From 4bc5ae9f4b14595e9fa90d92a3ba09d03f412165 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 8 Jun 2022 14:44:01 +0100 Subject: [PATCH 001/124] Upgrade to TypeDB 2.10.0 and associated build configurations. Won't build due to tensorflow issue. A previously used patch is no longer applicable to v0.8.0 of rules_python. --- .bazelversion | 2 +- WORKSPACE | 21 ++++++++++---------- dependencies/vaticle/artifacts.bzl | 2 +- dependencies/vaticle/repositories.bzl | 10 +++++----- requirements-dev.txt => requirements_dev.txt | 0 5 files changed, 18 insertions(+), 17 deletions(-) rename requirements-dev.txt => requirements_dev.txt (100%) diff --git a/.bazelversion b/.bazelversion index 0c89fc92..3bff0591 100644 --- a/.bazelversion +++ b/.bazelversion @@ -1 +1 @@ -4.0.0 \ No newline at end of file +5.1.1 \ No newline at end of file diff --git a/WORKSPACE b/WORKSPACE index 9c92ffc5..6bd1a71d 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -46,13 +46,13 @@ kt_register_toolchains() # Load //builder/python load("@vaticle_dependencies//builder/python:deps.bzl", python_deps = "deps") python_deps() -load("@rules_python//python:pip.bzl", "pip_install") +load("@rules_python//python:pip.bzl", "pip_repositories") +pip_repositories() # Load //builder/grpc load("@vaticle_dependencies//builder/grpc:deps.bzl", grpc_deps = "deps") grpc_deps() -load("@com_github_grpc_grpc//bazel:grpc_deps.bzl", -com_github_grpc_grpc_deps = "grpc_deps") +load("@com_github_grpc_grpc//bazel:grpc_deps.bzl", com_github_grpc_grpc_deps = "grpc_deps") com_github_grpc_grpc_deps() # Load //tool/common @@ -89,23 +89,24 @@ github_deps() # Load @vaticle dependencies # ################################ +load("//dependencies/vaticle:repositories.bzl", "vaticle_typedb_common") +vaticle_typedb_common() + +load("//dependencies/vaticle:artifacts.bzl", "vaticle_typedb_artifacts") +vaticle_typedb_artifacts() + load("//dependencies/vaticle:repositories.bzl", "vaticle_typedb_client_python") vaticle_typedb_client_python() +load("@rules_python//python:pip.bzl", "pip_install") pip_install( name = "vaticle_typedb_client_python_pip", requirements = "@vaticle_typedb_client_python//:requirements.txt", ) -load("//dependencies/vaticle:repositories.bzl", "vaticle_common") -vaticle_common() - -load("//dependencies/vaticle:artifacts.bzl", "vaticle_typedb_artifacts") -vaticle_typedb_artifacts() - pip_install( name = "vaticle_kglib_pip", - requirements = "//:requirements-dev.txt", + requirements = "//:requirements_dev.txt", ) ############################ diff --git a/dependencies/vaticle/artifacts.bzl b/dependencies/vaticle/artifacts.bzl index 05bae53b..2a6c7d07 100644 --- a/dependencies/vaticle/artifacts.bzl +++ b/dependencies/vaticle/artifacts.bzl @@ -29,5 +29,5 @@ def vaticle_typedb_artifacts(): artifact_name = "typedb-all-{platform}-{version}.{ext}", tag_source = deployment["artifact.release"], commit_source = deployment["artifact.snapshot"], - commit = "f91efad669b217805398b0ee05ebd24b9d9b4684", + tag = "2.10.0", ) diff --git a/dependencies/vaticle/repositories.bzl b/dependencies/vaticle/repositories.bzl index 13de3871..9ce0ac13 100644 --- a/dependencies/vaticle/repositories.bzl +++ b/dependencies/vaticle/repositories.bzl @@ -25,19 +25,19 @@ def vaticle_dependencies(): git_repository( name = "vaticle_dependencies", remote = "https://github.com/vaticle/dependencies", - commit = "a8b3b714a5e0562d41f4c05ca8f266d48b7d0fd3", # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_dependencies + commit = "34c260ae7858b08dd6247d43c8c373847b113d68", # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_dependencies ) def vaticle_typedb_client_python(): git_repository( name = "vaticle_typedb_client_python", remote = "https://github.com/vaticle/typedb-client-python", - commit = "d08a6c36ac4ef20f2df26017cf42e17fba230387" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_typedb_client_python + tag = "2.9.0" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_typedb_client_python ) -def vaticle_common(): +def vaticle_typedb_common(): git_repository( - name = "vaticle_common", + name = "vaticle_typedb_common", remote = "https://github.com/vaticle/typedb-common", - commit = "f0dd708adaea9fe1fdc3699180797a12166d33e8" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_common + tag = "2.9.0" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_common ) diff --git a/requirements-dev.txt b/requirements_dev.txt similarity index 100% rename from requirements-dev.txt rename to requirements_dev.txt From e01ed36cb13cb7ebf38896bb91e4c4c62505bacd Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 10 Jun 2022 12:02:03 +0100 Subject: [PATCH 002/124] Buildable state --- kglib/BUILD | 1 - kglib/kgcn_tensorflow/.images/BUILD | 32 ---- kglib/kgcn_tensorflow/BUILD | 41 ----- .../examples/diagnosis/.images/BUILD | 32 ---- .../kgcn_tensorflow/examples/diagnosis/BUILD | 62 -------- kglib/kgcn_tensorflow/learn/BUILD | 103 ------------ kglib/kgcn_tensorflow/models/BUILD | 150 ------------------ kglib/kgcn_tensorflow/pipeline/BUILD | 51 ------ kglib/kgcn_tensorflow/plot/BUILD | 93 ----------- kglib/tests/end_to_end/BUILD | 70 -------- kglib/utils/typedb/synthetic/BUILD | 3 +- requirements.txt | 44 +---- requirements_dev.txt | 42 +---- 13 files changed, 4 insertions(+), 720 deletions(-) delete mode 100644 kglib/kgcn_tensorflow/.images/BUILD delete mode 100644 kglib/kgcn_tensorflow/BUILD delete mode 100644 kglib/kgcn_tensorflow/examples/diagnosis/.images/BUILD delete mode 100644 kglib/kgcn_tensorflow/examples/diagnosis/BUILD delete mode 100644 kglib/kgcn_tensorflow/learn/BUILD delete mode 100644 kglib/kgcn_tensorflow/models/BUILD delete mode 100644 kglib/kgcn_tensorflow/pipeline/BUILD delete mode 100644 kglib/kgcn_tensorflow/plot/BUILD delete mode 100644 kglib/tests/end_to_end/BUILD diff --git a/kglib/BUILD b/kglib/BUILD index 56040a2e..747f1566 100644 --- a/kglib/BUILD +++ b/kglib/BUILD @@ -29,7 +29,6 @@ py_library( '__init__.py', ], deps = [ - '//kglib/kgcn_tensorflow:kgcn', '//kglib/utils', ], visibility=['//visibility:public'] diff --git a/kglib/kgcn_tensorflow/.images/BUILD b/kglib/kgcn_tensorflow/.images/BUILD deleted file mode 100644 index 3c5c660f..00000000 --- a/kglib/kgcn_tensorflow/.images/BUILD +++ /dev/null @@ -1,32 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - exclude = glob([ - "*.png", - "*.svg" - ]), - license_type = "apache-header", -) diff --git a/kglib/kgcn_tensorflow/BUILD b/kglib/kgcn_tensorflow/BUILD deleted file mode 100644 index 284d416e..00000000 --- a/kglib/kgcn_tensorflow/BUILD +++ /dev/null @@ -1,41 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_library") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_library( - name = "kgcn", - deps = [ - '//kglib/kgcn_tensorflow/examples/diagnosis', - '//kglib/kgcn_tensorflow/learn', - '//kglib/kgcn_tensorflow/models', - '//kglib/kgcn_tensorflow/pipeline', - '//kglib/kgcn_tensorflow/plot', - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/kgcn_tensorflow/examples/diagnosis/.images/BUILD b/kglib/kgcn_tensorflow/examples/diagnosis/.images/BUILD deleted file mode 100644 index 3c5c660f..00000000 --- a/kglib/kgcn_tensorflow/examples/diagnosis/.images/BUILD +++ /dev/null @@ -1,32 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - exclude = glob([ - "*.png", - "*.svg" - ]), - license_type = "apache-header", -) diff --git a/kglib/kgcn_tensorflow/examples/diagnosis/BUILD b/kglib/kgcn_tensorflow/examples/diagnosis/BUILD deleted file mode 100644 index 6d37c02d..00000000 --- a/kglib/kgcn_tensorflow/examples/diagnosis/BUILD +++ /dev/null @@ -1,62 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_test( - name = "diagnosis_test", - srcs = [ - "diagnosis_test.py" - ], - deps = [ - "diagnosis", - "//kglib/utils/graph/test", - vaticle_kglib_requirement('numpy'), - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('decorator'), - ] -) - -py_library( - name = "diagnosis", - srcs = [ - 'diagnosis.py' - ], - deps = [ - "//kglib/kgcn_tensorflow/pipeline", - "//kglib/kgcn_tensorflow/learn", - "//kglib/kgcn_tensorflow/plot", - "//kglib/kgcn_tensorflow/models", - "//kglib/utils/typedb/synthetic", - "//kglib/utils/typedb/type", - "@vaticle_typedb_client_python//:client_python", - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/kgcn_tensorflow/learn/BUILD b/kglib/kgcn_tensorflow/learn/BUILD deleted file mode 100644 index e6c2e103..00000000 --- a/kglib/kgcn_tensorflow/learn/BUILD +++ /dev/null @@ -1,103 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_test( - name = "metrics_test", - srcs = [ - "metrics_test.py" - ], - deps = [ - "learn" - ] -) - -py_test( - name = "learn_IT", - srcs = [ - "learn_IT.py" - ], - deps = [ - "learn", - "//kglib/kgcn_tensorflow/models", - "//kglib/kgcn_tensorflow/plot" - ] -) - -py_library( - name = "learn", - srcs = [ - 'feed.py', - 'learn.py', - 'loss.py', - 'metrics.py', - ], - deps = [ - # Networkx deps - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('decorator'), - - # Graph nets deps - vaticle_kglib_requirement('absl-py'), - vaticle_kglib_requirement('cloudpickle'), - vaticle_kglib_requirement('contextlib2'), - # vaticle_kglib_requirement('decorator'), - vaticle_kglib_requirement('dm-sonnet'), - vaticle_kglib_requirement('future'), - vaticle_kglib_requirement('graph-nets'), - # vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('numpy'), - vaticle_kglib_requirement('semantic-version'), - vaticle_kglib_requirement('six'), - vaticle_kglib_requirement('tensorflow-probability'), - vaticle_kglib_requirement('wrapt'), - - # Tensorflow deps - # vaticle_kglib_requirement('absl-py'), - vaticle_kglib_requirement('astor'), - vaticle_kglib_requirement('gast'), - vaticle_kglib_requirement('google-pasta'), - vaticle_kglib_requirement('keras-applications'), - vaticle_kglib_requirement('keras-preprocessing'), - # vaticle_kglib_requirement('six'), - vaticle_kglib_requirement('protobuf'), - vaticle_kglib_requirement('tensorboard'), - vaticle_kglib_requirement('tensorflow'), - vaticle_kglib_requirement('tensorflow-estimator'), - vaticle_kglib_requirement('termcolor'), - # vaticle_kglib_requirement('wrapt'), - - # Scipy deps - vaticle_kglib_requirement('scipy') - - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/kgcn_tensorflow/models/BUILD b/kglib/kgcn_tensorflow/models/BUILD deleted file mode 100644 index 75007c76..00000000 --- a/kglib/kgcn_tensorflow/models/BUILD +++ /dev/null @@ -1,150 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_test( - name = "attribute_test", - srcs = [ - "attribute_test.py" - ], - deps = [ - "//kglib/utils/test", - "models" - ] -) - -py_test( - name = "attribute_IT", - srcs = [ - "attribute_IT.py" - ], - deps = [ - "models" - ] -) - -py_test( - name = "embedding_test", - srcs = [ - "embedding_test.py" - ], - deps = [ - "//kglib/utils/test", - "models" - ] -) - -py_test( - name = "embedding_IT", - srcs = [ - "embedding_IT.py" - ], - deps = [ - "models" - ] -) - -py_test( - name = "typewise_test", - srcs = [ - "typewise_test.py" - ], - deps = [ - "//kglib/utils/test", - "models" - ] -) - -py_test( - name = "typewise_IT", - srcs = [ - "typewise_IT.py" - ], - deps = [ - "models" - ] -) - -py_test( - name = "core_IT", - srcs = [ - "core_IT.py" - ], - deps = [ - "models" - ] -) - -py_library( - name = "models", - srcs = [ - 'attribute.py', - 'core.py', - 'embedding.py', - 'typewise.py', - ], - deps = [ - # Networkx deps - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('decorator'), - - # Graph nets deps - vaticle_kglib_requirement('absl-py'), - vaticle_kglib_requirement('cloudpickle'), - vaticle_kglib_requirement('contextlib2'), - # vaticle_kglib_requirement('decorator'), - vaticle_kglib_requirement('dm-sonnet'), - vaticle_kglib_requirement('future'), - vaticle_kglib_requirement('graph-nets'), - # vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('numpy'), - vaticle_kglib_requirement('semantic-version'), - vaticle_kglib_requirement('six'), - vaticle_kglib_requirement('tensorflow-probability'), - vaticle_kglib_requirement('wrapt'), - - # Tensorflow deps - # vaticle_kglib_requirement('absl-py'), - vaticle_kglib_requirement('astor'), - vaticle_kglib_requirement('gast'), - vaticle_kglib_requirement('google-pasta'), - vaticle_kglib_requirement('keras-applications'), - vaticle_kglib_requirement('keras-preprocessing'), - # vaticle_kglib_requirement('six'), - vaticle_kglib_requirement('protobuf'), - vaticle_kglib_requirement('tensorboard'), - vaticle_kglib_requirement('tensorflow'), - vaticle_kglib_requirement('tensorflow-estimator'), - vaticle_kglib_requirement('termcolor'), - # vaticle_kglib_requirement('wrapt'), - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/kgcn_tensorflow/pipeline/BUILD b/kglib/kgcn_tensorflow/pipeline/BUILD deleted file mode 100644 index 17fe83bd..00000000 --- a/kglib/kgcn_tensorflow/pipeline/BUILD +++ /dev/null @@ -1,51 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_library( - name = "pipeline", - srcs = [ - 'pipeline.py', - ], - deps = [ - vaticle_kglib_requirement('graph_nets'), - vaticle_kglib_requirement('numpy'), - vaticle_kglib_requirement('dm-sonnet'), - vaticle_kglib_requirement('tensorflow'), - vaticle_kglib_requirement('tensorflow-probability'), - vaticle_kglib_requirement('semantic-version'), - vaticle_kglib_requirement('contextlib2'), - vaticle_kglib_requirement('wrapt'), - "//kglib/utils/graph", - "//kglib/kgcn_data_loader", - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/kgcn_tensorflow/plot/BUILD b/kglib/kgcn_tensorflow/plot/BUILD deleted file mode 100644 index 0c1c24e9..00000000 --- a/kglib/kgcn_tensorflow/plot/BUILD +++ /dev/null @@ -1,93 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_test( - name = "plotting_test", - srcs = [ - "plotting_test.py" - ], - deps = [ - "plot" - ] -) - -py_library( - name = "plot", - srcs = [ - 'draw.py', - 'plotting.py', - ], - deps = [ - # Matplotlib deps - vaticle_kglib_requirement('cycler'), - vaticle_kglib_requirement('kiwisolver'), - vaticle_kglib_requirement('matplotlib'), - vaticle_kglib_requirement('pyparsing'), - vaticle_kglib_requirement('python-dateutil'), - - # Networkx deps - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('decorator'), - - # Graph nets deps - vaticle_kglib_requirement('absl-py'), - vaticle_kglib_requirement('cloudpickle'), - vaticle_kglib_requirement('contextlib2'), - # vaticle_kglib_requirement('decorator'), - vaticle_kglib_requirement('dm-sonnet'), - vaticle_kglib_requirement('future'), - vaticle_kglib_requirement('graph-nets'), - # vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('numpy'), - vaticle_kglib_requirement('semantic-version'), - vaticle_kglib_requirement('six'), - vaticle_kglib_requirement('tensorflow-probability'), - vaticle_kglib_requirement('wrapt'), - - # Tensorflow deps - # vaticle_kglib_requirement('absl-py'), - vaticle_kglib_requirement('astor'), - vaticle_kglib_requirement('gast'), - vaticle_kglib_requirement('google-pasta'), - vaticle_kglib_requirement('keras-applications'), - vaticle_kglib_requirement('keras-preprocessing'), - # vaticle_kglib_requirement('six'), - vaticle_kglib_requirement('protobuf'), - vaticle_kglib_requirement('tensorboard'), - vaticle_kglib_requirement('tensorflow'), - vaticle_kglib_requirement('tensorflow-estimator'), - vaticle_kglib_requirement('termcolor'), - # vaticle_kglib_requirement('wrapt'), - - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD deleted file mode 100644 index 1a037d54..00000000 --- a/kglib/tests/end_to_end/BUILD +++ /dev/null @@ -1,70 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_test( - name = "diagnosis", - size = "enormous", - srcs = [ - "kgcn/diagnosis.py" - ], - deps = [ - "//kglib/kgcn_tensorflow/examples/diagnosis", - "//kglib/utils/typedb/test", - ], - data = [ - "//kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files", - "@vaticle_typedb_artifact_linux//file" - ], - args = [ - "$(locations //kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files)", - "$(location @vaticle_typedb_artifact_linux//file)", - ], -) - -py_test( - name = "diagnosis-debug", - size = "enormous", - srcs = [ - "kgcn/diagnosis_debug.py" - ], - main = "kgcn/diagnosis_debug.py", - deps = [ - "//kglib/kgcn_tensorflow/examples/diagnosis", - ], - data = [ - "//kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files", - ], - args = [ - "$(locations //kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files)", - ], -) - -checkstyle_test( - name = "checkstyle", - include = glob([ - "*", - "**/*" - ]), - license_type = "apache-header", -) diff --git a/kglib/utils/typedb/synthetic/BUILD b/kglib/utils/typedb/synthetic/BUILD index 29329537..fc106a1f 100644 --- a/kglib/utils/typedb/synthetic/BUILD +++ b/kglib/utils/typedb/synthetic/BUILD @@ -20,8 +20,7 @@ # load("@rules_python//python:defs.bzl", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") diff --git a/requirements.txt b/requirements.txt index 1533b947..61bd4b56 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,48 +19,8 @@ # under the License. # -absl-py==0.11.0 -astor==0.8.1 -behave==1.2.6 -cached-property==1.5.2 -cloudpickle==1.1.1 -contextlib2==0.6.0.post1 -cycler==0.10.0 -decorator==4.4.2 -dm-sonnet==1.35 -dm-tree==0.1.5 -future==0.18.2 -gast==0.2.2 -google-pasta==0.2.0 -typedb-client==2.1.0 -graph-nets==1.0.4 -grpcio==1.35.0 -h5py==3.1.0 -importlib-metadata==3.3.0 -Keras-Applications==1.0.8 -Keras-Preprocessing==1.1.2 -kiwisolver==1.3.1 -Markdown==3.3.3 -matplotlib==3.1.1 +decorator==5.1.1 networkx==2.5 numpy==1.19.5 pandas==0.25.1 -parse==1.18.0 -parse-type==0.5.2 -protobuf==3.14.0 -PyHamcrest==2.0.2 -pyparsing==2.4.7 -python-dateutil==2.8.1 -pytz==2020.5 -scipy==1.3.1 -semantic-version==2.8.5 -six==1.15.0 -tensorboard==1.14.0 -tensorflow==1.14.0 -tensorflow-estimator==1.14.0 -tensorflow-probability==0.7.0 -termcolor==1.1.0 -typing-extensions==3.7.4.3 -Werkzeug==1.0.1 -wrapt==1.12.1 -zipp==3.4.0 + diff --git a/requirements_dev.txt b/requirements_dev.txt index a9b3a4f8..f7e7da1a 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -21,47 +21,7 @@ --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple -absl-py==0.11.0 -astor==0.8.1 -behave==1.2.6 -cached-property==1.5.2 -cloudpickle==1.1.1 -contextlib2==0.6.0.post1 -cycler==0.10.0 -decorator==4.4.2 -dm-sonnet==1.35 -dm-tree==0.1.5 -future==0.18.2 -gast==0.2.2 -google-pasta==0.2.0 -graph-nets==1.0.4 -grpcio==1.35.0 -h5py==3.1.0 -importlib-metadata==3.3.0 -Keras-Applications==1.0.8 -Keras-Preprocessing==1.1.2 -kiwisolver==1.3.1 -Markdown==3.3.3 -matplotlib==3.1.1 +decorator==5.1.1 networkx==2.5 numpy==1.19.5 pandas==0.25.1 -parse==1.18.0 -parse-type==0.5.2 -protobuf==3.14.0 -PyHamcrest==2.0.2 -pyparsing==2.4.7 -python-dateutil==2.8.1 -pytz==2020.5 -scipy==1.3.1 -semantic-version==2.8.5 -six==1.15.0 -tensorboard==1.14.0 -tensorflow==1.14.0 -tensorflow-estimator==1.14.0 -tensorflow-probability==0.7.0 -termcolor==1.1.0 -typing-extensions==3.7.4.3 -Werkzeug==1.0.1 -wrapt==1.12.1 -zipp==3.4.0 From e2998c98314ddef1c24a8d9096362913cb185a4e Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 15 Jun 2022 10:13:11 +0100 Subject: [PATCH 003/124] Baseline example for CSV loads to PyTorch Geometric --- kglib/kgcn_data_loader/load_csv.py | 127 +++++++++++++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 kglib/kgcn_data_loader/load_csv.py diff --git a/kglib/kgcn_data_loader/load_csv.py b/kglib/kgcn_data_loader/load_csv.py new file mode 100644 index 00000000..edc4d72e --- /dev/null +++ b/kglib/kgcn_data_loader/load_csv.py @@ -0,0 +1,127 @@ +import os.path as osp + +import pandas as pd +import torch +from sentence_transformers import SentenceTransformer + +from torch_geometric.data import HeteroData, download_url, extract_zip +from torch_geometric.transforms import RandomLinkSplit, ToUndirected + +url = 'https://files.grouplens.org/datasets/movielens/ml-latest-small.zip' +root = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens') +extract_zip(download_url(url, root), root) +movie_path = osp.join(root, 'ml-latest-small', 'movies.csv') +rating_path = osp.join(root, 'ml-latest-small', 'ratings.csv') + + +def load_node_csv(path, index_col, encoders=None, **kwargs): + df = pd.read_csv(path, index_col=index_col, **kwargs) + mapping = {index: i for i, index in enumerate(df.index.unique())} + + x = None + if encoders is not None: + xs = [encoder(df[col]) for col, encoder in encoders.items()] + x = torch.cat(xs, dim=-1) + + return x, mapping + + +def load_edge_csv(path, src_index_col, src_mapping, dst_index_col, dst_mapping, + encoders=None, **kwargs): + df = pd.read_csv(path, **kwargs) + + src = [src_mapping[index] for index in df[src_index_col]] + dst = [dst_mapping[index] for index in df[dst_index_col]] + edge_index = torch.tensor([src, dst]) + + edge_attr = None + if encoders is not None: + edge_attrs = [encoder(df[col]) for col, encoder in encoders.items()] + edge_attr = torch.cat(edge_attrs, dim=-1) + + return edge_index, edge_attr + + +class SequenceEncoder(object): + # The 'SequenceEncoder' encodes raw column strings into embeddings. + def __init__(self, model_name='all-MiniLM-L6-v2', device=None): + self.device = device + self.model = SentenceTransformer(model_name, device=device) + + @torch.no_grad() + def __call__(self, df): + x = self.model.encode(df.values, show_progress_bar=True, + convert_to_tensor=True, device=self.device) + return x.cpu() + + +class GenresEncoder(object): + # The 'GenreEncoder' splits the raw column strings by 'sep' and converts + # individual elements to categorical labels. + def __init__(self, sep='|'): + self.sep = sep + + def __call__(self, df): + genres = set(g for col in df.values for g in col.split(self.sep)) + mapping = {genre: i for i, genre in enumerate(genres)} + + x = torch.zeros(len(df), len(mapping)) + for i, col in enumerate(df.values): + for genre in col.split(self.sep): + x[i, mapping[genre]] = 1 + return x + + +class IdentityEncoder(object): + # The 'IdentityEncoder' takes the raw column values and converts them to + # PyTorch tensors. + def __init__(self, dtype=None): + self.dtype = dtype + + def __call__(self, df): + return torch.from_numpy(df.values).view(-1, 1).to(self.dtype) + + +user_x, user_mapping = load_node_csv(rating_path, index_col='userId') + +movie_x, movie_mapping = load_node_csv( + movie_path, index_col='movieId', encoders={ + 'title': SequenceEncoder(), + 'genres': GenresEncoder() + }) + +edge_index, edge_label = load_edge_csv( + rating_path, + src_index_col='userId', + src_mapping=user_mapping, + dst_index_col='movieId', + dst_mapping=movie_mapping, + encoders={'rating': IdentityEncoder(dtype=torch.long)}, +) + +data = HeteroData() +data['user'].num_nodes = len(user_mapping) # Users do not have any features. +data['movie'].x = movie_x +data['user', 'rates', 'movie'].edge_index = edge_index +data['user', 'rates', 'movie'].edge_label = edge_label +print(data) + +# We can now convert `data` into an appropriate format for training a +# graph-based machine learning model: + +# 1. Add a reverse ('movie', 'rev_rates', 'user') relation for message passing. +data = ToUndirected()(data) +del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label. + +# 2. Perform a link-level split into training, validation, and test edges. +transform = RandomLinkSplit( + num_val=0.05, + num_test=0.1, + neg_sampling_ratio=0.0, + edge_types=[('user', 'rates', 'movie')], + rev_edge_types=[('movie', 'rev_rates', 'user')], +) +train_data, val_data, test_data = transform(data) +print(train_data) +print(val_data) +print(test_data) From b6c603c247597e86515dd7ca5c676ce5f22d4644 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 15 Jun 2022 17:40:23 +0100 Subject: [PATCH 004/124] WIP swapping out the KGCN for a PyTorch GCN --- .gitignore | 2 +- .grabl/automation.yml | 4 +- kglib/examples/diagnosis/.images/BUILD | 32 ++ .../diagnosis}/.images/basic_schema.png | Bin .../diagnosis}/.images/basic_schema.svg | 0 .../diagnosis/.images/diagnosis_schema.png | Bin .../diagnosis}/.images/edge_update.png | Bin .../diagnosis}/.images/edge_update.svg | 0 .../.images/edge_update_process.png | Bin .../examples/diagnosis/.images/graph.png | Bin .../.images/interaction_edge_update.png | Bin .../.images/interaction_edge_update.svg | 0 .../.images/interaction_node_update.png | Bin .../.images/interaction_node_update.svg | 0 .../knowledge_graph_machine_learning.png | Bin .../examples/diagnosis/.images/learning.png | Bin .../diagnosis}/.images/learning_pipeline.png | Bin .../diagnosis}/.images/learning_pipeline.svg | 0 .../diagnosis}/.images/message_passing.png | Bin .../diagnosis}/.images/node_update.png | Bin .../diagnosis}/.images/node_update.svg | 0 .../.images/node_update_process.png | Bin .../diagnosis/.images/queried_subgraph.png | Bin .../diagnosis}/.images/reasoning.png | Bin .../diagnosis}/.images/reasoning.svg | 0 .../diagnosis}/.images/subgraph.png | Bin .../diagnosis}/.images/subgraph.svg | 0 .../.images/subgraph_with_predictions.png | Bin .../.images/subgraph_with_predictions.svg | 0 .../diagnosis}/.images/ternary_diagnosis.png | Bin .../diagnosis}/.images/ternary_diagnosis.svg | 0 kglib/examples/diagnosis/BUILD | 58 ++++ .../examples/diagnosis/README.md | 2 +- .../examples/diagnosis/diagnosis.py | 72 +++-- .../examples/diagnosis/diagnosis_test.py | 2 +- .../encoding/standard_encode.py | 9 +- kglib/kgcn_data_loader/hetero_link_pred.py | 132 ++++++++ kglib/kgcn_data_loader/link_prediction.py | 115 +++++++ kglib/kgcn_data_loader/node_classification.py | 86 ++++++ .../transform/standard_kgcn_transform.py | 6 +- kglib/kgcn_data_loader/utils.py | 2 +- kglib/kgcn_tensorflow/README.md | 153 --------- kglib/kgcn_tensorflow/learn/feed.py | 57 ---- kglib/kgcn_tensorflow/learn/learn.py | 178 ----------- kglib/kgcn_tensorflow/learn/learn_IT.py | 63 ---- kglib/kgcn_tensorflow/learn/loss.py | 66 ---- kglib/kgcn_tensorflow/learn/metrics.py | 104 ------- kglib/kgcn_tensorflow/learn/metrics_test.py | 111 ------- kglib/kgcn_tensorflow/models/attribute.py | 74 ----- kglib/kgcn_tensorflow/models/attribute_IT.py | 39 --- .../kgcn_tensorflow/models/attribute_test.py | 67 ---- kglib/kgcn_tensorflow/models/core.py | 132 -------- kglib/kgcn_tensorflow/models/core_IT.py | 58 ---- kglib/kgcn_tensorflow/models/embedding.py | 138 --------- kglib/kgcn_tensorflow/models/embedding_IT.py | 80 ----- .../kgcn_tensorflow/models/embedding_test.py | 70 ----- kglib/kgcn_tensorflow/models/typewise.py | 84 ----- kglib/kgcn_tensorflow/models/typewise_IT.py | 55 ---- kglib/kgcn_tensorflow/models/typewise_test.py | 82 ----- kglib/kgcn_tensorflow/pipeline/pipeline.py | 112 ------- kglib/kgcn_tensorflow/plot/draw.py | 290 ------------------ kglib/kgcn_tensorflow/plot/plotting.py | 289 ----------------- kglib/kgcn_tensorflow/plot/plotting_test.py | 84 ----- kglib/tests/deployment/kgcn/diagnosis.py | 4 +- kglib/tests/end_to_end/BUILD | 70 +++++ kglib/tests/end_to_end/kgcn/diagnosis.py | 2 +- .../tests/end_to_end/kgcn/diagnosis_debug.py | 2 +- .../graph/thing/queries_to_networkx_graph.py | 1 - requirements.txt | 36 ++- requirements_dev.txt | 39 ++- 70 files changed, 634 insertions(+), 2428 deletions(-) create mode 100644 kglib/examples/diagnosis/.images/BUILD rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/basic_schema.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/basic_schema.svg (100%) rename kglib/{kgcn_tensorflow => }/examples/diagnosis/.images/diagnosis_schema.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/edge_update.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/edge_update.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/edge_update_process.png (100%) rename kglib/{kgcn_tensorflow => }/examples/diagnosis/.images/graph.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/interaction_edge_update.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/interaction_edge_update.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/interaction_node_update.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/interaction_node_update.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/knowledge_graph_machine_learning.png (100%) rename kglib/{kgcn_tensorflow => }/examples/diagnosis/.images/learning.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/learning_pipeline.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/learning_pipeline.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/message_passing.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/node_update.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/node_update.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/node_update_process.png (100%) rename kglib/{kgcn_tensorflow => }/examples/diagnosis/.images/queried_subgraph.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/reasoning.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/reasoning.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/subgraph.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/subgraph.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/subgraph_with_predictions.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/subgraph_with_predictions.svg (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/ternary_diagnosis.png (100%) rename kglib/{kgcn_tensorflow => examples/diagnosis}/.images/ternary_diagnosis.svg (100%) create mode 100644 kglib/examples/diagnosis/BUILD rename kglib/{kgcn_tensorflow => }/examples/diagnosis/README.md (98%) rename kglib/{kgcn_tensorflow => }/examples/diagnosis/diagnosis.py (85%) rename kglib/{kgcn_tensorflow => }/examples/diagnosis/diagnosis_test.py (97%) create mode 100644 kglib/kgcn_data_loader/hetero_link_pred.py create mode 100644 kglib/kgcn_data_loader/link_prediction.py create mode 100644 kglib/kgcn_data_loader/node_classification.py delete mode 100644 kglib/kgcn_tensorflow/README.md delete mode 100644 kglib/kgcn_tensorflow/learn/feed.py delete mode 100644 kglib/kgcn_tensorflow/learn/learn.py delete mode 100644 kglib/kgcn_tensorflow/learn/learn_IT.py delete mode 100644 kglib/kgcn_tensorflow/learn/loss.py delete mode 100644 kglib/kgcn_tensorflow/learn/metrics.py delete mode 100644 kglib/kgcn_tensorflow/learn/metrics_test.py delete mode 100644 kglib/kgcn_tensorflow/models/attribute.py delete mode 100644 kglib/kgcn_tensorflow/models/attribute_IT.py delete mode 100644 kglib/kgcn_tensorflow/models/attribute_test.py delete mode 100644 kglib/kgcn_tensorflow/models/core.py delete mode 100644 kglib/kgcn_tensorflow/models/core_IT.py delete mode 100644 kglib/kgcn_tensorflow/models/embedding.py delete mode 100644 kglib/kgcn_tensorflow/models/embedding_IT.py delete mode 100644 kglib/kgcn_tensorflow/models/embedding_test.py delete mode 100644 kglib/kgcn_tensorflow/models/typewise.py delete mode 100644 kglib/kgcn_tensorflow/models/typewise_IT.py delete mode 100644 kglib/kgcn_tensorflow/models/typewise_test.py delete mode 100644 kglib/kgcn_tensorflow/pipeline/pipeline.py delete mode 100644 kglib/kgcn_tensorflow/plot/draw.py delete mode 100644 kglib/kgcn_tensorflow/plot/plotting.py delete mode 100644 kglib/kgcn_tensorflow/plot/plotting_test.py create mode 100644 kglib/tests/end_to_end/BUILD diff --git a/.gitignore b/.gitignore index b88ea66e..1b7e5e04 100644 --- a/.gitignore +++ b/.gitignore @@ -35,6 +35,6 @@ tmp/ __pycache__/ # Data input/output directories -kglib/kgcn_tensorflow/examples/diagnosis/events/ +kglib/examples/diagnosis/events/ *.egg-info/ \ No newline at end of file diff --git a/.grabl/automation.yml b/.grabl/automation.yml index fd63976c..5c0c39af 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -60,13 +60,13 @@ build: pyenv install -s 3.6.3 pyenv global 3.6.3 system bazel test //kglib/kgcn_data_loader/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH - test-kgcn-tensorflow: + test-kgcn-examples: image: vaticle-ubuntu-20.04 timeout: "10m" command: | pyenv install -s 3.6.3 pyenv global 3.6.3 system - bazel test //kglib/kgcn_tensorflow/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + bazel test //kglib/examples/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-end-to-end: image: vaticle-ubuntu-20.04 timeout: "30m" diff --git a/kglib/examples/diagnosis/.images/BUILD b/kglib/examples/diagnosis/.images/BUILD new file mode 100644 index 00000000..a8ae9110 --- /dev/null +++ b/kglib/examples/diagnosis/.images/BUILD @@ -0,0 +1,32 @@ +# +# Copyright (C) 2021 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") + +checkstyle_test( + name = "checkstyle", + include = glob(["*"]), + exclude = glob([ + "*.png", + "*.svg" + ]), + license_type = "apache", +) diff --git a/kglib/kgcn_tensorflow/.images/basic_schema.png b/kglib/examples/diagnosis/.images/basic_schema.png similarity index 100% rename from kglib/kgcn_tensorflow/.images/basic_schema.png rename to kglib/examples/diagnosis/.images/basic_schema.png diff --git a/kglib/kgcn_tensorflow/.images/basic_schema.svg b/kglib/examples/diagnosis/.images/basic_schema.svg similarity index 100% rename from kglib/kgcn_tensorflow/.images/basic_schema.svg rename to kglib/examples/diagnosis/.images/basic_schema.svg diff --git a/kglib/kgcn_tensorflow/examples/diagnosis/.images/diagnosis_schema.png b/kglib/examples/diagnosis/.images/diagnosis_schema.png similarity index 100% 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b/kglib/examples/diagnosis/.images/subgraph_with_predictions.png similarity index 100% rename from kglib/kgcn_tensorflow/.images/subgraph_with_predictions.png rename to kglib/examples/diagnosis/.images/subgraph_with_predictions.png diff --git a/kglib/kgcn_tensorflow/.images/subgraph_with_predictions.svg b/kglib/examples/diagnosis/.images/subgraph_with_predictions.svg similarity index 100% rename from kglib/kgcn_tensorflow/.images/subgraph_with_predictions.svg rename to kglib/examples/diagnosis/.images/subgraph_with_predictions.svg diff --git a/kglib/kgcn_tensorflow/.images/ternary_diagnosis.png b/kglib/examples/diagnosis/.images/ternary_diagnosis.png similarity index 100% rename from kglib/kgcn_tensorflow/.images/ternary_diagnosis.png rename to kglib/examples/diagnosis/.images/ternary_diagnosis.png diff --git a/kglib/kgcn_tensorflow/.images/ternary_diagnosis.svg b/kglib/examples/diagnosis/.images/ternary_diagnosis.svg similarity index 100% rename from kglib/kgcn_tensorflow/.images/ternary_diagnosis.svg rename to kglib/examples/diagnosis/.images/ternary_diagnosis.svg diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD new file mode 100644 index 00000000..4f52f464 --- /dev/null +++ b/kglib/examples/diagnosis/BUILD @@ -0,0 +1,58 @@ +# +# Copyright (C) 2021 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +load("@rules_python//python:defs.bzl", "py_test", "py_library") +load("@vaticle_kglib_pip//:requirements.bzl", + vaticle_kglib_requirement = "requirement") +load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") + +py_test( + name = "diagnosis_test", + srcs = [ + "diagnosis_test.py" + ], + deps = [ + "diagnosis", + "//kglib/utils/graph/test", + vaticle_kglib_requirement('numpy'), + vaticle_kglib_requirement('networkx'), + vaticle_kglib_requirement('decorator'), + ] +) + +py_library( + name = "diagnosis", + srcs = [ + 'diagnosis.py' + ], + deps = [ + "//kglib/utils/typedb/synthetic", + "//kglib/utils/typedb/type", + "@vaticle_typedb_client_python//:client_python", + ], + visibility=['//visibility:public'] +) + +checkstyle_test( + name = "checkstyle", + include = glob(["*"]), + license_type = "apache", +) diff --git a/kglib/kgcn_tensorflow/examples/diagnosis/README.md b/kglib/examples/diagnosis/README.md similarity index 98% rename from kglib/kgcn_tensorflow/examples/diagnosis/README.md rename to kglib/examples/diagnosis/README.md index a7c3c26a..2474d43e 100644 --- a/kglib/kgcn_tensorflow/examples/diagnosis/README.md +++ b/kglib/examples/diagnosis/README.md @@ -12,7 +12,7 @@ Once you have [installed KGLIB via pip](../../#getting-started---running-the-mac 1. Make sure a TypeDB server is running -2. Run the example: `python -m kglib.kgcn_tensorflow.examples.diagnosis.diagnosis` +2. Run the example: `python -m kglib.examples.diagnosis.diagnosis` The database, schema and seed data will be created automatically. Data is generated synthetically. The whole example should complete in under 10 minutes diff --git a/kglib/kgcn_tensorflow/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py similarity index 85% rename from kglib/kgcn_tensorflow/examples/diagnosis/diagnosis.py rename to kglib/examples/diagnosis/diagnosis.py index cba7af28..26b7fee6 100644 --- a/kglib/kgcn_tensorflow/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -21,12 +21,13 @@ import inspect import time - +import subprocess as sp from typedb.client import * +from kglib.kgcn_data_loader.dataset.typedb_networkx_dataset import TypeDBNetworkxDataSet +from kglib.kgcn_data_loader.transform.standard_kgcn_transform import StandardKGCNNetworkxTransform from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.kgcn_tensorflow.pipeline.pipeline import pipeline -from kglib.utils.graph.iterate import multidigraph_data_iterator +from kglib.utils.graph.iterate import multidigraph_data_iterator, multidigraph_node_data_iterator from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data @@ -67,8 +68,8 @@ def diagnosis_example(typedb_binary_directory, num_training_iterations=50, database=DATABASE, address=ADDRESS, - schema_file_path="kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql", - seed_data_file_path="kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql"): + schema_file_path="/Users/jamesfletcher/programming/research/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql", + seed_data_file_path="/Users/jamesfletcher/programming/research/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql"): """ Run the diagnosis example from start to finish, including traceably ingesting predictions back into TypeDB @@ -87,6 +88,13 @@ def diagnosis_example(typedb_binary_directory, Final accuracies for training and for testing """ + # Delete the database each time + sp.check_call([ + './typedb', + 'console', + f'--command=database delete {database}', + ], cwd=typedb_binary_directory) + tr_ge_split = int(num_graphs*0.5) client = TypeDB.core_client(address) @@ -115,19 +123,47 @@ def diagnosis_example(typedb_binary_directory, print(f'Found node types: {node_types}') print(f'Found edge types: {edge_types}') - ge_graphs, solveds_tr, solveds_ge = pipeline(graphs, - tr_ge_split, - node_types, - edge_types, - num_processing_steps_tr=num_processing_steps_tr, - num_processing_steps_ge=num_processing_steps_ge, - num_training_iterations=num_training_iterations, - continuous_attributes=CONTINUOUS_ATTRIBUTES, - categorical_attributes=CATEGORICAL_ATTRIBUTES, - output_dir=f"./events/{time.time()}/") - - with session.transaction(TransactionType.WRITE) as tx: - write_predictions_to_typedb(ge_graphs, tx) + # for data in multidigraph_node_data_iterator(graphs[0]): + # features = create_feature_vector(data) + # target = data[self.target_name] + # data.clear() + # data["x"] = features + # data["y"] = target + + transform = StandardKGCNNetworkxTransform( + node_types, + edge_types, + target_name='solution', + obfuscate=None, + categorical=None, + continuous=None, + duplicate_in_reverse=True, + label_attribute="concept", + ) + + dataset = TypeDBNetworkxDataSet( + list(range(num_graphs)), + get_query_handles, + DATABASE, + ADDRESS, + session, + True, + transform + ) + + # ge_graphs, solveds_tr, solveds_ge = pipeline(graphs, + # tr_ge_split, + # node_types, + # edge_types, + # num_processing_steps_tr=num_processing_steps_tr, + # num_processing_steps_ge=num_processing_steps_ge, + # num_training_iterations=num_training_iterations, + # continuous_attributes=CONTINUOUS_ATTRIBUTES, + # categorical_attributes=CATEGORICAL_ATTRIBUTES, + # output_dir=f"./events/{time.time()}/") + # + # with session.transaction(TransactionType.WRITE) as tx: + # write_predictions_to_typedb(ge_graphs, tx) session.close() client.close() diff --git a/kglib/kgcn_tensorflow/examples/diagnosis/diagnosis_test.py b/kglib/examples/diagnosis/diagnosis_test.py similarity index 97% rename from kglib/kgcn_tensorflow/examples/diagnosis/diagnosis_test.py rename to kglib/examples/diagnosis/diagnosis_test.py index ea337e3a..262f415e 100644 --- a/kglib/kgcn_tensorflow/examples/diagnosis/diagnosis_test.py +++ b/kglib/examples/diagnosis/diagnosis_test.py @@ -27,7 +27,7 @@ import networkx as nx import numpy as np -from kglib.kgcn_tensorflow.examples.diagnosis.diagnosis import write_predictions_to_typedb, obfuscate_labels +from kglib.examples.diagnosis.diagnosis import write_predictions_to_typedb, obfuscate_labels from kglib.utils.typedb.object.thing import Thing from kglib.utils.graph.test.case import GraphTestCase diff --git a/kglib/kgcn_data_loader/encoding/standard_encode.py b/kglib/kgcn_data_loader/encoding/standard_encode.py index 176419f5..58619c5e 100644 --- a/kglib/kgcn_data_loader/encoding/standard_encode.py +++ b/kglib/kgcn_data_loader/encoding/standard_encode.py @@ -46,13 +46,14 @@ def encode_values(graph, categorical_attributes, continuous_attributes): return graph -def encode_types(graph, iterator_func, types): +def encode_types(graph, iterator_func, all_types): """ Encodes the type found in graph data as an integer according to the index it is found in `all_types` Args: graph: The graph to encode - iterator_func: An function to create an iterator of data in the graph (node data, edge data or combined node and edge data) - types: The full list of types to be encoded in this order + iterator_func: An function to create an iterator of data in the graph (node data, edge data or combined node + and edge data) + all_types: The full list of types to be encoded in this order Returns: The graph, which is also is updated in-place @@ -61,7 +62,7 @@ def encode_types(graph, iterator_func, types): iterator = iterator_func(graph) for data in iterator: - data['categorical_type'] = types.index(data['type']) + data['categorical_type'] = all_types.index(data['type']) return graph diff --git a/kglib/kgcn_data_loader/hetero_link_pred.py b/kglib/kgcn_data_loader/hetero_link_pred.py new file mode 100644 index 00000000..4b29477b --- /dev/null +++ b/kglib/kgcn_data_loader/hetero_link_pred.py @@ -0,0 +1,132 @@ +import argparse +import os.path as osp + +import torch +import torch.nn.functional as F +from torch.nn import Linear + +import torch_geometric.transforms as T +from torch_geometric.datasets import MovieLens +from torch_geometric.nn import SAGEConv, to_hetero + +parser = argparse.ArgumentParser() +parser.add_argument('--use_weighted_loss', action='store_true', + help='Whether to use weighted MSE loss.') +args = parser.parse_args() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +path = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens') +dataset = MovieLens(path, model_name='all-MiniLM-L6-v2') +data = dataset[0].to(device) + +# Add user node features for message passing: +data['user'].x = torch.eye(data['user'].num_nodes, device=device) +del data['user'].num_nodes + +# Add a reverse ('movie', 'rev_rates', 'user') relation for message passing: +data = T.ToUndirected()(data) +del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label. + +# Perform a link-level split into training, validation, and test edges: +train_data, val_data, test_data = T.RandomLinkSplit( + num_val=0.1, + num_test=0.1, + neg_sampling_ratio=0.0, + edge_types=[('user', 'rates', 'movie')], + rev_edge_types=[('movie', 'rev_rates', 'user')], +)(data) + +# We have an unbalanced dataset with many labels for rating 3 and 4, and very +# few for 0 and 1. Therefore we use a weighted MSE loss. +if args.use_weighted_loss: + weight = torch.bincount(train_data['user', 'movie'].edge_label) + weight = weight.max() / weight +else: + weight = None + + +def weighted_mse_loss(pred, target, weight=None): + weight = 1. if weight is None else weight[target].to(pred.dtype) + return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() + + +class GNNEncoder(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = SAGEConv((-1, -1), hidden_channels) + self.conv2 = SAGEConv((-1, -1), out_channels) + + def forward(self, x, edge_index): + x = self.conv1(x, edge_index).relu() + x = self.conv2(x, edge_index) + return x + + +class EdgeDecoder(torch.nn.Module): + def __init__(self, hidden_channels): + super().__init__() + self.lin1 = Linear(2 * hidden_channels, hidden_channels) + self.lin2 = Linear(hidden_channels, 1) + + def forward(self, z_dict, edge_label_index): + row, col = edge_label_index + z = torch.cat([z_dict['user'][row], z_dict['movie'][col]], dim=-1) + + z = self.lin1(z).relu() + z = self.lin2(z) + return z.view(-1) + + +class Model(torch.nn.Module): + def __init__(self, hidden_channels): + super().__init__() + self.encoder = GNNEncoder(hidden_channels, hidden_channels) + self.encoder = to_hetero(self.encoder, data.metadata(), aggr='sum') + self.decoder = EdgeDecoder(hidden_channels) + + def forward(self, x_dict, edge_index_dict, edge_label_index): + z_dict = self.encoder(x_dict, edge_index_dict) + return self.decoder(z_dict, edge_label_index) + + +model = Model(hidden_channels=32).to(device) + +# Due to lazy initialization, we need to run one model step so the number +# of parameters can be inferred: +with torch.no_grad(): + model.encoder(train_data.x_dict, train_data.edge_index_dict) + +optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + + +def train(): + model.train() + optimizer.zero_grad() + pred = model(train_data.x_dict, train_data.edge_index_dict, + train_data['user', 'movie'].edge_label_index) + target = train_data['user', 'movie'].edge_label + loss = weighted_mse_loss(pred, target, weight) + loss.backward() + optimizer.step() + return float(loss) + + +@torch.no_grad() +def test(data): + model.eval() + pred = model(data.x_dict, data.edge_index_dict, + data['user', 'movie'].edge_label_index) + pred = pred.clamp(min=0, max=5) + target = data['user', 'movie'].edge_label.float() + rmse = F.mse_loss(pred, target).sqrt() + return float(rmse) + + +for epoch in range(1, 301): + loss = train() + train_rmse = test(train_data) + val_rmse = test(val_data) + test_rmse = test(test_data) + print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_rmse:.4f}, ' + f'Val: {val_rmse:.4f}, Test: {test_rmse:.4f}') \ No newline at end of file diff --git a/kglib/kgcn_data_loader/link_prediction.py b/kglib/kgcn_data_loader/link_prediction.py new file mode 100644 index 00000000..a1a31bf4 --- /dev/null +++ b/kglib/kgcn_data_loader/link_prediction.py @@ -0,0 +1,115 @@ +import os.path as osp + +import torch +import torch.nn.functional as F +from sklearn.metrics import roc_auc_score + +from torch_geometric.utils import negative_sampling +from torch_geometric.datasets import Planetoid +import torch_geometric.transforms as T +from torch_geometric.nn import GCNConv +from torch_geometric.utils import train_test_split_edges + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +device = "cpu" + +# load the Cora dataset +dataset = 'Cora' +path = osp.join('.', 'data', dataset) +dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures()) +data = dataset[0] +print(dataset.data) + +# use train_test_split_edges to create neg and positive edges +data.train_mask = data.val_mask = data.test_mask = data.y = None +data = train_test_split_edges(data) +print(data) + + +class Net(torch.nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = GCNConv(dataset.num_features, 128) + self.conv2 = GCNConv(128, 64) + + def encode(self): + x = self.conv1(data.x, data.train_pos_edge_index) # convolution 1 + x = x.relu() + return self.conv2(x, data.train_pos_edge_index) # convolution 2 + + def decode(self, z, pos_edge_index, neg_edge_index): # only pos and neg edges + edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1) # concatenate pos and neg edges + logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1) # dot product + return logits + + def decode_all(self, z): + prob_adj = z @ z.t() # get adj NxN + return (prob_adj > 0).nonzero(as_tuple=False).t() # get predicted edge_list + + +model, data = Net().to(device), data.to(device) +optimizer = torch.optim.Adam(params=model.parameters(), lr=0.01) + + +def get_link_labels(pos_edge_index, neg_edge_index): + # returns a tensor: + # [1,1,1,1,...,0,0,0,0,0,..] with the number of ones is equel to the lenght of pos_edge_index + # and the number of zeros is equal to the length of neg_edge_index + E = pos_edge_index.size(1) + neg_edge_index.size(1) + link_labels = torch.zeros(E, dtype=torch.float, device=device) + link_labels[:pos_edge_index.size(1)] = 1. + return link_labels + + +def train(): + model.train() + + neg_edge_index = negative_sampling( + edge_index=data.train_pos_edge_index, #positive edges + num_nodes=data.num_nodes, # number of nodes + num_neg_samples=data.train_pos_edge_index.size(1)) # number of neg_sample equal to number of pos_edges + + optimizer.zero_grad() + + z = model.encode() #encode + link_logits = model.decode(z, data.train_pos_edge_index, neg_edge_index) # decode + + link_labels = get_link_labels(data.train_pos_edge_index, neg_edge_index) + loss = F.binary_cross_entropy_with_logits(link_logits, link_labels) + loss.backward() + optimizer.step() + + return loss + + +@torch.no_grad() +def test(): + model.eval() + perfs = [] + for prefix in ["val", "test"]: + pos_edge_index = data[f'{prefix}_pos_edge_index'] + neg_edge_index = data[f'{prefix}_neg_edge_index'] + + z = model.encode() # encode train + link_logits = model.decode(z, pos_edge_index, neg_edge_index) # decode test or val + link_probs = link_logits.sigmoid() # apply sigmoid + + link_labels = get_link_labels(pos_edge_index, neg_edge_index) # get link + + perfs.append(roc_auc_score(link_labels.cpu(), link_probs.cpu())) #compute roc_auc score + return perfs + + +best_val_perf = test_perf = 0 +for epoch in range(1, 101): + train_loss = train() + val_perf, tmp_test_perf = test() + if val_perf > best_val_perf: + best_val_perf = val_perf + test_perf = tmp_test_perf + log = 'Epoch: {:03d}, Loss: {:.4f}, Val: {:.4f}, Test: {:.4f}' + if epoch % 10 == 0: + print(log.format(epoch, train_loss, best_val_perf, test_perf)) + +z = model.encode() +final_edge_index = model.decode_all(z) diff --git a/kglib/kgcn_data_loader/node_classification.py b/kglib/kgcn_data_loader/node_classification.py new file mode 100644 index 00000000..b8c485d0 --- /dev/null +++ b/kglib/kgcn_data_loader/node_classification.py @@ -0,0 +1,86 @@ +import torch +from torch_geometric.nn import GCNConv +import torch.nn.functional as F +from torch_geometric.datasets import Planetoid +from torch_geometric.transforms import NormalizeFeatures + +dataset = Planetoid(root='data/Planetoid', name='Cora', transform=NormalizeFeatures()) + +print() +print(f'Dataset: {dataset}:') +print('======================') +print(f'Number of graphs: {len(dataset)}') +print(f'Number of features: {dataset.num_features}') +print(f'Number of classes: {dataset.num_classes}') + +data = dataset[0] # Get the first graph object. + +print() +print(data) +print('===========================================================================================================') + +# Gather some statistics about the graph. +print(f'Number of nodes: {data.num_nodes}') +print(f'Number of edges: {data.num_edges}') +print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}') +print(f'Number of training nodes: {data.train_mask.sum()}') +print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}') +print(f'Has isolated nodes: {data.has_isolated_nodes()}') +print(f'Has self-loops: {data.has_self_loops()}') +print(f'Is undirected: {data.is_undirected()}') + + +class GCN(torch.nn.Module): + def __init__(self, hidden_channels): + super().__init__() + torch.manual_seed(1234567) + self.conv1 = GCNConv(dataset.num_features, hidden_channels) + self.conv2 = GCNConv(hidden_channels, dataset.num_classes) + + def forward(self, x, edge_index): + x = self.conv1(x, edge_index) + x = x.relu() + x = F.dropout(x, p=0.5, training=self.training) + x = self.conv2(x, edge_index) + return x + + +model = GCN(hidden_channels=16) +print(model) + +model = GCN(hidden_channels=16) +model.eval() + +out = model(data.x, data.edge_index) +# visualize(out, color=data.y) + +model = GCN(hidden_channels=16) +optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) +criterion = torch.nn.CrossEntropyLoss() + + +def train(): + model.train() + optimizer.zero_grad() # Clear gradients. + out = model(data.x, data.edge_index) # Perform a single forward pass. + loss = criterion(out[data.train_mask], data.y[data.train_mask]) # Compute the loss solely based on the training nodes. + loss.backward() # Derive gradients. + optimizer.step() # Update parameters based on gradients. + return loss + + +def test(): + model.eval() + out = model(data.x, data.edge_index) + pred = out.argmax(dim=1) # Use the class with highest probability. + test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels. + test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions. + return test_acc + + +for epoch in range(1, 101): + loss = train() + print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}') + +test_acc = test() +print(f'Test Accuracy: {test_acc:.4f}') diff --git a/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py b/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py index 255673a8..a1aa3152 100644 --- a/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py +++ b/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py @@ -75,11 +75,9 @@ def __call__(self, graph): obfuscate_labels(graph, self.obfuscate) # Encode attribute values as number graph = encode_values(graph, self.categorical, self.continuous) - graph = nx.convert_node_labels_to_integers( - graph, label_attribute=self.label_attribute - ) + graph = nx.convert_node_labels_to_integers(graph, label_attribute=self.label_attribute) if self.duplicate: - graph = duplicate_edges_in_reverse(graph) + graph = duplicate_edges_in_reverse(graph) # TODO: This can use the equivalent function from PyTorch # Node or Edge Type as int graph = encode_types(graph, multidigraph_node_data_iterator, self.node_types) graph = encode_types(graph, multidigraph_edge_data_iterator, self.edge_types) diff --git a/kglib/kgcn_data_loader/utils.py b/kglib/kgcn_data_loader/utils.py index 63561783..6f4e493c 100644 --- a/kglib/kgcn_data_loader/utils.py +++ b/kglib/kgcn_data_loader/utils.py @@ -39,7 +39,7 @@ def load_typeql_data_file(database, typedb_binary_location, typeql_file_path): def _load_typeql_file(database, typeql_file_path, typedb_binary_location, schema_or_data): sp.check_call([ - 'typedb', + './typedb', 'console', f'--command=transaction {database} {schema_or_data} write', f'--command=source {typeql_file_path}', diff --git a/kglib/kgcn_tensorflow/README.md b/kglib/kgcn_tensorflow/README.md deleted file mode 100644 index 2447e290..00000000 --- a/kglib/kgcn_tensorflow/README.md +++ /dev/null @@ -1,153 +0,0 @@ -# KGCNs - Knowledge Graph Convolutional Networks - -This project introduces a novel model: the *Knowledge Graph Convolutional Network* (KGCN). - -### Getting Started - Running the Machine Learning Pipeline - -**Requirements** - -- Python >= 3.6 - -- KGLIB installed via pip: `pip install typedb-kglib`. - -- [TypeDB 2.1.1](https://github.com/vaticle/typedb/releases) running in the background - -- the Python TypeDB client 2.1.0 ([PyPi](https://pypi.org/project/typedb-client/), [GitHub release](https://github.com/vaticle/typedb-client-python/releases)) - -See the [full example](https://github.com/vaticle/kglib/tree/master/kglib/kgcn/examples/diagnosis/diagnosis.py) for how to use a KGCN for [Relation](https://docs.vaticle.com/docs/schema/concepts#relation) prediction. You can use the example as a template to create a KGCN for your own TypeDB data. If you need to customise the learning or model used, you'll need to make changes to your version of the [pipeline](https://github.com/vaticle/kglib/tree/master/kglib/kgcn/pipeline/pipeline.py). - -## How Do We Use Machine Learning over a Knowledge Graph? - -### Relation Prediction - -This KGCN framework is designed to provide a versatile means to perform learning tasks over a knowledge graph in TypeDB. - -Included in the [latest release](https://github.com/vaticle/kglib/releases/latest): - -- Predicting the existence of new [Relations](https://docs.vaticle.com/docs/schema/concepts#relation) between existing [Concepts](https://docs.vaticle.com/docs/concept-api/overview). These relations can be binary, **ternary** (3-way) or [**N-ary**]() (N-way), since Relations in TypeDB are graph [Hyperedges](https://en.wikipedia.org/wiki/Glossary_of_graph_theory_terms#hyperedge). - -Understand the full capabilities of KGCNs by examining the methodology outlined below. - -### Supervised Knowledge Graph Machine Learning - -We approach learning over a Knowledge Graph just as we do classical supervised learning. We learn from a ground truth set of training examples, but in this case each example is a subgraph. - -We extract these subgraphs from a TypeDB Knowledge Graph. Extracting subgraphs is performed by making Graql queries to TypeDB (multiple queries per example). - -![Knowledge Graph Machine Learning](.images/knowledge_graph_machine_learning.png) - -We then encode these graphs and feed them to the KGCN. As an output we receive the same graph with predicted node property values. Those predicted properties can be used to regress or classify the Concepts of our subgraphs as we see fit. - -Using this method we can frame Relation prediction as a node existence classification task. - -### Graphs In, Graphs Out - -We can directly ingest a graph into TensorFlow and learn over that graph. This leverages DeepMind's [Graph Nets](https://github.com/deepmind/graph_nets) framework, detailed in [their paper](https://arxiv.org/abs/1806.01261) (built in TensorFlow). This work is a generalisation of graph learning techniques, which offers plenty of ways to structure learning tailored to various knowledge graph problems. - -We extend this work for knowledge graphs in TypeDB, with a graph data flow as follows: - -![Pipeline](.images/learning_pipeline.png) - - - -## How Does Message Passing Work? - -Message passing is an iterative process. On each `superstep` messages are passed simultaneously (such that they don't influence each other) between elements of the graph. These messages are then used to update the state of graph elements. - -A KGCN is a learned graph message-passing algorithm. Neural network components transform the messages that are passed around the graph. This transformation is learned in order to pass useful updates around the graph. - -In our case, the algorithm looks like this: - -``` -for step in supersteps: - update all edges; - update all nodes; -``` - -![Message Passing](.images/message_passing.png) - -### Edge Block - -Use as input the current edge features and the features of the nodes it connects. Update the edge's features as the output of some neural network layers. Do this for all edges. - -![Edge Update](.images/edge_update.png) - -### Node Block - -Use as input the node's features and the most up-to-date features of the edges that connect to it. Update the node's features as the output of some neural network layers. Do this for all nodes. - -![Node Update](.images/node_update.png) - -### How is this Convolutional? - -This approach is described as convolutional since the same transformations are re-used across the graph. It may help your understanding to analogise this to convolution over images, where the same transformation is applied over all pixel neighbourhoods. - -## How Do We Frame Relation Prediction? - -In a typical use case, we have a specific Relation Type, `T`, that we want to predict. We want to predict the existence of `T` Relations based on the context that surrounds them in the graph. Our supervised learning approach requires ground truth examples. - -### Creating Ground Truth Examples - -Our approach is to extract subgraphs from a TypeDB knowledge graph to use as ground truth examples. - -Clearly, `T` Relations that are present are treated as fact, and given positive target labels. However, we must also consider *negative* examples of these `T` Relations. - -In our ground truth example subgraphs, we apply a [closed-world assumption](https://en.wikipedia.org/wiki/Closed-world_assumption). This means that for concepts `$a1`, `$a2`, ... ,`$aN` if a Relation does not exist in `($a1, $a2, ... ,$aN)`, this indicates that there is no such Relation in `($a1, $a2, ... ,$aN)`. - -Following this closed-world assumption, we use the absence of a `T` Relation as a negative target. - -Note that under an [open-world assumption](https://en.wikipedia.org/wiki/Open-world_assumption) a Relation in `($a1, $a2, ... ,$aN)` could exist but also be absent from the graph. - -### How Do We Represent Negative Examples? - -The KGCN needs to learn by example where it should predict new `T` Relations. Therefore, the learner needs to see all logically possible `T` Relations as candidates. - -To achieve this, wherever in the subgraph a `T` Relation *could* exist, but does not, we create one (see below), giving the new Relation a negative target label. - -The learner's job is then to classify those candidates to indicate their likelihood of true existence. - -Due to TypeDB's enforced schema, `T` Relations can logically only occur between certain Roleplayers. This means that the candidates to be added should be sparse - we don't see the combinatorial explosion of candidates that we would see in a homogenous subgraph. - -### Adding Negative Relations Dynamically - -Naturally, we don't wish to pollute our Knowledge Graph by inserting these `T` Relation candidates. Instead, we can make use of TypeDB's reasoning engine here by defining a logical [Rule](http://docs.vaticle.com/docs/schema/rules) to dynamically create these candidates (see the rule in the [example schema](../utils/typedb/synthetic/examples/diagnosis/schema.tql)). After training our learner we can simply `undefine` the rule to return to an unpolluted state. - -## Architectural Components - -Here we identify the core components used to build a working KGCN pipeline. - -### Application - -e.g. [diagnosis example](https://github.com/vaticle/kglib/tree/master/kglib/kgcn/examples/diagnosis) - -1. Fetch subgraphs, each subgraph is used as an *example* - - This requires specifying queries that will retrieve Concepts from TypeDB - - The answers from these queries are used to create subgraphs, stored in-memory as networkx graphs -2. Find the Types and Roles present in the schema. If any are not needed for learning then they should be excluded from the exhaustive list for better accuracy. -3. Run the pipeline -4. Write the predictions made to TypeDB - -### Pipeline - -Can be customised from [pipeline](https://github.com/vaticle/kglib/tree/master/kglib/kgcn/pipeline/pipeline.py). A pipeline performs the following: - -1. Take in graphs and the training/generalistaion split -2. Encode graph values (including type information) into numerics -3. Perform prerequisite graph formatting and manipulation -4. Build the neural network model, providing embedding models for Attribute Types -5. Run the learning process -6. Create visualisations -7. Record the predictions made, and return them in graphs - -### KGCNLearner -Found in [learn.py](https://github.com/vaticle/kglib/tree/master/kglib/kgcn/learn/learn.py). -- Performs the training loop -- Manages the loss function and optimiser -- Manages the TensorFlow session -- Prints results for the training and generalistaion datasets during training - -### KGCN - -Found in [core.py](https://github.com/vaticle/kglib/tree/master/kglib/kgcn/models/core.py). - -Defines the computation graph for a KGCN, including the initial embedding of values and the edge/node/graph feature update strategy during message-passing. This is the core that depends upon [Graph Nets](https://github.com/deepmind/graph_nets). diff --git a/kglib/kgcn_tensorflow/learn/feed.py b/kglib/kgcn_tensorflow/learn/feed.py deleted file mode 100644 index 1ba8a250..00000000 --- a/kglib/kgcn_tensorflow/learn/feed.py +++ /dev/null @@ -1,57 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -from graph_nets import utils_tf, utils_np - - -def create_placeholders(input_graphs, target_graphs): - """ - Creates placeholders for the model training and evaluation. - Returns: - input_ph: The input graph's placeholders, as a graph namedtuple. - target_ph: The target graph's placeholders, as a graph namedtuple. - """ - input_ph = utils_tf.placeholders_from_networkxs(input_graphs, name="input_placeholders_from_networksx") - target_ph = utils_tf.placeholders_from_networkxs(target_graphs, name="target_placeholders_from_networkxs") - return input_ph, target_ph - - -def create_feed_dict(input_ph, target_ph, inputs, targets): - """Creates the feed dict for the placeholders for the model training and evaluation. - - Args: - input_ph: The input graph's placeholders, as a graph namedtuple. - target_ph: The target graph's placeholders, as a graph namedtuple. - inputs: The input graphs - targets: The target graphs - - Returns: - feed_dict: The feed `dict` of input and target placeholders and data. - """ - input_graphs = utils_np.networkxs_to_graphs_tuple(inputs) - target_graphs = utils_np.networkxs_to_graphs_tuple(targets) - feed_dict = {input_ph: input_graphs, target_ph: target_graphs} - return feed_dict - - -def make_all_runnable_in_session(*args): - """Lets an iterable of TF graphs be output from a session as NP graphs.""" - return [utils_tf.make_runnable_in_session(a) for a in args] \ No newline at end of file diff --git a/kglib/kgcn_tensorflow/learn/learn.py b/kglib/kgcn_tensorflow/learn/learn.py deleted file mode 100644 index 74de9eee..00000000 --- a/kglib/kgcn_tensorflow/learn/learn.py +++ /dev/null @@ -1,178 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import time - -import tensorflow as tf - -from kglib.kgcn_tensorflow.learn.feed import create_placeholders, create_feed_dict, make_all_runnable_in_session -from kglib.kgcn_tensorflow.learn.loss import loss_ops_preexisting_no_penalty -from kglib.kgcn_tensorflow.learn.metrics import existence_accuracy - - -class KGCNLearner: - """ - Responsible for running a KGCN model - """ - def __init__(self, model, num_processing_steps_tr=10, num_processing_steps_ge=10): - self._model = model - self._num_processing_steps_tr = num_processing_steps_tr - self._num_processing_steps_ge = num_processing_steps_ge - - def __call__(self, - tr_input_graphs, - tr_target_graphs, - ge_input_graphs, - ge_target_graphs, - num_training_iterations=1000, - learning_rate=1e-3, - log_every_epochs=20, - log_dir=None): - """ - Args: - tr_graphs: In-memory graphs of TypeDB concepts for training - ge_graphs: In-memory graphs of TypeDB concepts for generalisation - num_processing_steps_tr: Number of processing (message-passing) steps for training. - num_processing_steps_ge: Number of processing (message-passing) steps for generalization. - num_training_iterations: Number of training iterations - log_every_seconds: The time to wait between logging and printing the next set of results. - log_dir: Directory to store TensorFlow events files - - Returns: - - """ - - tf.set_random_seed(1) - - input_ph, target_ph = create_placeholders(tr_input_graphs, tr_target_graphs) - - # A list of outputs, one per processing step. - output_ops_tr = self._model(input_ph, self._num_processing_steps_tr) - output_ops_ge = self._model(input_ph, self._num_processing_steps_ge) - - # Training loss. - loss_ops_tr = loss_ops_preexisting_no_penalty(target_ph, output_ops_tr) - # Loss across processing steps. - loss_op_tr = sum(loss_ops_tr) / self._num_processing_steps_tr - - tf.summary.scalar('loss_op_tr', loss_op_tr) - # Test/generalization loss. - loss_ops_ge = loss_ops_preexisting_no_penalty(target_ph, output_ops_ge) - loss_op_ge = loss_ops_ge[-1] # Loss from final processing step. - tf.summary.scalar('loss_op_ge', loss_op_ge) - - # Optimizer - optimizer = tf.train.AdamOptimizer(learning_rate) - gradients, variables = zip(*optimizer.compute_gradients(loss_op_tr)) - - for grad, var in zip(gradients, variables): - try: - print(var.name) - tf.summary.histogram('gradients/' + var.name, grad) - except: - pass - - gradients, _ = tf.clip_by_global_norm(gradients, 5.0) - step_op = optimizer.apply_gradients(zip(gradients, variables)) - - input_ph, target_ph = make_all_runnable_in_session(input_ph, target_ph) - - sess = tf.Session() - merged_summaries = tf.summary.merge_all() - - train_writer = None - - if log_dir is not None: - train_writer = tf.summary.FileWriter(log_dir, sess.graph) - - sess.run(tf.global_variables_initializer()) - - logged_iterations = [] - losses_tr = [] - corrects_tr = [] - solveds_tr = [] - losses_ge = [] - corrects_ge = [] - solveds_ge = [] - - print("# (iteration number), T (elapsed seconds), " - "Ltr (training loss), Lge (test/generalization loss), " - "Ctr (training fraction nodes/edges labeled correctly), " - "Str (training fraction examples solved correctly), " - "Cge (test/generalization fraction nodes/edges labeled correctly), " - "Sge (test/generalization fraction examples solved correctly)") - - start_time = time.time() - for iteration in range(num_training_iterations): - feed_dict = create_feed_dict(input_ph, target_ph, tr_input_graphs, tr_target_graphs) - - if iteration % log_every_epochs == 0: - - train_values = sess.run( - { - "step": step_op, - "target": target_ph, - "loss": loss_op_tr, - "outputs": output_ops_tr, - "summary": merged_summaries - }, - feed_dict=feed_dict) - - if train_writer is not None: - train_writer.add_summary(train_values["summary"], iteration) - - feed_dict = create_feed_dict(input_ph, target_ph, ge_input_graphs, ge_target_graphs) - test_values = sess.run( - { - "target": target_ph, - "loss": loss_op_ge, - "outputs": output_ops_ge - }, - feed_dict=feed_dict) - correct_tr, solved_tr = existence_accuracy( - train_values["target"], train_values["outputs"][-1], use_edges=False) - correct_ge, solved_ge = existence_accuracy( - test_values["target"], test_values["outputs"][-1], use_edges=False) - - elapsed = time.time() - start_time - losses_tr.append(train_values["loss"]) - corrects_tr.append(correct_tr) - solveds_tr.append(solved_tr) - losses_ge.append(test_values["loss"]) - corrects_ge.append(correct_ge) - solveds_ge.append(solved_ge) - logged_iterations.append(iteration) - print("# {:05d}, T {:.1f}, Ltr {:.4f}, Lge {:.4f}, Ctr {:.4f}, Str" - " {:.4f}, Cge {:.4f}, Sge {:.4f}".format( - iteration, elapsed, train_values["loss"], test_values["loss"], - correct_tr, solved_tr, correct_ge, solved_ge)) - else: - train_values = sess.run( - { - "step": step_op, - "target": target_ph, - "loss": loss_op_tr, - "outputs": output_ops_tr - }, - feed_dict=feed_dict) - - training_info = logged_iterations, losses_tr, losses_ge, corrects_tr, corrects_ge, solveds_tr, solveds_ge - return train_values, test_values, training_info diff --git a/kglib/kgcn_tensorflow/learn/learn_IT.py b/kglib/kgcn_tensorflow/learn/learn_IT.py deleted file mode 100644 index 29267c0b..00000000 --- a/kglib/kgcn_tensorflow/learn/learn_IT.py +++ /dev/null @@ -1,63 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import networkx as nx -import numpy as np - -from kglib.kgcn_tensorflow.learn.learn import KGCNLearner -from kglib.kgcn_tensorflow.models.core import KGCN -from kglib.kgcn_tensorflow.models.embedding import ThingEmbedder, RoleEmbedder - - -class ITKGCNLearner(unittest.TestCase): - def test_learner_runs(self): - input_graph = nx.MultiDiGraph() - input_graph.add_node(0, features=np.array([0, 1, 2], dtype=np.float32)) - input_graph.add_edge(1, 0, features=np.array([0, 1, 2], dtype=np.float32)) - input_graph.add_node(1, features=np.array([0, 1, 2], dtype=np.float32)) - input_graph.add_edge(1, 2, features=np.array([0, 1, 2], dtype=np.float32)) - input_graph.add_node(2, features=np.array([0, 1, 2], dtype=np.float32)) - input_graph.graph['features'] = np.zeros(5, dtype=np.float32) - - target_graph = nx.MultiDiGraph() - target_graph.add_node(0, features=np.array([0, 1, 0], dtype=np.float32)) - target_graph.add_edge(1, 0, features=np.array([0, 0, 1], dtype=np.float32)) - target_graph.add_node(1, features=np.array([0, 0, 1], dtype=np.float32)) - target_graph.add_edge(1, 2, features=np.array([0, 0, 1], dtype=np.float32)) - target_graph.add_node(2, features=np.array([0, 1, 0], dtype=np.float32)) - target_graph.graph['features'] = np.zeros(5, dtype=np.float32) - - thing_embedder = ThingEmbedder(node_types=['a', 'b', 'c'], type_embedding_dim=5, - attr_embedding_dim=6, categorical_attributes={}, continuous_attributes={}) - - role_embedder = RoleEmbedder(num_edge_types=2, type_embedding_dim=5) - - kgcn = KGCN(thing_embedder, role_embedder, edge_output_size=3, node_output_size=3) - - learner = KGCNLearner(kgcn, num_processing_steps_tr=2, num_processing_steps_ge=2) - - learner([input_graph], [target_graph], [input_graph], [target_graph], num_training_iterations=50) - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_tensorflow/learn/loss.py b/kglib/kgcn_tensorflow/learn/loss.py deleted file mode 100644 index 24c23ce4..00000000 --- a/kglib/kgcn_tensorflow/learn/loss.py +++ /dev/null @@ -1,66 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import numpy as np -import tensorflow as tf - - -def loss_ops_from_difference(target_op, output_ops): - """ - Loss operation which directly compares the target with the output over all nodes and edges - Args: - target_op: The target of the model - output_ops: A list of the outputs of the model, one for each message-passing step - - Returns: The loss for each message-passing step - - """ - loss_ops = [ - tf.losses.softmax_cross_entropy(target_op.nodes, output_op.nodes) - for output_op in output_ops - ] - return loss_ops - - -def loss_ops_preexisting_no_penalty(target_op, output_ops): - """ - Loss operation which doesn't penalise the output values for pre-existing nodes and edges, treating them as slack - variables - - Args: - target_op: The target of the model - output_ops: A list of the outputs of the model, one for each message-passing step - - Returns: The loss for each message-passing step - - """ - loss_ops = [] - for output_op in output_ops: - node_mask_op = tf.math.reduce_any( - tf.math.not_equal(target_op.nodes, tf.constant(np.array([1., 0., 0.]), dtype=tf.float32)), axis=1) - target_nodes = tf.boolean_mask(target_op.nodes, node_mask_op) - output_nodes = tf.boolean_mask(output_op.nodes, node_mask_op) - - loss_op = tf.losses.softmax_cross_entropy(target_nodes, output_nodes) - - loss_ops.append(loss_op) - - return loss_ops \ No newline at end of file diff --git a/kglib/kgcn_tensorflow/learn/metrics.py b/kglib/kgcn_tensorflow/learn/metrics.py deleted file mode 100644 index f9c9bf14..00000000 --- a/kglib/kgcn_tensorflow/learn/metrics.py +++ /dev/null @@ -1,104 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import numpy as np -from graph_nets import utils_np - -from scipy.special import softmax - - -def compute_accuracy(target, output, use_nodes=True, use_edges=True): - """Calculate model accuracy. - - Returns the number of elements correctly predicted to exist, and the number of completely correct graphs - (100% correct predictions). - - Args: - target: A `graphs.GraphsTuple` that contains the target graph. - output: A `graphs.GraphsTuple` that contains the output graph. - use_nodes: A `bool` indicator of whether to compute node accuracy or not. - use_edges: A `bool` indicator of whether to compute edge accuracy or not. - - Returns: - correct: A `float` fraction of correctly labeled nodes/edges. - solved: A `float` fraction of graphs that are completely correctly labeled. - - Raises: - ValueError: Nodes or edges (or both) must be used - """ - if not use_nodes and not use_edges: - raise ValueError("Nodes or edges (or both) must be used") - tdds = utils_np.graphs_tuple_to_data_dicts(target) - odds = utils_np.graphs_tuple_to_data_dicts(output) - cs = [] - ss = [] - for td, od in zip(tdds, odds): - xn = np.argmax(td["nodes"], axis=-1) - yn = np.argmax(od["nodes"], axis=-1) - xe = np.argmax(td["edges"], axis=-1) - ye = np.argmax(od["edges"], axis=-1) - c = [] - if use_nodes: - c.append(xn == yn) - if use_edges: - c.append(xe == ye) - c = np.concatenate(c, axis=0) - s = np.all(c) - cs.append(c) - ss.append(s) - correct = np.mean(np.concatenate(cs, axis=0)) - solved = np.mean(np.stack(ss)) - return correct, solved - - -def existence_accuracy(target, output, use_nodes=True, use_edges=True): - if not use_nodes and not use_edges: - raise ValueError("Nodes or edges (or both) must be used") - tdds = utils_np.graphs_tuple_to_data_dicts(target) - odds = utils_np.graphs_tuple_to_data_dicts(output) - cs = [] - ss = [] - for td, od in zip(tdds, odds): - - nodes_to_predict = td["nodes"][:, 0] == 0 - xn = np.argmax(td["nodes"][:, 1:], axis=-1) - xn = xn[nodes_to_predict] - yn = np.argmax(softmax(od["nodes"][:, 1:], axis=1), axis=-1) - yn = yn[nodes_to_predict] - - edges_to_predict = td["edges"][:, 0] == 0 - xe = np.argmax(td["edges"][:, 1:], axis=-1) - xe = xe[edges_to_predict] - ye = np.argmax(softmax(od["edges"][:, 1:], axis=1), axis=-1) - ye = ye[edges_to_predict] - - c = [] - if use_nodes: - c.append(xn == yn) - if use_edges: - c.append(xe == ye) - c = np.concatenate(c, axis=0) - s = np.all(c) - cs.append(c) - ss.append(s) - correct = np.mean(np.concatenate(cs, axis=0)) - solved = np.mean(np.stack(ss)) - return correct, solved diff --git a/kglib/kgcn_tensorflow/learn/metrics_test.py b/kglib/kgcn_tensorflow/learn/metrics_test.py deleted file mode 100644 index 0bcc9efc..00000000 --- a/kglib/kgcn_tensorflow/learn/metrics_test.py +++ /dev/null @@ -1,111 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import numpy as np -from graph_nets.graphs import GraphsTuple - -from kglib.kgcn_tensorflow.learn.metrics import compute_accuracy, existence_accuracy - - -class TestComputeAccuracy(unittest.TestCase): - - def test_compute_accuracy_is_as_expected(self): - - t_nodes = np.array([[1, 0], [1, 0], [0, 1]], dtype=np.float32) - o_nodes = np.array([[0, 1], [1, 0], [1, 0]], dtype=np.float32) - t_edges = np.array([[0, 1], [1, 0]], dtype=np.float32) - o_edges = np.array([[1, 0], [1, 0]], dtype=np.float32) - - globals = None - senders = np.array([0, 1]) - receivers = np.array([1, 2]) - n_node = np.array([3]) - n_edge = np.array([2]) - - target = GraphsTuple(nodes=t_nodes, - edges=t_edges, - globals=globals, - receivers=receivers, - senders=senders, - n_node=n_node, - n_edge=n_edge) - - output = GraphsTuple(nodes=o_nodes, - edges=o_edges, - globals=globals, - receivers=receivers, - senders=senders, - n_node=n_node, - n_edge=n_edge) - - correct, solved = compute_accuracy(target, output) - - expected_correct = 2 / 5 - expected_solved = 0 - - self.assertEqual(expected_correct, correct) - self.assertEqual(expected_solved, solved) - - -class TestExistenceAccuracy(unittest.TestCase): - - def test_compute_accuracy_is_as_expected(self): - - t_nodes = np.array([[1, 0, 0], [0, 0, 1], [0, 0, 1]], dtype=np.float32) - o_nodes = np.array([[0, 1, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float32) - t_edges = np.array([[0, 1, 0], [1, 0, 0]], dtype=np.float32) - o_edges = np.array([[1, 0, 0], [1, 0, 0]], dtype=np.float32) - - globals = None - senders = np.array([0, 1]) - receivers = np.array([1, 2]) - n_node = np.array([3]) - n_edge = np.array([2]) - - target = GraphsTuple(nodes=t_nodes, - edges=t_edges, - globals=globals, - receivers=receivers, - senders=senders, - n_node=n_node, - n_edge=n_edge) - - output = GraphsTuple(nodes=o_nodes, - edges=o_edges, - globals=globals, - receivers=receivers, - senders=senders, - n_node=n_node, - n_edge=n_edge) - - correct, solved = existence_accuracy(target, output) - - expected_correct = 2/3 - expected_solved = 0.0 - - self.assertEqual(expected_correct, correct) - self.assertEqual(expected_solved, solved) - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_tensorflow/models/attribute.py b/kglib/kgcn_tensorflow/models/attribute.py deleted file mode 100644 index 689401e5..00000000 --- a/kglib/kgcn_tensorflow/models/attribute.py +++ /dev/null @@ -1,74 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import abc - -import sonnet as snt -import tensorflow as tf - - -class Attribute(snt.AbstractModule, abc.ABC): - """ - Abstract base class for Attribute value embedding models - """ - def __init__(self, attr_embedding_dim, name='AttributeEmbedder'): - super(Attribute, self).__init__(name=name) - self._attr_embedding_dim = attr_embedding_dim - - -class ContinuousAttribute(Attribute): - def __init__(self, attr_embedding_dim, name='ContinuousAttributeEmbedder'): - super(ContinuousAttribute, self).__init__(attr_embedding_dim, name=name) - - def _build(self, attribute_value): - tf.summary.histogram('cont_attribute_value_histogram', attribute_value) - embedding = snt.Sequential([ - snt.nets.MLP([self._attr_embedding_dim] * 3, activate_final=True, use_dropout=True), - snt.LayerNorm(), - ])(tf.cast(attribute_value, dtype=tf.float32)) - tf.summary.histogram('cont_embedding_histogram', embedding) - return embedding - - -class CategoricalAttribute(Attribute): - def __init__(self, num_categories, attr_embedding_dim, name='CategoricalAttributeEmbedder'): - super(CategoricalAttribute, self).__init__(attr_embedding_dim, name=name) - - self._num_categories = num_categories - - def _build(self, attribute_value): - int_attribute_value = tf.cast(attribute_value, dtype=tf.int32) - tf.summary.histogram('cat_attribute_value_histogram', int_attribute_value) - embedding = snt.Embed(self._num_categories, self._attr_embedding_dim)(int_attribute_value) - tf.summary.histogram('cat_embedding_histogram', embedding) - return tf.squeeze(embedding, axis=1) - - -class BlankAttribute(Attribute): - - def __init__(self, attr_embedding_dim, name='BlankAttributeEmbedder'): - super(BlankAttribute, self).__init__(attr_embedding_dim, name=name) - - def _build(self, attribute_value): - shape = tf.stack([tf.shape(attribute_value)[0], self._attr_embedding_dim]) - - encoded_features = tf.zeros(shape, dtype=tf.float32) - return encoded_features diff --git a/kglib/kgcn_tensorflow/models/attribute_IT.py b/kglib/kgcn_tensorflow/models/attribute_IT.py deleted file mode 100644 index 53c6dc52..00000000 --- a/kglib/kgcn_tensorflow/models/attribute_IT.py +++ /dev/null @@ -1,39 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -from kglib.kgcn_tensorflow.models.attribute import CategoricalAttribute -import tensorflow as tf -import numpy as np - - -class ITCategoricalAttribute(unittest.TestCase): - def test_output_tensorspec(self): - cat = CategoricalAttribute(2, 5) - inp = tf.zeros((3, 1), dtype=tf.float32) - output = cat(inp) - np.testing.assert_array_equal(tf.TensorShape([3, 5]), output.shape) - np.testing.assert_equal(output.dtype, tf.float32) - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_tensorflow/models/attribute_test.py b/kglib/kgcn_tensorflow/models/attribute_test.py deleted file mode 100644 index 2c984173..00000000 --- a/kglib/kgcn_tensorflow/models/attribute_test.py +++ /dev/null @@ -1,67 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -from unittest.mock import Mock, patch - -from kglib.kgcn_tensorflow.models.attribute import CategoricalAttribute -import tensorflow as tf - -from kglib.utils.test.utils import get_call_args - - -class TestCategoricalAttribute(tf.test.TestCase): - - def setUp(self): - self._mock_embed_instance = Mock(return_value=tf.zeros((3, 1, 5), dtype=tf.float32)) - self._mock_embed_class = Mock(return_value=self._mock_embed_instance) - self._patcher = patch('kglib.kgcn_tensorflow.models.attribute.snt.Embed', new=self._mock_embed_class, - spec=True) - self._patcher.start() - - def tearDown(self): - self._patcher.stop() - - def test_embed_invoked_correctly(self): - attr_embedding_dim = 5 - cat = CategoricalAttribute(2, 5) - cat(tf.zeros((3, 1), tf.float32)) - self._mock_embed_class.assert_called_once_with(2, attr_embedding_dim) - - def test_output_is_as_expected(self): - inp = tf.zeros((3, 1), dtype=tf.float32) - expected_output = tf.zeros((3, 5), dtype=tf.float32) - cat = CategoricalAttribute(2, 5) - output = cat(inp) - self.assertAllClose(expected_output, output) - self.assertEqual(expected_output.dtype, output.dtype) - - def test_embed_instance_called_correctly(self): - inp = tf.zeros((3, 1), dtype=tf.float32) - cat = CategoricalAttribute(2, 5) - cat(inp) - self.assertAllClose(get_call_args(self._mock_embed_instance), [[tf.zeros((3, 1), dtype=tf.int32)]]) - self.assertEqual(get_call_args(self._mock_embed_instance)[0][0].dtype, tf.int32) - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_tensorflow/models/core.py b/kglib/kgcn_tensorflow/models/core.py deleted file mode 100644 index a1c309ec..00000000 --- a/kglib/kgcn_tensorflow/models/core.py +++ /dev/null @@ -1,132 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import numpy as np -import sonnet as snt -from graph_nets import modules -from graph_nets import utils_tf -from graph_nets.modules import GraphIndependent - - -def softmax(x): - return np.exp(x) / np.sum(np.exp(x)) - - -def make_mlp_model(latent_size=16, num_layers=2): - """Instantiates a new MLP, followed by LayerNorm. - - The parameters of each new MLP are not shared with others generated by - this function. - - Returns: - A Sonnet module which contains the MLP and LayerNorm. - """ - return snt.Sequential([ - snt.nets.MLP([latent_size] * num_layers, activate_final=True), - snt.LayerNorm() - ]) - - -class MLPGraphIndependent(snt.AbstractModule): - """GraphIndependent with MLP edge, node, and global models.""" - - def __init__(self, name="MLPGraphIndependent"): - super(MLPGraphIndependent, self).__init__(name=name) - with self._enter_variable_scope(): - self._network = GraphIndependent( - edge_model_fn=make_mlp_model, - node_model_fn=make_mlp_model) - - def _build(self, inputs): - return self._network(inputs) - - -class MLPInteractionNetwork(snt.AbstractModule): - """InteractionNetwork with MLP edge, node, and global models.""" - - def __init__(self, name="MLPInteractionNetwork"): - super(MLPInteractionNetwork, self).__init__(name=name) - with self._enter_variable_scope(): - self._network = modules.InteractionNetwork(make_mlp_model, make_mlp_model) - - def _build(self, inputs): - return self._network(inputs) - - -class KGCN(snt.AbstractModule): - """ - A KGCN Neural Network with Message Passing. Implemented as a Sonnet Module. - """ - - def __init__(self, - thing_embedder, - role_embedder, - edge_output_size=3, - node_output_size=3, - latent_size=16, - num_layers=2, - name="KGCN"): - super(KGCN, self).__init__(name=name) - - self._thing_embedder = thing_embedder - self._role_embedder = role_embedder - - self._latent_size = latent_size - self._num_layers = num_layers - - # Transforms the outputs into the appropriate shapes. - if edge_output_size is None: - edge_fn = None - else: - edge_fn = lambda: snt.Linear(edge_output_size, name="edge_output") - if node_output_size is None: - node_fn = None - else: - node_fn = lambda: snt.Linear(node_output_size, name="node_output") - with self._enter_variable_scope(): - self._encoder = self._kg_encoder() - self._core = MLPInteractionNetwork() - self._decoder = MLPGraphIndependent() - self._output_transform = modules.GraphIndependent(edge_fn, node_fn) - - def _edge_model(self): - return snt.Sequential([self._role_embedder, - snt.nets.MLP([self._latent_size] * self._num_layers, activate_final=True), - snt.LayerNorm()]) - - def _node_model(self): - return snt.Sequential([self._thing_embedder, - snt.nets.MLP([self._latent_size] * self._num_layers, activate_final=True), - snt.LayerNorm()]) - - def _kg_encoder(self): - return GraphIndependent(self._edge_model, self._node_model, name='kg_encoder') - - def _build(self, input_op, num_processing_steps): - latent = self._encoder(input_op) - latent0 = latent - output_ops = [] - for _ in range(num_processing_steps): - core_input = utils_tf.concat([latent0, latent], axis=1) - latent = self._core(core_input) - decoded_op = self._decoder(latent) - output_ops.append(self._output_transform(decoded_op)) - return output_ops diff --git a/kglib/kgcn_tensorflow/models/core_IT.py b/kglib/kgcn_tensorflow/models/core_IT.py deleted file mode 100644 index ff11639c..00000000 --- a/kglib/kgcn_tensorflow/models/core_IT.py +++ /dev/null @@ -1,58 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import numpy as np -import tensorflow as tf -from graph_nets.graphs import GraphsTuple - -from kglib.kgcn_tensorflow.models.core import KGCN -from kglib.kgcn_tensorflow.models.embedding import ThingEmbedder, RoleEmbedder - - -class ITKGCN(unittest.TestCase): - - def test_kgcn_runs(self): - tf.enable_eager_execution() - - graph = GraphsTuple(nodes=tf.convert_to_tensor(np.array([[1, 2, 0], [1, 0, 0], [1, 1, 0]], dtype=np.float32)), - edges=tf.convert_to_tensor(np.array([[1, 0, 0], [1, 0, 0]], dtype=np.float32)), - globals=tf.convert_to_tensor(np.array([[0, 0, 0, 0, 0]], dtype=np.float32)), - receivers=tf.convert_to_tensor(np.array([1, 2], dtype=np.int32)), - senders=tf.convert_to_tensor(np.array([0, 1], dtype=np.int32)), - n_node=tf.convert_to_tensor(np.array([3], dtype=np.int32)), - n_edge=tf.convert_to_tensor(np.array([2], dtype=np.int32))) - - thing_embedder = ThingEmbedder(node_types=['a', 'b', 'c'], type_embedding_dim=5, attr_embedding_dim=6, - categorical_attributes={'a': ['a1', 'a2', 'a3'], 'b': ['b1', 'b2', 'b3']}, - continuous_attributes={'c': (0, 1)}) - - role_embedder = RoleEmbedder(num_edge_types=2, type_embedding_dim=5) - - kgcn = KGCN(thing_embedder, role_embedder, edge_output_size=3, node_output_size=3) - - kgcn(graph, 2) - - -if __name__ == "__main__": - tf.enable_eager_execution() - unittest.main() diff --git a/kglib/kgcn_tensorflow/models/embedding.py b/kglib/kgcn_tensorflow/models/embedding.py deleted file mode 100644 index 693ebe08..00000000 --- a/kglib/kgcn_tensorflow/models/embedding.py +++ /dev/null @@ -1,138 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import tensorflow as tf -import sonnet as snt - -from kglib.kgcn_tensorflow.models.attribute import CategoricalAttribute, ContinuousAttribute, BlankAttribute -from kglib.kgcn_tensorflow.models.typewise import TypewiseEncoder - - -class ThingEmbedder(snt.AbstractModule): - def __init__(self, node_types, type_embedding_dim, attr_embedding_dim, categorical_attributes, - continuous_attributes, name="ThingEmbedder"): - super(ThingEmbedder, self).__init__(name=name) - - self._node_types = node_types - self._type_embedding_dim = type_embedding_dim - self._attr_embedding_dim = attr_embedding_dim - - # Create embedders for the different attribute types - self._attr_embedders = dict() - - if categorical_attributes is not None: - self._attr_embedders.update( - construct_categorical_embedders(node_types, attr_embedding_dim, categorical_attributes)) - - if continuous_attributes is not None: - self._attr_embedders.update( - construct_continuous_embedders(node_types, attr_embedding_dim, continuous_attributes)) - - self._attr_embedders.update( - construct_non_attribute_embedders(node_types, attr_embedding_dim, categorical_attributes, - continuous_attributes)) - - def _build(self, features): - return tf.concat([embed_type(features, len(self._node_types), self._type_embedding_dim), - embed_attribute(features, self._attr_embedders, self._attr_embedding_dim)], axis=1) - - -class RoleEmbedder(snt.AbstractModule): - def __init__(self, num_edge_types, type_embedding_dim, name="RoleEmbedder"): - super(RoleEmbedder, self).__init__(name=name) - self._num_edge_types = num_edge_types - self._type_embedding_dim = type_embedding_dim - - def _build(self, features): - return embed_type(features, self._num_edge_types, self._type_embedding_dim) - - -def embed_type(features, num_types, type_embedding_dim): - preexistance_feat = tf.expand_dims(tf.cast(features[:, 0], dtype=tf.float32), axis=1) - type_embedder = snt.Embed(num_types, type_embedding_dim) - norm = snt.LayerNorm() - type_embedding = norm(type_embedder(tf.cast(features[:, 1], tf.int32))) - tf.summary.histogram('type_embedding_histogram', type_embedding) - return tf.concat([preexistance_feat, type_embedding], axis=1) - - -def embed_attribute(features, attr_encoders, attr_embedding_dim): - typewise_attribute_encoder = TypewiseEncoder(attr_encoders, attr_embedding_dim) - attr_embedding = typewise_attribute_encoder(features[:, 1:]) - tf.summary.histogram('attribute_embedding_histogram', attr_embedding) - return attr_embedding - - -def construct_categorical_embedders(node_types, attr_embedding_dim, categorical_attributes): - attr_embedders = dict() - - # Construct attribute embedders - for attribute_type, categories in categorical_attributes.items(): - - attr_typ_index = node_types.index(attribute_type) - - def make_embedder(): - return CategoricalAttribute(len(categories), attr_embedding_dim, - name=attribute_type + '_cat_embedder') - - # Record the embedder, and the index of the type that it should encode - attr_embedders[make_embedder] = [attr_typ_index] - - return attr_embedders - - -def construct_continuous_embedders(node_types, attr_embedding_dim, continuous_attributes): - attr_embedders = dict() - - # Construct attribute embedders - for attribute_type in continuous_attributes.keys(): - - attr_typ_index = node_types.index(attribute_type) - - def make_embedder(): - return ContinuousAttribute(attr_embedding_dim, name=attribute_type + '_cat_embedder') - - # Record the embedder, and the index of the type that it should encode - attr_embedders[make_embedder] = [attr_typ_index] - - return attr_embedders - - -def construct_non_attribute_embedders(node_types, attr_embedding_dim, categorical_attributes, continuous_attributes): - - attribute_names = list(categorical_attributes.keys()) - attribute_names.extend(list(continuous_attributes.keys())) - - non_attribute_nodes = [] - for i, type in enumerate(node_types): - if type not in attribute_names: - non_attribute_nodes.append(i) - - # All entities and relations (non-attributes) also need an embedder with matching output dimension, which does - # nothing. This is provided as a list of their indices - def make_blank_embedder(): - return BlankAttribute(attr_embedding_dim) - - attr_embedders = dict() - - if len(non_attribute_nodes) > 0: - attr_embedders[make_blank_embedder] = non_attribute_nodes - return attr_embedders diff --git a/kglib/kgcn_tensorflow/models/embedding_IT.py b/kglib/kgcn_tensorflow/models/embedding_IT.py deleted file mode 100644 index b16bc5ac..00000000 --- a/kglib/kgcn_tensorflow/models/embedding_IT.py +++ /dev/null @@ -1,80 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -from kglib.kgcn_tensorflow.models.embedding import construct_categorical_embedders, construct_continuous_embedders, \ - construct_non_attribute_embedders - - -def construct_embedders(node_types, attr_embedding_dim, categorical_attributes, continuous_attributes): - attr_embedders = dict() - - if categorical_attributes is not None: - attr_embedders.update(construct_categorical_embedders(node_types, attr_embedding_dim, categorical_attributes)) - - if continuous_attributes is not None: - attr_embedders.update(construct_continuous_embedders(node_types, attr_embedding_dim, continuous_attributes)) - - attr_embedders.update(construct_non_attribute_embedders(node_types, attr_embedding_dim, categorical_attributes, - continuous_attributes)) - return attr_embedders - - -class TestConstructingEmbedders(unittest.TestCase): - - def test_all_types_encoded(self): - node_types = ['a', 'b', 'c'] - attr_embedding_dim = 5 - categorical_attributes = {'a': ['option1', 'option2']} - continuous_attributes = {'b': (0, 1)} - - attr_embedders = construct_embedders(node_types, attr_embedding_dim, categorical_attributes, - continuous_attributes) - all_types = [l for el in list(attr_embedders.values()) for l in el] - - expected_types = [0, 1, 2] - - self.assertListEqual(expected_types, all_types) - - def test_multiple_categorical_embedders(self): - node_types = ['a', 'b', 'c'] - attr_embedding_dim = 5 - categorical_attributes = {'a': ['option1', 'option2'], 'c': ['option3', 'option4']} - continuous_attributes = {'b': (0, 1)} - - attr_embedders = construct_embedders(node_types, attr_embedding_dim, categorical_attributes, - continuous_attributes) - - all_types = [l for el in list(attr_embedders.values()) for l in el] - all_types.sort() - - expected_types = [0, 1, 2] - print(attr_embedders) - - self.assertListEqual(expected_types, all_types) - - for types in attr_embedders.values(): - self.assertNotEqual(types, []) - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_tensorflow/models/embedding_test.py b/kglib/kgcn_tensorflow/models/embedding_test.py deleted file mode 100644 index f7e88b7c..00000000 --- a/kglib/kgcn_tensorflow/models/embedding_test.py +++ /dev/null @@ -1,70 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import numpy as np -import tensorflow as tf -from unittest.mock import Mock -from unittest.mock import patch -from kglib.kgcn_tensorflow.models.embedding import embed_type, embed_attribute -from kglib.utils.test.utils import get_call_args - - -class TestTypeEmbedding(unittest.TestCase): - def setUp(self): - tf.enable_eager_execution() - - def test_embedding_output_shape_as_expected(self): - features = np.array([[1, 0, 0.7], [1, 2, 0.7], [0, 1, 0.5]], dtype=np.float32) - type_embedding_dim = 5 - output = embed_type(features, 3, type_embedding_dim) - - np.testing.assert_array_equal(np.array([3, 6]), output.shape) - - -class TestAttributeEmbedding(unittest.TestCase): - def setUp(self): - tf.enable_eager_execution() - - def test_embedding_is_typewise(self): - features = np.array([[1, 0, 0.7], [1, 2, 0.7], [0, 1, 0.5]]) - - mock_instance = Mock(return_value=tf.convert_to_tensor(np.array([[1, 0.7], [1, 0.7], [0, 0.5]]))) - mock = Mock(return_value=mock_instance) - patcher = patch('kglib.kgcn_tensorflow.models.embedding.TypewiseEncoder', spec=True, new=mock) - mock_class = patcher.start() - - attr_encoders = Mock() - attr_embedding_dim = Mock() - - embed_attribute(features, attr_encoders, attr_embedding_dim) # Function under test - - mock_class.assert_called_once_with(attr_encoders, attr_embedding_dim) - call_args = get_call_args(mock_instance) - - np.testing.assert_array_equal([[np.array([[0, 0.7], [2, 0.7], [1, 0.5]])]], call_args) - - patcher.stop() - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_tensorflow/models/typewise.py b/kglib/kgcn_tensorflow/models/typewise.py deleted file mode 100644 index 641dd9c0..00000000 --- a/kglib/kgcn_tensorflow/models/typewise.py +++ /dev/null @@ -1,84 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import sonnet as snt -import tensorflow as tf - - -class TypewiseEncoder(snt.AbstractModule): - """ - Orchestrates encoding elements according to their types. Defers encoding of each feature to the appropriate encoder - for the type of that feature. Assumes that the type is given categorically as an integer value in the 0th position - of the provided features Tensor. - """ - def __init__(self, encoders_for_types, feature_length, name="typewise_encoder"): - """ - Args: - encoders_for_types: Dict - keys: encoders; values: a list of type categories the encoder should be used for - feature_length: The length of features to output for matrix initialisation - name: The name for this Module - """ - super(TypewiseEncoder, self).__init__(name=name) - - types_considered = [] - for a in encoders_for_types.values(): - types_considered.extend(a) - types_considered.sort() - - expected_types = list(range(max(types_considered) + 1)) - - if types_considered != expected_types: - raise ValueError( - f'Encoder categories are inconsistent. Expected {expected_types}, but got {types_considered}') - - self._feature_length = feature_length - self._encoders_for_types = encoders_for_types - - def _build(self, features): - - tf.summary.histogram('typewise_encoder_features_histogram', features) - - shape = tf.stack([tf.shape(features)[0], self._feature_length]) - - encoded_features = tf.zeros(shape, dtype=tf.float32) - - for encoder, types in self._encoders_for_types.items(): - - feat_types = tf.cast(features[:, 0], tf.int32) # The types for each feature, as integers - - # Expand dimensions ready for element-wise equality comparison - exp_types = tf.expand_dims(types, axis=0) - exp_feat_types = tf.expand_dims(feat_types, axis=1) - - elementwise_equality = tf.equal(exp_feat_types, exp_types) - - # Use this encoder when the feat_type matches any of the types - applicable_types_mask = tf.reduce_any(elementwise_equality, axis=1) - indices_to_encode = tf.where(applicable_types_mask) - - feats_to_encode = tf.squeeze(tf.gather(features[:, 1:], indices_to_encode), axis=1) - encoded_feats = encoder()(feats_to_encode) - - encoded_features += tf.scatter_nd(tf.cast(indices_to_encode, dtype=tf.int32), encoded_feats, shape) - - tf.summary.histogram('typewise_encoder_encoded_features_histogram', encoded_features) - - return encoded_features diff --git a/kglib/kgcn_tensorflow/models/typewise_IT.py b/kglib/kgcn_tensorflow/models/typewise_IT.py deleted file mode 100644 index a37247c6..00000000 --- a/kglib/kgcn_tensorflow/models/typewise_IT.py +++ /dev/null @@ -1,55 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import numpy as np -import tensorflow as tf -from tensorflow.python.framework.ops import EagerTensor - -from kglib.kgcn_tensorflow.models.typewise import TypewiseEncoder - - -class ITTypewiseEncoder(unittest.TestCase): - - def setUp(self): - tf.enable_eager_execution() - - def test_with_tensors(self): - tf.reset_default_graph() - tf.set_random_seed(1) - - things = tf.convert_to_tensor(np.array([[0, 0], [1, 0], [2, 0.5673]], dtype=np.float32)) - - entity_relation = lambda x: x - continuous_attribute = lambda x: x - - encoders_for_types = {lambda: entity_relation: [0, 1], lambda: continuous_attribute: [2]} - - tm = TypewiseEncoder(encoders_for_types, 1) - encoded_things = tm(things) # The function under test - - # Check that tensorflow was actually used - self.assertEqual(EagerTensor, type(encoded_things)) - - -if __name__ == '__main__': - unittest.main() diff --git a/kglib/kgcn_tensorflow/models/typewise_test.py b/kglib/kgcn_tensorflow/models/typewise_test.py deleted file mode 100644 index ee973deb..00000000 --- a/kglib/kgcn_tensorflow/models/typewise_test.py +++ /dev/null @@ -1,82 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import numpy as np -import tensorflow as tf -from unittest.mock import Mock - -from kglib.utils.test.utils import get_call_args -from kglib.kgcn_tensorflow.models.typewise import TypewiseEncoder - - -class TestTypewiseEncoder(unittest.TestCase): - def setUp(self): - tf.enable_eager_execution() - - def test_types_encoded_by_expected_functions(self): - things = np.array([[0, 0], [1, 0], [2, 0.5673]], dtype=np.float32) - - mock_entity_relation_encoder = Mock(return_value=np.array([[0, 0, 0], [0, 0, 0]], dtype=np.float32)) - - mock_attribute_encoder = Mock(return_value=np.array([[0.9527, 0.2367, 0.7582]], dtype=np.float32)) - - encoders_for_types = {lambda: mock_entity_relation_encoder: [0, 1], lambda: mock_attribute_encoder: [2]} - - tm = TypewiseEncoder(encoders_for_types, 3) - encoding = tm(things) # The function under test - - np.testing.assert_array_equal([[np.array([[0], [0]], dtype=np.float32)]], - get_call_args(mock_entity_relation_encoder)) - - np.testing.assert_array_equal([[np.array([[0.5673]], dtype=np.float32)]], get_call_args(mock_attribute_encoder)) - - expected_encoding = np.array([[0, 0, 0], [0, 0, 0], [0.9527, 0.2367, 0.7582]], dtype=np.float32) - np.testing.assert_array_equal(expected_encoding, encoding.numpy()) - - def test_basic_encoding(self): - things = np.array([[0], [1], [2]], dtype=np.float32) - - mock_entity_relation_encoder = Mock(return_value=np.array([[0.1, 0, 0], [0.1, 0, 0], [0.1, 0, 0]], dtype=np.float32)) - - encoders_for_types = {lambda: mock_entity_relation_encoder: [0, 1, 2]} - - tm = TypewiseEncoder(encoders_for_types, 3) - encoding = tm(things) # The function under test - - expected_encoding = np.array([[0.1, 0, 0], [0.1, 0, 0], [0.1, 0, 0]], dtype=np.float32) - np.testing.assert_array_equal(expected_encoding, encoding.numpy()) - - def test_encoders_do_not_fulfil_classes(self): - mock_entity_relation_encoder = Mock() - - encoders_for_types = {lambda: mock_entity_relation_encoder: [0, 2]} - - with self.assertRaises(ValueError) as context: - TypewiseEncoder(encoders_for_types, 3) - - self.assertEqual('Encoder categories are inconsistent. Expected [0, 1, 2], but got [0, 2]', - str(context.exception)) - - -if __name__ == '__main__': - unittest.main() diff --git a/kglib/kgcn_tensorflow/pipeline/pipeline.py b/kglib/kgcn_tensorflow/pipeline/pipeline.py deleted file mode 100644 index e7261be0..00000000 --- a/kglib/kgcn_tensorflow/pipeline/pipeline.py +++ /dev/null @@ -1,112 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import networkx as nx -import numpy as np -from graph_nets.utils_np import graphs_tuple_to_networkxs - -from kglib.kgcn_tensorflow.learn.learn import KGCNLearner -from kglib.kgcn_tensorflow.models.core import softmax, KGCN -from kglib.kgcn_tensorflow.models.embedding import ThingEmbedder, RoleEmbedder -from kglib.kgcn_tensorflow.plot.plotting import plot_across_training, plot_predictions - -from kglib.kgcn_data_loader.encoding.standard_encode import encode_types, create_input_graph, create_target_graph, encode_values -from kglib.kgcn_data_loader.utils import apply_logits_to_graphs, duplicate_edges_in_reverse -from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_data_iterator, \ - multidigraph_edge_data_iterator - -def pipeline(graphs, - tr_ge_split, - node_types, - edge_types, - num_processing_steps_tr=10, - num_processing_steps_ge=10, - num_training_iterations=10000, - continuous_attributes=None, - categorical_attributes=None, - type_embedding_dim=5, - attr_embedding_dim=6, - edge_output_size=3, - node_output_size=3, - output_dir=None): - - ############################################################ - # Manipulate the graph data - ############################################################ - - # Encode attribute values - graphs = [encode_values(graph, categorical_attributes, continuous_attributes) for graph in graphs] - - indexed_graphs = [nx.convert_node_labels_to_integers(graph, label_attribute='concept') for graph in graphs] - graphs = [duplicate_edges_in_reverse(graph) for graph in indexed_graphs] - - graphs = [encode_types(graph, multidigraph_node_data_iterator, node_types) for graph in graphs] - graphs = [encode_types(graph, multidigraph_edge_data_iterator, edge_types) for graph in graphs] - - input_graphs = [create_input_graph(graph) for graph in graphs] - target_graphs = [create_target_graph(graph) for graph in graphs] - - tr_input_graphs = input_graphs[:tr_ge_split] - tr_target_graphs = target_graphs[:tr_ge_split] - ge_input_graphs = input_graphs[tr_ge_split:] - ge_target_graphs = target_graphs[tr_ge_split:] - - ############################################################ - # Build and run the KGCN - ############################################################ - - thing_embedder = ThingEmbedder(node_types, type_embedding_dim, attr_embedding_dim, categorical_attributes, - continuous_attributes) - - role_embedder = RoleEmbedder(len(edge_types), type_embedding_dim) - - kgcn = KGCN(thing_embedder, - role_embedder, - edge_output_size=edge_output_size, - node_output_size=node_output_size) - - learner = KGCNLearner(kgcn, - num_processing_steps_tr=num_processing_steps_tr, - num_processing_steps_ge=num_processing_steps_ge) - - train_values, test_values, tr_info = learner(tr_input_graphs, - tr_target_graphs, - ge_input_graphs, - ge_target_graphs, - num_training_iterations=num_training_iterations, - log_dir=output_dir) - - plot_across_training(*tr_info, output_file=f'{output_dir}learning.png') - plot_predictions(graphs[tr_ge_split:], test_values, num_processing_steps_ge, output_file=f'{output_dir}graph.png') - - logit_graphs = graphs_tuple_to_networkxs(test_values["outputs"][-1]) - - indexed_ge_graphs = indexed_graphs[tr_ge_split:] - ge_graphs = [apply_logits_to_graphs(graph, logit_graph) for graph, logit_graph in - zip(indexed_ge_graphs, logit_graphs)] - - for ge_graph in ge_graphs: - for data in multidigraph_data_iterator(ge_graph): - data['probabilities'] = softmax(data['logits']) - data['prediction'] = int(np.argmax(data['probabilities'])) - - _, _, _, _, _, solveds_tr, solveds_ge = tr_info - return ge_graphs, solveds_tr, solveds_ge diff --git a/kglib/kgcn_tensorflow/plot/draw.py b/kglib/kgcn_tensorflow/plot/draw.py deleted file mode 100644 index 472ae918..00000000 --- a/kglib/kgcn_tensorflow/plot/draw.py +++ /dev/null @@ -1,290 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import networkx.utils - - -def draw_networkx_labels(G, pos, - labels=None, - font_size=12, - font_color=None, - font_family='sans-serif', - font_weight='normal', - alpha=None, - bbox=None, - ax=None, - **kwds): - """Draw node labels on the graph G. - - Parameters - ---------- - G : graph - A networkx graph - - pos : dictionary - A dictionary with nodes as keys and positions as values. - Positions should be sequences of length 2. - - labels : dictionary, optional (default=None) - Node labels in a dictionary keyed by node of text labels - Node-keys in labels should appear as keys in `pos`. - If needed use: `{n:lab for n,lab in labels.items() if n in pos}` - - font_size : int - Font size for text labels (default=12) - - font_color : string - Font color string (default='k' black) - - font_family : string - Font family (default='sans-serif') - - font_weight : string - Font weight (default='normal') - - alpha : float - The text transparency (default=1.0) - - ax : Matplotlib Axes object, optional - Draw the graph in the specified Matplotlib axes. - - Returns - ------- - dict - `dict` of labels keyed on the nodes - - Examples - -------- - >>> G = nx.dodecahedral_graph() - >>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G)) - - Also see the NetworkX drawing examples at - https://networkx.github.io/documentation/latest/auto_examples/index.html - - See Also - -------- - draw() - draw_networkx() - draw_networkx_nodes() - draw_networkx_edges() - draw_networkx_edge_labels() - """ - try: - import matplotlib.pyplot as plt - import matplotlib.cbook as cb - except ImportError: - raise ImportError("Matplotlib required for draw()") - except RuntimeError: - print("Matplotlib unable to open display") - raise - - if ax is None: - ax = plt.gca() - - if labels is None: - labels = dict((n, n) for n in G.nodes()) - - # set optional alignment - horizontalalignment = kwds.get('horizontalalignment', 'center') - verticalalignment = kwds.get('verticalalignment', 'center') - - text_items = {} # there is no text collection so we'll fake one - for n, label in labels.items(): - (x, y) = pos[n] - if not networkx.utils.is_string_like(label): - label = str(label) # this makes "1" and 1 labeled the same - t = ax.text(x, y, - label, - size=font_size, - color=font_color[n], - family=font_family, - weight=font_weight, - alpha=alpha[n], - horizontalalignment=horizontalalignment, - verticalalignment=verticalalignment, - transform=ax.transData, - bbox=bbox, - clip_on=True, - ) - text_items[n] = t - - plt.tick_params( - axis='both', - which='both', - bottom=False, - left=False, - labelbottom=False, - labelleft=False) - - return text_items - - -def draw_networkx_edge_labels(G, pos, - edge_labels=None, - label_pos=0.5, - font_size=10, - font_color=None, - font_family='sans-serif', - font_weight='normal', - alpha=None, - bbox=None, - ax=None, - rotate=True, - **kwds): - """Draw edge labels. - - Parameters - ---------- - G : graph - A networkx graph - - pos : dictionary - A dictionary with nodes as keys and positions as values. - Positions should be sequences of length 2. - - ax : Matplotlib Axes object, optional - Draw the graph in the specified Matplotlib axes. - - alpha : float - The text transparency (default=1.0) - - edge_labels : dictionary - Edge labels in a dictionary keyed by edge two-tuple of text - labels (default=None). Only labels for the keys in the dictionary - are drawn. - - label_pos : float - Position of edge label along edge (0=head, 0.5=center, 1=tail) - - font_size : int - Font size for text labels (default=12) - - font_color : string - Font color string (default='k' black) - - font_weight : string - Font weight (default='normal') - - font_family : string - Font family (default='sans-serif') - - bbox : Matplotlib bbox - Specify text box shape and colors. - - clip_on : bool - Turn on clipping at axis boundaries (default=True) - - Returns - ------- - dict - `dict` of labels keyed on the edges - - Examples - -------- - >>> G = nx.dodecahedral_graph() - >>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G)) - - Also see the NetworkX drawing examples at - https://networkx.github.io/documentation/latest/auto_examples/index.html - - See Also - -------- - draw() - draw_networkx() - draw_networkx_nodes() - draw_networkx_edges() - draw_networkx_labels() - """ - try: - import matplotlib.pyplot as plt - import numpy as np - except ImportError: - raise ImportError("Matplotlib required for draw()") - except RuntimeError: - print("Matplotlib unable to open display") - raise - - if ax is None: - ax = plt.gca() - if edge_labels is None: - labels = {(u, v): d for u, v, d in G.edges(data=True)} - else: - labels = edge_labels - text_items = {} - for (n1, n2), label in labels.items(): - (x1, y1) = pos[n1] - (x2, y2) = pos[n2] - (x, y) = (x1 * label_pos + x2 * (1.0 - label_pos), - y1 * label_pos + y2 * (1.0 - label_pos)) - - if rotate: - # in degrees - angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360 - # make label orientation "right-side-up" - if angle > 90: - angle -= 180 - if angle < - 90: - angle += 180 - # transform data coordinate angle to screen coordinate angle - xy = np.array((x, y)) - trans_angle = ax.transData.transform_angles(np.array((angle,)), - xy.reshape((1, 2)))[0] - else: - trans_angle = 0.0 - # use default box of white with white border - if bbox is None: - bbox = dict(boxstyle='round', - ec=(1.0, 1.0, 1.0), - fc=(1.0, 1.0, 1.0), - ) - if not networkx.utils.is_string_like(label): - label = str(label) # this makes "1" and 1 labeled the same - - # set optional alignment - horizontalalignment = kwds.get('horizontalalignment', 'center') - verticalalignment = kwds.get('verticalalignment', 'center') - - t = ax.text(x, y, - label, - size=font_size, - color=font_color[(n1, n2)], - family=font_family, - weight=font_weight, - alpha=alpha[(n1, n2)], - horizontalalignment=horizontalalignment, - verticalalignment=verticalalignment, - rotation=trans_angle, - transform=ax.transData, - bbox=bbox, - zorder=1, - clip_on=True, - ) - text_items[(n1, n2)] = t - - plt.tick_params( - axis='both', - which='both', - bottom=False, - left=False, - labelbottom=False, - labelleft=False) - - return text_items diff --git a/kglib/kgcn_tensorflow/plot/plotting.py b/kglib/kgcn_tensorflow/plot/plotting.py deleted file mode 100644 index 0a233f3a..00000000 --- a/kglib/kgcn_tensorflow/plot/plotting.py +++ /dev/null @@ -1,289 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import math - -import graph_nets.utils_np as utils_np -import matplotlib.pyplot as plt -import networkx as nx -import numpy as np - -import kglib.kgcn_tensorflow.plot.draw as custom_nx - - -def plot_across_training(logged_iterations, losses_tr, losses_ge, corrects_tr, corrects_ge, solveds_tr, solveds_ge, - output_file='./learning.png'): - # Plot results curves. - fig = plt.figure(1, figsize=(18, 3)) - fig.clf() - x = np.array(logged_iterations) - # Loss. - y_tr = losses_tr - y_ge = losses_ge - ax = fig.add_subplot(1, 3, 1) - ax.plot(x, y_tr, "k", label="Training") - ax.plot(x, y_ge, "k--", label="Test/generalization") - ax.set_title("Loss across training") - ax.set_xlabel("Training iteration") - ax.set_ylabel("Loss (binary cross-entropy)") - ax.legend() - # Correct. - y_tr = corrects_tr - y_ge = corrects_ge - ax = fig.add_subplot(1, 3, 2) - ax.plot(x, y_tr, "k", label="Training") - ax.plot(x, y_ge, "k--", label="Test/generalization") - ax.set_title("Fraction correct across training") - ax.set_xlabel("Training iteration") - ax.set_ylabel("Fraction nodes/edges correct") - # Solved. - y_tr = solveds_tr - y_ge = solveds_ge - ax = fig.add_subplot(1, 3, 3) - ax.plot(x, y_tr, "k", label="Training") - ax.plot(x, y_ge, "k--", label="Test/generalization") - ax.set_title("Fraction solved across training") - ax.set_xlabel("Training iteration") - ax.set_ylabel("Fraction examples solved") - - plt.savefig(output_file, bbox_inches='tight') - - -def plot_predictions(raw_graphs, test_values, num_processing_steps_ge, solution_weights=(-0.5, 0.5, 0.5), - output_file='./graph.png'): - - # # Plot graphs and results after each processing step. - # The white node is the start, and the black is the end. Other nodes are colored - # from red to purple to blue, where red means the model is confident the node is - # off the shortest path, blue means the model is confident the node is on the - # shortest path, and purplish colors mean the model isn't sure. - - max_graphs_to_plot = 10 - num_steps_to_plot = 3 - node_size = 120 - - num_graphs = len(raw_graphs) - targets = utils_np.graphs_tuple_to_data_dicts(test_values["target"]) - step_indices = np.floor( - np.linspace(0, num_processing_steps_ge - 1, - num_steps_to_plot)).astype(int).tolist() - outputs = list( - zip(*(utils_np.graphs_tuple_to_data_dicts(test_values["outputs"][i]) - for i in step_indices))) - h = min(num_graphs, max_graphs_to_plot) - w = num_steps_to_plot + 2 - fig = plt.figure(101, figsize=(18, h * 3)) - fig.clf() - for j, (graph, target, output) in enumerate(zip(raw_graphs, targets, outputs)): - if j >= h: - break - for s, r, d in graph.edges(data=True): - d['weight'] = solution_weights[d['solution']] # Looks good with high k - pos = nx.spring_layout(graph, k=3 / math.sqrt(graph.number_of_nodes()), seed=1, weight='weight', iterations=50) - # pos = nx.circular_layout(graph, scale=2) - ground_truth_node_prob = target["nodes"][:, -1] - ground_truth_edge_prob = target["edges"][:, -1] - - non_preexist_node_mask = mask_preexists(target["nodes"]) - non_preexist_edge_mask = mask_preexists(target["edges"]) - - # Ground truth. - iax = j * (2 + num_steps_to_plot) + 1 - ax = draw_subplot(graph, fig, pos, node_size, h, w, iax, ground_truth_node_prob, ground_truth_edge_prob, True) - - # Format the ground truth plot axes - ax.set_axis_on() - ax.set_xticks([]) - ax.set_yticks([]) - ax.spines['bottom'].set_color('blue') - ax.spines['top'].set_color('blue') - ax.spines['right'].set_color('blue') - ax.spines['left'].set_color('blue') - ax.grid(None) - ax.set_title("Ground truth") - - # Prediction. - for k, outp in enumerate(output): - iax = j * (2 + num_steps_to_plot) + 2 + k - node_prob = softmax_prob_last_dim(outp["nodes"]) * non_preexist_node_mask - edge_prob = softmax_prob_last_dim(outp["edges"]) * non_preexist_edge_mask - ax = draw_subplot(graph, fig, pos, node_size, h, w, iax, node_prob, edge_prob, False) - ax.set_title("Model-predicted\nStep {:02d} / {:02d}".format( - step_indices[k] + 1, step_indices[-1] + 1)) - - # Class Winners - # Displays whether the class represented by the last dimension was the winner - node_prob = last_dim_was_class_winner(output[-1]["nodes"]) * non_preexist_node_mask - edge_prob = last_dim_was_class_winner(output[-1]["edges"]) * non_preexist_edge_mask - - iax = j * (2 + num_steps_to_plot) + 2 + len(output) - ax = draw_subplot(graph, fig, pos, node_size, h, w, iax, node_prob, edge_prob, False) - - # Format the class winners plot axes - ax.set_axis_on() - ax.set_xticks([]) - ax.set_yticks([]) - ax.spines['bottom'].set_color('green') - ax.spines['top'].set_color('green') - ax.spines['right'].set_color('green') - ax.spines['left'].set_color('green') - ax.grid(None) - ax.set_title("Model-predicted winners") - - plt.savefig(output_file, bbox_inches='tight') - - -def mask_preexists(arr): - return (arr[:, 0] == 0) * 1 - - -def softmax_prob_last_dim(x): - e = np.exp(x) - return e[:, -1] / np.sum(e, axis=-1) - - -def last_dim_was_class_winner(x): - return (np.argmax(x, axis=-1) == 2) * 1 - - -def element_color(gt_plot, probability, element_props): - """ - Determine the color values to use for a node/edge and its label - gt plot: - blue for existing elements, green for those to infer, red candidates - - output plot: - blue for existing elements, green for those to infer, red for candidates, all with transparency - """ - - existing = 0 - candidate = 1 - to_infer = 2 - - solution = element_props.get('solution') - - color_config = { - to_infer: {'color': [0.0, 1.0, 0.0], 'gt_opacity': 1.0}, - candidate: {'color': [1.0, 0.0, 0.0], 'gt_opacity': 1.0}, - existing: {'color': [0.0, 0.0, 1.0], 'gt_opacity': 0.2} - } - - chosen_config = color_config[solution] - - if gt_plot: - opacity = chosen_config['gt_opacity'] - else: - opacity = probability - - label = np.array([0.0, 0.0, 0.0] + [opacity]) - color = np.array(chosen_config['color'] + [opacity]) - - return dict(element=color, label=label) - - -def draw_subplot(graph, fig, pos, node_size, h, w, iax, node_prob, edge_prob, gt_plot): - ax = fig.add_subplot(h, w, iax) - node_color = {} - node_label_color = {} - edge_color = {} - edge_label_color = {} - - for i, (n, props) in enumerate(graph.nodes(data=True)): - colors = element_color(gt_plot, node_prob[n], props) - - node_color[n] = colors['element'] - node_label_color[n] = colors['label'] - - for n, (sender, receiver, props) in enumerate(graph.edges(data=True)): - colors = element_color(gt_plot, edge_prob[n], props) - - edge_color[(sender, receiver)] = colors['element'] - edge_label_color[(sender, receiver)] = colors['label'] - - draw_graph(graph, pos, ax, node_size=node_size, node_color=node_color, node_label_color=node_label_color, - edge_color=edge_color, edge_label_color=edge_label_color) - return ax - - -def draw_graph(graph, - pos, - ax, - node_size=200, - node_color=(0.4, 0.8, 0.4), - node_label_color=None, - edge_color=(0.0, 0.0, 0.0), - edge_label_color=None, - node_linewidth=1.0, - edge_width=1.0, - font_size=6): - - def _draw(draw_function, zorder=None, **kwargs): - # draw_kwargs = self._make_draw_kwargs(**kwargs) - _base_draw_kwargs = dict(G=graph, pos=pos, ax=ax) - kwargs.update(_base_draw_kwargs) - collection = draw_function(**kwargs) - if collection is not None and zorder is not None: - try: - # This is for compatibility with older matplotlib. - collection.set_zorder(zorder) - except AttributeError: - # This is for compatibility with newer matplotlib. - collection[0].set_zorder(zorder) - return collection - - # Plot nodes. - c = [node_color[n] for n in graph.nodes()] - _draw(nx.draw_networkx_nodes, - node_size=node_size, - node_color=c, - linewidths=node_linewidth, - alpha=[node_color[n][-1] for n in graph.nodes()], - zorder=-10) - - # Plot edges. - e = [edge_color[(s, r)] for s, r, k in graph.edges] - _draw(nx.draw_networkx_edges, - edgelist=graph.edges, - width=edge_width, - zorder=-20, - edge_color=e - ) - - bbox_props = dict(boxstyle="square,pad=0.0", fc="none", ec="none", lw=1) - labels_dict = {node_id: graph.nodes[node_id]['type'] for node_id in graph.nodes} - edge_labels_dict = {(edge_id[0], edge_id[1]): graph.edges[edge_id]['type'] for edge_id in graph.edges} - - custom_nx.draw_networkx_labels(graph, - pos, - labels=labels_dict, - font_size=font_size, - font_color=node_label_color, - alpha=[node_label_color[n][-1] for n in graph.nodes()]) - - custom_nx.draw_networkx_edge_labels(graph, - pos, - edge_labels=edge_labels_dict, - font_size=font_size, - # font_color=np.array([0.0, 0.5, 0.0, 0.1]), - font_color=edge_label_color, - # alpha=0.2, - alpha={n: edge_label_color[n][-1] for n in graph.edges()}, - bbox=bbox_props) diff --git a/kglib/kgcn_tensorflow/plot/plotting_test.py b/kglib/kgcn_tensorflow/plot/plotting_test.py deleted file mode 100644 index c3b8860c..00000000 --- a/kglib/kgcn_tensorflow/plot/plotting_test.py +++ /dev/null @@ -1,84 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import datetime -import os -import unittest - -import networkx as nx -import numpy as np -from graph_nets.graphs import GraphsTuple - -from kglib.kgcn_tensorflow.plot.plotting import plot_predictions - - -class TestPlotPredictions(unittest.TestCase): - def test_plot_is_created(self): - num_processing_steps_ge = 6 - - graph = nx.MultiDiGraph(name=0) - - existing = dict(solution=0) - to_infer = dict(solution=2) - candidate = dict(solution=1) - - # people - graph.add_node(0, type='person', **existing) - graph.add_node(1, type='person', **candidate) - - # parentships - graph.add_node(2, type='parentship', **to_infer) - graph.add_edge(2, 0, type='parent', **to_infer) - graph.add_edge(2, 1, type='child', **candidate) - - graph_tuple_target = GraphsTuple(nodes=np.array([[1., 0., 0.], - [0., 1., 0.], - [0., 0., 1.]]), - edges=np.array([[0., 0., 1.], - [0., 1., 0.]]), - receivers=np.array([1, 2], dtype=np.int32), - senders=np.array([0, 1], dtype=np.int32), - globals=np.array([[0., 0., 0., 0., 0.]], dtype=np.float32), - n_node=np.array([3], dtype=np.int32), - n_edge=np.array([2], dtype=np.int32)) - - graph_tuple_output = GraphsTuple(nodes=np.array([[1., 0., 0.], - [1., 1., 0.], - [1., 0., 1.]]), - edges=np.array([[1., 0., 0.], - [1., 1., 0.]]), - receivers=np.array([1, 2], dtype=np.int32), - senders=np.array([0, 1], dtype=np.int32), - globals=np.array([[0., 0., 0., 0., 0.]], dtype=np.float32), - n_node=np.array([3], dtype=np.int32), - n_edge=np.array([2], dtype=np.int32)) - - test_values = {"target": graph_tuple_target, "outputs": [graph_tuple_output for _ in range(6)]} - - filename = f'./graph_{datetime.datetime.now()}.png' - - plot_predictions([graph], test_values, num_processing_steps_ge, output_file=filename) - - self.assertTrue(os.path.isfile(filename)) - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/tests/deployment/kgcn/diagnosis.py b/kglib/tests/deployment/kgcn/diagnosis.py index f4d417f6..481c71b3 100644 --- a/kglib/tests/deployment/kgcn/diagnosis.py +++ b/kglib/tests/deployment/kgcn/diagnosis.py @@ -21,13 +21,13 @@ import unittest -from kglib.kgcn_tensorflow.examples.diagnosis.diagnosis import diagnosis_example +from kglib.examples.diagnosis.diagnosis import diagnosis_example class TestDiagnosisExample(unittest.TestCase): def test_example_runs_without_exception(self): - diagnosis_example("./typedb-all-linux", + diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", num_graphs=6, num_processing_steps_tr=2, num_processing_steps_ge=2, diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD new file mode 100644 index 00000000..0d6720cf --- /dev/null +++ b/kglib/tests/end_to_end/BUILD @@ -0,0 +1,70 @@ +# +# Copyright (C) 2021 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +load("@rules_python//python:defs.bzl", "py_test") +load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") + +py_test( + name = "diagnosis", + size = "enormous", + srcs = [ + "kgcn/diagnosis.py" + ], + deps = [ + "//kglib/examples/diagnosis", + "//kglib/utils/typedb/test", + ], + data = [ + "//kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files", + "@vaticle_typedb_artifact_linux//file" + ], + args = [ + "$(locations //kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files)", + "$(location @vaticle_typedb_artifact_linux//file)", + ], +) + +py_test( + name = "diagnosis-debug", + size = "enormous", + srcs = [ + "kgcn/diagnosis_debug.py" + ], + main = "kgcn/diagnosis_debug.py", + deps = [ + "//kglib/examples/diagnosis", + ], + data = [ + "//kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files", + ], + args = [ + "$(locations //kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files)", + ], +) + +checkstyle_test( + name = "checkstyle", + include = glob([ + "*", + "**/*" + ]), + license_type = "apache", +) diff --git a/kglib/tests/end_to_end/kgcn/diagnosis.py b/kglib/tests/end_to_end/kgcn/diagnosis.py index 771fd879..acaa0e1f 100644 --- a/kglib/tests/end_to_end/kgcn/diagnosis.py +++ b/kglib/tests/end_to_end/kgcn/diagnosis.py @@ -21,7 +21,7 @@ import sys import unittest -from kglib.kgcn_tensorflow.examples.diagnosis.diagnosis import diagnosis_example +from kglib.examples.diagnosis.diagnosis import diagnosis_example from kglib.utils.typedb.test.base import TypeDBServer diff --git a/kglib/tests/end_to_end/kgcn/diagnosis_debug.py b/kglib/tests/end_to_end/kgcn/diagnosis_debug.py index daa18995..8ca0c232 100644 --- a/kglib/tests/end_to_end/kgcn/diagnosis_debug.py +++ b/kglib/tests/end_to_end/kgcn/diagnosis_debug.py @@ -22,7 +22,7 @@ import sys import unittest -from kglib.kgcn_tensorflow.examples.diagnosis.diagnosis import diagnosis_example +from kglib.examples.diagnosis.diagnosis import diagnosis_example class TestDiagnosisExampleDebug(unittest.TestCase): diff --git a/kglib/utils/graph/thing/queries_to_networkx_graph.py b/kglib/utils/graph/thing/queries_to_networkx_graph.py index 1acf1904..4d62a515 100644 --- a/kglib/utils/graph/thing/queries_to_networkx_graph.py +++ b/kglib/utils/graph/thing/queries_to_networkx_graph.py @@ -84,7 +84,6 @@ def combine_n_graphs(graphs_list): return reduce(lambda x, y: combine_2_graphs(x, y), graphs_list) - def build_graph_from_queries(query_sampler_variable_graph_tuples, typedb_transaction, concept_dict_converter=concept_dict_to_graph): """ diff --git a/requirements.txt b/requirements.txt index 61bd4b56..ae6ec700 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,7 +20,37 @@ # decorator==5.1.1 +filelock==3.7.1 +huggingface-hub==0.7.0 +idna==3.3 +importlib-metadata==4.11.4 +Jinja2==3.1.2 +joblib==1.1.0 +MarkupSafe==2.1.1 networkx==2.5 -numpy==1.19.5 -pandas==0.25.1 - +nltk==3.7 +numpy==1.21.6 +packaging==21.3 +pandas==1.3.5 +Pillow==9.1.1 +pyparsing==3.0.9 +python-dateutil==2.8.2 +pytz==2022.1 +PyYAML==6.0 +regex==2022.6.2 +requests==2.28.0 +scikit-learn==1.0.2 +scipy==1.7.3 +sentence-transformers==2.2.0 +sentencepiece==0.1.96 +six==1.16.0 +threadpoolctl==3.1.0 +tokenizers==0.12.1 +torch==1.11.0 +torch-geometric==2.0.4 +torchvision==0.12.0 +tqdm==4.64.0 +transformers==4.19.4 +typing-extensions==4.2.0 +urllib3==1.26.9 +zipp==3.8.0 diff --git a/requirements_dev.txt b/requirements_dev.txt index f7e7da1a..d737c2da 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -21,7 +21,42 @@ --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple +certifi==2022.5.18.1 +charset-normalizer==2.0.12 +click==8.1.3 decorator==5.1.1 +filelock==3.7.1 +huggingface-hub==0.7.0 +idna==3.3 +importlib-metadata==4.11.4 +Jinja2==3.1.2 +joblib==1.1.0 +MarkupSafe==2.1.1 networkx==2.5 -numpy==1.19.5 -pandas==0.25.1 +nltk==3.7 +numpy==1.21.6 +packaging==21.3 +pandas==1.3.5 +Pillow==9.1.1 +pyparsing==3.0.9 +python-dateutil==2.8.2 +pytz==2022.1 +PyYAML==6.0 +regex==2022.6.2 +requests==2.28.0 +scikit-learn==1.0.2 +scipy==1.7.3 +sentence-transformers==2.2.0 +sentencepiece==0.1.96 +six==1.16.0 +threadpoolctl==3.1.0 +tokenizers==0.12.1 +torch==1.11.0 +torch-geometric==2.0.4 +torchvision==0.12.0 +tqdm==4.64.0 +transformers==4.19.4 +typing-extensions==4.2.0 +urllib3==1.26.9 +zipp==3.8.0 + From 7b75171d9735882603448f0f891d7fbc10b41777 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 17 Jun 2022 16:07:11 +0100 Subject: [PATCH 005/124] WIP --- kglib/examples/diagnosis/diagnosis.py | 153 ++++++++++++------ .../hetero_link_pred.py | 25 ++- .../link_prediction.py | 0 .../load_csv.py | 0 .../node_classification.py | 0 .../dataset/typedb_networkx_dataset.py | 17 +- 6 files changed, 139 insertions(+), 56 deletions(-) rename kglib/{kgcn_data_loader => examples}/hetero_link_pred.py (80%) rename kglib/{kgcn_data_loader => examples}/link_prediction.py (100%) rename kglib/{kgcn_data_loader => examples}/load_csv.py (100%) rename kglib/{kgcn_data_loader => examples}/node_classification.py (100%) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 26b7fee6..9774c575 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -19,8 +19,11 @@ # under the License. # +import torch +from torch_geometric.nn import GCNConv +import torch.nn.functional as F +import torch_geometric.transforms as T import inspect -import time import subprocess as sp from typedb.client import * @@ -51,14 +54,33 @@ # Continuous Attribute types and their min and max values CONTINUOUS_ATTRIBUTES = {'severity': (0, 1), 'age': (7, 80), 'units-per-week': (3, 29)} -TYPES_TO_IGNORE = ['candidate-diagnosis', 'example-id', 'probability-exists', 'probability-non-exists', 'probability-preexists'] -ROLES_TO_IGNORE = ['candidate-patient', 'candidate-diagnosed-disease'] +TYPES_TO_IGNORE = [] +ROLES_TO_IGNORE = [] + +RELATION_TYPE_TO_PREDICT = 'diagnosis' +RELATION_TYPE_TO_PREDICT_ROLES = ['patient', 'diagnosis'] # The learner should see candidate relations the same as the ground truth relations, so adjust these candidates to # look like their ground truth counterparts -TYPES_AND_ROLES_TO_OBFUSCATE = {'candidate-diagnosis': 'diagnosis', - 'candidate-patient': 'patient', - 'candidate-diagnosed-disease': 'diagnosed-disease'} +TYPES_AND_ROLES_TO_OBFUSCATE = {} + + +def binary_relations_to_edges(graph): + pass + + +def get_types(session, types_to_ignore, roles_to_ignore): + with session.transaction(TransactionType.READ) as tx: + # The terminology changes from here onwards from thing -> node and role -> edge + node_types = get_thing_types(tx) + edge_types = get_role_types(tx) + # Ignore any types or roles that exist in the TypeDB instance but which aren't being used for learning to + # reduce the number of categories to embed + [node_types.remove(el) for el in types_to_ignore] + [edge_types.remove(el) for el in roles_to_ignore] + print(f'Found node types: {node_types}') + print(f'Found edge types: {edge_types}') + return node_types, edge_types def diagnosis_example(typedb_binary_directory, @@ -110,30 +132,15 @@ def diagnosis_example(typedb_binary_directory, session = client.session(database, SessionType.DATA) - print("Create concept graphs") - graphs = create_concept_graphs(list(range(num_graphs)), session, infer=True) - - with session.transaction(TransactionType.READ) as tx: - # Change the terminology here onwards from thing -> node and role -> edge - node_types = get_thing_types(tx) - [node_types.remove(el) for el in TYPES_TO_IGNORE] - - edge_types = get_role_types(tx) - [edge_types.remove(el) for el in ROLES_TO_IGNORE] - print(f'Found node types: {node_types}') - print(f'Found edge types: {edge_types}') + node_types, edge_types = get_types(session, TYPES_TO_IGNORE, ROLES_TO_IGNORE) - # for data in multidigraph_node_data_iterator(graphs[0]): - # features = create_feature_vector(data) - # target = data[self.target_name] - # data.clear() - # data["x"] = features - # data["y"] = target + binary_relations_to_edges(graph) # TODO: Should be a transform. Do we do this before or after generating features? + # This transform adds the features and labels to the graph transform = StandardKGCNNetworkxTransform( node_types, edge_types, - target_name='solution', + target_name='solution', # TODO: We're planning to do away with already having the graphs labelled at this point, so somehow we need to add negative samples and label accordingly obfuscate=None, categorical=None, continuous=None, @@ -141,6 +148,7 @@ def diagnosis_example(typedb_binary_directory, label_attribute="concept", ) + # Create a Dataset that will load graphs from TypeDB on-demand, based on an ID dataset = TypeDBNetworkxDataSet( list(range(num_graphs)), get_query_handles, @@ -151,19 +159,71 @@ def diagnosis_example(typedb_binary_directory, transform ) - # ge_graphs, solveds_tr, solveds_ge = pipeline(graphs, - # tr_ge_split, - # node_types, - # edge_types, - # num_processing_steps_tr=num_processing_steps_tr, - # num_processing_steps_ge=num_processing_steps_ge, - # num_training_iterations=num_training_iterations, - # continuous_attributes=CONTINUOUS_ATTRIBUTES, - # categorical_attributes=CATEGORICAL_ATTRIBUTES, - # output_dir=f"./events/{time.time()}/") - # - # with session.transaction(TransactionType.WRITE) as tx: - # write_predictions_to_typedb(ge_graphs, tx) + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + data = dataset[0].to(device) # Get the first graph object. + + data = T.ToUndirected()(data) + del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label. + + class GCN(torch.nn.Module): + def __init__(self, hidden_channels): + super().__init__() + torch.manual_seed(1234567) + # self.conv1 = GCNConv(dataset.num_features, hidden_channels) + # self.conv2 = GCNConv(hidden_channels, dataset.num_classes) + self.conv1 = GCNConv(3, hidden_channels) + self.conv2 = GCNConv(hidden_channels, 3) + + def forward(self, x, edge_index): + x = self.conv1(x, edge_index) + x = x.relu() + x = F.dropout(x, p=0.5, training=self.training) + x = self.conv2(x, edge_index) + return x + + model = GCN(hidden_channels=16) + print(model) + + model = GCN(hidden_channels=16) + model.eval() + + # out = model(data.x, data.edge_index) + # visualize(out, color=data.y) + + model = GCN(hidden_channels=16) + optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) + criterion = torch.nn.CrossEntropyLoss() + + def train(): + model.train() + optimizer.zero_grad() # Clear gradients. + out = model(data.x, data.edge_index) # Perform a single forward pass. + # loss = criterion(out[data.train_mask], data.y[data.train_mask]) # Compute the loss solely based on the training nodes. + loss = criterion(out, data.y) # Compute the loss solely based on the training nodes. + loss.backward() # Derive gradients. + optimizer.step() # Update parameters based on gradients. + return loss + + def test(): + model.eval() + out = model(data.x, data.edge_index) + pred = out.argmax(dim=1) # Use the class with highest probability. + # test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels. + test_correct = pred == data.y # Check against ground-truth labels. + # test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions. + test_acc = int(test_correct.sum()) / int(data.sum()) # Derive ratio of correct predictions. + return test_acc + + # for epoch in range(1, 101): + # loss = train() + # print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}') + + test_acc = test() + print(f'Test Accuracy: {test_acc:.4f}') + + with session.transaction(TransactionType.WRITE) as tx: + write_predictions_to_typedb(ge_graphs, tx) session.close() client.close() @@ -214,7 +274,7 @@ def obfuscate_labels(graph, types_and_roles_to_obfuscate): def get_query_handles(example_id): """ - Creates an iterable, each element containing a Graql query, a function to sample the answers, and a QueryGraph + Creates an iterable, each element containing a TypeQL query, a function to sample the answers, and a QueryGraph object which must be the TypeDB graph representation of the query. This tuple is termed a "query_handle" Args: @@ -224,10 +284,11 @@ def get_query_handles(example_id): Returns: query handles """ + assert example_id == 0 # === Hereditary Feature === hereditary_query = inspect.cleandoc(f'''match - $p isa person, has example-id {example_id}; + $p isa person; $par isa person; $ps(child: $p, parent: $par) isa parentship; $diag(patient:$par, diagnosed-disease: $d) isa diagnosis; @@ -245,7 +306,7 @@ def get_query_handles(example_id): # === Consumption Feature === consumption_query = inspect.cleandoc(f'''match - $p isa person, has example-id {example_id}; + $p isa person; $s isa substance, has name $n; $c(consumer: $p, consumed-substance: $s) isa consumption, has units-per-week $u;''') @@ -260,7 +321,7 @@ def get_query_handles(example_id): # === Age Feature === person_age_query = inspect.cleandoc(f'''match - $p isa person, has example-id {example_id}, has age $a; + $p isa person, has age $a; ''') vars = p, a = 'p', 'a' @@ -271,7 +332,7 @@ def get_query_handles(example_id): # === Risk Factors Feature === risk_factor_query = inspect.cleandoc(f'''match $d isa disease; - $p isa person, has example-id {example_id}; + $p isa person; $r(person-at-risk: $p, risked-disease: $d) isa risk-factor; ''') @@ -285,7 +346,7 @@ def get_query_handles(example_id): vars = p, s, sn, d, dn, sp, sev, c = 'p', 's', 'sn', 'd', 'dn', 'sp', 'sev', 'c' symptom_query = inspect.cleandoc(f'''match - $p isa person, has example-id {example_id}; + $p isa person; $s isa symptom, has name $sn; $d isa disease, has name $dn; $sp(presented-symptom: $s, symptomatic-patient: $p) isa symptom-presentation, has severity $sev; @@ -307,7 +368,7 @@ def get_query_handles(example_id): diag, d, p, dn = 'diag', 'd', 'p', 'dn' diagnosis_query = inspect.cleandoc(f'''match - $p isa person, has example-id {example_id}; + $p isa person; $d isa disease, has name $dn; $diag(patient: $p, diagnosed-disease: $d) isa diagnosis; ''') @@ -320,7 +381,7 @@ def get_query_handles(example_id): # === Candidate Diagnosis === candidate_diagnosis_query = inspect.cleandoc(f'''match - $p isa person, has example-id {example_id}; + $p isa person; $d isa disease, has name $dn; $diag(candidate-patient: $p, candidate-diagnosed-disease: $d) isa candidate-diagnosis; ''') diff --git a/kglib/kgcn_data_loader/hetero_link_pred.py b/kglib/examples/hetero_link_pred.py similarity index 80% rename from kglib/kgcn_data_loader/hetero_link_pred.py rename to kglib/examples/hetero_link_pred.py index 4b29477b..802a5071 100644 --- a/kglib/kgcn_data_loader/hetero_link_pred.py +++ b/kglib/examples/hetero_link_pred.py @@ -1,3 +1,24 @@ +# +# Copyright (C) 2021 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + import argparse import os.path as osp @@ -33,8 +54,8 @@ num_val=0.1, num_test=0.1, neg_sampling_ratio=0.0, - edge_types=[('user', 'rates', 'movie')], - rev_edge_types=[('movie', 'rev_rates', 'user')], + edge_types=[('user', 'rates', 'movie')], # Edge user -rates-> movie + rev_edge_types=[('movie', 'rev_rates', 'user')], # Edge user <-rev_rates- movie )(data) # We have an unbalanced dataset with many labels for rating 3 and 4, and very diff --git a/kglib/kgcn_data_loader/link_prediction.py b/kglib/examples/link_prediction.py similarity index 100% rename from kglib/kgcn_data_loader/link_prediction.py rename to kglib/examples/link_prediction.py diff --git a/kglib/kgcn_data_loader/load_csv.py b/kglib/examples/load_csv.py similarity index 100% rename from kglib/kgcn_data_loader/load_csv.py rename to kglib/examples/load_csv.py diff --git a/kglib/kgcn_data_loader/node_classification.py b/kglib/examples/node_classification.py similarity index 100% rename from kglib/kgcn_data_loader/node_classification.py rename to kglib/examples/node_classification.py diff --git a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py b/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py index 2ec351a8..62ed4707 100644 --- a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py +++ b/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py @@ -22,6 +22,7 @@ from typing import Sequence, Callable, Optional import networkx as nx +from torch_geometric.utils import from_networkx from typedb.client import TypeDB, TypeDBSession, SessionType, TypeDBOptions, TransactionType from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries @@ -35,7 +36,7 @@ class TypeDBNetworkxDataSet: def __init__( self, - example_indices: Sequence, + indices: Sequence, get_query_handles_for_id: Callable, database: Optional[str] = None, uri: Optional[str] = "localhost:1729", @@ -44,7 +45,7 @@ def __init__( transform: Optional[Callable[[nx.Graph], nx.Graph]] = None, ): assert (database and uri) or session - self._example_indices = example_indices + self._indices = indices self.get_query_handles_for_id = get_query_handles_for_id self._infer = infer self._transform = transform @@ -69,13 +70,13 @@ def typedb_session(self): return self._typedb_session def __len__(self): - return len(self._example_indices) + return len(self._indices) def __getitem__(self, idx): print(type(self._typedb_session)) - example_id = self._example_indices[idx] - print(f"Fetching subgraph for example {example_id}") - graph_query_handles = self.get_query_handles_for_id(example_id) + id = self._indices[idx] + print(f"Fetching graph for id: {id}") + graph_query_handles = self.get_query_handles_for_id(id) options = TypeDBOptions.core() options.infer = self._infer @@ -83,7 +84,7 @@ def __getitem__(self, idx): with self.typedb_session.transaction(TransactionType.READ, options=options) as tx: # Build a graph from the queries, samplers, and query graphs graph = build_graph_from_queries(graph_query_handles, tx) - graph.name = example_id + graph.name = id if self._transform: graph = self._transform(graph) - return graph \ No newline at end of file + return from_networkx(graph).to_heterogeneous(node_type, edge_type, node_type_names, edge_type_names) From c96fbb93061398a5d63fc1ae4e0e660543dc9451 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 17 Jun 2022 18:15:57 +0100 Subject: [PATCH 006/124] Adds binary_relations_to_edges --- kglib/examples/diagnosis/diagnosis.py | 130 +++++++++++++----- .../transform/standard_kgcn_transform.py | 2 +- 2 files changed, 97 insertions(+), 35 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 9774c575..c8634b38 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -21,10 +21,11 @@ import torch from torch_geometric.nn import GCNConv -import torch.nn.functional as F -import torch_geometric.transforms as T +import torch.nn.functional as functional +import torch_geometric.transforms as transforms import inspect import subprocess as sp +import networkx as nx from typedb.client import * from kglib.kgcn_data_loader.dataset.typedb_networkx_dataset import TypeDBNetworkxDataSet @@ -39,34 +40,85 @@ DATABASE = "diagnosis" ADDRESS = "localhost:1729" -# Existing elements in the graph are those that pre-exist in the graph, and should be predicted to continue to exist PREEXISTS = 0 -# Candidates are neither present in the input nor in the solution, they are negative samples -CANDIDATE = 1 - -# Elements to infer are the graph elements whose existence we want to predict to be true, they are positive samples -TO_INFER = 2 - # Categorical Attribute types and the values of their categories -CATEGORICAL_ATTRIBUTES = {'name': ['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', - 'Alcohol']} +CATEGORICAL_ATTRIBUTES = { + 'name': [ + 'Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol' + ] +} # Continuous Attribute types and their min and max values CONTINUOUS_ATTRIBUTES = {'severity': (0, 1), 'age': (7, 80), 'units-per-week': (3, 29)} TYPES_TO_IGNORE = [] ROLES_TO_IGNORE = [] -RELATION_TYPE_TO_PREDICT = 'diagnosis' -RELATION_TYPE_TO_PREDICT_ROLES = ['patient', 'diagnosis'] +# Note that this determines the edge direction when converting from a TypeDB relation +RELATION_TYPE_TO_PREDICT = ('patient', 'diagnosis', 'diagnosed-disease') # The learner should see candidate relations the same as the ground truth relations, so adjust these candidates to # look like their ground truth counterparts TYPES_AND_ROLES_TO_OBFUSCATE = {} -def binary_relations_to_edges(graph): - pass +def binary_relations_to_edges(graph: nx.MultiDiGraph, binary_relation_type): + """ + A function to convert a TypeDB relation (a hyperedge) to a directed binary edge. + + The replaced relations may not be: + - a roleplayer in any other relations + - own any attributes + - have more than two roles + and must: + - have exactly two outgoing role edges (which can be of the same role) + + This is useful because edges are often stored as an adjacency matrix. In PyTorch Geometric, for example, + this adjacency matrix expects binary edges. To be compatible we convert to binary edges so that when creating + negative samples we can simply change the values of the adjacency matrix. In the case of a hyperedge we cannot + simply do this to add negative edges. + + Args: + graph: The graph to modify in-place + binary_relation_type: A triple of the `(role1, relation, role2)` types to convert to a single edge labelled with `relation` + + Returns: + Dict of edges to the node each replaces + """ + replacement_edges = {} + for node, node_data in graph.nodes(data=True): + if node_data["type"] == binary_relation_type[1]: + if len(list(graph.in_edges(node))) > 0: + raise ValueError( + "The given binary relation can't be transformed into an edge because it plays a role in another " + "relation." + ) + roles = list(graph.edges(node, data=True)) # equivalent to out_edges() + assert len(roles) == 2 + new_edge_start = None + new_edge_end = None + for from_roleplayer, to_roleplayer, data in roles: + if data["type"] == binary_relation_type[0]: + if new_edge_start is None: + new_edge_start = to_roleplayer + else: + # In this case the given relation type is symmetric. Therefore direction is arbitrary (but + # expect to add a reverse edge elsewhere). + assert binary_relation_type[0] == binary_relation_type[2] + new_edge_end = to_roleplayer + elif data["type"] == binary_relation_type[2]: + new_edge_end = to_roleplayer + else: + raise ValueError( + f"Unexpected role in relation {binary_relation_type[1]}. Expected \"" + f"{binary_relation_type[0]}\" or \"{binary_relation_type[2]}\" but got \"{data['type']}\"." + ) + graph.remove_edge(from_roleplayer, to_roleplayer) + graph.add_edge(new_edge_start, new_edge_end, type=binary_relation_type[1]) + replacement_edges[(new_edge_start, new_edge_end)] = node + for node in replacement_edges.values(): + graph.remove_node(node) + return replacement_edges def get_types(session, types_to_ignore, roles_to_ignore): @@ -134,9 +186,18 @@ def diagnosis_example(typedb_binary_directory, node_types, edge_types = get_types(session, TYPES_TO_IGNORE, ROLES_TO_IGNORE) - binary_relations_to_edges(graph) # TODO: Should be a transform. Do we do this before or after generating features? + # TODO: Here temporarily, should be graph-by-graph + graph_query_handles = get_query_handles(0) + options = TypeDBOptions.core() + options.infer = True + with session.transaction(TransactionType.READ, options) as tx: + # Build a graph from the queries, samplers, and query graphs + graph = build_graph_from_queries(graph_query_handles, tx) + + binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT) # TODO: Should be a transform. Do we do this before or after generating features? # This transform adds the features and labels to the graph + # TODO: We should make the feature encoders more explicit transform = StandardKGCNNetworkxTransform( node_types, edge_types, @@ -160,12 +221,22 @@ def diagnosis_example(typedb_binary_directory, ) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - data = dataset[0].to(device) # Get the first graph object. - data = T.ToUndirected()(data) + data = transforms.ToUndirected()(data) del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label. + # TODO: How can the negative sampling know what types to add to the generated edges? It surely can't, so we have to + # deal with this + train_data, val_data, test_data = transforms.RandomLinkSplit( + num_val=0.1, + num_test=0.1, + add_negative_train_samples=True, + neg_sampling_ratio=1.0, + edge_types=[('user', 'rates', 'movie')], # Edge user -rates-> movie + rev_edge_types=[('movie', 'rev_rates', 'user')], # Edge user <-rev_rates- movie + )(data) + class GCN(torch.nn.Module): def __init__(self, hidden_channels): super().__init__() @@ -178,19 +249,10 @@ def __init__(self, hidden_channels): def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = x.relu() - x = F.dropout(x, p=0.5, training=self.training) + x = functional.dropout(x, p=0.5, training=self.training) x = self.conv2(x, edge_index) return x - model = GCN(hidden_channels=16) - print(model) - - model = GCN(hidden_channels=16) - model.eval() - - # out = model(data.x, data.edge_index) - # visualize(out, color=data.y) - model = GCN(hidden_channels=16) optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) criterion = torch.nn.CrossEntropyLoss() @@ -374,10 +436,10 @@ def get_query_handles(example_id): ''') diagnosis_query_graph = (QueryGraph() - .add_vars([diag], TO_INFER) + .add_vars([diag], PREEXISTS) .add_vars([d, p, dn], PREEXISTS) - .add_role_edge(diag, d, 'diagnosed-disease', TO_INFER) - .add_role_edge(diag, p, 'patient', TO_INFER)) + .add_role_edge(diag, d, 'diagnosed-disease', PREEXISTS) + .add_role_edge(diag, p, 'patient', PREEXISTS)) # === Candidate Diagnosis === candidate_diagnosis_query = inspect.cleandoc(f'''match @@ -387,10 +449,10 @@ def get_query_handles(example_id): ''') candidate_diagnosis_query_graph = (QueryGraph() - .add_vars([diag], CANDIDATE) + .add_vars([diag], PREEXISTS) .add_vars([d, p, dn], PREEXISTS) - .add_role_edge(diag, d, 'candidate-diagnosed-disease', CANDIDATE) - .add_role_edge(diag, p, 'candidate-patient', CANDIDATE)) + .add_role_edge(diag, d, 'candidate-diagnosed-disease', PREEXISTS) + .add_role_edge(diag, p, 'candidate-patient', PREEXISTS)) return [ (symptom_query, lambda x: x, symptom_query_graph), diff --git a/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py b/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py index a1aa3152..a3412da2 100644 --- a/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py +++ b/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py @@ -105,7 +105,7 @@ def create_feature_vector(node_or_edge_data_dict): Factored out of kglib.kgcn_tensorflow.pipeline.encode.create_input_graph. Args: - node_or_edge_dict: the dict coming describing a node or edge + node_or_edge_data_dict: the dict coming describing a node or edge obtained from an element of graph.nodes(data=True) or graph.edges(data=True) of a networkx graph. From 5c1fa52892bdf17af1f76260ead7ade86dcf3349 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 21 Jun 2022 18:59:57 +0100 Subject: [PATCH 007/124] WIP --- kglib/examples/diagnosis/diagnosis.py | 52 ++++++++++++++++-------- kglib/tests/deployment/kgcn/diagnosis.py | 5 +-- kglib/utils/typedb/type/type.py | 40 +++++++++++++++++- 3 files changed, 75 insertions(+), 22 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index c8634b38..b3277c40 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -35,7 +35,7 @@ from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data -from kglib.utils.typedb.type.type import get_thing_types, get_role_types +from kglib.utils.typedb.type.type import get_thing_types, get_role_types, get_role_triples, get_has_triples DATABASE = "diagnosis" ADDRESS = "localhost:1729" @@ -135,11 +135,22 @@ def get_types(session, types_to_ignore, roles_to_ignore): return node_types, edge_types +def get_edge_types(session): + # TODO: Naming is too close to get_types where the result is very different + with session.transaction(TransactionType.READ) as tx: + edge_types = get_role_triples(tx) + get_has_triples(tx) + return edge_types + + +def reverse_edge_types(edge_types): + reversed = [] + for edge_from, edge, edge_to in edge_types: + reversed.append((edge_to, f"rev_{edge}", edge_from)) + return reversed + + def diagnosis_example(typedb_binary_directory, num_graphs=100, - num_processing_steps_tr=3, - num_processing_steps_ge=3, - num_training_iterations=50, database=DATABASE, address=ADDRESS, schema_file_path="/Users/jamesfletcher/programming/research/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql", @@ -150,9 +161,6 @@ def diagnosis_example(typedb_binary_directory, Args: typedb_binary_directory: Location of the typedb binary for the purpose of loading initial schema and data num_graphs: Number of graphs to use for training and testing combined - num_processing_steps_tr: The number of message-passing steps for training - num_processing_steps_ge: The number of message-passing steps for testing - num_training_iterations: The number of training epochs database: The name of the database to retrieve example subgraphs from address: The address of the running TypeDB instance schema_file_path: Path to the diagnosis schema file @@ -172,11 +180,7 @@ def diagnosis_example(typedb_binary_directory, tr_ge_split = int(num_graphs*0.5) client = TypeDB.core_client(address) - if client.databases().contains(database): - raise ValueError( - f"There is already a database present with the name {database}. The Diagnosis example expects a clean DB. " - f"Please delete the {database} database, or use another database name") - client.databases().create(database) + create_database(client, database) load_typeql_schema_file(database, typedb_binary_directory, schema_file_path) load_typeql_data_file(database, typedb_binary_directory, seed_data_file_path) @@ -201,7 +205,8 @@ def diagnosis_example(typedb_binary_directory, transform = StandardKGCNNetworkxTransform( node_types, edge_types, - target_name='solution', # TODO: We're planning to do away with already having the graphs labelled at this point, so somehow we need to add negative samples and label accordingly + target_name='solution', # TODO: We're planning to do away with already having the graphs labelled at this + # point, so somehow we need to add negative samples and label accordingly obfuscate=None, categorical=None, continuous=None, @@ -223,8 +228,12 @@ def diagnosis_example(typedb_binary_directory, device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') data = dataset[0].to(device) # Get the first graph object. - data = transforms.ToUndirected()(data) - del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label. + edge_types = get_edge_types(session) + edge_types_reversed = reverse_edge_types(edge_types) + + data = transforms.ToUndirected()(data) # TODO: Consider whether we want to add reverse edges for the edge type being predicted + for edge_from, edge, edge_to in edge_types_reversed: + del data[edge_from, edge, edge_to].edge_label # Remove "reverse" label. # TODO: How can the negative sampling know what types to add to the generated edges? It surely can't, so we have to # deal with this @@ -233,10 +242,11 @@ def diagnosis_example(typedb_binary_directory, num_test=0.1, add_negative_train_samples=True, neg_sampling_ratio=1.0, - edge_types=[('user', 'rates', 'movie')], # Edge user -rates-> movie - rev_edge_types=[('movie', 'rev_rates', 'user')], # Edge user <-rev_rates- movie + edge_types=edge_types, + rev_edge_types=edge_types_reversed, )(data) + # TODO: Replace this model with that from hetero_link_pred class GCN(torch.nn.Module): def __init__(self, hidden_channels): super().__init__() @@ -293,6 +303,14 @@ def test(): return solveds_tr, solveds_ge +def create_database(client, database): + if client.databases().contains(database): + raise ValueError( + f"There is already a database present with the name {database}. The Diagnosis example expects a clean DB. " + f"Please delete the {database} database, or use another database name") + client.databases().create(database) + + def create_concept_graphs(example_indices, typedb_session, infer = True): """ Builds an in-memory graph for each example, with an example_id as an anchor for each example subgraph. diff --git a/kglib/tests/deployment/kgcn/diagnosis.py b/kglib/tests/deployment/kgcn/diagnosis.py index 481c71b3..6d82076b 100644 --- a/kglib/tests/deployment/kgcn/diagnosis.py +++ b/kglib/tests/deployment/kgcn/diagnosis.py @@ -28,10 +28,7 @@ class TestDiagnosisExample(unittest.TestCase): def test_example_runs_without_exception(self): diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", - num_graphs=6, - num_processing_steps_tr=2, - num_processing_steps_ge=2, - num_training_iterations=20) + num_graphs=6) if __name__ == "__main__": diff --git a/kglib/utils/typedb/type/type.py b/kglib/utils/typedb/type/type.py index a43fe89a..07349c5d 100644 --- a/kglib/utils/typedb/type/type.py +++ b/kglib/utils/typedb/type/type.py @@ -36,7 +36,7 @@ def get_thing_types(tx): def get_role_types(tx): """ - Get all schema roles, excluding those for implicit attribute relations, the base role type + Get all schema roles, excluding the base role type Args: tx: TypeDB transaction @@ -47,3 +47,41 @@ def get_role_types(tx): role_types = ['has'] + [role.get('r').get_label().name() for role in schema_concepts] role_types.remove('role') return role_types + + +def get_role_triples(tx): + """ + Get triples of all schema roles and the relation and roleplayer they connect + Args: + tx: TypeDB transaction + + Returns: + TypeDB role triples + """ + role_triples = [] + schema_concepts = tx.query().match("match $rel sub relation; $rel relates $r; $rp plays $r;") + for answer in schema_concepts: + relation = answer.get('rel').get_label().name() + role = answer.get('r').get_label().name() + player = answer.get("rp").get_label().name() + role_triples.append((relation, role, player)) + return role_triples + + +def get_has_triples(tx): + """ + Get triples of all ownerships: the owner type and owned attribute + Args: + tx: TypeDB transaction + + Returns: + TypeDB ownership triples + """ + has_triples = [] + schema_concepts = tx.query().match("match $owner sub relation owns $owned;") + for answer in schema_concepts: + owner = answer.get('owner').get_label().name() + owned = answer.get('owned').get_label().name() + has_triples.append((owner, "has", owned)) + return has_triples + From aacc48950366715167579b3af4f847728284b9ac Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 5 Jul 2022 10:14:32 +0100 Subject: [PATCH 008/124] Change StandardKGCNNetworkxTransform into explicitly a feature encoder, split out a separate labeller specifically for the binary link prediction case. --- kglib/examples/diagnosis/diagnosis.py | 332 +++++++----------- kglib/examples/diagnosis/diagnosis_test.py | 32 +- .../{standard_encode.py => encoding.py} | 59 +--- ...andard_encode_test.py => encoding_test.py} | 2 +- .../transform/binary_link_prediction.py | 102 ++++++ .../transform/standard_kgcn_transform.py | 137 -------- .../transform/typedb_graph_encoder.py | 122 +++++++ kglib/kgcn_data_loader/utils.py | 57 --- kglib/kgcn_data_loader/utils_test.py | 40 +-- kglib/utils/typedb/type/type.py | 19 +- 10 files changed, 379 insertions(+), 523 deletions(-) rename kglib/kgcn_data_loader/encoding/{standard_encode.py => encoding.py} (53%) rename kglib/kgcn_data_loader/encoding/{standard_encode_test.py => encoding_test.py} (95%) create mode 100644 kglib/kgcn_data_loader/transform/binary_link_prediction.py delete mode 100644 kglib/kgcn_data_loader/transform/standard_kgcn_transform.py create mode 100644 kglib/kgcn_data_loader/transform/typedb_graph_encoder.py diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index b3277c40..dea662eb 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -19,38 +19,35 @@ # under the License. # -import torch -from torch_geometric.nn import GCNConv -import torch.nn.functional as functional -import torch_geometric.transforms as transforms import inspect import subprocess as sp + import networkx as nx +import torch +import torch.nn.functional as functional +import torch_geometric.transforms as transforms +from torch.nn import Linear +from torch_geometric.nn import SAGEConv, to_hetero from typedb.client import * from kglib.kgcn_data_loader.dataset.typedb_networkx_dataset import TypeDBNetworkxDataSet -from kglib.kgcn_data_loader.transform.standard_kgcn_transform import StandardKGCNNetworkxTransform +from kglib.kgcn_data_loader.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges +from kglib.kgcn_data_loader.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ + ContinuousEncoder from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.utils.graph.iterate import multidigraph_data_iterator, multidigraph_node_data_iterator +from kglib.utils.graph.iterate import multidigraph_data_iterator from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data -from kglib.utils.typedb.type.type import get_thing_types, get_role_types, get_role_triples, get_has_triples +from kglib.utils.typedb.type.type import get_thing_types, get_role_types, get_edge_type_triplets, reverse_edge_type_triplets DATABASE = "diagnosis" ADDRESS = "localhost:1729" PREEXISTS = 0 -# Categorical Attribute types and the values of their categories -CATEGORICAL_ATTRIBUTES = { - 'name': [ - 'Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol' - ] -} -# Continuous Attribute types and their min and max values -CONTINUOUS_ATTRIBUTES = {'severity': (0, 1), 'age': (7, 80), 'units-per-week': (3, 29)} - +# Ignore any types or roles that exist in the TypeDB instance but which aren't being used for learning to reduce the +# number of categories to embed TYPES_TO_IGNORE = [] ROLES_TO_IGNORE = [] @@ -62,65 +59,6 @@ TYPES_AND_ROLES_TO_OBFUSCATE = {} -def binary_relations_to_edges(graph: nx.MultiDiGraph, binary_relation_type): - """ - A function to convert a TypeDB relation (a hyperedge) to a directed binary edge. - - The replaced relations may not be: - - a roleplayer in any other relations - - own any attributes - - have more than two roles - and must: - - have exactly two outgoing role edges (which can be of the same role) - - This is useful because edges are often stored as an adjacency matrix. In PyTorch Geometric, for example, - this adjacency matrix expects binary edges. To be compatible we convert to binary edges so that when creating - negative samples we can simply change the values of the adjacency matrix. In the case of a hyperedge we cannot - simply do this to add negative edges. - - Args: - graph: The graph to modify in-place - binary_relation_type: A triple of the `(role1, relation, role2)` types to convert to a single edge labelled with `relation` - - Returns: - Dict of edges to the node each replaces - """ - replacement_edges = {} - for node, node_data in graph.nodes(data=True): - if node_data["type"] == binary_relation_type[1]: - if len(list(graph.in_edges(node))) > 0: - raise ValueError( - "The given binary relation can't be transformed into an edge because it plays a role in another " - "relation." - ) - roles = list(graph.edges(node, data=True)) # equivalent to out_edges() - assert len(roles) == 2 - new_edge_start = None - new_edge_end = None - for from_roleplayer, to_roleplayer, data in roles: - if data["type"] == binary_relation_type[0]: - if new_edge_start is None: - new_edge_start = to_roleplayer - else: - # In this case the given relation type is symmetric. Therefore direction is arbitrary (but - # expect to add a reverse edge elsewhere). - assert binary_relation_type[0] == binary_relation_type[2] - new_edge_end = to_roleplayer - elif data["type"] == binary_relation_type[2]: - new_edge_end = to_roleplayer - else: - raise ValueError( - f"Unexpected role in relation {binary_relation_type[1]}. Expected \"" - f"{binary_relation_type[0]}\" or \"{binary_relation_type[2]}\" but got \"{data['type']}\"." - ) - graph.remove_edge(from_roleplayer, to_roleplayer) - graph.add_edge(new_edge_start, new_edge_end, type=binary_relation_type[1]) - replacement_edges[(new_edge_start, new_edge_end)] = node - for node in replacement_edges.values(): - graph.remove_node(node) - return replacement_edges - - def get_types(session, types_to_ignore, roles_to_ignore): with session.transaction(TransactionType.READ) as tx: # The terminology changes from here onwards from thing -> node and role -> edge @@ -135,20 +73,6 @@ def get_types(session, types_to_ignore, roles_to_ignore): return node_types, edge_types -def get_edge_types(session): - # TODO: Naming is too close to get_types where the result is very different - with session.transaction(TransactionType.READ) as tx: - edge_types = get_role_triples(tx) + get_has_triples(tx) - return edge_types - - -def reverse_edge_types(edge_types): - reversed = [] - for edge_from, edge, edge_to in edge_types: - reversed.append((edge_to, f"rev_{edge}", edge_from)) - return reversed - - def diagnosis_example(typedb_binary_directory, num_graphs=100, database=DATABASE, @@ -177,8 +101,6 @@ def diagnosis_example(typedb_binary_directory, f'--command=database delete {database}', ], cwd=typedb_binary_directory) - tr_ge_split = int(num_graphs*0.5) - client = TypeDB.core_client(address) create_database(client, database) @@ -188,51 +110,57 @@ def diagnosis_example(typedb_binary_directory, session = client.session(database, SessionType.DATA) + # TODO: This considers the node_type_and edge_type lists, but not the edge type triples that PyG takes in + # TODO: This means that the triples also need to be manipulated based on the conversion from a relation to a binary edge node_types, edge_types = get_types(session, TYPES_TO_IGNORE, ROLES_TO_IGNORE) - - # TODO: Here temporarily, should be graph-by-graph - graph_query_handles = get_query_handles(0) - options = TypeDBOptions.core() - options.infer = True - with session.transaction(TransactionType.READ, options) as tx: - # Build a graph from the queries, samplers, and query graphs - graph = build_graph_from_queries(graph_query_handles, tx) - - binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT) # TODO: Should be a transform. Do we do this before or after generating features? - - # This transform adds the features and labels to the graph - # TODO: We should make the feature encoders more explicit - transform = StandardKGCNNetworkxTransform( - node_types, - edge_types, - target_name='solution', # TODO: We're planning to do away with already having the graphs labelled at this - # point, so somehow we need to add negative samples and label accordingly - obfuscate=None, - categorical=None, - continuous=None, - duplicate_in_reverse=True, - label_attribute="concept", - ) + # During the transforms below we convert the *relations to predict* to simple edges, which means the relation + # changes from a node to an edge. We therefore need to update the node_types and edge_types accordingly + node_types.remove(RELATION_TYPE_TO_PREDICT[1]) + edge_types.add(RELATION_TYPE_TO_PREDICT[1]) + # Remove the relation's roles from the edge_types list + edge_types.remove(RELATION_TYPE_TO_PREDICT[0]) + if RELATION_TYPE_TO_PREDICT[0] != RELATION_TYPE_TO_PREDICT[2]: + edge_types.remove(RELATION_TYPE_TO_PREDICT[2]) + + edge_type_triplets = get_edge_type_triplets(session) + edge_type_triplets_reversed = reverse_edge_type_triplets(edge_type_triplets) + + # Attribute encoders encode the value of each attribute into a fixed-length feature vector. The encoders are + # defined on a per-type basis. Easily define your own encoders for specific attribute data in your TypeDB database + attribute_encoding_size = 16 + attribute_encoders = { + # Categorical Attribute types and the values of their categories + # TODO: Use a sentence encoder for this instead to demonstrate how to use one + 'name': CategoricalEncoder(['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol']), + # Continuous Attribute types and their min and max values + 'severity': ContinuousEncoder(0, 1), + 'age': ContinuousEncoder(7, 80), + 'units-per-week': ContinuousEncoder(3, 29) + } + + def prepare_graph(graph): + # TODO: We likely need to know the relations that were replaced with binary edges later on + replaced_edges = binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT), + return nx.convert_node_labels_to_integers(graph, label_attribute="concept") + + transform = transforms.Compose([ + prepare_graph, + GraphFeatureEncoder(node_types, edge_types, attribute_encoders, attribute_encoding_size), + LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[1]) + ]) + # TODO: Consider clearing unneeded node and edge data # Create a Dataset that will load graphs from TypeDB on-demand, based on an ID - dataset = TypeDBNetworkxDataSet( - list(range(num_graphs)), - get_query_handles, - DATABASE, - ADDRESS, - session, - True, - transform - ) + dataset = TypeDBNetworkxDataSet([0], get_query_handles, DATABASE, ADDRESS, session, True, transform) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') data = dataset[0].to(device) # Get the first graph object. - edge_types = get_edge_types(session) - edge_types_reversed = reverse_edge_types(edge_types) - - data = transforms.ToUndirected()(data) # TODO: Consider whether we want to add reverse edges for the edge type being predicted - for edge_from, edge, edge_to in edge_types_reversed: + # TODO: Consider whether we want to add reverse edges for the edge type being predicted and whether to do the + # before or after other transforms + data = transforms.ToUndirected()(data) + for edge_from, edge, edge_to in edge_type_triplets_reversed: + # TODO: It's unclear in the PyG examples why this is necessary del data[edge_from, edge, edge_to].edge_label # Remove "reverse" label. # TODO: How can the negative sampling know what types to add to the generated edges? It surely can't, so we have to @@ -242,54 +170,87 @@ def diagnosis_example(typedb_binary_directory, num_test=0.1, add_negative_train_samples=True, neg_sampling_ratio=1.0, - edge_types=edge_types, - rev_edge_types=edge_types_reversed, + edge_types=edge_type_triplets, + rev_edge_types=edge_type_triplets_reversed, )(data) - # TODO: Replace this model with that from hetero_link_pred - class GCN(torch.nn.Module): - def __init__(self, hidden_channels): + class GNNEncoder(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): super().__init__() - torch.manual_seed(1234567) - # self.conv1 = GCNConv(dataset.num_features, hidden_channels) - # self.conv2 = GCNConv(hidden_channels, dataset.num_classes) - self.conv1 = GCNConv(3, hidden_channels) - self.conv2 = GCNConv(hidden_channels, 3) + self.conv1 = SAGEConv((-1, -1), hidden_channels) + self.conv2 = SAGEConv((-1, -1), out_channels) def forward(self, x, edge_index): - x = self.conv1(x, edge_index) - x = x.relu() - x = functional.dropout(x, p=0.5, training=self.training) + x = self.conv1(x, edge_index).relu() x = self.conv2(x, edge_index) return x - model = GCN(hidden_channels=16) - optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) - criterion = torch.nn.CrossEntropyLoss() + class EdgeDecoder(torch.nn.Module): + def __init__(self, hidden_channels): + super().__init__() + self.lin1 = Linear(2 * hidden_channels, hidden_channels) + self.lin2 = Linear(hidden_channels, 1) + + def forward(self, z_dict, edge_label_index): + row, col = edge_label_index + z = torch.cat([z_dict['user'][row], z_dict['movie'][col]], dim=-1) + + z = self.lin1(z).relu() + z = self.lin2(z) + return z.view(-1) + + class Model(torch.nn.Module): + def __init__(self, hidden_channels): + super().__init__() + self.encoder = GNNEncoder(hidden_channels, hidden_channels) + self.encoder = to_hetero(self.encoder, data.metadata(), aggr='sum') + self.decoder = EdgeDecoder(hidden_channels) + + def forward(self, x_dict, edge_index_dict, edge_label_index): + z_dict = self.encoder(x_dict, edge_index_dict) + return self.decoder(z_dict, edge_label_index) + + model = Model(hidden_channels=32).to(device) + + # Due to lazy initialization, we need to run one model step so the number + # of parameters can be inferred: + with torch.no_grad(): + model.encoder(train_data.x_dict, train_data.edge_index_dict) + + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + + def weighted_mse_loss(pred, target, weight=None): + weight = 1. if weight is None else weight[target].to(pred.dtype) + return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() def train(): model.train() - optimizer.zero_grad() # Clear gradients. - out = model(data.x, data.edge_index) # Perform a single forward pass. - # loss = criterion(out[data.train_mask], data.y[data.train_mask]) # Compute the loss solely based on the training nodes. - loss = criterion(out, data.y) # Compute the loss solely based on the training nodes. - loss.backward() # Derive gradients. - optimizer.step() # Update parameters based on gradients. - return loss - - def test(): + optimizer.zero_grad() + pred = model(train_data.x_dict, train_data.edge_index_dict, + train_data['user', 'movie'].edge_label_index) + target = train_data['user', 'movie'].edge_label + loss = weighted_mse_loss(pred, target, None) # TODO: Consider using weighting to balance the positive/negative example sets + loss.backward() + optimizer.step() + return float(loss) + + @torch.no_grad() + def test(data): model.eval() - out = model(data.x, data.edge_index) - pred = out.argmax(dim=1) # Use the class with highest probability. - # test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels. - test_correct = pred == data.y # Check against ground-truth labels. - # test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions. - test_acc = int(test_correct.sum()) / int(data.sum()) # Derive ratio of correct predictions. - return test_acc - - # for epoch in range(1, 101): - # loss = train() - # print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}') + pred = model(data.x_dict, data.edge_index_dict, + data['user', 'movie'].edge_label_index) + pred = pred.clamp(min=0, max=5) + target = data['user', 'movie'].edge_label.float() + rmse = functional.mse_loss(pred, target).sqrt() + return float(rmse) + + for epoch in range(1, 301): + loss = train() + train_rmse = test(train_data) + val_rmse = test(val_data) + test_rmse = test(test_data) + print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_rmse:.4f}, ' + f'Val: {val_rmse:.4f}, Test: {test_rmse:.4f}') test_acc = test() print(f'Test Accuracy: {test_acc:.4f}') @@ -311,47 +272,6 @@ def create_database(client, database): client.databases().create(database) -def create_concept_graphs(example_indices, typedb_session, infer = True): - """ - Builds an in-memory graph for each example, with an example_id as an anchor for each example subgraph. - Args: - example_indices: The values used to anchor the subgraph queries within the entire knowledge graph - typedb_session: TypeDB Session - - Returns: - In-memory graphs of TypeDB subgraphs - """ - - graphs = [] - - options = TypeDBOptions.core() - options.infer = infer - - for example_id in example_indices: - print(f'Creating graph for example {example_id}') - graph_query_handles = get_query_handles(example_id) - - with typedb_session.transaction(TransactionType.READ, options) as tx: - # Build a graph from the queries, samplers, and query graphs - graph = build_graph_from_queries(graph_query_handles, tx) - - obfuscate_labels(graph, TYPES_AND_ROLES_TO_OBFUSCATE) - - graph.name = example_id - graphs.append(graph) - - return graphs - - -def obfuscate_labels(graph, types_and_roles_to_obfuscate): - # Remove label leakage - change type labels that indicate candidates into non-candidates - for data in multidigraph_data_iterator(graph): - for label_to_obfuscate, with_label in types_and_roles_to_obfuscate.items(): - if data['type'] == label_to_obfuscate: - data.update(type=with_label) - break - - def get_query_handles(example_id): """ Creates an iterable, each element containing a TypeQL query, a function to sample the answers, and a QueryGraph diff --git a/kglib/examples/diagnosis/diagnosis_test.py b/kglib/examples/diagnosis/diagnosis_test.py index 262f415e..c3c9a6ff 100644 --- a/kglib/examples/diagnosis/diagnosis_test.py +++ b/kglib/examples/diagnosis/diagnosis_test.py @@ -22,14 +22,12 @@ import unittest from unittest.mock import MagicMock -from typedb.api.query.query_manager import QueryManager from typedb.client import * import networkx as nx import numpy as np -from kglib.examples.diagnosis.diagnosis import write_predictions_to_typedb, obfuscate_labels +from kglib.examples.diagnosis.diagnosis import write_predictions_to_typedb from kglib.utils.typedb.object.thing import Thing -from kglib.utils.graph.test.case import GraphTestCase class TestWritePredictionsToTypeDB(unittest.TestCase): @@ -94,33 +92,5 @@ def test_query_made_only_if_relation_wins(self): tx.commit.assert_called() -class TestObfuscateLabels(GraphTestCase): - - def test_labels_obfuscated_as_expected(self): - - graph = nx.MultiDiGraph() - - graph.add_node(0, type='person') - graph.add_node(1, type='disease') - graph.add_node(2, type='candidate-diagnosis') - - graph.add_edge(2, 0, type='candidate-patient') - graph.add_edge(2, 1, type='candidate-diagnosed-disease') - - obfuscate_labels(graph, {'candidate-diagnosis': 'diagnosis', - 'candidate-patient': 'patient', - 'candidate-diagnosed-disease': 'diagnosed-disease'}) - - expected_graph = nx.MultiDiGraph() - expected_graph.add_node(0, type='person') - expected_graph.add_node(1, type='disease') - expected_graph.add_node(2, type='diagnosis') - - expected_graph.add_edge(2, 0, type='patient') - expected_graph.add_edge(2, 1, type='diagnosed-disease') - - self.assertGraphsEqual(graph, expected_graph) - - if __name__ == "__main__": unittest.main() diff --git a/kglib/kgcn_data_loader/encoding/standard_encode.py b/kglib/kgcn_data_loader/encoding/encoding.py similarity index 53% rename from kglib/kgcn_data_loader/encoding/standard_encode.py rename to kglib/kgcn_data_loader/encoding/encoding.py index 58619c5e..8aacc7cb 100644 --- a/kglib/kgcn_data_loader/encoding/standard_encode.py +++ b/kglib/kgcn_data_loader/encoding/encoding.py @@ -21,29 +21,20 @@ import numpy as np -from kglib.utils.graph.iterate import multidigraph_data_iterator, multidigraph_node_data_iterator, \ - multidigraph_edge_data_iterator +from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator -def encode_values(graph, categorical_attributes, continuous_attributes): +def encode_values(graph, attribute_encoders, attribute_encoding_size): for node_data in multidigraph_node_data_iterator(graph): typ = node_data['type'] - - if categorical_attributes is not None and typ in categorical_attributes.keys(): + if typ in attribute_encoders.keys(): # Add the integer value of the category for each categorical attribute instance - category_values = categorical_attributes[typ] - node_data['encoded_value'] = category_values.index(node_data['value']) - - elif continuous_attributes is not None and typ in continuous_attributes.keys(): - min_val, max_val = continuous_attributes[typ] - node_data['encoded_value'] = (node_data['value'] - min_val) / (max_val - min_val) - + node_data['encoded_value'] = attribute_encoders[typ].encode(node_data['value']) else: - node_data['encoded_value'] = 0 - for edge_data in multidigraph_edge_data_iterator(graph): - edge_data['encoded_value'] = 0 + node_data['encoded_value'] = [0] * attribute_encoding_size - return graph + for edge_data in multidigraph_edge_data_iterator(graph): + edge_data['encoded_value'] = [0] * attribute_encoding_size def encode_types(graph, iterator_func, all_types): @@ -54,9 +45,9 @@ def encode_types(graph, iterator_func, all_types): iterator_func: An function to create an iterator of data in the graph (node data, edge data or combined node and edge data) all_types: The full list of types to be encoded in this order - + Returns: - The graph, which is also is updated in-place + Nothing, the graph is updated in-place """ iterator = iterator_func(graph) @@ -64,38 +55,6 @@ def encode_types(graph, iterator_func, all_types): for data in iterator: data['categorical_type'] = all_types.index(data['type']) - return graph - - -def create_input_graph(graph): - input_graph = graph.copy() - - for data in multidigraph_data_iterator(input_graph): - if data["solution"] == 0: - preexists = 1 - else: - preexists = 0 - - features = stack_features([preexists, data["categorical_type"], data["encoded_value"]]) - data.clear() - data["features"] = features - - input_graph.graph["features"] = np.array([0.0] * 5, dtype=np.float32) - return input_graph - - -def create_target_graph(graph): - target_graph = graph.copy() - solution_one_hot_encoding = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]], dtype=np.float32) - - for data in multidigraph_data_iterator(target_graph): - features = solution_one_hot_encoding[data["solution"]] - data.clear() - data["features"] = features - - target_graph.graph["features"] = np.array([0.0] * 5, dtype=np.float32) - return target_graph - def stack_features(features): """ diff --git a/kglib/kgcn_data_loader/encoding/standard_encode_test.py b/kglib/kgcn_data_loader/encoding/encoding_test.py similarity index 95% rename from kglib/kgcn_data_loader/encoding/standard_encode_test.py rename to kglib/kgcn_data_loader/encoding/encoding_test.py index 87b77fa6..eadcf9b0 100644 --- a/kglib/kgcn_data_loader/encoding/standard_encode_test.py +++ b/kglib/kgcn_data_loader/encoding/encoding_test.py @@ -23,7 +23,7 @@ import numpy as np -from kglib.kgcn_data_loader.encoding.standard_encode import stack_features +from kglib.kgcn_data_loader.encoding.encoding import stack_features class TestAugmentDataFields(unittest.TestCase): diff --git a/kglib/kgcn_data_loader/transform/binary_link_prediction.py b/kglib/kgcn_data_loader/transform/binary_link_prediction.py new file mode 100644 index 00000000..74f9c066 --- /dev/null +++ b/kglib/kgcn_data_loader/transform/binary_link_prediction.py @@ -0,0 +1,102 @@ +# +# Copyright (C) 2021 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +import networkx as nx + +from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator + + +class LinkPredictionLabeller: + + def __init__(self, edge_type_to_predict): + self.edge_type_to_predict = edge_type_to_predict + + def __call__(self, graph): + self.label_edges(graph) + + # def label_nodes(self, graph): + # for data in multidigraph_node_data_iterator(graph): + # data["y"] = 0 + + def label_edges(self, graph): + for data in multidigraph_edge_data_iterator(graph): + if data["type"] == self.edge_type_to_predict: + data["y_edge"] = 1 + else: + data["y_edge"] = 0 + + +def binary_relations_to_edges(graph: nx.MultiDiGraph, binary_relation_type): + """ + A function to convert a TypeDB relation (a hyperedge) to a directed binary edge. + + The replaced relations may not be: + - a roleplayer in any other relations + - own any attributes + - have more than two roles + and must: + - have exactly two outgoing role edges (which can be of the same role) + + This is useful because edges are often stored as an adjacency matrix. In PyTorch Geometric, for example, + this adjacency matrix expects binary edges. To be compatible we convert to binary edges so that when creating + negative samples we can simply change the values of the adjacency matrix. In the case of a hyperedge we cannot + simply do this to add negative edges. + + Args: + graph: The graph to modify in-place + binary_relation_type: A triple of the `(role1, relation, role2)` types to convert to a single edge labelled with `relation` + + Returns: + Dict of edges to the node each replaces + """ + replacement_edges = {} + for node, node_data in graph.nodes(data=True): + if node_data["type"] == binary_relation_type[1]: + if len(list(graph.in_edges(node))) > 0: + raise ValueError( + "The given binary relation can't be transformed into an edge because it plays a role in another " + "relation." + ) + roles = list(graph.edges(node, data=True)) # equivalent to out_edges() + assert len(roles) == 2 + new_edge_start = None + new_edge_end = None + for from_roleplayer, to_roleplayer, data in roles: + if data["type"] == binary_relation_type[0]: + if new_edge_start is None: + new_edge_start = to_roleplayer + else: + # In this case the given relation type is symmetric. Therefore direction is arbitrary (but + # expect to add a reverse edge elsewhere). + assert binary_relation_type[0] == binary_relation_type[2] + new_edge_end = to_roleplayer + elif data["type"] == binary_relation_type[2]: + new_edge_end = to_roleplayer + else: + raise ValueError( + f"Unexpected role in relation {binary_relation_type[1]}. Expected \"" + f"{binary_relation_type[0]}\" or \"{binary_relation_type[2]}\" but got \"{data['type']}\"." + ) + graph.remove_edge(from_roleplayer, to_roleplayer) + graph.add_edge(new_edge_start, new_edge_end, type=binary_relation_type[1]) + replacement_edges[(new_edge_start, new_edge_end)] = node + for node in replacement_edges.values(): + graph.remove_node(node) + return replacement_edges diff --git a/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py b/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py deleted file mode 100644 index a3412da2..00000000 --- a/kglib/kgcn_data_loader/transform/standard_kgcn_transform.py +++ /dev/null @@ -1,137 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import networkx as nx -from kglib.utils.graph.iterate import ( - multidigraph_node_data_iterator, - multidigraph_data_iterator, - multidigraph_edge_data_iterator, -) - -from kglib.kgcn_data_loader.encoding.standard_encode import encode_types, encode_values, stack_features -from kglib.kgcn_data_loader.utils import duplicate_edges_in_reverse - - -class StandardKGCNNetworkxTransform: - """Transform of the networkx graph as it comes out of TypeDB - to a networkx graph that pytorch geometric likes to ingest. - Now this is very much geared to pytorch geometric especially - because I set the attribute names to things like "x" and - "edge_attr" which are the standard names in pytorch geometric. - - One thing I encountered when trying to load the graph form the - original kglib example directly in pytorch geometric is that - since in the original example the feature vector on the nodes - and the edges were both called "features", the stock function - from pytorch geometric: torch_geometric.utils.from_networkx - does not deal with this well (it ends up overwriting node - features with edge features). - - :arg graph: networkx graph object - :returns: networkx graph object - - """ - - def __init__( - self, - node_types, - edge_types, - target_name, - obfuscate=None, - categorical=None, - continuous=None, - duplicate_in_reverse=True, - label_attribute="concept", - ): - self.node_types = node_types - self.edge_types = edge_types - self.target_name = target_name - self.obfuscate = obfuscate or {} - self.categorical = categorical or {} - self.continuous = continuous or {} - self.duplicate = duplicate_in_reverse - self.label_attribute = label_attribute - - def __call__(self, graph): - if self.obfuscate: - obfuscate_labels(graph, self.obfuscate) - # Encode attribute values as number - graph = encode_values(graph, self.categorical, self.continuous) - graph = nx.convert_node_labels_to_integers(graph, label_attribute=self.label_attribute) - if self.duplicate: - graph = duplicate_edges_in_reverse(graph) # TODO: This can use the equivalent function from PyTorch - # Node or Edge Type as int - graph = encode_types(graph, multidigraph_node_data_iterator, self.node_types) - graph = encode_types(graph, multidigraph_edge_data_iterator, self.edge_types) - - for data in multidigraph_node_data_iterator(graph): - features = create_feature_vector(data) - target = data[self.target_name] - data.clear() - data["x"] = features - data["y"] = target - - for data in multidigraph_edge_data_iterator(graph): - features = create_feature_vector(data) - target = data[self.target_name] - data.clear() - data["edge_attr"] = features - data["y_edge"] = target - - return graph - - -def create_feature_vector(node_or_edge_data_dict): - """Make a feature 3-dimensional feature vector, - - Factored out of kglib.kgcn_tensorflow.pipeline.encode.create_input_graph. - - Args: - node_or_edge_data_dict: the dict coming describing a node or edge - obtained from an element of graph.nodes(data=True) or graph.edges(data=True) - of a networkx graph. - - Returns: - Numpy array (vector) of stacked features - - """ - if node_or_edge_data_dict["solution"] == -1: - preexists = 1 - else: - preexists = 0 - features = stack_features( - [ - preexists, - node_or_edge_data_dict["categorical_type"], - node_or_edge_data_dict["encoded_value"], - ] - ) - return features - - -def obfuscate_labels(graph, types_and_roles_to_obfuscate): - """Taken directly from diagnosis.py from the kglib example""" - # Remove label leakage - change type labels that indicate candidates into non-candidates - for data in multidigraph_data_iterator(graph): - for label_to_obfuscate, with_label in types_and_roles_to_obfuscate.items(): - if data["type"] == label_to_obfuscate: - data.update(type=with_label) - break diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py new file mode 100644 index 00000000..cb8f67ae --- /dev/null +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -0,0 +1,122 @@ +# +# Copyright (C) 2021 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +from typing import List + +import numpy as np + +from kglib.utils.graph.iterate import ( + multidigraph_node_data_iterator, + multidigraph_edge_data_iterator, +) + + +class GraphFeatureEncoder: + """Transform of the networkx graph as it comes out of TypeDB + to a networkx graph that pytorch geometric likes to ingest. + Now this is very much geared to pytorch geometric especially + because I set the attribute names to things like "x" and + "edge_attr" which are the standard names in pytorch geometric. + + One thing I encountered when trying to load the graph form the + original kglib example directly in pytorch geometric is that + since in the original example the feature vector on the nodes + and the edges were both called "features", the stock function + from pytorch geometric: torch_geometric.utils.from_networkx + does not deal with this well (it ends up overwriting node + features with edge features). + + :arg graph: networkx graph object + :returns: networkx graph object + + """ + + def __init__(self, node_types, edge_types, attribute_encoders, attribute_encoding_size): + self.node_types = node_types + self.edge_types = edge_types + self.attribute_encoders = attribute_encoders + self.attribute_encoding_size = attribute_encoding_size + + def __call__(self, graph): + self.encode_node_features(graph) + self.encode_edge_features(graph) + return graph + + def encode_node_features(self, graph): + for node_data in multidigraph_node_data_iterator(graph): + typ = node_data['type'] + if typ in self.attribute_encoders.keys(): + # Add the integer value of the category for each categorical attribute instance + encoded_value = self.attribute_encoders[typ](node_data['value']) + assert len(encoded_value) == self.attribute_encoding_size + else: + # encoded_value = [0] * self.attribute_encoding_size + encoded_value = [] + + one_hot_encoded_type = self.node_types.index(node_data['type']) + node_data["x"] = np.hstack( + [np.array(one_hot_encoded_type, dtype=np.float32), np.array(encoded_value, dtype=np.float32)] + ) + + def encode_edge_features(self, graph): + for edge_data in multidigraph_edge_data_iterator(graph): + one_hot_encoded_type = self.edge_types.index(edge_data['type']) + edge_data["edge_attr"] = np.array(one_hot_encoded_type, dtype=np.float32) + + +class CategoricalEncoder: + + def __init__(self, categories: List): + self.categories = categories + + def __call__(self, value): + return self.categories.index(value) + + +class ContinuousEncoder: + def __init__(self, min_val, max_val): + self.max_val = max_val + self.min_val = min_val + + def __call__(self, value): + return (value - self.min_val) / (self.max_val - self.min_val) + + +# class SequenceEncoder(object): +# # The 'SequenceEncoder' encodes raw column strings into embeddings. +# def __init__(self, model_name='all-MiniLM-L6-v2', device=None): +# self.device = device +# self.model = SentenceTransformer(model_name, device=device) +# +# @torch.no_grad() +# def __call__(self, df): +# x = self.model.encode(df.values, show_progress_bar=True, +# convert_to_tensor=True, device=self.device) +# return x.cpu() +# +# +# class IdentityEncoder(object): +# # The 'IdentityEncoder' takes the raw column values and converts them to +# # PyTorch tensors. +# def __init__(self, dtype=None): +# self.dtype = dtype +# +# def __call__(self, df): +# return torch.from_numpy(df.values).view(-1, 1).to(self.dtype) diff --git a/kglib/kgcn_data_loader/utils.py b/kglib/kgcn_data_loader/utils.py index 6f4e493c..7e349859 100644 --- a/kglib/kgcn_data_loader/utils.py +++ b/kglib/kgcn_data_loader/utils.py @@ -20,11 +20,6 @@ # import subprocess as sp -from typing import List - -from typedb.client import * - -from kglib.utils.typedb.type.type import get_thing_types, get_role_types def load_typeql_schema_file(database, typedb_binary_location, typeql_file_path): @@ -47,22 +42,6 @@ def _load_typeql_file(database, typeql_file_path, typedb_binary_location, schema ], cwd=typedb_binary_location) -def duplicate_edges_in_reverse(graph): - """ - Takes in a directed multi graph, and creates duplicates of all edges, the duplicates having reversed direction to - the originals. This is useful since directed edges constrain the direction of messages passed. We want to permit - omni-directional message passing. - Args: - graph: The graph - - Returns: - The graph with duplicated edges, reversed, with all original edge properties attached to the duplicates - """ - for sender, receiver, keys, data in graph.edges(data=True, keys=True): - graph.add_edge(receiver, sender, keys, **data) - return graph - - def apply_logits_to_graphs(graph, logits_graph): """ Take in a graph that describes the logits of the graph of interest, and store those logits on the graph as the @@ -88,39 +67,3 @@ def apply_logits_to_graphs(graph, logits_graph): data['logits'] = list(logits_graph.edges[sender, receiver, keys]['features']) return graph - - -def get_node_types_for_training(session: TypeDBSession, types_to_ignore: List[str]) -> List[str]: - """ - Takes in a list of node types to ignore and returns all node types in schema that are not to be ignored. - - Args: - session: TypeDB rpc session of type SessionType.DATA - types_to_ignore: list of strings of schema type labels - - Returns: - list of strings of schema type labels to include in training - """ - with session.transaction(TransactionType.READ) as tx: - node_types = get_thing_types(tx) - [node_types.remove(el) for el in types_to_ignore] - print(f"Found node types: {node_types}") - return node_types - - -def get_edge_types_for_training(session: TypeDBSession, roles_to_ignore: List[str]) -> List[str]: - """ - Takes in a list of role types to ignore and returns all role types in schema that are not to be ignored. - - Args: - session: TypeDB rpc session of type SessionType.DATA - roles_to_ignore: list of strings of role type labels - - Returns: - list of strings of role type labels to include in training - """ - with session.transaction(TransactionType.READ) as tx: - edge_types = get_role_types(tx) - [edge_types.remove(el) for el in roles_to_ignore] - print(f"Found edge types: {edge_types}") - return edge_types diff --git a/kglib/kgcn_data_loader/utils_test.py b/kglib/kgcn_data_loader/utils_test.py index f5ec5cf8..32c76c7f 100644 --- a/kglib/kgcn_data_loader/utils_test.py +++ b/kglib/kgcn_data_loader/utils_test.py @@ -24,47 +24,9 @@ import networkx as nx import numpy as np -from kglib.utils.typedb.object.thing import Thing from kglib.utils.graph.test.case import GraphTestCase -from kglib.kgcn_data_loader.utils import duplicate_edges_in_reverse, apply_logits_to_graphs - - -class TestDuplicateEdgesInReverse(GraphTestCase): - - def test_edges_are_duplicated_as_expected(self): - graph = nx.MultiDiGraph(name=0) - - p0 = Thing('V123', 'person', 'entity') - p1 = Thing('V456', 'person', 'entity') - par0 = Thing('V789', 'parentship', 'relation') - - # people - graph.add_node(p0, type='person', solution=1) - graph.add_node(p1, type='person', solution=1) - - # parentships - graph.add_node(par0, type='parentship', solution=1) - graph.add_edge(par0, p0, type='parent', solution=1) - graph.add_edge(par0, p1, type='child', solution=1) - - duplicate_edges_in_reverse(graph) - - expected_graph = nx.MultiDiGraph(name=0) - - # people - expected_graph.add_node(p0, type='person', solution=1) - expected_graph.add_node(p1, type='person', solution=1) - - # parentships - expected_graph.add_node(par0, type='parentship', solution=1) - expected_graph.add_edge(par0, p0, type='parent', solution=1) - expected_graph.add_edge(par0, p1, type='child', solution=1) - - # Duplicates - expected_graph.add_edge(p0, par0, type='parent', solution=1) - expected_graph.add_edge(p1, par0, type='child', solution=1) - self.assertGraphsEqual(expected_graph, graph) +from kglib.kgcn_data_loader.utils import apply_logits_to_graphs class TestApplyLogitsToGraphs(GraphTestCase): diff --git a/kglib/utils/typedb/type/type.py b/kglib/utils/typedb/type/type.py index 07349c5d..bd2a774c 100644 --- a/kglib/utils/typedb/type/type.py +++ b/kglib/utils/typedb/type/type.py @@ -18,6 +18,8 @@ # specific language governing permissions and limitations # under the License. # +from typedb.api.connection.transaction import TransactionType + def get_thing_types(tx): """ @@ -49,7 +51,7 @@ def get_role_types(tx): return role_types -def get_role_triples(tx): +def get_role_triplets(tx): """ Get triples of all schema roles and the relation and roleplayer they connect Args: @@ -68,7 +70,7 @@ def get_role_triples(tx): return role_triples -def get_has_triples(tx): +def get_has_triplets(tx): """ Get triples of all ownerships: the owner type and owned attribute Args: @@ -85,3 +87,16 @@ def get_has_triples(tx): has_triples.append((owner, "has", owned)) return has_triples + +def get_edge_type_triplets(session): + # TODO: Naming is too close to get_types where the result is very different + with session.transaction(TransactionType.READ) as tx: + edge_types = get_role_triplets(tx) + get_has_triplets(tx) + return edge_types + + +def reverse_edge_type_triplets(edge_types): + reversed_edge_triples = [] + for edge_from, edge, edge_to in edge_types: + reversed_edge_triples.append((edge_to, f"rev_{edge}", edge_from)) + return reversed_edge_triples From d2a6f3d7a13cf469b58b9cb5e2fb12abde6a1ba1 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 5 Jul 2022 11:56:43 +0100 Subject: [PATCH 009/124] Properly prepare nodes and edges with necessary additions/deletions for binary link prediction --- kglib/examples/diagnosis/diagnosis.py | 44 +++++++++---------- .../transform/binary_link_prediction.py | 28 ++++++++++++ .../transform/typedb_graph_encoder.py | 1 - kglib/utils/typedb/type/type.py | 11 ++--- 4 files changed, 54 insertions(+), 30 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index dea662eb..79bf099a 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -31,28 +31,26 @@ from typedb.client import * from kglib.kgcn_data_loader.dataset.typedb_networkx_dataset import TypeDBNetworkxDataSet -from kglib.kgcn_data_loader.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges +from kglib.kgcn_data_loader.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges, \ + prepare_edge_triplets, prepare_node_types from kglib.kgcn_data_loader.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ ContinuousEncoder from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.utils.graph.iterate import multidigraph_data_iterator from kglib.utils.graph.query.query_graph import QueryGraph -from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data -from kglib.utils.typedb.type.type import get_thing_types, get_role_types, get_edge_type_triplets, reverse_edge_type_triplets +from kglib.utils.typedb.type.type import get_thing_types, get_role_types DATABASE = "diagnosis" ADDRESS = "localhost:1729" PREEXISTS = 0 -# Ignore any types or roles that exist in the TypeDB instance but which aren't being used for learning to reduce the +# Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed -TYPES_TO_IGNORE = [] -ROLES_TO_IGNORE = [] +TYPES_TO_IGNORE = {'person-at-risk'} # Note that this determines the edge direction when converting from a TypeDB relation -RELATION_TYPE_TO_PREDICT = ('patient', 'diagnosis', 'diagnosed-disease') +RELATION_TYPE_TO_PREDICT = ('person', 'patient', 'diagnosis', 'diagnosed-disease', 'disease') # The learner should see candidate relations the same as the ground truth relations, so adjust these candidates to # look like their ground truth counterparts @@ -94,7 +92,7 @@ def diagnosis_example(typedb_binary_directory, Final accuracies for training and for testing """ - # Delete the database each time + # Delete the database each time # TODO: Remove sp.check_call([ './typedb', 'console', @@ -110,20 +108,10 @@ def diagnosis_example(typedb_binary_directory, session = client.session(database, SessionType.DATA) - # TODO: This considers the node_type_and edge_type lists, but not the edge type triples that PyG takes in - # TODO: This means that the triples also need to be manipulated based on the conversion from a relation to a binary edge - node_types, edge_types = get_types(session, TYPES_TO_IGNORE, ROLES_TO_IGNORE) # During the transforms below we convert the *relations to predict* to simple edges, which means the relation # changes from a node to an edge. We therefore need to update the node_types and edge_types accordingly - node_types.remove(RELATION_TYPE_TO_PREDICT[1]) - edge_types.add(RELATION_TYPE_TO_PREDICT[1]) - # Remove the relation's roles from the edge_types list - edge_types.remove(RELATION_TYPE_TO_PREDICT[0]) - if RELATION_TYPE_TO_PREDICT[0] != RELATION_TYPE_TO_PREDICT[2]: - edge_types.remove(RELATION_TYPE_TO_PREDICT[2]) - - edge_type_triplets = get_edge_type_triplets(session) - edge_type_triplets_reversed = reverse_edge_type_triplets(edge_type_triplets) + node_types = prepare_node_types(session, RELATION_TYPE_TO_PREDICT, TYPES_TO_IGNORE) + edge_type_triplets, edge_type_triplets_reversed = prepare_edge_triplets(session, RELATION_TYPE_TO_PREDICT, TYPES_TO_IGNORE) # Attribute encoders encode the value of each attribute into a fixed-length feature vector. The encoders are # defined on a per-type basis. Easily define your own encoders for specific attribute data in your TypeDB database @@ -131,7 +119,9 @@ def diagnosis_example(typedb_binary_directory, attribute_encoders = { # Categorical Attribute types and the values of their categories # TODO: Use a sentence encoder for this instead to demonstrate how to use one - 'name': CategoricalEncoder(['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol']), + 'name': CategoricalEncoder( + ['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol'] + ), # Continuous Attribute types and their min and max values 'severity': ContinuousEncoder(0, 1), 'age': ContinuousEncoder(7, 80), @@ -140,13 +130,14 @@ def diagnosis_example(typedb_binary_directory, def prepare_graph(graph): # TODO: We likely need to know the relations that were replaced with binary edges later on - replaced_edges = binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT), + replaced_edges = binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT[1:4]), return nx.convert_node_labels_to_integers(graph, label_attribute="concept") + edge_types = list({triplet[1] for triplet in edge_type_triplets}) transform = transforms.Compose([ prepare_graph, GraphFeatureEncoder(node_types, edge_types, attribute_encoders, attribute_encoding_size), - LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[1]) + LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[2]) ]) # TODO: Consider clearing unneeded node and edge data @@ -441,3 +432,8 @@ def write_predictions_to_typedb(graphs, tx): f'has probability-preexists {p[0]:.3f};') tx.query().insert(query) tx.commit() + + +if __name__ == '__main__': + # TODO: Remove + diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", num_graphs=6) diff --git a/kglib/kgcn_data_loader/transform/binary_link_prediction.py b/kglib/kgcn_data_loader/transform/binary_link_prediction.py index 74f9c066..0df4b417 100644 --- a/kglib/kgcn_data_loader/transform/binary_link_prediction.py +++ b/kglib/kgcn_data_loader/transform/binary_link_prediction.py @@ -21,6 +21,7 @@ import networkx as nx from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator +from kglib.utils.typedb.type.type import get_edge_type_triplets, reverse_edge_type_triplets, get_thing_types class LinkPredictionLabeller: @@ -100,3 +101,30 @@ def binary_relations_to_edges(graph: nx.MultiDiGraph, binary_relation_type): for node in replacement_edges.values(): graph.remove_node(node) return replacement_edges + + +def prepare_edge_triplets(session, relation_type_to_predict, types_to_ignore): + edge_type_triplets = get_edge_type_triplets(session) + edge_type_triplets = [e for e in edge_type_triplets if not (set(e).intersection(types_to_ignore))] + replace_relation_with_binary_edge(edge_type_triplets, relation_type_to_predict) + edge_type_triplets_reversed = reverse_edge_type_triplets(edge_type_triplets) + return edge_type_triplets, edge_type_triplets_reversed + + +def replace_relation_with_binary_edge(edge_type_triplets, relation_type_to_predict): + # Remove the two roles of the relation_to_predict and replace them with a binary edge that uses the name of the + # binary relation it represents + edge_type_triplets.remove(relation_type_to_predict[2:]) + if relation_type_to_predict[1] != relation_type_to_predict[3]: + edge_type_triplets.remove(tuple(reversed(relation_type_to_predict[:3]))) + edge_type_triplets.append((relation_type_to_predict[0], relation_type_to_predict[2], relation_type_to_predict[4])) + + +def prepare_node_types(session, relation_type_to_predict, types_to_ignore): + node_types = get_thing_types(session) + for el in types_to_ignore: + if el in node_types: + node_types.remove(el) + # Remove the relation_to_predict as a node, since we will be using it as a binary edge instead + node_types.remove(relation_type_to_predict[2]) + return node_types diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index cb8f67ae..6181e021 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -65,7 +65,6 @@ def encode_node_features(self, graph): if typ in self.attribute_encoders.keys(): # Add the integer value of the category for each categorical attribute instance encoded_value = self.attribute_encoders[typ](node_data['value']) - assert len(encoded_value) == self.attribute_encoding_size else: # encoded_value = [0] * self.attribute_encoding_size encoded_value = [] diff --git a/kglib/utils/typedb/type/type.py b/kglib/utils/typedb/type/type.py index bd2a774c..be670798 100644 --- a/kglib/utils/typedb/type/type.py +++ b/kglib/utils/typedb/type/type.py @@ -21,17 +21,18 @@ from typedb.api.connection.transaction import TransactionType -def get_thing_types(tx): +def get_thing_types(session): """ Get all schema types, excluding those for implicit attribute relations and base types Args: - tx: TypeDB transaction + session: TypeDB session Returns: TypeDB types """ - schema_concepts = tx.query().match("match $x sub thing;") - thing_types = [schema_concept.get('x').get_label().name() for schema_concept in schema_concepts] + with session.transaction(TransactionType.READ) as tx: + schema_concepts = tx.query().match("match $x sub thing;") + thing_types = [schema_concept.get('x').get_label().name() for schema_concept in schema_concepts] [thing_types.remove(el) for el in ['thing', 'relation', 'entity', 'attribute']] return thing_types @@ -80,7 +81,7 @@ def get_has_triplets(tx): TypeDB ownership triples """ has_triples = [] - schema_concepts = tx.query().match("match $owner sub relation owns $owned;") + schema_concepts = tx.query().match("match $owner sub relation, owns $owned;") for answer in schema_concepts: owner = answer.get('owner').get_label().name() owned = answer.get('owned').get_label().name() From 55da3db5f4d72ed88ea7e8411a3abd96296dfbcf Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 5 Jul 2022 12:21:09 +0100 Subject: [PATCH 010/124] Include all role supertypes in case the user uses them in their queries (as in the diagnosis example) --- kglib/examples/diagnosis/diagnosis.py | 17 +---------------- kglib/utils/typedb/type/type.py | 21 ++++----------------- 2 files changed, 5 insertions(+), 33 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 79bf099a..b08e4345 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -38,7 +38,6 @@ from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data -from kglib.utils.typedb.type.type import get_thing_types, get_role_types DATABASE = "diagnosis" ADDRESS = "localhost:1729" @@ -47,7 +46,7 @@ # Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed -TYPES_TO_IGNORE = {'person-at-risk'} +TYPES_TO_IGNORE = {} # Note that this determines the edge direction when converting from a TypeDB relation RELATION_TYPE_TO_PREDICT = ('person', 'patient', 'diagnosis', 'diagnosed-disease', 'disease') @@ -57,20 +56,6 @@ TYPES_AND_ROLES_TO_OBFUSCATE = {} -def get_types(session, types_to_ignore, roles_to_ignore): - with session.transaction(TransactionType.READ) as tx: - # The terminology changes from here onwards from thing -> node and role -> edge - node_types = get_thing_types(tx) - edge_types = get_role_types(tx) - # Ignore any types or roles that exist in the TypeDB instance but which aren't being used for learning to - # reduce the number of categories to embed - [node_types.remove(el) for el in types_to_ignore] - [edge_types.remove(el) for el in roles_to_ignore] - print(f'Found node types: {node_types}') - print(f'Found edge types: {edge_types}') - return node_types, edge_types - - def diagnosis_example(typedb_binary_directory, num_graphs=100, database=DATABASE, diff --git a/kglib/utils/typedb/type/type.py b/kglib/utils/typedb/type/type.py index be670798..992a5a8f 100644 --- a/kglib/utils/typedb/type/type.py +++ b/kglib/utils/typedb/type/type.py @@ -37,21 +37,6 @@ def get_thing_types(session): return thing_types -def get_role_types(tx): - """ - Get all schema roles, excluding the base role type - Args: - tx: TypeDB transaction - - Returns: - TypeDB roles - """ - schema_concepts = tx.query().match("match $rel sub relation, relates $r;") - role_types = ['has'] + [role.get('r').get_label().name() for role in schema_concepts] - role_types.remove('role') - return role_types - - def get_role_triplets(tx): """ Get triples of all schema roles and the relation and roleplayer they connect @@ -65,9 +50,11 @@ def get_role_triplets(tx): schema_concepts = tx.query().match("match $rel sub relation; $rel relates $r; $rp plays $r;") for answer in schema_concepts: relation = answer.get('rel').get_label().name() - role = answer.get('r').get_label().name() + roles = [r.get_label().name() for r in answer.get('r').as_remote(tx).get_supertypes()] + roles.remove('role') player = answer.get("rp").get_label().name() - role_triples.append((relation, role, player)) + for role in roles: + role_triples.append((relation, role, player)) return role_triples From aabe9f8906178dbddd8667c2583660e8f1bd83b0 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 5 Jul 2022 17:56:12 +0100 Subject: [PATCH 011/124] Finish conversion to a HeteroData object --- kglib/examples/diagnosis/diagnosis.py | 36 ++++++++++++++++--- .../dataset/typedb_networkx_dataset.py | 19 +++++++++- .../transform/binary_link_prediction.py | 1 + .../transform/typedb_graph_encoder.py | 11 +++--- kglib/utils/typedb/type/type.py | 2 +- 5 files changed, 58 insertions(+), 11 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index b08e4345..4aeb0ce5 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -24,6 +24,7 @@ import networkx as nx import torch +from torch import as_tensor import torch.nn.functional as functional import torch_geometric.transforms as transforms from torch.nn import Linear @@ -36,6 +37,7 @@ from kglib.kgcn_data_loader.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ ContinuousEncoder from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file +from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data @@ -60,6 +62,7 @@ def diagnosis_example(typedb_binary_directory, num_graphs=100, database=DATABASE, address=ADDRESS, + # TODO: remove hard-coding schema_file_path="/Users/jamesfletcher/programming/research/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql", seed_data_file_path="/Users/jamesfletcher/programming/research/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql"): """ @@ -118,19 +121,44 @@ def prepare_graph(graph): replaced_edges = binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT[1:4]), return nx.convert_node_labels_to_integers(graph, label_attribute="concept") + def clear_unneeded_fields(graph): + for node_data in multidigraph_node_data_iterator(graph): + x = node_data["x"] + # y = node_data["y"] + t = node_data["type"] + node_data.clear() + node_data["x"] = x + # node_data["y"] = y + node_data["type"] = t + + for edge_data in multidigraph_edge_data_iterator(graph): + x = edge_data["edge_attr"] + y = edge_data["y_edge"] + t = edge_data["type"] + edge_data.clear() + edge_data["edge_attr"] = x + edge_data["y_edge"] = y + edge_data["type"] = t + return graph + edge_types = list({triplet[1] for triplet in edge_type_triplets}) transform = transforms.Compose([ prepare_graph, GraphFeatureEncoder(node_types, edge_types, attribute_encoders, attribute_encoding_size), - LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[2]) + LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[2]), + clear_unneeded_fields ]) - # TODO: Consider clearing unneeded node and edge data # Create a Dataset that will load graphs from TypeDB on-demand, based on an ID - dataset = TypeDBNetworkxDataSet([0], get_query_handles, DATABASE, ADDRESS, session, True, transform) + dataset = TypeDBNetworkxDataSet( + [0], node_types, edge_type_triplets, get_query_handles, DATABASE, ADDRESS, session, True, transform + ) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - data = dataset[0].to(device) # Get the first graph object. + data, node_type_indices, edge_type_indices = dataset[0] + data = data.to_heterogeneous( + as_tensor(node_type_indices), as_tensor(edge_type_indices), node_types, edge_type_triplets + ).to(device) # Get the first graph object. # TODO: Consider whether we want to add reverse edges for the edge type being predicted and whether to do the # before or after other transforms diff --git a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py b/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py index 62ed4707..f3475aa4 100644 --- a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py +++ b/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py @@ -37,6 +37,8 @@ class TypeDBNetworkxDataSet: def __init__( self, indices: Sequence, + node_types, + edge_type_triplets, get_query_handles_for_id: Callable, database: Optional[str] = None, uri: Optional[str] = "localhost:1729", @@ -46,6 +48,8 @@ def __init__( ): assert (database and uri) or session self._indices = indices + self._node_types = node_types + self._edge_type_triplets = edge_type_triplets self.get_query_handles_for_id = get_query_handles_for_id self._infer = infer self._transform = transform @@ -87,4 +91,17 @@ def __getitem__(self, idx): graph.name = id if self._transform: graph = self._transform(graph) - return from_networkx(graph).to_heterogeneous(node_type, edge_type, node_type_names, edge_type_names) + return from_networkx(graph), self.node_type_indices(graph), self.edge_type_indices(graph) + + def node_type_indices(self, graph): + indices = [] + for _, data in graph.nodes(data=True): + indices.append(self._node_types.index(data['type'])) + return indices + + def edge_type_indices(self, graph): + indices = [] + for src, dst, data in graph.edges(data=True): + # indices.append(self._edge_type_triplets.index(data['type'])) + indices.append(self._edge_type_triplets.index((graph.nodes[src]["type"], data["type"], graph.nodes[dst]["type"]))) + return indices diff --git a/kglib/kgcn_data_loader/transform/binary_link_prediction.py b/kglib/kgcn_data_loader/transform/binary_link_prediction.py index 0df4b417..ed861f07 100644 --- a/kglib/kgcn_data_loader/transform/binary_link_prediction.py +++ b/kglib/kgcn_data_loader/transform/binary_link_prediction.py @@ -31,6 +31,7 @@ def __init__(self, edge_type_to_predict): def __call__(self, graph): self.label_edges(graph) + return graph # def label_nodes(self, graph): # for data in multidigraph_node_data_iterator(graph): diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index 6181e021..c161d951 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -67,17 +67,18 @@ def encode_node_features(self, graph): encoded_value = self.attribute_encoders[typ](node_data['value']) else: # encoded_value = [0] * self.attribute_encoding_size - encoded_value = [] + encoded_value = [0] one_hot_encoded_type = self.node_types.index(node_data['type']) - node_data["x"] = np.hstack( + node_data['x'] = np.hstack( [np.array(one_hot_encoded_type, dtype=np.float32), np.array(encoded_value, dtype=np.float32)] ) def encode_edge_features(self, graph): for edge_data in multidigraph_edge_data_iterator(graph): one_hot_encoded_type = self.edge_types.index(edge_data['type']) - edge_data["edge_attr"] = np.array(one_hot_encoded_type, dtype=np.float32) + # TODO: Unnecessary empty array added to have common features size between nodes and edges + edge_data['edge_attr'] = np.hstack([np.array(one_hot_encoded_type, dtype=np.float32), np.array([0], dtype=np.float32)]) class CategoricalEncoder: @@ -86,7 +87,7 @@ def __init__(self, categories: List): self.categories = categories def __call__(self, value): - return self.categories.index(value) + return [self.categories.index(value)] class ContinuousEncoder: @@ -95,7 +96,7 @@ def __init__(self, min_val, max_val): self.min_val = min_val def __call__(self, value): - return (value - self.min_val) / (self.max_val - self.min_val) + return [(value - self.min_val) / (self.max_val - self.min_val)] # class SequenceEncoder(object): diff --git a/kglib/utils/typedb/type/type.py b/kglib/utils/typedb/type/type.py index 992a5a8f..2487be46 100644 --- a/kglib/utils/typedb/type/type.py +++ b/kglib/utils/typedb/type/type.py @@ -68,7 +68,7 @@ def get_has_triplets(tx): TypeDB ownership triples """ has_triples = [] - schema_concepts = tx.query().match("match $owner sub relation, owns $owned;") + schema_concepts = tx.query().match("match $owner sub thing, owns $owned;") for answer in schema_concepts: owner = answer.get('owner').get_label().name() owned = answer.get('owned').get_label().name() From 05456fb984c3e9bff2015620d37cba67d119022f Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 6 Jul 2022 10:42:35 +0100 Subject: [PATCH 012/124] Give the random link splitter only the edge we want to predict --- kglib/examples/diagnosis/diagnosis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 4aeb0ce5..82ea212c 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -174,8 +174,8 @@ def clear_unneeded_fields(graph): num_test=0.1, add_negative_train_samples=True, neg_sampling_ratio=1.0, - edge_types=edge_type_triplets, - rev_edge_types=edge_type_triplets_reversed, + edge_types=('person', 'diagnosis', 'disease'), + rev_edge_types=('disease', 'rev_diagnosis', 'person'), )(data) class GNNEncoder(torch.nn.Module): From 49f6768992642965a0028dea49da8b3c2b282e01 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 6 Jul 2022 10:49:30 +0100 Subject: [PATCH 013/124] Remove candidate-diagnosis, kgcn and doctor concepts from the diagnosis example. --- kglib/examples/diagnosis/diagnosis.py | 14 ------------- .../synthetic/examples/diagnosis/generate.py | 4 ++-- .../synthetic/examples/diagnosis/schema.tql | 21 ------------------- .../examples/diagnosis/seed_data.tql | 3 +-- 4 files changed, 3 insertions(+), 39 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 82ea212c..adfe214f 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -383,23 +383,9 @@ def get_query_handles(example_id): .add_role_edge(diag, d, 'diagnosed-disease', PREEXISTS) .add_role_edge(diag, p, 'patient', PREEXISTS)) - # === Candidate Diagnosis === - candidate_diagnosis_query = inspect.cleandoc(f'''match - $p isa person; - $d isa disease, has name $dn; - $diag(candidate-patient: $p, candidate-diagnosed-disease: $d) isa candidate-diagnosis; - ''') - - candidate_diagnosis_query_graph = (QueryGraph() - .add_vars([diag], PREEXISTS) - .add_vars([d, p, dn], PREEXISTS) - .add_role_edge(diag, d, 'candidate-diagnosed-disease', PREEXISTS) - .add_role_edge(diag, p, 'candidate-patient', PREEXISTS)) - return [ (symptom_query, lambda x: x, symptom_query_graph), (diagnosis_query, lambda x: x, diagnosis_query_graph), - (candidate_diagnosis_query, lambda x: x, candidate_diagnosis_query_graph), (risk_factor_query, lambda x: x, risk_factor_query_graph), (person_age_query, lambda x: x, person_age_query_graph), (consumption_query, lambda x: x, consumption_query_graph), diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py index 625df675..c798eeab 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py @@ -41,7 +41,7 @@ def get_example_queries(pmf, example_id): $p isa person, has example-id {example_id}; $doc isa person, has example-id {20000 + example_id}; insert - $diagnosis (patient: $p, diagnosed-disease: $d, doctor: $doc) isa diagnosis; + $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; $p has age {int(variable_values['Multiple Sclerosis']['age']())};''')) if variable_values['Diabetes Type II'] is not False: @@ -51,7 +51,7 @@ def get_example_queries(pmf, example_id): $d isa disease, has name "Diabetes Type II"; $doc isa person, has example-id {20000 + example_id}; insert - $diagnosis (patient: $p, diagnosed-disease: $d, doctor: $doc) isa diagnosis; + $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; $p has age {int(variable_values['Diabetes Type II']['age']())};''')) if variable_values['Fatigue'] is not False: diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql index 2de2f633..73f79a65 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql @@ -40,8 +40,6 @@ person sub entity, owns example-id @key, owns age, plays diagnosis:patient, - plays diagnosis:doctor, - plays candidate-diagnosis:candidate-patient, plays symptom-presentation:symptomatic-patient, plays consumption:consumer, plays age-risk-factor:person-at-age-risk, @@ -55,7 +53,6 @@ disease sub entity, owns name @key, plays causality:cause, plays diagnosis:diagnosed-disease, - plays candidate-diagnosis:candidate-diagnosed-disease, plays age-risk-factor:age-risked-disease, plays hereditary-risk-factor:hereditary-risked-disease, plays smoking-risk-factor:smoking-risked-disease, @@ -72,16 +69,10 @@ parentship sub relation, diagnosis sub relation, relates patient, relates diagnosed-disease, - relates doctor, - relates diagnoser, owns probability-exists, owns probability-non-exists, owns probability-preexists; -candidate-diagnosis sub relation, - relates candidate-patient, - relates candidate-diagnosed-disease; - probability-exists sub attribute, value double; @@ -91,18 +82,6 @@ probability-non-exists sub attribute, probability-preexists sub attribute, value double; -kgcn sub entity, - plays diagnosis:diagnoser; - -rule where-no-diagnosis-add-candidate-diagnosis: - when { - $p isa person; - $d isa disease; - not{ (patient: $p, diagnosed-disease: $d) isa diagnosis; }; - } then { - (candidate-patient: $p, candidate-diagnosed-disease: $d) isa candidate-diagnosis; - }; - causality sub relation, relates cause, relates effect; diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql b/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql index 966a4a55..1a576bb2 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql @@ -46,5 +46,4 @@ $vision isa symptom, has name "Blurred vision"; insert $c3(cause: $diabetes, effect: $vision) isa causality; insert $s isa substance, has name "Alcohol"; -insert $s isa substance, has name "Cigarettes"; -insert $kgcn isa kgcn; \ No newline at end of file +insert $s isa substance, has name "Cigarettes"; \ No newline at end of file From 756bbe74cecd9bbcc2a4ad93268e79cfeebe6e8e Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 6 Jul 2022 16:39:52 +0100 Subject: [PATCH 014/124] Handle an error for missing data and tell the user what to do --- kglib/examples/diagnosis/diagnosis.py | 41 +++++++++++++------ .../transform/typedb_graph_encoder.py | 2 + 2 files changed, 31 insertions(+), 12 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index adfe214f..a3879e9c 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -48,8 +48,8 @@ # Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed -TYPES_TO_IGNORE = {} - +TYPES_TO_IGNORE = {'substance', 'probability-exists', 'units-per-week', 'probability-preexists', 'smoking-risk-factor', + 'example-id', 'consumption', 'risk-factor', 'probability-non-exists', 'alcohol-risk-factor'} # Note that this determines the edge direction when converting from a TypeDB relation RELATION_TYPE_TO_PREDICT = ('person', 'patient', 'diagnosis', 'diagnosed-disease', 'disease') @@ -172,10 +172,9 @@ def clear_unneeded_fields(graph): train_data, val_data, test_data = transforms.RandomLinkSplit( num_val=0.1, num_test=0.1, - add_negative_train_samples=True, - neg_sampling_ratio=1.0, - edge_types=('person', 'diagnosis', 'disease'), - rev_edge_types=('disease', 'rev_diagnosis', 'person'), + neg_sampling_ratio=0.0, + edge_types=RELATION_TYPE_TO_PREDICT[::2], # Evaluates to: ('person', 'diagnosis', 'disease'), + rev_edge_types=edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person'), )(data) class GNNEncoder(torch.nn.Module): @@ -197,7 +196,7 @@ def __init__(self, hidden_channels): def forward(self, z_dict, edge_label_index): row, col = edge_label_index - z = torch.cat([z_dict['user'][row], z_dict['movie'][col]], dim=-1) + z = torch.cat([z_dict['person'][row], z_dict['disease'][col]], dim=-1) z = self.lin1(z).relu() z = self.lin2(z) @@ -219,7 +218,25 @@ def forward(self, x_dict, edge_index_dict, edge_label_index): # Due to lazy initialization, we need to run one model step so the number # of parameters can be inferred: with torch.no_grad(): - model.encoder(train_data.x_dict, train_data.edge_index_dict) + try: + model.encoder(train_data.x_dict, train_data.edge_index_dict) + except AttributeError as e: + if str(e) == "'NoneType' object has no attribute 'dim'": + # Node types with no instances need to be ignored + to_ignore = set() + for key, value in train_data.x_dict.items(): + if len(value) == 0: + to_ignore.add(key) + for key, value in train_data.edge_index_dict.items(): + if len(value) == 0 and to_ignore.intersection(set(key)): + to_ignore.add(key[1]) + print(to_ignore) + raise ValueError( + f'This pipeline requires all un-ignored types to have instances. Some types don\'t have instances. ' + f'Try adding/retrieving data for the following types or add them to the ignored types:\n{to_ignore}' + ) + else: + raise e optimizer = torch.optim.Adam(model.parameters(), lr=0.01) @@ -231,8 +248,8 @@ def train(): model.train() optimizer.zero_grad() pred = model(train_data.x_dict, train_data.edge_index_dict, - train_data['user', 'movie'].edge_label_index) - target = train_data['user', 'movie'].edge_label + train_data['person', 'disease'].edge_label_index) + target = train_data['person', 'disease'].edge_label loss = weighted_mse_loss(pred, target, None) # TODO: Consider using weighting to balance the positive/negative example sets loss.backward() optimizer.step() @@ -242,9 +259,9 @@ def train(): def test(data): model.eval() pred = model(data.x_dict, data.edge_index_dict, - data['user', 'movie'].edge_label_index) + data['person', 'disease'].edge_label_index) pred = pred.clamp(min=0, max=5) - target = data['user', 'movie'].edge_label.float() + target = data['person', 'disease'].edge_label.float() rmse = functional.mse_loss(pred, target).sqrt() return float(rmse) diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index c161d951..6af14034 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -69,6 +69,8 @@ def encode_node_features(self, graph): # encoded_value = [0] * self.attribute_encoding_size encoded_value = [0] + # TODO: We can skip encoding type information as we are using the hetero models from PyTorch to take care + # of that for us one_hot_encoded_type = self.node_types.index(node_data['type']) node_data['x'] = np.hstack( [np.array(one_hot_encoded_type, dtype=np.float32), np.array(encoded_value, dtype=np.float32)] From 8d1e8f888acbb42056342f54edaff593ee78bbde Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 6 Jul 2022 17:52:32 +0100 Subject: [PATCH 015/124] Some fixes for the generated data --- kglib/examples/diagnosis/diagnosis.py | 7 +++--- .../synthetic/examples/diagnosis/generate.py | 24 +++++++------------ .../synthetic/examples/diagnosis/schema.tql | 4 ++-- 3 files changed, 14 insertions(+), 21 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index a3879e9c..875ef938 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -48,8 +48,7 @@ # Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed -TYPES_TO_IGNORE = {'substance', 'probability-exists', 'units-per-week', 'probability-preexists', 'smoking-risk-factor', - 'example-id', 'consumption', 'risk-factor', 'probability-non-exists', 'alcohol-risk-factor'} +TYPES_TO_IGNORE = {'risk-factor', 'person-id', 'probability-preexists', 'probability-exists', 'probability-non-exists'} # Note that this determines the edge direction when converting from a TypeDB relation RELATION_TYPE_TO_PREDICT = ('person', 'patient', 'diagnosis', 'diagnosed-disease', 'disease') @@ -59,7 +58,7 @@ def diagnosis_example(typedb_binary_directory, - num_graphs=100, + num_graphs, database=DATABASE, address=ADDRESS, # TODO: remove hard-coding @@ -452,4 +451,4 @@ def write_predictions_to_typedb(graphs, tx): if __name__ == '__main__': # TODO: Remove - diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", num_graphs=6) + diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", 50) diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py index c798eeab..62760287 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py @@ -31,15 +31,13 @@ def get_example_queries(pmf, example_id): variable_values = pmf.select() - queries = [f'insert $p isa person, has example-id {example_id};', - f'insert $doc isa person, has example-id {20000 + example_id};'] + queries = [f'insert $p isa person, has person-id {example_id};'] if variable_values['Multiple Sclerosis'] is not False: queries.append(inspect.cleandoc(f''' match $d isa disease, has name "Multiple Sclerosis"; - $p isa person, has example-id {example_id}; - $doc isa person, has example-id {20000 + example_id}; + $p isa person, has person-id {example_id}; insert $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; $p has age {int(variable_values['Multiple Sclerosis']['age']())};''')) @@ -47,9 +45,8 @@ def get_example_queries(pmf, example_id): if variable_values['Diabetes Type II'] is not False: queries.append(inspect.cleandoc(f''' match - $p isa person, has example-id {example_id}; + $p isa person, has person-id {example_id}; $d isa disease, has name "Diabetes Type II"; - $doc isa person, has example-id {20000 + example_id}; insert $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; $p has age {int(variable_values['Diabetes Type II']['age']())};''')) @@ -57,7 +54,7 @@ def get_example_queries(pmf, example_id): if variable_values['Fatigue'] is not False: queries.append(inspect.cleandoc(f''' match - $p isa person, has example-id {example_id}; + $p isa person, has person-id {example_id}; $s isa symptom, has name "Fatigue"; insert $sp (presented-symptom: $s, symptomatic-patient: $p) isa @@ -66,7 +63,7 @@ def get_example_queries(pmf, example_id): if variable_values['Blurred vision'] is not False: queries.append(inspect.cleandoc(f''' match - $p isa person, has example-id {example_id}; + $p isa person, has person-id {example_id}; $s isa symptom, has name "Blurred vision"; insert $sp (presented-symptom: $s, symptomatic-patient: $p) isa @@ -75,7 +72,7 @@ def get_example_queries(pmf, example_id): if variable_values['Drinking'] is not False: queries.append(inspect.cleandoc(f''' match - $p isa person, has example-id {example_id}; + $p isa person, has person-id {example_id}; $s isa substance, has name "Alcohol"; insert $c (consumer: $p, consumed-substance: $s) isa consumption, @@ -84,18 +81,18 @@ def get_example_queries(pmf, example_id): if variable_values['Parent has Diabetes Type II'] is not False: queries.append(inspect.cleandoc(f''' match - $p isa person, has example-id {example_id}; + $p isa person, has person-id {example_id}; $d isa disease, has name "Diabetes Type II"; insert (parent: $parent, child: $p) isa parentship; - $parent isa person, has example-id {example_id + 10000}; + $parent isa person, has person-id {example_id + 10000}; $diagnosis (patient: $parent, diagnosed-disease: $d) isa diagnosis; ''')) if variable_values['Cigarettes'] is not False: queries.append(inspect.cleandoc(f''' match - $p isa person, has example-id {example_id}; + $p isa person, has person-id {example_id}; $s isa substance, has name "Cigarettes"; insert $c (consumer: $p, consumed-substance: $s) isa consumption, @@ -135,12 +132,9 @@ def normal_dist(mean, var): 'Cigarettes': [False, {'units-per-week': normal_dist(5, 1)}, {'units-per-week': normal_dist(20, 3)}], }, pmf_array, seed=0) - # print(pmf.to_dataframe()) # TODO Remove pandas if this is not needed now - for example_id in range(0, num_examples): tx = session.transaction(TransactionType.WRITE) for query in get_example_queries(pmf, example_id): - #print(query) tx.query().insert(query) tx.commit() diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql index 73f79a65..60c1531b 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql @@ -21,7 +21,7 @@ define -example-id sub attribute, +person-id sub attribute, value long; age sub attribute, @@ -37,7 +37,7 @@ units-per-week sub attribute, value long; person sub entity, - owns example-id @key, + owns person-id @key, owns age, plays diagnosis:patient, plays symptom-presentation:symptomatic-patient, From 715e86fe48765f14d913f5fc9bf50764a141b10d Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 7 Jul 2022 12:59:17 +0100 Subject: [PATCH 016/124] There were more missing types to be detected, now fixed, but encoder still throws an error --- kglib/examples/diagnosis/diagnosis.py | 32 ++++++++++++++++----------- 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 875ef938..ce00b5db 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -48,7 +48,10 @@ # Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed -TYPES_TO_IGNORE = {'risk-factor', 'person-id', 'probability-preexists', 'probability-exists', 'probability-non-exists'} +TYPES_TO_IGNORE = {'risk-factor', 'person-id', 'probability-preexists', 'probability-exists', 'probability-non-exists', + 'alcohol-risked-disease', 'person-at-alcohol-risk', 'person-at-hereditary-risk', + 'hereditary-risked-disease', 'smoking-risked-disease', 'person-at-smoking-risk', + 'person-at-age-risk', 'age-risked-disease'} # Note that this determines the edge direction when converting from a TypeDB relation RELATION_TYPE_TO_PREDICT = ('person', 'patient', 'diagnosis', 'diagnosed-disease', 'disease') @@ -219,17 +222,21 @@ def forward(self, x_dict, edge_index_dict, edge_label_index): with torch.no_grad(): try: model.encoder(train_data.x_dict, train_data.edge_index_dict) - except AttributeError as e: - if str(e) == "'NoneType' object has no attribute 'dim'": - # Node types with no instances need to be ignored - to_ignore = set() - for key, value in train_data.x_dict.items(): - if len(value) == 0: - to_ignore.add(key) - for key, value in train_data.edge_index_dict.items(): - if len(value) == 0 and to_ignore.intersection(set(key)): + except Exception as e: + # if str(e) == "'NoneType' object has no attribute 'dim'": + # Node types with no instances need to be ignored + to_ignore = set() + for key, value in train_data.x_dict.items(): + if torch.numel(value) == 0: + to_ignore.add(key) + for key, value in train_data.edge_index_dict.items(): + if torch.numel(value) == 0: + if not to_ignore.intersection(set(key)): + to_ignore.add(key[0]) to_ignore.add(key[1]) - print(to_ignore) + to_ignore.add(key[2]) + to_ignore = {i for i in to_ignore if not i[:4] == "rev_"} + if to_ignore: raise ValueError( f'This pipeline requires all un-ignored types to have instances. Some types don\'t have instances. ' f'Try adding/retrieving data for the following types or add them to the ignored types:\n{to_ignore}' @@ -439,9 +446,8 @@ def write_predictions_to_typedb(graphs, tx): query = (f'match ' f'$p iid {person.iid};' f'$d iid {disease.iid};' - f'$kgcn isa kgcn;' f'insert ' - f'$pd(patient: $p, diagnosed-disease: $d, diagnoser: $kgcn) isa diagnosis,' + f'$pd(patient: $p, diagnosed-disease: $d) isa diagnosis,' f'has probability-exists {p[2]:.3f},' f'has probability-non-exists {p[1]:.3f},' f'has probability-preexists {p[0]:.3f};') From 552ee34313b10ed71ff3c19fd1fcba23191c1a71 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 7 Jul 2022 18:19:10 +0100 Subject: [PATCH 017/124] Use a different model, called HAN. Evades several errors that perhaps came from the conversion of a homogeneous graph network into a heterogeneous one. Seemed impossible to debug the call graph creation in pytorch. Now the model runs, but the current issue is getting the model to see the negative samples too. Perhaps a different approach is required (NxN adjacency for the edge concerned). --- kglib/examples/diagnosis/diagnosis.py | 145 +++++++++++--------------- 1 file changed, 59 insertions(+), 86 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index ce00b5db..700a944c 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -28,7 +28,7 @@ import torch.nn.functional as functional import torch_geometric.transforms as transforms from torch.nn import Linear -from torch_geometric.nn import SAGEConv, to_hetero +from torch_geometric.nn import HANConv from typedb.client import * from kglib.kgcn_data_loader.dataset.typedb_networkx_dataset import TypeDBNetworkxDataSet @@ -174,110 +174,83 @@ def clear_unneeded_fields(graph): train_data, val_data, test_data = transforms.RandomLinkSplit( num_val=0.1, num_test=0.1, - neg_sampling_ratio=0.0, + neg_sampling_ratio=1.0, + # edge_types=edge_type_triplets, # Evaluates to: ('person', 'diagnosis', 'disease'), + # rev_edge_types=edge_type_triplets_reversed, # Evaluates to: ('disease', 'rev_diagnosis', 'person'), edge_types=RELATION_TYPE_TO_PREDICT[::2], # Evaluates to: ('person', 'diagnosis', 'disease'), rev_edge_types=edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person'), )(data) - class GNNEncoder(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), out_channels) - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index).relu() - x = self.conv2(x, edge_index) - return x - - class EdgeDecoder(torch.nn.Module): - def __init__(self, hidden_channels): + class HAN(torch.nn.Module): + def __init__(self, in_channels: Union[int, Dict[str, int]], + out_channels: int, hidden_channels=128, heads=8): super().__init__() + self.han_conv = HANConv(in_channels, hidden_channels, heads=heads, + dropout=0.6, metadata=train_data.metadata()) self.lin1 = Linear(2 * hidden_channels, hidden_channels) - self.lin2 = Linear(hidden_channels, 1) - - def forward(self, z_dict, edge_label_index): - row, col = edge_label_index - z = torch.cat([z_dict['person'][row], z_dict['disease'][col]], dim=-1) + self.lin2 = Linear(hidden_channels, out_channels) + def forward(self, x_dict, edge_index_dict): + out = self.han_conv(x_dict, edge_index_dict) + # out = self.lin(out['person', 'disease']) + row, col = edge_index_dict[('person', 'diagnosis', 'disease')] + z = torch.cat([out['person'][row], out['disease'][col]], dim=-1) z = self.lin1(z).relu() z = self.lin2(z) - return z.view(-1) + # return z.view(-1) + return z - class Model(torch.nn.Module): - def __init__(self, hidden_channels): - super().__init__() - self.encoder = GNNEncoder(hidden_channels, hidden_channels) - self.encoder = to_hetero(self.encoder, data.metadata(), aggr='sum') - self.decoder = EdgeDecoder(hidden_channels) - - def forward(self, x_dict, edge_index_dict, edge_label_index): - z_dict = self.encoder(x_dict, edge_index_dict) - return self.decoder(z_dict, edge_label_index) - - model = Model(hidden_channels=32).to(device) - - # Due to lazy initialization, we need to run one model step so the number - # of parameters can be inferred: - with torch.no_grad(): - try: - model.encoder(train_data.x_dict, train_data.edge_index_dict) - except Exception as e: - # if str(e) == "'NoneType' object has no attribute 'dim'": - # Node types with no instances need to be ignored - to_ignore = set() - for key, value in train_data.x_dict.items(): - if torch.numel(value) == 0: - to_ignore.add(key) - for key, value in train_data.edge_index_dict.items(): - if torch.numel(value) == 0: - if not to_ignore.intersection(set(key)): - to_ignore.add(key[0]) - to_ignore.add(key[1]) - to_ignore.add(key[2]) - to_ignore = {i for i in to_ignore if not i[:4] == "rev_"} - if to_ignore: - raise ValueError( - f'This pipeline requires all un-ignored types to have instances. Some types don\'t have instances. ' - f'Try adding/retrieving data for the following types or add them to the ignored types:\n{to_ignore}' - ) - else: - raise e - - optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - - def weighted_mse_loss(pred, target, weight=None): - weight = 1. if weight is None else weight[target].to(pred.dtype) - return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() - - def train(): + model = HAN(in_channels=-1, out_channels=2) + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + data, model = data.to(device), model.to(device) + + with torch.no_grad(): # Initialize lazy modules. + out = model(train_data.x_dict, train_data.edge_index_dict) + + optimizer = torch.optim.Adam(model.parameters(), lr=0.005, weight_decay=0.001) + + def train() -> float: model.train() optimizer.zero_grad() - pred = model(train_data.x_dict, train_data.edge_index_dict, - train_data['person', 'disease'].edge_label_index) - target = train_data['person', 'disease'].edge_label - loss = weighted_mse_loss(pred, target, None) # TODO: Consider using weighting to balance the positive/negative example sets + # TODO: Does the model automatically use `edge_label_index_dict` and `edge_label` for training to include the negative samples? It seems like it can't be + # in which case we want to use the below, but it fails because `edge_label_index_dict` contains only one edge's information + # pred = model(train_data.x_dict, train_data.edge_label_index_dict) + # loss = functional.cross_entropy(pred, train_data[('person', 'diagnosis', 'disease')].edge_label) + pred = model(train_data.x_dict, train_data.edge_index_dict) + loss = functional.cross_entropy(pred, train_data[('person', 'diagnosis', 'disease')].y_edge) loss.backward() optimizer.step() return float(loss) @torch.no_grad() - def test(data): + def test() -> List[float]: model.eval() - pred = model(data.x_dict, data.edge_index_dict, - data['person', 'disease'].edge_label_index) - pred = pred.clamp(min=0, max=5) - target = data['person', 'disease'].edge_label.float() - rmse = functional.mse_loss(pred, target).sqrt() - return float(rmse) - - for epoch in range(1, 301): + accs = [] + for split in train_data, val_data, test_data: + # We use `edge_index_dict` and `y_edge` for validation and testing to exclude the negative samples + pred = model(split.x_dict, split.edge_index_dict).argmax(dim=-1) + acc = (pred == split['person', 'disease'].y_edge).sum() / split['person', 'disease'].y_edge.numel() + accs.append(float(acc)) + return accs + + best_val_acc = 0 + start_patience = patience = 100 + for epoch in range(1, 200): loss = train() - train_rmse = test(train_data) - val_rmse = test(val_data) - test_rmse = test(test_data) - print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_rmse:.4f}, ' - f'Val: {val_rmse:.4f}, Test: {test_rmse:.4f}') + train_acc, val_acc, test_acc = test() + print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, ' + f'Val: {val_acc:.4f}, Test: {test_acc:.4f}') + + if best_val_acc <= val_acc: + patience = start_patience + best_val_acc = val_acc + else: + patience -= 1 + + if patience <= 0: + print('Stopping training as validation accuracy did not improve ' + f'for {start_patience} epochs') + break test_acc = test() print(f'Test Accuracy: {test_acc:.4f}') From 243803fbd5eb61b1691b45bb0e2addcbbfefb783 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 8 Jul 2022 12:45:53 +0100 Subject: [PATCH 018/124] The learner now sees the negative training examples --- kglib/examples/diagnosis/diagnosis.py | 30 +++++++++++++-------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 700a944c..82e34b85 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -175,8 +175,8 @@ def clear_unneeded_fields(graph): num_val=0.1, num_test=0.1, neg_sampling_ratio=1.0, - # edge_types=edge_type_triplets, # Evaluates to: ('person', 'diagnosis', 'disease'), - # rev_edge_types=edge_type_triplets_reversed, # Evaluates to: ('disease', 'rev_diagnosis', 'person'), + # edge_types=edge_type_triplets, + # rev_edge_types=edge_type_triplets_reversed, edge_types=RELATION_TYPE_TO_PREDICT[::2], # Evaluates to: ('person', 'diagnosis', 'disease'), rev_edge_types=edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person'), )(data) @@ -190,11 +190,12 @@ def __init__(self, in_channels: Union[int, Dict[str, int]], self.lin1 = Linear(2 * hidden_channels, hidden_channels) self.lin2 = Linear(hidden_channels, out_channels) - def forward(self, x_dict, edge_index_dict): - out = self.han_conv(x_dict, edge_index_dict) - # out = self.lin(out['person', 'disease']) - row, col = edge_index_dict[('person', 'diagnosis', 'disease')] - z = torch.cat([out['person'][row], out['disease'][col]], dim=-1) + def encode(self, x_dict, edge_index_dict): + return self.han_conv(x_dict, edge_index_dict) + + def decode(self, z, edge_label_index_dict): + row, col = edge_label_index_dict[('person', 'diagnosis', 'disease')] + z = torch.cat([z['person'][row], z['disease'][col]], dim=-1) z = self.lin1(z).relu() z = self.lin2(z) # return z.view(-1) @@ -205,7 +206,7 @@ def forward(self, x_dict, edge_index_dict): data, model = data.to(device), model.to(device) with torch.no_grad(): # Initialize lazy modules. - out = model(train_data.x_dict, train_data.edge_index_dict) + z = model.encode(train_data.x_dict, train_data.edge_index_dict) optimizer = torch.optim.Adam(model.parameters(), lr=0.005, weight_decay=0.001) @@ -216,8 +217,9 @@ def train() -> float: # in which case we want to use the below, but it fails because `edge_label_index_dict` contains only one edge's information # pred = model(train_data.x_dict, train_data.edge_label_index_dict) # loss = functional.cross_entropy(pred, train_data[('person', 'diagnosis', 'disease')].edge_label) - pred = model(train_data.x_dict, train_data.edge_index_dict) - loss = functional.cross_entropy(pred, train_data[('person', 'diagnosis', 'disease')].y_edge) + z = model.encode(train_data.x_dict, train_data.edge_index_dict) + pred = model.decode(z, train_data.edge_label_index_dict) + loss = functional.cross_entropy(pred, torch.tensor(train_data[('person', 'diagnosis', 'disease')].edge_label, dtype=torch.long)) loss.backward() optimizer.step() return float(loss) @@ -228,8 +230,9 @@ def test() -> List[float]: accs = [] for split in train_data, val_data, test_data: # We use `edge_index_dict` and `y_edge` for validation and testing to exclude the negative samples - pred = model(split.x_dict, split.edge_index_dict).argmax(dim=-1) - acc = (pred == split['person', 'disease'].y_edge).sum() / split['person', 'disease'].y_edge.numel() + z = model.encode(split.x_dict, split.edge_index_dict) + pred = model.decode(z, split.edge_label_index_dict).argmax(dim=-1) + acc = (pred == split['person', 'disease'].edge_label).sum() / split['person', 'disease'].edge_label.numel() accs.append(float(acc)) return accs @@ -252,9 +255,6 @@ def test() -> List[float]: f'for {start_patience} epochs') break - test_acc = test() - print(f'Test Accuracy: {test_acc:.4f}') - with session.transaction(TransactionType.WRITE) as tx: write_predictions_to_typedb(ge_graphs, tx) From 2d7bad46ed2436dd59db53292ce3082fb2214be9 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 8 Jul 2022 15:10:36 +0100 Subject: [PATCH 019/124] Use a simple dot product to compute edge logits and therefore the approach is easily applicable to predicting all edges in an adjacency matric, as per the link_prediction example. --- kglib/examples/diagnosis/diagnosis.py | 47 ++++++++++++--------------- 1 file changed, 20 insertions(+), 27 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 82e34b85..05a9461b 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -27,7 +27,6 @@ from torch import as_tensor import torch.nn.functional as functional import torch_geometric.transforms as transforms -from torch.nn import Linear from torch_geometric.nn import HANConv from typedb.client import * @@ -162,46 +161,39 @@ def clear_unneeded_fields(graph): as_tensor(node_type_indices), as_tensor(edge_type_indices), node_types, edge_type_triplets ).to(device) # Get the first graph object. - # TODO: Consider whether we want to add reverse edges for the edge type being predicted and whether to do the - # before or after other transforms data = transforms.ToUndirected()(data) for edge_from, edge, edge_to in edge_type_triplets_reversed: - # TODO: It's unclear in the PyG examples why this is necessary + # This seems to be necessary so that the reverse edges are present for message-passing but the labels aren't + # considered for node and edge representations del data[edge_from, edge, edge_to].edge_label # Remove "reverse" label. - # TODO: How can the negative sampling know what types to add to the generated edges? It surely can't, so we have to - # deal with this train_data, val_data, test_data = transforms.RandomLinkSplit( num_val=0.1, num_test=0.1, neg_sampling_ratio=1.0, - # edge_types=edge_type_triplets, - # rev_edge_types=edge_type_triplets_reversed, edge_types=RELATION_TYPE_TO_PREDICT[::2], # Evaluates to: ('person', 'diagnosis', 'disease'), rev_edge_types=edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person'), )(data) class HAN(torch.nn.Module): - def __init__(self, in_channels: Union[int, Dict[str, int]], - out_channels: int, hidden_channels=128, heads=8): + def __init__(self, in_channels: Union[int, Dict[str, int]], hidden_channels=128, heads=8): super().__init__() self.han_conv = HANConv(in_channels, hidden_channels, heads=heads, dropout=0.6, metadata=train_data.metadata()) - self.lin1 = Linear(2 * hidden_channels, hidden_channels) - self.lin2 = Linear(hidden_channels, out_channels) def encode(self, x_dict, edge_index_dict): return self.han_conv(x_dict, edge_index_dict) def decode(self, z, edge_label_index_dict): row, col = edge_label_index_dict[('person', 'diagnosis', 'disease')] - z = torch.cat([z['person'][row], z['disease'][col]], dim=-1) - z = self.lin1(z).relu() - z = self.lin2(z) - # return z.view(-1) - return z + logits = (z['person'][row] * z['disease'][col]).sum(dim=-1) + return logits - model = HAN(in_channels=-1, out_channels=2) + def decode_all(self, z): + prob_adj = z['person'] @ z['disease'].t() + return (prob_adj > 0).nonzero(as_tuple=False).t() + + model = HAN(in_channels=-1) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') data, model = data.to(device), model.to(device) @@ -213,13 +205,9 @@ def decode(self, z, edge_label_index_dict): def train() -> float: model.train() optimizer.zero_grad() - # TODO: Does the model automatically use `edge_label_index_dict` and `edge_label` for training to include the negative samples? It seems like it can't be - # in which case we want to use the below, but it fails because `edge_label_index_dict` contains only one edge's information - # pred = model(train_data.x_dict, train_data.edge_label_index_dict) - # loss = functional.cross_entropy(pred, train_data[('person', 'diagnosis', 'disease')].edge_label) z = model.encode(train_data.x_dict, train_data.edge_index_dict) - pred = model.decode(z, train_data.edge_label_index_dict) - loss = functional.cross_entropy(pred, torch.tensor(train_data[('person', 'diagnosis', 'disease')].edge_label, dtype=torch.long)) + logits = model.decode(z, train_data.edge_label_index_dict) + loss = functional.binary_cross_entropy_with_logits(logits, train_data[('person', 'diagnosis', 'disease')].edge_label) loss.backward() optimizer.step() return float(loss) @@ -231,14 +219,15 @@ def test() -> List[float]: for split in train_data, val_data, test_data: # We use `edge_index_dict` and `y_edge` for validation and testing to exclude the negative samples z = model.encode(split.x_dict, split.edge_index_dict) - pred = model.decode(z, split.edge_label_index_dict).argmax(dim=-1) - acc = (pred == split['person', 'disease'].edge_label).sum() / split['person', 'disease'].edge_label.numel() + link_logits = model.decode(z, split.edge_label_index_dict) + link_probs = link_logits.sigmoid() + acc = ((link_probs > 0.5) == (split['person', 'disease'].edge_label == 1)).sum() / split['person', 'disease'].edge_label.numel() accs.append(float(acc)) return accs best_val_acc = 0 start_patience = patience = 100 - for epoch in range(1, 200): + for epoch in range(1, 20): loss = train() train_acc, val_acc, test_acc = test() print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, ' @@ -255,6 +244,10 @@ def test() -> List[float]: f'for {start_patience} epochs') break + z = model.encode(data.x_dict, data.edge_index_dict) + final_edge_index = model.decode_all(z) + print(final_edge_index) + with session.transaction(TransactionType.WRITE) as tx: write_predictions_to_typedb(ge_graphs, tx) From 4f72eef8dac80c328a580a1f81e6f9f78e55c0ec Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 8 Jul 2022 16:26:28 +0100 Subject: [PATCH 020/124] WIP improving features --- kglib/examples/diagnosis/diagnosis.py | 14 ++++---- .../transform/typedb_graph_encoder.py | 32 ++++++++++--------- 2 files changed, 25 insertions(+), 21 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 05a9461b..a8c4b698 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -101,6 +101,7 @@ def diagnosis_example(typedb_binary_directory, # changes from a node to an edge. We therefore need to update the node_types and edge_types accordingly node_types = prepare_node_types(session, RELATION_TYPE_TO_PREDICT, TYPES_TO_IGNORE) edge_type_triplets, edge_type_triplets_reversed = prepare_edge_triplets(session, RELATION_TYPE_TO_PREDICT, TYPES_TO_IGNORE) + type_encoding_size = 16 # Attribute encoders encode the value of each attribute into a fixed-length feature vector. The encoders are # defined on a per-type basis. Easily define your own encoders for specific attribute data in your TypeDB database @@ -109,12 +110,13 @@ def diagnosis_example(typedb_binary_directory, # Categorical Attribute types and the values of their categories # TODO: Use a sentence encoder for this instead to demonstrate how to use one 'name': CategoricalEncoder( - ['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol'] + ['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol'], + attribute_encoding_size ), # Continuous Attribute types and their min and max values - 'severity': ContinuousEncoder(0, 1), - 'age': ContinuousEncoder(7, 80), - 'units-per-week': ContinuousEncoder(3, 29) + 'severity': ContinuousEncoder(0, 1, attribute_encoding_size), + 'age': ContinuousEncoder(7, 80, attribute_encoding_size), + 'units-per-week': ContinuousEncoder(3, 29, attribute_encoding_size) } def prepare_graph(graph): @@ -145,7 +147,7 @@ def clear_unneeded_fields(graph): edge_types = list({triplet[1] for triplet in edge_type_triplets}) transform = transforms.Compose([ prepare_graph, - GraphFeatureEncoder(node_types, edge_types, attribute_encoders, attribute_encoding_size), + GraphFeatureEncoder(node_types, edge_types, type_encoding_size, attribute_encoders, attribute_encoding_size), LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[2]), clear_unneeded_fields ]) @@ -227,7 +229,7 @@ def test() -> List[float]: best_val_acc = 0 start_patience = patience = 100 - for epoch in range(1, 20): + for epoch in range(1, 200): loss = train() train_acc, val_acc, test_acc = test() print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, ' diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index 6af14034..85503bd4 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -20,6 +20,9 @@ # from typing import List +import torch +from torch.nn import Embedding + import numpy as np from kglib.utils.graph.iterate import ( @@ -48,11 +51,13 @@ class GraphFeatureEncoder: """ - def __init__(self, node_types, edge_types, attribute_encoders, attribute_encoding_size): + def __init__(self, node_types, edge_types, type_encoding_size, attribute_encoders, attribute_encoding_size): self.node_types = node_types self.edge_types = edge_types self.attribute_encoders = attribute_encoders self.attribute_encoding_size = attribute_encoding_size + self.node_type_embedding = Embedding(len(self.node_types), type_encoding_size) + self.edge_type_embedding = Embedding(len(self.edge_types), type_encoding_size) def __call__(self, graph): self.encode_node_features(graph) @@ -66,39 +71,36 @@ def encode_node_features(self, graph): # Add the integer value of the category for each categorical attribute instance encoded_value = self.attribute_encoders[typ](node_data['value']) else: - # encoded_value = [0] * self.attribute_encoding_size - encoded_value = [0] + encoded_value = [0] * self.attribute_encoding_size - # TODO: We can skip encoding type information as we are using the hetero models from PyTorch to take care - # of that for us - one_hot_encoded_type = self.node_types.index(node_data['type']) - node_data['x'] = np.hstack( - [np.array(one_hot_encoded_type, dtype=np.float32), np.array(encoded_value, dtype=np.float32)] - ) + type_embedding = self.node_type_embedding(torch.as_tensor(self.node_types.index(node_data['type']))) + node_data['x'] = torch.hstack([type_embedding, torch.as_tensor(encoded_value)]) def encode_edge_features(self, graph): for edge_data in multidigraph_edge_data_iterator(graph): - one_hot_encoded_type = self.edge_types.index(edge_data['type']) + type_embedding = self.node_type_embedding(torch.as_tensor(self.edge_types.index(edge_data['type']))) # TODO: Unnecessary empty array added to have common features size between nodes and edges - edge_data['edge_attr'] = np.hstack([np.array(one_hot_encoded_type, dtype=np.float32), np.array([0], dtype=np.float32)]) + edge_data['edge_attr'] = torch.hstack([type_embedding, torch.as_tensor([0] * self.attribute_encoding_size)]) class CategoricalEncoder: - def __init__(self, categories: List): + def __init__(self, categories: List, attribute_encoding_size): self.categories = categories + self.embedding = Embedding(len(self.categories), attribute_encoding_size) def __call__(self, value): - return [self.categories.index(value)] + return self.embedding(torch.as_tensor(self.categories.index(value))) class ContinuousEncoder: - def __init__(self, min_val, max_val): + def __init__(self, min_val, max_val, attribute_encoding_size): + self.attribute_encoding_size = attribute_encoding_size self.max_val = max_val self.min_val = min_val def __call__(self, value): - return [(value - self.min_val) / (self.max_val - self.min_val)] + return [(value - self.min_val) / (self.max_val - self.min_val)] * self.attribute_encoding_size # class SequenceEncoder(object): From 1d54acd4d5d62de6f44ae3d2507612fc2b2412cb Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 8 Jul 2022 17:27:49 +0100 Subject: [PATCH 021/124] Non-ideal fix to the graph structure breaking. It breaks when the features are given as tensors manually for each node. When given as numpy arrays this way of adding features works fine. --- kglib/kgcn_data_loader/transform/typedb_graph_encoder.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index 85503bd4..9c182118 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -74,13 +74,15 @@ def encode_node_features(self, graph): encoded_value = [0] * self.attribute_encoding_size type_embedding = self.node_type_embedding(torch.as_tensor(self.node_types.index(node_data['type']))) - node_data['x'] = torch.hstack([type_embedding, torch.as_tensor(encoded_value)]) + node_data['x'] = torch.hstack([type_embedding, torch.as_tensor(encoded_value)])\ + .cpu().detach().numpy() # TODO: Conversion to numpy array, otherwise the graph representation breaks def encode_edge_features(self, graph): for edge_data in multidigraph_edge_data_iterator(graph): type_embedding = self.node_type_embedding(torch.as_tensor(self.edge_types.index(edge_data['type']))) # TODO: Unnecessary empty array added to have common features size between nodes and edges - edge_data['edge_attr'] = torch.hstack([type_embedding, torch.as_tensor([0] * self.attribute_encoding_size)]) + edge_data['edge_attr'] = torch.hstack([type_embedding, torch.as_tensor([0] * self.attribute_encoding_size)])\ + .cpu().detach().numpy() # TODO: Conversion to numpy array, otherwise the graph representation breaks class CategoricalEncoder: From d065eeba091b19469028ca8df540568ccea1f86d Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 12 Jul 2022 12:45:28 +0100 Subject: [PATCH 022/124] Log scalars and histograms to tensorboard --- kglib/examples/diagnosis/diagnosis.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index a8c4b698..ef59b03e 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -24,9 +24,10 @@ import networkx as nx import torch -from torch import as_tensor import torch.nn.functional as functional import torch_geometric.transforms as transforms +from torch import as_tensor +from torch.utils.tensorboard import SummaryWriter from torch_geometric.nn import HANConv from typedb.client import * @@ -227,11 +228,23 @@ def test() -> List[float]: accs.append(float(acc)) return accs + writer = SummaryWriter() + for edge_type, edge_store in zip(data.edge_types, data.edge_stores): + writer.add_histogram('('+', '.join(edge_type) + ')/edge_attr', edge_store["edge_attr"]) + writer.add_histogram('('+', '.join(edge_type) + ')/y_edge', edge_store["y_edge"]) + + for node_type, node_store in zip(data.node_types, data.node_stores): + writer.add_histogram(node_type + '/x', node_store["x"]) + best_val_acc = 0 start_patience = patience = 100 for epoch in range(1, 200): loss = train() + writer.add_scalar('Loss/train', loss, epoch) train_acc, val_acc, test_acc = test() + writer.add_scalar('Accuracy/train', train_acc, epoch) + writer.add_scalar('Accuracy/val', val_acc, epoch) + writer.add_scalar('Accuracy/test', test_acc, epoch) print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, ' f'Val: {val_acc:.4f}, Test: {test_acc:.4f}') From cd95ef0b966aaae6480fbba39aa62d64ee513204 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 12 Jul 2022 14:18:53 +0100 Subject: [PATCH 023/124] Use a HGTConv rather than a HANConv as the heterogeneous convolution layer, and now the learner learns with 100% accuracy on train and validation and 91% on test! --- kglib/examples/diagnosis/diagnosis.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index ef59b03e..244ed61b 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -28,7 +28,7 @@ import torch_geometric.transforms as transforms from torch import as_tensor from torch.utils.tensorboard import SummaryWriter -from torch_geometric.nn import HANConv +from torch_geometric.nn import HGTConv from typedb.client import * from kglib.kgcn_data_loader.dataset.typedb_networkx_dataset import TypeDBNetworkxDataSet @@ -178,14 +178,13 @@ def clear_unneeded_fields(graph): rev_edge_types=edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person'), )(data) - class HAN(torch.nn.Module): + class LinkPredictionModel(torch.nn.Module): def __init__(self, in_channels: Union[int, Dict[str, int]], hidden_channels=128, heads=8): super().__init__() - self.han_conv = HANConv(in_channels, hidden_channels, heads=heads, - dropout=0.6, metadata=train_data.metadata()) + self.conv = HGTConv(in_channels, hidden_channels, heads=heads, metadata=train_data.metadata()) def encode(self, x_dict, edge_index_dict): - return self.han_conv(x_dict, edge_index_dict) + return self.conv(x_dict, edge_index_dict) def decode(self, z, edge_label_index_dict): row, col = edge_label_index_dict[('person', 'diagnosis', 'disease')] @@ -196,7 +195,7 @@ def decode_all(self, z): prob_adj = z['person'] @ z['disease'].t() return (prob_adj > 0).nonzero(as_tuple=False).t() - model = HAN(in_channels=-1) + model = LinkPredictionModel(in_channels=-1) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') data, model = data.to(device), model.to(device) From 77ef6ef9832504d3667edbce85fa67ffa58f3ffe Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 12 Jul 2022 18:04:44 +0100 Subject: [PATCH 024/124] WIP removing data leak of diagnosis edges. edge_label needs to be saved for later use as well as the entry from edge_label_index_dict --- kglib/examples/diagnosis/diagnosis.py | 28 +++++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 244ed61b..a7755c62 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -170,24 +170,40 @@ def clear_unneeded_fields(graph): # considered for node and edge representations del data[edge_from, edge, edge_to].edge_label # Remove "reverse" label. + binary_edge_to_predict = RELATION_TYPE_TO_PREDICT[::2] # Evaluates to: ('person', 'diagnosis', 'disease') + binary_rev_edge_to_predict = edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person') + train_data, val_data, test_data = transforms.RandomLinkSplit( num_val=0.1, num_test=0.1, neg_sampling_ratio=1.0, - edge_types=RELATION_TYPE_TO_PREDICT[::2], # Evaluates to: ('person', 'diagnosis', 'disease'), - rev_edge_types=edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person'), + edge_types=binary_edge_to_predict, + rev_edge_types=binary_rev_edge_to_predict )(data) + # Add a new `links` attribute to store the edges for prediction so that they aren't used for training + train_data.links = train_data.edge_label_index_dict[binary_edge_to_predict] + val_data.links = val_data.edge_label_index_dict[binary_edge_to_predict] + test_data.links = test_data.edge_label_index_dict[binary_edge_to_predict] + + del train_data['person', 'diagnosis', 'disease'] + del train_data['disease', 'rev_diagnosis', 'person'] + del val_data['person', 'diagnosis', 'disease'] + del val_data['disease', 'rev_diagnosis', 'person'] + del test_data['person', 'diagnosis', 'disease'] + del test_data['disease', 'rev_diagnosis', 'person'] + class LinkPredictionModel(torch.nn.Module): def __init__(self, in_channels: Union[int, Dict[str, int]], hidden_channels=128, heads=8): super().__init__() self.conv = HGTConv(in_channels, hidden_channels, heads=heads, metadata=train_data.metadata()) def encode(self, x_dict, edge_index_dict): + # TODO: Ideally we would mask the edge being predicted so that it isn't used for message passing return self.conv(x_dict, edge_index_dict) def decode(self, z, edge_label_index_dict): - row, col = edge_label_index_dict[('person', 'diagnosis', 'disease')] + row, col = edge_label_index_dict logits = (z['person'][row] * z['disease'][col]).sum(dim=-1) return logits @@ -208,8 +224,8 @@ def train() -> float: model.train() optimizer.zero_grad() z = model.encode(train_data.x_dict, train_data.edge_index_dict) - logits = model.decode(z, train_data.edge_label_index_dict) - loss = functional.binary_cross_entropy_with_logits(logits, train_data[('person', 'diagnosis', 'disease')].edge_label) + logits = model.decode(z, train_data.links) + loss = functional.binary_cross_entropy_with_logits(logits, train_data[binary_edge_to_predict].edge_label) loss.backward() optimizer.step() return float(loss) @@ -221,7 +237,7 @@ def test() -> List[float]: for split in train_data, val_data, test_data: # We use `edge_index_dict` and `y_edge` for validation and testing to exclude the negative samples z = model.encode(split.x_dict, split.edge_index_dict) - link_logits = model.decode(z, split.edge_label_index_dict) + link_logits = model.decode(z, split.links) link_probs = link_logits.sigmoid() acc = ((link_probs > 0.5) == (split['person', 'disease'].edge_label == 1)).sum() / split['person', 'disease'].edge_label.numel() accs.append(float(acc)) From aea16d07ddaf11257e59c604b1d417d54d6ef785 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 14 Jul 2022 17:08:50 +0100 Subject: [PATCH 025/124] Make age a less significant feature --- .../synthetic/examples/diagnosis/generate.py | 32 ++++++++++--------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py index 62760287..2bdee288 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py @@ -31,7 +31,7 @@ def get_example_queries(pmf, example_id): variable_values = pmf.select() - queries = [f'insert $p isa person, has person-id {example_id};'] + queries = [f'insert $p isa person, has person-id {example_id}, has age {int(normal_dist(60, 10)())};'] if variable_values['Multiple Sclerosis'] is not False: queries.append(inspect.cleandoc(f''' @@ -39,8 +39,7 @@ def get_example_queries(pmf, example_id): $d isa disease, has name "Multiple Sclerosis"; $p isa person, has person-id {example_id}; insert - $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; - $p has age {int(variable_values['Multiple Sclerosis']['age']())};''')) + $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis;''')) if variable_values['Diabetes Type II'] is not False: queries.append(inspect.cleandoc(f''' @@ -48,8 +47,7 @@ def get_example_queries(pmf, example_id): $p isa person, has person-id {example_id}; $d isa disease, has name "Diabetes Type II"; insert - $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; - $p has age {int(variable_values['Diabetes Type II']['age']())};''')) + $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis;''')) if variable_values['Fatigue'] is not False: queries.append(inspect.cleandoc(f''' @@ -106,25 +104,25 @@ def generate_example_data(client, num_examples, database="diagnosis"): session = client.session(database, SessionType.DATA) pmf_array = np.zeros([2, 2, 2, 2, 3, 2, 3], dtype=np.float) - pmf_array[0, 1, 0, 1, 0, 0, 0] = 0.1 + # Diabetes Type II pmf_array[1, 0, 1, 0, 0, 0, 0] = 0.05 pmf_array[1, 0, 1, 0, 2, 0, 0] = 0.1 - pmf_array[0, 1, 1, 0, 0, 0, 0] = 0.05 pmf_array[1, 0, 0, 1, 0, 0, 0] = 0.19 pmf_array[1, 0, 0, 1, 0, 1, 0] = 0.15 - pmf_array[1, 1, 1, 1, 0, 0, 0] = 0.01 + pmf_array[1, 0, 1, 1, 0, 0, 0] = 0.05 + pmf_array[1, 0, 1, 1, 2, 1, 2] = 0.1 + # Multiple Sclerosis + pmf_array[0, 1, 0, 1, 0, 0, 0] = 0.1 + pmf_array[0, 1, 1, 0, 0, 0, 0] = 0.05 pmf_array[0, 1, 1, 1, 0, 0, 0] = 0.05 pmf_array[0, 1, 1, 1, 0, 0, 1] = 0.05 pmf_array[0, 1, 1, 1, 0, 0, 2] = 0.1 - pmf_array[1, 0, 1, 1, 0, 0, 0] = 0.05 - pmf_array[1, 0, 1, 1, 2, 1, 2] = 0.1 - - def normal_dist(mean, var): - return lambda: round(np.random.normal(mean, var, 1)[0], 2) + # Both + pmf_array[1, 1, 1, 1, 0, 0, 0] = 0.01 pmf = PMF({ - 'Diabetes Type II': [False, {'age': normal_dist(60, 10)}], - 'Multiple Sclerosis': [False, {'age': normal_dist(30, 10)}], + 'Diabetes Type II': [False, True], + 'Multiple Sclerosis': [False, True], 'Fatigue': [False, {'severity': normal_dist(0.3, 0.1)}], 'Blurred vision': [False, {'severity': normal_dist(0.5, 0.2)}], 'Drinking': [False, {'units-per-week': normal_dist(5, 1)}, {'units-per-week': normal_dist(20, 3)}], @@ -141,6 +139,10 @@ def normal_dist(mean, var): session.close() +def normal_dist(mean, var): + return lambda: round(np.random.normal(mean, var, 1)[0], 2) + + if __name__ == '__main__': with TypeDB.core_client("localhost:1729") as client: client.databases().create("diagnosis") From d6351514efcf0fb7b34e1a7668bb7e9548cf0c6f Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 14 Jul 2022 17:10:38 +0100 Subject: [PATCH 026/124] Remove diagnosis edges to ensure there's no data leakage. Might also be able to do this using disjoint parameter on RandomLinkSplit. Use a sigmoid in decode_all so that we can see it is used the same way as decode. --- kglib/examples/diagnosis/diagnosis.py | 38 ++++++++++++++++++--------- 1 file changed, 25 insertions(+), 13 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index a7755c62..9be4d926 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -128,11 +128,9 @@ def prepare_graph(graph): def clear_unneeded_fields(graph): for node_data in multidigraph_node_data_iterator(graph): x = node_data["x"] - # y = node_data["y"] t = node_data["type"] node_data.clear() node_data["x"] = x - # node_data["y"] = y node_data["type"] = t for edge_data in multidigraph_edge_data_iterator(graph): @@ -176,16 +174,23 @@ def clear_unneeded_fields(graph): train_data, val_data, test_data = transforms.RandomLinkSplit( num_val=0.1, num_test=0.1, - neg_sampling_ratio=1.0, + neg_sampling_ratio=0.8, # The generated dataset limits the number of possible negative edges to less than 1.0. Putting it greater than that makes the train, val, test splits have different proportions of negative examples edge_types=binary_edge_to_predict, rev_edge_types=binary_rev_edge_to_predict )(data) # Add a new `links` attribute to store the edges for prediction so that they aren't used for training - train_data.links = train_data.edge_label_index_dict[binary_edge_to_predict] - val_data.links = val_data.edge_label_index_dict[binary_edge_to_predict] - test_data.links = test_data.edge_label_index_dict[binary_edge_to_predict] - + train_data.link_index = train_data.edge_label_index_dict[binary_edge_to_predict] + train_data.link_labels = train_data.edge_label_dict[binary_edge_to_predict] + val_data.link_index = val_data.edge_label_index_dict[binary_edge_to_predict] + val_data.link_labels = val_data.edge_label_dict[binary_edge_to_predict] + test_data.link_index = test_data.edge_label_index_dict[binary_edge_to_predict] + test_data.link_labels = test_data.edge_label_dict[binary_edge_to_predict] + + data.links = data['person', 'diagnosis', 'disease'] + data.rev_links = data['disease', 'rev_diagnosis', 'person'] + del data['person', 'diagnosis', 'disease'] + del data['disease', 'rev_diagnosis', 'person'] del train_data['person', 'diagnosis', 'disease'] del train_data['disease', 'rev_diagnosis', 'person'] del val_data['person', 'diagnosis', 'disease'] @@ -209,7 +214,8 @@ def decode(self, z, edge_label_index_dict): def decode_all(self, z): prob_adj = z['person'] @ z['disease'].t() - return (prob_adj > 0).nonzero(as_tuple=False).t() + # return (prob_adj.sigmoid() > 0.5).nonzero(as_tuple=False).t() + return prob_adj.sigmoid() > 0.5 model = LinkPredictionModel(in_channels=-1) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -224,8 +230,8 @@ def train() -> float: model.train() optimizer.zero_grad() z = model.encode(train_data.x_dict, train_data.edge_index_dict) - logits = model.decode(z, train_data.links) - loss = functional.binary_cross_entropy_with_logits(logits, train_data[binary_edge_to_predict].edge_label) + logits = model.decode(z, train_data.link_index) + loss = functional.binary_cross_entropy_with_logits(logits, train_data.link_labels) loss.backward() optimizer.step() return float(loss) @@ -237,9 +243,9 @@ def test() -> List[float]: for split in train_data, val_data, test_data: # We use `edge_index_dict` and `y_edge` for validation and testing to exclude the negative samples z = model.encode(split.x_dict, split.edge_index_dict) - link_logits = model.decode(z, split.links) + link_logits = model.decode(z, split.link_index) link_probs = link_logits.sigmoid() - acc = ((link_probs > 0.5) == (split['person', 'disease'].edge_label == 1)).sum() / split['person', 'disease'].edge_label.numel() + acc = ((link_probs > 0.5) == (split.link_labels == 1)).sum() / split.link_labels.numel() accs.append(float(acc)) return accs @@ -253,7 +259,7 @@ def test() -> List[float]: best_val_acc = 0 start_patience = patience = 100 - for epoch in range(1, 200): + for epoch in range(1, 100): loss = train() writer.add_scalar('Loss/train', loss, epoch) train_acc, val_acc, test_acc = test() @@ -274,9 +280,15 @@ def test() -> List[float]: f'for {start_patience} epochs') break + # Put back the diagnosis edges + data['person', 'diagnosis', 'disease'] = data.links + data['disease', 'rev_diagnosis', 'person'] = data.rev_links + z = model.encode(data.x_dict, data.edge_index_dict) final_edge_index = model.decode_all(z) print(final_edge_index) + print(final_edge_index.nonzero(as_tuple=False).t()) + # TODO: Cross-reference all predictions with actual (but this will include training edges) with session.transaction(TransactionType.WRITE) as tx: write_predictions_to_typedb(ge_graphs, tx) From ad9bc2bed50ef10898d55815541bd49bd580cfab Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 14 Jul 2022 17:23:29 +0100 Subject: [PATCH 027/124] Change the type used to represent past history of diagnosis of a parent. This is reasonable as this type should be being used as a feature, not for prediction, and will be conflating the actual objective of this demo. --- kglib/examples/diagnosis/diagnosis.py | 10 +++++----- .../typedb/synthetic/examples/diagnosis/generate.py | 2 +- .../typedb/synthetic/examples/diagnosis/schema.tql | 8 +++++++- 3 files changed, 13 insertions(+), 7 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 9be4d926..9d7ebfce 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -213,9 +213,9 @@ def decode(self, z, edge_label_index_dict): return logits def decode_all(self, z): - prob_adj = z['person'] @ z['disease'].t() - # return (prob_adj.sigmoid() > 0.5).nonzero(as_tuple=False).t() - return prob_adj.sigmoid() > 0.5 + logits = z['person'] @ z['disease'].t() + # return (logits.sigmoid() > 0.5).nonzero(as_tuple=False).t() + return logits model = LinkPredictionModel(in_channels=-1) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -285,7 +285,7 @@ def test() -> List[float]: data['disease', 'rev_diagnosis', 'person'] = data.rev_links z = model.encode(data.x_dict, data.edge_index_dict) - final_edge_index = model.decode_all(z) + final_edge_index = model.decode_all(z).sigmoid() > 0.5 print(final_edge_index) print(final_edge_index.nonzero(as_tuple=False).t()) # TODO: Cross-reference all predictions with actual (but this will include training edges) @@ -326,7 +326,7 @@ def get_query_handles(example_id): $p isa person; $par isa person; $ps(child: $p, parent: $par) isa parentship; - $diag(patient:$par, diagnosed-disease: $d) isa diagnosis; + $diag(patient:$par, diagnosed-disease: $d) isa familial-diagnosis; $d isa disease, has name $n; ''') diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py index 2bdee288..a9e39bda 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py @@ -84,7 +84,7 @@ def get_example_queries(pmf, example_id): insert (parent: $parent, child: $p) isa parentship; $parent isa person, has person-id {example_id + 10000}; - $diagnosis (patient: $parent, diagnosed-disease: $d) isa diagnosis; + $diagnosis (patient: $parent, diagnosed-disease: $d) isa familial-diagnosis; ''')) if variable_values['Cigarettes'] is not False: diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql index 60c1531b..0ab7de48 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql @@ -40,6 +40,7 @@ person sub entity, owns person-id @key, owns age, plays diagnosis:patient, + plays familial-diagnosis:patient, plays symptom-presentation:symptomatic-patient, plays consumption:consumer, plays age-risk-factor:person-at-age-risk, @@ -53,6 +54,7 @@ disease sub entity, owns name @key, plays causality:cause, plays diagnosis:diagnosed-disease, + plays familial-diagnosis:diagnosed-disease, plays age-risk-factor:age-risked-disease, plays hereditary-risk-factor:hereditary-risked-disease, plays smoking-risk-factor:smoking-risked-disease, @@ -73,6 +75,10 @@ diagnosis sub relation, owns probability-non-exists, owns probability-preexists; +familial-diagnosis sub relation, + relates patient, + relates diagnosed-disease; + probability-exists sub attribute, value double; @@ -131,7 +137,7 @@ when { $p isa person; $parent isa person; $cause(parent: $parent, child: $p) isa parentship; - $dia(patient: $parent, diagnosed-disease: $d) isa diagnosis; + $dia(patient: $parent, diagnosed-disease: $d) isa familial-diagnosis; $d isa disease, has name "Diabetes Type II"; } then { (person-at-hereditary-risk: $p, hereditary-risked-disease: $d) isa hereditary-risk-factor; From d204d2421152b66737d6641d6c6f0854dbde9837 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 14 Jul 2022 18:11:01 +0100 Subject: [PATCH 028/124] Clean up encoder --- .../transform/typedb_graph_encoder.py | 25 ------------------- 1 file changed, 25 deletions(-) diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index 9c182118..575402ce 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -23,8 +23,6 @@ import torch from torch.nn import Embedding -import numpy as np - from kglib.utils.graph.iterate import ( multidigraph_node_data_iterator, multidigraph_edge_data_iterator, @@ -103,26 +101,3 @@ def __init__(self, min_val, max_val, attribute_encoding_size): def __call__(self, value): return [(value - self.min_val) / (self.max_val - self.min_val)] * self.attribute_encoding_size - - -# class SequenceEncoder(object): -# # The 'SequenceEncoder' encodes raw column strings into embeddings. -# def __init__(self, model_name='all-MiniLM-L6-v2', device=None): -# self.device = device -# self.model = SentenceTransformer(model_name, device=device) -# -# @torch.no_grad() -# def __call__(self, df): -# x = self.model.encode(df.values, show_progress_bar=True, -# convert_to_tensor=True, device=self.device) -# return x.cpu() -# -# -# class IdentityEncoder(object): -# # The 'IdentityEncoder' takes the raw column values and converts them to -# # PyTorch tensors. -# def __init__(self, dtype=None): -# self.dtype = dtype -# -# def __call__(self, df): -# return torch.from_numpy(df.values).view(-1, 1).to(self.dtype) From 42ac773ee065059fe268cd3b84fadd013461adf0 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Mon, 18 Jul 2022 11:47:31 +0100 Subject: [PATCH 029/124] Minor changes --- kglib/examples/diagnosis/diagnosis.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 9d7ebfce..7f4ce4ef 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -37,7 +37,7 @@ from kglib.kgcn_data_loader.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ ContinuousEncoder from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator +from kglib.utils.graph.iterate import multidigraph_edge_data_iterator, multidigraph_node_data_iterator from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data @@ -82,7 +82,7 @@ def diagnosis_example(typedb_binary_directory, Final accuracies for training and for testing """ - # Delete the database each time # TODO: Remove + # Delete the database each time # TODO: Remove when adapting to your own data! sp.check_call([ './typedb', 'console', @@ -204,7 +204,6 @@ def __init__(self, in_channels: Union[int, Dict[str, int]], hidden_channels=128, self.conv = HGTConv(in_channels, hidden_channels, heads=heads, metadata=train_data.metadata()) def encode(self, x_dict, edge_index_dict): - # TODO: Ideally we would mask the edge being predicted so that it isn't used for message passing return self.conv(x_dict, edge_index_dict) def decode(self, z, edge_label_index_dict): @@ -465,4 +464,4 @@ def write_predictions_to_typedb(graphs, tx): if __name__ == '__main__': # TODO: Remove - diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", 50) + diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", 200) From d9c819f9a43fcc4b26845d66a026b60e0d8e1c07 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Mon, 18 Jul 2022 15:00:46 +0100 Subject: [PATCH 030/124] Remove PyG example scripts --- kglib/examples/hetero_link_pred.py | 153 -------------------------- kglib/examples/link_prediction.py | 115 ------------------- kglib/examples/load_csv.py | 127 --------------------- kglib/examples/node_classification.py | 86 --------------- 4 files changed, 481 deletions(-) delete mode 100644 kglib/examples/hetero_link_pred.py delete mode 100644 kglib/examples/link_prediction.py delete mode 100644 kglib/examples/load_csv.py delete mode 100644 kglib/examples/node_classification.py diff --git a/kglib/examples/hetero_link_pred.py b/kglib/examples/hetero_link_pred.py deleted file mode 100644 index 802a5071..00000000 --- a/kglib/examples/hetero_link_pred.py +++ /dev/null @@ -1,153 +0,0 @@ -# -# Copyright (C) 2021 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import argparse -import os.path as osp - -import torch -import torch.nn.functional as F -from torch.nn import Linear - -import torch_geometric.transforms as T -from torch_geometric.datasets import MovieLens -from torch_geometric.nn import SAGEConv, to_hetero - -parser = argparse.ArgumentParser() -parser.add_argument('--use_weighted_loss', action='store_true', - help='Whether to use weighted MSE loss.') -args = parser.parse_args() - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - -path = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens') -dataset = MovieLens(path, model_name='all-MiniLM-L6-v2') -data = dataset[0].to(device) - -# Add user node features for message passing: -data['user'].x = torch.eye(data['user'].num_nodes, device=device) -del data['user'].num_nodes - -# Add a reverse ('movie', 'rev_rates', 'user') relation for message passing: -data = T.ToUndirected()(data) -del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label. - -# Perform a link-level split into training, validation, and test edges: -train_data, val_data, test_data = T.RandomLinkSplit( - num_val=0.1, - num_test=0.1, - neg_sampling_ratio=0.0, - edge_types=[('user', 'rates', 'movie')], # Edge user -rates-> movie - rev_edge_types=[('movie', 'rev_rates', 'user')], # Edge user <-rev_rates- movie -)(data) - -# We have an unbalanced dataset with many labels for rating 3 and 4, and very -# few for 0 and 1. Therefore we use a weighted MSE loss. -if args.use_weighted_loss: - weight = torch.bincount(train_data['user', 'movie'].edge_label) - weight = weight.max() / weight -else: - weight = None - - -def weighted_mse_loss(pred, target, weight=None): - weight = 1. if weight is None else weight[target].to(pred.dtype) - return (weight * (pred - target.to(pred.dtype)).pow(2)).mean() - - -class GNNEncoder(torch.nn.Module): - def __init__(self, hidden_channels, out_channels): - super().__init__() - self.conv1 = SAGEConv((-1, -1), hidden_channels) - self.conv2 = SAGEConv((-1, -1), out_channels) - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index).relu() - x = self.conv2(x, edge_index) - return x - - -class EdgeDecoder(torch.nn.Module): - def __init__(self, hidden_channels): - super().__init__() - self.lin1 = Linear(2 * hidden_channels, hidden_channels) - self.lin2 = Linear(hidden_channels, 1) - - def forward(self, z_dict, edge_label_index): - row, col = edge_label_index - z = torch.cat([z_dict['user'][row], z_dict['movie'][col]], dim=-1) - - z = self.lin1(z).relu() - z = self.lin2(z) - return z.view(-1) - - -class Model(torch.nn.Module): - def __init__(self, hidden_channels): - super().__init__() - self.encoder = GNNEncoder(hidden_channels, hidden_channels) - self.encoder = to_hetero(self.encoder, data.metadata(), aggr='sum') - self.decoder = EdgeDecoder(hidden_channels) - - def forward(self, x_dict, edge_index_dict, edge_label_index): - z_dict = self.encoder(x_dict, edge_index_dict) - return self.decoder(z_dict, edge_label_index) - - -model = Model(hidden_channels=32).to(device) - -# Due to lazy initialization, we need to run one model step so the number -# of parameters can be inferred: -with torch.no_grad(): - model.encoder(train_data.x_dict, train_data.edge_index_dict) - -optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - - -def train(): - model.train() - optimizer.zero_grad() - pred = model(train_data.x_dict, train_data.edge_index_dict, - train_data['user', 'movie'].edge_label_index) - target = train_data['user', 'movie'].edge_label - loss = weighted_mse_loss(pred, target, weight) - loss.backward() - optimizer.step() - return float(loss) - - -@torch.no_grad() -def test(data): - model.eval() - pred = model(data.x_dict, data.edge_index_dict, - data['user', 'movie'].edge_label_index) - pred = pred.clamp(min=0, max=5) - target = data['user', 'movie'].edge_label.float() - rmse = F.mse_loss(pred, target).sqrt() - return float(rmse) - - -for epoch in range(1, 301): - loss = train() - train_rmse = test(train_data) - val_rmse = test(val_data) - test_rmse = test(test_data) - print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_rmse:.4f}, ' - f'Val: {val_rmse:.4f}, Test: {test_rmse:.4f}') \ No newline at end of file diff --git a/kglib/examples/link_prediction.py b/kglib/examples/link_prediction.py deleted file mode 100644 index a1a31bf4..00000000 --- a/kglib/examples/link_prediction.py +++ /dev/null @@ -1,115 +0,0 @@ -import os.path as osp - -import torch -import torch.nn.functional as F -from sklearn.metrics import roc_auc_score - -from torch_geometric.utils import negative_sampling -from torch_geometric.datasets import Planetoid -import torch_geometric.transforms as T -from torch_geometric.nn import GCNConv -from torch_geometric.utils import train_test_split_edges - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -device = "cpu" - -# load the Cora dataset -dataset = 'Cora' -path = osp.join('.', 'data', dataset) -dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures()) -data = dataset[0] -print(dataset.data) - -# use train_test_split_edges to create neg and positive edges -data.train_mask = data.val_mask = data.test_mask = data.y = None -data = train_test_split_edges(data) -print(data) - - -class Net(torch.nn.Module): - def __init__(self): - super(Net, self).__init__() - self.conv1 = GCNConv(dataset.num_features, 128) - self.conv2 = GCNConv(128, 64) - - def encode(self): - x = self.conv1(data.x, data.train_pos_edge_index) # convolution 1 - x = x.relu() - return self.conv2(x, data.train_pos_edge_index) # convolution 2 - - def decode(self, z, pos_edge_index, neg_edge_index): # only pos and neg edges - edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1) # concatenate pos and neg edges - logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1) # dot product - return logits - - def decode_all(self, z): - prob_adj = z @ z.t() # get adj NxN - return (prob_adj > 0).nonzero(as_tuple=False).t() # get predicted edge_list - - -model, data = Net().to(device), data.to(device) -optimizer = torch.optim.Adam(params=model.parameters(), lr=0.01) - - -def get_link_labels(pos_edge_index, neg_edge_index): - # returns a tensor: - # [1,1,1,1,...,0,0,0,0,0,..] with the number of ones is equel to the lenght of pos_edge_index - # and the number of zeros is equal to the length of neg_edge_index - E = pos_edge_index.size(1) + neg_edge_index.size(1) - link_labels = torch.zeros(E, dtype=torch.float, device=device) - link_labels[:pos_edge_index.size(1)] = 1. - return link_labels - - -def train(): - model.train() - - neg_edge_index = negative_sampling( - edge_index=data.train_pos_edge_index, #positive edges - num_nodes=data.num_nodes, # number of nodes - num_neg_samples=data.train_pos_edge_index.size(1)) # number of neg_sample equal to number of pos_edges - - optimizer.zero_grad() - - z = model.encode() #encode - link_logits = model.decode(z, data.train_pos_edge_index, neg_edge_index) # decode - - link_labels = get_link_labels(data.train_pos_edge_index, neg_edge_index) - loss = F.binary_cross_entropy_with_logits(link_logits, link_labels) - loss.backward() - optimizer.step() - - return loss - - -@torch.no_grad() -def test(): - model.eval() - perfs = [] - for prefix in ["val", "test"]: - pos_edge_index = data[f'{prefix}_pos_edge_index'] - neg_edge_index = data[f'{prefix}_neg_edge_index'] - - z = model.encode() # encode train - link_logits = model.decode(z, pos_edge_index, neg_edge_index) # decode test or val - link_probs = link_logits.sigmoid() # apply sigmoid - - link_labels = get_link_labels(pos_edge_index, neg_edge_index) # get link - - perfs.append(roc_auc_score(link_labels.cpu(), link_probs.cpu())) #compute roc_auc score - return perfs - - -best_val_perf = test_perf = 0 -for epoch in range(1, 101): - train_loss = train() - val_perf, tmp_test_perf = test() - if val_perf > best_val_perf: - best_val_perf = val_perf - test_perf = tmp_test_perf - log = 'Epoch: {:03d}, Loss: {:.4f}, Val: {:.4f}, Test: {:.4f}' - if epoch % 10 == 0: - print(log.format(epoch, train_loss, best_val_perf, test_perf)) - -z = model.encode() -final_edge_index = model.decode_all(z) diff --git a/kglib/examples/load_csv.py b/kglib/examples/load_csv.py deleted file mode 100644 index edc4d72e..00000000 --- a/kglib/examples/load_csv.py +++ /dev/null @@ -1,127 +0,0 @@ -import os.path as osp - -import pandas as pd -import torch -from sentence_transformers import SentenceTransformer - -from torch_geometric.data import HeteroData, download_url, extract_zip -from torch_geometric.transforms import RandomLinkSplit, ToUndirected - -url = 'https://files.grouplens.org/datasets/movielens/ml-latest-small.zip' -root = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens') -extract_zip(download_url(url, root), root) -movie_path = osp.join(root, 'ml-latest-small', 'movies.csv') -rating_path = osp.join(root, 'ml-latest-small', 'ratings.csv') - - -def load_node_csv(path, index_col, encoders=None, **kwargs): - df = pd.read_csv(path, index_col=index_col, **kwargs) - mapping = {index: i for i, index in enumerate(df.index.unique())} - - x = None - if encoders is not None: - xs = [encoder(df[col]) for col, encoder in encoders.items()] - x = torch.cat(xs, dim=-1) - - return x, mapping - - -def load_edge_csv(path, src_index_col, src_mapping, dst_index_col, dst_mapping, - encoders=None, **kwargs): - df = pd.read_csv(path, **kwargs) - - src = [src_mapping[index] for index in df[src_index_col]] - dst = [dst_mapping[index] for index in df[dst_index_col]] - edge_index = torch.tensor([src, dst]) - - edge_attr = None - if encoders is not None: - edge_attrs = [encoder(df[col]) for col, encoder in encoders.items()] - edge_attr = torch.cat(edge_attrs, dim=-1) - - return edge_index, edge_attr - - -class SequenceEncoder(object): - # The 'SequenceEncoder' encodes raw column strings into embeddings. - def __init__(self, model_name='all-MiniLM-L6-v2', device=None): - self.device = device - self.model = SentenceTransformer(model_name, device=device) - - @torch.no_grad() - def __call__(self, df): - x = self.model.encode(df.values, show_progress_bar=True, - convert_to_tensor=True, device=self.device) - return x.cpu() - - -class GenresEncoder(object): - # The 'GenreEncoder' splits the raw column strings by 'sep' and converts - # individual elements to categorical labels. - def __init__(self, sep='|'): - self.sep = sep - - def __call__(self, df): - genres = set(g for col in df.values for g in col.split(self.sep)) - mapping = {genre: i for i, genre in enumerate(genres)} - - x = torch.zeros(len(df), len(mapping)) - for i, col in enumerate(df.values): - for genre in col.split(self.sep): - x[i, mapping[genre]] = 1 - return x - - -class IdentityEncoder(object): - # The 'IdentityEncoder' takes the raw column values and converts them to - # PyTorch tensors. - def __init__(self, dtype=None): - self.dtype = dtype - - def __call__(self, df): - return torch.from_numpy(df.values).view(-1, 1).to(self.dtype) - - -user_x, user_mapping = load_node_csv(rating_path, index_col='userId') - -movie_x, movie_mapping = load_node_csv( - movie_path, index_col='movieId', encoders={ - 'title': SequenceEncoder(), - 'genres': GenresEncoder() - }) - -edge_index, edge_label = load_edge_csv( - rating_path, - src_index_col='userId', - src_mapping=user_mapping, - dst_index_col='movieId', - dst_mapping=movie_mapping, - encoders={'rating': IdentityEncoder(dtype=torch.long)}, -) - -data = HeteroData() -data['user'].num_nodes = len(user_mapping) # Users do not have any features. -data['movie'].x = movie_x -data['user', 'rates', 'movie'].edge_index = edge_index -data['user', 'rates', 'movie'].edge_label = edge_label -print(data) - -# We can now convert `data` into an appropriate format for training a -# graph-based machine learning model: - -# 1. Add a reverse ('movie', 'rev_rates', 'user') relation for message passing. -data = ToUndirected()(data) -del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label. - -# 2. Perform a link-level split into training, validation, and test edges. -transform = RandomLinkSplit( - num_val=0.05, - num_test=0.1, - neg_sampling_ratio=0.0, - edge_types=[('user', 'rates', 'movie')], - rev_edge_types=[('movie', 'rev_rates', 'user')], -) -train_data, val_data, test_data = transform(data) -print(train_data) -print(val_data) -print(test_data) diff --git a/kglib/examples/node_classification.py b/kglib/examples/node_classification.py deleted file mode 100644 index b8c485d0..00000000 --- a/kglib/examples/node_classification.py +++ /dev/null @@ -1,86 +0,0 @@ -import torch -from torch_geometric.nn import GCNConv -import torch.nn.functional as F -from torch_geometric.datasets import Planetoid -from torch_geometric.transforms import NormalizeFeatures - -dataset = Planetoid(root='data/Planetoid', name='Cora', transform=NormalizeFeatures()) - -print() -print(f'Dataset: {dataset}:') -print('======================') -print(f'Number of graphs: {len(dataset)}') -print(f'Number of features: {dataset.num_features}') -print(f'Number of classes: {dataset.num_classes}') - -data = dataset[0] # Get the first graph object. - -print() -print(data) -print('===========================================================================================================') - -# Gather some statistics about the graph. -print(f'Number of nodes: {data.num_nodes}') -print(f'Number of edges: {data.num_edges}') -print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}') -print(f'Number of training nodes: {data.train_mask.sum()}') -print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}') -print(f'Has isolated nodes: {data.has_isolated_nodes()}') -print(f'Has self-loops: {data.has_self_loops()}') -print(f'Is undirected: {data.is_undirected()}') - - -class GCN(torch.nn.Module): - def __init__(self, hidden_channels): - super().__init__() - torch.manual_seed(1234567) - self.conv1 = GCNConv(dataset.num_features, hidden_channels) - self.conv2 = GCNConv(hidden_channels, dataset.num_classes) - - def forward(self, x, edge_index): - x = self.conv1(x, edge_index) - x = x.relu() - x = F.dropout(x, p=0.5, training=self.training) - x = self.conv2(x, edge_index) - return x - - -model = GCN(hidden_channels=16) -print(model) - -model = GCN(hidden_channels=16) -model.eval() - -out = model(data.x, data.edge_index) -# visualize(out, color=data.y) - -model = GCN(hidden_channels=16) -optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) -criterion = torch.nn.CrossEntropyLoss() - - -def train(): - model.train() - optimizer.zero_grad() # Clear gradients. - out = model(data.x, data.edge_index) # Perform a single forward pass. - loss = criterion(out[data.train_mask], data.y[data.train_mask]) # Compute the loss solely based on the training nodes. - loss.backward() # Derive gradients. - optimizer.step() # Update parameters based on gradients. - return loss - - -def test(): - model.eval() - out = model(data.x, data.edge_index) - pred = out.argmax(dim=1) # Use the class with highest probability. - test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels. - test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions. - return test_acc - - -for epoch in range(1, 101): - loss = train() - print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}') - -test_acc = test() -print(f'Test Accuracy: {test_acc:.4f}') From 503a150088f11d85eab832b7c1470b966a4c5706 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Mon, 18 Jul 2022 18:39:07 +0100 Subject: [PATCH 031/124] Created a dictionary of concepts by type in order to be able to map back from the predictions to the concepts they indicate. Fixed a coupe of issues with the data Fixed the metrics to show proper accuracy and add precision and recall. --- kglib/examples/diagnosis/diagnosis.py | 116 +++++++++--------- .../dataset/typedb_networkx_dataset.py | 4 +- .../transform/typedb_graph_encoder.py | 12 ++ kglib/tests/end_to_end/kgcn/diagnosis.py | 17 +-- .../tests/end_to_end/kgcn/diagnosis_debug.py | 17 +-- .../synthetic/examples/diagnosis/generate.py | 18 +-- .../synthetic/examples/diagnosis/schema.tql | 26 ++-- 7 files changed, 116 insertions(+), 94 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 7f4ce4ef..bd0d011b 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -35,7 +35,7 @@ from kglib.kgcn_data_loader.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges, \ prepare_edge_triplets, prepare_node_types from kglib.kgcn_data_loader.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ - ContinuousEncoder + ContinuousEncoder, store_concepts_by_type from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file from kglib.utils.graph.iterate import multidigraph_edge_data_iterator, multidigraph_node_data_iterator from kglib.utils.graph.query.query_graph import QueryGraph @@ -48,10 +48,9 @@ # Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed -TYPES_TO_IGNORE = {'risk-factor', 'person-id', 'probability-preexists', 'probability-exists', 'probability-non-exists', - 'alcohol-risked-disease', 'person-at-alcohol-risk', 'person-at-hereditary-risk', - 'hereditary-risked-disease', 'smoking-risked-disease', 'person-at-smoking-risk', - 'person-at-age-risk', 'age-risked-disease'} +TYPES_TO_IGNORE = {'risk-factor', 'person-id', 'alcohol-risked-disease', 'person-at-alcohol-risk', + 'person-at-hereditary-risk', 'hereditary-risked-disease', 'smoking-risked-disease', + 'person-at-smoking-risk', 'person-at-age-risk', 'age-risked-disease', 'predicted-diagnosis'} # Note that this determines the edge direction when converting from a TypeDB relation RELATION_TYPE_TO_PREDICT = ('person', 'patient', 'diagnosis', 'diagnosed-disease', 'disease') @@ -148,6 +147,7 @@ def clear_unneeded_fields(graph): prepare_graph, GraphFeatureEncoder(node_types, edge_types, type_encoding_size, attribute_encoders, attribute_encoding_size), LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[2]), + store_concepts_by_type, # store the concepts we have in type-wise lists so that we can interpret the concepts for our predictions later clear_unneeded_fields ]) @@ -172,9 +172,9 @@ def clear_unneeded_fields(graph): binary_rev_edge_to_predict = edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person') train_data, val_data, test_data = transforms.RandomLinkSplit( - num_val=0.1, - num_test=0.1, - neg_sampling_ratio=0.8, # The generated dataset limits the number of possible negative edges to less than 1.0. Putting it greater than that makes the train, val, test splits have different proportions of negative examples + num_val=0.2, + num_test=0.2, + neg_sampling_ratio=1.0, # Putting this ration greater than that makes the train, val, test splits have different proportions of negative examples due to a limited number of places to add negative edges in this example generated data. edge_types=binary_edge_to_predict, rev_edge_types=binary_rev_edge_to_predict )(data) @@ -236,17 +236,27 @@ def train() -> float: return float(loss) @torch.no_grad() - def test() -> List[float]: + def test() -> Tuple[List[float], List[float], List[float]]: model.eval() - accs = [] + accuracies = [] + precisions = [] + recalls = [] for split in train_data, val_data, test_data: # We use `edge_index_dict` and `y_edge` for validation and testing to exclude the negative samples z = model.encode(split.x_dict, split.edge_index_dict) link_logits = model.decode(z, split.link_index) link_probs = link_logits.sigmoid() - acc = ((link_probs > 0.5) == (split.link_labels == 1)).sum() / split.link_labels.numel() - accs.append(float(acc)) - return accs + tp = ((link_probs > 0.5) * (split.link_labels == 1)).sum() + tn = ((link_probs < 0.5) * (split.link_labels == 0)).sum() + pos = (split.link_labels == 1).sum() + neg = (split.link_labels == 0).sum() + precision = tn / neg + recall = tp / pos + acc = (tp + tn) / (pos + neg) + accuracies.append(float(acc)) + precisions.append(precision) + recalls.append(recall) + return accuracies, precisions, recalls writer = SummaryWriter() for edge_type, edge_store in zip(data.edge_types, data.edge_stores): @@ -261,16 +271,19 @@ def test() -> List[float]: for epoch in range(1, 100): loss = train() writer.add_scalar('Loss/train', loss, epoch) - train_acc, val_acc, test_acc = test() - writer.add_scalar('Accuracy/train', train_acc, epoch) - writer.add_scalar('Accuracy/val', val_acc, epoch) - writer.add_scalar('Accuracy/test', test_acc, epoch) - print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, ' - f'Val: {val_acc:.4f}, Test: {test_acc:.4f}') - - if best_val_acc <= val_acc: + train_results, val_results, test_results = test() + writer.add_scalar('Accuracy/train', train_results[0], epoch) + writer.add_scalar('Accuracy/val', val_results[0], epoch) + writer.add_scalar('Accuracy/test', test_results[0], epoch) + writer.add_scalar('Precision/test', test_results[1], epoch) + writer.add_scalar('Recall/test', test_results[2], epoch) + print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_results[0]:.4f}, ' + f'Val: {val_results[0]:.4f}, Test: {test_results[0]:.4f}, Test Precision: {test_results[1]:.4f}, ' + f'Test Recall: {test_results[2]:.4f}') + + if best_val_acc <= val_results[0]: patience = start_patience - best_val_acc = val_acc + best_val_acc = val_results[0] else: patience -= 1 @@ -284,18 +297,27 @@ def test() -> List[float]: data['disease', 'rev_diagnosis', 'person'] = data.rev_links z = model.encode(data.x_dict, data.edge_index_dict) - final_edge_index = model.decode_all(z).sigmoid() > 0.5 - print(final_edge_index) - print(final_edge_index.nonzero(as_tuple=False).t()) + final_edge_index = (model.decode_all(z).sigmoid() > 0.5).nonzero(as_tuple=False).cpu().detach().numpy() # TODO: Cross-reference all predictions with actual (but this will include training edges) + predicted_links = [] + for p, d in final_edge_index: + predicted_links.append( + { + 'person': data['concepts_by_type']['person'][p], + 'disease': data['concepts_by_type']['disease'][d] + } + ) + print("The following links have been predicted:") + print(predicted_links) # Bear in mind this is predicted links across *all* data: train, val and test + with session.transaction(TransactionType.WRITE) as tx: - write_predictions_to_typedb(ge_graphs, tx) + write_predictions_to_typedb(predicted_links, tx) session.close() client.close() - return solveds_tr, solveds_ge + return train_results[0], test_results[0] def create_database(client, database): @@ -323,7 +345,7 @@ def get_query_handles(example_id): # === Hereditary Feature === hereditary_query = inspect.cleandoc(f'''match $p isa person; - $par isa person; + $par isa parent; $ps(child: $p, parent: $par) isa parentship; $diag(patient:$par, diagnosed-disease: $d) isa familial-diagnosis; $d isa disease, has name $n; @@ -423,42 +445,26 @@ def get_query_handles(example_id): ] -def write_predictions_to_typedb(graphs, tx): +def write_predictions_to_typedb(predicted_links, tx): """ Take predictions from the ML model, and insert representations of those predictions back into the graph. Args: - graphs: graphs containing the concepts, with their class predictions and class probabilities + predicted_links: pairs of concepts that are predicted links tx: TypeDB write transaction to use Returns: None """ - for graph in graphs: - for node, data in graph.nodes(data=True): - if data['prediction'] == 2: - concept = data['concept'] - concept_type = concept.type_label - if concept_type == 'diagnosis' or concept_type == 'candidate-diagnosis': - neighbours = graph.neighbors(node) - - for neighbour in neighbours: - concept = graph.nodes[neighbour]['concept'] - if concept.type_label == 'person': - person = concept - else: - disease = concept - - p = data['probabilities'] - query = (f'match ' - f'$p iid {person.iid};' - f'$d iid {disease.iid};' - f'insert ' - f'$pd(patient: $p, diagnosed-disease: $d) isa diagnosis,' - f'has probability-exists {p[2]:.3f},' - f'has probability-non-exists {p[1]:.3f},' - f'has probability-preexists {p[0]:.3f};') - tx.query().insert(query) + for predicted_link in predicted_links: + person = predicted_link['person'] + disease = predicted_link['disease'] + query = (f'match ' + f'$p iid {person.iid}; ' + f'$d iid {disease.iid}; ' + f'insert ' + f'$pd(patient: $p, diagnosed-disease: $d) isa predicted-diagnosis;') + tx.query().insert(query) tx.commit() diff --git a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py b/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py index f3475aa4..b4509277 100644 --- a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py +++ b/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py @@ -91,7 +91,9 @@ def __getitem__(self, idx): graph.name = id if self._transform: graph = self._transform(graph) - return from_networkx(graph), self.node_type_indices(graph), self.edge_type_indices(graph) + data = from_networkx(graph) + data.concepts_by_type = graph.concepts_by_type + return data, self.node_type_indices(graph), self.edge_type_indices(graph) def node_type_indices(self, graph): indices = [] diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index 575402ce..0fed21ab 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -101,3 +101,15 @@ def __init__(self, min_val, max_val, attribute_encoding_size): def __call__(self, value): return [(value - self.min_val) / (self.max_val - self.min_val)] * self.attribute_encoding_size + + +def store_concepts_by_type(graph): + concepts_by_type = {} + for node_data in multidigraph_node_data_iterator(graph): + typ = node_data['type'] + if typ in concepts_by_type: + concepts_by_type[typ].append(node_data['concept']) + else: + concepts_by_type[typ] = [node_data['concept']] + graph.concepts_by_type = concepts_by_type + return graph diff --git a/kglib/tests/end_to_end/kgcn/diagnosis.py b/kglib/tests/end_to_end/kgcn/diagnosis.py index acaa0e1f..c1c1fe5b 100644 --- a/kglib/tests/end_to_end/kgcn/diagnosis.py +++ b/kglib/tests/end_to_end/kgcn/diagnosis.py @@ -37,13 +37,16 @@ def tearDown(self): self._tdb.stop() def test_learning_is_done(self): - solveds_tr, solveds_ge = diagnosis_example(self._typedb_binary_location, - schema_file_path=self._schema_file_location, - seed_data_file_path=self._data_file_location) - self.assertGreaterEqual(solveds_tr[-1], 0.7) - self.assertLessEqual(solveds_tr[-1], 0.99) - self.assertGreaterEqual(solveds_ge[-1], 0.7) - self.assertLessEqual(solveds_ge[-1], 0.99) + train_accuracy, test_accuracy = diagnosis_example( + self._typedb_binary_location, + 200, + schema_file_path=self._schema_file_location, + seed_data_file_path=self._data_file_location + ) + self.assertGreaterEqual(train_accuracy, 0.7) + self.assertLessEqual(train_accuracy, 0.99) + self.assertGreaterEqual(test_accuracy, 0.7) + self.assertLessEqual(test_accuracy, 0.99) if __name__ == "__main__": diff --git a/kglib/tests/end_to_end/kgcn/diagnosis_debug.py b/kglib/tests/end_to_end/kgcn/diagnosis_debug.py index 8ca0c232..58190383 100644 --- a/kglib/tests/end_to_end/kgcn/diagnosis_debug.py +++ b/kglib/tests/end_to_end/kgcn/diagnosis_debug.py @@ -39,13 +39,16 @@ def setUp(self): self._schema_file_location = base_dir + sys.argv.pop() def test_learning_is_done(self): - solveds_tr, solveds_ge = diagnosis_example(self._typedb_binary_location, - schema_file_path=self._schema_file_location, - seed_data_file_path=self._data_file_location) - self.assertGreaterEqual(solveds_tr[-1], 0.7) - self.assertLessEqual(solveds_tr[-1], 0.99) - self.assertGreaterEqual(solveds_ge[-1], 0.7) - self.assertLessEqual(solveds_ge[-1], 0.99) + train_accuracy, test_accuracy = diagnosis_example( + self._typedb_binary_location, + 200, + schema_file_path=self._schema_file_location, + seed_data_file_path=self._data_file_location + ) + self.assertGreaterEqual(train_accuracy, 0.7) + self.assertLessEqual(train_accuracy, 0.99) + self.assertGreaterEqual(test_accuracy, 0.7) + self.assertLessEqual(test_accuracy, 0.99) if __name__ == "__main__": diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py index a9e39bda..d81257f1 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py @@ -31,7 +31,7 @@ def get_example_queries(pmf, example_id): variable_values = pmf.select() - queries = [f'insert $p isa person, has person-id {example_id}, has age {int(normal_dist(60, 10)())};'] + queries = [f'insert $p isa person, has person-id {example_id};'] if variable_values['Multiple Sclerosis'] is not False: queries.append(inspect.cleandoc(f''' @@ -39,7 +39,8 @@ def get_example_queries(pmf, example_id): $d isa disease, has name "Multiple Sclerosis"; $p isa person, has person-id {example_id}; insert - $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis;''')) + $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; + $p has age {int(variable_values['Multiple Sclerosis']['age']())};''')) if variable_values['Diabetes Type II'] is not False: queries.append(inspect.cleandoc(f''' @@ -47,7 +48,8 @@ def get_example_queries(pmf, example_id): $p isa person, has person-id {example_id}; $d isa disease, has name "Diabetes Type II"; insert - $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis;''')) + $diagnosis (patient: $p, diagnosed-disease: $d) isa diagnosis; + $p has age {int(variable_values['Diabetes Type II']['age']())};''')) if variable_values['Fatigue'] is not False: queries.append(inspect.cleandoc(f''' @@ -83,7 +85,7 @@ def get_example_queries(pmf, example_id): $d isa disease, has name "Diabetes Type II"; insert (parent: $parent, child: $p) isa parentship; - $parent isa person, has person-id {example_id + 10000}; + $parent isa parent; $diagnosis (patient: $parent, diagnosed-disease: $d) isa familial-diagnosis; ''')) @@ -107,7 +109,7 @@ def generate_example_data(client, num_examples, database="diagnosis"): # Diabetes Type II pmf_array[1, 0, 1, 0, 0, 0, 0] = 0.05 pmf_array[1, 0, 1, 0, 2, 0, 0] = 0.1 - pmf_array[1, 0, 0, 1, 0, 0, 0] = 0.19 + pmf_array[1, 0, 0, 1, 0, 0, 0] = 0.2 pmf_array[1, 0, 0, 1, 0, 1, 0] = 0.15 pmf_array[1, 0, 1, 1, 0, 0, 0] = 0.05 pmf_array[1, 0, 1, 1, 2, 1, 2] = 0.1 @@ -117,12 +119,10 @@ def generate_example_data(client, num_examples, database="diagnosis"): pmf_array[0, 1, 1, 1, 0, 0, 0] = 0.05 pmf_array[0, 1, 1, 1, 0, 0, 1] = 0.05 pmf_array[0, 1, 1, 1, 0, 0, 2] = 0.1 - # Both - pmf_array[1, 1, 1, 1, 0, 0, 0] = 0.01 pmf = PMF({ - 'Diabetes Type II': [False, True], - 'Multiple Sclerosis': [False, True], + 'Diabetes Type II': [False, {'age': normal_dist(45, 10)}], + 'Multiple Sclerosis': [False, {'age': normal_dist(30, 10)}], 'Fatigue': [False, {'severity': normal_dist(0.3, 0.1)}], 'Blurred vision': [False, {'severity': normal_dist(0.5, 0.2)}], 'Drinking': [False, {'units-per-week': normal_dist(5, 1)}, {'units-per-week': normal_dist(20, 3)}], diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql index 0ab7de48..53ab29a8 100644 --- a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql +++ b/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql @@ -40,14 +40,13 @@ person sub entity, owns person-id @key, owns age, plays diagnosis:patient, - plays familial-diagnosis:patient, + plays predicted-diagnosis:patient, plays symptom-presentation:symptomatic-patient, plays consumption:consumer, plays age-risk-factor:person-at-age-risk, plays hereditary-risk-factor:person-at-hereditary-risk, plays smoking-risk-factor:person-at-smoking-risk, plays alcohol-risk-factor:person-at-alcohol-risk, - plays parentship:parent, plays parentship:child; disease sub entity, @@ -55,11 +54,16 @@ disease sub entity, plays causality:cause, plays diagnosis:diagnosed-disease, plays familial-diagnosis:diagnosed-disease, + plays predicted-diagnosis:diagnosed-disease, plays age-risk-factor:age-risked-disease, plays hereditary-risk-factor:hereditary-risked-disease, plays smoking-risk-factor:smoking-risked-disease, plays alcohol-risk-factor:alcohol-risked-disease; +parent sub entity, + plays parentship:parent, + plays familial-diagnosis:patient; + substance sub entity, owns name @key, plays consumption:consumed-substance; @@ -70,23 +74,15 @@ parentship sub relation, diagnosis sub relation, relates patient, - relates diagnosed-disease, - owns probability-exists, - owns probability-non-exists, - owns probability-preexists; + relates diagnosed-disease; familial-diagnosis sub relation, relates patient, relates diagnosed-disease; -probability-exists sub attribute, - value double; - -probability-non-exists sub attribute, - value double; - -probability-preexists sub attribute, - value double; +predicted-diagnosis sub relation, + relates patient, + relates diagnosed-disease; causality sub relation, relates cause, @@ -135,7 +131,7 @@ when { rule heriditary-risk-of-diabetes: when { $p isa person; - $parent isa person; + $parent isa parent; $cause(parent: $parent, child: $p) isa parentship; $dia(patient: $parent, diagnosed-disease: $d) isa familial-diagnosis; $d isa disease, has name "Diabetes Type II"; From 23dfe73100ca171ac9f5866fff409bb91592d1de Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 19 Jul 2022 11:55:38 +0100 Subject: [PATCH 032/124] Remove unneeded tests and update BUILD files --- kglib/examples/diagnosis/BUILD | 14 ---- kglib/examples/diagnosis/diagnosis.py | 2 + kglib/examples/diagnosis/diagnosis_test.py | 96 ---------------------- kglib/kgcn_data_loader/BUILD | 18 +--- kglib/kgcn_data_loader/utils.py | 29 +------ kglib/kgcn_data_loader/utils_test.py | 57 ------------- 6 files changed, 5 insertions(+), 211 deletions(-) delete mode 100644 kglib/examples/diagnosis/diagnosis_test.py delete mode 100644 kglib/kgcn_data_loader/utils_test.py diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 4f52f464..1f3ce99c 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -24,20 +24,6 @@ load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") -py_test( - name = "diagnosis_test", - srcs = [ - "diagnosis_test.py" - ], - deps = [ - "diagnosis", - "//kglib/utils/graph/test", - vaticle_kglib_requirement('numpy'), - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('decorator'), - ] -) - py_library( name = "diagnosis", srcs = [ diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index bd0d011b..c5f65352 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -253,6 +253,8 @@ def test() -> Tuple[List[float], List[float], List[float]]: precision = tn / neg recall = tp / pos acc = (tp + tn) / (pos + neg) + assert acc >= ((precision + recall) / 2) - 0.001 + assert acc <= ((precision + recall) / 2) + 0.001 accuracies.append(float(acc)) precisions.append(precision) recalls.append(recall) diff --git a/kglib/examples/diagnosis/diagnosis_test.py b/kglib/examples/diagnosis/diagnosis_test.py deleted file mode 100644 index c3c9a6ff..00000000 --- a/kglib/examples/diagnosis/diagnosis_test.py +++ /dev/null @@ -1,96 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest -from unittest.mock import MagicMock - -from typedb.client import * -import networkx as nx -import numpy as np - -from kglib.examples.diagnosis.diagnosis import write_predictions_to_typedb -from kglib.utils.typedb.object.thing import Thing - - -class TestWritePredictionsToTypeDB(unittest.TestCase): - def test_query_made_as_expected(self): - graph = nx.MultiDiGraph() - - graph.add_node(0, concept=Thing('V123', 'person', 'entity'), probabilities=np.array([1.0, 0.0, 0.0]), - prediction=0) - graph.add_node(1, concept=Thing('V1235', 'disease', 'entity'), probabilities=np.array([1.0, 0.0, 0.0]), - prediction=0) - graph.add_node(2, concept=Thing('V6543', 'diagnosis', 'relation'), probabilities=np.array([0.0, 0.0071, 0.9927]), - prediction=2) - - graph.add_edge(2, 0) - graph.add_edge(2, 1) - - graphs = [graph] - tx = MagicMock(TypeDBTransaction) - - tx.commit = MagicMock() - tx.query.return_value = query = MagicMock(QueryManager) - - write_predictions_to_typedb(graphs, tx) - - expected_query = (f'match ' - f'$p iid V123;' - f'$d iid V1235;' - f'$kgcn isa kgcn;' - f'insert ' - f'$pd(patient: $p, diagnosed-disease: $d, diagnoser: $kgcn) isa diagnosis,' - f'has probability-exists 0.993,' - f'has probability-non-exists 0.007,' - f'has probability-preexists 0.000;') - - query.insert.assert_called_with(expected_query) - - tx.commit.assert_called() - - def test_query_made_only_if_relation_wins(self): - graph = nx.MultiDiGraph() - - graph.add_node(0, concept=Thing('V123', 'person', 'entity'), - probabilities=np.array([1.0, 0.0, 0.0]), prediction=0) - graph.add_node(1, concept=Thing('V1235', 'disease', 'entity'), - probabilities=np.array([1.0, 0.0, 0.0]), prediction=0) - graph.add_node(2, concept=Thing('V6543', 'diagnosis', 'relation'), - probabilities=np.array([0.0, 0.0, 1.0]), prediction=1) - - graph.add_edge(2, 0) - graph.add_edge(2, 1) - - graphs = [graph] - tx = MagicMock(TypeDBTransaction) - - tx.commit = MagicMock() - tx.query = MagicMock(QueryManager) - - write_predictions_to_typedb(graphs, tx) - - tx.query.assert_not_called() - - tx.commit.assert_called() - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_data_loader/BUILD b/kglib/kgcn_data_loader/BUILD index 77257580..979a463a 100644 --- a/kglib/kgcn_data_loader/BUILD +++ b/kglib/kgcn_data_loader/BUILD @@ -33,9 +33,9 @@ py_library( ) py_test( - name = "standard_encode_test", + name = "encoding_test", srcs = [ - "encoding/standard_encode_test.py" + "encoding/encoding_test.py" ], deps = [ "kgcn_data_loader", @@ -43,20 +43,6 @@ py_test( ] ) -py_test( - name = "utils_test", - srcs = [ - "utils_test.py" - ], - deps = [ - "kgcn_data_loader", - "//kglib/utils/graph/test", - "//kglib/utils/typedb", - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('numpy'), - ] -) - checkstyle_test( name = "checkstyle", include = glob([ diff --git a/kglib/kgcn_data_loader/utils.py b/kglib/kgcn_data_loader/utils.py index 7e349859..840e10cc 100644 --- a/kglib/kgcn_data_loader/utils.py +++ b/kglib/kgcn_data_loader/utils.py @@ -24,7 +24,7 @@ def load_typeql_schema_file(database, typedb_binary_location, typeql_file_path): """Load a schema from a file""" - _load_typeql_file(database, typeql_file_path, typedb_binary_location, "schema") + _load_typeql_file(database, typeql_file_path, typedb_binary_location, "schema") # TODO compress def load_typeql_data_file(database, typedb_binary_location, typeql_file_path): @@ -40,30 +40,3 @@ def _load_typeql_file(database, typeql_file_path, typedb_binary_location, schema f'--command=source {typeql_file_path}', f'--command=commit' ], cwd=typedb_binary_location) - - -def apply_logits_to_graphs(graph, logits_graph): - """ - Take in a graph that describes the logits of the graph of interest, and store those logits on the graph as the - property 'logits'. The graphs must correspond with one another - - Args: - graph: Graph to apply logits to - logits_graph: Graph containing logits - - Returns: - graph with logits added as property 'logits' - """ - - for node, data in logits_graph.nodes(data=True): - graph.nodes[node]['logits'] = list(data['features']) - - # TODO This is the desired implementation, but the graphs are altered by the model to have duplicated reversed - # edges, so this won't work for now - # for sender, receiver, keys, data in logit_graph.edges(keys=True, data=True): - # graph.edges[sender, receiver, keys]['logits'] = list(data['features']) - - for sender, receiver, keys, data in graph.edges(keys=True, data=True): - data['logits'] = list(logits_graph.edges[sender, receiver, keys]['features']) - - return graph diff --git a/kglib/kgcn_data_loader/utils_test.py b/kglib/kgcn_data_loader/utils_test.py deleted file mode 100644 index 32c76c7f..00000000 --- a/kglib/kgcn_data_loader/utils_test.py +++ /dev/null @@ -1,57 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import networkx as nx -import numpy as np - -from kglib.utils.graph.test.case import GraphTestCase - -from kglib.kgcn_data_loader.utils import apply_logits_to_graphs - - -class TestApplyLogitsToGraphs(GraphTestCase): - def test_logits_applied_as_expected(self): - - graph = nx.MultiDiGraph(name=0) - graph.add_node(0) - graph.add_node(1) - graph.add_edge(0, 1) - - logits_graph = nx.MultiDiGraph(name=0) - logits_graph.add_node(0, features=np.array([0.2, 0.3, 0.01])) - logits_graph.add_node(1, features=np.array([0.56, -0.04, 0.05])) - logits_graph.add_edge(0, 1, features=np.array([0.5, 0.008, -0.1])) - logits_graph.add_edge(1, 0, features=np.array([0.5, 0.008, -0.1])) - - expected_graph = nx.MultiDiGraph(name=0) - expected_graph.add_node(0, logits=[0.2, 0.3, 0.01]) - expected_graph.add_node(1, logits=[0.56, -0.04, 0.05]) - expected_graph.add_edge(0, 1, logits=[0.5, 0.008, -0.1]) - - graph_with_logits = apply_logits_to_graphs(graph, logits_graph) - - self.assertGraphsEqual(expected_graph, graph_with_logits) - - -if __name__ == '__main__': - unittest.main() From 3fcefdfaacff517ba8d3ce475f69b260049027b9 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 19 Jul 2022 11:57:41 +0100 Subject: [PATCH 033/124] Fix automation.yml --- .grabl/automation.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 5c0c39af..693afc17 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -60,7 +60,7 @@ build: pyenv install -s 3.6.3 pyenv global 3.6.3 system bazel test //kglib/kgcn_data_loader/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH - test-kgcn-examples: + test-examples: image: vaticle-ubuntu-20.04 timeout: "10m" command: | @@ -76,7 +76,7 @@ build: bazel test //kglib/tests/end_to_end:diagnosis --test_output=streamed --spawn_strategy=standalone --action_env=PATH deploy-pip-snapshot: image: vaticle-ubuntu-20.04 - dependencies: [build, test-kgcn-data-loader, test-utils, test-kgcn-tensorflow, test-end-to-end] + dependencies: [build, test-kgcn-data-loader, test-utils, test-examples, test-end-to-end] command: | export DEPLOY_PIP_USERNAME=$REPO_VATICLE_USERNAME export DEPLOY_PIP_PASSWORD=$REPO_VATICLE_PASSWORD From 7b5c5b200eaa491e1871db7fe07a45ff523e1f08 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 19 Jul 2022 12:25:41 +0100 Subject: [PATCH 034/124] Upgrade to Python 3.7.2 --- .grabl/automation.yml | 20 ++++++++++---------- BUILD | 2 +- README.md | 6 +++--- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 693afc17..101169c1 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -37,8 +37,8 @@ build: build: image: vaticle-ubuntu-20.04 command: | - pyenv install -s 3.6.3 - pyenv global 3.6.3 system + pyenv install -s 3.7.2 + pyenv global 3.7.2 system bazel build //... bazel run @vaticle_dependencies//tool/checkstyle:test-coverage bazel test $(bazel query 'kind(checkstyle_test, //...)') --test_output=errors @@ -50,29 +50,29 @@ build: image: vaticle-ubuntu-20.04 timeout: "10m" command: | - pyenv install -s 3.6.3 - pyenv global 3.6.3 system + pyenv install -s 3.7.2 + pyenv global 3.7.2 system bazel test //kglib/utils/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-utils: image: vaticle-ubuntu-20.04 timeout: "10m" command: | - pyenv install -s 3.6.3 - pyenv global 3.6.3 system + pyenv install -s 3.7.2 + pyenv global 3.7.2 system bazel test //kglib/kgcn_data_loader/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-examples: image: vaticle-ubuntu-20.04 timeout: "10m" command: | - pyenv install -s 3.6.3 - pyenv global 3.6.3 system + pyenv install -s 3.7.2 + pyenv global 3.7.2 system bazel test //kglib/examples/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-end-to-end: image: vaticle-ubuntu-20.04 timeout: "30m" command: | - pyenv install -s 3.6.3 - pyenv global 3.6.3 system + pyenv install -s 3.7.2 + pyenv global 3.7.2 system bazel test //kglib/tests/end_to_end:diagnosis --test_output=streamed --spawn_strategy=standalone --action_env=PATH deploy-pip-snapshot: image: vaticle-ubuntu-20.04 diff --git a/BUILD b/BUILD index b4ffda49..b3521517 100644 --- a/BUILD +++ b/BUILD @@ -43,7 +43,7 @@ assemble_pip( package_name = "vaticle-kglib", classifiers = [ "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Intended Audience :: Developers", diff --git a/README.md b/README.md index bbb860a2..e150ae42 100644 --- a/README.md +++ b/README.md @@ -37,13 +37,13 @@ You may find the following resources useful: ### Requirements -- Python >= 3.6, <= 3.7.x (TensorFlow 1.14.0 doesn't support later Python versions). +- Python >= 3.7.x - KGLIB installed via pip: `pip install typedb-kglib`. -- [TypeDB 2.1.1](https://github.com/vaticle/typedb/releases) running in the background. +- [TypeDB 2.11.1](https://github.com/vaticle/typedb/releases) running in the background. -- `typedb-client-python` 2.1.0 ([PyPi](https://pypi.org/project/typedb-client/), [GitHub release](https://github.com/vaticle/typedb-client-python/releases)). This should be installed automatically when you `pip install typedb-kglib`. +- `typedb-client-python` 2.11.x ([PyPi](https://pypi.org/project/typedb-client/), [GitHub release](https://github.com/vaticle/typedb-client-python/releases)). This should be installed automatically when you `pip install typedb-kglib`. ### Run the Example From 1b8bff6df964203bb63beb6d06a1959eb78316f1 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Tue, 19 Jul 2022 18:17:54 +0100 Subject: [PATCH 035/124] Fix result reporting --- kglib/examples/diagnosis/diagnosis.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index c5f65352..0a67d41d 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -236,11 +236,9 @@ def train() -> float: return float(loss) @torch.no_grad() - def test() -> Tuple[List[float], List[float], List[float]]: + def test() -> List[Tuple[float, float, float]]: model.eval() - accuracies = [] - precisions = [] - recalls = [] + results = [] for split in train_data, val_data, test_data: # We use `edge_index_dict` and `y_edge` for validation and testing to exclude the negative samples z = model.encode(split.x_dict, split.edge_index_dict) @@ -253,12 +251,8 @@ def test() -> Tuple[List[float], List[float], List[float]]: precision = tn / neg recall = tp / pos acc = (tp + tn) / (pos + neg) - assert acc >= ((precision + recall) / 2) - 0.001 - assert acc <= ((precision + recall) / 2) + 0.001 - accuracies.append(float(acc)) - precisions.append(precision) - recalls.append(recall) - return accuracies, precisions, recalls + results.append((float(acc), precision, recall)) + return results writer = SummaryWriter() for edge_type, edge_store in zip(data.edge_types, data.edge_stores): From 4b2e95c068ebd814b3958d941eb4dc2bf4d8fe7b Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 12:09:59 +0100 Subject: [PATCH 036/124] Cleaning, refactoring, added confusion matrix from TypeDB --- kglib/examples/diagnosis/diagnosis.py | 154 +++++++++--------- ...{typedb_networkx_dataset.py => dataset.py} | 2 +- kglib/kgcn_data_loader/encoding/encoding.py | 71 -------- .../encoding/encoding_test.py | 51 ------ .../transform/binary_link_prediction.py | 22 +-- kglib/kgcn_data_loader/transform/common.py | 58 +++++++ .../transform/typedb_graph_encoder.py | 12 -- kglib/tests/deployment/kgcn/diagnosis.py | 4 +- kglib/tests/end_to_end/kgcn/diagnosis.py | 4 +- .../tests/end_to_end/kgcn/diagnosis_debug.py | 4 +- 10 files changed, 144 insertions(+), 238 deletions(-) rename kglib/kgcn_data_loader/dataset/{typedb_networkx_dataset.py => dataset.py} (99%) delete mode 100644 kglib/kgcn_data_loader/encoding/encoding.py delete mode 100644 kglib/kgcn_data_loader/encoding/encoding_test.py create mode 100644 kglib/kgcn_data_loader/transform/common.py diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 0a67d41d..f7036be4 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -31,20 +31,21 @@ from torch_geometric.nn import HGTConv from typedb.client import * -from kglib.kgcn_data_loader.dataset.typedb_networkx_dataset import TypeDBNetworkxDataSet +from kglib.kgcn_data_loader.dataset.dataset import DataSet from kglib.kgcn_data_loader.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges, \ - prepare_edge_triplets, prepare_node_types + binary_link_prediction_edge_triplets +from kglib.kgcn_data_loader.transform.common import clear_unneeded_fields, store_concepts_by_type from kglib.kgcn_data_loader.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ - ContinuousEncoder, store_concepts_by_type + ContinuousEncoder from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.utils.graph.iterate import multidigraph_edge_data_iterator, multidigraph_node_data_iterator from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data +from kglib.utils.typedb.type.type import get_thing_types DATABASE = "diagnosis" ADDRESS = "localhost:1729" -PREEXISTS = 0 +PREEXISTS = 0 # TODO Remove, now unnecessary # Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed @@ -54,9 +55,23 @@ # Note that this determines the edge direction when converting from a TypeDB relation RELATION_TYPE_TO_PREDICT = ('person', 'patient', 'diagnosis', 'diagnosed-disease', 'disease') -# The learner should see candidate relations the same as the ground truth relations, so adjust these candidates to -# look like their ground truth counterparts -TYPES_AND_ROLES_TO_OBFUSCATE = {} +TYPE_ENCODING_SIZE = 16 +ATTRIBUTE_ENCODING_SIZE = 16 + +# Attribute encoders encode the value of each attribute into a fixed-length feature vector. The encoders are +# defined on a per-type basis. Easily define your own encoders for specific attribute data in your TypeDB database +ATTRIBUTE_ENCODERS = { + # Categorical Attribute types and the values of their categories + # TODO: Use a sentence encoder for this instead to demonstrate how to use one + 'name': CategoricalEncoder( + ['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol'], + ATTRIBUTE_ENCODING_SIZE + ), + # Continuous Attribute types and their min and max values + 'severity': ContinuousEncoder(0, 1, ATTRIBUTE_ENCODING_SIZE), + 'age': ContinuousEncoder(7, 80, ATTRIBUTE_ENCODING_SIZE), + 'units-per-week': ContinuousEncoder(3, 29, ATTRIBUTE_ENCODING_SIZE) +} def diagnosis_example(typedb_binary_directory, @@ -97,62 +112,31 @@ def diagnosis_example(typedb_binary_directory, session = client.session(database, SessionType.DATA) - # During the transforms below we convert the *relations to predict* to simple edges, which means the relation + # During the transforms below we convert the relations to predict to simple binary edges, which means the relation # changes from a node to an edge. We therefore need to update the node_types and edge_types accordingly - node_types = prepare_node_types(session, RELATION_TYPE_TO_PREDICT, TYPES_TO_IGNORE) - edge_type_triplets, edge_type_triplets_reversed = prepare_edge_triplets(session, RELATION_TYPE_TO_PREDICT, TYPES_TO_IGNORE) - type_encoding_size = 16 - - # Attribute encoders encode the value of each attribute into a fixed-length feature vector. The encoders are - # defined on a per-type basis. Easily define your own encoders for specific attribute data in your TypeDB database - attribute_encoding_size = 16 - attribute_encoders = { - # Categorical Attribute types and the values of their categories - # TODO: Use a sentence encoder for this instead to demonstrate how to use one - 'name': CategoricalEncoder( - ['Diabetes Type II', 'Multiple Sclerosis', 'Blurred vision', 'Fatigue', 'Cigarettes', 'Alcohol'], - attribute_encoding_size - ), - # Continuous Attribute types and their min and max values - 'severity': ContinuousEncoder(0, 1, attribute_encoding_size), - 'age': ContinuousEncoder(7, 80, attribute_encoding_size), - 'units-per-week': ContinuousEncoder(3, 29, attribute_encoding_size) - } - - def prepare_graph(graph): - # TODO: We likely need to know the relations that were replaced with binary edges later on - replaced_edges = binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT[1:4]), - return nx.convert_node_labels_to_integers(graph, label_attribute="concept") - - def clear_unneeded_fields(graph): - for node_data in multidigraph_node_data_iterator(graph): - x = node_data["x"] - t = node_data["type"] - node_data.clear() - node_data["x"] = x - node_data["type"] = t - - for edge_data in multidigraph_edge_data_iterator(graph): - x = edge_data["edge_attr"] - y = edge_data["y_edge"] - t = edge_data["type"] - edge_data.clear() - edge_data["edge_attr"] = x - edge_data["y_edge"] = y - edge_data["type"] = t - return graph + + # Remove the relation from the node types, since we will be using it as a binary edge instead + to_ignore = list(TYPES_TO_IGNORE) + [RELATION_TYPE_TO_PREDICT[2]] + node_types = [t for t in get_thing_types(session) if t not in to_ignore] + edge_type_triplets, edge_type_triplets_reversed = binary_link_prediction_edge_triplets( + session, RELATION_TYPE_TO_PREDICT, TYPES_TO_IGNORE + ) + + binary_edge_to_predict = RELATION_TYPE_TO_PREDICT[::2] # Evaluates to: ('person', 'diagnosis', 'disease') + binary_rev_edge_to_predict = edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person') edge_types = list({triplet[1] for triplet in edge_type_triplets}) transform = transforms.Compose([ - prepare_graph, - GraphFeatureEncoder(node_types, edge_types, type_encoding_size, attribute_encoders, attribute_encoding_size), + lambda graph: binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT[1:4]), + lambda graph: nx.convert_node_labels_to_integers(graph, label_attribute="concept"), + GraphFeatureEncoder(node_types, edge_types, TYPE_ENCODING_SIZE, ATTRIBUTE_ENCODERS, ATTRIBUTE_ENCODING_SIZE), LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[2]), - store_concepts_by_type, # store the concepts we have in type-wise lists so that we can interpret the concepts for our predictions later + store_concepts_by_type, clear_unneeded_fields ]) # Create a Dataset that will load graphs from TypeDB on-demand, based on an ID - dataset = TypeDBNetworkxDataSet( + dataset = DataSet( [0], node_types, edge_type_triplets, get_query_handles, DATABASE, ADDRESS, session, True, transform ) @@ -162,24 +146,24 @@ def clear_unneeded_fields(graph): as_tensor(node_type_indices), as_tensor(edge_type_indices), node_types, edge_type_triplets ).to(device) # Get the first graph object. + # Reverse edges need to be present for bi-directional message-passing but the labels should not be considered + # for node and edge representations data = transforms.ToUndirected()(data) for edge_from, edge, edge_to in edge_type_triplets_reversed: - # This seems to be necessary so that the reverse edges are present for message-passing but the labels aren't - # considered for node and edge representations del data[edge_from, edge, edge_to].edge_label # Remove "reverse" label. - binary_edge_to_predict = RELATION_TYPE_TO_PREDICT[::2] # Evaluates to: ('person', 'diagnosis', 'disease') - binary_rev_edge_to_predict = edge_type_triplets_reversed[edge_type_triplets.index(RELATION_TYPE_TO_PREDICT[::2])] # Evaluates to: ('disease', 'rev_diagnosis', 'person') - + # Setting the neg_sampling_ratio higher than the number of places that a negative sample can be added causes the + # training set to have too few negative samples! + # Consider using other samplers from Pytorch Geometric in place of this one, depending on your use case train_data, val_data, test_data = transforms.RandomLinkSplit( num_val=0.2, num_test=0.2, - neg_sampling_ratio=1.0, # Putting this ration greater than that makes the train, val, test splits have different proportions of negative examples due to a limited number of places to add negative edges in this example generated data. + neg_sampling_ratio=1.0, edge_types=binary_edge_to_predict, rev_edge_types=binary_rev_edge_to_predict )(data) - # Add a new `links` attribute to store the edges for prediction so that they aren't used for training + # Add a new `links` attribute to store the edges for prediction so that they are definitely isolated from training train_data.link_index = train_data.edge_label_index_dict[binary_edge_to_predict] train_data.link_labels = train_data.edge_label_dict[binary_edge_to_predict] val_data.link_index = val_data.edge_label_index_dict[binary_edge_to_predict] @@ -187,16 +171,17 @@ def clear_unneeded_fields(graph): test_data.link_index = test_data.edge_label_index_dict[binary_edge_to_predict] test_data.link_labels = test_data.edge_label_dict[binary_edge_to_predict] - data.links = data['person', 'diagnosis', 'disease'] - data.rev_links = data['disease', 'rev_diagnosis', 'person'] - del data['person', 'diagnosis', 'disease'] - del data['disease', 'rev_diagnosis', 'person'] - del train_data['person', 'diagnosis', 'disease'] - del train_data['disease', 'rev_diagnosis', 'person'] - del val_data['person', 'diagnosis', 'disease'] - del val_data['disease', 'rev_diagnosis', 'person'] - del test_data['person', 'diagnosis', 'disease'] - del test_data['disease', 'rev_diagnosis', 'person'] + # Delete the stores for the predicted edge now that we have stored it elsewhere above + data.links = data[binary_edge_to_predict] + data.rev_links = data[binary_rev_edge_to_predict] + del data[binary_edge_to_predict] + del data[binary_rev_edge_to_predict] + del train_data[binary_edge_to_predict] + del train_data[binary_rev_edge_to_predict] + del val_data[binary_edge_to_predict] + del val_data[binary_rev_edge_to_predict] + del test_data[binary_edge_to_predict] + del test_data[binary_rev_edge_to_predict] class LinkPredictionModel(torch.nn.Module): def __init__(self, in_channels: Union[int, Dict[str, int]], hidden_channels=128, heads=8): @@ -213,16 +198,12 @@ def decode(self, z, edge_label_index_dict): def decode_all(self, z): logits = z['person'] @ z['disease'].t() - # return (logits.sigmoid() > 0.5).nonzero(as_tuple=False).t() return logits model = LinkPredictionModel(in_channels=-1) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') data, model = data.to(device), model.to(device) - with torch.no_grad(): # Initialize lazy modules. - z = model.encode(train_data.x_dict, train_data.edge_index_dict) - optimizer = torch.optim.Adam(model.parameters(), lr=0.005, weight_decay=0.001) def train() -> float: @@ -264,7 +245,9 @@ def test() -> List[Tuple[float, float, float]]: best_val_acc = 0 start_patience = patience = 100 - for epoch in range(1, 100): + train_results = None + test_results = None + for epoch in range(1, 10): loss = train() writer.add_scalar('Loss/train', loss, epoch) train_results, val_results, test_results = test() @@ -289,8 +272,8 @@ def test() -> List[Tuple[float, float, float]]: break # Put back the diagnosis edges - data['person', 'diagnosis', 'disease'] = data.links - data['disease', 'rev_diagnosis', 'person'] = data.rev_links + # data[binary_edge_to_predict] = data.links + # data[binary_rev_edge_to_predict] = data.rev_links z = model.encode(data.x_dict, data.edge_index_dict) final_edge_index = (model.decode_all(z).sigmoid() > 0.5).nonzero(as_tuple=False).cpu().detach().numpy() @@ -310,6 +293,19 @@ def test() -> List[Tuple[float, float, float]]: with session.transaction(TransactionType.WRITE) as tx: write_predictions_to_typedb(predicted_links, tx) + # Now we can get the confusion matrix from querying TypeDB! + with session.transaction(TransactionType.READ) as tx: + tp = tx.query().match_aggregate("match $p isa person; $d isa disease; ($p, $d) isa diagnosis; " + "($p, $d) isa predicted-diagnosis; count;").get().as_int() + tn = tx.query().match_aggregate("match $p isa person; $d isa disease; not{($p, $d) isa diagnosis;}; " + "not{($p, $d) isa predicted-diagnosis;}; count;").get().as_int() + fp = tx.query().match_aggregate("match $p isa person; $d isa disease; not{($p, $d) isa diagnosis;}; " + "($p, $d) isa predicted-diagnosis; count;").get().as_int() + fn = tx.query().match_aggregate("match $p isa person; $d isa disease; ($p, $d) isa diagnosis; " + "not{($p, $d) isa predicted-diagnosis;}; count;").get().as_int() + print("Confusion matrix") + print(f"{tp} {fn}\n{fp} {tn}") + session.close() client.close() diff --git a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py b/kglib/kgcn_data_loader/dataset/dataset.py similarity index 99% rename from kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py rename to kglib/kgcn_data_loader/dataset/dataset.py index b4509277..1754afa8 100644 --- a/kglib/kgcn_data_loader/dataset/typedb_networkx_dataset.py +++ b/kglib/kgcn_data_loader/dataset/dataset.py @@ -28,7 +28,7 @@ from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries -class TypeDBNetworkxDataSet: +class DataSet: """ Loading graphs based on queries from TypeDB. Note: not dependent on PyTorch or Pytorch Geometric. diff --git a/kglib/kgcn_data_loader/encoding/encoding.py b/kglib/kgcn_data_loader/encoding/encoding.py deleted file mode 100644 index 8aacc7cb..00000000 --- a/kglib/kgcn_data_loader/encoding/encoding.py +++ /dev/null @@ -1,71 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import numpy as np - -from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator - - -def encode_values(graph, attribute_encoders, attribute_encoding_size): - for node_data in multidigraph_node_data_iterator(graph): - typ = node_data['type'] - if typ in attribute_encoders.keys(): - # Add the integer value of the category for each categorical attribute instance - node_data['encoded_value'] = attribute_encoders[typ].encode(node_data['value']) - else: - node_data['encoded_value'] = [0] * attribute_encoding_size - - for edge_data in multidigraph_edge_data_iterator(graph): - edge_data['encoded_value'] = [0] * attribute_encoding_size - - -def encode_types(graph, iterator_func, all_types): - """ - Encodes the type found in graph data as an integer according to the index it is found in `all_types` - Args: - graph: The graph to encode - iterator_func: An function to create an iterator of data in the graph (node data, edge data or combined node - and edge data) - all_types: The full list of types to be encoded in this order - - Returns: - Nothing, the graph is updated in-place - - """ - iterator = iterator_func(graph) - - for data in iterator: - data['categorical_type'] = all_types.index(data['type']) - - -def stack_features(features): - """ - Stacks features together into a single vector - - Args: - features: iterable of features, features can be a single value or iterable - - Returns: - Numpy array (vector) of stacked features - - """ - - return np.hstack([np.array(feature, dtype=np.float32) for feature in features]) diff --git a/kglib/kgcn_data_loader/encoding/encoding_test.py b/kglib/kgcn_data_loader/encoding/encoding_test.py deleted file mode 100644 index eadcf9b0..00000000 --- a/kglib/kgcn_data_loader/encoding/encoding_test.py +++ /dev/null @@ -1,51 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -import unittest - -import numpy as np - -from kglib.kgcn_data_loader.encoding.encoding import stack_features - - -class TestAugmentDataFields(unittest.TestCase): - - def test_numpy_fields_augmented_as_expected(self): - features = [np.array([0, 1, 0]), np.array([5])] - - stacked = stack_features(features) - - expected = np.array([0, 1, 0, 5]) - - np.testing.assert_equal(expected, stacked) - - def test_augmenting_non_numpy_numeric(self): - data = [np.array([0, 1, 0]), 5] - - stacked = stack_features(data) - - expected = np.array([0, 1, 0, 5]) - - np.testing.assert_equal(stacked, expected) - - -if __name__ == "__main__": - unittest.main() diff --git a/kglib/kgcn_data_loader/transform/binary_link_prediction.py b/kglib/kgcn_data_loader/transform/binary_link_prediction.py index ed861f07..0c23924c 100644 --- a/kglib/kgcn_data_loader/transform/binary_link_prediction.py +++ b/kglib/kgcn_data_loader/transform/binary_link_prediction.py @@ -20,8 +20,8 @@ # import networkx as nx -from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator -from kglib.utils.typedb.type.type import get_edge_type_triplets, reverse_edge_type_triplets, get_thing_types +from kglib.utils.graph.iterate import multidigraph_edge_data_iterator +from kglib.utils.typedb.type.type import get_edge_type_triplets, reverse_edge_type_triplets class LinkPredictionLabeller: @@ -33,10 +33,6 @@ def __call__(self, graph): self.label_edges(graph) return graph - # def label_nodes(self, graph): - # for data in multidigraph_node_data_iterator(graph): - # data["y"] = 0 - def label_edges(self, graph): for data in multidigraph_edge_data_iterator(graph): if data["type"] == self.edge_type_to_predict: @@ -101,10 +97,10 @@ def binary_relations_to_edges(graph: nx.MultiDiGraph, binary_relation_type): replacement_edges[(new_edge_start, new_edge_end)] = node for node in replacement_edges.values(): graph.remove_node(node) - return replacement_edges + return graph -def prepare_edge_triplets(session, relation_type_to_predict, types_to_ignore): +def binary_link_prediction_edge_triplets(session, relation_type_to_predict, types_to_ignore): edge_type_triplets = get_edge_type_triplets(session) edge_type_triplets = [e for e in edge_type_triplets if not (set(e).intersection(types_to_ignore))] replace_relation_with_binary_edge(edge_type_triplets, relation_type_to_predict) @@ -119,13 +115,3 @@ def replace_relation_with_binary_edge(edge_type_triplets, relation_type_to_predi if relation_type_to_predict[1] != relation_type_to_predict[3]: edge_type_triplets.remove(tuple(reversed(relation_type_to_predict[:3]))) edge_type_triplets.append((relation_type_to_predict[0], relation_type_to_predict[2], relation_type_to_predict[4])) - - -def prepare_node_types(session, relation_type_to_predict, types_to_ignore): - node_types = get_thing_types(session) - for el in types_to_ignore: - if el in node_types: - node_types.remove(el) - # Remove the relation_to_predict as a node, since we will be using it as a binary edge instead - node_types.remove(relation_type_to_predict[2]) - return node_types diff --git a/kglib/kgcn_data_loader/transform/common.py b/kglib/kgcn_data_loader/transform/common.py new file mode 100644 index 00000000..838b2d36 --- /dev/null +++ b/kglib/kgcn_data_loader/transform/common.py @@ -0,0 +1,58 @@ +# +# Copyright (C) 2021 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator + + +def clear_unneeded_fields(graph): + for node_data in multidigraph_node_data_iterator(graph): + x = node_data["x"] + t = node_data["type"] + node_data.clear() + node_data["x"] = x + node_data["type"] = t + + for edge_data in multidigraph_edge_data_iterator(graph): + x = edge_data["edge_attr"] + y = edge_data["y_edge"] + t = edge_data["type"] + edge_data.clear() + edge_data["edge_attr"] = x + edge_data["y_edge"] = y + edge_data["type"] = t + return graph + + +def store_concepts_by_type(graph): + """ + Organises concepts by type the same way `data.to_heterogeneous()` organises nodes by type. This is necessary to + be able to map back from a `HeteroData` object to the TypeDB concepts that the nodes refer to. + :param graph: NetworkX graph to operate on + :return: The same graph, with a field `concepts_by_type` holding concepts organised by type + """ + concepts_by_type = {} + for node_data in multidigraph_node_data_iterator(graph): + typ = node_data['type'] + if typ in concepts_by_type: + concepts_by_type[typ].append(node_data['concept']) + else: + concepts_by_type[typ] = [node_data['concept']] + graph.concepts_by_type = concepts_by_type + return graph diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py index 0fed21ab..575402ce 100644 --- a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py +++ b/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py @@ -101,15 +101,3 @@ def __init__(self, min_val, max_val, attribute_encoding_size): def __call__(self, value): return [(value - self.min_val) / (self.max_val - self.min_val)] * self.attribute_encoding_size - - -def store_concepts_by_type(graph): - concepts_by_type = {} - for node_data in multidigraph_node_data_iterator(graph): - typ = node_data['type'] - if typ in concepts_by_type: - concepts_by_type[typ].append(node_data['concept']) - else: - concepts_by_type[typ] = [node_data['concept']] - graph.concepts_by_type = concepts_by_type - return graph diff --git a/kglib/tests/deployment/kgcn/diagnosis.py b/kglib/tests/deployment/kgcn/diagnosis.py index 6d82076b..bffcfbf0 100644 --- a/kglib/tests/deployment/kgcn/diagnosis.py +++ b/kglib/tests/deployment/kgcn/diagnosis.py @@ -27,8 +27,8 @@ class TestDiagnosisExample(unittest.TestCase): def test_example_runs_without_exception(self): - diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", - num_graphs=6) + # TODO: Remove hard-coding + diagnosis_example("/Users/jamesfletcher/programming/typedb-dists/typedb-all-mac-2.11.0", num_graphs=6) if __name__ == "__main__": diff --git a/kglib/tests/end_to_end/kgcn/diagnosis.py b/kglib/tests/end_to_end/kgcn/diagnosis.py index c1c1fe5b..99d08fe8 100644 --- a/kglib/tests/end_to_end/kgcn/diagnosis.py +++ b/kglib/tests/end_to_end/kgcn/diagnosis.py @@ -43,9 +43,9 @@ def test_learning_is_done(self): schema_file_path=self._schema_file_location, seed_data_file_path=self._data_file_location ) - self.assertGreaterEqual(train_accuracy, 0.7) + self.assertGreaterEqual(train_accuracy, 0.9) self.assertLessEqual(train_accuracy, 0.99) - self.assertGreaterEqual(test_accuracy, 0.7) + self.assertGreaterEqual(test_accuracy, 0.9) self.assertLessEqual(test_accuracy, 0.99) diff --git a/kglib/tests/end_to_end/kgcn/diagnosis_debug.py b/kglib/tests/end_to_end/kgcn/diagnosis_debug.py index 58190383..ac82244b 100644 --- a/kglib/tests/end_to_end/kgcn/diagnosis_debug.py +++ b/kglib/tests/end_to_end/kgcn/diagnosis_debug.py @@ -45,9 +45,9 @@ def test_learning_is_done(self): schema_file_path=self._schema_file_location, seed_data_file_path=self._data_file_location ) - self.assertGreaterEqual(train_accuracy, 0.7) + self.assertGreaterEqual(train_accuracy, 0.9) self.assertLessEqual(train_accuracy, 0.99) - self.assertGreaterEqual(test_accuracy, 0.7) + self.assertGreaterEqual(test_accuracy, 0.9) self.assertLessEqual(test_accuracy, 0.99) From 7e910c334d3613f7e3ec4af309f17a09f5018d6d Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 12:21:49 +0100 Subject: [PATCH 037/124] Update dependencies and fix build issues --- dependencies/vaticle/repositories.bzl | 6 +++--- kglib/examples/diagnosis/.images/BUILD | 2 +- kglib/examples/diagnosis/BUILD | 2 +- kglib/kgcn_data_loader/BUILD | 11 ----------- kglib/tests/end_to_end/BUILD | 2 +- 5 files changed, 6 insertions(+), 17 deletions(-) diff --git a/dependencies/vaticle/repositories.bzl b/dependencies/vaticle/repositories.bzl index 9ce0ac13..b95f404e 100644 --- a/dependencies/vaticle/repositories.bzl +++ b/dependencies/vaticle/repositories.bzl @@ -25,19 +25,19 @@ def vaticle_dependencies(): git_repository( name = "vaticle_dependencies", remote = "https://github.com/vaticle/dependencies", - commit = "34c260ae7858b08dd6247d43c8c373847b113d68", # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_dependencies + commit = "a8b3b714a5e0562d41f4c05ca8f266d48b7d0fd3", # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_dependencies ) def vaticle_typedb_client_python(): git_repository( name = "vaticle_typedb_client_python", remote = "https://github.com/vaticle/typedb-client-python", - tag = "2.9.0" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_typedb_client_python + commit = "d08a6c36ac4ef20f2df26017cf42e17fba230387" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_typedb_client_python ) def vaticle_typedb_common(): git_repository( name = "vaticle_typedb_common", remote = "https://github.com/vaticle/typedb-common", - tag = "2.9.0" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_common + commit = "f0dd708adaea9fe1fdc3699180797a12166d33e8" # sync-marker: do not remove this comment, this is used for sync-dependencies by @vaticle_common ) diff --git a/kglib/examples/diagnosis/.images/BUILD b/kglib/examples/diagnosis/.images/BUILD index a8ae9110..a3814b9a 100644 --- a/kglib/examples/diagnosis/.images/BUILD +++ b/kglib/examples/diagnosis/.images/BUILD @@ -28,5 +28,5 @@ checkstyle_test( "*.png", "*.svg" ]), - license_type = "apache", + license_type = "apache-header", ) diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 1f3ce99c..7659384d 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -40,5 +40,5 @@ py_library( checkstyle_test( name = "checkstyle", include = glob(["*"]), - license_type = "apache", + license_type = "apache-header", ) diff --git a/kglib/kgcn_data_loader/BUILD b/kglib/kgcn_data_loader/BUILD index 979a463a..605a5b90 100644 --- a/kglib/kgcn_data_loader/BUILD +++ b/kglib/kgcn_data_loader/BUILD @@ -32,17 +32,6 @@ py_library( visibility=['//visibility:public'] ) -py_test( - name = "encoding_test", - srcs = [ - "encoding/encoding_test.py" - ], - deps = [ - "kgcn_data_loader", - "//kglib/utils" - ] -) - checkstyle_test( name = "checkstyle", include = glob([ diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD index 0d6720cf..49aef43f 100644 --- a/kglib/tests/end_to_end/BUILD +++ b/kglib/tests/end_to_end/BUILD @@ -66,5 +66,5 @@ checkstyle_test( "*", "**/*" ]), - license_type = "apache", + license_type = "apache-header", ) From 7105217755f69768ae4509e240593c18dad1c0ed Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 12:32:32 +0100 Subject: [PATCH 038/124] Rename kgcn_data_loader to pytorch_geometric --- .grabl/automation.yml | 2 +- kglib/examples/diagnosis/diagnosis.py | 10 +++++----- kglib/{kgcn_data_loader => pytorch_geometric}/BUILD | 2 +- .../dataset/dataset.py | 0 .../transform/binary_link_prediction.py | 0 .../transform/common.py | 0 .../transform/typedb_graph_encoder.py | 0 kglib/{kgcn_data_loader => pytorch_geometric}/utils.py | 0 setup.py | 4 ++-- 9 files changed, 9 insertions(+), 9 deletions(-) rename kglib/{kgcn_data_loader => pytorch_geometric}/BUILD (97%) rename kglib/{kgcn_data_loader => pytorch_geometric}/dataset/dataset.py (100%) rename kglib/{kgcn_data_loader => pytorch_geometric}/transform/binary_link_prediction.py (100%) rename kglib/{kgcn_data_loader => pytorch_geometric}/transform/common.py (100%) rename kglib/{kgcn_data_loader => pytorch_geometric}/transform/typedb_graph_encoder.py (100%) rename kglib/{kgcn_data_loader => pytorch_geometric}/utils.py (100%) diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 101169c1..e578ec6a 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -59,7 +59,7 @@ build: command: | pyenv install -s 3.7.2 pyenv global 3.7.2 system - bazel test //kglib/kgcn_data_loader/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + bazel test //kglib/pytorch_geometric/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-examples: image: vaticle-ubuntu-20.04 timeout: "10m" diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index f7036be4..3ec151b0 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -31,13 +31,13 @@ from torch_geometric.nn import HGTConv from typedb.client import * -from kglib.kgcn_data_loader.dataset.dataset import DataSet -from kglib.kgcn_data_loader.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges, \ +from kglib.pytorch_geometric.dataset.dataset import DataSet +from kglib.pytorch_geometric.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges, \ binary_link_prediction_edge_triplets -from kglib.kgcn_data_loader.transform.common import clear_unneeded_fields, store_concepts_by_type -from kglib.kgcn_data_loader.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ +from kglib.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type +from kglib.pytorch_geometric.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ ContinuousEncoder -from kglib.kgcn_data_loader.utils import load_typeql_schema_file, load_typeql_data_file +from kglib.pytorch_geometric.utils import load_typeql_schema_file, load_typeql_data_file from kglib.utils.graph.query.query_graph import QueryGraph from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data from kglib.utils.typedb.type.type import get_thing_types diff --git a/kglib/kgcn_data_loader/BUILD b/kglib/pytorch_geometric/BUILD similarity index 97% rename from kglib/kgcn_data_loader/BUILD rename to kglib/pytorch_geometric/BUILD index 605a5b90..afd485de 100644 --- a/kglib/kgcn_data_loader/BUILD +++ b/kglib/pytorch_geometric/BUILD @@ -24,7 +24,7 @@ load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") py_library( - name = "kgcn_data_loader", + name = "pytorch_geometric", srcs = glob([ "*.py", "**/*.py" diff --git a/kglib/kgcn_data_loader/dataset/dataset.py b/kglib/pytorch_geometric/dataset/dataset.py similarity index 100% rename from kglib/kgcn_data_loader/dataset/dataset.py rename to kglib/pytorch_geometric/dataset/dataset.py diff --git a/kglib/kgcn_data_loader/transform/binary_link_prediction.py b/kglib/pytorch_geometric/transform/binary_link_prediction.py similarity index 100% rename from kglib/kgcn_data_loader/transform/binary_link_prediction.py rename to kglib/pytorch_geometric/transform/binary_link_prediction.py diff --git a/kglib/kgcn_data_loader/transform/common.py b/kglib/pytorch_geometric/transform/common.py similarity index 100% rename from kglib/kgcn_data_loader/transform/common.py rename to kglib/pytorch_geometric/transform/common.py diff --git a/kglib/kgcn_data_loader/transform/typedb_graph_encoder.py b/kglib/pytorch_geometric/transform/typedb_graph_encoder.py similarity index 100% rename from kglib/kgcn_data_loader/transform/typedb_graph_encoder.py rename to kglib/pytorch_geometric/transform/typedb_graph_encoder.py diff --git a/kglib/kgcn_data_loader/utils.py b/kglib/pytorch_geometric/utils.py similarity index 100% rename from kglib/kgcn_data_loader/utils.py rename to kglib/pytorch_geometric/utils.py diff --git a/setup.py b/setup.py index 76af24a0..c084f7e5 100644 --- a/setup.py +++ b/setup.py @@ -24,10 +24,10 @@ setup( name='kglib', version='', - packages=['kglib', 'kglib.utils', 'kglib.kgcn_tensorflow', 'kglib.kgcn_data_loader'], + packages=['kglib', 'kglib.utils', 'kglib.pytorch_geometric'], url='', license='', - author='jms_fltchr', + author='jmsfltchr', author_email='', description='' ) From 9a001cda462af4273e8e8aca8064b8f73af8aece Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 14:30:55 +0100 Subject: [PATCH 039/124] Update automation.yml to match --- .grabl/automation.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.grabl/automation.yml b/.grabl/automation.yml index e578ec6a..36e36947 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -46,7 +46,7 @@ build: image: vaticle-ubuntu-20.04 command: | find . -name \*.md | xargs -L1 npx markdown-link-check@3.8.0 -v - test-kgcn-data-loader: + test-pytorch-geometric: image: vaticle-ubuntu-20.04 timeout: "10m" command: | @@ -76,7 +76,7 @@ build: bazel test //kglib/tests/end_to_end:diagnosis --test_output=streamed --spawn_strategy=standalone --action_env=PATH deploy-pip-snapshot: image: vaticle-ubuntu-20.04 - dependencies: [build, test-kgcn-data-loader, test-utils, test-examples, test-end-to-end] + dependencies: [build, test-pytorch-geometric, test-utils, test-examples, test-end-to-end] command: | export DEPLOY_PIP_USERNAME=$REPO_VATICLE_USERNAME export DEPLOY_PIP_PASSWORD=$REPO_VATICLE_PASSWORD From 1ed0cf266b8253c95e62bbd224516b07388bab3c Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 14:51:18 +0100 Subject: [PATCH 040/124] Move all of the code relating to the diagnosis example into one place --- kglib/examples/diagnosis/BUILD | 2 +- .../diagnosis/dataset}/generate.py | 0 .../diagnosis/dataset}/pmf.py | 0 .../diagnosis/dataset}/pmf_test.py | 0 .../diagnosis/dataset}/schema.tql | 0 .../diagnosis/dataset}/seed_data.tql | 0 kglib/tests/end_to_end/BUILD | 8 +-- kglib/utils/typedb/BUILD | 1 - kglib/utils/typedb/synthetic/BUILD | 40 ------------- kglib/utils/typedb/synthetic/examples/BUILD | 55 ------------------ kglib/utils/typedb/synthetic/statistics/BUILD | 56 ------------------- 11 files changed, 5 insertions(+), 157 deletions(-) rename kglib/{utils/typedb/synthetic/examples/diagnosis => examples/diagnosis/dataset}/generate.py (100%) rename kglib/{utils/typedb/synthetic/statistics => examples/diagnosis/dataset}/pmf.py (100%) rename kglib/{utils/typedb/synthetic/statistics => examples/diagnosis/dataset}/pmf_test.py (100%) rename kglib/{utils/typedb/synthetic/examples/diagnosis => examples/diagnosis/dataset}/schema.tql (100%) rename kglib/{utils/typedb/synthetic/examples/diagnosis => examples/diagnosis/dataset}/seed_data.tql (100%) delete mode 100644 kglib/utils/typedb/synthetic/BUILD delete mode 100644 kglib/utils/typedb/synthetic/examples/BUILD delete mode 100644 kglib/utils/typedb/synthetic/statistics/BUILD diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 7659384d..c6e6a7f4 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -30,7 +30,7 @@ py_library( 'diagnosis.py' ], deps = [ - "//kglib/utils/typedb/synthetic", + "//kglib/examples/diagnosis/dataset", "//kglib/utils/typedb/type", "@vaticle_typedb_client_python//:client_python", ], diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/generate.py b/kglib/examples/diagnosis/dataset/generate.py similarity index 100% rename from kglib/utils/typedb/synthetic/examples/diagnosis/generate.py rename to kglib/examples/diagnosis/dataset/generate.py diff --git a/kglib/utils/typedb/synthetic/statistics/pmf.py b/kglib/examples/diagnosis/dataset/pmf.py similarity index 100% rename from kglib/utils/typedb/synthetic/statistics/pmf.py rename to kglib/examples/diagnosis/dataset/pmf.py diff --git a/kglib/utils/typedb/synthetic/statistics/pmf_test.py b/kglib/examples/diagnosis/dataset/pmf_test.py similarity index 100% rename from kglib/utils/typedb/synthetic/statistics/pmf_test.py rename to kglib/examples/diagnosis/dataset/pmf_test.py diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql b/kglib/examples/diagnosis/dataset/schema.tql similarity index 100% rename from kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql rename to kglib/examples/diagnosis/dataset/schema.tql diff --git a/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql b/kglib/examples/diagnosis/dataset/seed_data.tql similarity index 100% rename from kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql rename to kglib/examples/diagnosis/dataset/seed_data.tql diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD index 49aef43f..ea5f1274 100644 --- a/kglib/tests/end_to_end/BUILD +++ b/kglib/tests/end_to_end/BUILD @@ -33,11 +33,11 @@ py_test( "//kglib/utils/typedb/test", ], data = [ - "//kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files", + "//kglib/examples/diagnosis/dataset:diagnosis-example-typeql-files", "@vaticle_typedb_artifact_linux//file" ], args = [ - "$(locations //kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files)", + "$(locations //kglib/examples/diagnosis/dataset:diagnosis-example-typeql-files)", "$(location @vaticle_typedb_artifact_linux//file)", ], ) @@ -53,10 +53,10 @@ py_test( "//kglib/examples/diagnosis", ], data = [ - "//kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files", + "//kglib/examples/diagnosis/dataset:diagnosis-example-typeql-files", ], args = [ - "$(locations //kglib/utils/typedb/synthetic/examples:diagnosis-example-typeql-files)", + "$(locations //kglib/examples/diagnosis/dataset:diagnosis-example-typeql-files)", ], ) diff --git a/kglib/utils/typedb/BUILD b/kglib/utils/typedb/BUILD index 4c7f65fe..7851eafd 100644 --- a/kglib/utils/typedb/BUILD +++ b/kglib/utils/typedb/BUILD @@ -29,7 +29,6 @@ py_library( deps = [ '//kglib/utils/typedb/test', '//kglib/utils/typedb/object', - '//kglib/utils/typedb/synthetic', '//kglib/utils/typedb/type', ], visibility=['//visibility:public'] diff --git a/kglib/utils/typedb/synthetic/BUILD b/kglib/utils/typedb/synthetic/BUILD deleted file mode 100644 index fc106a1f..00000000 --- a/kglib/utils/typedb/synthetic/BUILD +++ /dev/null @@ -1,40 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - - -py_library( - name = "synthetic", - deps = [ - '//kglib/utils/typedb/synthetic/statistics', - '//kglib/utils/typedb/synthetic/examples', - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/utils/typedb/synthetic/examples/BUILD b/kglib/utils/typedb/synthetic/examples/BUILD deleted file mode 100644 index 2a897558..00000000 --- a/kglib/utils/typedb/synthetic/examples/BUILD +++ /dev/null @@ -1,55 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -filegroup( - name = "diagnosis-example-typeql-files", - srcs = [ - "diagnosis/schema.tql", - "diagnosis/seed_data.tql", - ], - visibility = ["//visibility:public"], -) - -py_library( - name = "examples", - srcs = [ - 'diagnosis/generate.py', - ], - deps = [ - "@vaticle_typedb_client_python//:client_python", - vaticle_kglib_requirement('numpy'), - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob([ - "*", - "diagnosis/*" - ]), - license_type = "apache-header", -) diff --git a/kglib/utils/typedb/synthetic/statistics/BUILD b/kglib/utils/typedb/synthetic/statistics/BUILD deleted file mode 100644 index 16bcae3b..00000000 --- a/kglib/utils/typedb/synthetic/statistics/BUILD +++ /dev/null @@ -1,56 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_test( - name = "pmf_test", - srcs = [ - "pmf_test.py" - ], - deps = [ - "statistics" - ] -) - -py_library( - name = "statistics", - srcs = [ - 'pmf.py', - ], - deps = [ - vaticle_kglib_requirement('numpy'), - vaticle_kglib_requirement('pandas'), - vaticle_kglib_requirement('pytz'), - vaticle_kglib_requirement('python-dateutil'), - vaticle_kglib_requirement('six') - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) From 27eaf0405a254b3f0311dc603803423589dee6a2 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 15:01:36 +0100 Subject: [PATCH 041/124] Move thing and type libraries up a level --- kglib/examples/diagnosis/BUILD | 2 +- kglib/examples/diagnosis/dataset/BUILD | 48 ++++++++++++++++++++++++ kglib/utils/graph/thing/BUILD | 2 +- kglib/utils/typedb/BUILD | 7 +++- kglib/utils/typedb/object/BUILD | 42 --------------------- kglib/utils/typedb/{object => }/thing.py | 0 kglib/utils/typedb/{type => }/type.py | 0 kglib/utils/typedb/type/BUILD | 39 ------------------- 8 files changed, 55 insertions(+), 85 deletions(-) create mode 100644 kglib/examples/diagnosis/dataset/BUILD delete mode 100644 kglib/utils/typedb/object/BUILD rename kglib/utils/typedb/{object => }/thing.py (100%) rename kglib/utils/typedb/{type => }/type.py (100%) delete mode 100644 kglib/utils/typedb/type/BUILD diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index c6e6a7f4..638ace05 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -31,7 +31,7 @@ py_library( ], deps = [ "//kglib/examples/diagnosis/dataset", - "//kglib/utils/typedb/type", + "//kglib/utils/typedb", "@vaticle_typedb_client_python//:client_python", ], visibility=['//visibility:public'] diff --git a/kglib/examples/diagnosis/dataset/BUILD b/kglib/examples/diagnosis/dataset/BUILD new file mode 100644 index 00000000..aa13c804 --- /dev/null +++ b/kglib/examples/diagnosis/dataset/BUILD @@ -0,0 +1,48 @@ +load("@rules_python//python:defs.bzl", "py_test", "py_library") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") +load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") + +py_library( + name = "dataset", + srcs = [ + 'generate.py', + 'pmf.py', + ], + deps = [ + "@vaticle_typedb_client_python//:client_python", + vaticle_kglib_requirement('numpy'), + vaticle_kglib_requirement('pandas'), + vaticle_kglib_requirement('pytz'), + vaticle_kglib_requirement('python-dateutil'), + vaticle_kglib_requirement('six') + ], + visibility=['//visibility:public'] +) + +filegroup( + name = "diagnosis-example-typeql-files", + srcs = [ + "schema.tql", + "seed_data.tql", + ], + visibility = ["//visibility:public"], +) + +py_test( + name = "pmf_test", + srcs = [ + "pmf_test.py" + ], + deps = [ + "dataset" + ] +) + +checkstyle_test( + name = "checkstyle", + include = glob([ + "*", + "diagnosis/*" + ]), + license_type = "apache-header", +) diff --git a/kglib/utils/graph/thing/BUILD b/kglib/utils/graph/thing/BUILD index 4b795846..789faf68 100644 --- a/kglib/utils/graph/thing/BUILD +++ b/kglib/utils/graph/thing/BUILD @@ -71,7 +71,7 @@ py_library( 'queries_to_networkx_graph.py' ], deps = [ - "//kglib/utils/typedb/object", + "//kglib/utils/typedb", vaticle_kglib_requirement('networkx'), vaticle_kglib_requirement('decorator'), ], diff --git a/kglib/utils/typedb/BUILD b/kglib/utils/typedb/BUILD index 7851eafd..315f42b1 100644 --- a/kglib/utils/typedb/BUILD +++ b/kglib/utils/typedb/BUILD @@ -26,10 +26,13 @@ load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") py_library( name = "typedb", + srcs = [ + 'type.py', + 'thing.py', + ], deps = [ '//kglib/utils/typedb/test', - '//kglib/utils/typedb/object', - '//kglib/utils/typedb/type', + "@vaticle_typedb_client_python//:client_python", ], visibility=['//visibility:public'] ) diff --git a/kglib/utils/typedb/object/BUILD b/kglib/utils/typedb/object/BUILD deleted file mode 100644 index 0a2cb033..00000000 --- a/kglib/utils/typedb/object/BUILD +++ /dev/null @@ -1,42 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_library( - name = "object", - srcs = [ - 'thing.py', - ], - deps = [ - "@vaticle_typedb_client_python//:client_python", - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/utils/typedb/object/thing.py b/kglib/utils/typedb/thing.py similarity index 100% rename from kglib/utils/typedb/object/thing.py rename to kglib/utils/typedb/thing.py diff --git a/kglib/utils/typedb/type/type.py b/kglib/utils/typedb/type.py similarity index 100% rename from kglib/utils/typedb/type/type.py rename to kglib/utils/typedb/type.py diff --git a/kglib/utils/typedb/type/BUILD b/kglib/utils/typedb/type/BUILD deleted file mode 100644 index 71428367..00000000 --- a/kglib/utils/typedb/type/BUILD +++ /dev/null @@ -1,39 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_library( - name = "type", - srcs = [ - 'type.py', - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) From a705f21297bf585a021f47ab75870888bc36b042 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 15:32:20 +0100 Subject: [PATCH 042/124] Fix some license headers --- kglib/examples/diagnosis/.images/BUILD | 2 +- kglib/examples/diagnosis/BUILD | 2 +- kglib/pytorch_geometric/transform/binary_link_prediction.py | 2 +- kglib/pytorch_geometric/transform/common.py | 2 +- kglib/pytorch_geometric/transform/typedb_graph_encoder.py | 2 +- kglib/tests/end_to_end/BUILD | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/kglib/examples/diagnosis/.images/BUILD b/kglib/examples/diagnosis/.images/BUILD index a3814b9a..3c5c660f 100644 --- a/kglib/examples/diagnosis/.images/BUILD +++ b/kglib/examples/diagnosis/.images/BUILD @@ -1,5 +1,5 @@ # -# Copyright (C) 2021 Vaticle +# Copyright (C) 2022 Vaticle # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 638ace05..bfb00f6c 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -1,5 +1,5 @@ # -# Copyright (C) 2021 Vaticle +# Copyright (C) 2022 Vaticle # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file diff --git a/kglib/pytorch_geometric/transform/binary_link_prediction.py b/kglib/pytorch_geometric/transform/binary_link_prediction.py index 0c23924c..d59261c2 100644 --- a/kglib/pytorch_geometric/transform/binary_link_prediction.py +++ b/kglib/pytorch_geometric/transform/binary_link_prediction.py @@ -1,5 +1,5 @@ # -# Copyright (C) 2021 Vaticle +# Copyright (C) 2022 Vaticle # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file diff --git a/kglib/pytorch_geometric/transform/common.py b/kglib/pytorch_geometric/transform/common.py index 838b2d36..b8f828f1 100644 --- a/kglib/pytorch_geometric/transform/common.py +++ b/kglib/pytorch_geometric/transform/common.py @@ -1,5 +1,5 @@ # -# Copyright (C) 2021 Vaticle +# Copyright (C) 2022 Vaticle # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file diff --git a/kglib/pytorch_geometric/transform/typedb_graph_encoder.py b/kglib/pytorch_geometric/transform/typedb_graph_encoder.py index 575402ce..e1fad3cc 100644 --- a/kglib/pytorch_geometric/transform/typedb_graph_encoder.py +++ b/kglib/pytorch_geometric/transform/typedb_graph_encoder.py @@ -1,5 +1,5 @@ # -# Copyright (C) 2021 Vaticle +# Copyright (C) 2022 Vaticle # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD index ea5f1274..24455911 100644 --- a/kglib/tests/end_to_end/BUILD +++ b/kglib/tests/end_to_end/BUILD @@ -1,5 +1,5 @@ # -# Copyright (C) 2021 Vaticle +# Copyright (C) 2022 Vaticle # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file From 152815a75c3c3f9f19c0e902546bb4af3d13d22a Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 15:41:08 +0100 Subject: [PATCH 043/124] Remove superfluous test file --- kglib/utils/BUILD | 1 - kglib/utils/test/BUILD | 35 ----------------------------------- kglib/utils/test/utils.py | 37 ------------------------------------- 3 files changed, 73 deletions(-) delete mode 100644 kglib/utils/test/BUILD delete mode 100644 kglib/utils/test/utils.py diff --git a/kglib/utils/BUILD b/kglib/utils/BUILD index f0bc0193..24ebd255 100644 --- a/kglib/utils/BUILD +++ b/kglib/utils/BUILD @@ -27,7 +27,6 @@ py_library( deps = [ '//kglib/utils/typedb', '//kglib/utils/graph', - '//kglib/utils/test' ], visibility=['//visibility:public'] ) diff --git a/kglib/utils/test/BUILD b/kglib/utils/test/BUILD deleted file mode 100644 index 13238d0a..00000000 --- a/kglib/utils/test/BUILD +++ /dev/null @@ -1,35 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_library") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_library( - name = "test", - srcs = glob(['**/*.py']), - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/utils/test/utils.py b/kglib/utils/test/utils.py deleted file mode 100644 index 58126508..00000000 --- a/kglib/utils/test/utils.py +++ /dev/null @@ -1,37 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -def get_call_args(mock): - """ - Get the arguments used to call a mock for each call to the mock. Necessary since `.assert_has_calls` won't work - for numpy arrays - Args: - mock: the mock - - Returns: - A list of lists. The outer list is the calls made, the inner list is the arguments given for that call - """ - flat_args = [] - args_list = mock.call_args_list - for call in args_list: - args, kwargs = call - flat_args.append(args) - return flat_args From eb7126a7fb173b709421aefdbd6ed735ff1f89c2 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 15:53:32 +0100 Subject: [PATCH 044/124] Remove references to kgcn from test structure --- kglib/tests/deployment/BUILD | 1 - kglib/tests/deployment/{kgcn => }/diagnosis.py | 0 kglib/tests/end_to_end/BUILD | 6 +++--- kglib/tests/end_to_end/{kgcn => }/diagnosis.py | 0 kglib/tests/end_to_end/{kgcn => }/diagnosis_debug.py | 0 5 files changed, 3 insertions(+), 4 deletions(-) rename kglib/tests/deployment/{kgcn => }/diagnosis.py (100%) rename kglib/tests/end_to_end/{kgcn => }/diagnosis.py (100%) rename kglib/tests/end_to_end/{kgcn => }/diagnosis_debug.py (100%) diff --git a/kglib/tests/deployment/BUILD b/kglib/tests/deployment/BUILD index 1a4bf662..7b46fb58 100644 --- a/kglib/tests/deployment/BUILD +++ b/kglib/tests/deployment/BUILD @@ -25,7 +25,6 @@ checkstyle_test( name = "checkstyle", include = glob([ "*", - "**/*" ]), license_type = "apache-header", ) diff --git a/kglib/tests/deployment/kgcn/diagnosis.py b/kglib/tests/deployment/diagnosis.py similarity index 100% rename from kglib/tests/deployment/kgcn/diagnosis.py rename to kglib/tests/deployment/diagnosis.py diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD index 24455911..57e382c0 100644 --- a/kglib/tests/end_to_end/BUILD +++ b/kglib/tests/end_to_end/BUILD @@ -26,7 +26,7 @@ py_test( name = "diagnosis", size = "enormous", srcs = [ - "kgcn/diagnosis.py" + "diagnosis.py" ], deps = [ "//kglib/examples/diagnosis", @@ -46,9 +46,9 @@ py_test( name = "diagnosis-debug", size = "enormous", srcs = [ - "kgcn/diagnosis_debug.py" + "diagnosis_debug.py" ], - main = "kgcn/diagnosis_debug.py", + main = "diagnosis_debug.py", deps = [ "//kglib/examples/diagnosis", ], diff --git a/kglib/tests/end_to_end/kgcn/diagnosis.py b/kglib/tests/end_to_end/diagnosis.py similarity index 100% rename from kglib/tests/end_to_end/kgcn/diagnosis.py rename to kglib/tests/end_to_end/diagnosis.py diff --git a/kglib/tests/end_to_end/kgcn/diagnosis_debug.py b/kglib/tests/end_to_end/diagnosis_debug.py similarity index 100% rename from kglib/tests/end_to_end/kgcn/diagnosis_debug.py rename to kglib/tests/end_to_end/diagnosis_debug.py From 119eea5748404d6839e8e5752085327dc90723b2 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 16:05:18 +0100 Subject: [PATCH 045/124] Move simple test parent class next to its usages --- kglib/utils/graph/test/BUILD | 37 ------------------- kglib/utils/graph/thing/BUILD | 12 +++--- .../case.py => thing/graph_test_case.py} | 0 3 files changed, 6 insertions(+), 43 deletions(-) delete mode 100644 kglib/utils/graph/test/BUILD rename kglib/utils/graph/{test/case.py => thing/graph_test_case.py} (100%) diff --git a/kglib/utils/graph/test/BUILD b/kglib/utils/graph/test/BUILD deleted file mode 100644 index 781dcce6..00000000 --- a/kglib/utils/graph/test/BUILD +++ /dev/null @@ -1,37 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_library( - name = "test", - srcs = [ - 'case.py', - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/utils/graph/thing/BUILD b/kglib/utils/graph/thing/BUILD index 789faf68..6565135e 100644 --- a/kglib/utils/graph/thing/BUILD +++ b/kglib/utils/graph/thing/BUILD @@ -28,24 +28,24 @@ load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") py_test( name = "queries_to_networkx_graph_test", srcs = [ - "queries_to_networkx_graph_test.py" + "queries_to_networkx_graph_test.py", + "graph_test_case.py" ], deps = [ "thing", "//kglib/utils/typedb/test", - "//kglib/utils/graph/test", ] ) py_test( name = "queries_to_networkx_graph_it", srcs = [ - "queries_to_networkx_graph_it.py" + "queries_to_networkx_graph_it.py", + "graph_test_case.py" ], deps = [ "thing", "//kglib/utils/typedb/test", - "//kglib/utils/graph/test", "@vaticle_typedb_client_python//:client_python", ], data = ["@vaticle_typedb_artifact_linux//file"], @@ -56,11 +56,11 @@ py_test( py_test( name = "concept_dict_to_graph_test", srcs = [ - "concept_dict_to_graph_test.py" + "concept_dict_to_graph_test.py", + "graph_test_case.py" ], deps = [ "thing", - "//kglib/utils/graph/test", ], ) diff --git a/kglib/utils/graph/test/case.py b/kglib/utils/graph/thing/graph_test_case.py similarity index 100% rename from kglib/utils/graph/test/case.py rename to kglib/utils/graph/thing/graph_test_case.py From 410d7eb3e3d00dc027f66a4b3a1199056c10bf79 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 16:22:38 +0100 Subject: [PATCH 046/124] Update checkstyle to exclude inappropriate files --- BUILD | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/BUILD b/BUILD index b3521517..b0e3f4fd 100644 --- a/BUILD +++ b/BUILD @@ -89,6 +89,11 @@ checkstyle_test( "*", ".grabl/*", ]), + exclude = [ + ".bazelversion", + "LICENSE", + "VERSION", + ], license_type = "apache-header", ) From ffdff099b86d17291235f7aaa6b0e23e24489c7d Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 16:27:27 +0100 Subject: [PATCH 047/124] Add missing exclusions for checkstyles for .md files --- BUILD | 4 +++- kglib/examples/diagnosis/BUILD | 3 +++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/BUILD b/BUILD index b0e3f4fd..ca017c1f 100644 --- a/BUILD +++ b/BUILD @@ -89,7 +89,9 @@ checkstyle_test( "*", ".grabl/*", ]), - exclude = [ + exclude = glob([ + *.md + ]) + [ ".bazelversion", "LICENSE", "VERSION", diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index bfb00f6c..88ff4e8d 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -40,5 +40,8 @@ py_library( checkstyle_test( name = "checkstyle", include = glob(["*"]), + exclude = glob([ + *.md + ]), license_type = "apache-header", ) From 23645b2e24ce8e1812a2f62dd543ed07d8755956 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 16:42:49 +0100 Subject: [PATCH 048/124] Use Python 3.7.2 for deployment --- .grabl/automation.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 36e36947..48973e21 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -89,11 +89,11 @@ build: branch: master type: foreground command: | - pyenv global 3.6.10 + pyenv global 3.7.2 pip3 install -U pip sudo unlink /usr/bin/python3 sudo ln -s $(which python3) /usr/bin/python3 - sudo ln -s /usr/share/pyshared/lsb_release.py /opt/pyenv/versions/3.6.10/lib/python3.6/site-packages/lsb_release.py + sudo ln -s /usr/share/pyshared/lsb_release.py /opt/pyenv/versions/3.7.2/lib/python3.6/site-packages/lsb_release.py bazel run //test:typedb-extractor -- typedb-all-linux ./typedb-all-linux/typedb server & pip install --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple typedb-kglib==0.0.0-$GRABL_COMMIT @@ -113,8 +113,8 @@ release: deploy-github: image: vaticle-ubuntu-20.04 command: | - pyenv install -s 3.6.10 - pyenv global 3.6.10 system + pyenv install -s 3.7.2 + pyenv global 3.7.2 system pip3 install -U pip pip install certifi export ARTIFACT_USERNAME=$REPO_VATICLE_USERNAME @@ -127,8 +127,8 @@ release: deploy-pip-release: image: vaticle-ubuntu-20.04 command: | - pyenv install -s 3.6.10 - pyenv global 3.6.10 system + pyenv install -s 3.7.2 + pyenv global 3.7.2 system pip3 install -U pip export ARTIFACT_USERNAME=$REPO_VATICLE_USERNAME export ARTIFACT_PASSWORD=$REPO_VATICLE_PASSWORD From ce042a8e8fbe5c5c664f10272f8ee666ce2ea433 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 16:42:57 +0100 Subject: [PATCH 049/124] Fix imports --- kglib/utils/graph/thing/concept_dict_to_graph_test.py | 4 ++-- kglib/utils/graph/thing/queries_to_networkx_graph_it.py | 4 ++-- kglib/utils/graph/thing/queries_to_networkx_graph_test.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/kglib/utils/graph/thing/concept_dict_to_graph_test.py b/kglib/utils/graph/thing/concept_dict_to_graph_test.py index fe7e5710..e819aed7 100644 --- a/kglib/utils/graph/thing/concept_dict_to_graph_test.py +++ b/kglib/utils/graph/thing/concept_dict_to_graph_test.py @@ -22,9 +22,9 @@ import unittest import networkx as nx -from kglib.utils.typedb.object.thing import Thing +from kglib.utils.typedb.thing import Thing from kglib.utils.graph.thing.concept_dict_to_networkx_graph import concept_dict_to_graph -from kglib.utils.graph.test.case import GraphTestCase +from kglib.utils.graph.thing.graph_test_case import GraphTestCase class TestConceptDictToTypeDBGraph(GraphTestCase): diff --git a/kglib/utils/graph/thing/queries_to_networkx_graph_it.py b/kglib/utils/graph/thing/queries_to_networkx_graph_it.py index 770605cc..b03d8971 100644 --- a/kglib/utils/graph/thing/queries_to_networkx_graph_it.py +++ b/kglib/utils/graph/thing/queries_to_networkx_graph_it.py @@ -26,9 +26,9 @@ from typedb.api.concept.type.attribute_type import AttributeType from typedb.client import * -from kglib.utils.graph.test.case import GraphTestCase +from kglib.utils.graph.thing.graph_test_case import GraphTestCase from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries -from kglib.utils.typedb.object.thing import build_thing +from kglib.utils.typedb.thing import build_thing from kglib.utils.typedb.test.base import TypeDBServer diff --git a/kglib/utils/graph/thing/queries_to_networkx_graph_test.py b/kglib/utils/graph/thing/queries_to_networkx_graph_test.py index 0576e8e5..ece6af5c 100644 --- a/kglib/utils/graph/thing/queries_to_networkx_graph_test.py +++ b/kglib/utils/graph/thing/queries_to_networkx_graph_test.py @@ -24,10 +24,10 @@ import networkx as nx from typedb.api.concept.type.attribute_type import AttributeType -from kglib.utils.graph.test.case import GraphTestCase +from kglib.utils.graph.thing.graph_test_case import GraphTestCase from kglib.utils.graph.thing.queries_to_networkx_graph import concept_dict_from_concept_map, \ combine_n_graphs, build_graph_from_queries -from kglib.utils.typedb.object.thing import Thing +from kglib.utils.typedb.thing import Thing from kglib.utils.typedb.test.mock.answer import MockConceptMap from kglib.utils.typedb.test.mock.concept import MockType, MockAttributeType, MockThing, MockAttribute From b989e07b893ad02e76f34827ab9fabc8cf82168a Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 16:44:30 +0100 Subject: [PATCH 050/124] Fix README exclusions again --- BUILD | 2 +- kglib/examples/diagnosis/BUILD | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/BUILD b/BUILD index ca017c1f..b0ae9fc9 100644 --- a/BUILD +++ b/BUILD @@ -90,7 +90,7 @@ checkstyle_test( ".grabl/*", ]), exclude = glob([ - *.md + "*.md" ]) + [ ".bazelversion", "LICENSE", diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 88ff4e8d..3f6f7f72 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -41,7 +41,7 @@ checkstyle_test( name = "checkstyle", include = glob(["*"]), exclude = glob([ - *.md + "*.md" ]), license_type = "apache-header", ) From 721d03b1554e7e2ce4a6eba5ace8d2da88cee6a8 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 16:59:01 +0100 Subject: [PATCH 051/124] Move typedb related code to a module and networkx to another --- .grabl/automation.yml | 15 ++++++-- kglib/BUILD | 4 +- kglib/examples/diagnosis/BUILD | 2 +- kglib/{utils/graph => networkx}/BUILD | 6 +-- kglib/{utils/graph => networkx}/iterate.py | 0 kglib/{utils/graph => networkx}/query/BUILD | 0 .../graph => networkx}/query/query_graph.py | 0 .../query/query_graph_test.py | 0 kglib/{utils/graph => networkx}/thing/BUILD | 6 +-- .../thing/concept_dict_to_graph_test.py | 0 .../thing/concept_dict_to_networkx_graph.py | 0 .../thing/graph_test_case.py | 0 .../thing/queries_to_networkx_graph.py | 0 .../thing/queries_to_networkx_graph_it.py | 0 .../thing/queries_to_networkx_graph_test.py | 0 kglib/tests/end_to_end/BUILD | 2 +- kglib/{utils => }/typedb/BUILD | 2 +- kglib/{utils => }/typedb/test/BUILD | 0 kglib/{utils => }/typedb/test/base.py | 0 kglib/{utils => }/typedb/test/mock/answer.py | 0 kglib/{utils => }/typedb/test/mock/concept.py | 0 kglib/{utils => }/typedb/thing.py | 0 kglib/{utils => }/typedb/type.py | 0 kglib/utils/BUILD | 38 ------------------- kglib/utils/__init__.py | 21 ---------- 25 files changed, 23 insertions(+), 73 deletions(-) rename kglib/{utils/graph => networkx}/BUILD (92%) rename kglib/{utils/graph => networkx}/iterate.py (100%) rename kglib/{utils/graph => networkx}/query/BUILD (100%) rename kglib/{utils/graph => networkx}/query/query_graph.py (100%) rename kglib/{utils/graph => networkx}/query/query_graph_test.py (100%) rename kglib/{utils/graph => networkx}/thing/BUILD (95%) rename kglib/{utils/graph => networkx}/thing/concept_dict_to_graph_test.py (100%) rename kglib/{utils/graph => networkx}/thing/concept_dict_to_networkx_graph.py (100%) rename kglib/{utils/graph => networkx}/thing/graph_test_case.py (100%) rename kglib/{utils/graph => networkx}/thing/queries_to_networkx_graph.py (100%) rename kglib/{utils/graph => networkx}/thing/queries_to_networkx_graph_it.py (100%) rename kglib/{utils/graph => networkx}/thing/queries_to_networkx_graph_test.py (100%) rename kglib/{utils => }/typedb/BUILD (97%) rename kglib/{utils => }/typedb/test/BUILD (100%) rename kglib/{utils => }/typedb/test/base.py (100%) rename kglib/{utils => }/typedb/test/mock/answer.py (100%) rename kglib/{utils => }/typedb/test/mock/concept.py (100%) rename kglib/{utils => }/typedb/thing.py (100%) rename kglib/{utils => }/typedb/type.py (100%) delete mode 100644 kglib/utils/BUILD delete mode 100644 kglib/utils/__init__.py diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 48973e21..83b20bd3 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -52,14 +52,21 @@ build: command: | pyenv install -s 3.7.2 pyenv global 3.7.2 system - bazel test //kglib/utils/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH - test-utils: + bazel test //kglib/pytorch_geometric/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + test-typedb: image: vaticle-ubuntu-20.04 timeout: "10m" command: | pyenv install -s 3.7.2 pyenv global 3.7.2 system - bazel test //kglib/pytorch_geometric/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + bazel test //kglib/typedb/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + test-neworkx: + image: vaticle-ubuntu-20.04 + timeout: "10m" + command: | + pyenv install -s 3.7.2 + pyenv global 3.7.2 system + bazel test //kglib/neworkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-examples: image: vaticle-ubuntu-20.04 timeout: "10m" @@ -76,7 +83,7 @@ build: bazel test //kglib/tests/end_to_end:diagnosis --test_output=streamed --spawn_strategy=standalone --action_env=PATH deploy-pip-snapshot: image: vaticle-ubuntu-20.04 - dependencies: [build, test-pytorch-geometric, test-utils, test-examples, test-end-to-end] + dependencies: [build, test-pytorch-geometric, test-typedb, test-networkx, test-examples, test-end-to-end] command: | export DEPLOY_PIP_USERNAME=$REPO_VATICLE_USERNAME export DEPLOY_PIP_PASSWORD=$REPO_VATICLE_PASSWORD diff --git a/kglib/BUILD b/kglib/BUILD index 747f1566..73a661f0 100644 --- a/kglib/BUILD +++ b/kglib/BUILD @@ -29,7 +29,9 @@ py_library( '__init__.py', ], deps = [ - '//kglib/utils', + '//kglib/typedb', + '//kglib/networkx', + '//kglib/pytorch_geometric', ], visibility=['//visibility:public'] ) diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 3f6f7f72..9915133d 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -31,7 +31,7 @@ py_library( ], deps = [ "//kglib/examples/diagnosis/dataset", - "//kglib/utils/typedb", + "//kglib/typedb", "@vaticle_typedb_client_python//:client_python", ], visibility=['//visibility:public'] diff --git a/kglib/utils/graph/BUILD b/kglib/networkx/BUILD similarity index 92% rename from kglib/utils/graph/BUILD rename to kglib/networkx/BUILD index 519134a9..7496af23 100644 --- a/kglib/utils/graph/BUILD +++ b/kglib/networkx/BUILD @@ -23,11 +23,11 @@ load("@rules_python//python:defs.bzl", "py_library") load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") py_library( - name = "graph", + name = "networkx", srcs = glob(['**/*.py']), deps = [ - '//kglib/utils/graph/query', - '//kglib/utils/graph/thing', + '//kglib/networkx/query', + '//kglib/networkx/thing', ], visibility=['//visibility:public'] ) diff --git a/kglib/utils/graph/iterate.py b/kglib/networkx/iterate.py similarity index 100% rename from kglib/utils/graph/iterate.py rename to kglib/networkx/iterate.py diff --git a/kglib/utils/graph/query/BUILD b/kglib/networkx/query/BUILD similarity index 100% rename from kglib/utils/graph/query/BUILD rename to kglib/networkx/query/BUILD diff --git a/kglib/utils/graph/query/query_graph.py b/kglib/networkx/query/query_graph.py similarity index 100% rename from kglib/utils/graph/query/query_graph.py rename to kglib/networkx/query/query_graph.py diff --git a/kglib/utils/graph/query/query_graph_test.py b/kglib/networkx/query/query_graph_test.py similarity index 100% rename from kglib/utils/graph/query/query_graph_test.py rename to kglib/networkx/query/query_graph_test.py diff --git a/kglib/utils/graph/thing/BUILD b/kglib/networkx/thing/BUILD similarity index 95% rename from kglib/utils/graph/thing/BUILD rename to kglib/networkx/thing/BUILD index 6565135e..9b9e3ddf 100644 --- a/kglib/utils/graph/thing/BUILD +++ b/kglib/networkx/thing/BUILD @@ -33,7 +33,7 @@ py_test( ], deps = [ "thing", - "//kglib/utils/typedb/test", + "//kglib/typedb/test", ] ) @@ -45,7 +45,7 @@ py_test( ], deps = [ "thing", - "//kglib/utils/typedb/test", + "//kglib/typedb/test", "@vaticle_typedb_client_python//:client_python", ], data = ["@vaticle_typedb_artifact_linux//file"], @@ -71,7 +71,7 @@ py_library( 'queries_to_networkx_graph.py' ], deps = [ - "//kglib/utils/typedb", + "//kglib/typedb", vaticle_kglib_requirement('networkx'), vaticle_kglib_requirement('decorator'), ], diff --git a/kglib/utils/graph/thing/concept_dict_to_graph_test.py b/kglib/networkx/thing/concept_dict_to_graph_test.py similarity index 100% rename from kglib/utils/graph/thing/concept_dict_to_graph_test.py rename to kglib/networkx/thing/concept_dict_to_graph_test.py diff --git a/kglib/utils/graph/thing/concept_dict_to_networkx_graph.py b/kglib/networkx/thing/concept_dict_to_networkx_graph.py similarity index 100% rename from kglib/utils/graph/thing/concept_dict_to_networkx_graph.py rename to kglib/networkx/thing/concept_dict_to_networkx_graph.py diff --git a/kglib/utils/graph/thing/graph_test_case.py b/kglib/networkx/thing/graph_test_case.py similarity index 100% rename from kglib/utils/graph/thing/graph_test_case.py rename to kglib/networkx/thing/graph_test_case.py diff --git a/kglib/utils/graph/thing/queries_to_networkx_graph.py b/kglib/networkx/thing/queries_to_networkx_graph.py similarity index 100% rename from kglib/utils/graph/thing/queries_to_networkx_graph.py rename to kglib/networkx/thing/queries_to_networkx_graph.py diff --git a/kglib/utils/graph/thing/queries_to_networkx_graph_it.py b/kglib/networkx/thing/queries_to_networkx_graph_it.py similarity index 100% rename from kglib/utils/graph/thing/queries_to_networkx_graph_it.py rename to kglib/networkx/thing/queries_to_networkx_graph_it.py diff --git a/kglib/utils/graph/thing/queries_to_networkx_graph_test.py b/kglib/networkx/thing/queries_to_networkx_graph_test.py similarity index 100% rename from kglib/utils/graph/thing/queries_to_networkx_graph_test.py rename to kglib/networkx/thing/queries_to_networkx_graph_test.py diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD index 57e382c0..d17b707c 100644 --- a/kglib/tests/end_to_end/BUILD +++ b/kglib/tests/end_to_end/BUILD @@ -30,7 +30,7 @@ py_test( ], deps = [ "//kglib/examples/diagnosis", - "//kglib/utils/typedb/test", + "//kglib/typedb/test", ], data = [ "//kglib/examples/diagnosis/dataset:diagnosis-example-typeql-files", diff --git a/kglib/utils/typedb/BUILD b/kglib/typedb/BUILD similarity index 97% rename from kglib/utils/typedb/BUILD rename to kglib/typedb/BUILD index 315f42b1..8d0fc780 100644 --- a/kglib/utils/typedb/BUILD +++ b/kglib/typedb/BUILD @@ -31,7 +31,7 @@ py_library( 'thing.py', ], deps = [ - '//kglib/utils/typedb/test', + '//kglib/typedb/test', "@vaticle_typedb_client_python//:client_python", ], visibility=['//visibility:public'] diff --git a/kglib/utils/typedb/test/BUILD b/kglib/typedb/test/BUILD similarity index 100% rename from kglib/utils/typedb/test/BUILD rename to kglib/typedb/test/BUILD diff --git a/kglib/utils/typedb/test/base.py b/kglib/typedb/test/base.py similarity index 100% rename from kglib/utils/typedb/test/base.py rename to kglib/typedb/test/base.py diff --git a/kglib/utils/typedb/test/mock/answer.py b/kglib/typedb/test/mock/answer.py similarity index 100% rename from kglib/utils/typedb/test/mock/answer.py rename to kglib/typedb/test/mock/answer.py diff --git a/kglib/utils/typedb/test/mock/concept.py b/kglib/typedb/test/mock/concept.py similarity index 100% rename from kglib/utils/typedb/test/mock/concept.py rename to kglib/typedb/test/mock/concept.py diff --git a/kglib/utils/typedb/thing.py b/kglib/typedb/thing.py similarity index 100% rename from kglib/utils/typedb/thing.py rename to kglib/typedb/thing.py diff --git a/kglib/utils/typedb/type.py b/kglib/typedb/type.py similarity index 100% rename from kglib/utils/typedb/type.py rename to kglib/typedb/type.py diff --git a/kglib/utils/BUILD b/kglib/utils/BUILD deleted file mode 100644 index 24ebd255..00000000 --- a/kglib/utils/BUILD +++ /dev/null @@ -1,38 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_library") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - -py_library( - name = "utils", - deps = [ - '//kglib/utils/typedb', - '//kglib/utils/graph', - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/utils/__init__.py b/kglib/utils/__init__.py deleted file mode 100644 index 4e916487..00000000 --- a/kglib/utils/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - From e101e220fffb4d3f60fd77e843dc2328d1a495cd Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 17:01:38 +0100 Subject: [PATCH 052/124] Missing license header --- kglib/examples/diagnosis/dataset/BUILD | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/kglib/examples/diagnosis/dataset/BUILD b/kglib/examples/diagnosis/dataset/BUILD index aa13c804..a1a21c64 100644 --- a/kglib/examples/diagnosis/dataset/BUILD +++ b/kglib/examples/diagnosis/dataset/BUILD @@ -1,3 +1,24 @@ +# +# Copyright (C) 2022 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + load("@rules_python//python:defs.bzl", "py_test", "py_library") load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") From 0438a517a4abdef411cac69034dc649d244e41b8 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 17:23:14 +0100 Subject: [PATCH 053/124] Fix imports --- kglib/networkx/BUILD | 2 +- kglib/networkx/query/query_graph_test.py | 2 +- kglib/networkx/thing/BUILD | 3 +-- kglib/networkx/thing/concept_dict_to_graph_test.py | 6 +++--- kglib/networkx/thing/queries_to_networkx_graph.py | 4 ++-- kglib/networkx/thing/queries_to_networkx_graph_it.py | 8 ++++---- kglib/networkx/thing/queries_to_networkx_graph_test.py | 10 +++++----- 7 files changed, 17 insertions(+), 18 deletions(-) diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index 7496af23..4f1ed78b 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -24,7 +24,7 @@ load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") py_library( name = "networkx", - srcs = glob(['**/*.py']), + srcs = glob(['*.py']), deps = [ '//kglib/networkx/query', '//kglib/networkx/thing', diff --git a/kglib/networkx/query/query_graph_test.py b/kglib/networkx/query/query_graph_test.py index 74ab9e4b..1ba364de 100644 --- a/kglib/networkx/query/query_graph_test.py +++ b/kglib/networkx/query/query_graph_test.py @@ -21,7 +21,7 @@ import unittest -from kglib.utils.graph.query.query_graph import QueryGraph +from kglib.networkx.query.query_graph import QueryGraph class TestQueryGraph(unittest.TestCase): diff --git a/kglib/networkx/thing/BUILD b/kglib/networkx/thing/BUILD index 9b9e3ddf..16b75459 100644 --- a/kglib/networkx/thing/BUILD +++ b/kglib/networkx/thing/BUILD @@ -20,8 +20,7 @@ # load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") diff --git a/kglib/networkx/thing/concept_dict_to_graph_test.py b/kglib/networkx/thing/concept_dict_to_graph_test.py index e819aed7..c9c4df07 100644 --- a/kglib/networkx/thing/concept_dict_to_graph_test.py +++ b/kglib/networkx/thing/concept_dict_to_graph_test.py @@ -22,9 +22,9 @@ import unittest import networkx as nx -from kglib.utils.typedb.thing import Thing -from kglib.utils.graph.thing.concept_dict_to_networkx_graph import concept_dict_to_graph -from kglib.utils.graph.thing.graph_test_case import GraphTestCase +from kglib.typedb.thing import Thing +from kglib.networkx.thing.concept_dict_to_networkx_graph import concept_dict_to_graph +from kglib.networkx.thing.graph_test_case import GraphTestCase class TestConceptDictToTypeDBGraph(GraphTestCase): diff --git a/kglib/networkx/thing/queries_to_networkx_graph.py b/kglib/networkx/thing/queries_to_networkx_graph.py index 4d62a515..f5fca256 100644 --- a/kglib/networkx/thing/queries_to_networkx_graph.py +++ b/kglib/networkx/thing/queries_to_networkx_graph.py @@ -24,8 +24,8 @@ from functools import reduce import networkx as nx -from kglib.utils.typedb.object.thing import build_thing -from kglib.utils.graph.thing.concept_dict_to_networkx_graph import concept_dict_to_graph +from kglib.typedb.thing import build_thing +from kglib.networkx.thing.concept_dict_to_networkx_graph import concept_dict_to_graph def concept_dict_from_concept_map(concept_map): diff --git a/kglib/networkx/thing/queries_to_networkx_graph_it.py b/kglib/networkx/thing/queries_to_networkx_graph_it.py index b03d8971..28561358 100644 --- a/kglib/networkx/thing/queries_to_networkx_graph_it.py +++ b/kglib/networkx/thing/queries_to_networkx_graph_it.py @@ -26,10 +26,10 @@ from typedb.api.concept.type.attribute_type import AttributeType from typedb.client import * -from kglib.utils.graph.thing.graph_test_case import GraphTestCase -from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries -from kglib.utils.typedb.thing import build_thing -from kglib.utils.typedb.test.base import TypeDBServer +from kglib.networkx.thing.graph_test_case import GraphTestCase +from kglib.networkx.thing.queries_to_networkx_graph import build_graph_from_queries +from kglib.typedb.thing import build_thing +from kglib.typedb.test.base import TypeDBServer class ITBuildGraphFromQueriesWithRealTypeDB(GraphTestCase): diff --git a/kglib/networkx/thing/queries_to_networkx_graph_test.py b/kglib/networkx/thing/queries_to_networkx_graph_test.py index ece6af5c..68d065d7 100644 --- a/kglib/networkx/thing/queries_to_networkx_graph_test.py +++ b/kglib/networkx/thing/queries_to_networkx_graph_test.py @@ -24,12 +24,12 @@ import networkx as nx from typedb.api.concept.type.attribute_type import AttributeType -from kglib.utils.graph.thing.graph_test_case import GraphTestCase -from kglib.utils.graph.thing.queries_to_networkx_graph import concept_dict_from_concept_map, \ +from kglib.networkx.thing.graph_test_case import GraphTestCase +from kglib.networkx.thing.queries_to_networkx_graph import concept_dict_from_concept_map, \ combine_n_graphs, build_graph_from_queries -from kglib.utils.typedb.thing import Thing -from kglib.utils.typedb.test.mock.answer import MockConceptMap -from kglib.utils.typedb.test.mock.concept import MockType, MockAttributeType, MockThing, MockAttribute +from kglib.typedb.thing import Thing +from kglib.typedb.test.mock.answer import MockConceptMap +from kglib.typedb.test.mock.concept import MockType, MockAttributeType, MockThing, MockAttribute def mock_sampler(input_iter): From ebadf399e8c3c5c066d5e2b951a82706001d88a9 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 17:45:05 +0100 Subject: [PATCH 054/124] Fix paths --- kglib/examples/diagnosis/dataset/generate.py | 2 +- kglib/examples/diagnosis/dataset/pmf_test.py | 2 +- kglib/examples/diagnosis/diagnosis.py | 6 +++--- kglib/pytorch_geometric/dataset/dataset.py | 2 +- kglib/pytorch_geometric/transform/binary_link_prediction.py | 4 ++-- kglib/pytorch_geometric/transform/common.py | 2 +- kglib/pytorch_geometric/transform/typedb_graph_encoder.py | 2 +- kglib/tests/end_to_end/diagnosis.py | 2 +- 8 files changed, 11 insertions(+), 11 deletions(-) diff --git a/kglib/examples/diagnosis/dataset/generate.py b/kglib/examples/diagnosis/dataset/generate.py index d81257f1..017ee7d0 100644 --- a/kglib/examples/diagnosis/dataset/generate.py +++ b/kglib/examples/diagnosis/dataset/generate.py @@ -24,7 +24,7 @@ import numpy as np from typedb.client import * -from kglib.utils.typedb.synthetic.statistics.pmf import PMF +from kglib.typedb.synthetic.statistics.pmf import PMF def get_example_queries(pmf, example_id): diff --git a/kglib/examples/diagnosis/dataset/pmf_test.py b/kglib/examples/diagnosis/dataset/pmf_test.py index a0405842..b8cb9012 100644 --- a/kglib/examples/diagnosis/dataset/pmf_test.py +++ b/kglib/examples/diagnosis/dataset/pmf_test.py @@ -24,7 +24,7 @@ import numpy as np import pandas as pd -from kglib.utils.typedb.synthetic.statistics.pmf import PMF +from pmf import PMF class TestPMF(unittest.TestCase): diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 3ec151b0..74ef9c56 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -38,9 +38,9 @@ from kglib.pytorch_geometric.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ ContinuousEncoder from kglib.pytorch_geometric.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.utils.graph.query.query_graph import QueryGraph -from kglib.utils.typedb.synthetic.examples.diagnosis.generate import generate_example_data -from kglib.utils.typedb.type.type import get_thing_types +from kglib.networkx.query.query_graph import QueryGraph +from kglib.examples.diagnosis.dataset.generate import generate_example_data +from kglib.typedb.type import get_thing_types DATABASE = "diagnosis" ADDRESS = "localhost:1729" diff --git a/kglib/pytorch_geometric/dataset/dataset.py b/kglib/pytorch_geometric/dataset/dataset.py index 1754afa8..3edb3ee1 100644 --- a/kglib/pytorch_geometric/dataset/dataset.py +++ b/kglib/pytorch_geometric/dataset/dataset.py @@ -25,7 +25,7 @@ from torch_geometric.utils import from_networkx from typedb.client import TypeDB, TypeDBSession, SessionType, TypeDBOptions, TransactionType -from kglib.utils.graph.thing.queries_to_networkx_graph import build_graph_from_queries +from kglib.networkx.thing.queries_to_networkx_graph import build_graph_from_queries class DataSet: diff --git a/kglib/pytorch_geometric/transform/binary_link_prediction.py b/kglib/pytorch_geometric/transform/binary_link_prediction.py index d59261c2..f6ea6d7f 100644 --- a/kglib/pytorch_geometric/transform/binary_link_prediction.py +++ b/kglib/pytorch_geometric/transform/binary_link_prediction.py @@ -20,8 +20,8 @@ # import networkx as nx -from kglib.utils.graph.iterate import multidigraph_edge_data_iterator -from kglib.utils.typedb.type.type import get_edge_type_triplets, reverse_edge_type_triplets +from kglib.networkx.iterate import multidigraph_edge_data_iterator +from kglib.typedb.type.type import get_edge_type_triplets, reverse_edge_type_triplets class LinkPredictionLabeller: diff --git a/kglib/pytorch_geometric/transform/common.py b/kglib/pytorch_geometric/transform/common.py index b8f828f1..29310453 100644 --- a/kglib/pytorch_geometric/transform/common.py +++ b/kglib/pytorch_geometric/transform/common.py @@ -18,7 +18,7 @@ # specific language governing permissions and limitations # under the License. # -from kglib.utils.graph.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator +from kglib.networkx.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator def clear_unneeded_fields(graph): diff --git a/kglib/pytorch_geometric/transform/typedb_graph_encoder.py b/kglib/pytorch_geometric/transform/typedb_graph_encoder.py index e1fad3cc..7910147c 100644 --- a/kglib/pytorch_geometric/transform/typedb_graph_encoder.py +++ b/kglib/pytorch_geometric/transform/typedb_graph_encoder.py @@ -23,7 +23,7 @@ import torch from torch.nn import Embedding -from kglib.utils.graph.iterate import ( +from kglib.networkx.iterate import ( multidigraph_node_data_iterator, multidigraph_edge_data_iterator, ) diff --git a/kglib/tests/end_to_end/diagnosis.py b/kglib/tests/end_to_end/diagnosis.py index 99d08fe8..f21384ca 100644 --- a/kglib/tests/end_to_end/diagnosis.py +++ b/kglib/tests/end_to_end/diagnosis.py @@ -22,7 +22,7 @@ import unittest from kglib.examples.diagnosis.diagnosis import diagnosis_example -from kglib.utils.typedb.test.base import TypeDBServer +from kglib.typedb.test.base import TypeDBServer class TestDiagnosisExample(unittest.TestCase): From bd8ed879b125c2a523e62175bdce6837e4c96353 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 17:49:54 +0100 Subject: [PATCH 055/124] Typo in automation --- .grabl/automation.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 83b20bd3..220abf39 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -60,13 +60,13 @@ build: pyenv install -s 3.7.2 pyenv global 3.7.2 system bazel test //kglib/typedb/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH - test-neworkx: + test-networkx: image: vaticle-ubuntu-20.04 timeout: "10m" command: | pyenv install -s 3.7.2 pyenv global 3.7.2 system - bazel test //kglib/neworkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + bazel test //kglib/networkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-examples: image: vaticle-ubuntu-20.04 timeout: "10m" From 14f3bba691a63f406c7a667f4165c11a39f04364 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 18:11:54 +0100 Subject: [PATCH 056/124] Flatten the networkx package --- .grabl/automation.yml | 2 +- kglib/examples/diagnosis/diagnosis.py | 2 +- kglib/networkx/BUILD | 68 ++++++++++++++- .../{thing => }/concept_dict_to_graph_test.py | 4 +- .../concept_dict_to_networkx_graph.py | 0 kglib/networkx/{thing => }/graph_test_case.py | 0 .../{thing => }/queries_to_networkx_graph.py | 2 +- .../queries_to_networkx_graph_it.py | 4 +- .../queries_to_networkx_graph_test.py | 4 +- kglib/networkx/query/BUILD | 54 ------------ kglib/networkx/{query => }/query_graph.py | 0 .../networkx/{query => }/query_graph_test.py | 2 +- kglib/networkx/thing/BUILD | 84 ------------------- kglib/pytorch_geometric/dataset/dataset.py | 2 +- 14 files changed, 75 insertions(+), 153 deletions(-) rename kglib/networkx/{thing => }/concept_dict_to_graph_test.py (98%) rename kglib/networkx/{thing => }/concept_dict_to_networkx_graph.py (100%) rename kglib/networkx/{thing => }/graph_test_case.py (100%) rename kglib/networkx/{thing => }/queries_to_networkx_graph.py (98%) rename kglib/networkx/{thing => }/queries_to_networkx_graph_it.py (97%) rename kglib/networkx/{thing => }/queries_to_networkx_graph_test.py (98%) delete mode 100644 kglib/networkx/query/BUILD rename kglib/networkx/{query => }/query_graph.py (100%) rename kglib/networkx/{query => }/query_graph_test.py (97%) delete mode 100644 kglib/networkx/thing/BUILD diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 220abf39..0e919adf 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -66,7 +66,7 @@ build: command: | pyenv install -s 3.7.2 pyenv global 3.7.2 system - bazel test //kglib/networkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + bazel test //kglib.networkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-examples: image: vaticle-ubuntu-20.04 timeout: "10m" diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 74ef9c56..7c2ecccf 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -38,7 +38,7 @@ from kglib.pytorch_geometric.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ ContinuousEncoder from kglib.pytorch_geometric.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.networkx.query.query_graph import QueryGraph +from kglib.networkx.query_graph import QueryGraph from kglib.examples.diagnosis.dataset.generate import generate_example_data from kglib.typedb.type import get_thing_types diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index 4f1ed78b..c301f6ee 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -20,20 +20,80 @@ # load("@rules_python//python:defs.bzl", "py_library") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") py_library( name = "networkx", - srcs = glob(['*.py']), + srcs = [ + 'query_graph.py', + 'concept_dict_to_networkx_graph.py', + 'queries_to_networkx_graph.py' + ], deps = [ - '//kglib/networkx/query', - '//kglib/networkx/thing', + "//kglib/typedb", + vaticle_kglib_requirement('networkx'), + vaticle_kglib_requirement('decorator'), ], visibility=['//visibility:public'] ) +py_test( + name = "query_graph_test", + srcs = [ + "query_graph_test.py", + "query_graph.py" + ], + deps = [ + vaticle_kglib_requirement('networkx'), + ] +) + +py_test( + name = "queries_to_networkx_graph_test", + srcs = [ + "queries_to_networkx_graph_test.py", + "queries_to_networkx_graph.py", + "graph_test_case.py" + ], + deps = [ + "//kglib/typedb", + vaticle_kglib_requirement('networkx'), + ] +) + +py_test( + name = "queries_to_networkx_graph_it", + srcs = [ + "queries_to_networkx_graph_it.py", + "queries_to_networkx_graph.py", + "graph_test_case.py" + ], + deps = [ + "//kglib/typedb/test", + "@vaticle_typedb_client_python//:client_python", + vaticle_kglib_requirement('networkx'), + ], + data = ["@vaticle_typedb_artifact_linux//file"], + args = ["$(location @vaticle_typedb_artifact_linux//file)"], +) + + +py_test( + name = "concept_dict_to_graph_test", + srcs = [ + "concept_dict_to_graph_test.py", + "concept_dict_to_networkx_graph.py", + "graph_test_case.py" + ], + deps = [ + "//kglib/typedb", + vaticle_kglib_requirement('networkx'), + ], +) + checkstyle_test( name = "checkstyle", include = glob(["*"]), license_type = "apache-header", -) +) \ No newline at end of file diff --git a/kglib/networkx/thing/concept_dict_to_graph_test.py b/kglib/networkx/concept_dict_to_graph_test.py similarity index 98% rename from kglib/networkx/thing/concept_dict_to_graph_test.py rename to kglib/networkx/concept_dict_to_graph_test.py index c9c4df07..5f6dad51 100644 --- a/kglib/networkx/thing/concept_dict_to_graph_test.py +++ b/kglib/networkx/concept_dict_to_graph_test.py @@ -23,8 +23,8 @@ import networkx as nx from kglib.typedb.thing import Thing -from kglib.networkx.thing.concept_dict_to_networkx_graph import concept_dict_to_graph -from kglib.networkx.thing.graph_test_case import GraphTestCase +from kglib.networkx.concept_dict_to_networkx_graph import concept_dict_to_graph +from kglib.networkx.graph_test_case import GraphTestCase class TestConceptDictToTypeDBGraph(GraphTestCase): diff --git a/kglib/networkx/thing/concept_dict_to_networkx_graph.py b/kglib/networkx/concept_dict_to_networkx_graph.py similarity index 100% rename from kglib/networkx/thing/concept_dict_to_networkx_graph.py rename to kglib/networkx/concept_dict_to_networkx_graph.py diff --git a/kglib/networkx/thing/graph_test_case.py b/kglib/networkx/graph_test_case.py similarity index 100% rename from kglib/networkx/thing/graph_test_case.py rename to kglib/networkx/graph_test_case.py diff --git a/kglib/networkx/thing/queries_to_networkx_graph.py b/kglib/networkx/queries_to_networkx_graph.py similarity index 98% rename from kglib/networkx/thing/queries_to_networkx_graph.py rename to kglib/networkx/queries_to_networkx_graph.py index f5fca256..7d7f7487 100644 --- a/kglib/networkx/thing/queries_to_networkx_graph.py +++ b/kglib/networkx/queries_to_networkx_graph.py @@ -25,7 +25,7 @@ import networkx as nx from kglib.typedb.thing import build_thing -from kglib.networkx.thing.concept_dict_to_networkx_graph import concept_dict_to_graph +from kglib.networkx.concept_dict_to_networkx_graph import concept_dict_to_graph def concept_dict_from_concept_map(concept_map): diff --git a/kglib/networkx/thing/queries_to_networkx_graph_it.py b/kglib/networkx/queries_to_networkx_graph_it.py similarity index 97% rename from kglib/networkx/thing/queries_to_networkx_graph_it.py rename to kglib/networkx/queries_to_networkx_graph_it.py index 28561358..792d256c 100644 --- a/kglib/networkx/thing/queries_to_networkx_graph_it.py +++ b/kglib/networkx/queries_to_networkx_graph_it.py @@ -26,8 +26,8 @@ from typedb.api.concept.type.attribute_type import AttributeType from typedb.client import * -from kglib.networkx.thing.graph_test_case import GraphTestCase -from kglib.networkx.thing.queries_to_networkx_graph import build_graph_from_queries +from kglib.networkx.graph_test_case import GraphTestCase +from kglib.networkx.queries_to_networkx_graph import build_graph_from_queries from kglib.typedb.thing import build_thing from kglib.typedb.test.base import TypeDBServer diff --git a/kglib/networkx/thing/queries_to_networkx_graph_test.py b/kglib/networkx/queries_to_networkx_graph_test.py similarity index 98% rename from kglib/networkx/thing/queries_to_networkx_graph_test.py rename to kglib/networkx/queries_to_networkx_graph_test.py index 68d065d7..70775bd4 100644 --- a/kglib/networkx/thing/queries_to_networkx_graph_test.py +++ b/kglib/networkx/queries_to_networkx_graph_test.py @@ -24,8 +24,8 @@ import networkx as nx from typedb.api.concept.type.attribute_type import AttributeType -from kglib.networkx.thing.graph_test_case import GraphTestCase -from kglib.networkx.thing.queries_to_networkx_graph import concept_dict_from_concept_map, \ +from kglib.networkx.graph_test_case import GraphTestCase +from kglib.networkx.queries_to_networkx_graph import concept_dict_from_concept_map, \ combine_n_graphs, build_graph_from_queries from kglib.typedb.thing import Thing from kglib.typedb.test.mock.answer import MockConceptMap diff --git a/kglib/networkx/query/BUILD b/kglib/networkx/query/BUILD deleted file mode 100644 index bb705dc0..00000000 --- a/kglib/networkx/query/BUILD +++ /dev/null @@ -1,54 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - - -py_test( - name = "query_graph_test", - srcs = [ - "query_graph_test.py" - ], - deps = [ - "query", - ], -) - -py_library( - name = "query", - srcs = [ - 'query_graph.py', - ], - deps = [ - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('decorator'), - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/networkx/query/query_graph.py b/kglib/networkx/query_graph.py similarity index 100% rename from kglib/networkx/query/query_graph.py rename to kglib/networkx/query_graph.py diff --git a/kglib/networkx/query/query_graph_test.py b/kglib/networkx/query_graph_test.py similarity index 97% rename from kglib/networkx/query/query_graph_test.py rename to kglib/networkx/query_graph_test.py index 1ba364de..d4407889 100644 --- a/kglib/networkx/query/query_graph_test.py +++ b/kglib/networkx/query_graph_test.py @@ -21,7 +21,7 @@ import unittest -from kglib.networkx.query.query_graph import QueryGraph +from kglib.networkx.query_graph import QueryGraph class TestQueryGraph(unittest.TestCase): diff --git a/kglib/networkx/thing/BUILD b/kglib/networkx/thing/BUILD deleted file mode 100644 index 16b75459..00000000 --- a/kglib/networkx/thing/BUILD +++ /dev/null @@ -1,84 +0,0 @@ -# -# Copyright (C) 2022 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") -load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") - - -py_test( - name = "queries_to_networkx_graph_test", - srcs = [ - "queries_to_networkx_graph_test.py", - "graph_test_case.py" - ], - deps = [ - "thing", - "//kglib/typedb/test", - ] -) - -py_test( - name = "queries_to_networkx_graph_it", - srcs = [ - "queries_to_networkx_graph_it.py", - "graph_test_case.py" - ], - deps = [ - "thing", - "//kglib/typedb/test", - "@vaticle_typedb_client_python//:client_python", - ], - data = ["@vaticle_typedb_artifact_linux//file"], - args = ["$(location @vaticle_typedb_artifact_linux//file)"], -) - - -py_test( - name = "concept_dict_to_graph_test", - srcs = [ - "concept_dict_to_graph_test.py", - "graph_test_case.py" - ], - deps = [ - "thing", - ], -) - -py_library( - name = "thing", - srcs = [ - 'concept_dict_to_networkx_graph.py', - 'queries_to_networkx_graph.py' - ], - deps = [ - "//kglib/typedb", - vaticle_kglib_requirement('networkx'), - vaticle_kglib_requirement('decorator'), - ], - visibility=['//visibility:public'] -) - -checkstyle_test( - name = "checkstyle", - include = glob(["*"]), - license_type = "apache-header", -) diff --git a/kglib/pytorch_geometric/dataset/dataset.py b/kglib/pytorch_geometric/dataset/dataset.py index 3edb3ee1..c982162a 100644 --- a/kglib/pytorch_geometric/dataset/dataset.py +++ b/kglib/pytorch_geometric/dataset/dataset.py @@ -25,7 +25,7 @@ from torch_geometric.utils import from_networkx from typedb.client import TypeDB, TypeDBSession, SessionType, TypeDBOptions, TransactionType -from kglib.networkx.thing.queries_to_networkx_graph import build_graph_from_queries +from kglib.networkx.queries_to_networkx_graph import build_graph_from_queries class DataSet: From 313531405eccfa912cf3829c98ea4b80cabc7c11 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Wed, 20 Jul 2022 18:19:28 +0100 Subject: [PATCH 057/124] Add omitted requirements --- kglib/examples/diagnosis/BUILD | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 9915133d..30feb27b 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -20,8 +20,7 @@ # load("@rules_python//python:defs.bzl", "py_test", "py_library") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") py_library( @@ -33,6 +32,9 @@ py_library( "//kglib/examples/diagnosis/dataset", "//kglib/typedb", "@vaticle_typedb_client_python//:client_python", + vaticle_kglib_requirement("networkx"), + vaticle_kglib_requirement("torch"), + vaticle_kglib_requirement("torch_geometric"), ], visibility=['//visibility:public'] ) From f514bba9cf5ac0732bea23e91f6cf8000fef6378 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 09:49:42 +0100 Subject: [PATCH 058/124] Remove reference to non-existant dir --- kglib/examples/diagnosis/dataset/BUILD | 1 - 1 file changed, 1 deletion(-) diff --git a/kglib/examples/diagnosis/dataset/BUILD b/kglib/examples/diagnosis/dataset/BUILD index a1a21c64..368253b6 100644 --- a/kglib/examples/diagnosis/dataset/BUILD +++ b/kglib/examples/diagnosis/dataset/BUILD @@ -63,7 +63,6 @@ checkstyle_test( name = "checkstyle", include = glob([ "*", - "diagnosis/*" ]), license_type = "apache-header", ) From 6c5a0c1866e228bbfc28b125b3e67ae83504970d Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 09:58:34 +0100 Subject: [PATCH 059/124] Correct path to networkx package --- .grabl/automation.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.grabl/automation.yml b/.grabl/automation.yml index 0e919adf..220abf39 100644 --- a/.grabl/automation.yml +++ b/.grabl/automation.yml @@ -66,7 +66,7 @@ build: command: | pyenv install -s 3.7.2 pyenv global 3.7.2 system - bazel test //kglib.networkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH + bazel test //kglib/networkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH test-examples: image: vaticle-ubuntu-20.04 timeout: "10m" From 8b43d4b18f094f443e36c98c3333b023646fd8d8 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 11:07:37 +0100 Subject: [PATCH 060/124] Update requirements to slimmest possible --- BUILD | 2 +- kglib/examples/diagnosis/BUILD | 1 + requirements.txt | 44 ++++++++++++++++++++------------- requirements_dev.txt | 45 +++++++++++++++++++--------------- 4 files changed, 54 insertions(+), 38 deletions(-) diff --git a/BUILD b/BUILD index b0ae9fc9..2fe27ba4 100644 --- a/BUILD +++ b/BUILD @@ -40,7 +40,7 @@ load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") assemble_pip( name = "assemble-pip", target = "//kglib:kglib", - package_name = "vaticle-kglib", + package_name = "typedb-kglib", classifiers = [ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7", diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 30feb27b..742cf4c1 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -31,6 +31,7 @@ py_library( deps = [ "//kglib/examples/diagnosis/dataset", "//kglib/typedb", + "//kglib/pytorch_geometric", "@vaticle_typedb_client_python//:client_python", vaticle_kglib_requirement("networkx"), vaticle_kglib_requirement("torch"), diff --git a/requirements.txt b/requirements.txt index ae6ec700..8dee8cee 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,38 +19,48 @@ # under the License. # +absl-py==1.2.0 +cachetools==5.2.0 +certifi==2022.6.15 +charset-normalizer==2.1.0 decorator==5.1.1 -filelock==3.7.1 -huggingface-hub==0.7.0 +google-auth==2.9.1 +google-auth-oauthlib==0.4.6 +grpcio==1.43.0 idna==3.3 -importlib-metadata==4.11.4 +importlib-metadata==4.12.0 Jinja2==3.1.2 joblib==1.1.0 +Markdown==3.4.1 MarkupSafe==2.1.1 networkx==2.5 -nltk==3.7 numpy==1.21.6 -packaging==21.3 +oauthlib==3.2.0 pandas==1.3.5 -Pillow==9.1.1 +protobuf==3.15.5 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.1 -PyYAML==6.0 -regex==2022.6.2 -requests==2.28.0 +requests==2.28.1 +requests-oauthlib==1.3.1 +rsa==4.9 scikit-learn==1.0.2 scipy==1.7.3 -sentence-transformers==2.2.0 -sentencepiece==0.1.96 six==1.16.0 +tensorboard==2.9.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 -tokenizers==0.12.1 torch==1.11.0 torch-geometric==2.0.4 -torchvision==0.12.0 +torch-scatter==2.0.9 +torch-sparse==0.6.14 tqdm==4.64.0 -transformers==4.19.4 -typing-extensions==4.2.0 -urllib3==1.26.9 -zipp==3.8.0 +typedb-client==2.9.0 +typedb-protocol==2.9.0 +typing-extensions==4.3.0 +urllib3==1.26.10 +Werkzeug==2.1.2 +zipp==3.8.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index d737c2da..d659d1bd 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -21,42 +21,47 @@ --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple -certifi==2022.5.18.1 -charset-normalizer==2.0.12 -click==8.1.3 +absl-py==1.2.0 +cachetools==5.2.0 +certifi==2022.6.15 +charset-normalizer==2.1.0 decorator==5.1.1 -filelock==3.7.1 -huggingface-hub==0.7.0 +google-auth==2.9.1 +google-auth-oauthlib==0.4.6 +grpcio==1.43.0 idna==3.3 -importlib-metadata==4.11.4 +importlib-metadata==4.12.0 Jinja2==3.1.2 joblib==1.1.0 +Markdown==3.4.1 MarkupSafe==2.1.1 networkx==2.5 -nltk==3.7 numpy==1.21.6 -packaging==21.3 +oauthlib==3.2.0 pandas==1.3.5 -Pillow==9.1.1 +protobuf==3.15.5 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.1 -PyYAML==6.0 -regex==2022.6.2 -requests==2.28.0 +requests==2.28.1 +requests-oauthlib==1.3.1 +rsa==4.9 scikit-learn==1.0.2 scipy==1.7.3 -sentence-transformers==2.2.0 -sentencepiece==0.1.96 six==1.16.0 +tensorboard==2.9.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 -tokenizers==0.12.1 torch==1.11.0 torch-geometric==2.0.4 -torchvision==0.12.0 +torch-scatter==2.0.9 +torch-sparse==0.6.14 tqdm==4.64.0 -transformers==4.19.4 -typing-extensions==4.2.0 -urllib3==1.26.9 -zipp==3.8.0 +typing-extensions==4.3.0 +urllib3==1.26.10 +Werkzeug==2.1.2 +zipp==3.8.1 From 51492bf7f311728927eb6bbeaeb5a4af6958bac2 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 11:16:20 +0100 Subject: [PATCH 061/124] Change package name back to vaticle-kglib for now --- BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/BUILD b/BUILD index 2fe27ba4..b0ae9fc9 100644 --- a/BUILD +++ b/BUILD @@ -40,7 +40,7 @@ load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") assemble_pip( name = "assemble-pip", target = "//kglib:kglib", - package_name = "typedb-kglib", + package_name = "vaticle-kglib", classifiers = [ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7", From 767c946563427cf90cf76912e1f7a01612066a83 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 11:46:16 +0100 Subject: [PATCH 062/124] Revert "Update requirements to slimmest possible" This reverts commit 8b43d4b18f094f443e36c98c3333b023646fd8d8. --- kglib/examples/diagnosis/BUILD | 1 - requirements.txt | 44 +++++++++++++-------------------- requirements_dev.txt | 45 +++++++++++++++------------------- 3 files changed, 37 insertions(+), 53 deletions(-) diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 742cf4c1..30feb27b 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -31,7 +31,6 @@ py_library( deps = [ "//kglib/examples/diagnosis/dataset", "//kglib/typedb", - "//kglib/pytorch_geometric", "@vaticle_typedb_client_python//:client_python", vaticle_kglib_requirement("networkx"), vaticle_kglib_requirement("torch"), diff --git a/requirements.txt b/requirements.txt index 8dee8cee..ae6ec700 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,48 +19,38 @@ # under the License. # -absl-py==1.2.0 -cachetools==5.2.0 -certifi==2022.6.15 -charset-normalizer==2.1.0 decorator==5.1.1 -google-auth==2.9.1 -google-auth-oauthlib==0.4.6 -grpcio==1.43.0 +filelock==3.7.1 +huggingface-hub==0.7.0 idna==3.3 -importlib-metadata==4.12.0 +importlib-metadata==4.11.4 Jinja2==3.1.2 joblib==1.1.0 -Markdown==3.4.1 MarkupSafe==2.1.1 networkx==2.5 +nltk==3.7 numpy==1.21.6 -oauthlib==3.2.0 +packaging==21.3 pandas==1.3.5 -protobuf==3.15.5 -pyasn1==0.4.8 -pyasn1-modules==0.2.8 +Pillow==9.1.1 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.1 -requests==2.28.1 -requests-oauthlib==1.3.1 -rsa==4.9 +PyYAML==6.0 +regex==2022.6.2 +requests==2.28.0 scikit-learn==1.0.2 scipy==1.7.3 +sentence-transformers==2.2.0 +sentencepiece==0.1.96 six==1.16.0 -tensorboard==2.9.1 -tensorboard-data-server==0.6.1 -tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 +tokenizers==0.12.1 torch==1.11.0 torch-geometric==2.0.4 -torch-scatter==2.0.9 -torch-sparse==0.6.14 +torchvision==0.12.0 tqdm==4.64.0 -typedb-client==2.9.0 -typedb-protocol==2.9.0 -typing-extensions==4.3.0 -urllib3==1.26.10 -Werkzeug==2.1.2 -zipp==3.8.1 +transformers==4.19.4 +typing-extensions==4.2.0 +urllib3==1.26.9 +zipp==3.8.0 diff --git a/requirements_dev.txt b/requirements_dev.txt index d659d1bd..d737c2da 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -21,47 +21,42 @@ --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple -absl-py==1.2.0 -cachetools==5.2.0 -certifi==2022.6.15 -charset-normalizer==2.1.0 +certifi==2022.5.18.1 +charset-normalizer==2.0.12 +click==8.1.3 decorator==5.1.1 -google-auth==2.9.1 -google-auth-oauthlib==0.4.6 -grpcio==1.43.0 +filelock==3.7.1 +huggingface-hub==0.7.0 idna==3.3 -importlib-metadata==4.12.0 +importlib-metadata==4.11.4 Jinja2==3.1.2 joblib==1.1.0 -Markdown==3.4.1 MarkupSafe==2.1.1 networkx==2.5 +nltk==3.7 numpy==1.21.6 -oauthlib==3.2.0 +packaging==21.3 pandas==1.3.5 -protobuf==3.15.5 -pyasn1==0.4.8 -pyasn1-modules==0.2.8 +Pillow==9.1.1 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.1 -requests==2.28.1 -requests-oauthlib==1.3.1 -rsa==4.9 +PyYAML==6.0 +regex==2022.6.2 +requests==2.28.0 scikit-learn==1.0.2 scipy==1.7.3 +sentence-transformers==2.2.0 +sentencepiece==0.1.96 six==1.16.0 -tensorboard==2.9.1 -tensorboard-data-server==0.6.1 -tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 +tokenizers==0.12.1 torch==1.11.0 torch-geometric==2.0.4 -torch-scatter==2.0.9 -torch-sparse==0.6.14 +torchvision==0.12.0 tqdm==4.64.0 -typing-extensions==4.3.0 -urllib3==1.26.10 -Werkzeug==2.1.2 -zipp==3.8.1 +transformers==4.19.4 +typing-extensions==4.2.0 +urllib3==1.26.9 +zipp==3.8.0 From 152dfba84e32695e77c81dac32592482f6ad4ec0 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 12:10:54 +0100 Subject: [PATCH 063/124] Figure out that it's torch-scatter and torch-sparse throwing an error that they can't find torch (despite it just having been found by pip) --- requirements.txt | 44 ++++++++++++++++++++++++++----------------- requirements_dev.txt | 45 ++++++++++++++++++++++++-------------------- 2 files changed, 52 insertions(+), 37 deletions(-) diff --git a/requirements.txt b/requirements.txt index ae6ec700..a0ab5f65 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,38 +19,48 @@ # under the License. # +absl-py==1.2.0 +cachetools==5.2.0 +certifi==2022.6.15 +charset-normalizer==2.1.0 decorator==5.1.1 -filelock==3.7.1 -huggingface-hub==0.7.0 +google-auth==2.9.1 +google-auth-oauthlib==0.4.6 +grpcio==1.43.0 idna==3.3 -importlib-metadata==4.11.4 +importlib-metadata==4.12.0 Jinja2==3.1.2 joblib==1.1.0 +Markdown==3.4.1 MarkupSafe==2.1.1 networkx==2.5 -nltk==3.7 numpy==1.21.6 -packaging==21.3 +oauthlib==3.2.0 pandas==1.3.5 -Pillow==9.1.1 +protobuf==3.15.5 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.1 -PyYAML==6.0 -regex==2022.6.2 -requests==2.28.0 +requests==2.28.1 +requests-oauthlib==1.3.1 +rsa==4.9 scikit-learn==1.0.2 scipy==1.7.3 -sentence-transformers==2.2.0 -sentencepiece==0.1.96 six==1.16.0 +tensorboard==2.9.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 -tokenizers==0.12.1 torch==1.11.0 torch-geometric==2.0.4 -torchvision==0.12.0 +# torch-scatter==2.0.9 +# torch-sparse==0.6.14 tqdm==4.64.0 -transformers==4.19.4 -typing-extensions==4.2.0 -urllib3==1.26.9 -zipp==3.8.0 +typedb-client==2.9.0 +typedb-protocol==2.9.0 +typing-extensions==4.3.0 +urllib3==1.26.10 +Werkzeug==2.1.2 +zipp==3.8.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index d737c2da..33d06ebc 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -21,42 +21,47 @@ --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple -certifi==2022.5.18.1 -charset-normalizer==2.0.12 -click==8.1.3 +absl-py==1.2.0 +cachetools==5.2.0 +certifi==2022.6.15 +charset-normalizer==2.1.0 decorator==5.1.1 -filelock==3.7.1 -huggingface-hub==0.7.0 +google-auth==2.9.1 +google-auth-oauthlib==0.4.6 +grpcio==1.43.0 idna==3.3 -importlib-metadata==4.11.4 +importlib-metadata==4.12.0 Jinja2==3.1.2 joblib==1.1.0 +Markdown==3.4.1 MarkupSafe==2.1.1 networkx==2.5 -nltk==3.7 numpy==1.21.6 -packaging==21.3 +oauthlib==3.2.0 pandas==1.3.5 -Pillow==9.1.1 +protobuf==3.15.5 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.1 -PyYAML==6.0 -regex==2022.6.2 -requests==2.28.0 +requests==2.28.1 +requests-oauthlib==1.3.1 +rsa==4.9 scikit-learn==1.0.2 scipy==1.7.3 -sentence-transformers==2.2.0 -sentencepiece==0.1.96 six==1.16.0 +tensorboard==2.9.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 -tokenizers==0.12.1 torch==1.11.0 torch-geometric==2.0.4 -torchvision==0.12.0 +# torch-scatter==2.0.9 +# torch-sparse==0.6.14 tqdm==4.64.0 -transformers==4.19.4 -typing-extensions==4.2.0 -urllib3==1.26.9 -zipp==3.8.0 +typing-extensions==4.3.0 +urllib3==1.26.10 +Werkzeug==2.1.2 +zipp==3.8.1 From c69db2f3bb44c9394f2b162788e047a8e12de9f5 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 12:40:28 +0100 Subject: [PATCH 064/124] Add missing dependency --- kglib/networkx/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index c301f6ee..615de495 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -54,6 +54,7 @@ py_test( srcs = [ "queries_to_networkx_graph_test.py", "queries_to_networkx_graph.py", + "concept_dict_to_networkx_graph.py" "graph_test_case.py" ], deps = [ From 54993aa35736b8326bcded9b3b8a0d398f510883 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 12:43:01 +0100 Subject: [PATCH 065/124] Simplify naming in networkx packaage --- kglib/networkx/BUILD | 24 +++++++++---------- kglib/networkx/concept_dict_to_graph_test.py | 2 +- kglib/networkx/queries_to_networkx_graph.py | 2 +- .../networkx/queries_to_networkx_graph_it.py | 2 +- .../queries_to_networkx_graph_test.py | 2 +- kglib/pytorch_geometric/dataset/dataset.py | 2 +- 6 files changed, 17 insertions(+), 17 deletions(-) diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index 615de495..f5acba5b 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -27,8 +27,8 @@ py_library( name = "networkx", srcs = [ 'query_graph.py', - 'concept_dict_to_networkx_graph.py', - 'queries_to_networkx_graph.py' + 'concept_dict_to_networkx.py', + 'queries_to_networkx.py' ], deps = [ "//kglib/typedb", @@ -50,11 +50,11 @@ py_test( ) py_test( - name = "queries_to_networkx_graph_test", + name = "queries_to_networkx_test", srcs = [ - "queries_to_networkx_graph_test.py", - "queries_to_networkx_graph.py", - "concept_dict_to_networkx_graph.py" + "queries_to_networkx_test.py", + "queries_to_networkx.py", + "concept_dict_to_networkx.py" "graph_test_case.py" ], deps = [ @@ -64,10 +64,10 @@ py_test( ) py_test( - name = "queries_to_networkx_graph_it", + name = "queries_to_networkx_it", srcs = [ - "queries_to_networkx_graph_it.py", - "queries_to_networkx_graph.py", + "queries_to_networkx_it.py", + "queries_to_networkx.py", "graph_test_case.py" ], deps = [ @@ -81,10 +81,10 @@ py_test( py_test( - name = "concept_dict_to_graph_test", + name = "concept_dict_to_networkx_test", srcs = [ - "concept_dict_to_graph_test.py", - "concept_dict_to_networkx_graph.py", + "concept_dict_to_networkx_test.py", + "concept_dict_to_networkx.py", "graph_test_case.py" ], deps = [ diff --git a/kglib/networkx/concept_dict_to_graph_test.py b/kglib/networkx/concept_dict_to_graph_test.py index 5f6dad51..2cd021c7 100644 --- a/kglib/networkx/concept_dict_to_graph_test.py +++ b/kglib/networkx/concept_dict_to_graph_test.py @@ -23,7 +23,7 @@ import networkx as nx from kglib.typedb.thing import Thing -from kglib.networkx.concept_dict_to_networkx_graph import concept_dict_to_graph +from kglib.networkx.concept_dict_to_networkx import concept_dict_to_graph from kglib.networkx.graph_test_case import GraphTestCase diff --git a/kglib/networkx/queries_to_networkx_graph.py b/kglib/networkx/queries_to_networkx_graph.py index 7d7f7487..9dd28f79 100644 --- a/kglib/networkx/queries_to_networkx_graph.py +++ b/kglib/networkx/queries_to_networkx_graph.py @@ -25,7 +25,7 @@ import networkx as nx from kglib.typedb.thing import build_thing -from kglib.networkx.concept_dict_to_networkx_graph import concept_dict_to_graph +from kglib.networkx.concept_dict_to_networkx import concept_dict_to_graph def concept_dict_from_concept_map(concept_map): diff --git a/kglib/networkx/queries_to_networkx_graph_it.py b/kglib/networkx/queries_to_networkx_graph_it.py index 792d256c..ecb1caff 100644 --- a/kglib/networkx/queries_to_networkx_graph_it.py +++ b/kglib/networkx/queries_to_networkx_graph_it.py @@ -27,7 +27,7 @@ from typedb.client import * from kglib.networkx.graph_test_case import GraphTestCase -from kglib.networkx.queries_to_networkx_graph import build_graph_from_queries +from kglib.networkx.queries_to_networkx import build_graph_from_queries from kglib.typedb.thing import build_thing from kglib.typedb.test.base import TypeDBServer diff --git a/kglib/networkx/queries_to_networkx_graph_test.py b/kglib/networkx/queries_to_networkx_graph_test.py index 70775bd4..adea98e9 100644 --- a/kglib/networkx/queries_to_networkx_graph_test.py +++ b/kglib/networkx/queries_to_networkx_graph_test.py @@ -25,7 +25,7 @@ from typedb.api.concept.type.attribute_type import AttributeType from kglib.networkx.graph_test_case import GraphTestCase -from kglib.networkx.queries_to_networkx_graph import concept_dict_from_concept_map, \ +from kglib.networkx.queries_to_networkx import concept_dict_from_concept_map, \ combine_n_graphs, build_graph_from_queries from kglib.typedb.thing import Thing from kglib.typedb.test.mock.answer import MockConceptMap diff --git a/kglib/pytorch_geometric/dataset/dataset.py b/kglib/pytorch_geometric/dataset/dataset.py index c982162a..3500195a 100644 --- a/kglib/pytorch_geometric/dataset/dataset.py +++ b/kglib/pytorch_geometric/dataset/dataset.py @@ -25,7 +25,7 @@ from torch_geometric.utils import from_networkx from typedb.client import TypeDB, TypeDBSession, SessionType, TypeDBOptions, TransactionType -from kglib.networkx.queries_to_networkx_graph import build_graph_from_queries +from kglib.networkx.queries_to_networkx import build_graph_from_queries class DataSet: From 14b24288b09f39378820ed8450c2d313856e49a3 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 12:55:58 +0100 Subject: [PATCH 066/124] Fixes for networkx rename --- kglib/networkx/BUILD | 2 +- ...pt_dict_to_networkx_graph.py => concept_dict_to_networkx.py} | 0 ...t_dict_to_graph_test.py => concept_dict_to_networkx_test.py} | 0 .../{queries_to_networkx_graph.py => queries_to_networkx.py} | 0 ...ueries_to_networkx_graph_it.py => queries_to_networkx_it.py} | 0 ...es_to_networkx_graph_test.py => queries_to_networkx_test.py} | 0 6 files changed, 1 insertion(+), 1 deletion(-) rename kglib/networkx/{concept_dict_to_networkx_graph.py => concept_dict_to_networkx.py} (100%) rename kglib/networkx/{concept_dict_to_graph_test.py => concept_dict_to_networkx_test.py} (100%) rename kglib/networkx/{queries_to_networkx_graph.py => queries_to_networkx.py} (100%) rename kglib/networkx/{queries_to_networkx_graph_it.py => queries_to_networkx_it.py} (100%) rename kglib/networkx/{queries_to_networkx_graph_test.py => queries_to_networkx_test.py} (100%) diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index f5acba5b..c5379195 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -54,7 +54,7 @@ py_test( srcs = [ "queries_to_networkx_test.py", "queries_to_networkx.py", - "concept_dict_to_networkx.py" + "concept_dict_to_networkx.py", "graph_test_case.py" ], deps = [ diff --git a/kglib/networkx/concept_dict_to_networkx_graph.py b/kglib/networkx/concept_dict_to_networkx.py similarity index 100% rename from kglib/networkx/concept_dict_to_networkx_graph.py rename to kglib/networkx/concept_dict_to_networkx.py diff --git a/kglib/networkx/concept_dict_to_graph_test.py b/kglib/networkx/concept_dict_to_networkx_test.py similarity index 100% rename from kglib/networkx/concept_dict_to_graph_test.py rename to kglib/networkx/concept_dict_to_networkx_test.py diff --git a/kglib/networkx/queries_to_networkx_graph.py b/kglib/networkx/queries_to_networkx.py similarity index 100% rename from kglib/networkx/queries_to_networkx_graph.py rename to kglib/networkx/queries_to_networkx.py diff --git a/kglib/networkx/queries_to_networkx_graph_it.py b/kglib/networkx/queries_to_networkx_it.py similarity index 100% rename from kglib/networkx/queries_to_networkx_graph_it.py rename to kglib/networkx/queries_to_networkx_it.py diff --git a/kglib/networkx/queries_to_networkx_graph_test.py b/kglib/networkx/queries_to_networkx_test.py similarity index 100% rename from kglib/networkx/queries_to_networkx_graph_test.py rename to kglib/networkx/queries_to_networkx_test.py From 2a141509a88417bffdfb46eac9302d85fedf5b60 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 13:21:00 +0100 Subject: [PATCH 067/124] Fix the ModuleError torch for missing torch, see https://github.com/pyg-team/pytorch_geometric/issues/861 --- requirements.txt | 7 +++++-- requirements_dev.txt | 7 ++++--- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/requirements.txt b/requirements.txt index a0ab5f65..9da4c4f8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,6 +19,9 @@ # under the License. # +-f https://download.pytorch.org/whl/cpu/torch_stable.html +-f https://data.pyg.org/whl/torch-1.11.0+cpu.html + absl-py==1.2.0 cachetools==5.2.0 certifi==2022.6.15 @@ -55,8 +58,8 @@ tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 torch==1.11.0 torch-geometric==2.0.4 -# torch-scatter==2.0.9 -# torch-sparse==0.6.14 +torch-scatter==2.0.9 +torch-sparse==0.6.14 tqdm==4.64.0 typedb-client==2.9.0 typedb-protocol==2.9.0 diff --git a/requirements_dev.txt b/requirements_dev.txt index 33d06ebc..9bde2902 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -20,6 +20,8 @@ # --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple +-f https://download.pytorch.org/whl/cpu/torch_stable.html +-f https://data.pyg.org/whl/torch-1.11.0+cpu.html absl-py==1.2.0 cachetools==5.2.0 @@ -57,11 +59,10 @@ tensorboard-plugin-wit==1.8.1 threadpoolctl==3.1.0 torch==1.11.0 torch-geometric==2.0.4 -# torch-scatter==2.0.9 -# torch-sparse==0.6.14 +torch-scatter==2.0.9 +torch-sparse==0.6.14 tqdm==4.64.0 typing-extensions==4.3.0 urllib3==1.26.10 Werkzeug==2.1.2 zipp==3.8.1 - From f683b82a782790896900d0ce707e7894998e1790 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 14:23:14 +0100 Subject: [PATCH 068/124] Add missing dep --- kglib/networkx/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index c5379195..cf1e90b7 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -71,6 +71,7 @@ py_test( "graph_test_case.py" ], deps = [ + "//kglib/typedb", "//kglib/typedb/test", "@vaticle_typedb_client_python//:client_python", vaticle_kglib_requirement('networkx'), From f713ae36fcd4e34df00cae7bc75794dcdb8467aa Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 14:39:56 +0100 Subject: [PATCH 069/124] Add more deps --- kglib/examples/diagnosis/BUILD | 2 ++ kglib/pytorch_geometric/BUILD | 10 ++++++++-- .../transform/binary_link_prediction.py | 2 +- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 30feb27b..8bce2a0d 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -35,6 +35,8 @@ py_library( vaticle_kglib_requirement("networkx"), vaticle_kglib_requirement("torch"), vaticle_kglib_requirement("torch_geometric"), + vaticle_kglib_requirement("torch_sparse"), + vaticle_kglib_requirement("torch_scatter"), ], visibility=['//visibility:public'] ) diff --git a/kglib/pytorch_geometric/BUILD b/kglib/pytorch_geometric/BUILD index afd485de..7f0c91b2 100644 --- a/kglib/pytorch_geometric/BUILD +++ b/kglib/pytorch_geometric/BUILD @@ -20,8 +20,7 @@ # load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") py_library( name = "pytorch_geometric", @@ -29,6 +28,13 @@ py_library( "*.py", "**/*.py" ]), + deps = [ + vaticle_kglib_requirement("networkx"), + vaticle_kglib_requirement("torch"), + vaticle_kglib_requirement("torch_geometric"), + vaticle_kglib_requirement("torch_sparse"), + vaticle_kglib_requirement("torch_scatter"), + ], visibility=['//visibility:public'] ) diff --git a/kglib/pytorch_geometric/transform/binary_link_prediction.py b/kglib/pytorch_geometric/transform/binary_link_prediction.py index f6ea6d7f..1b296e62 100644 --- a/kglib/pytorch_geometric/transform/binary_link_prediction.py +++ b/kglib/pytorch_geometric/transform/binary_link_prediction.py @@ -21,7 +21,7 @@ import networkx as nx from kglib.networkx.iterate import multidigraph_edge_data_iterator -from kglib.typedb.type.type import get_edge_type_triplets, reverse_edge_type_triplets +from kglib.typedb.type import get_edge_type_triplets, reverse_edge_type_triplets class LinkPredictionLabeller: From dadde8e4aa44a7d9641286ffe57726f0684f759c Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 14:47:16 +0100 Subject: [PATCH 070/124] More deps fixes --- kglib/networkx/BUILD | 3 ++- kglib/networkx/concept_dict_to_networkx.py | 2 +- .../networkx/concept_dict_to_networkx_test.py | 20 +++++++++---------- kglib/networkx/queries_to_networkx.py | 4 ++-- 4 files changed, 15 insertions(+), 14 deletions(-) diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index cf1e90b7..5e6a07b9 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -68,6 +68,7 @@ py_test( srcs = [ "queries_to_networkx_it.py", "queries_to_networkx.py", + "concept_dict_to_networkx.py", "graph_test_case.py" ], deps = [ @@ -98,4 +99,4 @@ checkstyle_test( name = "checkstyle", include = glob(["*"]), license_type = "apache-header", -) \ No newline at end of file +) diff --git a/kglib/networkx/concept_dict_to_networkx.py b/kglib/networkx/concept_dict_to_networkx.py index 14ce059e..a8631f62 100644 --- a/kglib/networkx/concept_dict_to_networkx.py +++ b/kglib/networkx/concept_dict_to_networkx.py @@ -22,7 +22,7 @@ import networkx as nx -def concept_dict_to_graph(concept_dict, variable_graph): +def concept_dict_to_networkx(concept_dict, variable_graph): """ Create a new graph from a concept_dict, based on a `variable_graph` that describes the interactions between the variables in the `concept_dict` diff --git a/kglib/networkx/concept_dict_to_networkx_test.py b/kglib/networkx/concept_dict_to_networkx_test.py index 2cd021c7..3842fff1 100644 --- a/kglib/networkx/concept_dict_to_networkx_test.py +++ b/kglib/networkx/concept_dict_to_networkx_test.py @@ -23,7 +23,7 @@ import networkx as nx from kglib.typedb.thing import Thing -from kglib.networkx.concept_dict_to_networkx import concept_dict_to_graph +from kglib.networkx.concept_dict_to_networkx import concept_dict_to_networkx from kglib.networkx.graph_test_case import GraphTestCase @@ -35,7 +35,7 @@ def test_single_entity_graph_is_as_expected(self): person = Thing('V123', 'person', 'entity') concept_dict = {'x': person} - typedb_graph = concept_dict_to_graph(concept_dict, variable_graph) + typedb_graph = concept_dict_to_networkx(concept_dict, variable_graph) expected_typedb_graph = nx.MultiDiGraph() expected_typedb_graph.add_node(person, type='person') @@ -48,7 +48,7 @@ def test_single_attribute_graph_is_as_expected(self): name = Thing('V123', 'name', 'attribute', value_type='string', value='Bob') concept_dict = {'x': name} - typedb_graph = concept_dict_to_graph(concept_dict, variable_graph) + typedb_graph = concept_dict_to_networkx(concept_dict, variable_graph) expected_typedb_graph = nx.MultiDiGraph() expected_typedb_graph.add_node(name, type='name', value_type='string', value='Bob') @@ -64,7 +64,7 @@ def test_single_entity_single_relation_graph_is_as_expected(self): employment = Thing('V456', 'employment', 'relation') concept_dict = {'x': person, 'y': employment} - typedb_graph = concept_dict_to_graph(concept_dict, variable_graph) + typedb_graph = concept_dict_to_networkx(concept_dict, variable_graph) expected_typedb_graph = nx.MultiDiGraph() expected_typedb_graph.add_node(person, type='person') expected_typedb_graph.add_node(employment, type='employment') @@ -85,7 +85,7 @@ def test_two_entity_single_relation_graph_is_as_expected(self): employment = Thing('V12345', 'employment', 'relation') concept_dict = {'x': person, 'y': company, 'r': employment} - typedb_graph = concept_dict_to_graph(concept_dict, variable_graph) + typedb_graph = concept_dict_to_networkx(concept_dict, variable_graph) expected_typedb_graph = nx.MultiDiGraph() expected_typedb_graph.add_node(person, type='person') @@ -106,7 +106,7 @@ def test_same_thing_occurs_in_two_different_variables(self): concept_dict = {'x': person, 'y': person2} - typedb_graph = concept_dict_to_graph(concept_dict, variable_graph) + typedb_graph = concept_dict_to_networkx(concept_dict, variable_graph) expected_typedb_graph = nx.MultiDiGraph() expected_typedb_graph.add_node(person, type='person') @@ -123,7 +123,7 @@ def test_edge_starting_from_entity_throws_exception(self): concept_dict = {'x': person, 'y': employment} with self.assertRaises(ValueError) as context: - _ = concept_dict_to_graph(concept_dict, variable_graph) + _ = concept_dict_to_networkx(concept_dict, variable_graph) self.assertEqual('An edge in the variable_graph originates from a non-relation, check the variable_graph!', str(context.exception)) @@ -139,7 +139,7 @@ def test_edge_starting_from_attribute_throws_exception(self): concept_dict = {'x': name, 'y': employment} with self.assertRaises(ValueError) as context: - _ = concept_dict_to_graph(concept_dict, variable_graph) + _ = concept_dict_to_networkx(concept_dict, variable_graph) self.assertEqual('An edge in the variable_graph originates from a non-relation, check the variable_graph!', str(context.exception)) @@ -156,7 +156,7 @@ def test_exception_if_sets_of_variables_differ(self): 'a': thing} with self.assertRaises(ValueError) as context: - _ = concept_dict_to_graph(concept_dict, variable_graph) + _ = concept_dict_to_networkx(concept_dict, variable_graph) self.assertEqual( 'The variables in the variable_graph must match those in the concept_dict\n' @@ -174,7 +174,7 @@ def test_variable_graph_properties_are_transferred_to_graph(self): employment = Thing('V456', 'employment', 'relation') concept_dict = {'x': person, 'y': employment} - typedb_graph = concept_dict_to_graph(concept_dict, variable_graph) + typedb_graph = concept_dict_to_networkx(concept_dict, variable_graph) expected_typedb_graph = nx.MultiDiGraph() expected_typedb_graph.add_node(person, type='person', input=1, solution=1) expected_typedb_graph.add_node(employment, type='employment', input=1, solution=1) diff --git a/kglib/networkx/queries_to_networkx.py b/kglib/networkx/queries_to_networkx.py index 9dd28f79..9fba41e8 100644 --- a/kglib/networkx/queries_to_networkx.py +++ b/kglib/networkx/queries_to_networkx.py @@ -25,7 +25,7 @@ import networkx as nx from kglib.typedb.thing import build_thing -from kglib.networkx.concept_dict_to_networkx import concept_dict_to_graph +from kglib.networkx.concept_dict_to_networkx import concept_dict_to_networkx def concept_dict_from_concept_map(concept_map): @@ -85,7 +85,7 @@ def combine_n_graphs(graphs_list): def build_graph_from_queries(query_sampler_variable_graph_tuples, typedb_transaction, - concept_dict_converter=concept_dict_to_graph): + concept_dict_converter=concept_dict_to_networkx): """ Builds a graph of Things, interconnected by roles (and *has*), from a set of queries and graphs representing those queries (variable graphs)of those queries, over a TypeDB transaction From 9b5613e6769948e5af02e5f3ef9c215ec38e4273 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 14:54:11 +0100 Subject: [PATCH 071/124] Deps fixes for end-to-end test --- kglib/examples/diagnosis/BUILD | 3 +++ kglib/examples/diagnosis/dataset/generate.py | 2 +- kglib/networkx/BUILD | 3 ++- 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index 8bce2a0d..ac2607d7 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -31,12 +31,15 @@ py_library( deps = [ "//kglib/examples/diagnosis/dataset", "//kglib/typedb", + "//kglib/pytorch_geometric", + "//kglib/networkx", "@vaticle_typedb_client_python//:client_python", vaticle_kglib_requirement("networkx"), vaticle_kglib_requirement("torch"), vaticle_kglib_requirement("torch_geometric"), vaticle_kglib_requirement("torch_sparse"), vaticle_kglib_requirement("torch_scatter"), + vaticle_kglib_requirement("tensorboard"), ], visibility=['//visibility:public'] ) diff --git a/kglib/examples/diagnosis/dataset/generate.py b/kglib/examples/diagnosis/dataset/generate.py index 017ee7d0..a37cb604 100644 --- a/kglib/examples/diagnosis/dataset/generate.py +++ b/kglib/examples/diagnosis/dataset/generate.py @@ -24,7 +24,7 @@ import numpy as np from typedb.client import * -from kglib.typedb.synthetic.statistics.pmf import PMF +from kglib.examples.diagnosis.dataset.pmf import PMF def get_example_queries(pmf, example_id): diff --git a/kglib/networkx/BUILD b/kglib/networkx/BUILD index 5e6a07b9..c14f867f 100644 --- a/kglib/networkx/BUILD +++ b/kglib/networkx/BUILD @@ -28,7 +28,8 @@ py_library( srcs = [ 'query_graph.py', 'concept_dict_to_networkx.py', - 'queries_to_networkx.py' + 'queries_to_networkx.py', + 'iterate.py', ], deps = [ "//kglib/typedb", From a218275e4e6c832ac4c1c24ac8b95985d5a52cd7 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 15:11:26 +0100 Subject: [PATCH 072/124] Remove auto-deleting diagnosis database on each run. Can't be enabled if running in CI --- kglib/examples/diagnosis/diagnosis.py | 63 ++++++++++++--------------- 1 file changed, 28 insertions(+), 35 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 7c2ecccf..0fbea0fa 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -96,13 +96,6 @@ def diagnosis_example(typedb_binary_directory, Final accuracies for training and for testing """ - # Delete the database each time # TODO: Remove when adapting to your own data! - sp.check_call([ - './typedb', - 'console', - f'--command=database delete {database}', - ], cwd=typedb_binary_directory) - client = TypeDB.core_client(address) create_database(client, database) @@ -345,12 +338,12 @@ def get_query_handles(example_id): vars = p, par, ps, d, diag, n = 'p', 'par', 'ps', 'd', 'diag', 'n' hereditary_query_graph = (QueryGraph() - .add_vars(vars, PREEXISTS) - .add_role_edge(ps, p, 'child', PREEXISTS) - .add_role_edge(ps, par, 'parent', PREEXISTS) - .add_role_edge(diag, par, 'patient', PREEXISTS) - .add_role_edge(diag, d, 'diagnosed-disease', PREEXISTS) - .add_has_edge(d, n, PREEXISTS)) + .add_vars(vars) + .add_role_edge(ps, p, 'child') + .add_role_edge(ps, par, 'parent') + .add_role_edge(diag, par, 'patient') + .add_role_edge(diag, d, 'diagnosed-disease') + .add_has_edge(d, n)) # === Consumption Feature === consumption_query = inspect.cleandoc(f'''match @@ -361,11 +354,11 @@ def get_query_handles(example_id): vars = p, s, n, c, u = 'p', 's', 'n', 'c', 'u' consumption_query_graph = (QueryGraph() - .add_vars(vars, PREEXISTS) - .add_has_edge(s, n, PREEXISTS) - .add_role_edge(c, p, 'consumer', PREEXISTS) - .add_role_edge(c, s, 'consumed-substance', PREEXISTS) - .add_has_edge(c, u, PREEXISTS)) + .add_vars(vars) + .add_has_edge(s, n) + .add_role_edge(c, p, 'consumer') + .add_role_edge(c, s, 'consumed-substance') + .add_has_edge(c, u)) # === Age Feature === person_age_query = inspect.cleandoc(f'''match @@ -374,8 +367,8 @@ def get_query_handles(example_id): vars = p, a = 'p', 'a' person_age_query_graph = (QueryGraph() - .add_vars(vars, PREEXISTS) - .add_has_edge(p, a, PREEXISTS)) + .add_vars(vars) + .add_has_edge(p, a)) # === Risk Factors Feature === risk_factor_query = inspect.cleandoc(f'''match @@ -386,9 +379,9 @@ def get_query_handles(example_id): vars = p, d, r = 'p', 'd', 'r' risk_factor_query_graph = (QueryGraph() - .add_vars(vars, PREEXISTS) - .add_role_edge(r, p, 'person-at-risk', PREEXISTS) - .add_role_edge(r, d, 'risked-disease', PREEXISTS)) + .add_vars(vars) + .add_role_edge(r, p, 'person-at-risk') + .add_role_edge(r, d, 'risked-disease')) # === Symptom === vars = p, s, sn, d, dn, sp, sev, c = 'p', 's', 'sn', 'd', 'dn', 'sp', 'sev', 'c' @@ -402,14 +395,14 @@ def get_query_handles(example_id): ''') symptom_query_graph = (QueryGraph() - .add_vars(vars, PREEXISTS) - .add_has_edge(s, sn, PREEXISTS) - .add_has_edge(d, dn, PREEXISTS) - .add_role_edge(sp, s, 'presented-symptom', PREEXISTS) - .add_has_edge(sp, sev, PREEXISTS) - .add_role_edge(sp, p, 'symptomatic-patient', PREEXISTS) - .add_role_edge(c, s, 'effect', PREEXISTS) - .add_role_edge(c, d, 'cause', PREEXISTS)) + .add_vars(vars) + .add_has_edge(s, sn) + .add_has_edge(d, dn) + .add_role_edge(sp, s, 'presented-symptom') + .add_has_edge(sp, sev) + .add_role_edge(sp, p, 'symptomatic-patient') + .add_role_edge(c, s, 'effect') + .add_role_edge(c, d, 'cause')) # === Diagnosis === @@ -422,10 +415,10 @@ def get_query_handles(example_id): ''') diagnosis_query_graph = (QueryGraph() - .add_vars([diag], PREEXISTS) - .add_vars([d, p, dn], PREEXISTS) - .add_role_edge(diag, d, 'diagnosed-disease', PREEXISTS) - .add_role_edge(diag, p, 'patient', PREEXISTS)) + .add_vars([diag]) + .add_vars([d, p, dn]) + .add_role_edge(diag, d, 'diagnosed-disease') + .add_role_edge(diag, p, 'patient')) return [ (symptom_query, lambda x: x, symptom_query_graph), From d16a8e77ec6037e6c6b8f06457d698e676c218cf Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 15:28:52 +0100 Subject: [PATCH 073/124] Refactor to remove the solution field from networkx graphs. No longer needed. --- kglib/examples/diagnosis/diagnosis.py | 8 ++--- .../networkx/concept_dict_to_networkx_test.py | 12 +++---- kglib/networkx/queries_to_networkx.py | 14 ++++---- kglib/networkx/queries_to_networkx_test.py | 32 +++++++++---------- kglib/networkx/query_graph.py | 19 +++++------ kglib/networkx/query_graph_test.py | 18 +++++------ 6 files changed, 51 insertions(+), 52 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 0fbea0fa..bf1e81f4 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -20,7 +20,6 @@ # import inspect -import subprocess as sp import networkx as nx import torch @@ -31,15 +30,16 @@ from torch_geometric.nn import HGTConv from typedb.client import * +from kglib.examples.diagnosis.dataset.generate import generate_example_data +from kglib.networkx.query_graph import QueryGraph from kglib.pytorch_geometric.dataset.dataset import DataSet -from kglib.pytorch_geometric.transform.binary_link_prediction import LinkPredictionLabeller, binary_relations_to_edges, \ +from kglib.pytorch_geometric.transform.binary_link_prediction import LinkPredictionLabeller, \ + binary_relations_to_edges, \ binary_link_prediction_edge_triplets from kglib.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type from kglib.pytorch_geometric.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ ContinuousEncoder from kglib.pytorch_geometric.utils import load_typeql_schema_file, load_typeql_data_file -from kglib.networkx.query_graph import QueryGraph -from kglib.examples.diagnosis.dataset.generate import generate_example_data from kglib.typedb.type import get_thing_types DATABASE = "diagnosis" diff --git a/kglib/networkx/concept_dict_to_networkx_test.py b/kglib/networkx/concept_dict_to_networkx_test.py index 3842fff1..06266df0 100644 --- a/kglib/networkx/concept_dict_to_networkx_test.py +++ b/kglib/networkx/concept_dict_to_networkx_test.py @@ -166,9 +166,9 @@ def test_exception_if_sets_of_variables_differ(self): def test_variable_graph_properties_are_transferred_to_graph(self): variable_graph = nx.MultiDiGraph() - variable_graph.add_node('x', input=1, solution=1) - variable_graph.add_node('y', input=1, solution=1) - variable_graph.add_edge('y', 'x', type='employee', input=0, solution=1) + variable_graph.add_node('x', input=1) + variable_graph.add_node('y', input=1) + variable_graph.add_edge('y', 'x', type='employee', input=0) person = Thing('V123', 'person', 'entity') employment = Thing('V456', 'employment', 'relation') @@ -176,9 +176,9 @@ def test_variable_graph_properties_are_transferred_to_graph(self): typedb_graph = concept_dict_to_networkx(concept_dict, variable_graph) expected_typedb_graph = nx.MultiDiGraph() - expected_typedb_graph.add_node(person, type='person', input=1, solution=1) - expected_typedb_graph.add_node(employment, type='employment', input=1, solution=1) - expected_typedb_graph.add_edge(employment, person, type='employee', input=0, solution=1) + expected_typedb_graph.add_node(person, type='person', input=1) + expected_typedb_graph.add_node(employment, type='employment', input=1) + expected_typedb_graph.add_edge(employment, person, type='employee', input=0) self.assertGraphsEqual(expected_typedb_graph, typedb_graph) diff --git a/kglib/networkx/queries_to_networkx.py b/kglib/networkx/queries_to_networkx.py index 9fba41e8..9cca8987 100644 --- a/kglib/networkx/queries_to_networkx.py +++ b/kglib/networkx/queries_to_networkx.py @@ -84,17 +84,16 @@ def combine_n_graphs(graphs_list): return reduce(lambda x, y: combine_2_graphs(x, y), graphs_list) -def build_graph_from_queries(query_sampler_variable_graph_tuples, typedb_transaction, +def build_graph_from_queries(query_sampler_variable_graph_tuples, transaction, concept_dict_converter=concept_dict_to_networkx): """ Builds a graph of Things, interconnected by roles (and *has*), from a set of queries and graphs representing those queries (variable graphs)of those queries, over a TypeDB transaction Args: - infer: whether to use TypeDB's inference engine query_sampler_variable_graph_tuples: A list of tuples, each tuple containing a query, a sampling function, and a variable_graph - typedb_transaction: A TypeDB transaction + transaction: A TypeDB transaction concept_dict_converter: The function to use to convert from concept_dicts to a TypeDB model. This could be a typical model or a mathematical model @@ -107,7 +106,7 @@ def build_graph_from_queries(query_sampler_variable_graph_tuples, typedb_transac for query, sampler, variable_graph in query_sampler_variable_graph_tuples: print("working on query: " + query) - concept_maps = sampler(typedb_transaction.query().match(query)) + concept_maps = sampler(transaction.query().match(query)) print("query completed") concept_dicts = [concept_dict_from_concept_map(concept_map) for concept_map in concept_maps] @@ -132,8 +131,11 @@ def build_graph_from_queries(query_sampler_variable_graph_tuples, typedb_transac if len(query_concept_graphs) == 0: # Raise exception when none of the queries returned any results - raise RuntimeError(f'The graph from queries: {[query_sampler_variable_graph_tuple[0] for query_sampler_variable_graph_tuple in query_sampler_variable_graph_tuples]}\n' - f'could not be created, since none of these queries returned results') + raise RuntimeError( + f'The graph from queries: ' + f'{[query_sampler_variable_graph_tuple[0] for query_sampler_variable_graph_tuple in query_sampler_variable_graph_tuples]}\n' + f'could not be created, since none of these queries returned results' + ) concept_graph = combine_n_graphs(query_concept_graphs) return concept_graph diff --git a/kglib/networkx/queries_to_networkx_test.py b/kglib/networkx/queries_to_networkx_test.py index adea98e9..81fcadaa 100644 --- a/kglib/networkx/queries_to_networkx_test.py +++ b/kglib/networkx/queries_to_networkx_test.py @@ -214,47 +214,47 @@ def test_when_graph_node_properties_are_mismatched_exception_is_raised(self): person_a = Thing('V123', 'person', 'entity') name_a = Thing('V1234', 'name', 'attribute', value_type='string', value='Bob') typedb_graph_a = nx.MultiDiGraph(name='a') - typedb_graph_a.add_node(person_a, input=1, solution=1) - typedb_graph_a.add_node(name_a, input=1, solution=1) - typedb_graph_a.add_edge(person_a, name_a, type='has', input=0, solution=1) + typedb_graph_a.add_node(person_a, input=1) + typedb_graph_a.add_node(name_a, input=1) + typedb_graph_a.add_edge(person_a, name_a, type='has', input=0) person_b = Thing('V123', 'person', 'entity') name_b = Thing('V1234', 'name', 'attribute', value_type='string', value='Bob') typedb_graph_b = nx.MultiDiGraph(name='b') - typedb_graph_b.add_node(person_b, input=1, solution=1) - typedb_graph_b.add_node(name_b, input=0, solution=1) - typedb_graph_b.add_edge(person_b, name_b, type='has', input=0, solution=1) + typedb_graph_b.add_node(person_b, input=1) + typedb_graph_b.add_node(name_b, input=0) + typedb_graph_b.add_edge(person_b, name_b, type='has', input=0) with self.assertRaises(ValueError) as context: combine_n_graphs([typedb_graph_a, typedb_graph_b]) self.assertEqual(('Found non-matching node properties for node ' 'between graphs a and b:\n' - 'In graph a: {\'input\': 1, \'solution\': 1}\n' - 'In graph b: {\'input\': 0, \'solution\': 1}'), str(context.exception)) + 'In graph a: {\'input\': 1}\n' + 'In graph b: {\'input\': 0}'), str(context.exception)) def test_when_graph_edge_properties_are_mismatched_exception_is_raised(self): person_a = Thing('V123', 'person', 'entity') name_a = Thing('V1234', 'name', 'attribute', value_type='string', value='Bob') typedb_graph_a = nx.MultiDiGraph(name='a') - typedb_graph_a.add_node(person_a, input=1, solution=1) - typedb_graph_a.add_node(name_a, input=1, solution=1) - typedb_graph_a.add_edge(person_a, name_a, type='has', input=0, solution=1) + typedb_graph_a.add_node(person_a, input=1) + typedb_graph_a.add_node(name_a, input=1) + typedb_graph_a.add_edge(person_a, name_a, type='has', input=0) person_b = Thing('V123', 'person', 'entity') name_b = Thing('V1234', 'name', 'attribute', value_type='string', value='Bob') typedb_graph_b = nx.MultiDiGraph(name='b') - typedb_graph_b.add_node(person_b, input=1, solution=1) - typedb_graph_b.add_node(name_b, input=1, solution=1) - typedb_graph_b.add_edge(person_b, name_b, type='has', input=1, solution=0) + typedb_graph_b.add_node(person_b, input=1) + typedb_graph_b.add_node(name_b, input=1) + typedb_graph_b.add_edge(person_b, name_b, type='has', input=1) with self.assertRaises(ValueError) as context: combine_n_graphs([typedb_graph_a, typedb_graph_b]) self.assertEqual(('Found non-matching edge properties for edge (, , 0) ' 'between graphs a and b:\n' - 'In graph a: {\'type\': \'has\', \'input\': 0, \'solution\': 1}\n' - 'In graph b: {\'type\': \'has\', \'input\': 1, \'solution\': 0}'), str(context.exception)) + 'In graph a: {\'type\': \'has\', \'input\': 0}\n' + 'In graph b: {\'type\': \'has\', \'input\': 1}'), str(context.exception)) if __name__ == "__main__": diff --git a/kglib/networkx/query_graph.py b/kglib/networkx/query_graph.py index 5445f67a..2b2be890 100644 --- a/kglib/networkx/query_graph.py +++ b/kglib/networkx/query_graph.py @@ -24,48 +24,45 @@ class QueryGraph(nx.MultiDiGraph): """ - A custom graph to represent a query. Has additional helper methods specific to adding Graql patterns. + A custom graph to represent a query. Has additional helper methods specific to adding TypeQL patterns. """ - def add_vars(self, vars, solution): + def add_vars(self, vars): """ - Add Graql variables, stored as nodes in the graph + Add variables, stored as nodes in the graph Args: vars: String variables - solution: Indicator of the ground truth class that the variables belongs to Returns: self """ for var in vars: - self.add_node(var, solution=solution) + self.add_node(var) return self - def add_has_edge(self, owner_var, attribute_var, solution): + def add_has_edge(self, owner_var, attribute_var): """ Add a "has" edge to represent ownership of an attribute Args: owner_var: The variable of the owner attribute_var: The variable of the owned attribute - solution: Indicator of the ground truth class that the edge belongs to Returns: self """ - self.add_edge(owner_var, attribute_var, type='has', solution=solution) + self.add_edge(owner_var, attribute_var, type='has') return self - def add_role_edge(self, relation_var, roleplayer_var, role_label, solution): + def add_role_edge(self, relation_var, roleplayer_var, role_label): """ Add an edge to represent the role a variable plays in a relation Args: relation_var: The variable of the relation roleplayer_var: The variable of the roleplayer in the relation role_label: The role the roleplayer plays in the relation - solution: Indicator of the ground truth class that the edge belongs to Returns: self """ - self.add_edge(relation_var, roleplayer_var, type=role_label, solution=solution) + self.add_edge(relation_var, roleplayer_var, type=role_label) return self diff --git a/kglib/networkx/query_graph_test.py b/kglib/networkx/query_graph_test.py index d4407889..d63b3060 100644 --- a/kglib/networkx/query_graph_test.py +++ b/kglib/networkx/query_graph_test.py @@ -28,30 +28,30 @@ class TestQueryGraph(unittest.TestCase): def test_add_single_var_adds_variable_node_as_expected(self): g = QueryGraph() - g.add_vars(['a'], 0) - self.assertDictEqual({'solution': 0}, g.nodes['a']) + g.add_vars(['a']) + self.assertDictEqual({}, g.nodes['a']) def test_add_vars_adds_variable_nodes_as_expected(self): g = QueryGraph() - g.add_vars(['a', 'b'], 0) + g.add_vars(['a', 'b']) nodes = {node for node in g.nodes} self.assertSetEqual({'a', 'b'}, nodes) def test_add_has_edge_adds_edge_as_expected(self): g = QueryGraph() - g.add_vars('a', 'b') - g.add_has_edge('a', 'b', 0) + g.add_vars('a') + g.add_has_edge('a', 'b') edges = [edge for edge in g.edges] self.assertEqual(1, len(edges)) - self.assertDictEqual({'type': 'has', 'solution': 0}, g.edges['a', 'b', 0]) + self.assertDictEqual({'type': 'has'}, g.edges['a', 'b', 0]) def test_add_role_edge_adds_role_as_expected(self): g = QueryGraph() - g.add_vars('a', 'b') - g.add_role_edge('a', 'b', 'role_label', 1) + g.add_vars('a') + g.add_role_edge('a', 'b', 'role_label') edges = [edge for edge in g.edges] self.assertEqual(1, len(edges)) - self.assertDictEqual({'type': 'role_label', 'solution': 1}, g.edges['a', 'b', 0]) + self.assertDictEqual({'type': 'role_label'}, g.edges['a', 'b', 0]) if __name__ == "__main__": From b3719d44dde8e3b9be671644b3c43b2e62e91c45 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 15:33:48 +0100 Subject: [PATCH 074/124] Try using a requirements file specifically to fill the install_requires for pypi --- BUILD | 8 ++---- install_requires.txt | 66 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 69 insertions(+), 5 deletions(-) create mode 100644 install_requires.txt diff --git a/BUILD b/BUILD index b0ae9fc9..b7139c99 100644 --- a/BUILD +++ b/BUILD @@ -23,13 +23,11 @@ exports_files(["requirements.txt", "RELEASE_TEMPLATE.md"]) load("@rules_python//python:defs.bzl", "py_library", "py_test") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_bazel_distribution//github:rules.bzl", "deploy_github") load("@vaticle_bazel_distribution//pip:rules.bzl", "assemble_pip", "deploy_pip") -load("@vaticle_kglib_pip//:requirements.bzl", - vaticle_kglib_requirement = "requirement") +load("@vaticle_kglib_pip//:requirements.bzl", vaticle_kglib_requirement = "requirement") load("@vaticle_dependencies//distribution:deployment.bzl", "deployment") load("//:deployment.bzl", github_deployment = "deployment") @@ -58,7 +56,7 @@ assemble_pip( author = "Vaticle", author_email = "community@vaticle.com", license = "Apache-2.0", - requirements_file = "//:requirements.txt", + requirements_file = "//:install_requires.txt", keywords = ["machine learning", "logical reasoning", "knowledege graph", "typedb", "database", "graph", "knowledgebase", "knowledge-engineering"], diff --git a/install_requires.txt b/install_requires.txt new file mode 100644 index 00000000..8dee8cee --- /dev/null +++ b/install_requires.txt @@ -0,0 +1,66 @@ +# +# Copyright (C) 2022 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +absl-py==1.2.0 +cachetools==5.2.0 +certifi==2022.6.15 +charset-normalizer==2.1.0 +decorator==5.1.1 +google-auth==2.9.1 +google-auth-oauthlib==0.4.6 +grpcio==1.43.0 +idna==3.3 +importlib-metadata==4.12.0 +Jinja2==3.1.2 +joblib==1.1.0 +Markdown==3.4.1 +MarkupSafe==2.1.1 +networkx==2.5 +numpy==1.21.6 +oauthlib==3.2.0 +pandas==1.3.5 +protobuf==3.15.5 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pyparsing==3.0.9 +python-dateutil==2.8.2 +pytz==2022.1 +requests==2.28.1 +requests-oauthlib==1.3.1 +rsa==4.9 +scikit-learn==1.0.2 +scipy==1.7.3 +six==1.16.0 +tensorboard==2.9.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 +threadpoolctl==3.1.0 +torch==1.11.0 +torch-geometric==2.0.4 +torch-scatter==2.0.9 +torch-sparse==0.6.14 +tqdm==4.64.0 +typedb-client==2.9.0 +typedb-protocol==2.9.0 +typing-extensions==4.3.0 +urllib3==1.26.10 +Werkzeug==2.1.2 +zipp==3.8.1 From 945a3484cbebffde12dff9e1fe97154ac5a7b227 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 17:53:23 +0100 Subject: [PATCH 075/124] Try using absolute path to the schema and data files --- kglib/tests/end_to_end/BUILD | 1 - kglib/tests/end_to_end/diagnosis.py | 6 ++++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/kglib/tests/end_to_end/BUILD b/kglib/tests/end_to_end/BUILD index d17b707c..9b049bd8 100644 --- a/kglib/tests/end_to_end/BUILD +++ b/kglib/tests/end_to_end/BUILD @@ -64,7 +64,6 @@ checkstyle_test( name = "checkstyle", include = glob([ "*", - "**/*" ]), license_type = "apache-header", ) diff --git a/kglib/tests/end_to_end/diagnosis.py b/kglib/tests/end_to_end/diagnosis.py index f21384ca..173b32d6 100644 --- a/kglib/tests/end_to_end/diagnosis.py +++ b/kglib/tests/end_to_end/diagnosis.py @@ -20,6 +20,7 @@ # import sys import unittest +import os from kglib.examples.diagnosis.diagnosis import diagnosis_example from kglib.typedb.test.base import TypeDBServer @@ -27,11 +28,12 @@ class TestDiagnosisExample(unittest.TestCase): def setUp(self): + cwd = os.getcwd() self._tdb = TypeDBServer(sys.argv.pop()) self._tdb.start() self._typedb_binary_location = self._tdb.typedb_binary_location - self._data_file_location = sys.argv.pop() - self._schema_file_location = sys.argv.pop() + self._data_file_location = cwd + '/' + sys.argv.pop() + self._schema_file_location = cwd + '/' + sys.argv.pop() def tearDown(self): self._tdb.stop() From a64453ffc208d8e68f91288b9acdc72afe1f82a2 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Thu, 21 Jul 2022 18:04:47 +0100 Subject: [PATCH 076/124] Lower the threshold accuracy required to pass end-to-end tests (having raised it previously) --- kglib/tests/end_to_end/diagnosis.py | 4 ++-- kglib/tests/end_to_end/diagnosis_debug.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/kglib/tests/end_to_end/diagnosis.py b/kglib/tests/end_to_end/diagnosis.py index 173b32d6..7e4eace5 100644 --- a/kglib/tests/end_to_end/diagnosis.py +++ b/kglib/tests/end_to_end/diagnosis.py @@ -45,9 +45,9 @@ def test_learning_is_done(self): schema_file_path=self._schema_file_location, seed_data_file_path=self._data_file_location ) - self.assertGreaterEqual(train_accuracy, 0.9) + self.assertGreaterEqual(train_accuracy, 0.85) self.assertLessEqual(train_accuracy, 0.99) - self.assertGreaterEqual(test_accuracy, 0.9) + self.assertGreaterEqual(test_accuracy, 0.85) self.assertLessEqual(test_accuracy, 0.99) diff --git a/kglib/tests/end_to_end/diagnosis_debug.py b/kglib/tests/end_to_end/diagnosis_debug.py index ac82244b..d5fb9365 100644 --- a/kglib/tests/end_to_end/diagnosis_debug.py +++ b/kglib/tests/end_to_end/diagnosis_debug.py @@ -45,9 +45,9 @@ def test_learning_is_done(self): schema_file_path=self._schema_file_location, seed_data_file_path=self._data_file_location ) - self.assertGreaterEqual(train_accuracy, 0.9) + self.assertGreaterEqual(train_accuracy, 0.85) self.assertLessEqual(train_accuracy, 0.99) - self.assertGreaterEqual(test_accuracy, 0.9) + self.assertGreaterEqual(test_accuracy, 0.85) self.assertLessEqual(test_accuracy, 0.99) From 66a8e2f7252e68b996fa0aba13c3dc9e1809ce7b Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 22 Jul 2022 10:07:59 +0100 Subject: [PATCH 077/124] README updates and relevant amendments --- README.md | 34 ++++-- kglib/examples/diagnosis/diagnosis.py | 4 +- kglib/networkx/queries_to_networkx.py | 113 +++++++++--------- .../{typedb_graph_encoder.py => encode.py} | 3 +- 4 files changed, 84 insertions(+), 70 deletions(-) rename kglib/pytorch_geometric/transform/{typedb_graph_encoder.py => encode.py} (99%) diff --git a/README.md b/README.md index e150ae42..e0d3ac3b 100644 --- a/README.md +++ b/README.md @@ -6,15 +6,25 @@ # TypeDB KGLIB (Knowledge Graph Library) -**KGLIB provides tools to enable machine learning with [TypeDB](https://github.com/vaticle/typedb).** +**KGLIB provides tools to enable graph algorithms and machine learning with [TypeDB](https://github.com/vaticle/typedb).** -This library is under development and will henceforth be transformed into primarily infrastructure tools and integrations between TypeDB and machine learning libraries. +There are integrations for [NetworkX](https://networkx.org) and for [PyTorch Geometric (PyG)](https://github.com/pyg-team/pytorch_geometric). -## Machine Learning Pipeline +[NetworkX](https://networkx.org) integration allows you to use a [large library of algorithms](https://networkx.org/documentation/stable/reference/algorithms/index.html) over graphs you export into NetworkX from TypeDB. + +[PyTorch Geometric (PyG)](https://github.com/pyg-team/pytorch_geometric) integration gives a toolbox to build Graph Neural Networks (GNNs) over your TypeDB data, with an example included for link prediction (binary relation prediction in TypeDB terms). -![Flow Diagram](kglib/kgcn_tensorflow/.images/knowledge_graph_machine_learning.png) +## Features -The pipeline provided helps by allowing us to extract subgraphs from TypeDB. Each subgraph is a training example, which are sent to the learner in batches. Algorithms using this approach are scalable since they do not need to hold the whole graph in memory for training. +### NetworkX +- Declare the graph structure of your queries: "graph handles", with sampling functions. +- Use query handles to query a TypeDB instance and combine many results across many queries into a single graph (`build_graph_from_queries`). +### PyTorch Geometric +- A `DataSet` object to lazily load graphs from a TypeDB instance. Each graph is converted to a PyG `Data` object. +- It's most natural to work with `HeteroData` objects since all data in TypeDB has a type. This conversion is available by default in PyG, but KGLIB provides `store_concepts_by_type` to map concepts by type so that they can be reassociated after learning is finished. +- A `FeatureEncoder` + +## Machine Learning Pipeline The pipeline is as follows: 1. Extract data from `TypeDB` into Python [NetworkX](https://networkx.org) in-memory subgraphs by specifying multiple [TypeQL](https://github.com/vaticle/typeql) queries. @@ -39,7 +49,9 @@ You may find the following resources useful: - Python >= 3.7.x -- KGLIB installed via pip: `pip install typedb-kglib`. +- Grab the `requirements.txt` from [here](requirements.txt) and install the requirements with `pip install requirements.txt`. This is due to some intricacies installing PyG's dependencies, see [here](https://github.com/pyg-team/pytorch_geometric/issues/861) for details. + +- Installed KGLIB: `pip install typedb-kglib`. - [TypeDB 2.11.1](https://github.com/vaticle/typedb/releases) running in the background. @@ -47,11 +59,11 @@ You may find the following resources useful: ### Run the Example -Take a look at [*Knowledge Graph Convolutional Networks* (KGCNs)](kglib/kgcn_tensorflow) to see a walkthrough of how to use the library. +Take a look at [The PyTorch Geometric heterogeneous data example](kglib/examples/diagnosis) to see a walkthrough of how to use KGLIB to build a GNN that runs over data from TypeDB. ### Building from source -It's expected that you will use Pip to install, but should you need to make your own changes to the library, and import it into your project, you can build from source as follows. +It's expected that you will use Pip to install, but should you need to make your own changes to the library, and import it into your project, you can build from source as follows: Clone KGLIB: @@ -71,7 +83,7 @@ Build all targets: bazel build //... ``` -Run all tests. Requires Python 3.6+ on your `PATH`. Test dependencies are for Linux since that is the CI environment: +Run all tests. Requires Python 3.7+ on your `PATH`. Test dependencies are for Linux since that is the CI environment: ``` bazel test //kglib/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH @@ -85,6 +97,6 @@ bazel build //:assemble-pip ## Development -To follow the development conversation, please join the [Vaticle Discord](https://discord.com/invite/grakn), and join the `#kglib` channel. Alternatively, start a new topic on the [Vaticle Discussion Forum](https://forum.vaticle.com). +To follow the development conversation, please join the [Vaticle Discord](https://discord.com/invite/vaticle), and join the `#kglib` channel. Alternatively, start a new topic on the [Vaticle Discussion Forum](https://forum.vaticle.com). -KGLIB requires that you have migrated your data into a [TypeDB](https://github.com/vaticle/typedb) or TypeDB Cluster instance. There is an [official examples repo](https://github.com/vaticle/examples) for how to go about this, and information available on [migration in the docs](https://docs.vaticle.com/docs/examples/phone-calls-migration-python). Alternatively, there are fantastic community-led projects growing in the [TypeDB OSI](https://typedb.org) to facilitate fast and easy data loading. +KGLIB requires that you have migrated your data into a [TypeDB](https://github.com/vaticle/typedb) or TypeDB Cluster instance. There is an [official examples repo](https://github.com/vaticle/examples) for how to go about this, and information available on [migration in the docs](https://docs.vaticle.com/docs/examples/phone-calls-migration-python). Alternatively, there are fantastic community-led projects growing in the [TypeDB OSI](https://typedb.org) to facilitate fast and easy data loading, for example [TypeDB Loader](https://github.com/typedb-osi/typedb-loader). diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index bf1e81f4..7de70b06 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -37,7 +37,7 @@ binary_relations_to_edges, \ binary_link_prediction_edge_triplets from kglib.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type -from kglib.pytorch_geometric.transform.typedb_graph_encoder import GraphFeatureEncoder, CategoricalEncoder, \ +from kglib.pytorch_geometric.transform.encode import FeatureEncoder, CategoricalEncoder, \ ContinuousEncoder from kglib.pytorch_geometric.utils import load_typeql_schema_file, load_typeql_data_file from kglib.typedb.type import get_thing_types @@ -122,7 +122,7 @@ def diagnosis_example(typedb_binary_directory, transform = transforms.Compose([ lambda graph: binary_relations_to_edges(graph, RELATION_TYPE_TO_PREDICT[1:4]), lambda graph: nx.convert_node_labels_to_integers(graph, label_attribute="concept"), - GraphFeatureEncoder(node_types, edge_types, TYPE_ENCODING_SIZE, ATTRIBUTE_ENCODERS, ATTRIBUTE_ENCODING_SIZE), + FeatureEncoder(node_types, edge_types, TYPE_ENCODING_SIZE, ATTRIBUTE_ENCODERS, ATTRIBUTE_ENCODING_SIZE), LinkPredictionLabeller(RELATION_TYPE_TO_PREDICT[2]), store_concepts_by_type, clear_unneeded_fields diff --git a/kglib/networkx/queries_to_networkx.py b/kglib/networkx/queries_to_networkx.py index 9cca8987..fdf281dc 100644 --- a/kglib/networkx/queries_to_networkx.py +++ b/kglib/networkx/queries_to_networkx.py @@ -28,62 +28,6 @@ from kglib.networkx.concept_dict_to_networkx import concept_dict_to_networkx -def concept_dict_from_concept_map(concept_map): - """ - Given a concept map, build a dictionary of the variables present and the concepts they refer to, locally storing any - information required about those concepts. - - Args: - concept_map: A dict of Concepts provided by TypeDB keyed by query variables - - Returns: - A dictionary of concepts keyed by query variables - """ - return {variable: build_thing(typedb_concept) for variable, typedb_concept in concept_map.map().items()} - - -def combine_2_graphs(graph1, graph2): - """ - Combine two graphs into one. Do this by recognising common nodes between the two. - Args: - graph1: Graph to compare - graph2: Graph to compare - Returns: - Combined graph - """ - - for node, data in graph1.nodes(data=True): - if graph2.has_node(node): - data2 = graph2.nodes[node] - if data2 != data: - raise ValueError((f'Found non-matching node properties for node {node} ' - f'between graphs {graph1} and {graph2}:\n' - f'In graph {graph1}: {data}\n' - f'In graph {graph2}: {data2}')) - - for sender, receiver, keys, data in graph1.edges(data=True, keys=True): - if graph2.has_edge(sender, receiver, keys): - data2 = graph2.edges[sender, receiver, keys] - if data2 != data: - raise ValueError((f'Found non-matching edge properties for edge {sender, receiver, keys} ' - f'between graphs {graph1} and {graph2}:\n' - f'In graph {graph1}: {data}\n' - f'In graph {graph2}: {data2}')) - - return nx.compose(graph1, graph2) - - -def combine_n_graphs(graphs_list): - """ - Combine N graphs into one. Do this by recognising common nodes between the two. - Args: - graphs_list: List of graphs to combine - Returns: - Combined graph - """ - return reduce(lambda x, y: combine_2_graphs(x, y), graphs_list) - - def build_graph_from_queries(query_sampler_variable_graph_tuples, transaction, concept_dict_converter=concept_dict_to_networkx): """ @@ -139,3 +83,60 @@ def build_graph_from_queries(query_sampler_variable_graph_tuples, transaction, concept_graph = combine_n_graphs(query_concept_graphs) return concept_graph + + +def concept_dict_from_concept_map(concept_map): + """ + Given a concept map, build a dictionary of the variables present and the concepts they refer to, locally storing any + information required about those concepts. + + Args: + concept_map: A dict of Concepts provided by TypeDB keyed by query variables + + Returns: + A dictionary of concepts keyed by query variables + """ + return {variable: build_thing(typedb_concept) for variable, typedb_concept in concept_map.map().items()} + + +def combine_2_graphs(graph1, graph2): + """ + Combine two graphs into one. Do this by recognising common nodes between the two. + Args: + graph1: Graph to compare + graph2: Graph to compare + Returns: + Combined graph + """ + + for node, data in graph1.nodes(data=True): + if graph2.has_node(node): + data2 = graph2.nodes[node] + if data2 != data: + raise ValueError((f'Found non-matching node properties for node {node} ' + f'between graphs {graph1} and {graph2}:\n' + f'In graph {graph1}: {data}\n' + f'In graph {graph2}: {data2}')) + + for sender, receiver, keys, data in graph1.edges(data=True, keys=True): + if graph2.has_edge(sender, receiver, keys): + data2 = graph2.edges[sender, receiver, keys] + if data2 != data: + raise ValueError((f'Found non-matching edge properties for edge {sender, receiver, keys} ' + f'between graphs {graph1} and {graph2}:\n' + f'In graph {graph1}: {data}\n' + f'In graph {graph2}: {data2}')) + + return nx.compose(graph1, graph2) + + +def combine_n_graphs(graphs_list): + """ + Combine N graphs into one. Do this by recognising common nodes between the two. + Args: + graphs_list: List of graphs to combine + Returns: + Combined graph + """ + return reduce(lambda x, y: combine_2_graphs(x, y), graphs_list) + diff --git a/kglib/pytorch_geometric/transform/typedb_graph_encoder.py b/kglib/pytorch_geometric/transform/encode.py similarity index 99% rename from kglib/pytorch_geometric/transform/typedb_graph_encoder.py rename to kglib/pytorch_geometric/transform/encode.py index 7910147c..ffae2e60 100644 --- a/kglib/pytorch_geometric/transform/typedb_graph_encoder.py +++ b/kglib/pytorch_geometric/transform/encode.py @@ -29,7 +29,8 @@ ) -class GraphFeatureEncoder: +class FeatureEncoder: + # TODO: Rephrase """Transform of the networkx graph as it comes out of TypeDB to a networkx graph that pytorch geometric likes to ingest. Now this is very much geared to pytorch geometric especially From 42b1b618f6693742b31c2d6c94cad4a58b1fdfd6 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 22 Jul 2022 10:16:36 +0100 Subject: [PATCH 078/124] Extra comments --- kglib/examples/diagnosis/diagnosis.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 7de70b06..c41eba51 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -45,8 +45,6 @@ DATABASE = "diagnosis" ADDRESS = "localhost:1729" -PREEXISTS = 0 # TODO Remove, now unnecessary - # Ignore any types that exist in the TypeDB instance but which aren't being used for learning to reduce the # number of categories to embed TYPES_TO_IGNORE = {'risk-factor', 'person-id', 'alcohol-risked-disease', 'person-at-alcohol-risk', @@ -270,8 +268,8 @@ def test() -> List[Tuple[float, float, float]]: z = model.encode(data.x_dict, data.edge_index_dict) final_edge_index = (model.decode_all(z).sigmoid() > 0.5).nonzero(as_tuple=False).cpu().detach().numpy() - # TODO: Cross-reference all predictions with actual (but this will include training edges) + # Get back the concepts for each of links predicted in order to insert the predictions into TypeDB predicted_links = [] for p, d in final_edge_index: predicted_links.append( @@ -286,7 +284,8 @@ def test() -> List[Tuple[float, float, float]]: with session.transaction(TransactionType.WRITE) as tx: write_predictions_to_typedb(predicted_links, tx) - # Now we can get the confusion matrix from querying TypeDB! + # Now we can get the confusion matrix from querying TypeDB! (Note that this includes training and validation + # examples, but serves as a demo for seeing the predictions made.) with session.transaction(TransactionType.READ) as tx: tp = tx.query().match_aggregate("match $p isa person; $d isa disease; ($p, $d) isa diagnosis; " "($p, $d) isa predicted-diagnosis; count;").get().as_int() From 0e2c3ce9bf030767f0815d1eb71478abf7037f64 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 22 Jul 2022 10:52:19 +0100 Subject: [PATCH 079/124] Refactoring --- kglib/examples/diagnosis/BUILD | 2 +- kglib/examples/diagnosis/diagnosis.py | 19 ++++++-------- kglib/pytorch_geometric/transform/common.py | 6 +++-- kglib/typedb/BUILD | 1 + .../utils.py => typedb/load.py} | 26 ++++++++++++------- kglib/typedb/type.py | 1 - setup.py | 2 +- 7 files changed, 32 insertions(+), 25 deletions(-) rename kglib/{pytorch_geometric/utils.py => typedb/load.py} (60%) diff --git a/kglib/examples/diagnosis/BUILD b/kglib/examples/diagnosis/BUILD index ac2607d7..524d122f 100644 --- a/kglib/examples/diagnosis/BUILD +++ b/kglib/examples/diagnosis/BUILD @@ -30,9 +30,9 @@ py_library( ], deps = [ "//kglib/examples/diagnosis/dataset", - "//kglib/typedb", "//kglib/pytorch_geometric", "//kglib/networkx", + "//kglib/typedb", "@vaticle_typedb_client_python//:client_python", vaticle_kglib_requirement("networkx"), vaticle_kglib_requirement("torch"), diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index c41eba51..39448b11 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -39,7 +39,7 @@ from kglib.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type from kglib.pytorch_geometric.transform.encode import FeatureEncoder, CategoricalEncoder, \ ContinuousEncoder -from kglib.pytorch_geometric.utils import load_typeql_schema_file, load_typeql_data_file +from kglib.typedb.load import load_typeql_file, TransactionType from kglib.typedb.type import get_thing_types DATABASE = "diagnosis" @@ -76,19 +76,16 @@ def diagnosis_example(typedb_binary_directory, num_graphs, database=DATABASE, address=ADDRESS, - # TODO: remove hard-coding - schema_file_path="/Users/jamesfletcher/programming/research/kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql", - seed_data_file_path="/Users/jamesfletcher/programming/research/kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql"): + schema_file_path="kglib/utils/typedb/synthetic/examples/diagnosis/schema.tql", + seed_data_file_path="kglib/utils/typedb/synthetic/examples/diagnosis/seed_data.tql"): """ - Run the diagnosis example from start to finish, including traceably ingesting predictions back into TypeDB - Args: - typedb_binary_directory: Location of the typedb binary for the purpose of loading initial schema and data + typedb_binary_directory: Location of the TypeDB binary for the purpose of loading initial schema and data num_graphs: Number of graphs to use for training and testing combined - database: The name of the database to retrieve example subgraphs from + database: The name of the database to retrieve data from address: The address of the running TypeDB instance schema_file_path: Path to the diagnosis schema file - seed_data_file_path: Path to the file containing seed data, that doesn't grow as synthetic data is added + seed_data_file_path: Path to the file containing seed data Returns: Final accuracies for training and for testing @@ -97,8 +94,8 @@ def diagnosis_example(typedb_binary_directory, client = TypeDB.core_client(address) create_database(client, database) - load_typeql_schema_file(database, typedb_binary_directory, schema_file_path) - load_typeql_data_file(database, typedb_binary_directory, seed_data_file_path) + load_typeql_file(typedb_binary_directory, database, schema_file_path, TransactionType.Schema) + load_typeql_file(typedb_binary_directory, database, seed_data_file_path, TransactionType.Data) generate_example_data(client, num_graphs, database=database) session = client.session(database, SessionType.DATA) diff --git a/kglib/pytorch_geometric/transform/common.py b/kglib/pytorch_geometric/transform/common.py index 29310453..cd4dd652 100644 --- a/kglib/pytorch_geometric/transform/common.py +++ b/kglib/pytorch_geometric/transform/common.py @@ -44,8 +44,10 @@ def store_concepts_by_type(graph): """ Organises concepts by type the same way `data.to_heterogeneous()` organises nodes by type. This is necessary to be able to map back from a `HeteroData` object to the TypeDB concepts that the nodes refer to. - :param graph: NetworkX graph to operate on - :return: The same graph, with a field `concepts_by_type` holding concepts organised by type + Args: + graph: NetworkX graph to operate on + Returns: + The same graph, with a field `concepts_by_type` holding concepts organised by type """ concepts_by_type = {} for node_data in multidigraph_node_data_iterator(graph): diff --git a/kglib/typedb/BUILD b/kglib/typedb/BUILD index 8d0fc780..d939ba84 100644 --- a/kglib/typedb/BUILD +++ b/kglib/typedb/BUILD @@ -29,6 +29,7 @@ py_library( srcs = [ 'type.py', 'thing.py', + 'load.py', ], deps = [ '//kglib/typedb/test', diff --git a/kglib/pytorch_geometric/utils.py b/kglib/typedb/load.py similarity index 60% rename from kglib/pytorch_geometric/utils.py rename to kglib/typedb/load.py index 840e10cc..a7242da8 100644 --- a/kglib/pytorch_geometric/utils.py +++ b/kglib/typedb/load.py @@ -20,23 +20,31 @@ # import subprocess as sp +from enum import Enum -def load_typeql_schema_file(database, typedb_binary_location, typeql_file_path): - """Load a schema from a file""" - _load_typeql_file(database, typeql_file_path, typedb_binary_location, "schema") # TODO compress +class TransactionType(Enum): + Schema = 1 + Data = 2 -def load_typeql_data_file(database, typedb_binary_location, typeql_file_path): - """Load data from a file""" - _load_typeql_file(database, typeql_file_path, typedb_binary_location, "data") +def load_typeql_file(typedb_binary_location: str, database_name: str, typeql_file_path: str, + transaction_type: TransactionType): + """ + Load a file into a TypeDB database - -def _load_typeql_file(database, typeql_file_path, typedb_binary_location, schema_or_data): + Args: + typedb_binary_location: the location of TypeDB + database_name: The name of the TypeDB database to load into + typeql_file_path: The path to the file to load + transaction_type: The content of the file and therefore the transaction type to use, either schema or data + Returns: + None + """ sp.check_call([ './typedb', 'console', - f'--command=transaction {database} {schema_or_data} write', + f'--command=transaction {database_name} {transaction_type} write', f'--command=source {typeql_file_path}', f'--command=commit' ], cwd=typedb_binary_location) diff --git a/kglib/typedb/type.py b/kglib/typedb/type.py index 2487be46..22fc043b 100644 --- a/kglib/typedb/type.py +++ b/kglib/typedb/type.py @@ -77,7 +77,6 @@ def get_has_triplets(tx): def get_edge_type_triplets(session): - # TODO: Naming is too close to get_types where the result is very different with session.transaction(TransactionType.READ) as tx: edge_types = get_role_triplets(tx) + get_has_triplets(tx) return edge_types diff --git a/setup.py b/setup.py index c084f7e5..e81775b8 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,7 @@ setup( name='kglib', version='', - packages=['kglib', 'kglib.utils', 'kglib.pytorch_geometric'], + packages=['kglib', 'kglib.networkx', 'kglib.pytorch_geometric', 'kglib.typedb'], url='', license='', author='jmsfltchr', From ff2bc07d8cdf4f346f01ae3be19514c468f1c056 Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 22 Jul 2022 11:17:17 +0100 Subject: [PATCH 080/124] Use enum value --- kglib/typedb/load.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/kglib/typedb/load.py b/kglib/typedb/load.py index a7242da8..aacab011 100644 --- a/kglib/typedb/load.py +++ b/kglib/typedb/load.py @@ -24,8 +24,8 @@ class TransactionType(Enum): - Schema = 1 - Data = 2 + Schema = "schema" + Data = "data" def load_typeql_file(typedb_binary_location: str, database_name: str, typeql_file_path: str, @@ -44,7 +44,7 @@ def load_typeql_file(typedb_binary_location: str, database_name: str, typeql_fil sp.check_call([ './typedb', 'console', - f'--command=transaction {database_name} {transaction_type} write', + f'--command=transaction {database_name} {transaction_type.value} write', f'--command=source {typeql_file_path}', f'--command=commit' ], cwd=typedb_binary_location) From 4f854388262e850609b154159690cd9857bb5c1f Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 22 Jul 2022 11:28:03 +0100 Subject: [PATCH 081/124] Fix overloaded name --- kglib/examples/diagnosis/diagnosis.py | 6 +++--- kglib/typedb/load.py | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/kglib/examples/diagnosis/diagnosis.py b/kglib/examples/diagnosis/diagnosis.py index 39448b11..079f2898 100644 --- a/kglib/examples/diagnosis/diagnosis.py +++ b/kglib/examples/diagnosis/diagnosis.py @@ -39,7 +39,7 @@ from kglib.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type from kglib.pytorch_geometric.transform.encode import FeatureEncoder, CategoricalEncoder, \ ContinuousEncoder -from kglib.typedb.load import load_typeql_file, TransactionType +from kglib.typedb.load import load_typeql_file, FileType from kglib.typedb.type import get_thing_types DATABASE = "diagnosis" @@ -94,8 +94,8 @@ def diagnosis_example(typedb_binary_directory, client = TypeDB.core_client(address) create_database(client, database) - load_typeql_file(typedb_binary_directory, database, schema_file_path, TransactionType.Schema) - load_typeql_file(typedb_binary_directory, database, seed_data_file_path, TransactionType.Data) + load_typeql_file(typedb_binary_directory, database, schema_file_path, FileType.Schema) + load_typeql_file(typedb_binary_directory, database, seed_data_file_path, FileType.Data) generate_example_data(client, num_graphs, database=database) session = client.session(database, SessionType.DATA) diff --git a/kglib/typedb/load.py b/kglib/typedb/load.py index aacab011..fb0c90f3 100644 --- a/kglib/typedb/load.py +++ b/kglib/typedb/load.py @@ -23,13 +23,13 @@ from enum import Enum -class TransactionType(Enum): +class FileType(Enum): Schema = "schema" Data = "data" def load_typeql_file(typedb_binary_location: str, database_name: str, typeql_file_path: str, - transaction_type: TransactionType): + file_type: FileType): """ Load a file into a TypeDB database @@ -37,14 +37,14 @@ def load_typeql_file(typedb_binary_location: str, database_name: str, typeql_fil typedb_binary_location: the location of TypeDB database_name: The name of the TypeDB database to load into typeql_file_path: The path to the file to load - transaction_type: The content of the file and therefore the transaction type to use, either schema or data + file_type: The content of the file and therefore the transaction type to use, either schema or data Returns: None """ sp.check_call([ './typedb', 'console', - f'--command=transaction {database_name} {transaction_type.value} write', + f'--command=transaction {database_name} {file_type.value} write', f'--command=source {typeql_file_path}', f'--command=commit' ], cwd=typedb_binary_location) From 837e5ca16d690fd9100cbf3b53451c8ec121b3ab Mon Sep 17 00:00:00 2001 From: James Fletcher Date: Fri, 22 Jul 2022 12:49:44 +0100 Subject: [PATCH 082/124] WIP updating READMEs and their images --- README.md | 12 +- .../diagnosis/.images/basic_schema.png | Bin 66076 -> 0 bytes .../diagnosis/.images/basic_schema.svg | 3 - .../diagnosis/.images/diagnosis_schema.png | Bin 1118653 -> 421537 bytes .../diagnosis/.images/edge_update.png | Bin 129510 -> 0 bytes .../diagnosis/.images/edge_update.svg | 3 - 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It's most natural to work with `HeteroData` objects since all data in TypeDB has a type. This conversion is available by default in PyG, but KGLIB provides `store_concepts_by_type` to map concepts by type so that they can be reassociated after learning is finished. - A `FeatureEncoder` + ## Machine Learning Pipeline The pipeline is as follows: @@ -33,11 +34,6 @@ The pipeline is as follows: 4. Feed the features to a learning algorithm (see below) 5. Optionally, store the predictions made by the learner in TypeDB. These predictions can then be queried using TypeQL. This means we can trivially run more learning tasks over the knowledge base, including the newly made predictions. This is knowledge graph completion. -## Learning Algorithms -This repo contains one algorithmic implementation: [*Knowledge Graph Convolutional Network* (KGCN)](kglib/kgcn_tensorflow). This is a generic method for relation predication over any TypeDB database. There is a [full worked example](kglib/kgcn_tensorflow/examples/diagnosis) and an explanation of how the approach works. - -You are encouraged to use the tools available in KGLIB to interface TypeDB to your own algorithmic implementations, or to use/leverage prebuilt implementations available from popular libraries such as [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric) or [Graph Nets](https://github.com/deepmind/graph_nets) (TensorFlow/Sonnet). - ## Resources You may find the following resources useful: - [Strongly Typed Data for Machine Learning](https://www.youtube.com/watch?v=qhUyurWMiSQ) (YouTube) @@ -45,11 +41,11 @@ You may find the following resources useful: ## Quickstart -### Requirements +### Install - Python >= 3.7.x -- Grab the `requirements.txt` from [here](requirements.txt) and install the requirements with `pip install requirements.txt`. This is due to some intricacies installing PyG's dependencies, see [here](https://github.com/pyg-team/pytorch_geometric/issues/861) for details. +- Grab the `requirements.txt` file from [here](requirements.txt) and install the requirements with `pip install requirements.txt`. This is due to some intricacies installing PyG's dependencies, see [here](https://github.com/pyg-team/pytorch_geometric/issues/861) for details. - Installed KGLIB: `pip install typedb-kglib`. @@ -59,7 +55,7 @@ You may find the following resources useful: ### Run the Example -Take a look at [The PyTorch Geometric heterogeneous data example](kglib/examples/diagnosis) to see a walkthrough of how to use KGLIB to build a GNN that runs over data from TypeDB. +Take a look at the [PyTorch Geometric heterogeneous link prediction example](kglib/examples/diagnosis) to see how to use KGLIB to build a GNN on TypeDB data. ### Building from source diff --git a/kglib/examples/diagnosis/.images/basic_schema.png b/kglib/examples/diagnosis/.images/basic_schema.png deleted file mode 100644 index 3d3eca097a14b0b8cdbf564ce900aa368ee12879..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 66076 zcmZ^~1zgkL_dmXof*?pr2`C`#fDw}#U88G&;DC)Wdemq(R8Ub2q>(U?5JW+c1{FcN 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"Diabetes Type II"
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