Use typedb_ml as the root package rather than typedb due to otherwi…

…se having an import conflict with the client package also called `typedb`.

.gitignore

@@ -35,6 +35,6 @@ tmp/
 __pycache__/
 
 # Data input/output directories
-typedb/ml/examples/diagnosis/events/
+typedb_ml/examples/diagnosis/events/
 
 *.egg-info/

.grabl/automation.yml

@@ -50,35 +50,35 @@ build:
       command: |
         pyenv install -s 3.7.2
         pyenv global 3.7.2 system
-        bazel test //typedb/ml/pytorch_geometric/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
+        bazel test //typedb_ml/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 //typedb/ml/typedb/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
+        bazel test //typedb_ml/typedb/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
     test-networkx:
       image: vaticle-ubuntu-20.04
       timeout: "10m"
       command: |
         pyenv install -s 3.7.2
         pyenv global 3.7.2 system
-        bazel test //typedb/ml/networkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
+        bazel test //typedb_ml/networkx/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
     test-examples:
       image: vaticle-ubuntu-20.04
       timeout: "10m"
       command: |
         pyenv install -s 3.7.2
         pyenv global 3.7.2 system
-        bazel test //typedb/ml/examples/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
+        bazel test //typedb_ml/examples/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
     test-end-to-end:
       image: vaticle-ubuntu-20.04
       timeout: "10m"
       command: |
         pyenv install -s 3.7.2
         pyenv global 3.7.2 system
-        bazel test //typedb/ml/tests/end_to_end:diagnosis --test_output=streamed --spawn_strategy=standalone --action_env=PATH
+        bazel test //typedb_ml/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-typedb, test-networkx, test-examples, test-end-to-end]
@@ -106,7 +106,7 @@ build:
         ./typedb-all-linux/typedb server &
         pip install -r requirements.txt
         pip install --extra-index-url https://repo.vaticle.com/repository/pypi-snapshot/simple typedb-ml==0.0.0-$GRABL_COMMIT
-        python -m typedb.ml.examples.diagnosis.diagnosis "./typedb-all-linux" && export TEST_SUCCESS=0 ||
+        python -m typedb_ml.examples.diagnosis.diagnosis "./typedb-all-linux" && export TEST_SUCCESS=0 ||
         export TEST_SUCCESS=1
         kill $(jps | awk '/TypeDBServer/ {print $1}')
         exit $TEST_SUCCESS

BUILD

@@ -37,7 +37,7 @@ load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test")
 
 assemble_pip(
     name = "assemble-pip",
-    target = "//typedb:ml",
+    target = "//typedb_ml:typedb-ml",
     package_name = "typedb-ml",
     classifiers = [
         "Programming Language :: Python :: 3",

README.md

@@ -25,7 +25,7 @@ There are integrations for [NetworkX](https://networkx.org) and for [PyTorch Geo
 - 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 TypeDB-ML provides `store_concepts_by_type` to map concepts by type so that they can be re-associated after learning is finished.
 - A `FeatureEncoder` to orchestrate encoders to generate features for graphs.
 - Encoders for Continuous and Categorical values to apply encodings/embedding spaces to the types and attribute values present in TypeDB data.
-- A [full example for link prediction](typedb/ml/examples/diagnosis)
+- A [full example for link prediction](typedb_ml/examples/diagnosis)
 ### Other
 - Example usage of Tensorboard for PyG `HeteroData`
 
@@ -50,7 +50,7 @@ You may find the following resources useful, particularly to understand why Type
 
 ### Run the Example
 
-Take a look at the [PyTorch Geometric heterogeneous link prediction example](typedb/ml/examples/diagnosis) to see how to use TypeDB-ML to build a GNN on TypeDB data.
+Take a look at the [PyTorch Geometric heterogeneous link prediction example](typedb_ml/examples/diagnosis) to see how to use TypeDB-ML to build a GNN on TypeDB data.
 
 ### Building from source
 
@@ -77,7 +77,7 @@ bazel build //...
 Run all tests. Requires Python 3.7+ on your `PATH`. Test dependencies are for Linux since that is the CI environment: 
 
 ```
-bazel test //typedb/ml/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
+bazel test //typedb_ml/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH
 ```
 
 Build the pip distribution. Outputs to `bazel-bin`:

setup.py

@@ -22,9 +22,9 @@
 from setuptools import setup
 
 setup(
-    name='typedb-ml',
+    name='typedb_ml',
     version='',
-    packages=['typedb', 'ml', 'ml.networkx', 'ml.pytorch_geometric', 'ml.typedb'],
+    packages=['typedb_ml', 'typedb_ml.networkx', 'typedb_ml.pytorch_geometric', 'typedb_ml.typedb'],
     url='',
     license='',
     author='jmsfltchr',

typedb/BUILD → typedb_ml/BUILD

@@ -24,11 +24,14 @@ load("@vaticle_bazel_distribution_pip//:requirements.bzl", deployment_requiremen
 load("@vaticle_dependencies//tool/checkstyle:rules.bzl", "checkstyle_test")
 
 py_library(
-    name = "ml",
+    name = "typedb-ml",
+    srcs = [
+        '__init__.py',
+    ],
     deps = [
-        '//typedb/ml/typedb',
-        '//typedb/ml/networkx',
-        '//typedb/ml/pytorch_geometric',
+        '//typedb_ml/typedb',
+        '//typedb_ml/networkx',
+        '//typedb_ml/pytorch_geometric',
     ],
     visibility=['//visibility:public']
 )

typedb_ml/__init__.py

@@ -0,0 +1,20 @@
+#
+# 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.
+#

typedb/ml/examples/diagnosis/BUILD → typedb_ml/examples/diagnosis/BUILD

@@ -29,10 +29,10 @@ py_library(
         'diagnosis.py'
     ],
     deps = [
-        "//typedb/ml/examples/diagnosis/dataset",
-        "//typedb/ml/pytorch_geometric",
-        "//typedb/ml/networkx",
-        "//typedb/ml/typedb",
+        "//typedb_ml/examples/diagnosis/dataset",
+        "//typedb_ml/pytorch_geometric",
+        "//typedb_ml/networkx",
+        "//typedb_ml/typedb",
         "@vaticle_typedb_client_python//:client_python",
         vaticle_typedb_ml_requirement("networkx"),
         vaticle_typedb_ml_requirement("torch"),

typedb/ml/examples/diagnosis/README.md → typedb_ml/examples/diagnosis/README.md

@@ -10,7 +10,7 @@ Once you have installed TypeDB-ML (see root README for instructions) you can run
 
 1. Make sure a TypeDB server (version 2.11.1 or later) is running locally   
 
-2. Clone TypeDB-ML (shallow clone: `git clone --depth 1`) and from the project root, run the example: `python -m typedb.ml.examples.diagnosis.diagnosis "/path/to/my/typedb/install/directory"`
+2. Clone TypeDB-ML (shallow clone: `git clone --depth 1`) and from the project root, run the example: `python -m typedb_ml.examples.diagnosis.diagnosis "/path/to/my/typedb/install/directory"`
 
 3. The database, schema and seed data will be set up and data will be generated synthetically. You should see console output to indicate that the pipeline is running and that the model is learning. Finally, the predicted relations are shown, and they are written back into TypeDB as the type `predicted-diagnosis`.
 

typedb/ml/examples/diagnosis/dataset/generate.py → typedb_ml/examples/diagnosis/dataset/generate.py

@@ -24,7 +24,7 @@
 import numpy as np
 from typedb.client import *
 
-from typedb.ml.examples.diagnosis.dataset.pmf import PMF
+from typedb_ml.examples.diagnosis.dataset.pmf import PMF
 
 
 def get_example_queries(pmf, example_id):

typedb/ml/examples/diagnosis/diagnosis.py → typedb_ml/examples/diagnosis/diagnosis.py

@@ -31,15 +31,15 @@
 from torch_geometric.nn import HGTConv
 from typedb.client import *
 
-from typedb.ml.examples.diagnosis.dataset.generate import generate_example_data
-from typedb.ml.networkx.query_graph import QueryGraph
-from typedb.ml.pytorch_geometric.dataset.dataset import DataSet
-from typedb.ml.pytorch_geometric.transform.binary_link_prediction import LinkPredictionLabeller, \
+from typedb_ml.examples.diagnosis.dataset.generate import generate_example_data
+from typedb_ml.networkx.query_graph import QueryGraph
+from typedb_ml.pytorch_geometric.dataset.dataset import DataSet
+from typedb_ml.pytorch_geometric.transform.binary_link_prediction import LinkPredictionLabeller, \
     binary_relations_to_edges, binary_link_prediction_edge_triplets
-from typedb.ml.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type
-from typedb.ml.pytorch_geometric.transform.encode import FeatureEncoder, CategoricalEncoder, ContinuousEncoder
-from typedb.ml.typedb.load import load_typeql_file, FileType
-from typedb.ml.typedb.type import get_thing_types
+from typedb_ml.pytorch_geometric.transform.common import clear_unneeded_fields, store_concepts_by_type
+from typedb_ml.pytorch_geometric.transform.encode import FeatureEncoder, CategoricalEncoder, ContinuousEncoder
+from typedb_ml.typedb.load import load_typeql_file, FileType
+from typedb_ml.typedb.type import get_thing_types
 
 DATABASE = "diagnosis"
 ADDRESS = "localhost:1729"
@@ -74,8 +74,8 @@ def diagnosis_example(typedb_binary_directory,
                       num_graphs,
                       database=DATABASE,
                       address=ADDRESS,
-                      schema_file_path="typedb/ml/examples/diagnosis/dataset/schema.tql",
-                      seed_data_file_path="typedb/ml/examples/diagnosis/dataset/seed_data.tql"):
+                      schema_file_path="typedb_ml/examples/diagnosis/dataset/schema.tql",
+                      seed_data_file_path="typedb_ml/examples/diagnosis/dataset/seed_data.tql"):
     """
     Args:
         typedb_binary_directory: Location of the TypeDB binary for the purpose of loading initial schema and data
@@ -458,5 +458,5 @@ def write_predictions_to_typedb(predicted_links, tx):
     diagnosis_example(args.typedb, args.graphs,
                       database=DATABASE,
                       address=ADDRESS,
-                      schema_file_path=cwd + '/' + "typedb/ml/examples/diagnosis/dataset/schema.tql",
-                      seed_data_file_path=cwd + '/' + "typedb/ml/examples/diagnosis/dataset/seed_data.tql")
+                      schema_file_path=cwd + '/' + "typedb_ml/examples/diagnosis/dataset/schema.tql",
+                      seed_data_file_path=cwd + '/' + "typedb_ml/examples/diagnosis/dataset/seed_data.tql")

typedb/ml/networkx/BUILD → typedb_ml/networkx/BUILD

@@ -32,7 +32,7 @@ py_library(
         'iterate.py',
     ],
     deps = [
-        "//typedb/ml/typedb",
+        "//typedb_ml/typedb",
         vaticle_typedb_ml_requirement('networkx'),
         vaticle_typedb_ml_requirement('decorator'),
     ],
@@ -59,7 +59,7 @@ py_test(
         "graph_test_case.py"
     ],
     deps = [
-        "//typedb/ml/typedb",
+        "//typedb_ml/typedb",
         vaticle_typedb_ml_requirement('networkx'),
     ]
 )
@@ -73,8 +73,8 @@ py_test(
         "graph_test_case.py"
     ],
     deps = [
-        "//typedb/ml/typedb",
-        "//typedb/ml/typedb/test",
+        "//typedb_ml/typedb",
+        "//typedb_ml/typedb/test",
         "@vaticle_typedb_client_python//:client_python",
         vaticle_typedb_ml_requirement('networkx'),
     ],
@@ -91,7 +91,7 @@ py_test(
         "graph_test_case.py"
     ],
     deps = [
-        "//typedb/ml/typedb",
+        "//typedb_ml/typedb",
         vaticle_typedb_ml_requirement('networkx'),
     ],
 )

typedb/ml/networkx/concept_dict_to_networkx_test.py → typedb_ml/networkx/concept_dict_to_networkx_test.py

@@ -22,9 +22,9 @@
 import unittest
 import networkx as nx
 
-from typedb.ml.typedb.thing import Thing
-from typedb.ml.networkx.concept_dict_to_networkx import concept_dict_to_networkx
-from typedb.ml.networkx.graph_test_case import GraphTestCase
+from typedb_ml.typedb.thing import Thing
+from typedb_ml.networkx.concept_dict_to_networkx import concept_dict_to_networkx
+from typedb_ml.networkx.graph_test_case import GraphTestCase
 
 
 class TestConceptDictToTypeDBGraph(GraphTestCase):

typedb/ml/networkx/queries_to_networkx.py → typedb_ml/networkx/queries_to_networkx.py

@@ -24,8 +24,8 @@
 from functools import reduce
 import networkx as nx
 
-from typedb.ml.typedb.thing import build_thing
-from typedb.ml.networkx.concept_dict_to_networkx import concept_dict_to_networkx
+from typedb_ml.typedb.thing import build_thing
+from typedb_ml.networkx.concept_dict_to_networkx import concept_dict_to_networkx
 
 
 def build_graph_from_queries(query_sampler_variable_graph_tuples, transaction,

typedb/ml/networkx/queries_to_networkx_it.py → typedb_ml/networkx/queries_to_networkx_it.py

@@ -26,10 +26,10 @@
 from typedb.api.concept.type.attribute_type import AttributeType
 from typedb.client import *
 
-from typedb.ml.networkx.graph_test_case import GraphTestCase
-from typedb.ml.networkx.queries_to_networkx import build_graph_from_queries
-from typedb.ml.typedb.thing import build_thing
-from typedb.ml.typedb.test.base import TypeDBServer
+from typedb_ml.networkx.graph_test_case import GraphTestCase
+from typedb_ml.networkx.queries_to_networkx import build_graph_from_queries
+from typedb_ml.typedb.thing import build_thing
+from typedb_ml.typedb.test.base import TypeDBServer
 
 
 class ITBuildGraphFromQueriesWithRealTypeDB(GraphTestCase):

typedb/ml/networkx/queries_to_networkx_test.py → typedb_ml/networkx/queries_to_networkx_test.py

@@ -24,12 +24,12 @@
 import networkx as nx
 from typedb.api.concept.type.attribute_type import AttributeType
 
-from typedb.ml.networkx.graph_test_case import GraphTestCase
-from typedb.ml.networkx.queries_to_networkx import concept_dict_from_concept_map, \
+from typedb_ml.networkx.graph_test_case import GraphTestCase
+from typedb_ml.networkx.queries_to_networkx import concept_dict_from_concept_map, \
     combine_n_graphs, build_graph_from_queries
-from typedb.ml.typedb.thing import Thing
-from typedb.ml.typedb.test.mock.answer import MockConceptMap
-from typedb.ml.typedb.test.mock.concept import MockType, MockAttributeType, MockThing, MockAttribute
+from typedb_ml.typedb.thing import Thing
+from typedb_ml.typedb.test.mock.answer import MockConceptMap
+from typedb_ml.typedb.test.mock.concept import MockType, MockAttributeType, MockThing, MockAttribute
 
 
 def mock_sampler(input_iter):

typedb/ml/networkx/query_graph_test.py → typedb_ml/networkx/query_graph_test.py

@@ -21,7 +21,7 @@
 
 import unittest
 
-from typedb.ml.networkx.query_graph import QueryGraph
+from typedb_ml.networkx.query_graph import QueryGraph
 
 
 class TestQueryGraph(unittest.TestCase):

typedb/ml/pytorch_geometric/dataset/dataset.py → typedb_ml/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 typedb.ml.networkx.queries_to_networkx import build_graph_from_queries
+from typedb_ml.networkx.queries_to_networkx import build_graph_from_queries
 
 
 class DataSet:

typedb/ml/pytorch_geometric/transform/binary_link_prediction.py → typedb_ml/pytorch_geometric/transform/binary_link_prediction.py

@@ -20,8 +20,8 @@
 #
 import networkx as nx
 
-from typedb.ml.networkx.iterate import multidigraph_edge_data_iterator
-from typedb.ml.typedb.type import get_edge_type_triplets, reverse_edge_type_triplets
+from typedb_ml.networkx.iterate import multidigraph_edge_data_iterator
+from typedb_ml.typedb.type import get_edge_type_triplets, reverse_edge_type_triplets
 
 
 class LinkPredictionLabeller:

typedb/ml/pytorch_geometric/transform/common.py → typedb_ml/pytorch_geometric/transform/common.py

@@ -18,7 +18,7 @@
 #  specific language governing permissions and limitations
 #  under the License.
 #
-from typedb.ml.networkx.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator
+from typedb_ml.networkx.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator
 
 
 def clear_unneeded_fields(graph):

typedb/ml/pytorch_geometric/transform/encode.py → typedb_ml/pytorch_geometric/transform/encode.py

@@ -23,7 +23,7 @@
 import torch
 from torch.nn import Embedding
 
-from typedb.ml.networkx.iterate import (
+from typedb_ml.networkx.iterate import (
     multidigraph_node_data_iterator,
     multidigraph_edge_data_iterator,
 )