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Don't use numpy directly for similarity (#3362)
* Don't use numpy directly for similarity * Contributor agreement
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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The SCA applies to any contribution that you make to any product or project
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managed by us (the **"project"**), and sets out the intellectual property rights
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you grant to us in the contributed materials. The term **"us"** shall mean
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[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
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**"you"** shall mean the person or entity identified below.
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If you agree to be bound by these terms, fill in the information requested
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below and include the filled-in version with your first pull request, under the
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folder [`.github/contributors/`](/.github/contributors/). The name of the file
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should be your GitHub username, with the extension `.md`. For example, the user
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example_user would create the file `.github/contributors/example_user.md`.
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Read this agreement carefully before signing. These terms and conditions
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constitute a binding legal agreement.
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## Contributor Agreement
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1. The term "contribution" or "contributed materials" means any source code,
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object code, patch, tool, sample, graphic, specification, manual,
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documentation, or any other material posted or submitted by you to the project.
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2. With respect to any worldwide copyrights, or copyright applications and
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registrations, in your contribution:
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* you hereby assign to us joint ownership, and to the extent that such
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assignment is or becomes invalid, ineffective or unenforceable, you hereby
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grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
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royalty-free, unrestricted license to exercise all rights under those
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copyrights. This includes, at our option, the right to sublicense these same
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rights to third parties through multiple levels of sublicensees or other
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licensing arrangements;
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* you agree that each of us can do all things in relation to your
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contribution as if each of us were the sole owners, and if one of us makes
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a derivative work of your contribution, the one who makes the derivative
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work (or has it made will be the sole owner of that derivative work;
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* you agree that you will not assert any moral rights in your contribution
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against us, our licensees or transferees;
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* you agree that we may register a copyright in your contribution and
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exercise all ownership rights associated with it; and
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* you agree that neither of us has any duty to consult with, obtain the
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consent of, pay or render an accounting to the other for any use or
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distribution of your contribution.
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3. With respect to any patents you own, or that you can license without payment
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to any third party, you hereby grant to us a perpetual, irrevocable,
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non-exclusive, worldwide, no-charge, royalty-free license to:
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* make, have made, use, sell, offer to sell, import, and otherwise transfer
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your contribution in whole or in part, alone or in combination with or
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included in any product, work or materials arising out of the project to
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which your contribution was submitted, and
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* at our option, to sublicense these same rights to third parties through
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multiple levels of sublicensees or other licensing arrangements.
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4. Except as set out above, you keep all right, title, and interest in your
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contribution. The rights that you grant to us under these terms are effective
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on the date you first submitted a contribution to us, even if your submission
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took place before the date you sign these terms.
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5. You covenant, represent, warrant and agree that:
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* Each contribution that you submit is and shall be an original work of
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authorship and you can legally grant the rights set out in this SCA;
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* to the best of your knowledge, each contribution will not violate any
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third party's copyrights, trademarks, patents, or other intellectual
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property rights; and
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* each contribution shall be in compliance with U.S. export control laws and
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other applicable export and import laws. You agree to notify us if you
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become aware of any circumstance which would make any of the foregoing
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representations inaccurate in any respect. We may publicly disclose your
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participation in the project, including the fact that you have signed the SCA.
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6. This SCA is governed by the laws of the State of California and applicable
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U.S. Federal law. Any choice of law rules will not apply.
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7. Please place an “x” on one of the applicable statement below. Please do NOT
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mark both statements:
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* [ ] I am signing on behalf of myself as an individual and no other person
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or entity, including my employer, has or will have rights with respect to my
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contributions.
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* [ ] I am signing on behalf of my employer or a legal entity and I have the
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actual authority to contractually bind that entity.
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Daniel King |
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| Company name (if applicable) | Allen Institute for Artificial Intelligence |
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| Title or role (if applicable) | Predoctoral Young Investigator |
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| Date | 03/06/2019 |
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| GitHub username | danielkingai2 |
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| Website (optional) | |
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@ -8,6 +8,7 @@ cimport numpy as np
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np.import_array()
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np.import_array()
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from libc.string cimport memset
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from libc.string cimport memset
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import numpy
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import numpy
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from thinc.neural.util import get_array_module
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from .typedefs cimport attr_t, flags_t
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from .typedefs cimport attr_t, flags_t
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from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
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from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
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@ -123,7 +124,10 @@ cdef class Lexeme:
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return 1.0
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return 1.0
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if self.vector_norm == 0 or other.vector_norm == 0:
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if self.vector_norm == 0 or other.vector_norm == 0:
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return 0.0
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return 0.0
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return (numpy.dot(self.vector, other.vector) /
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vector = self.vector
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xp = get_array_module(vector)
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return (xp.dot(self.vector, other.vector) /
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(self.vector_norm * other.vector_norm))
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(self.vector_norm * other.vector_norm))
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def to_bytes(self):
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def to_bytes(self):
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@ -321,7 +321,10 @@ cdef class Doc:
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if self.vector_norm == 0 or other.vector_norm == 0:
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if self.vector_norm == 0 or other.vector_norm == 0:
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return 0.0
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return 0.0
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return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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vector = self.vector
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xp = get_array_module(vector)
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return xp.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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property has_vector:
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property has_vector:
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"""A boolean value indicating whether a word vector is associated with
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"""A boolean value indicating whether a word vector is associated with
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@ -356,10 +359,7 @@ cdef class Doc:
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dtype='f')
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dtype='f')
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return self._vector
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return self._vector
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elif self.vocab.vectors.data.size > 0:
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elif self.vocab.vectors.data.size > 0:
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vector = numpy.zeros((self.vocab.vectors_length,), dtype='f')
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self._vector = sum(t.vector for t in self) / len(self)
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for token in self.c[:self.length]:
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vector += self.vocab.get_vector(token.lex.orth)
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self._vector = vector / len(self)
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return self._vector
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return self._vector
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elif self.tensor.size > 0:
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elif self.tensor.size > 0:
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self._vector = self.tensor.mean(axis=0)
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self._vector = self.tensor.mean(axis=0)
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@ -6,6 +6,7 @@ cimport numpy as np
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import numpy
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import numpy
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import numpy.linalg
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import numpy.linalg
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from libc.math cimport sqrt
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from libc.math cimport sqrt
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from thinc.neural.util import get_array_module
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from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
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from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
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from .token cimport TokenC
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from .token cimport TokenC
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@ -214,7 +215,10 @@ cdef class Span:
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return 1.0
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return 1.0
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if self.vector_norm == 0.0 or other.vector_norm == 0.0:
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if self.vector_norm == 0.0 or other.vector_norm == 0.0:
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return 0.0
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return 0.0
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return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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vector = self.vector
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xp = get_array_module(vector)
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return xp.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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cpdef np.ndarray to_array(self, object py_attr_ids):
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cpdef np.ndarray to_array(self, object py_attr_ids):
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"""Given a list of M attribute IDs, export the tokens to a numpy
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"""Given a list of M attribute IDs, export the tokens to a numpy
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@ -9,6 +9,7 @@ from cython.view cimport array as cvarray
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cimport numpy as np
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cimport numpy as np
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np.import_array()
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np.import_array()
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import numpy
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import numpy
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from thinc.neural.util import get_array_module
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from ..typedefs cimport hash_t
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from ..typedefs cimport hash_t
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from ..lexeme cimport Lexeme
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from ..lexeme cimport Lexeme
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@ -165,7 +166,10 @@ cdef class Token:
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other_norm = other.vector_norm
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other_norm = other.vector_norm
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if self_norm == 0 or other_norm == 0:
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if self_norm == 0 or other_norm == 0:
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return 0.0
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return 0.0
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return (numpy.dot(self.vector, other.vector) /
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vector = self.vector
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xp = get_array_module(self.vector)
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return (xp.dot(self.vector, other.vector) /
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(self_norm * other_norm))
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(self_norm * other_norm))
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property lex_id:
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property lex_id:
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