damccorm commented on code in PR #34459:
URL: https://github.com/apache/beam/pull/34459#discussion_r2019014506
##########
examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_zscore.ipynb:
##########
@@ -0,0 +1,653 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/shunping/beam/blob/anomaly-detection-notebook-1/anomaly_detection_zscore.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>"
Review Comment:
Please remove in favor of a cell below the title like
https://github.com/apache/beam/blob/4ac20255c993906045bb720618324575b50689d7/examples/notebooks/beam-ml/automatic_model_refresh.ipynb#L45
(we do this across all our notebooks)
##########
examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_zscore.ipynb:
##########
@@ -0,0 +1,653 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/shunping/beam/blob/anomaly-detection-notebook-1/anomaly_detection_zscore.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2d79fe3a-952b-478f-ba78-44cafddc91d1",
+ "metadata": {
+ "cellView": "form",
+ "id": "2d79fe3a-952b-478f-ba78-44cafddc91d1"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF),
Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n";,
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Use Apache Beam for Anomaly Detection on Streaming Data
(Z-Score)\n",
+ "This notebook demonstrates how to perform anomaly detection on
streaming data using the `AnomalyDetection` PTransform, a new feature
introduced in Apache Beam 2.64.0.\n",
+ "\n",
+ "We will first generate a synthetic dataset that incorporates various
types of concept drift (changes in the underlying data distribution). This data
will then be published to a Pub/Sub topic to simulate a real-time input stream.
Our Beam pipeline will read from this topic, apply the `AnomalyDetection`
PTransform with the Z-Score algorithm, and publish the detected anomalies to a
second Pub/Sub topic. Finally, we will visualize the labeled data points in an
animated plot."
+ ],
+ "metadata": {
+ "id": "vOwDXurVLkxO"
+ },
+ "id": "vOwDXurVLkxO"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Preparation\n",
+ "To get started with this notebook, you'll need to install the Apache
Beam Python SDK and its associated extras. Make sure your installation is
version 2.64.0 or later."
+ ],
+ "metadata": {
+ "id": "UQx1jKZ2LHHR"
+ },
+ "id": "UQx1jKZ2LHHR"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "! pip install apache_beam[interactive,gcp]==2.64.0rc2 --quiet"
+ ],
+ "metadata": {
+ "collapsed": true,
+ "id": "SafqA6dALKvo"
+ },
+ "id": "SafqA6dALKvo",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "To proceed, import the essential modules: matplotlib, numpy, pandas,
Beam, and others as needed."
+ ],
+ "metadata": {
+ "id": "h5gJCJGpStSb"
+ },
+ "id": "h5gJCJGpStSb"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8fb71376-b0eb-474b-ab51-2161dfa60e2d",
+ "metadata": {
+ "id": "8fb71376-b0eb-474b-ab51-2161dfa60e2d"
+ },
+ "outputs": [],
+ "source": [
+ "# Imports Required for the notebook\n",
+ "import json\n",
+ "import os\n",
+ "import random\n",
+ "import threading\n",
+ "import time\n",
+ "import warnings\n",
+ "from typing import Any\n",
+ "from typing import Iterable\n",
+ "from typing import Tuple\n",
+ "\n",
+ "import matplotlib.animation\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from IPython.display import HTML, Javascript\n",
+ "from google.api_core import retry\n",
+ "from google.api_core.exceptions import AlreadyExists\n",
+ "from google.cloud import pubsub_v1\n",
+ "from google.cloud.exceptions import NotFound\n",
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.io.gcp.pubsub import PubsubMessage\n",
+ "from apache_beam.ml.anomaly.base import AnomalyResult\n",
+ "from apache_beam.ml.anomaly.base import AnomalyPrediction\n",
+ "from apache_beam.ml.anomaly.detectors.zscore import ZScore\n",
+ "from apache_beam.ml.anomaly.transforms import AnomalyDetection\n",
+ "from apache_beam.ml.anomaly.univariate.mean import
IncSlidingMeanTracker\n",
+ "from apache_beam.ml.anomaly.univariate.stdev import
IncSlidingStdevTracker\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "# Suppress logging warnings\n",
+ "os.environ[\"GRPC_VERBOSITY\"] = \"ERROR\"\n",
+ "os.environ[\"GLOG_minloglevel\"] = \"2\"\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ " Next, replace `<PROJECT_ID>` with your Google Cloud project ID."
+ ],
+ "metadata": {
+ "id": "C2GhkkvSUXdu"
+ },
+ "id": "C2GhkkvSUXdu"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29cda7f0-a24e-4e74-ba6e-166413ab532c",
+ "metadata": {
+ "id": "29cda7f0-a24e-4e74-ba6e-166413ab532c"
+ },
+ "outputs": [],
+ "source": [
+ "# GCP-related constant are listed below\n",
+ "\n",
+ "# GCP project id\n",
+ "PROJECT_ID = '<PROJECT_ID>' # @param {type:'string'}\n",
+ "\n",
+ "SUFFIX = str(random.randint(0, 10000))\n",
+ "\n",
+ "# Pubsub topic and subscription for retrieving input data\n",
+ "INPUT_TOPIC = 'anomaly-input-' + SUFFIX\n",
+ "INPUT_SUB = INPUT_TOPIC + '-sub'\n",
+ "\n",
+ "# Pubsub topic and subscription for collecting output result\n",
+ "OUTPUT_TOPIC = 'anomaly-output-' + SUFFIX\n",
+ "OUTPUT_SUB = OUTPUT_TOPIC + '-sub'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The last preparation step needs to authenticate your Google account
and authorize your Colab notebook to access Google Cloud Platform (GCP)
resources associated with the project set above."
+ ],
+ "metadata": {
+ "id": "coZF7R8zTBsF"
+ },
+ "id": "coZF7R8zTBsF"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import auth\n",
+ "auth.authenticate_user(project_id=PROJECT_ID)"
+ ],
+ "metadata": {
+ "id": "51jml7JvMpbD"
+ },
+ "id": "51jml7JvMpbD",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0064575d-5e60-4f8b-a970-9dc39db8d331",
+ "metadata": {
+ "id": "0064575d-5e60-4f8b-a970-9dc39db8d331"
+ },
+ "source": [
+ "## Generating Synthetic Data with Concept Drifting"
Review Comment:
```suggestion
"## Generating Synthetic Data with Concept Drift"
```
Nit: I think this is the right tense/may help with SEO
##########
examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_zscore.ipynb:
##########
@@ -0,0 +1,653 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/shunping/beam/blob/anomaly-detection-notebook-1/anomaly_detection_zscore.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2d79fe3a-952b-478f-ba78-44cafddc91d1",
+ "metadata": {
+ "cellView": "form",
+ "id": "2d79fe3a-952b-478f-ba78-44cafddc91d1"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF),
Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n";,
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Use Apache Beam for Anomaly Detection on Streaming Data
(Z-Score)\n",
+ "This notebook demonstrates how to perform anomaly detection on
streaming data using the `AnomalyDetection` PTransform, a new feature
introduced in Apache Beam 2.64.0.\n",
+ "\n",
+ "We will first generate a synthetic dataset that incorporates various
types of concept drift (changes in the underlying data distribution). This data
will then be published to a Pub/Sub topic to simulate a real-time input stream.
Our Beam pipeline will read from this topic, apply the `AnomalyDetection`
PTransform with the Z-Score algorithm, and publish the detected anomalies to a
second Pub/Sub topic. Finally, we will visualize the labeled data points in an
animated plot."
+ ],
+ "metadata": {
+ "id": "vOwDXurVLkxO"
+ },
+ "id": "vOwDXurVLkxO"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Preparation\n",
+ "To get started with this notebook, you'll need to install the Apache
Beam Python SDK and its associated extras. Make sure your installation is
version 2.64.0 or later."
+ ],
+ "metadata": {
+ "id": "UQx1jKZ2LHHR"
+ },
+ "id": "UQx1jKZ2LHHR"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "! pip install apache_beam[interactive,gcp]==2.64.0rc2 --quiet"
Review Comment:
```suggestion
"! pip install apache_beam[interactive,gcp]>=2.64.0 --quiet"
```
Adding this so we don't forget
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