diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..329ec8356c701f7802f39cb0ba185cf8cbe8c4ce
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+venv
+__pycache__
+.idea
diff --git a/Authors b/Authors
new file mode 100644
index 0000000000000000000000000000000000000000..280c7f4f5ea9a6140b2189125bcdc3550554f142
--- /dev/null
+++ b/Authors
@@ -0,0 +1,10 @@
+:: PEACEWORD ::
+
+
+ Authors:
+ Olivier Caron
+ Alexander Bassett
+ Julie Jacques
+ Julien Baste
+
+
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ <program> Copyright (C) <year> <name of author>
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. 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
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/README.md b/README.md
index bd377df308c3629bda53c9d975513b50855bbc7d..e2a7365f21b7bc59c15342d6cc4f03e89b659f85 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,93 @@
# PEACEWORD
-Prototype for Extracing And Considering the Explainability of WORD Embeddings
+Prototype for Extracting And Considering the Explainability of WORD embeddings.
+
+This simple Git project contains two classic heuristics for assessing their suitability
+for word embeddings (proofs of concepts).
+
+This project is a work from the research team ORKAD of the CRIStAL laboratory of the University of Lille
+[:globe_with_meridians: ORKAD team web site](https://orkad.univ-lille.fr)
+
+
+
+## Required Elements
+
+* `Python compiler` (version 3.12 or higher)
+* `Git`
+
+
+## Quick installation
+Default installation can be summarized as follows:
+
+```bash
+git clone https://gitlab.cristal.univ-lille.fr/orkad-public/peaceword.git
+cd peaceword
+python3 -m venv venv
+. venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Folder organisation
+
+- **models** the folder containing the downloaded datasets, note that the 'text8' dataset
+ (based on Wikipedia text) is constantly evolving. The resulting cosine similarity may therefore
+ vary depending on when the dataset was downloaded.
+- **methods** python package containing two approaches (hillclimbing and greedy)
+- **project root** or **.** contains the different main programs (described below)
+
+
+## Programs
+
+This section describes the various python programs included in this Git project
+
+### Downloading datasets
+
+There are two programs, the first (_load_model.py_)
+allows you to download a model from the gensim library,
+the second (_load_glove_model.py_) is specific to glove-XXX datasets.
+
+It's easy to use: launch the python code with the name of the dataset as argument, and the loaded model is stored in the *models* directory.
+
+Here's an example for the 'text8' dataset.
+
+```bash
+python3 load_model.py text8
+```
+
+### The greedy method
+
+The main program **run_greedy.py** requires several parameters:
+
+- `dataset` : the dataset location
+- `only_pos` : 'yes' if the research is limited to positive words, 'no' otherwise.
+- `min_d` : the minimum distance between two dimension values for them to be considered
+closed (double value).
+- `min_p` : the minimum percentage (integer value) of close dimensions for selecting a word
+- `threshold` : the minimum absolute double value for which a dimension value is considered relevant.
+- `target` : target word name
+
+Here is an example:
+```bash
+python3 run_greedy.py ./models/text8_article yes 0.0279 5 0.2233 yes queen
+```
+
+## Information
+
+### Authors
+See [Authors](./Authors)
+
+### License
+
+PEACEWORD is licensed under the following license :
+
+* [GNU General Public License version 3 (GPLv3)](./LICENSE) GPL refers to the GNU General Public License as published by the Free Software Foundation;
+ either version 3 of the License, or (at your option) any later version.
+
+
+
+
+
+
+
diff --git a/load_glove_model.py b/load_glove_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..654b73502fba11f4e95c717a8f9ad63655c33eb7
--- /dev/null
+++ b/load_glove_model.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+import sys
+import gensim.downloader as api
+
+def main():
+ """
+ This simple program loads a glove dataset from the gensim library and store it into the "models" directory
+ Example : python3 load_glove_model.py glove_wiki-gigaword-100
+ """
+ if len(sys.argv) < 2:
+ print('Usage: load_glove_model.py gensimModelName')
+ sys.exit()
+ model = api.load(sys.argv[1])
+ model.save_word2vec_format(f"./models/{sys.argv[1]}", binary=True) # save in binary format
+ print(f"INFO :: model Trained {sys.argv[1]} saved in ./models")
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/load_model.py b/load_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bec431ae77d82d889308a55439707e97c0737f2a
--- /dev/null
+++ b/load_model.py
@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+
+from gensim.models.word2vec import Word2Vec
+
+import sys
+import gensim.downloader as api
+
+
+
+
+
+def main():
+ """
+ This simple program loads a gensim dataset and store it into the "models" directory
+ Example : python3 load_model_pretrained text8
+ """
+ if len(sys.argv) < 2:
+ print('Usage: load_model_pretrained.py gensimModelName')
+ sys.exit()
+ corpus = api.load(sys.argv[1])
+ model = Word2Vec(corpus)
+ model.save(f"./models/{sys.argv[1]}")
+ print(f"INFO :: model Trained {sys.argv[1]} saved in ./models")
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/methods/__init__.py b/methods/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/methods/common.py b/methods/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..efea003ed74e2f6db08597590f6ea2b206897fb0
--- /dev/null
+++ b/methods/common.py
@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+
+from numpy import dot
+from numpy.linalg import norm
+import numpy as np
+
+def normalize_model(model):
+ """
+ normalization of gensim KeyedVectors
+ Args:
+ model: the no normalized downloaded model
+
+ Returns: the corresponding map where all dimensions of the vector words are normalized
+ and the elapsed time of the normalization
+ """
+ # Retrieve keys and corresponding vectors
+ keys = list(model.key_to_index.keys())
+ vectors = np.stack([model[key] for key in keys])
+ normalized_model = {}
+ for i_dim in range(model.vector_size):
+ vectors.T[i_dim] = vectors.T[i_dim] / max(abs(vectors.T[i_dim]))
+ nb = 0
+ for word in keys:
+ normalized_model[word] = vectors[nb]
+ nb = nb + 1
+
+ return normalized_model
+
+def cosine_similarity(vector_a, vector_b) -> float:
+ """
+ compute the cosine similarity between two vectors
+
+ Arguments:
+ vector_a - the first vector
+ vector_b - the second vector
+ """
+ a = vector_a
+ b = vector_b
+ if len(vector_a) == 0:
+ return 0.0
+
+ norm_a = norm(a)
+ norm_b = norm(b)
+ if norm_a == 0.0 or norm_b == 0.0:
+ return 0.0
+ cos_sim = dot(a, b) / (norm_a * norm_b)
+ return cos_sim
\ No newline at end of file
diff --git a/methods/greedy/__init__.py b/methods/greedy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/methods/greedy/greedy.py b/methods/greedy/greedy.py
new file mode 100755
index 0000000000000000000000000000000000000000..0b04fd686a4e142208f89bac588bc32455b9bf14
--- /dev/null
+++ b/methods/greedy/greedy.py
@@ -0,0 +1,239 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+
+from methods.solution import Solution
+from methods.common import cosine_similarity
+
+
+def coverage_similarity(vector_a, vector_b, min_d) -> int:
+ """
+ compute the number of dimensions that are equivalent (according to given min_d)
+
+ Arguments:
+ vector_a - the first word vector
+ vector_b - the second word vector
+ min_d - the minimum distance to consider dimension values as equivalent
+ """
+ counter = 0
+ for value1, value2 in zip(vector_a, vector_b):
+ if abs(value1-value2)<=min_d:
+ counter = counter + 1
+ return counter
+
+def sub_vector(v, coverage):
+ """
+ returns the sub vector of v according to a coverage
+
+ Arguments:
+ v - the given vector
+ coverage - a vector that contains all vector indexes to take into account
+ """
+ return [v[i] for i in coverage]
+
+def cosine_closest_match(vocab, vector_words, target_word_vector, coverage, banned_words):
+ """
+ returns the word from the model base that is the closest_match according to cosine_similarity and given coverage
+
+ Arguments:
+ vocab - array containing the words including target_word
+ vector_words - dictionary of vector words
+ target_word_vector - the given target word vector
+ coverage - a vector that contains all vector indexes to take into account
+ banned_words - list of words to not taking into account
+ """
+ best = None
+ best_cosine = None
+ for name in vocab:
+ if name not in banned_words:
+ cosine = cosine_similarity(sub_vector(vector_words[name],coverage), sub_vector(target_word_vector,coverage))
+ if best is None:
+ best = name
+ best_cosine = cosine
+ else:
+ if cosine > best_cosine:
+ best = name
+ best_cosine = cosine
+ return best
+
+def coverage_closest_match(vocab, vector_words, target_word_vector, coverage, banned_words, min_d):
+ """
+ returns the word from the model base that is the closest_match according to coverage_similarity and given coverage
+
+ Arguments:
+ vocab - array containing the words including target_word
+ vector_words - dictionary of vector words
+ target_word_vector - the given target word vector
+ coverage - a vector that contains all vector indexes to take into account
+ banned_words - list of words to not taking into account
+ min_d - the minimum distance to consider dimension values as equivalent
+ """
+ best=None
+ best_match = None
+ for name in vocab:
+ if name not in banned_words:
+ nb = coverage_similarity(sub_vector(vector_words[name],coverage), sub_vector(target_word_vector,coverage),min_d)
+ if best is None:
+ best = name
+ best_match = nb
+ else:
+ if nb > best_match:
+ best = name
+ best_match = nb
+ return best
+
+def test_filter(vector_a, vector_b, vector_size, min_d, min_p):
+ """
+ this function returns true if it exists at least x percent of dimensions equivalent between vector_a and vector_b,
+ returns false otherwise
+ Args:
+ vector_a: first given word vector
+ vector_b: second given word vector
+ vector_size : the size of the two given word vectors
+ min_d: the value used for comparing dimensions
+ min_p : the required percentage of equivalent dimensions to produce a true result
+
+ Returns: true if vectors are considered as equivalent
+ """
+ if vector_size == 0:
+ return False
+ counter = coverage_similarity(vector_a, vector_b, min_d) # coincidence test of word
+ return ((counter * 100) / vector_size) >= min_p
+
+def greedy_prepare_data(norm_model, pos_only, min_d, min_p, threshold, target_word):
+ """
+ Step 1 of the greedy algorithm, returns the vector of words and the pertinent coverage
+ :param norm_model: the dataset
+ :param pos_only: only manage positive words if equals to True
+ :param min_d - the minimum distance to compare for each dimension of the word vector
+ :param min_p - percentage minimum of dimensions (step 1),
+ if equals to zero, consider all words
+ :param threshold - this parameter allows to reduce the coverage for all abs(values) of the target vector > threshold
+ if threshold is equals to 0, the resulting coverage contains all dimensions
+ :param target_word: the name of the target
+ :return: the map of word vectors, the pertinent coverage .
+ """
+
+ print("before step 1")
+ print("initial number of words:", len(norm_model.keys()))
+ print("pos_only:",pos_only)
+ print("min_d:",min_d, "min_p:",min_p)
+ print("threshold:",threshold)
+ # step 1 : complete the base with negative words if pos_only is equal to False
+ print("Step 1: ")
+ if not pos_only:
+ print("complete base with negative words")
+ vector_words = {}
+ if target_word[0] == "-":
+ target_word_vector = -1 * norm_model[target_word[1:]]
+ else:
+ target_word_vector = norm_model[target_word]
+ wv_size = len(target_word_vector)
+ print("size word:",wv_size)
+ if threshold == 0:
+ coverage = list(range(wv_size)) # init coverage
+ else:
+ coverage = []
+ for i in range(wv_size):
+ if abs(target_word_vector[i]) > threshold:
+ coverage.append(i)
+ for word in norm_model.keys():
+ current_wv = norm_model[word]
+ if (word == target_word or (min_p == 0) or
+ (test_filter(sub_vector(current_wv, coverage), sub_vector(target_word_vector, coverage),
+ len(coverage), min_d, min_p))):
+ vector_words[word] = current_wv
+ if not pos_only:
+ inverse_word = f"-{word}"
+ inverse_wv = -1 * current_wv # works with np.ndarray
+ # inverse_wv = [x * -1 for x in current_wv]
+ if (inverse_word == target_word or (min_p == 0) or
+ (test_filter(sub_vector(inverse_wv, coverage), sub_vector(target_word_vector, coverage),
+ len(coverage), min_d, min_p))):
+ vector_words[inverse_word] = inverse_wv
+ print("after step 1")
+ print("number of words:", len(vector_words))
+ print("size of coverage:",len(coverage))
+ return vector_words, coverage
+
+def algo_greedy(vector_words, target_word, min_d, max_number_words, test_improve, coverage):
+ """
+ Greedy algorithm for finding semantically related words
+
+ Arguments:
+ model - the model base provided by the gensim library
+ target_word - the given target word
+ min_d - the initial minimum distance to compare for each dimension of the word vector
+ max_number_words - max size of the result vector
+ test_improve - if True, the found word (step 2) must improve the solution to be taken into account
+ coverage - a vector of index of dimensions to take into account
+
+ returns the found words, the resulting solution and the used coverage size at the end of the process
+ """
+ solution = Solution([], [])
+ target_word_vector = vector_words[target_word]
+ wv_size = len(target_word_vector)
+ vocab = list(vector_words.keys())
+
+ iteration = 1
+ delta_min_d = (min_d / max_number_words) /2
+ best_cs = -1 # init
+ banned_words = [ target_word ]
+
+ while iteration <= max_number_words and len(coverage)>0:
+ # Step 2 : find the closest word
+ print(f"Step 2 (find the closest word by cosine similarity), iteration number :", iteration)
+ print("coverage size:",len(coverage))
+ new_word = cosine_closest_match(vocab, vector_words, target_word_vector, coverage,banned_words)
+ if new_word is not None:
+ improve = True
+ banned_words.append(new_word)
+ if test_improve: # test if new_word improves the solution
+ temp_sol = solution.word_vector(vector_words,wv_size) + vector_words[new_word]
+ current_cs = cosine_similarity(temp_sol, target_word_vector)
+ if current_cs < best_cs:
+ improve = False
+ else:
+ best_cs = current_cs
+ if improve:
+ solution.add(new_word)
+ print("After Step 2 : found word:",new_word)
+ # Step 3 :
+ print("Step 3 - update coverage")
+ coverage = update_coverage(vector_words[new_word], target_word_vector, min_d, coverage)
+ print("After Step 3 : coverage size : ", len(coverage))
+ else:
+ print("After Step 2 : the found word:", new_word, "does not improve the solution")
+
+ iteration = iteration + 1
+ min_d = min_d - delta_min_d
+
+ return solution, len(coverage)
+
+
+def update_coverage(word_vector, target_word_vector, min_d, coverage):
+ """
+ compute a new coverage by subtracting equivalent dimension (according to min_d)
+ between a word vector and the target word vector
+
+ Arguments:
+ word_vector - the word_vector to compare with the target one
+ target_word_vector the target word vector
+ min_d - the minimum delta for each dimension
+ coverage - the current coverage
+ """
+
+ for i,(v1,v2) in enumerate(zip(word_vector,target_word_vector)):
+ if i in coverage and abs(v1-v2)<=min_d:
+ coverage.remove(i)
+ return coverage
+
+
diff --git a/methods/hillclimbing/__init__.py b/methods/hillclimbing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/methods/hillclimbing/hillclimbing.py b/methods/hillclimbing/hillclimbing.py
new file mode 100644
index 0000000000000000000000000000000000000000..e006468096dc4b8fd072a9b6e8d923d8682b5c74
--- /dev/null
+++ b/methods/hillclimbing/hillclimbing.py
@@ -0,0 +1,93 @@
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+
+import copy
+
+class HillClimbing:
+ """
+ class that implements the hillclimbing local research algorithm
+ """
+
+
+ def __init__(self, word_embedding, solution, target_word, banned_words=None) -> None:
+ """
+ class constructor, initialization of instance variables
+ :param word_embedding: the class that contains the dataset
+ :param solution: an initial solution
+ :param target_word: the target word
+ :param banned_words: list of words to be banned
+ """
+ if banned_words is None:
+ banned_words = []
+ self.model = word_embedding
+ self.initial_sol = solution
+ self.current_sol = solution
+ self.target_word = target_word
+ self.vocab = copy.deepcopy(self.model.get_vocab())
+ for word in banned_words:
+ print(word)
+ self.vocab.remove(word)
+
+ self.iterations = 0
+ self.evals = 0
+ self.eval_method = self.model.evaluate
+
+ def step(self):
+ """
+ perform one step of the algorithm
+ :return: the score of a better solution, returns False if no found better solution
+ """
+ score = self.evaluate(self.current_sol, self.target_word)
+ for neighbor in self.model.neighbor_solutions(self.current_sol, self.vocab, self.target_word):
+ next_score = self.evaluate(neighbor, self.target_word)
+ if next_score - score > 0.05:
+ score = next_score
+ self.set_current_solution(neighbor)
+ return score
+ return False
+
+ def evaluate(self, sol, predict_word):
+ """
+ computes an evaluation of a given solution
+ :param sol: the current solution
+ :param predict_word: the target word
+ :return:
+ """
+ self.evals += 1
+ return self.eval_method(sol, predict_word)
+
+ def set_current_solution(self, new_sol):
+ """
+ set the current solution
+ :param new_sol: the selected solution
+ :return:
+ """
+ self.current_sol = new_sol
+ self.iterations +=1
+
+ def get_current_solution(self):
+ """
+ provides the current solution
+ :return:
+ """
+ return self.current_sol
+
+ def get_iterations(self):
+ """
+ provides the number of iterations
+ """
+ return self.iterations
+
+ def __str__(self) -> str:
+ return f"HillClimbing (model={self.model}, init={self.initial_sol})"
+
+
+
diff --git a/methods/hillclimbing/utils.py b/methods/hillclimbing/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..be1cc1676a8bf9aa97638b74a423ac4a2ff01cb6
--- /dev/null
+++ b/methods/hillclimbing/utils.py
@@ -0,0 +1,140 @@
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+
+
+import copy
+import random
+
+from methods.solution import Solution
+from methods.common import cosine_similarity
+
+
+class WordEmbedding:
+ """
+ class that contains the dataset, provides getter methods and a method for calculating a new neighborhood
+ """
+ def __init__(self, vectors, pos_only, seed) -> None:
+ """
+ class constructor, initialize the instance variables
+ :param vectors: KeyedVectors object from gensim library
+ :param pos_only: boolean value, restricted to positive words if set to True
+ :param seed: the random seed
+ """
+ self.wv = vectors
+ self.pos_only = pos_only
+ self.list_keys = list(self.wv.keys())
+ random.seed(seed)
+
+ def get_vocab_size(self):
+ """ provides the dataset size """
+ return len(self.list_keys)
+
+ def get_vocab(self):
+ """ provides the list of words managed by the dataset"""
+ return self.list_keys
+
+ def get_random_word(self):
+ """ provides a random word from the data set """
+ return random.choice(self.list_keys)
+
+ def random_solution(self, nb_words, eval_word):
+ """ provides a random solution containing nb_words
+ Arguments:
+ nb_words : the solution size to generate
+ eval_word : the target word, this word must not be in the solution
+ """
+ positives = []
+ negatives = []
+ couple = (positives, negatives)
+ for i in range(nb_words):
+ new_word = self.get_random_word()
+ while new_word in (positives+negatives) or new_word == eval_word:
+ new_word = self.get_random_word()
+ if self.pos_only:
+ positives.append(new_word)
+ else:
+ random.choice(couple).append(new_word)
+ return Solution(positives, negatives)
+
+ def evaluate(self, solution, predict_word):
+ """
+ provides the cosine_similarity of the solution and the target word
+ :param solution: a solution
+ :param predict_word: the target word
+ :return: the cosine similarity
+ """
+ result = 0
+ if len(solution.positive) == 0 and len(solution.negative) == 0:
+ print("WARNING : EMPTY SOLUTION PASSED")
+ return 0
+ for word in solution.positive:
+ result = result + self.wv[word]
+ for word in solution.negative:
+ result = result - self.wv[word]
+
+ return round(cosine_similarity(result, self.wv[predict_word]), 6)
+
+ def neighbor_solutions(self, solution, vocab, eval_word):
+ """
+ calculates a list of neighboring solutions from a given solution;
+ neighboring solutions differ from the initial solutions by a single word
+ :param solution: the current solution
+ :param vocab: vocabulary
+ :param eval_word: the target word
+ """
+ neighbors = []
+ #part 1 : removing a word
+ for word in solution.positive:
+ s_copy = copy.deepcopy(solution)
+ s_copy.positive.remove(word)
+ neighbors.append(s_copy)
+ if not self.pos_only:
+ for word in solution.negative:
+ s_copy = copy.deepcopy(solution)
+ s_copy.negative.remove(word)
+ neighbors.append(s_copy)
+
+ #part 2 : changing a word
+ for word in solution.positive:
+ s_copy = copy.deepcopy(solution)
+ s_copy.positive.remove(word)
+ for v in vocab:
+ if v not in (solution.positive+solution.negative) and v != eval_word and v != word:
+ replaced = copy.deepcopy(s_copy)
+ replaced.positive.append(v)
+ neighbors.append(replaced)
+ if not self.pos_only:
+ for word in solution.negative:
+ s_copy = copy.deepcopy(solution)
+ s_copy.negative.remove(word)
+ for v in vocab:
+ if v not in (solution.positive + solution.negative) and v != eval_word and v != word:
+ replaced = copy.deepcopy(s_copy)
+ replaced.negative.append(v)
+ neighbors.append(replaced)
+
+ # part 3 : add a word
+ for word in vocab:
+ if word not in (solution.positive + solution.negative) and word != eval_word:
+ s_copy = copy.deepcopy(solution)
+ s_copy.positive.append(word)
+ neighbors.append(s_copy)
+ if not self.pos_only and word not in (solution.positive + solution.negative) and word != eval_word:
+ s_copy = copy.deepcopy(solution)
+ s_copy.negative.append(word)
+ neighbors.append(s_copy)
+
+ # remove empty solutions
+ for n in neighbors:
+ if n.is_empty():
+ neighbors.remove(n)
+
+ return neighbors
\ No newline at end of file
diff --git a/methods/solution.py b/methods/solution.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc4ac06ce9ad8ca8af1186cf45522155be431529
--- /dev/null
+++ b/methods/solution.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+
+
+import numpy as np
+
+class Solution:
+ def __init__(self, positives=None, negatives=None) -> None:
+ """ initialisation of a solution with positive and negative set of words"""
+ if positives is None:
+ self.positive = []
+ else:
+ self.positive = positives
+ if negatives is None:
+ self.negative = []
+ else:
+ self.negative = negatives
+
+ def is_empty(self):
+ """
+ :return: true if the solution contains no word, false otherwise
+ """
+ return len(self.positive) == 0 and len(self.negative) == 0
+
+ def word_vector(self, map_word_vector, vector_size):
+ """ computes the corresponding word vector of the solution
+ Args:
+ map_word_vector : the map corresponding to the dataset
+ vector_size: the vector size
+ Returns:
+ result: the corresponding word vector of the solution
+ """
+ result = np.zeros(vector_size)
+ for word in self.positive:
+ # result = [x + y for x, y in zip(result, map_word_vector[word])]
+ result = result + map_word_vector[word]
+ for word in self.negative:
+ # result = [x + y for x, y in zip(result, map_word_vector["-"+word])]
+ result = result + map_word_vector["-"+word] # to test
+ return result
+
+ def add(self,word):
+ """ add a new word in the solution
+ Args:
+ word: the new word, if word starts with the character '-', it is a negative word
+ """
+ if word[0] == '-':
+ self.negative.append(word[1:])
+ else:
+ self.positive.append(word)
+
+
+
+
+ def __str__(self) -> str:
+ return f"Solution (positive={self.positive}, negative={self.negative})"
\ No newline at end of file
diff --git a/models/readme.txt b/models/readme.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e2157d063121a11ac3f6ce5da2e5ea21b65a0bd8
--- /dev/null
+++ b/models/readme.txt
@@ -0,0 +1 @@
+this folder will contain downloaded datasets
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..033ae27bbce526cff812a062ca8cdb370c9c8db3
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+numpy==1.26.4
+gensim==4.3.3
+pandas==2.2.3
diff --git a/runHillClimbing.sh b/runHillClimbing.sh
new file mode 100755
index 0000000000000000000000000000000000000000..0fbeeba814be936ec007ca80f51856190d9c4892
--- /dev/null
+++ b/runHillClimbing.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+words="queen berlin brother euro athens"
+dataset=$1
+pos_only=$2
+
+for word in $words
+do
+ for ((seed=1; seed<= 50; seed++))
+ do
+ sbatch we_slurm.sl $dataset $pos_only $seed $word
+ done
+done
diff --git a/run_greedy.py b/run_greedy.py
new file mode 100644
index 0000000000000000000000000000000000000000..8590e27d854b94d06b90adb4be1ab8d457a87ed5
--- /dev/null
+++ b/run_greedy.py
@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+import sys
+import csv
+import re
+import os
+import time
+
+from gensim.models.word2vec import Word2Vec
+from gensim.models import KeyedVectors
+
+from methods.greedy.greedy import algo_greedy, greedy_prepare_data
+from methods.common import cosine_similarity, normalize_model
+
+
+
+def main():
+ if len(sys.argv) != 8:
+ print('Usage: python3 run_greedy.py dataset_location pos_only (yes or no) ',
+ ' min_d (real), min_p (0-10) '
+ 'threshold (0 or limit value) test_improve (yes or no) eval_word')
+ sys.exit()
+
+ # parameters :
+
+ max_number_words = 6
+
+ dataset_location = sys.argv[1]
+ param_pos_only = sys.argv[2]
+ param_min_d = sys.argv[3]
+ param_min_p = sys.argv[4]
+ param_threshold = sys.argv[5]
+ param_test_improve = sys.argv[6]
+ target_word=sys.argv[7]
+
+ regex = '[+-]?[0-9]+.?[0-9]*'
+
+ if param_pos_only not in ['yes','no']:
+ print(f'bad pos_only parameter : {param_pos_only} (yes or no are only supported)')
+ sys.exit()
+ if not re.search(regex, param_threshold):
+ print(f'bad threshold parameter : {param_threshold} must be a number')
+ sys.exit()
+ if not re.search(regex, param_min_d):
+ print(f'bad min_d parameter : {param_min_d} must be a number')
+ sys.exit()
+ min_d = float(param_min_d)
+ if not re.search(regex, param_min_p):
+ print(f'bad min_p parameter : {param_min_p} must be a number')
+ sys.exit()
+ if param_test_improve not in ['yes','no']:
+ print(f'bad test_improve parameter : {param_test_improve} (yes or no are only supported)')
+ sys.exit()
+
+ path, filename = os.path.split(dataset_location)
+ if "glove" in dataset_location:
+ model = KeyedVectors.load_word2vec_format(dataset_location, binary=True)
+ else:
+ model = Word2Vec.load(dataset_location)
+ model = model.wv
+
+ norm_model = normalize_model(model)
+ start_time = time.time()
+ vector_words, coverage =greedy_prepare_data (norm_model,
+ (param_pos_only=='yes'),
+ min_d, float(param_min_p),
+ float(param_threshold),target_word)
+
+ word_size = len(vector_words[target_word])
+ nb_unused_dimensions = word_size - len(coverage)
+ print(f"unused_dimensions: {nb_unused_dimensions}")
+
+ solution, last_coverage_size = algo_greedy(vector_words, target_word, min_d,
+ max_number_words, (param_test_improve=='yes'), coverage)
+ end_time = time.time()
+ print(f"found solution for target {target_word} :", solution)
+
+
+ run_time=end_time - start_time
+ print(f"Total execution time: {run_time} seconds")
+ print("solution:", solution)
+
+ cs = cosine_similarity(solution.word_vector(vector_words,word_size), vector_words[target_word])
+ print(f"cosine similarity of solution :{cs}")
+
+ print(f"last coverage size:{last_coverage_size}")
+
+
+ with open(f"greedy_{filename}_{target_word}.csv", "a", newline='', encoding='utf-8') as csvfile:
+ fwriter = csv.writer(csvfile, delimiter=';', quoting=csv.QUOTE_NONE, escapechar='@')
+ fwriter.writerow([filename, target_word, last_coverage_size, str(run_time), cs,
+ param_pos_only, param_min_p, param_min_d,
+ param_threshold, param_test_improve,solution,nb_unused_dimensions])
+ csvfile.close()
+ print(filename, target_word, last_coverage_size
+ , str(run_time), cs,
+ param_pos_only, param_min_p,param_min_d,
+ param_threshold, param_test_improve, solution, nb_unused_dimensions)
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/run_hillclimbing.py b/run_hillclimbing.py
new file mode 100644
index 0000000000000000000000000000000000000000..105ed779cb98577fcf922b7e261a717d560a26ee
--- /dev/null
+++ b/run_hillclimbing.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+PEACEWORD, Prototype for Extracting And Considering
+ the Explainability of WORD embeddings.
+ (c) 2025 University of Lille, CNRS
+
+copyright: Peaceword developers (see Authors file),
+ GPL v3 License (see LICENSE file)
+"""
+
+
+from gensim.models.word2vec import Word2Vec
+from gensim.models import KeyedVectors
+
+import time
+import sys
+import csv
+import os
+
+
+from methods.hillclimbing.utils import WordEmbedding
+from methods.hillclimbing.hillclimbing import HillClimbing
+from methods.common import normalize_model
+
+def main():
+ if len(sys.argv) != 5:
+ print('Usage: python3 run_hillclimbing.py dataset_location pos_only seed eval_word')
+ sys.exit()
+ dataset_location = sys.argv[1]
+ path, dataset_name = os.path.split(dataset_location)
+ param_pos_only = sys.argv[2]
+ seed = sys.argv[3]
+ eval_word = sys.argv[4]
+ if param_pos_only not in ['yes','no']:
+ print(f'bad pos_only parameter : {param_pos_only} (yes or no are only supported)')
+ sys.exit()
+ else:
+ pos_only = (param_pos_only == 'yes')
+
+ if "glove" in dataset_location:
+ model = KeyedVectors.load_word2vec_format(dataset_location, binary=True)
+ else:
+ model = Word2Vec.load(dataset_location)
+ model = model.wv
+
+ norm_model = normalize_model(model)
+ print("INFO :: model Trained")
+ print("run No", seed)
+ word_embedding = WordEmbedding(norm_model, pos_only, seed)
+
+ start_time = time.time()
+ start_sol = word_embedding.random_solution(6, eval_word)
+ hc = HillClimbing(word_embedding, start_sol, eval_word, None)
+ step = True
+ print("starting hc")
+ score = 0
+ while step:
+ step = hc.step()
+ if not step == False:
+ score = step
+ print(f"{str(hc.get_current_solution())} : score {step}")
+ end_time = time.time()
+ print("--------------")
+ print(f"{str(hc.get_current_solution())} : score {score}")
+ print(f"iterations : {hc.iterations}")
+
+ run_time = end_time - start_time
+ print(f"Total execution time: {run_time} seconds")
+
+ with open(f"hillclimbing_{dataset_name}_{param_pos_only}_{eval_word}.csv", "a", newline='', encoding='utf-8') as csvfile:
+ fwriter = csv.writer(csvfile, delimiter=';', quoting=csv.QUOTE_NONE, escapechar='@')
+ fwriter.writerow(
+ [seed, str(start_sol), eval_word, str(score), hc.iterations, str(run_time), str(hc.get_current_solution())])
+ csvfile.close()
+ print(seed, start_sol, eval_word, score, hc.iterations, run_time, hc.get_current_solution())
+
+
+if __name__ == '__main__':
+ main()
+
diff --git a/we_slurm.sl b/we_slurm.sl
new file mode 100644
index 0000000000000000000000000000000000000000..6e5e8318e20d7d4d35ffad5e8152f474bf7ab4be
--- /dev/null
+++ b/we_slurm.sl
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+DATASET=$1
+POS_ONLY=$2
+SEED=$3
+WORD=$4
+
+
+
+RUNPATH=/media/softs_orkad/olivier/peaceword
+
+#SBATCH --job-name=HC_${DATASET}_${WORD}_${SEED}
+#SBATCH --partition=debug
+#SBATCH --nodes=1
+
+
+cd $RUNPATH
+source $RUNPATH/venv/bin/activate
+python3 run_hillclimbing.py $DATASET $POS_ONLY $SEED $WORD