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emgraph-1.0.0rc1
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
A Python library for knowledge graph representation learning (graph embedding)
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | emgraph-1.0.0rc1 |
| نام | emgraph |
| نسخه کتابخانه | 1.0.0rc1 |
| نگهدارنده | ['Soran Ghaderi'] |
| ایمیل نگهدارنده | ['soran.gdr.cd@gmail.com'] |
| نویسنده | Soran Ghaderi |
| ایمیل نویسنده | soran.gdr.cd@gmail.com |
| آدرس صفحه اصلی | https://bi-graph.github.io/Emgraph |
| آدرس اینترنتی | https://pypi.org/project/emgraph/ |
| مجوز | BSD |
<h1><b>Emgraph</b></h1>
<div>
[//]: # (<a href="https://badge.fury.io/py/emgraph"><img src="https://badge.fury.io/py/emgraph.svg" alt="PyPI version" height="18"></a>)
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[//]: # (<img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/emgraph">)
[comment]: <> (<img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dw/emgraph">)
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[comment]: <> (<img alt="PyPI - Implementation" src="https://img.shields.io/pypi/implementation/emgraph">)
[comment]: <> (<img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/m/bi-graph/emgraph">)
[comment]: <> (<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/bi-graph/emgraph">)
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/bi-graph/emgraph?style=social">
</div>
<p><b>Emgraph</b> is a Python toolkit for graph embedding.</p>
[//]: # (<ul>)
[//]: # ( <li><b>Bug reports:</b> https://github.com/bi-graph/emgraph/issues</li>)
[//]: # (</ul>)
[//]: # (> Node based similarities and Katz has been implemented. you can find algorithms in emgraph module. Algorithms implemented so far:)
<div align="center">
<table>
<caption><b>Algorithms table</b></caption>
<tr>
<td><b>Number</b></td>
<td align="center"><b>Algorithm</b></td>
</tr>
<tr>
<td align="center">1</td>
<td><code><b>TransE</b></code></td>
</tr>
<tr>
<td align="center">2</td>
<td><code><b>ComplEx</b></code></td>
</tr>
<tr>
<td align="center">3</td>
<td><code><b>HolE</b></code></td>
</tr>
<tr>
<td align="center">4</td>
<td><code><b>DistMult</b></code></td>
</tr>
<tr>
<td align="center">5</td>
<td><code><b>ConvE</b></code></td>
</tr>
<tr>
<td align="center">6</td>
<td><code><b>ConvKB</b></code></td>
</tr>
<tr>
<td align="center">7</td>
<td><code><b>RandomBaseline</b></code></td>
</tr>
</table>
</div>
<div>
<h2>Installation</h2>
<p>Install the latest version of <b>Emgraph</b>:</p>
<pre>$ pip install emgraph</pre>
</div>
<div>
<h2>Documentation</h2>
<p>Soon</p>
[//]: # (<p> <a href="https://emgraph.readthedocs.io/en/latest/index.html">https://emgraph.readthedocs.io/en/latest/</a></p>)
</div>
<h2>Simple example</h2>
<p>Embedding wordnet11 graph using
<code><b>TransE</b></code> model:</p>
```python
from sklearn.metrics import brier_score_loss, log_loss
from scipy.special import expit
from emgraph.datasets import BaseDataset, DatasetType
from emgraph.models import TransE
def train_transe():
X = BaseDataset.load_dataset(DatasetType.WN11)
model = TransE(batches_count=64, seed=0, epochs=20, k=100, eta=20,
optimizer='adam', optimizer_params={'lr': 0.0001},
loss='pairwise', verbose=True, large_graphs=False)
model.fit(X['train'])
scores = model.predict(X['test'])
print("Scores: ", scores)
print("Brier score loss:", brier_score_loss(X['test_labels'], expit(scores)))
# Executing the function
if __name__ == '__main__':
train_transe()
```
<p>Evaluating <code><b>ComplEx</b></code> model after training:<br>
```python
import numpy as np
from emgraph.datasets import BaseDataset, DatasetType
from emgraph.models import ComplEx
from emgraph.evaluation import evaluate_performance
def complex_performance():
X = BaseDataset.load_dataset(DatasetType.WN18)
model = ComplEx(batches_count=10, seed=0, epochs=20, k=150, eta=1,
loss='nll', optimizer='adam')
model.fit(np.concatenate((X['train'], X['valid'])))
filter_triples = np.concatenate((X['train'], X['valid'], X['test']))
ranks = evaluate_performance(X['test'][:5], model=model,
filter_triples=filter_triples,
corrupt_side='s+o',
use_default_protocol=False)
return ranks
# Executing the function
if __name__ == '__main__':
ranks = complex_performance()
print("ranks {}".format(ranks))
```
<div>
<h2>Call for Contributions</h2>
<p>The <b>Emgraph</b> project welcomes your expertise and enthusiasm!</p>
<p>Ways to contribute to <b>Emgraph</b>:</p>
<ul>
<li>Writing code</li>
<li>Review pull requests</li>
<li>Develop tutorials, presentations, and other educational materials</li>
<li>Translate documentation and readme contents</li>
</ul>
</div>
<div>
<h2>Issues</h2>
<p>If you happened to encounter any issue in the codes, please report it
<a href="https://github.com/bi-graph/emgraph/issues">here</a>.
A better way is to fork the repository on <b>Github</b> and/or create a pull request.</p>
</div>
[//]: # (<h3>Metrics</h3>)
[//]: # (<p>Metrics that are calculated during evaluation:</p>)
[//]: # ()
[//]: # (> * For further usages and calculating different metrics)
[//]: # ()
[//]: # (<h3>Dataset format</h3>)
[//]: # (<p>Your dataset should be in the following format (Exclude the 'Row' column):</p>)
<h3>More examples</h3>
<p>Embedding wordnet11 graph using
<code><b>DistMult</b></code> model:</p>
```python
from sklearn.metrics import brier_score_loss, log_loss
from scipy.special import expit
from emgraph.datasets import BaseDataset, DatasetType
from emgraph.models import DistMult
def train_dist_mult():
X = BaseDataset.load_dataset(DatasetType.WN11)
model = DistMult(batches_count=1, seed=555, epochs=20, k=10, loss='pairwise',
loss_params={'margin': 5})
model.fit(X['train'])
scores = model.predict(X['test'])
print("Scores: ", scores)
print("Brier score loss:", brier_score_loss(X['test_labels'], expit(scores)))
# Executing the function
if __name__ == '__main__':
train_dist_mult()
```
[//]: # (<h3>References</h3>)
[//]: # (<div>)
[//]: # (<table>)
[//]: # (<caption><b>References table</b></caption>)
[//]: # ( <tr>)
[//]: # ( <td><b>Number</b></td>)
[//]: # ( <td align="center"><b>Reference</b></td>)
[//]: # ( <td align="center"><b>Year</b></td>)
[//]: # ( </tr>)
[//]: # ( <tr>)
[//]: # ( <td align="center">1</td>)
[//]: # ( <td><code>Yang, Y., Lichtenwalter, R.N. & Chawla, N.V. Evaluating link prediction methods. Knowl Inf Syst 45, 751–782 (2015).</code> <a href="https://doi.org/10.1007/s10115-014-0789-0")
[//]: # (target="_blank">https://doi.org/10.1007/s10115-014-0789-0</a></td>)
[//]: # ( <td align="center"><b>2015</b></td>)
[//]: # ( </tr>)
[//]: # ( <tr>)
[//]: # ( <td align="center">2</td>)
[//]: # ( <td><code>Liben-nowell, David & Kleinberg, Jon. (2003). The Link Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology.</code><a href="https://doi.org/58.10.1002/asi.20591")
[//]: # (target="_blank">https://doi.org/58.10.1002/asi.20591</a></td>)
[//]: # ( <td align="center"><b>2003</b></td>)
[//]: # ( </tr>)
[//]: # ( <tr>)
[//]: # ( <td align="center">2</td>)
[//]: # ( <td><code>...</code></td>)
[//]: # ( <td align="center"><b>...</b></td>)
[//]: # ( </tr>)
[//]: # (</table>)
[//]: # (</div>)
<h3>Future work</h3>
- [x] Modulate the functions
- [ ] Add more algorithms
- [x] Run on CUDA cores
- [x] Make it faster using vectorization etc.
- [x] Add more preprocessors
- [ ] Add dataset, graph, and dataframe manipulations
- [x] Unify and reconstruct the architecture and eliminate redundancy
<h2>If you found it helpful, please give us a <span>:star:</span></h2>
<h2>License</h3>
<p>Released under the BSD license</p>
<div class="footer"><pre>Copyright © 2019-2022 <b>Emgraph</b> Developers
<a href="https://www.linkedin.com/in/soran-ghaderi/">Soran Ghaderi</a> (soran.gdr.cs@gmail.com)
<a href="https://uk.linkedin.com/in/taleb-zarhesh">Taleb Zarhesh</a> (taleb.zarhesh@gmail.com)</pre>
</div>
نیازمندی
| مقدار | نام |
|---|---|
| >=4.64.0,<5.0.0 | tqdm |
| >=1.23.1,<2.0.0 | numpy |
| >=1.4.3,<2.0.0 | pandas |
| >=1.9.0,<2.0.0 | scipy |
| >=2.8.0,<3.0.0 | tensorflow |
| >=7.1.2,<8.0.0 | pytest |
| >=6.2.0,<7.0.0 | rdflib |
| >=1.9.1,<2.0.0 | pydantic |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.10,<3.11 | Python |
نحوه نصب
نصب پکیج whl emgraph-1.0.0rc1:
pip install emgraph-1.0.0rc1.whl
نصب پکیج tar.gz emgraph-1.0.0rc1:
pip install emgraph-1.0.0rc1.tar.gz