Add STS module for end-to-end anomaly detection with Gibbs sampling.

PiperOrigin-RevId: 387237124

Author: davmre

tensorflow_probability/python/sts/BUILD

@@ -35,6 +35,7 @@ py_library(
         ":regularization",
         ":structural_time_series",
         "//tensorflow_probability/python/internal:all_util",
+        "//tensorflow_probability/python/sts/anomaly_detection",
         "//tensorflow_probability/python/sts/components",
     ],
 )

tensorflow_probability/python/sts/anomaly_detection/BUILD

@@ -0,0 +1,72 @@
+# Copyright 2020 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.
+# ============================================================================
+# Description:
+#   Gibbs sampling for Bayesian structural time series models
+
+licenses(["notice"])
+
+package(
+    default_visibility = [
+        "//tensorflow_probability:__subpackages__",
+    ],
+)
+
+exports_files(["LICENSE"])
+
+py_library(
+    name = "anomaly_detection",
+    srcs = ["__init__.py"],
+    srcs_version = "PY3",
+    deps = [
+        ":anomaly_detection_lib",
+    ],
+)
+
+py_library(
+    name = "anomaly_detection_lib",
+    srcs = ["anomaly_detection_lib.py"],
+    srcs_version = "PY3",
+    deps = [
+        # numpy dep,
+        # tensorflow dep,
+        "//tensorflow_probability/python/distributions",
+        "//tensorflow_probability/python/experimental/sts_gibbs:gibbs_sampler",
+        "//tensorflow_probability/python/experimental/util",
+        "//tensorflow_probability/python/internal:distribution_util",
+        "//tensorflow_probability/python/internal:prefer_static",
+        "//tensorflow_probability/python/math",
+        "//tensorflow_probability/python/sts:fitting",
+        "//tensorflow_probability/python/sts:forecast",
+        "//tensorflow_probability/python/sts:regularization",
+        "//tensorflow_probability/python/sts/components",
+        "//tensorflow_probability/python/sts/internal",
+    ],
+)
+
+py_test(
+    name = "anomaly_detection_test",
+    size = "medium",
+    srcs = ["anomaly_detection_test.py"],
+    srcs_version = "PY3",
+    deps = [
+        # absl/testing:parameterized dep,
+        # numpy dep,
+        # pandas dep,
+        # tensorflow dep,
+        "//tensorflow_probability",
+        "//tensorflow_probability/python/experimental/sts_gibbs",
+        "//tensorflow_probability/python/internal:test_util",
+    ],
+)

tensorflow_probability/python/sts/anomaly_detection/__init__.py

@@ -0,0 +1,27 @@
+# Copyright 2020 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.
+# ============================================================================
+"""Anomaly detection with structural time series models."""
+
+from tensorflow_probability.python.internal import all_util
+from tensorflow_probability.python.sts.anomaly_detection.anomaly_detection_lib import detect_anomalies
+from tensorflow_probability.python.sts.anomaly_detection.anomaly_detection_lib import PredictionOutput
+
+_allowed_symbols = [
+    'detect_anomalies',
+    'PredictionOutput'
+]
+
+all_util.remove_undocumented(__name__, _allowed_symbols)
+