first commit

Author: conex-kge

KGs.zip

@@ -0,0 +1,24 @@
+# Convolutional Complex Knowledge Graph Embeddings
+
+
+This repository contains the implementation of our approach along with experimental results for the reproducibility.
+
+## Installation
+
+First clone the repostiroy:
+```
+git clone https://github.com/XXX/XXX.git.
+```
+Then obtain the required libraries:
+```
+conda env create -f environment.yml
+source activate conex
+```
+The code is compatible with Python 3.6.4
+
+## Reproducing reported results
+- Please unzip KGs.zip and PretrainedModels.zip
+- python reproduce_reported_results_FB15K-237.py
+- python reproduce_reported_results_WN18RR.py
+- python reproduce_reported_results_KINSHIP.py
+- python reproduce_reported_results_UMLS.py

PretrainedModels.zip

@@ -0,0 +1,169 @@
+name: conex
+channels:
+  - dglteam
+  - pytorch
+  - anaconda
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1
+  - attrs=19.3.0
+  - backcall=0.1.0
+  - blas=1.0
+  - bleach=3.1.0
+  - bzip2=1.0.8
+  - ca-certificates=2019.11.28
+  - certifi=2019.11.28
+  - cffi=1.13.2
+  - cpuonly=1.0
+  - cycler=0.10.0
+  - dbus=1.13.6
+  - decorator=4.4.1
+  - defusedxml=0.6.0
+  - dgl=0.4.1
+  - entrypoints=0.3
+  - expat=2.2.5
+  - ffmpeg=4.2
+  - fontconfig=2.13.1
+  - freetype=2.9.1
+  - gettext=0.19.8.1
+  - glib=2.58.3
+  - gmp=6.1.2
+  - gnutls=3.6.5
+  - gst-plugins-base=1.14.5
+  - gstreamer=1.14.5
+  - icu=58.2
+  - importlib_metadata=1.1.0
+  - intel-openmp=2019.4
+  - ipykernel=5.1.3
+  - ipython=7.9.0
+  - ipython_genutils=0.2.0
+  - jedi=0.15.1
+  - jinja2=2.10.3
+  - joblib=0.13.2
+  - jpeg=9b
+  - json5=0.8.5
+  - jsonschema=3.2.0
+  - jupyter_client=5.3.4
+  - jupyter_core=4.6.1
+  - jupyterlab=1.2.3
+  - jupyterlab_server=1.0.6
+  - kiwisolver=1.1.0
+  - lame=3.100
+  - libedit=3.1.20181209
+  - libffi=3.2.1
+  - libgcc-ng=9.1.0
+  - libgfortran-ng=7.3.0
+  - libiconv=1.15
+  - libpng=1.6.37
+  - libsodium=1.0.16
+  - libstdcxx-ng=9.1.0
+  - libtiff=4.1.0
+  - libuuid=2.32.1
+  - libxcb=1.13
+  - libxml2=2.9.9
+  - markupsafe=1.1.1
+  - matplotlib=3.1.1
+  - mistune=0.8.4
+  - mkl=2019.4
+  - mkl-service=2.3.0
+  - mkl_fft=1.0.14
+  - mkl_random=1.1.0
+  - more-itertools=7.2.0
+  - nbconvert=5.6.1
+  - nbformat=4.4.0
+  - ncurses=6.1
+  - nettle=3.4.1
+  - networkx=2.4
+  - ninja=1.9.0
+  - notebook=6.0.1
+  - numpy=1.17.2
+  - numpy-base=1.17.2
+  - olefile=0.46
+  - openh264=1.8.0
+  - openssl=1.0.2t
+  - pandas=0.25.1
+  - pandoc=2.8.0.1
+  - pandocfilters=1.4.2
+  - parso=0.5.1
+  - pcre=8.43
+  - pexpect=4.7.0
+  - pickleshare=0.7.5
+  - pillow=6.2.1
+  - pip=19.2.3
+  - prometheus_client=0.7.1
+  - prompt_toolkit=2.0.10
+  - pthread-stubs=0.4
+  - ptyprocess=0.6.0
+  - pycparser=2.19
+  - pygments=2.4.2
+  - pyparsing=2.4.5
+  - pyqt=5.9.2
+  - pyrsistent=0.15.6
+  - python=3.6.4
+  - python-dateutil=2.8.0
+  - pytorch=1.3.1
+  - pytz=2019.3
+  - pyzmq=18.1.0
+  - qt=5.9.6
+  - readline=7.0
+  - scikit-learn=0.21.3
+  - scipy=1.3.1
+  - send2trash=1.5.0
+  - setuptools=41.4.0
+  - sip=4.19.8
+  - six=1.12.0
+  - sqlite=3.30.0
+  - terminado=0.8.3
+  - testpath=0.4.4
+  - tk=8.6.8
+  - torchvision=0.4.2
+  - tornado=6.0.3
+  - traitlets=4.3.3
+  - wcwidth=0.1.7
+  - webencodings=0.5.1
+  - wheel=0.33.6
+  - x264=1!152.20180806
+  - xorg-libxau=1.0.9
+  - xorg-libxdmcp=1.1.3
+  - xz=5.2.4
+  - zeromq=4.3.1
+  - zipp=0.6.0
+  - zlib=1.2.11
+  - zstd=1.3.7
+  - pip:
+    - absl-py==0.8.1
+    - astor==0.8.0
+    - cachetools==3.1.1
+    - chardet==3.0.4
+    - click==7.0
+    - cython==0.29.13
+    - downhill==0.4.0
+    - gast==0.2.2
+    - google-auth==1.7.1
+    - google-auth-oauthlib==0.4.1
+    - google-pasta==0.1.8
+    - grpcio==1.25.0
+    - h5py==2.10.0
+    - hdbscan==0.8.23
+    - idna==2.8
+    - keras-applications==1.0.8
+    - keras-preprocessing==1.1.0
+    - markdown==3.1.1
+    - oauthlib==3.1.0
+    - opt-einsum==3.1.0
+    - protobuf==3.10.0
+    - pyasn1==0.4.8
+    - pyasn1-modules==0.2.7
+    - requests==2.22.0
+    - requests-oauthlib==1.3.0
+    - rsa==4.0
+    - tensorboard==2.0.1
+    - tensorflow==2.0.0
+    - tensorflow-estimator==2.0.1
+    - termcolor==1.1.0
+    - urllib3==1.25.7
+    - werkzeug==0.16.0
+    - wrapt==1.11.2
+prefix: /home/xxx/anaconda3/envs/conex
+

README.md

@@ -0,0 +1,34 @@
+class Data:
+
+    def __init__(self, data_dir=None, reverse=False):
+
+        self.info={'dataset':data_dir,
+                   'dataset_augmentation':reverse}
+
+        self.train_data = self.load_data(data_dir, "train", reverse=reverse)
+        self.valid_data = self.load_data(data_dir, "valid", reverse=reverse)
+        self.test_data = self.load_data(data_dir, "test")
+        self.data = self.train_data + self.valid_data + self.test_data
+        self.entities = self.get_entities(self.data)
+        self.train_relations = self.get_relations(self.train_data)
+        self.valid_relations = self.get_relations(self.valid_data)
+        self.test_relations = self.get_relations(self.test_data)
+        self.relations = self.train_relations + [i for i in self.valid_relations \
+                if i not in self.train_relations] + [i for i in self.test_relations \
+                if i not in self.train_relations]
+
+    def load_data(self, data_dir, data_type="train", reverse=False):
+        with open("%s%s.txt" % (data_dir, data_type), "r") as f:
+            data = f.read().strip().split("\n")
+            data = [i.split() for i in data]
+            if reverse:
+                data += [[i[2], i[1]+"_reverse", i[0]] for i in data]
+        return data
+
+    def get_relations(self, data):
+        relations = sorted(list(set([d[1] for d in data])))
+        return relations
+
+    def get_entities(self, data):
+        entities = sorted(list(set([d[0] for d in data]+[d[2] for d in data])))
+        return entities

environment.yml

@@ -0,0 +1,34 @@
+import datetime
+import logging
+import os
+
+
+def create_experiment_folder(folder_name='Experiments'):
+    directory = os.getcwd() + '/' + folder_name + '/'
+    folder_name = str(datetime.datetime.now())
+    path_of_folder = directory + folder_name
+    os.makedirs(path_of_folder)
+    return path_of_folder, path_of_folder[:path_of_folder.rfind('/')]
+
+
+def create_logger(*, name, p):
+    logger = logging.getLogger(name)
+
+    logger.setLevel(logging.INFO)
+    # create file handler which logs even debug messages
+    fh = logging.FileHandler(p + '/info.log')
+    fh.setLevel(logging.INFO)
+
+    # create console handler with a higher log level
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.INFO)
+
+    # create formatter and add it to the handlers
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    ch.setFormatter(formatter)
+    fh.setFormatter(formatter)
+    # add the handlers to logger
+    logger.addHandler(ch)
+    logger.addHandler(fh)
+
+    return logger