merged

tensorflow_probability/python/experimental/autobnn/BUILD

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+# Copyright 2023 The TensorFlow Probability Authors.
+#
+# Licensed 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.
+# ============================================================================
+# Code for AutoBNN.  See README.md for more information.
+
+# Placeholder: py_library
+# Placeholder: py_test
+
+licenses(["notice"])
+
+package(
+    # default_applicable_licenses
+    default_visibility = ["//visibility:public"],
+)
+
+py_library(
+    name = "bnn",
+    srcs = ["bnn.py"],
+    deps = [
+        ":likelihoods",
+        # flax:core dep,
+        # jax dep,
+        # jaxtyping dep,
+        "//tensorflow_probability/python/distributions:distribution.jax",
+    ],
+)
+
+py_test(
+    name = "bnn_test",
+    srcs = ["bnn_test.py"],
+    deps = [
+        ":bnn",
+        # absl/testing:absltest dep,
+        # google/protobuf:use_fast_cpp_protos dep,
+        # jax dep,
+        "//tensorflow_probability:jax",
+        "//tensorflow_probability/python/distributions:lognormal.jax",
+        "//tensorflow_probability/python/distributions:normal.jax",
+    ],
+)
+
+py_library(
+    name = "kernels",
+    srcs = ["kernels.py"],
+    deps = [
+        ":bnn",
+        # flax dep,
+        # flax:core dep,
+        # jax dep,
+        "//tensorflow_probability/python/distributions:lognormal.jax",
+        "//tensorflow_probability/python/distributions:normal.jax",
+        "//tensorflow_probability/python/distributions:student_t.jax",
+        "//tensorflow_probability/python/distributions:uniform.jax",
+    ],
+)
+
+py_test(
+    name = "kernels_test",
+    srcs = ["kernels_test.py"],
+    deps = [
+        ":kernels",
+        ":util",
+        # absl/testing:absltest dep,
+        # absl/testing:parameterized dep,
+        # google/protobuf:use_fast_cpp_protos dep,
+        # jax dep,
+        "//tensorflow_probability/python/distributions:lognormal.jax",
+    ],
+)
+
+py_library(
+    name = "likelihoods",
+    srcs = ["likelihoods.py"],
+    deps = [
+        # flax:core dep,
+        # jax dep,
+        # jaxtyping dep,
+        "//tensorflow_probability:jax",
+        "//tensorflow_probability/python/bijectors:softplus.jax",
+        "//tensorflow_probability/python/distributions:distribution.jax",
+        "//tensorflow_probability/python/distributions:inflated.jax",
+        "//tensorflow_probability/python/distributions:logistic.jax",
+        "//tensorflow_probability/python/distributions:lognormal.jax",
+        "//tensorflow_probability/python/distributions:negative_binomial.jax",
+        "//tensorflow_probability/python/distributions:normal.jax",
+        "//tensorflow_probability/python/distributions:transformed_distribution.jax",
+    ],
+)
+
+py_test(
+    name = "likelihoods_test",
+    srcs = ["likelihoods_test.py"],
+    deps = [
+        ":likelihoods",
+        # absl/testing:absltest dep,
+        # absl/testing:parameterized dep,
+        # jax dep,
+    ],
+)
+
+py_library(
+    name = "models",
+    srcs = ["models.py"],
+    deps = [
+        ":bnn",
+        ":bnn_tree",
+        ":kernels",
+        ":likelihoods",
+        ":operators",
+        # jax dep,
+    ],
+)
+
+py_test(
+    name = "models_test",
+    srcs = ["models_test.py"],
+    shard_count = 3,
+    deps = [
+        ":likelihoods",
+        ":models",
+        ":operators",
+        # absl/testing:absltest dep,
+        # absl/testing:parameterized dep,
+        # jax dep,
+    ],
+)
+
+py_library(
+    name = "operators",
+    srcs = ["operators.py"],
+    deps = [
+        ":bnn",
+        ":likelihoods",
+        # flax:core dep,
+        # jax dep,
+        "//tensorflow_probability:jax",
+        "//tensorflow_probability/python/bijectors:chain.jax",
+        "//tensorflow_probability/python/bijectors:scale.jax",
+        "//tensorflow_probability/python/bijectors:shift.jax",
+        "//tensorflow_probability/python/distributions:beta.jax",
+        "//tensorflow_probability/python/distributions:dirichlet.jax",
+        "//tensorflow_probability/python/distributions:half_normal.jax",
+        "//tensorflow_probability/python/distributions:normal.jax",
+        "//tensorflow_probability/python/distributions:transformed_distribution.jax",
+    ],
+)
+
+py_test(
+    name = "operators_test",
+    srcs = ["operators_test.py"],
+    deps = [
+        ":kernels",
+        ":operators",
+        ":util",
+        # absl/testing:absltest dep,
+        # absl/testing:parameterized dep,
+        # google/protobuf:use_fast_cpp_protos dep,
+        # jax dep,
+        # numpy dep,
+        "//tensorflow_probability/python/distributions:distribution.jax",
+    ],
+)
+
+py_library(
+    name = "bnn_tree",
+    srcs = ["bnn_tree.py"],
+    deps = [
+        ":bnn",
+        ":kernels",
+        ":operators",
+        ":util",
+        # flax:core dep,
+        # jax dep,
+    ],
+)
+
+py_test(
+    name = "bnn_tree_test",
+    timeout = "long",
+    srcs = ["bnn_tree_test.py"],
+    shard_count = 3,
+    deps = [
+        ":bnn_tree",
+        ":kernels",
+        # absl/testing:absltest dep,
+        # absl/testing:parameterized dep,
+        # flax dep,
+        # google/protobuf:use_fast_cpp_protos dep,
+        # jax dep,
+    ],
+)
+
+py_library(
+    name = "util",
+    srcs = ["util.py"],
+    deps = [
+        ":bnn",
+        # jax dep,
+        # numpy dep,
+        # scipy dep,
+        "//tensorflow_probability/python/distributions:distribution.jax",
+    ],
+)
+
+py_test(
+    name = "util_test",
+    srcs = ["util_test.py"],
+    deps = [
+        ":kernels",
+        ":util",
+        # google/protobuf:use_fast_cpp_protos dep,
+        # jax dep,
+        # numpy dep,
+        "//tensorflow_probability/python/internal:test_util",
+    ],
+)

tensorflow_probability/python/experimental/autobnn/README.md

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+# AutoBNN
+
+This library contains code to specify BNNs that correspond to various useful GP
+kernels and assemble them into models using operators such as Addition,
+Multiplication and Changepoint.
+
+It is based on the ideas in the following papers:
+
+* Lassi Meronen, Martin Trapp, Arno Solin. _Periodic Activation Functions
+Induce Stationarity_. NeurIPS 2021.
+
+* Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely.
+_Expressive Priors in Bayesian Neural Networks: Kernel Combinations and
+Periodic Functions_. UAI 2019.
+
+* Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous,
+Vikash K. Mansinghka.  _Sequential Monte Carlo Learning for Time Series
+Structure Discovery_. ICML 2023.
+
+
+## Setup
+
+AutoBNN has three additional dependencies beyond those used by the core
+Tensorflow Probability package:  flax, scipy and jaxtyping.  These
+can be installed by running `setup\_autobnn.sh`.