From 8e8abd15891e3740ef85064d3c25343d075b17ba Mon Sep 17 00:00:00 2001 From: Aryan Ahadinia <62179268+AryanAhadinia@users.noreply.github.com> Date: Sat, 11 Mar 2023 04:13:04 +0330 Subject: [PATCH] Initial Commit --- .gitignore | 247 ++++++++++++++++++++++++++++++++ README.md | 37 +++++ data/LFR-100-200/A_GT.csv | 100 +++++++++++++ data/LFR-100-200/A_Ob.csv | 100 +++++++++++++ data/LFR-100-200/MatrixC.txt | 100 +++++++++++++ data/LFR-100-200/MatrixCASL.txt | 100 +++++++++++++ data/LFR-100-200/MatrixG.txt | 100 +++++++++++++ data/LFR-100-200/MatrixGASL.txt | 100 +++++++++++++ data/LFR-100-200/S_GT.csv | 100 +++++++++++++ data/LFR-100-200/S_Ob.csv | 100 +++++++++++++ data/converter/convert.py | 37 +++++ evaluate/__init__.py | 0 evaluate/evaluate.py | 66 +++++++++ jwnmf/__init__.py | 0 jwnmf/jwnmf.py | 104 ++++++++++++++ main.py | 192 +++++++++++++++++++++++++ 16 files changed, 1483 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 data/LFR-100-200/A_GT.csv create mode 100644 data/LFR-100-200/A_Ob.csv create mode 100644 data/LFR-100-200/MatrixC.txt create mode 100644 data/LFR-100-200/MatrixCASL.txt create mode 100644 data/LFR-100-200/MatrixG.txt create mode 100644 data/LFR-100-200/MatrixGASL.txt create mode 100644 data/LFR-100-200/S_GT.csv create mode 100644 data/LFR-100-200/S_Ob.csv create mode 100644 data/converter/convert.py create mode 100644 evaluate/__init__.py create mode 100644 evaluate/evaluate.py create mode 100644 jwnmf/__init__.py create mode 100644 jwnmf/jwnmf.py create mode 100644 main.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7cd3430 --- /dev/null +++ b/.gitignore @@ -0,0 +1,247 @@ +# Created by https://www.toptal.com/developers/gitignore/api/python,macos,linux,windows +# Edit at https://www.toptal.com/developers/gitignore?templates=python,macos,linux,windows + +### Linux ### +*~ + +# temporary files which can be created if a process still has a handle open of a deleted file +.fuse_hidden* + +# KDE directory preferences +.directory + +# Linux trash folder which might appear on any partition or disk +.Trash-* + +# .nfs files are created when an open file is removed but is still being accessed +.nfs* + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### macOS Patch ### +# iCloud generated files +*.icloud + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +### Windows ### +# Windows thumbnail cache files +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db + +# Dump file +*.stackdump + +# Folder config file +[Dd]esktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msix +*.msm +*.msp + +# Windows shortcuts +*.lnk + +# End of https://www.toptal.com/developers/gitignore/api/python,macos,linux,windows \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..cfc1020 --- /dev/null +++ b/README.md @@ -0,0 +1,37 @@ +# JWNMF + +This is an **Unofficial** implementation of method proposed by Tang in [1]. + +## Sample Data + +In this repository, we've placed a test data which is a sythensized graph generated using method +proposed in [2] and simulated cascades of posts exchanged between social network nodes. Underlying +graph in this data has 100 nodes and we've simulated 200 i.i.d. cascades on that. To use this to +for testing proposed method in [1], we've randomly deleted some links and cascade participations. + +## How to run + +Convert data + +```Python +python converter/convert.py -d +``` + +Run Code + +```Python +python main.py -d +``` + +## Parameters + +## References + +[1] Tang, M. (2022). A Joint Weighted Nonnegative Matrix Factorization Model via Fusing Attribute +Information for Link Prediction. In: Chenggang, Y., Honggang, W., Yun, L. (eds) Mobile Multimedia +Communications. MobiMedia 2022. Lecture Notes of the Institute for Computer Sciences, Social +Informatics and Telecommunications Engineering, vol 451. Springer, Cham. + + +[2] Lancichinetti, A., Fortunato, S., & Radicchi, F. (2008). Benchmark graphs for testing +community detection algorithms. 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+import numpy as np +import pandas as pd + + +def main(): + # get path arg + parser = argparse.ArgumentParser() + parser.add_argument("-d", "--dataset", help="Dataset name") + args = parser.parse_args() + + dataset = args.dataset + + A_GT = pd.read_csv( + f"{dataset}/MatrixCASL.txt", header=None, index_col=False + ).to_numpy() + A_GT = np.where(A_GT != 0, 1, 0) + pd.DataFrame(A_GT).to_csv(f"{dataset}/A_GT.csv", header=False, index=False) + + A_Ob = pd.read_csv( + f"{dataset}/MatrixC.txt", header=None, index_col=False + ).to_numpy() + A_Ob = np.where(A_Ob != 0, 1, 0) + pd.DataFrame(A_Ob).to_csv(f"{dataset}/A_Ob.csv", header=False, index=False) + + S_GT = pd.read_csv(f"{dataset}/MatrixGASL.txt", header=None, index_col=False) + S_GT = S_GT.to_numpy().astype(int) + pd.DataFrame(S_GT).to_csv(f"{dataset}/S_GT.csv", header=False, index=False) + + S_Ob = pd.read_csv(f"{dataset}/MatrixG.txt", header=None, index_col=False) + S_Ob = S_Ob.to_numpy().astype(int) + pd.DataFrame(S_Ob).to_csv(f"{dataset}/S_Ob.csv", header=False, index=False) + + +if __name__ == "__main__": + main() diff --git a/evaluate/__init__.py b/evaluate/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evaluate/evaluate.py b/evaluate/evaluate.py new file mode 100644 index 0000000..63dbf89 --- /dev/null +++ b/evaluate/evaluate.py @@ -0,0 +1,66 @@ +import numpy as np + +from sklearn.metrics import roc_auc_score +from sklearn.metrics import accuracy_score +from sklearn.metrics import matthews_corrcoef +from sklearn.metrics import precision_recall_curve + + +def ground_truth_for_eval(gt, ob): + return gt[ob != 1] + + +def predicted_scores_for_eval(pr_scores, ob): + return pr_scores[ob != 1] + + +def get_best_f1(gt, pr_scores): + precision, recall, thresholds = precision_recall_curve(gt, pr_scores) + f1_scores = 2 * recall * precision / (recall + precision) + f1_scores = np.nan_to_num(f1_scores) + index = np.argmax(f1_scores) + return precision[index], recall[index], f1_scores[index], thresholds[index] + + +def auc_score(gt, pr_scores): + return roc_auc_score(gt, pr_scores) + + +def accuracy(gt, pr_binary): + return accuracy_score(gt, pr_binary) + + +def sre(gt, pr_binary): + return np.linalg.norm(gt) / np.linalg.norm(gt - pr_binary) + + +def mcc(gt, pr_binary): + return matthews_corrcoef(gt, pr_binary) + + +def evaluate(gt, pr_scores): + precision, recall, f1_score, threshold = get_best_f1(gt, pr_scores) + pr_binary = pr_scores > threshold + return { + "best_f1": f1_score, + "best_threshold": threshold, + "precision": precision, + "recall": recall, + "auc": auc_score(gt, pr_scores), + "accuracy": accuracy(gt, pr_binary), + "sre": sre(gt, pr_binary), + "mcc": mcc(gt, pr_binary), + } + + +def evaluate_cascade(gt, pr_scores): + precision, recall, f1_score, threshold = get_best_f1(gt, pr_scores) + pr_binary = pr_scores > threshold + return { + "best_f1": f1_score, + "best_threshold": threshold, + "precision": precision, + "recall": recall, + "auc": auc_score(gt, pr_scores), + "accuracy": accuracy(gt, pr_binary), + } diff --git a/jwnmf/__init__.py b/jwnmf/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/jwnmf/jwnmf.py b/jwnmf/jwnmf.py new file mode 100644 index 0000000..8e1ad2c --- /dev/null +++ b/jwnmf/jwnmf.py @@ -0,0 +1,104 @@ +import numpy as np +from tqdm import tqdm + + +def loss(S, A, V, U, W, lambda_coefficient): + mask_S = S + mask_A = A + network_loss = np.linalg.norm(np.multiply(mask_S, S - V @ V.T), "fro") ** 2 + attributes_loss = np.linalg.norm(np.multiply(mask_A, A @ W - V @ U.T), "fro") ** 2 + total_loss = network_loss + lambda_coefficient * attributes_loss + return total_loss + + +def normalize_W(W): + return W / np.trace(W) + + +def normalize_S(S): + return S / S.sum() + + +def normalize_A(A): + return A / A.sum() + + +def initial_V(n, k): + np.random.seed(0) + _V = np.random.standard_normal((n, k)) + _V[_V <= 0] = 0 + return _V + + +def initial_U(m, k): + np.random.seed(0) + _U = np.random.standard_normal((m, k)) + _U[_U <= 0] = 0 + return _U + + +def initial_W(m): + np.random.seed(0) + _W = np.diag(np.random.standard_normal(m)) + _W[_W <= 0] = 0 + return normalize_W(_W) + + +def initial(m, n, k): + return initial_V(n, k), initial_U(m, k), initial_W(m) + + +def update_V(S, A, V, U, W, lambda_coefficient): + numerator = S @ V + S.T @ V + lambda_coefficient * A @ W @ U + denominator = ( + 2 * V @ V.T @ V + lambda_coefficient * V @ U.T @ U + np.finfo(float).eps + ) + return np.multiply(V, numerator / denominator) + + +def update_U(S, A, V, U, W, lambda_coefficient): + numerator = W @ A.T @ V + denominator = U @ V.T @ V + np.finfo(float).eps + return np.multiply(U, numerator / denominator) + + +def update_W(S, A, V, U, W, lambda_coefficient): + numerator = A.T @ V @ U.T + denominator = A.T @ A @ W + np.finfo(float).eps + return normalize_W(np.multiply(W, numerator / denominator)) + + +def update(S, A, V, U, W, lambda_coefficient): + _V = update_V(S, A, V, U, W, lambda_coefficient) + _U = update_U(S, A, V, U, W, lambda_coefficient) + _W = update_W(S, A, V, U, W, lambda_coefficient) + if _V[_V < 0] or _U[_U < 0] or _W[_W < 0]: + print("Error in negative value of factorized matrices") + return _V, _U, _W + + +def train(S, A, m, n, k, lambda_coefficient=0.1, max_iterations=1000, epsilon=1e-6): + assert S.shape == (n, n) + assert A.shape == (n, m) + # S = normalize_S(S) + # A = normalize_A(A) + V, U, W = initial(m, n, k) + losses = [] + best_index = 0 + best_loss = np.inf + best_parameters = None + with tqdm(range(max_iterations)) as pbar: + _loss = loss(S, A, V, U, W, lambda_coefficient) + pbar.set_description(f"Loss: {_loss:.8f}") + for i in pbar: + V, U, W = update(S, A, V, U, W, lambda_coefficient) + _loss = loss(S, A, V, U, W, lambda_coefficient) + losses.append(_loss) + pbar.set_description(f"Loss: {_loss:.8f}") + pbar.update(1) + if _loss < best_loss and i > 100: + best_index = i + best_loss = _loss + best_parameters = V, U, W + V, U, W = best_parameters + return V, U, W, losses, best_index diff --git a/main.py b/main.py new file mode 100644 index 0000000..f6dc2b9 --- /dev/null +++ b/main.py @@ -0,0 +1,192 @@ +import argparse +from pathlib import Path +import os + +import numpy as np +import pandas as pd +from matplotlib import pyplot as plt + +from tabulate import tabulate + +from evaluate import evaluate +from jwnmf import jwnmf + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-d", + "--dataset", + help="Dataset Path; Must Contain A.csv and S.csv.", + type=Path, + required=True, + ) + parser.add_argument( + "-k", + "--latent_dim", + help="Dimension of latent factor", + type=int, + default=10, + ) + parser.add_argument( + "-l", + "--lambda_coefficient", + help="Lambda", + type=float, + default=0.1, + ) + parser.add_argument( + "-i", + "--max_iterations", + type=int, + default=4000, + ) + parser.add_argument( + "-e", + "--epsilon", + type=float, + default=1e-6, + ) + parser.add_argument( + "-o", + "--output_dir", + help="Output Directory", + type=Path, + ) + return parser.parse_args() + + +def read_matrix(path): + return pd.read_csv(path, header=None, index_col=False).to_numpy() + + +def write_matrix(matrix, path): + pd.DataFrame(matrix).to_csv(path, header=None, index=False) + + +def plot_loss(losses, path): + plt.figure() + plt.plot(losses) + plt.xlabel("Iterations") + plt.ylabel("Losses") + plt.savefig(path) + + +def main(): + args = parse_args() + + dataset = args.dataset + + A_Ob = read_matrix(dataset / "A_Ob.csv") + S_Ob = read_matrix(dataset / "S_Ob.csv") + + S_height, S_width = S_Ob.shape + n, m = A_Ob.shape + assert S_height == S_width == n + + k = args.latent_dim + lambda_coefficient = args.lambda_coefficient + max_iters = args.max_iterations + epsilon = args.epsilon + + output_dir = args.output_dir if args.output_dir else dataset / "JWNMF_results" + os.mkdir(output_dir) + + table_data = [ + ("dataset", dataset), + ("output", output_dir), + ("num users", n), + ("num attributes", m), + ("latent dim", k), + ("lambda", lambda_coefficient), + ("max iterations", max_iters), + ("epsilon", epsilon), + ] + + table = tabulate( + table_data, + headers=["Parameter", "Value"], + tablefmt="orgtbl", + ) + + print(table) + + V, U, W, losses, i = jwnmf.train( + S_Ob, A_Ob, m, n, k, lambda_coefficient, max_iters, epsilon + ) + print("Terminated") + + table_data.append(("iterations", i)) + table = tabulate( + table_data, + headers=["Parameter", "Value"], + tablefmt="orgtbl", + ) + print(table) + + with open(output_dir / "info.txt", "w") as f: + f.write(table) + + write_matrix(V, output_dir / "V.csv") + write_matrix(U, output_dir / "U.csv") + write_matrix(W, output_dir / "W.csv") + + S_Pr = V @ V.T + A_Pr = V @ U.T + + S_Pr[S_Pr > 1] = 1 + S_Pr[S_Pr < 0] = 0 + + A_Pr[A_Pr > 1] = 1 + A_Pr[A_Pr < 0] = 0 + + write_matrix(S_Pr, output_dir / "S_Pr.csv") + write_matrix(A_Pr, output_dir / "A_Pr.csv") + + plot_loss(losses, output_dir / "losses.png") + + try: + A_Gt = read_matrix(dataset / "A_GT.csv") + S_Gt = read_matrix(dataset / "S_GT.csv") + except: + print("Ground Truth Not Found") + return + + assert A_Gt.shape == A_Ob.shape + assert S_Gt.shape == S_Ob.shape + + predicted_for_eval_S = evaluate.predicted_scores_for_eval(S_Pr, S_Ob) + ground_truth_for_eval_S = evaluate.ground_truth_for_eval(S_Gt, S_Ob) + + with open(output_dir / "unobserved_inferred.txt", "w") as f: + f.write(",".join(map(str, list(predicted_for_eval_S)))) + + with open(output_dir / "unobserved_gt.txt", "w") as f: + f.write(",".join(map(str, list(ground_truth_for_eval_S)))) + + metrics = evaluate.evaluate(ground_truth_for_eval_S, predicted_for_eval_S) + table = tabulate( + {(k, v) for k, v in metrics.items()}, + headers=["Parameter", "Value"], + tablefmt="orgtbl", + ) + print(table) + with open(output_dir / "metrics.txt", "w") as f: + f.write(table) + + predicted_for_eval_A = evaluate.predicted_scores_for_eval(A_Pr, A_Ob) + ground_truth_for_eval_A = evaluate.ground_truth_for_eval(A_Gt, A_Ob) + + metrics = evaluate.evaluate_cascade(ground_truth_for_eval_A, predicted_for_eval_A) + table = tabulate( + {(k, v) for k, v in metrics.items()}, + headers=["Parameter", "Value"], + tablefmt="orgtbl", + ) + print(table) + with open(output_dir / "metrics_A.txt", "a") as f: + f.write(table) + + +if __name__ == "__main__": + main()