From 9ad3ba687202206c4190fbd1625028a1a99db0d0 Mon Sep 17 00:00:00 2001 From: garrafao Date: Sun, 1 Sep 2019 15:59:10 +0200 Subject: [PATCH] Python 2 to 3, full readme, pipeline, correct VI alignment --- .gitignore | 5 + LICENSE.txt | 674 ++++++++++++++++++ README.md | 163 ++++- alignment/ci_align.py | 75 +- alignment/sgns_vi.py | 35 +- alignment/srv_align.py | 129 ++-- alignment/wi.py | 54 +- corpora/test/corpus.txt.gz | Bin 453193 -> 0 bytes corpora/test/corpus1/corpus1.txt.gz | Bin 0 -> 81654 bytes corpora/test/corpus2/corpus2.txt.gz | Bin 0 -> 131250 bytes corpora/test/corpus_wi/corpus_wi.txt.gz | Bin 0 -> 435670 bytes corpora/test_wi/corpus.txt.gz | Bin 455073 -> 0 bytes evaluation/spearman.py | 26 +- measures/cd.py | 78 +- measures/{subtract.py => diff.py} | 28 +- measures/entropy.py | 66 +- measures/freq.py | 67 +- measures/lnd.py | 97 ++- measures/{transform.py => trsf.py} | 22 +- measures/types.py | 52 +- modules/__pycache__/utils_.cpython-37.pyc | Bin 0 -> 6459 bytes modules/composes/__init__.py | 12 - modules/composes/composition/__init__.py | 0 .../composes/composition/composition_model.py | 259 ------- modules/composes/composition/dilation.py | 91 --- modules/composes/composition/full_additive.py | 139 ---- .../composes/composition/lexical_function.py | 288 -------- .../composes/composition/multiplicative.py | 42 -- .../composes/composition/weighted_additive.py | 143 ---- modules/composes/exception/__init__.py | 0 .../composes/exception/illegal_state_error.py | 18 - .../exception/invalid_argument_error.py | 6 - modules/composes/matrix/__init__.py | 0 modules/composes/matrix/dense_matrix.py | 362 ---------- modules/composes/matrix/linalg.py | 406 ----------- modules/composes/matrix/matrix.py | 152 ---- modules/composes/matrix/sparse_matrix.py | 413 ----------- modules/composes/semantic_space/__init__.py | 0 modules/composes/semantic_space/operation.py | 248 ------- .../semantic_space/peripheral_space.py | 160 ----- modules/composes/semantic_space/space.py | 649 ----------------- modules/composes/similarity/__init__.py | 0 modules/composes/similarity/cos.py | 39 - modules/composes/similarity/dot_prod.py | 20 - modules/composes/similarity/euclidean.py | 18 - modules/composes/similarity/lin.py | 33 - modules/composes/similarity/similarity.py | 46 -- modules/composes/transformation/__init__.py | 0 .../transformation/dim_reduction/__init__.py | 0 .../dim_reduction/dimensionality_reduction.py | 37 - .../transformation/dim_reduction/nmf.py | 136 ---- .../transformation/dim_reduction/svd.py | 33 - .../feature_selection/__init__.py | 0 .../feature_selection/feature_selection.py | 27 - .../top_feature_selection.py | 54 -- .../transformation/scaling/__init__.py | 0 .../transformation/scaling/epmi_weighting.py | 52 -- .../transformation/scaling/normalization.py | 55 -- .../transformation/scaling/plmi_weighting.py | 22 - .../transformation/scaling/plog_weighting.py | 29 - .../transformation/scaling/ppmi_weighting.py | 30 - .../scaling/row_normalization.py | 46 -- .../transformation/scaling/scaling.py | 29 - modules/composes/utils/__init__.py | 0 .../composes/utils/crossvalidation_utils.py | 35 - modules/composes/utils/gen_utils.py | 29 - modules/composes/utils/io_utils.py | 272 ------- modules/composes/utils/log_utils.py | 110 --- modules/composes/utils/matrix_utils.py | 103 --- modules/composes/utils/mem_utils.py | 16 - modules/composes/utils/num_utils.py | 15 - modules/composes/utils/py_matrix_utils.py | 35 - modules/composes/utils/regression_learner.py | 106 --- modules/composes/utils/scoring_utils.py | 64 -- modules/composes/utils/space_utils.py | 56 -- modules/dsm.py | 171 ----- modules/dsm.pyc | Bin 6625 -> 0 bytes modules/utils_.py | 195 +++++ modules/utils_.pyc | Bin 0 -> 5633 bytes representations/count.py | 41 +- representations/ppmi.py | 78 +- representations/ri.py | 123 ++-- representations/sgns.py | 34 +- representations/svd.py | 53 +- scripts/make_folders.sh | 54 ++ scripts/make_results_disp.sh | 51 +- scripts/make_results_sim.sh | 50 +- scripts/make_results_wi.sh | 38 +- scripts/parameters_durel.sh | 79 +- scripts/parameters_surel.sh | 79 +- scripts/parameters_test.sh | 79 +- scripts/run_CD.sh | 4 +- scripts/run_CI.sh | 4 +- scripts/run_CNT.sh | 2 +- scripts/run_CNT_WI.sh | 5 - scripts/run_DIFF.sh | 8 + scripts/run_ENTR.sh | 5 +- scripts/run_FREQ.sh | 2 +- scripts/run_LND.sh | 4 +- scripts/run_NENTR.sh | 5 +- scripts/run_NFREQ.sh | 2 +- scripts/run_NTYPE.sh | 4 +- scripts/run_OP+.sh | 4 +- scripts/run_OP-.sh | 4 +- scripts/run_OP.sh | 4 +- scripts/run_PPMI.sh | 4 +- scripts/run_RI.sh | 4 +- scripts/run_SBTR.sh | 8 - scripts/run_SGNS.sh | 2 +- scripts/run_SGNS_VI.sh | 4 +- scripts/run_SGNS_WI.sh | 14 - scripts/run_SPR.sh | 7 +- scripts/run_SRV.sh | 4 +- scripts/run_SVD.sh | 4 +- scripts/run_TRSF.sh | 4 +- scripts/run_TYPE.sh | 5 +- scripts/run_WI.sh | 3 + scripts/run_durel.sh | 51 ++ scripts/run_surel.sh | 53 ++ scripts/run_test.sh | 36 + testsets/test/gold.tsv | 2 +- 121 files changed, 1772 insertions(+), 6121 deletions(-) create mode 100644 .gitignore create mode 100644 LICENSE.txt delete mode 100644 corpora/test/corpus.txt.gz create mode 100644 corpora/test/corpus1/corpus1.txt.gz create mode 100644 corpora/test/corpus2/corpus2.txt.gz create mode 100644 corpora/test/corpus_wi/corpus_wi.txt.gz delete mode 100644 corpora/test_wi/corpus.txt.gz rename measures/{subtract.py => diff.py} (66%) rename measures/{transform.py => trsf.py} (69%) create mode 100644 modules/__pycache__/utils_.cpython-37.pyc delete mode 100755 modules/composes/__init__.py delete mode 100755 modules/composes/composition/__init__.py delete mode 100755 modules/composes/composition/composition_model.py delete mode 100755 modules/composes/composition/dilation.py delete mode 100755 modules/composes/composition/full_additive.py delete mode 100755 modules/composes/composition/lexical_function.py delete mode 100755 modules/composes/composition/multiplicative.py delete mode 100755 modules/composes/composition/weighted_additive.py delete mode 100755 modules/composes/exception/__init__.py delete mode 100755 modules/composes/exception/illegal_state_error.py delete mode 100755 modules/composes/exception/invalid_argument_error.py delete mode 100755 modules/composes/matrix/__init__.py delete mode 100755 modules/composes/matrix/dense_matrix.py delete mode 100755 modules/composes/matrix/linalg.py delete mode 100755 modules/composes/matrix/matrix.py delete mode 100755 modules/composes/matrix/sparse_matrix.py delete mode 100755 modules/composes/semantic_space/__init__.py delete mode 100755 modules/composes/semantic_space/operation.py delete mode 100755 modules/composes/semantic_space/peripheral_space.py delete mode 100755 modules/composes/semantic_space/space.py delete mode 100755 modules/composes/similarity/__init__.py delete mode 100755 modules/composes/similarity/cos.py delete mode 100755 modules/composes/similarity/dot_prod.py delete mode 100755 modules/composes/similarity/euclidean.py delete mode 100755 modules/composes/similarity/lin.py delete mode 100755 modules/composes/similarity/similarity.py delete mode 100755 modules/composes/transformation/__init__.py delete mode 100755 modules/composes/transformation/dim_reduction/__init__.py delete mode 100755 modules/composes/transformation/dim_reduction/dimensionality_reduction.py delete mode 100755 modules/composes/transformation/dim_reduction/nmf.py delete mode 100755 modules/composes/transformation/dim_reduction/svd.py delete mode 100755 modules/composes/transformation/feature_selection/__init__.py delete mode 100755 modules/composes/transformation/feature_selection/feature_selection.py delete mode 100755 modules/composes/transformation/feature_selection/top_feature_selection.py delete mode 100755 modules/composes/transformation/scaling/__init__.py delete mode 100755 modules/composes/transformation/scaling/epmi_weighting.py delete mode 100755 modules/composes/transformation/scaling/normalization.py delete mode 100755 modules/composes/transformation/scaling/plmi_weighting.py delete mode 100755 modules/composes/transformation/scaling/plog_weighting.py delete mode 100755 modules/composes/transformation/scaling/ppmi_weighting.py delete mode 100755 modules/composes/transformation/scaling/row_normalization.py delete mode 100755 modules/composes/transformation/scaling/scaling.py delete mode 100755 modules/composes/utils/__init__.py delete mode 100755 modules/composes/utils/crossvalidation_utils.py delete mode 100755 modules/composes/utils/gen_utils.py delete mode 100755 modules/composes/utils/io_utils.py delete mode 100755 modules/composes/utils/log_utils.py delete mode 100755 modules/composes/utils/matrix_utils.py delete mode 100755 modules/composes/utils/mem_utils.py delete mode 100755 modules/composes/utils/num_utils.py delete mode 100755 modules/composes/utils/py_matrix_utils.py delete mode 100755 modules/composes/utils/regression_learner.py delete mode 100755 modules/composes/utils/scoring_utils.py delete mode 100755 modules/composes/utils/space_utils.py delete mode 100644 modules/dsm.py delete mode 100644 modules/dsm.pyc create mode 100644 modules/utils_.py create mode 100644 modules/utils_.pyc create mode 100644 scripts/make_folders.sh delete mode 100644 scripts/run_CNT_WI.sh create mode 100644 scripts/run_DIFF.sh delete mode 100644 scripts/run_SBTR.sh delete mode 100644 scripts/run_SGNS_WI.sh create mode 100644 scripts/run_WI.sh create mode 100644 scripts/run_durel.sh create mode 100644 scripts/run_surel.sh create mode 100644 scripts/run_test.sh diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c140248 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +todo.txt +matrices +results +corpora/durel +corpora/surel diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..94a9ed0 --- 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 2541cc1..f16ff71 100644 --- a/README.md +++ b/README.md @@ -1,53 +1,168 @@ # LSCDetection Data Sets and Models for Evaluation of Lexical Semantic Change Detection. -If you use this software for academic research, [please cite this paper](#bibtex) and make sure you give appropriate credit to the below-mentioned software this repository strongly depends on. +If you use this software for academic research, please [cite](#bibtex) this paper: -The code heavily relies on [DISSECT](http://clic.cimec.unitn.it/composes/toolkit/introduction.html) (modules/composes). For aligning embeddings (SGNS/SVD/RI) we used [VecMap](https://github.com/artetxem/vecmap) (alignment/map_embeddings.py). We used the implementation of [gensim](https://github.com/rare-technologies/gensim) for SGNS. +- Dominik Schlechtweg, Anna Hätty, Marco del Tredici, and Sabine Schulte im Walde. 2019. [A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains](https://www.aclweb.org/anthology/papers/P/P19/P19-1072/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 732-746, Florence, Italy. ACL. -### Testsets +Also make sure you give appropriate credit to the below-mentioned software this repository depends on. -In `testsets/` we provide the testset versions of DURel and SURel as used in the paper. +Parts of the code rely on [DISSECT](https://github.com/composes-toolkit/dissect), [gensim](https://github.com/rare-technologies/gensim), [numpy](https://pypi.org/project/numpy/), [scikit-learn](https://pypi.org/project/scikit-learn/), [scipy](https://pypi.org/project/scipy/), [VecMap](https://github.com/artetxem/vecmap). -### Usage Note +### Usage -The scripts should be run directly from the main directory. If you wish to do otherwise, you may have to change the path you add to the path attribute in `sys.path.append('./modules/')` in the scripts. All scripts can be run directly from the command line, e.g.: +The scripts should be run directly from the main directory. If you wish to do otherwise, you may have to change the path you add to the path attribute in `sys.path.append('./modules/')` in the scripts. All scripts can be run directly from the command line: - python representations/count.py + python3 representations/count.py -We recommend you to run the scripts with the Python Anaconda distribution (Python 2.7.15), only for VecMap Python 3 is needed. You will have to install some additional packages such as: docopt, gensim, i.a. Those that aren't available from the Anaconda installer can be installed via EasyInstall, or by running `pip install -r requirements.txt`. +e.g. -### Pipeline + python3 representations/count.py corpora/test/corpus1/ test_matrix1 1 -Under `scripts/` you find an example of a full pipeline for the models on a small test corpus. Assuming you are working on a UNIX-based system, first make the scripts executable with +The usage of each script can be understood by running it with help option `-h`, e.g.: + + python3 representations/count.py -h + +We recommend you to run the scripts within a [virtual environment](https://pypi.org/project/virtualenv/) with Python 3.7.4. Install the required packages running `pip install -r requirements.txt`. + +### Models + +A standard model of LSC detection executes three consecutive steps: + +1. learn semantic representations from corpora (`representations/`) +2. align representations (`alignment/`) +3. measure change (`measures/`) + +As an example, consider a very simple model (CNT+CI+CD) going through these steps: + +1. learn count vectors from each corpus to compare (`representations/count.py`) +2. align them by intersecting their columns (`alignment/ci_align.py`) +3. measure change with cosine distance (`measures/cd.py`) + +You can apply this model to the testing data using the following commands: + + python3 representations/count.py corpora/test/corpus1/ test_matrix1 1 + python3 representations/count.py corpora/test/corpus2/ test_matrix2 1 + + python3 alignment/ci_align.py test_matrix1 test_matrix2 test_matrix1_aligned test_matrix2_aligned + + python3 measures/cd.py -s testsets/test/targets.tsv test_matrix1_aligned test_matrix2_aligned test_results.tsv + +__Input Format__: All the scripts in this repository can handle two types of matrix input formats: + +- sparse scipy matrices stored in [npz format](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.save_npz.html) +- dense matrices stored in [word2vec plain text format](https://tedboy.github.io/nlps/generated/generated/gensim.models.Word2Vec.save_word2vec_format.html) + +To learn more about how matrices are loaded and stored check out `modules/utils_.py`. + +The scripts assume a corpus format of one sentence per line in UTF-8 encoded (optionally zipped) text files. You can specify either a file path or a folder. In the latter case the scripts will iterate over all files in the folder. + +#### Semantic Representations + +|Name | Code | Type | +| --- | --- | --- | +| Count | `representations/count.py` | VSM | +| PPMI | `representations/ppmi.py` | VSM | +| SVD | `representations/svd.py` | VSM | +| RI | `representations/ri.py` | VSM | +| SGNS | `representations/sgns.py` | VSM | +| SCAN | [repository](https://github.com/ColiLea/scan) | TP | + +Table: VSM=Vector Space Model, TP=Topic Model + +Note that SCAN takes a slightly different corpus input format than the other models. + +#### Alignment + +|Name | Code | Applicability | +| --- | --- | --- | +| CI | `alignment/ci_align.py` | Count, PPMI | +| SRV | `alignment/srv_align.py` | RI | +| OP | `alignment/map_embeddings.py` | SVD, RI, SGNS | +| VI | `alignment/sgns_vi.py` | SGNS | +| WI | `alignment/wi.py` | Count, PPMI, SVD, RI, SGNS | + +The script `alignment/map_embeddings.py` is drawn from [VecMap](https://github.com/artetxem/vecmap), where you can find instructions how to use it. Find examples of how to obtain OP, OP- and OP+ under `scripts/`. + +Instead of WI, consider using the more advanced [Temporal Referencing](https://github.com/Garrafao/TemporalReferencing). + +#### Measures + +|Name | Code | Applicability | +| --- | --- | --- | +| CD | `measures/cd.py` | Count, PPMI, SVD, RI, SGNS | +| LND | `measures/lnd.py` | Count, PPMI, SVD, RI, SGNS | +| JSD | - | SCAN | +| FD | `measures/freq.py` | from corpus | +| TD | `measures/types.py` |Count| +| HD | `measures/entropy.py` | Count | + +FD, TD and HD need additional applications of `measures/diff.py` and optionally `measures/trsf.py`. + +### Parameter Settings + +For better performance, RI and SRV should be run with `-a` option, instead of specifying the seed number manually. + +Consider the application of column mean centering to RI and SGNS embeddings before applying a change measure. + +Find more detailed notes on model performances and optimal parameter settings in [these papers](#bibtex). + +### Evaluation + +The evaluation framework of this repository is based on the comparison of a set of target words across two corpora. Hence, models can be evaluated on a triple (dataset, corpus1, corpus2), where the dataset provides gold values for the change of target words between corpus1 and corpus2. + +| Dataset | Corpus 1 | Corpus 2 | Download | +| --- | --- | --- | --- | +| DURel | DTA18 | DTA19 | [Dataset](https://www.ims.uni-stuttgart.de/forschung/ressourcen/experiment-daten/durel.html), [Corpora](https://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/wocc.html) | +| SURel | SDEWAC | COOK | [Dataset](https://www.ims.uni-stuttgart.de/forschung/ressourcen/experiment-daten/surel.html), [Corpora](https://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/wocc.html) | + +You don't have to download the data manually. In `testsets/` we provide the testset versions of DURel and SURel as used in Schlechtweg et al. (2019). Additionally, we provide an evaluation pipeline, downloading the corpora and evaluating the models to the above-mentioned datasets, see [pipeline](#pipeline). + +#### Metrics + +|Name | Code | Applicability | +| --- | --- | --- | +| Spearman correlation | `evaluation/spearman.py` | DURel, SURel | + +The script `evaluation/spearman.py` outputs the Spearman correlation of the two input rankings (column 3), as well as the significance of the obtained result (column 4). + +Consider uploading your results for DURel as a submission to the shared task [Lexical Semantic Change Detection in German](https://codalab.lri.fr/competitions/560). + +#### Pipeline + +Under `scripts/` you find an example of a full evaluation pipeline for the models on two small test corpora. Assuming you are working on a UNIX-based system, first make the scripts executable with chmod 755 scripts/*.sh -Then run either of +Then run + + bash -e scripts/run_test.sh + +The script first reads the two gzipped test corpora `corpora/test/corpus1/` and `corpora/test/corpus2/`. Then it produces model predictions for the targets in `testsets/test/targets.tsv` and writes them under `results/`. It finally writes the Spearman correlation between each model's predictions and the gold rank (`testsets/test/gold.tsv`) under the respective folder in `results/`. Note that the gold values for the test data are meaningless, as they were randomly assigned. - bash -e scripts/make_results_sim.sh - bash -e scripts/make_results_disp.sh - bash -e scripts/make_results_wi.sh +We also provide scripts to reproduce the results from Schlechtweg et al. (2019), including the corpus download. For this run either of -The script `make_results_sim.sh` produces results for the similarity measures (Cosine Distance, Local Neighborhood Distance) for all vector space and alignment types except for Word Injection. It first reads the gzipped test corpus in `corpora/test/corpus.txt.gz` with each line in the following format: + bash -e scripts/run_durel.sh + bash -e scripts/run_surel.sh - year [tab] word1 word2 word3... +You may want to change the parameters in `scripts/parameters_durel.sh` and `scripts/parameters_surel.sh` (e.g. vector dimensionality, iterations), as running the scripts on the full parameter set will take several days and require a large amount of disk space. -It then produces model predictions for the targets in `testsets/test/targets.tsv`, writes them under `results/` and correlates the predictions with the gold rank `testsets/test/gold.tsv`. It finally writes the Spearman correlation between each model prediction and the gold rank under `results/`. +### Important Changes -The scripts `make_results_disp.sh` and `make_results_wi.sh` do similarly for the dispersion measures (Frequency, Types, Entropy Difference) and the similarity measures for Word Injection. +September 1, 2019: Python scripts were updated from Python 2 to Python 3. BibTex -------- ``` @inproceedings{Schlechtwegetal19, -title = {{A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains}}, -author = {Dominik Schlechtweg and Anna H\"{a}tty and Marco del Tredici and Sabine {Schulte im Walde}}, -booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", -year = "2019", -address = "Florence, Italy", -publisher = "Association for Computational Linguistics" + title = {{A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains}}, + author = {Dominik Schlechtweg and Anna H\"{a}tty and Marco del Tredici and Sabine {Schulte im Walde}}, + booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, + year = {2019}, + address = {Florence, Italy}, + publisher = {Association for Computational Linguistics}, + pages = {732--746} } ``` diff --git a/alignment/ci_align.py b/alignment/ci_align.py index dc82033..471e671 100644 --- a/alignment/ci_align.py +++ b/alignment/ci_align.py @@ -2,12 +2,9 @@ sys.path.append('./modules/') from docopt import docopt -from dsm import load_pkl_files, save_pkl_files -from composes.semantic_space.space import Space -from composes.matrix.sparse_matrix import SparseMatrix -from scipy.sparse import linalg import logging import time +from utils_ import Space def main(): @@ -19,21 +16,17 @@ def main(): args = docopt('''Align two sparse matrices by intersecting their columns. Usage: - ci_align.py [-l] + ci_align.py - = output path for aligned space 1 - = output path for aligned space 2 - = path to pickled space1 without suffix - = path to pickled space2 without suffix - - Options: - -l, --len normalize final vectors to unit length + = path to matrix1 + = path to matrix2 + = output path for aligned matrix 1 + = output path for aligned matrix 2 ''') - is_len = args['--len'] - spacePrefix1 = args[''] - spacePrefix2 = args[''] + matrix1 = args[''] + matrix2 = args[''] outPath1 = args[''] outPath2 = args[''] @@ -41,42 +34,30 @@ def main(): logging.info(__file__.upper()) start_time = time.time() - # Get the two matrices as spaces and intersect their columns - space1 = load_pkl_files(spacePrefix1) - space2 = load_pkl_files(spacePrefix2) - id2row1 = space1.get_id2row() - id2row2 = space2.get_id2row() - id2column1 = space1.get_id2column() - id2column2 = space2.get_id2column() - column2id1 = space1.get_column2id() - column2id2 = space2.get_column2id() - intersected_columns = list(set(id2column1).intersection(id2column2)) + # Load matrices, rows and columns + space1 = Space(matrix1) + space2 = Space(matrix2) + matrix1 = space1.matrix + rows1 = space1.rows + columns1 = space1.columns + column2id1 = space1.column2id + matrix2 = space2.matrix + rows2 = space2.rows + columns2 = space2.columns + column2id2 = space2.column2id + + # Intersect columns of matrices + intersected_columns = sorted(list(set(columns1).intersection(columns2))) intersected_columns_id1 = [column2id1[item] for item in intersected_columns] intersected_columns_id2 = [column2id2[item] for item in intersected_columns] - reduced_matrix1 = space1.get_cooccurrence_matrix()[:, intersected_columns_id1].get_mat() - reduced_matrix2 = space2.get_cooccurrence_matrix()[:, intersected_columns_id2].get_mat() - - if is_len: - # L2-normalize vectors - l2norm1 = linalg.norm(reduced_matrix1, axis=1, ord=2) - l2norm2 = linalg.norm(reduced_matrix2, axis=1, ord=2) - l2norm1[l2norm1==0.0] = 1.0 # Convert 0 values to 1 - l2norm2[l2norm2==0.0] = 1.0 # Convert 0 values to 1 - reduced_matrix1 /= l2norm1.reshape(len(l2norm1),1) - reduced_matrix2 /= l2norm2.reshape(len(l2norm2),1) - - # Make new spaces - reduced_space1 = Space(SparseMatrix(reduced_matrix1), id2row1, intersected_columns) - reduced_space2 = Space(SparseMatrix(reduced_matrix2), id2row2, intersected_columns) - - if reduced_space1.get_id2column()!=reduced_space2.get_id2column(): - sys.exit('Two spaces not properly aligned!') + reduced_matrix1 = matrix1[:, intersected_columns_id1] + reduced_matrix2 = matrix2[:, intersected_columns_id2] - # Save the Space object in pickle format - save_pkl_files(reduced_space1, outPath1 + '.sm', save_in_one_file=True) - save_pkl_files(reduced_space2, outPath2 + '.sm', save_in_one_file=True) + # Save matrices + Space(matrix=reduced_matrix1, rows=rows1, columns=intersected_columns).save(outPath1) + Space(matrix=reduced_matrix2, rows=rows2, columns=intersected_columns).save(outPath2) - logging.info("--- %s seconds ---" % (time.time() - start_time)) + logging.info("--- %s seconds ---" % (time.time() - start_time)) if __name__ == '__main__': diff --git a/alignment/sgns_vi.py b/alignment/sgns_vi.py index 588c074..6c4954a 100644 --- a/alignment/sgns_vi.py +++ b/alignment/sgns_vi.py @@ -1,19 +1,12 @@ import sys sys.path.append('./modules/') -import codecs -from collections import defaultdict -import os -from os.path import basename -import zipfile from docopt import docopt import logging -import logging.config import time import gensim -from gensim.models.word2vec import Word2Vec +from gensim.models.word2vec import PathLineSentences from gensim.models import KeyedVectors -from dsm import PathLineSentences_mod def intersection_dic(t1, t2): @@ -34,10 +27,12 @@ def main(): args = docopt("""Make comparable embedding vector spaces with Skip-Gram with Negative Sampling and Vector Initialization from corpus. Usage: - sgns_vi.py [-l] + sgns_vi.py [-l] Arguments: + = path to corpus directory with zipped files, each sentence in form 'year\tword1 word2 word3...' + = output path for vectors = vectors on which model should be initialized = the linear distance of context words to consider in each direction = dimensionality of embeddings @@ -45,10 +40,6 @@ def main(): = threshold for subsampling = number of occurrences for a word to be included in the vocabulary = number of iterations - = path to corpus directory with zipped files, each sentence in form 'year\tword1 word2 word3...' - = output path for vectors - = lower bound for time period - = upper bound for time period Options: -l, --len normalize final vectors to unit length @@ -60,6 +51,8 @@ def main(): is_len = args['--len'] initVectorsPath = args[''] + corpDir = args[''] + outPath = args[''] windowSize = int(args['']) dim = int(args['']) k = int(args['']) @@ -69,12 +62,7 @@ def main(): t = float(args['']) minCount = int(args['']) itera = int(args['']) - corpDir = args[''] - outPath = args[''] - lowerBound = int(args['']) - upperBound = int(args['']) - logging.config.dictConfig({'version': 1, 'disable_existing_loggers': True,}) logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) logging.info(__file__.upper()) start_time = time.time() @@ -92,22 +80,23 @@ def main(): # Initialize vocabulary vocab_initVectors = initVectors.vocab - # Intersect vocabulary - vocab_sentences_t_2 = PathLineSentences_mod(corpDir, lowerBound=lowerBound, upperBound=upperBound) + # Intersect vocabulary + vocab_sentences_t_2 = PathLineSentences(corpDir) + logging.getLogger('gensim').setLevel(logging.ERROR) vocab_intersect = intersection_dic([[token] for token in vocab_initVectors],vocab_sentences_t_2) model.build_vocab(vocab_intersect) # Train - sentences = PathLineSentences_mod(corpDir, lowerBound=lowerBound, upperBound=upperBound) + sentences = PathLineSentences(corpDir) model.intersect_word2vec_format(initVectorsPath, lockf=1) - model.train(sentences, total_examples=model.corpus_count, epochs=model.iter) + model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) if is_len: # L2-normalize vectors model.init_sims(replace=True) # Save the vectors and the model - model.wv.save_word2vec_format(outPath + '.w2v') + model.wv.save_word2vec_format(outPath) #model.save(outPath + '.model') logging.info("--- %s seconds ---" % (time.time() - start_time)) diff --git a/alignment/srv_align.py b/alignment/srv_align.py index e217fb0..f10faf5 100644 --- a/alignment/srv_align.py +++ b/alignment/srv_align.py @@ -1,18 +1,13 @@ import sys sys.path.append('./modules/') -import os from docopt import docopt -from dsm import load_pkl_files, save_pkl_files import logging import time -import codecs import numpy as np -from composes.semantic_space.space import Space -from composes.matrix.dense_matrix import DenseMatrix -from composes.matrix.sparse_matrix import SparseMatrix -from scipy.sparse import lil_matrix, csr_matrix, csc_matrix, hstack, vstack from sklearn.random_projection import sparse_random_matrix +from scipy.sparse import lil_matrix, csc_matrix, hstack, vstack +from utils_ import Space def main(): @@ -25,18 +20,16 @@ def main(): args = docopt('''Create two aligned low-dimensional vector spaces by sparse random indexing from two co-occurrence matrices. Usage: - srv_align.py [-l] (-s | -a) + srv_align.py [-l] (-s | -a) - = number negative samples, expressed as percentage of positive samples - = smoothing parameter for negative sampling = number of non-zero values in each random vector - = number of dimensions for random vectors - = threshold for downsampling (if t=None, no subsampling is applied) + = path to matrix1 + = path to matrix2 = output path for aligned space 1 = output path for aligned space 2 - = path to pickled space without suffix - = path to pickled space without suffix = output path for elemental space (context vectors) + = number of dimensions for random vectors + = threshold for downsampling (if t=None, no subsampling is applied) Options: -l, --len normalize final vectors to unit length @@ -57,61 +50,65 @@ def main(): if is_seeds: seeds = int(args['']) is_aut = args['--aut'] + matrixPath1 = args[''] + matrixPath2 = args[''] + outPath1 = args[''] + outPath2 = args[''] + outPathElement = args[''] dim = int(args['']) if args['']=='None': t = None else: t = float(args['']) - outPath1 = args[''] - outPath2 = args[''] - outPathElement = args[''] - spacePrefix1 = args[''] - spacePrefix2 = args[''] logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) logging.info(__file__.upper()) start_time = time.time() - # Load input spaces - space1 = load_pkl_files(spacePrefix1) - space2 = load_pkl_files(spacePrefix2) - matrix1 = csc_matrix(space1.get_cooccurrence_matrix().get_mat()) - matrix2 = csc_matrix(space2.get_cooccurrence_matrix().get_mat()) - + # Load input matrices + space1 = Space(matrixPath1) + matrix1 = space1.matrix + space2 = Space(matrixPath2) + matrix2 = space2.matrix + # Get mappings between rows/columns and words - id2row1 = space1.get_id2row() - id2row2 = space2.get_id2row() - row2id_1 = space1.get_row2id() - row2id_2 = space2.get_row2id() - id2column1 = space1.get_id2column() - id2column2 = space2.get_id2column() + rows1 = space1.rows + id2row1 = space1.id2row + row2id1 = space1.row2id + columns1 = space1.columns + column2id1 = space1.column2id + rows2 = space2.rows + id2row2 = space2.id2row + row2id2 = space2.row2id + columns2 = space2.columns + column2id2 = space2.column2id # Get union of rows and columns in both spaces - unified_rows = sorted(list(set(id2row1).union(id2row2))) - unified_columns = sorted(list(set(id2column1).union(id2column2))) - columns_diff1 = list(set(unified_columns) - set(id2column1)) - columns_diff2 = list(set(unified_columns) - set(id2column2)) + unified_rows = sorted(list(set(rows1).union(rows2))) + unified_columns = sorted(list(set(columns1).union(columns2))) + columns_diff1 = sorted(list(set(unified_columns) - set(columns1))) + columns_diff2 = sorted(list(set(unified_columns) - set(columns2))) # Get mappings of indices of columns in original spaces to indices of columns in unified space c2i = {w: i for i, w in enumerate(unified_columns)} - cj2i1 = {j: c2i[w] for j, w in enumerate(id2column1+columns_diff1)} - cj2i2 = {j: c2i[w] for j, w in enumerate(id2column2+columns_diff2)} + cj2i1 = {j: c2i[w] for j, w in enumerate(columns1+columns_diff1)} + cj2i2 = {j: c2i[w] for j, w in enumerate(columns2+columns_diff2)} if t!=None: - rows_diff1 = list(set(unified_rows) - set(id2row1)) - rows_diff2 = list(set(unified_rows) - set(id2row2)) + rows_diff1 = list(set(unified_rows) - set(rows1)) + rows_diff2 = list(set(unified_rows) - set(rows2)) r2i = {w: i for i, w in enumerate(unified_rows)} - rj2i1 = {j: r2i[w] for j, w in enumerate(id2row1+rows_diff1)} - rj2i2 = {j: r2i[w] for j, w in enumerate(id2row2+rows_diff2)} + rj2i1 = {j: r2i[w] for j, w in enumerate(rows1+rows_diff1)} + rj2i2 = {j: r2i[w] for j, w in enumerate(rows2+rows_diff2)} # Build spaces with unified COLUMNS - new_columns1 = csc_matrix((len(id2row1),len(columns_diff1))) # Get empty columns for additional context words - unified_matrix1 = hstack((matrix1,new_columns1))[:,sorted(cj2i1, key=cj2i1.get)] # First concatenate matrix and empty columns and then order columns according to unified_columns + new_columns1 = csc_matrix((len(rows1),len(columns_diff1))) # Get empty columns for additional context words + unified_matrix1 = csc_matrix(hstack((matrix1,new_columns1)))[:,sorted(cj2i1, key=cj2i1.get)] # First concatenate matrix and empty columns and then order columns according to unified_columns - new_columns2 = csc_matrix((len(id2row2),len(columns_diff2))) - unified_matrix2 = hstack((matrix2,new_columns2))[:,sorted(cj2i2, key=cj2i2.get)] + new_columns2 = csc_matrix((len(rows2),len(columns_diff2))) + unified_matrix2 = csc_matrix(hstack((matrix2,new_columns2)))[:,sorted(cj2i2, key=cj2i2.get)] # Build spaces with unified ROWS new_rows1 = csc_matrix((len(rows_diff1),len(unified_columns))) @@ -139,9 +136,9 @@ def main(): elementalMatrix = lil_matrix((len(unified_columns),dim)) # Generate base vector for random vectors baseVector = np.zeros(dim) # Note: Make sure that number of seeds is not greater than dimensions - for i in range(0,seeds/2): + for i in range(0,int(seeds/2)): baseVector[i] = 1.0 - for i in range(seeds/2,seeds): + for i in range(int(seeds/2),seeds): baseVector[i] = -1.0 for i in range(len(unified_columns)): # To-do: make this more efficient by generating random indices for a whole array np.random.shuffle(baseVector) @@ -150,16 +147,16 @@ def main(): elementalMatrix = sparse_random_matrix(dim,len(unified_columns)).T # Initialize target vectors - alignedMatrix1 = np.zeros((len(id2row1),dim)) - alignedMatrix2 = np.zeros((len(id2row2),dim)) + alignedMatrix1 = np.zeros((len(rows1),dim)) + alignedMatrix2 = np.zeros((len(rows2),dim)) # Iterate over rows of space, find context words and update aligned matrix with low-dimensional random vectors of these context words - for (space,id2row,cj2i,alignedMatrix) in [(space1,id2row1,cj2i1,alignedMatrix1),(space2,id2row2,cj2i2,alignedMatrix2)]: + for (matrix,id2row,cj2i,alignedMatrix) in [(matrix1,id2row1,cj2i1,alignedMatrix1),(matrix2,id2row2,cj2i2,alignedMatrix2)]: # Iterate over targets - for i, target in enumerate(id2row): + for i in id2row: # Get co-occurrence values as matrix - m = space.get_row(target).get_mat() + m = matrix[i] # Get nonzero indexes nonzeros = m.nonzero() nonzeros = [cj2i[j] for j in nonzeros[1]] @@ -173,26 +170,20 @@ def main(): # Weight context vectors by occurrence frequency pos_context_vectors = pos_context_vectors.multiply(data.reshape(-1,1)) # Add up context vectors and store as row for target - alignedMatrix[i] = np.sum(pos_context_vectors, axis=0) - + alignedMatrix[i] = np.sum(pos_context_vectors, axis=0) + + outSpace1 = Space(matrix=alignedMatrix1, rows=rows1, columns=[]) + outSpace2 = Space(matrix=alignedMatrix2, rows=rows2, columns=[]) + if is_len: # L2-normalize vectors - l2norm1 = np.linalg.norm(alignedMatrix1, axis=1, ord=2) - l2norm2 = np.linalg.norm(alignedMatrix2, axis=1, ord=2) - l2norm1[l2norm1==0.0] = 1.0 # Convert 0 values to 1 - l2norm2[l2norm2==0.0] = 1.0 # Convert 0 values to 1 - alignedMatrix1 /= l2norm1.reshape(len(l2norm1),1) - alignedMatrix2 /= l2norm2.reshape(len(l2norm2),1) - - # Make spaces - alignedSpace1 = Space(DenseMatrix(alignedMatrix1), id2row1, []) - alignedSpace2 = Space(DenseMatrix(alignedMatrix2), id2row2, []) - elementalSpace = Space(SparseMatrix(elementalMatrix), unified_columns, []) + outSpace1.l2_normalize() + outSpace2.l2_normalize() - # Save the Space objects in pickle format - save_pkl_files(alignedSpace1, outPath1 + '.dm', save_in_one_file=False) - save_pkl_files(alignedSpace2, outPath2 + '.dm', save_in_one_file=False) - save_pkl_files(elementalSpace, outPathElement + '.dm', save_in_one_file=False) + # Save the matrices + outSpace1.save(outPath1) + outSpace2.save(outPath2) + Space(matrix=elementalMatrix, rows=unified_columns, columns=[]).save(outPathElement) logging.info("--- %s seconds ---" % (time.time() - start_time)) diff --git a/alignment/wi.py b/alignment/wi.py index 7cf7590..34eb8e3 100644 --- a/alignment/wi.py +++ b/alignment/wi.py @@ -1,7 +1,6 @@ import sys sys.path.append('./modules/') -import codecs import os from docopt import docopt import logging @@ -9,6 +8,7 @@ import re import random import numpy as np +from gensim.models.word2vec import PathLineSentences def main(): @@ -21,29 +21,21 @@ def main(): Usage: - wi.py + wi.py Arguments: - = first corpus - = second corpus - = lower bound for time period in first corpus - = upper bound for time period in first corpus - = lower bound for time period in second corpus - = upper bound for time period in second corpus - = target words (to substitute in one corpus) - = path+filename to target corpus (2 corpora combined, with substitution) + = path to file with target words in first column (to substitute in one corpus) + = path to first corpus directory with corpus files + = path to second corpus directory + = output path for word-injected corpus """) + targets = args[''] corp1 = args[''] corp2 = args[''] - lowerBound1 = int(args['']) - upperBound1 = int(args['']) - lowerBound2 = int(args['']) - upperBound2 = int(args['']) - targWords = args[''] - outFile = args[''] + outPath = args[''] logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) logging.info(__file__.upper()) @@ -51,40 +43,36 @@ def main(): # get seeds words seedList = [] - for line in codecs.open(targWords, "r", 'utf-8'): + for line in open(targets, "r", encoding='utf-8'): line = line.strip().split("\t")[0] seedList.append(line) searchPat = re.compile(r'(\b(?:%s)\b)' % '|'.join(seedList), re.UNICODE) - + lines1 = PathLineSentences(corp1) + lines2 = PathLineSentences(corp2) + lineCt = 0 - wFile = codecs.open("tempOutFile.txt", "w", 'utf-8') - for line in codecs.open(corp1, "r", 'utf-8'): - date = int(line.split("\t")[0]) - if not lowerBound1 <= date <= upperBound1: # skip every sentence which is not in timeframe - continue + wFile = open("tempOutFile.txt", "w", encoding='utf-8') + for line in lines1: + line = ' '.join(line) newLine = re.sub(searchPat, r"\1_", line) - wFile.write(newLine) + wFile.write(newLine + '\n') lineCt +=1 - for line in codecs.open(corp2, "r", 'utf-8'): - date = int(line.split("\t")[0]) - if not lowerBound2 <= date <= upperBound2: # skip every sentence which is not in timeframe - continue - wFile.write(line) + for line in lines2: + line = ' '.join(line) + wFile.write(line + '\n') lineCt +=1 print("Seed words substituted. Total number of lines: %d" % (lineCt)) indList = list(range(lineCt)) random.shuffle(indList) sublists = np.array_split(indList, 5) - # make sure that you do not append at the outFile form the last iteration - open(outFile, 'w').close() - wFile = codecs.open(outFile, "a", 'utf-8') + wFile = open(outPath, "w", encoding='utf-8') for nrSub, sublist in enumerate(sublists): sublist = set(sublist) print("Processing %d part ..." % (nrSub)) smallLineList = [] - for nrL, line in enumerate(codecs.open("tempOutFile.txt", "r", 'utf-8')): + for nrL, line in enumerate(open("tempOutFile.txt", "r", encoding='utf-8')): if nrL in sublist: smallLineList.append(line) random.shuffle(smallLineList) diff --git a/corpora/test/corpus.txt.gz b/corpora/test/corpus.txt.gz deleted file mode 100644 index f090cee8377cca46d15edc2c3625b17f961ecee4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 453193 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