted a contribution to us, even if your submission
+took place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+    * Each contribution that you submit is and shall be an original work of
+    authorship and you can legally grant the rights set out in this SCA;
+
+    * to the best of your knowledge, each contribution will not violate any
+    third party's copyrights, trademarks, patents, or other intellectual
+    property rights; and
+
+    * each contribution shall be in compliance with U.S. export control laws and
+    other applicable export and import laws. You agree to notify us if you
+    become aware of any circumstance which would make any of the foregoing
+    representations inaccurate in any respect. We may publicly disclose your
+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [x] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect to my
+    contributions.
+
+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                          | Entry                |
+|------------------------------- | -------------------- |
+| Name                           | Ben Taylor           |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           | October 2, 2019      |
+| GitHub username                | bintay               |
+| Website (optional)             | bentaylor.xyz        |

README.md

@@ -14,7 +14,7 @@ It's commercial open-source software, released under the MIT license.
 💫 **Version 2.2 out now!**
 [Check out the release notes here.](https://github.com/explosion/spaCy/releases)
 
-[![Azure Pipelines](<https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-devops&style=flat-square&label=build+(3.x)>)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
+[![Azure Pipelines](<https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build+(3.x)>)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
 [![Travis Build Status](<https://img.shields.io/travis/explosion/spaCy/master.svg?style=flat-square&logo=travis-ci&logoColor=white&label=build+(2.7)>)](https://travis-ci.org/explosion/spaCy)
 [![Current Release Version](https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square&logo=github)](https://github.com/explosion/spaCy/releases)
 [![pypi Version](https://img.shields.io/pypi/v/spacy.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy/)
@@ -22,7 +22,7 @@ It's commercial open-source software, released under the MIT license.
 [![Python wheels](https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true&style=flat-square&logo=python&logoColor=white)](https://github.com/explosion/wheelwright/releases)
 [![PyPi downloads](https://img.shields.io/pypi/dm/spacy?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy/)
 [![Conda downloads](https://img.shields.io/conda/dn/conda-forge/spacy?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/spacy)
-[![Model downloads](https://img.shields.io/github/downloads/explosion/spacy-models/total?style=flat-square&label=model+downloads)](https://github.com/explosion/spacy-models)
+[![Model downloads](https://img.shields.io/github/downloads/explosion/spacy-models/total?style=flat-square&label=model+downloads)](https://github.com/explosion/spacy-models/releases)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)
 [![spaCy on Twitter](https://img.shields.io/twitter/follow/spacy_io.svg?style=social&label=Follow)](https://twitter.com/spacy_io)
 

spacy/tests/vocab_vectors/test_vectors.py

@@ -277,9 +277,9 @@ def test_vocab_prune_vectors():
     _ = vocab["dog"]  # noqa: F841
     _ = vocab["kitten"]  # noqa: F841
     data = numpy.ndarray((5, 3), dtype="f")
-    data[0] = 1.0
+    data[0] = [1.0, 1.2, 1.1]
-    data[1] = 2.0
+    data[1] = [0.3, 1.3, 1.0]
-    data[2] = 1.1
+    data[2] = [0.9, 1.22, 1.05]
     vocab.set_vector("cat", data[0])
     vocab.set_vector("dog", data[1])
     vocab.set_vector("kitten", data[2])

spacy/vectors.pyx

@@ -303,8 +303,8 @@ cdef class Vectors:
                 self._unset.erase(self._unset.find(row))
         return row
 
-    def most_similar(self, queries, *, batch_size=1024):
+    def most_similar(self, queries, *, batch_size=1024, n=1, sort=True):
-        """For each of the given vectors, find the single entry most similar
+        """For each of the given vectors, find the n most similar entries
         to it, by cosine.
 
         Queries are by vector. Results are returned as a `(keys, best_rows,
@@ -314,15 +314,17 @@ cdef class Vectors:
 
         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)`
+        n (int): The number of entries to return for each query.
+        sort (bool): Whether to sort the n entries returned by score.
+        RETURNS (tuple): The most similar entries 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)
 
-        best_rows = xp.zeros((queries.shape[0],), dtype='i')
+        best_rows = xp.zeros((queries.shape[0], n), dtype='i')
-        scores = xp.zeros((queries.shape[0],), dtype='f')
+        scores = xp.zeros((queries.shape[0], n), dtype='f')
         # Work in batches, to avoid memory problems.
         for i in range(0, queries.shape[0], batch_size):
             batch = queries[i : i+batch_size]
@@ -331,12 +333,19 @@ cdef class Vectors:
             # vectors e.g. (10000, 300)
             # sims    e.g. (1024, 10000)
             sims = xp.dot(batch, vectors.T)
-            best_rows[i:i+batch_size] = sims.argmax(axis=1)
+            best_rows[i:i+batch_size] = xp.argpartition(sims, -n, axis=1)[:,-n:]
-            scores[i:i+batch_size] = sims.max(axis=1)
+            scores[i:i+batch_size] = xp.partition(sims, -n, axis=1)[:,-n:]
+
+            if sort:
+                sorted_index = xp.arange(scores.shape[0])[:,None],xp.argsort(scores, axis=1)[:,::-1]
+                scores[i:i+batch_size] = scores[sorted_index]
+                best_rows[i:i+batch_size] = best_rows[sorted_index]
+
         xp = get_array_module(self.data)
         row2key = {row: key for key, row in self.key2row.items()}
         keys = xp.asarray(
-            [row2key[row] for row in best_rows if row in row2key], dtype="uint64")
+            [[row2key[row] for row in best_rows[i] if row in row2key] 
+                    for i in range(len(queries)) ], dtype="uint64")
         return (keys, best_rows, scores)
 
     def from_glove(self, path):

spacy/vocab.pyx

@@ -324,10 +324,10 @@ cdef class Vocab:
         syn_keys, syn_rows, scores = self.vectors.most_similar(toss, batch_size=batch_size)
         remap = {}
         for i, key in enumerate(keys[nr_row:]):
-            self.vectors.add(key, row=syn_rows[i])
+            self.vectors.add(key, row=syn_rows[i][0])
             word = self.strings[key]
-            synonym = self.strings[syn_keys[i]]
+            synonym = self.strings[syn_keys[i][0]]
-            score = scores[i]
+            score = scores[i][0]
             remap[word] = (synonym, score)
         link_vectors_to_models(self)
         return remap

website/docs/api/vectors.md

@@ -303,6 +303,29 @@ vectors, they will be counted individually.
 | ----------- | ---- | ------------------------------------ |
 | **RETURNS** | int  | The number of all keys in the table. |
 
+## Vectors.most_similar {#most_similar tag="method"}
+
+For each of the given vectors, find the `n` most similar entries to it, by
+cosine. 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.
+
+> #### Example
+>
+> ```python
+> queries = numpy.asarray([numpy.random.uniform(-1, 1, (300,))])
+> most_similar = nlp.vectors.most_similar(queries, n=10)
+> ```
+
+| Name         | Type      | Description                                                        |
+| ------------ | --------- | ------------------------------------------------------------------ |
+| `queries`    | `ndarray` | An array with one or more vectors.                                 |
+| `batch_size` | int       | The batch size to use. Default to `1024`.                          |
+| `n`          | int       | The number of entries to return for each query. Defaults to `1`.   |
+| `sort`       | bool      | Whether to sort the entries returned by score. Defaults to `True`. |
+| **RETURNS**  | tuple     | The most similar entries as a `(keys, best_rows, scores)` tuple.   |
+
 ## Vectors.from_glove {#from_glove tag="method"}
 
 Load [GloVe](https://nlp.stanford.edu/projects/glove/) vectors from a directory.