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| #!/usr/bin/env python3 | |
| """ | |
| Copyright 2019 Brian Thompson | |
| Licensed under the Apache License, Version 2.0 (the "License"); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| https://www.apache.org/licenses/LICENSE-2.0 | |
| Unless required by applicable law or agreed to in writing, software | |
| distributed under the License is distributed on an "AS IS" BASIS, | |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| See the License for the specific language governing permissions and | |
| limitations under the License. | |
| This is a standalone example of creating a document vector from sentence vectors | |
| following https://aclanthology.org/2020.emnlp-main.483 | |
| """ | |
| import numpy as np | |
| from mcerp import PERT # pip install mcerp # see https://github.com/tisimst/mcerp/blob/master/mcerp/__init__.py | |
| NUM_TIME_SLOTS = 16 | |
| PERT_G = 20 | |
| # PERT is very slow (50ms per distribution) so we cache a bank of PERT distributions | |
| _num_banks = 100 | |
| _xx = np.linspace(start=0, stop=1, num=NUM_TIME_SLOTS) | |
| PERT_BANKS = [] | |
| for _pp in np.linspace(0, 1, num=_num_banks): | |
| if _pp == 0.5: # some special case that makes g do nothing | |
| _pp += 0.001 | |
| pert = PERT(low=-0.001, peak=_pp, high=1.001, g=PERT_G, tag=None) | |
| _yy = pert.rv.pdf(_xx) | |
| _yy = _yy / sum(_yy) # normalize | |
| PERT_BANKS.append(_yy) | |
| np.set_printoptions(threshold=50, precision=5) | |
| def build_doc_embedding(sent_vecs, sent_counts): | |
| # ensure sentence counds are >= 1 | |
| sent_counts = np.clip(sent_counts, a_min=1, a_max=None) | |
| # scale each sent vec by 1/count | |
| sent_weights = 1.0/np.array(sent_counts) | |
| scaled_sent_vecs = np.multiply(sent_vecs.T, sent_weights).T | |
| # equally space sentences | |
| sent_centers = np.linspace(0, 1, len(scaled_sent_vecs)) | |
| # find weighting for each sentence, for each time slot | |
| sentence_loc_weights = np.zeros((len(sent_centers), NUM_TIME_SLOTS)) | |
| for sent_ii, p in enumerate(sent_centers): | |
| bank_idx = int(p * (len(PERT_BANKS) - 1)) # find the nearest cached pert distribution | |
| sentence_loc_weights[sent_ii, :] = PERT_BANKS[bank_idx] | |
| # make each chunk vector | |
| doc_chunk_vec = np.matmul(scaled_sent_vecs.T, sentence_loc_weights).T | |
| # concatenate chunk vectors into a single vector for the full document | |
| doc_vec = doc_chunk_vec.flatten() | |
| # normalize document vector | |
| doc_vec = doc_vec / (np.linalg.norm(doc_vec) + 1e-5) | |
| return doc_vec | |
| def demo(): | |
| # Replace sent_vecs with laser/LaBSE/etc embeddings of each sentence in your document, | |
| # after projecting the sentence embeddings into a lower-dimensional space using something like PCA (see paper for details). | |
| sent_emb_size = 32 # Document embedding size will be sent_emb_size * NUM_TIME_SLOTS | |
| n_sents = 7 | |
| sent_vecs = np.random.rand(n_sents, sent_emb_size)-0.5 | |
| # Replace sent_counts with the number of times each sentence has been seen in your corpus. | |
| sent_counts = np.random.randint(low=1, high=50, size=n_sents) | |
| doc_emb = build_doc_embedding(sent_vecs, sent_counts) | |
| print('Document Embedding:', doc_emb) | |
| print('Document Embedding Size:', doc_emb.shape) | |
| if __name__ == '__main__': | |
| demo() | |