diff --git a/.gitignore b/.gitignore index 3e759b7..89e0420 100644 --- a/.gitignore +++ b/.gitignore @@ -328,3 +328,5 @@ ASALocalRun/ # MFractors (Xamarin productivity tool) working folder .mfractor/ + +.vscode/ \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md index 9f92a3d..d3f5a12 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,13 +1 @@ -## [project-title] Changelog - -# x.y.z (yyyy-mm-dd) - -*Features* -* ... - -*Bug Fixes* -* ... - -*Breaking Changes* -* ... diff --git a/README.md b/README.md index 7337541..482b3e5 100644 --- a/README.md +++ b/README.md @@ -1,64 +1,86 @@ ---- -page_type: sample -languages: -- csharp -- java -- python -- typescript -- go -- ruby -products: -- azure -- azure-cognitive-services -- azure-anomaly-detector -description: "This repository contains samples for Anomaly Detector API. The Anomaly Detector API enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume." -urlFragment: AnomalyDetector ---- - -# Anomaly Detector API Samples - -This repository contains samples for [Anomaly Detector API](https://aka.ms/anomalydetector). The Anomaly Detector API enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume. Using your time series data, the API can find anomalies as a batch throughout your data, or determine if your latest data point is an anomaly. - -## Contents - -| File/folder | Description | -|-------------|-------------| -| `example-data` | Example data to be sent to the API, along with example API responses. | -| `ipython-notebook` | Jupyter notebooks. | -| `quickstarts` | Sample code for the Anomaly Detector API quickstarts. | +# Anomaly Detector Samples + +This repository contains API samples and SDK samples for [Anomaly Detector service](https://aka.ms/anomalydetector). Anomaly Detector enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume. + +## What's new? + +### March 2024: Anomaly Detector Now Available on PYPI 🎉 + +In March 2024, we proudly announce the release of the Anomaly Detector package on PYPI! + +While the existing Anomaly Detector as a service will be deprecated by 2026, you can now seamlessly utilize the new package directly on your local machine. No need to create an Azure Anomaly Detector resource—simply install the package and start detecting anomalies right away. + +For the latest details and usage instructions, refer to our Python notebook available here: [anomaly-detector-pypi-demo.ipynb](anomaly-detector-pypi-demo.ipynb) + +## 👋About Anomaly Detector +[Anomaly Detector](https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview) is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little ML knowledge, either batch validation or real-time inference. + +[Univariate Anomaly Detection API](https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/how-to/identify-anomalies) enables you to monitor and detect abnormalities in your single variable without having to know machine learning. The Anomaly Detector API's algorithms adapt by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies. + +[Multivariate anomaly detection API](https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/how-to/create-resource) further enable developers by easily integrating advanced AI for detecting anomalies from groups of metrics, without the need for machine learning knowledge or labeled data. Dependencies and inter-correlations between up to 300 different signals are now automatically counted as key factors. This new capability helps you to proactively protect your complex systems such as software applications, servers, factory machines, spacecraft, or even your business, from failures. ## Prerequisites -You must have an [Anomaly Detector API resource](https://aka.ms/adnew). Before continuing, you will need the API key and the endpoint from your Azure dashboard. +You must have an [Anomaly Detector API resource](https://aka.ms/adnew). Before continuing, you will need the API **key** and the **endpoint** from your Azure dashboard. ![Get Anomaly Detector access keys](./media/cognitive-services-get-access-keys.png "Get Anomaly Detector access keys") Or you could create a 7-day free resource of Anomaly Detector from [here](https://azure.microsoft.com/en-us/try/cognitive-services/my-apis/). -If you want to run the notebook with an on-premise version of [Anomaly Detector as container](https://aka.ms/adcontainerdocs), there're four prerequisites that must be met: +## Content -1. You have access to the Azure Container Registry which hosts the Anomaly Detector container images. Please complete and submit the [Anomaly Detector Container Request form](https://aka.ms/adcontainer) to request access to the container. -1. You have created an Anomaly Detector resource on Azure. -1. You have the proper container environment ready to host the Anomaly Detector container. Please read [Prerequisites](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/anomaly-detector-container-howto#prerequisites) and [The host computer](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/anomaly-detector-container-howto#the-host-computer) for details. -1. You have [Jupyter Notebook](https://jupyter.org/install.html) installed on your computer. We recommend installing Python and Jupyter using the [Anaconda Distribution](https://www.anaconda.com/downloads). +This repository is organized in the following structure, we recommend you go to `demo-notebook` first to try the simple samples if you are a fan of Python. 🤗 -After you pull the container image and spin it up, ensure there's an HTTP endpoint accessible to the APIs and this will be your **endpoint** for the demo. +| Folder | Description | +|-------------|-------------| +| 🆕[ipython-notebook](/ipython-notebook/) | [API](/ipython-notebook/API%20Sample/) and [SDK](/ipython-notebook/SDK%20Sample/) sample codes written in python notebook for UVAD adn MVAD. The latest update will start from here first. 😉 | +| [sampledata](/sampledata/) | All the sample datasets that are used in this repository. | +| [sample-multivariate](/samples-multivariate/) | Sample SDK codes for MVAD(preview version) using 4 languages. | +| [sample-univariate](/samples-univariate/) | Sample API and SDK codes for UVAD using 4 languages. | +| [univariate-live-demo](/univariate-live-demo/)| This includes a live demo that you could clone directly and ran on your data or make any modifications. | +| [postman-demo](/postman-demo/) | This includes the tutorial of using postman to trigger Anomaly Detector, which could help better understand from API perspective. | -## Online demo +## 🔗Important links -To quickly start using the Anomaly Detector API, try an [online demo](https://notebooks.azure.com/AzureAnomalyDetection/projects/anomalydetector). This demo runs in a web-hosted Jupyter notebook and shows you how to send an API request, and visualize the result. +### 1️⃣Microsoft Learn - Anomaly Detector -To run the demo, complete the following steps: - -1. Sign in, and click **Clone**, in the upper right corner. -1. Uncheck the "public" option in the dialog box before completing the clone operation, otherwise your notebook, including any subscription keys, will be public. -1. Click **Run on free compute** -1. Open one of the notebooks, for example, Anomaly Finder API Example Private Preview (Batch Method).ipynb -1. Fill in the API key and the endpoint from your Anomaly Detector resource on Azure -1. In the Notebook main menu, click Cell->run all +- Learning module: [Identify abnormal time-series data with Anomaly Detector](https://learn.microsoft.com/en-us/training/modules/identify-abnormal-time-series-data-anomaly-detector/?WT.mc_id=data-12171-ruyakubu) + +### 2️⃣API/SDK Sample +- [Anomaly Detector Sample](https://github.com/Azure-Samples/AnomalyDetector) +- [Anomaly Detector Sample in python notebook](https://github.com/Azure-Samples/AnomalyDetector/tree/master/ipython-notebook) + +### 3️⃣Anomaly Detector in Synapse + +- [Tutorial: Use Multivariate Anomaly Detector in Azure Synapse Analytics](https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/tutorials/multivariate-anomaly-detection-synapse) +- [Sample notebook for MVAD in Synapse](https://github.com/jr-MS/MVAD-in-Synapse) + +### 4️⃣Anomaly Detector in Azure Databricks + +- [Blog: Detect Anomalies in Equipment with Anomaly Detector in Azure Databricks](https://techcommunity.microsoft.com/t5/ai-cognitive-services-blog/detect-anomalies-in-equipment-with-anomaly-detector-in-azure/ba-p/3390688) + +### 5️⃣Anomaly Detector in Azure Data Explorer + +- [Blog: Announcing Univariate Anomaly Detector in Azure Data Explorer](https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/announcing-univariate-anomaly-detector-in-azure-data-explorer/ba-p/3285400) +- [Documentation about anomaly detection function in ADX](https://learn.microsoft.com/en-us/azure/data-explorer/kusto/functions-library/series-uv-anomalies-fl?tabs=adhoc) + +### 6️⃣Anomaly Detector PowerBI + +- [Anomaly Detection in PowerBI - UI](https://learn.microsoft.com/en-us/power-bi/visuals/power-bi-visualization-anomaly-detection) +- [Anomaly Detection in PowerBI - PowerQuery](https://learn.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/tutorials/batch-anomaly-detection-powerbi) ## Container demo +(Only support UVAD) + +If you want to run the notebook with an on-premise UVAD version of [Anomaly Detector as container](https://aka.ms/adcontainerdocs), there're four prerequisites that must be met: + +1. You have access to the Azure Container Registry which hosts the Anomaly Detector container images. Please complete and submit the [Anomaly Detector Container Request form](https://aka.ms/adcontainer) to request access to the container. +1. You have created an Anomaly Detector resource on Azure. +1. You have the proper container environment ready to host the Anomaly Detector container. Please read [Prerequisites](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/anomaly-detector-container-howto#prerequisites) and [The host computer](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/anomaly-detector-container-howto#the-host-computer) for details. +1. You have [Jupyter Notebook](https://jupyter.org/install.html) installed on your computer. We recommend installing Python and Jupyter using the [Anaconda Distribution](https://www.anaconda.com/downloads). + +After you pull the container image and spin it up, ensure there's an HTTP endpoint accessible to the APIs and this will be your **endpoint** for the demo. To run the notebook with your Anomaly Detector container instance, complete the following steps: 1. Clone this project to your local directory @@ -69,11 +91,6 @@ To run the notebook with your Anomaly Detector container instance, complete the 1. Fill in the API key (from your Anomaly Detector resource on Azure) and the endpoint (from your Anomaly Detector container instance) 1. In the Notebook main menu, click Cell->run all -## Data requirements - -Example data is provided in this repository, along with example JSON responses from the API. To use the Anomaly Detector API on your time series data, ensure the following: -* Your data points are separated by the same interval, with no missing points. -* Your data has at least 13 data points if it doesn't have clear perodicity. -* Your data has at least 4 periods if it does have clear perodicity. -Please read [Best practices for using the Anomaly Detector API](https://aka.ms/adbest) for details. +## ❤️Support +Need support? [Join the Anomaly Detector Community](https://forms.office.com/pages/responsepage.aspx?id=v4j5cvGGr0GRqy180BHbR2Ci-wb6-iNDoBoNxrnEk9VURjNXUU1VREpOT0U1UEdURkc0OVRLSkZBNC4u). diff --git a/anomaly-detector-pypi-demo.ipynb b/anomaly-detector-pypi-demo.ipynb new file mode 100644 index 0000000..c172f74 --- /dev/null +++ b/anomaly-detector-pypi-demo.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a2f4ea7a79dcc5e", + "metadata": { + "collapsed": false + }, + "source": [ + "## Install package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0f996e3f768461f", + "metadata": { + "collapsed": false, + "vscode": { + "languageId": "powershell" + } + }, + "outputs": [], + "source": [ + "pip install time-series-anomaly-detector" + ] + }, + { + "cell_type": "markdown", + "id": "8346a504", + "metadata": {}, + "source": [ + "## Multivariate Anomaly Detection" + ] + }, + { + "cell_type": "markdown", + "id": "4f25e1b2", + "metadata": {}, + "source": [ + "### 1. Train model\n", + "To train a mvad model, the type of training data should be DataFrame type. And you must specify the sliding_window and device in params. \n", + "\n", + "Moreover, please note that in mvad, timestamp of the training data is optional." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5da3531", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from anomaly_detector import MultivariateAnomalyDetector\n", + "\n", + "import json\n", + "from pprint import pprint\n", + "\n", + "data_size = 1000\n", + "var_num = 20\n", + "\n", + "training_data = np.random.randn(data_size, var_num)\n", + "columns = [f\"variable_{i}\" for i in range(var_num)]\n", + "training_data = pd.DataFrame(training_data, columns=columns)\n", + "\n", + "# Optional\n", + "timestamps = pd.date_range(start=\"2023-01-03\", periods=data_size, freq=\"H\")\n", + "training_data[\"timestamp\"] = timestamps.strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n", + "training_data = training_data.set_index(\"timestamp\", drop=True)\n", + "\n", + "params = {\"sliding_window\": 200, \"device\": \"cpu\"}\n", + "\n", + "model = MultivariateAnomalyDetector()\n", + "\n", + "# Train model\n", + "model.fit(training_data, params=params)" + ] + }, + { + "cell_type": "markdown", + "id": "b918d943", + "metadata": {}, + "source": [ + "### 2. Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3a010d7", + "metadata": {}, + "outputs": [], + "source": [ + "eval_data = np.random.randn(201, var_num)\n", + "eval_data[-1, :] += 100\n", + "eval_data = pd.DataFrame(eval_data, columns=columns)\n", + "\n", + "# Optional\n", + "timestamps = pd.date_range(start=\"2023-01-03\", periods=201, freq=\"H\")\n", + "eval_data[\"timestamp\"] = timestamps.strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n", + "eval_data = eval_data.set_index(\"timestamp\", drop=True)\n", + "\n", + "# prediction\n", + "results = model.predict(data=eval_data, context=None)\n", + "\n", + "pprint(results)" + ] + }, + { + "cell_type": "markdown", + "id": "1cc129ba", + "metadata": {}, + "source": [ + "## Univariate Anomaly Detection" + ] + }, + { + "cell_type": "markdown", + "id": "5d12be4c", + "metadata": {}, + "source": [ + "### Inference\n", + "\n", + "Please note that the univariate anomaly detection does not need to train before inference, and timestamp of the eval_data must be specified." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "232963b5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from anomaly_detector import EntireAnomalyDetector\n", + "\n", + "params = {\n", + " \"granularity\": \"monthly\", \n", + " \"maxAnomalyRatio\": 0.25, \n", + " \"sensitivity\": 95, \n", + " \"imputeMode\": \"auto\"\n", + "}\n", + "\n", + "\n", + "model = EntireAnomalyDetector()\n", + "\n", + "eval_data = np.ones(20)\n", + "eval_data[-1] = 0\n", + "eval_data = pd.DataFrame(eval_data, columns=[\"value\"])\n", + "\n", + "timestamps = pd.date_range(start=\"1962-01-01\", periods=20, freq=\"ME\")\n", + "eval_data[\"timestamp\"] = timestamps\n", + "\n", + "results = model.predict(\n", + " data=eval_data,\n", + " params=params,\n", + " context=None\n", + ")\n", + "print(results)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/example-data/README.md b/example-data/README.md deleted file mode 100644 index 196226c..0000000 --- a/example-data/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Please don't use the sample data in this folder for the preview feature "multivariate anomaly detection", those sample data is for the previous univariate anomaly detection only. -To learn more on the multivariate preview APIs: -1. Read the blog: https://aka.ms/ADMultivariateBlog -2. Understand API flow through Jupyter Notebook: https://aka.ms/ad-multivariate-python-demo -3. QuickStarts: https://aka.ms/ADMultivariateQuickstarts -4. Best practices: https://aka.ms/ADBestPractices -5. Build predictive maintenance solution: https://aka.ms/ADBuildPMSolution diff --git a/example-data/example-data.xlsx b/example-data/example-data.xlsx deleted file mode 100644 index bc71a0d..0000000 Binary files a/example-data/example-data.xlsx and /dev/null differ diff --git a/ipython-notebook/API Sample/Batch anomaly detection with the Anomaly Detector API.ipynb b/ipython-notebook/API Sample/Batch anomaly detection with the Anomaly Detector API.ipynb index f7a2bb7..099f3d4 100644 --- a/ipython-notebook/API Sample/Batch anomaly detection with the Anomaly Detector API.ipynb +++ b/ipython-notebook/API Sample/Batch anomaly detection with the Anomaly Detector API.ipynb @@ -19,17 +19,26 @@ "* Anomaly detection boundaries \n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To start sending requests to the Anomaly Detector API, paste your Anomaly Detector resource access key below,\n", + "and replace the endpoint variable with the endpoint for your region or your on-premise container endpoint. \n", + "\n", + "Endpoint examples:\n", + "\n", + "`https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/entire/detect`\n", + "\n", + "`http://127.0.0.1:5000/anomalydetector/v1.0/timeseries/entire/detect`" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# To start sending requests to the Anomaly Detector API, paste your Anomaly Detector resource access key below,\n", - "# and replace the endpoint variable with the endpoint for your region or your on-premise container endpoint. \n", - "# Endpoint examples:\n", - "# https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/entire/detect\n", - "# http://127.0.0.1:5000/anomalydetector/v1.0/timeseries/entire/detect\n", "apikey = '[Placeholder: Your Anomaly Detector resource access key]' \n", "endpoint = '[Placeholder: Your Anomaly Detector resource endpoint]/anomalydetector/v1.0/timeseries/entire/detect'" ] @@ -116,9 +125,9 @@ " lowerband = response['expectedValues'] -response['lowerMargins']\n", " band_x = np.append(label, label[::-1])\n", " band_y = np.append(lowerband, upperband[::-1])\n", - " boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend='Boundary')\n", - " p.line(label, values, legend='Value', color=\"#2222aa\", line_width=1)\n", - " p.line(label, response['expectedValues'], legend='ExpectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n", + " boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend_label='Boundary')\n", + " p.line(label, values, legend_label='Value', color=\"#2222aa\", line_width=1)\n", + " p.line(label, response['expectedValues'], legend_label='ExpectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n", " anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n", " anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n", " p.legend.border_line_width = 1\n", @@ -149,7 +158,7 @@ "outputs": [], "source": [ "# Hourly Sample\n", - "sample_data = json.load(open('univariate_sample_hourly.json'))\n", + "sample_data = json.load(open('../../sampledata/univariate/univariate_sample_hourly.json'))\n", "sample_data['granularity'] = 'hourly'\n", "sample_data['period'] = 24\n", "# 95 sensitivity\n", @@ -190,7 +199,7 @@ "outputs": [], "source": [ "#daily sample\n", - "sample_data = json.load(open('univariate_sample_daily.json'))\n", + "sample_data = json.load(open('../../sampledata/univariate/univariate_sample_daily.json'))\n", "sample_data['granularity'] = 'daily'\n", "# 95 sensitivity\n", "build_figure(sample_data,95)" @@ -215,11 +224,18 @@ "# 85 sensitivity\n", "build_figure(sample_data,80)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -233,9 +249,14 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "530dd268f570c5cdc0ceeb4c9b04e15d56fa20bfd35f8d335952cdbe40d1c280" + } } }, "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "nbformat_minor": 4 +} diff --git a/ipython-notebook/API Sample/Latest point detection with the Anomaly Detector API.ipynb b/ipython-notebook/API Sample/Latest point detection with the Anomaly Detector API.ipynb index 81620ba..87765b4 100644 --- a/ipython-notebook/API Sample/Latest point detection with the Anomaly Detector API.ipynb +++ b/ipython-notebook/API Sample/Latest point detection with the Anomaly Detector API.ipynb @@ -23,17 +23,26 @@ "The following example simulates using the Anomaly Detector API on streaming data. Sections of the example time series are sent to the API over multiple iterations, and the anomaly status of each section's last data point is saved. The data set used in this example has a pattern that repeats roughly every 7 data points (the `period` in the request's JSON file), so for best results, the data set is sent in groups of 29 points (`4 * + an extra data point`. See [Best practices for using the Anomaly Detector API](https://docs.microsoft.com/azure/cognitive-services/anomaly-detector/concepts/anomaly-detection-best-practices) for more information). " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To start sending requests to the Anomaly Detector API, paste your Anomaly Detector resource access key below,\n", + "and replace the endpoint variable with the endpoint for your region or your on-premise container endpoint. \n", + "\n", + "Endpoint examples:\n", + "\n", + "`https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/last/detect`\n", + "\n", + "`http://127.0.0.1:5000/anomalydetector/v1.0/timeseries/last/detect`" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# To start sending requests to the Anomaly Detector API, paste your Anomaly Detector resource access key below,\n", - "# and replace the endpoint variable with the endpoint for your region or your on-premise container endpoint. \n", - "# Endpoint examples:\n", - "# https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/last/detect\n", - "# http://127.0.0.1:5000/anomalydetector/v1.0/timeseries/last/detect\n", "apikey = '[Placeholder: Your Anomaly Detector resource access key]' \n", "endpoint_latest = '[Placeholder: Your Anomaly Detector resource endpoint]/anomalydetector/v1.0/timeseries/last/detect'" ] @@ -117,9 +126,9 @@ " lowerband = response['expectedValues'] -response['lowerMargins']\n", " band_x = np.append(label, label[::-1])\n", " band_y = np.append(lowerband, upperband[::-1])\n", - " boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend='Boundary')\n", - " p.line(label, values, legend='value', color=\"#2222aa\", line_width=1)\n", - " p.line(label, response['expectedValues'], legend='expectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n", + " boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend_label='Boundary')\n", + " p.line(label, values, legend_label='value', color=\"#2222aa\", line_width=1)\n", + " p.line(label, response['expectedValues'], legend_label='expectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n", " anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n", " anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n", " p.legend.border_line_width = 1\n", @@ -198,7 +207,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -212,9 +221,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.10.9" } }, "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + "nbformat_minor": 4 +} diff --git a/ipython-notebook/API Sample/Multivariate API Demo Notebook - Sensor Case.ipynb b/ipython-notebook/API Sample/Multivariate API Demo Notebook - Sensor Case.ipynb deleted file mode 100644 index 4f52178..0000000 --- a/ipython-notebook/API Sample/Multivariate API Demo Notebook - Sensor Case.ipynb +++ /dev/null @@ -1,793 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "import json" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Endpoints" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Multivariate Anomaly Detection Endpoint\n", - "\n", - "`[PlaceHolder].cognitiveservices.azure.com/anomalydetector/v1.1-preview`\n", - "\n", - "Multivariate Anomaly Detection APIs\n", - "- List models `[GET] /multivariate/models`\n", - "- Train a model `[POST] /multivariate/models`\n", - "- Get a model `[GET] /multivariate/models/{model_id}`\n", - "- Delete a model `[DELETE] /multivariate/models/{model_id}`\n", - "- Detect anomalies `[POST] /multivariate/models/{model_id}/detect`\n", - "- Export a model `[GET] /multivariate/models/{model_id}/export`\n", - "- Get detection result `[GET] /multivariate/results/{result_id}`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ENDPOINT = \"[PlaceHolder].cognitiveservices.azure.com/anomalydetector/v1.1-preview\"\n", - "HEADERS = {\n", - " \"Ocp-Apim-Subscription-Key\": \"[PlaceHolder]\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "API_MODEL = \"https://{endpoint}/multivariate/models\"\n", - "API_MODEL_STATUS = \"https://{endpoint}/multivariate/models/{model_id}\"\n", - "API_MODEL_INFERENCE = \"https://{endpoint}/multivariate/models/{model_id}/detect\"\n", - "API_RESULTS = \"https://{endpoint}/multivariate/results/{result_id}\"\n", - "API_EXPORT = \"https://{endpoint}/multivariate/models/{model_id}/export\"\n", - "API_DELETE = \"https://{endpoint}/multivariate/models/{model_id}\"\n", - "SOURCE_BLOB_SAS = \"https://multiadsample.blob.core.windows.net/data/sample-engine-simple.zip?sp=r&st=2021-07-09T08:55:40Z&se=2026-07-09T16:55:40Z&spr=https&sv=2020-08-04&sr=b&sig=NQkLncjR46d4mi4Goj3Z0BFmRCynBeUQ13sm6PzNjIk%3D\"\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## List Models\n", - "```http\n", - "[GET] https://{endpoint}/multivariate/models?$skip=0&$top=5\n", - "```\n", - "#### Sample\n", - " - Request\n", - "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "```\n", - " - Response\n", - "```json\n", - "response={\n", - " \"models\": [\n", - " {\n", - " \"createdTime\":\"2020-12-01T09:43:45Z\",\n", - " \"displayName\":\"DevOps-Test\",\n", - " \"lastUpdatedTime\":\"2020-12-01T09:46:13Z\",\n", - " \"modelId\":\"b4c1616c-33b9-11eb-824e-0242ac110002\",\n", - " \"status\":\"READY\",\n", - " \"variablesCount\":18\n", - " },\n", - " {\n", - " \"createdTime\":\"2020-12-01T09:43:30Z\",\n", - " \"displayName\":\"DevOps-Test\",\n", - " \"lastUpdatedTime\":\"2020-12-01T09:45:10Z\",\n", - " \"modelId\":\"ab9d3e30-33b9-11eb-a3f4-0242ac110002\",\n", - " \"status\":\"READY\",\n", - " \"variablesCount\":18\n", - " },\n", - " ],\n", - " \"currentCount\": 1, # Current count of trained multivariate models.\n", - " \"maxCount\": 50, # Max number of models that can be trained for this subscription.\n", - " \"nextLink\": \"string\" # next link to fetch more models\n", - "}\n", - "```\n", - " - Error Response\n", - " ```json\n", - "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", - "}\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = requests.get(API_MODEL.format(endpoint=ENDPOINT), headers=HEADERS)\n", - "assert res.status_code == 200, f\"Error occured. Error message: {res.content}\"\n", - "print(res.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train Model \n", - "```HTTP\n", - "[POST] https://{endpoint}/multivariate/models\n", - "```\n", - "- #### Request\n", - " - **slidingWindow** An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point.\n", - " - **alignPolicy** An optional field, since those multivariate need to be aligned in the same timestamp before starting the detection.\n", - " - **alignMode**, An optional field, indicates how we merge different variables into the same time-range which is required by the model\n", - " ```json\n", - " {Inner, Outer}\n", - " ```\n", - " - **fillNAMethod**, optional field, indicates how missed valus will be filled with. Can not be set to *NotFill*, when alignMode is *Outer*\n", - " ```json\n", - " {Previous, Subsequent, Linear, Zero, Fix, NotFill}\n", - " ```\n", - " - **paddingValue**, optional field, only be usefull if fillNAMethod is set to *Fix*. \n", - " - **source**, required field, source path contain the training zip file. \n", - " ```bash\n", - " Source file link of the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, an extra meta.json can be included in th zip file if you would like to rename a variable.Be default, the file name of the variable will be used as the variable name.\",example: \"https://multiadsample.blob.core.windows.net/data/sample_data_2_1000.zip?sp=rl&st=2020-12-04T06:03:47Z&se=2022-12-05T06:03:00Z&sv=2019-12-12&sr=b&sig=AZTbvZ7fcp3MdqGY%2FvGHJXJjUgjS4DneCGl7U5omq5c%3D\"\n", - " ```\n", - " - **startTime**, require field, it means start time of data you want to use for training, should be %Y-%m-%dT%H:%M:%SZ \n", - " format.\n", - " - **endTime**, require field, it means end time of data you want to use for training, should be %Y-%m-%dT%H:%M:%SZ format.\n", - " - **displayName**, optional field, it means model name.\n", - " \n", - "- #### Response\n", - " - model location in headers\n", - "\n", - "- #### Sample\n", - " - Request\n", - " ```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "request=\n", - "{\n", - " \"slidingWindow\": 200,\n", - " \"alignPolicy\": {\n", - " \"alignMode\": \"Outer\",\n", - " \"fillNAMethod\": \"Linear\", \n", - " \"paddingValue\": 0\n", - " },\n", - " \"source\": SOURCE_BLOB_SAS,\n", - " \"startTime\": \"2021-01-01T00:00:00Z\", \n", - " \"endTime\": \"2021-01-02T12:00:00Z\", \n", - " \"displayName\": \"SampleRequest\"\n", - "}\n", - " ```\n", - " - Response\n", - " ```json\n", - " Location:\t{endpoint}/multivariate/models/927e2af8-a657-11ea-8cae-0242ac110002 # GUID\n", - " ```\n", - " - Error Response\n", - " ```json\n", - " {\n", - " \"code\": \"string\",\n", - " \"message\": \"string\"\n", - " } ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SLIDING_WINDOW = 200\n", - "data = {\n", - " 'slidingWindow': SLIDING_WINDOW,\n", - " 'alignPolicy': {\n", - " 'alignMode': 'Outer',\n", - " 'fillNAMethod': 'Linear', \n", - " 'paddingValue': 0\n", - " },\n", - " 'source': SOURCE_BLOB_SAS,\n", - " 'startTime': '2019-04-30 10:00:00', \n", - " 'endTime': '2019-05-02 10:00:00', \n", - " 'displayName': 'SampleRequest'\n", - "}\n", - "\n", - "res = requests.post(API_MODEL.format(endpoint=ENDPOINT), data=json.dumps(data), headers=HEADERS)\n", - "assert res.status_code == 201, f\"Error occured. Error message: {res.content}\"\n", - "print(res.content)\n", - "location = res.headers['Location']\n", - "print(location)\n", - "model_id = location[location.rindex('/')+1:]\n", - "print(model_id)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get Multivaraite Model Status by modelid\n", - "```http\n", - "[GET] https://{endpoint}/multivariate/models/{model_id}\n", - "```\n", - "#### Sample\n", - " - Request\n", - "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "```\n", - " - Response\n", - " \n", - "```json\n", - "response = {\n", - " \"createdTime\":\"2020-07-01T07:58:37Z\",\n", - " \"lastUpdatedTime\":\"2020-07-01T07:59:55Z\",\n", - " \"modelId\":\"ab888466-bb70-11ea-958f-0242ac110002\",\n", - " \"modelInfo\":{ # Training Status of the model.\n", - " \"diagnoseInfo\":{\n", - " \"modelState\":{\n", - " \"epochIds\":[10, 20, 30, 40, 50, 60, 70, 80, 90, 100], # 100 epoc in total\n", - " \"latenciesInSeconds\":[0.5837657451629639, 0.5688292980194092, 0.5959596633911133, 0.5251538753509521, 0.6021878719329834, 0.6459534168243408, 0.5391685962677002, 0.5622642040252686, 0.5487074851989746, 0.6336326599121094],\n", - " \"trainLosses\":[1.682054042816162, 0.7844524383544922, 0.6616984605789185, 0.6293938159942627, 0.6323581337928772, 0.6257774233818054, 0.5985430479049683, 0.6037595868110657, 0.5779791474342346, 0.5583345293998718],\n", - " \"validationLosses\":[0.8330008387565613, 0.6937242150306702, 0.7329594492912292, 0.6103720664978027, 0.6125020980834961, 0.5729937553405762, 0.5761528611183167, 0.5710235238075256, 0.5679566264152527, 0.5674979090690613]\n", - " },\n", - " \"variableStates\":[\n", - " { \n", - " \"effectiveCount\":1441,\n", - " \"endTime\":\"2019-04-02T00:00:00Z\",\n", - " \"errors\":[],\n", - " \"filledNARatio\":0.0,\n", - " \"startTime\":\"2019-04-01T00:00:00Z\",\n", - " \"variable\":\"established_connections\"\n", - " },\n", - " { \n", - " \"effectiveCount\":1441, # Effective time-series points count.\n", - " \"endTime\":\"2019-04-02T00:00:00Z\", # End time of a variable\n", - " \"errors\":[],\n", - " \"filledNARatio\":0.0, # NA ratio of a variable.\n", - " \"startTime\":\"2019-04-01T00:00:00Z\", # Start time of a variable\n", - " \"variable\":\"memory\"\n", - " }\n", - " ]\n", - " },\n", - " \"errors\":[], # Error message when creating or training model fails.\n", - " \"status\":\"READY\",\n", - " \"alignPolicy\":{\n", - " \"alignMode\":\"Outer\",\n", - " \"fillNAMethod\":\"Linear\",\n", - " \"paddingValue\":0\n", - " },\n", - " \"displayName\":\"DevOps-Test\",\n", - " \"endTime\":\"2019-04-02T00:00:00Z\",\n", - " \"slidingWindow\":28,\n", - " \"source\":\"/data/sample_data.zip\",\n", - " \"startTime\":\"2019-04-01T00:00:00Z\"\n", - " }\n", - "}\n", - " \n", - "```\n", - " - Error Response\n", - " ```json\n", - "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", - "}\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = requests.get(API_MODEL_STATUS.format(endpoint=ENDPOINT, model_id = model_id), headers=HEADERS)\n", - "assert res.status_code == 200, f\"Error occured. Error message: {res.content}\"\n", - "res_content = json.loads(res.content)\n", - "print(json.dumps(res_content))\n", - "print(res_content['modelInfo']['status'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Detection with the trained model\n", - "```http\n", - "[POST] https://{endpoint}/multivariate/models/{model_id}/detect\n", - "```\n", - "inference api will return an resultid, you can get result by get inference result api.\n", - "\n", - "- #### Request\n", - " - **source**, required field, source path contain the training zip file.\n", - "```json\n", - " Source file link of the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, an extra meta.json can be included in th zip file if you would like to rename a variable.Be default, the file name of the variable will be used as the variable name.\",example: \"https://multiadsample.blob.core.windows.net/data/sample_data_2_1000.zip?sp=rl&st=2020-12-04T06:03:47Z&se=2022-12-05T06:03:00Z&sv=2019-12-12&sr=b&sig=AZTbvZ7fcp3MdqGY%2FvGHJXJjUgjS4DneCGl7U5omq5c%3D\"\n", - "```\n", - " - **startTime**, a require field, it means start time of data you want to use to inference, should be %Y-%m-%dT%H:%M:%SZ format. \n", - " - **endTime**, a require field, it means end time of data you want to use to inference, should be %Y-%m-%dT%H:%M:%SZ format. \n", - "\n", - "- #### Response\n", - " - result location in headers\n", - " \n", - "- #### Sample \n", - "- Request\n", - "\n", - "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "request={\n", - " \"source\": SOURCE_BLOB_SAS,\n", - " \"startTime\": \"2020-01-01T00:00:00Z\",\n", - " \"endTime\": \"2020-02-01T00:00:00Z\"\n", - "}\n", - "```\n", - "- Response\n", - "```json\n", - "Location: {endpoint}/multivariate/results/927e2af8-a657-11ea-8cae-0242ac110002 # GUID\n", - " ```\n", - "- Error Response\n", - " ```json\n", - "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", - "}\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = {\n", - " 'source': SOURCE_BLOB_SAS,\n", - " 'startTime': '2019-05-02 11:00:00', \n", - " 'endTime': '2019-05-02 14:00:00', \n", - "}\n", - "\n", - "res = requests.post(API_MODEL_INFERENCE.format(endpoint=ENDPOINT, model_id=model_id), \n", - " data=json.dumps(data), headers=HEADERS)\n", - "assert res.status_code == 201, f\"Error occured. Error message: {res.content}\"\n", - "print(res.content)\n", - "result_id = res.headers['location'].split(\"/\")[-1]\n", - "print(f\"result id = {result_id}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get Detection Result\n", - "```http\n", - "[GET] https://{endpoint}/multivariate/results/{result_id}\n", - "```\n", - "- #### Sample\n", - "- Request\n", - "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "```\n", - "- Response\n", - "```json\n", - "Response={\n", - " \"resultId\": \"45aad126-aafd-11ea-b8fb-d89ef3400c5f\",\n", - " \"summary\": {\n", - " \"status\": \"READY\", # Multivariate anomaly detection status\n", - " \"errors\": [ # Error message when inference fails.\n", - " {\n", - " \"code\": \"string\",\n", - " \"message\": \"string\"\n", - " }\n", - " ],\n", - " \"variableStates\": [\n", - " {\n", - " \"variable\": \"ad_input\",\n", - " \"filledNARatio\": 0,\n", - " \"effectiveCount\": 26,\n", - " \"startTime\": \"2019-04-01T00:00:00Z\",\n", - " \"endTime\": \"2019-04-01T00:25:00Z\",\n", - " \"errors\": []\n", - " },\n", - " {\n", - " \"variable\": \"ad_ontimer_output\",\n", - " \"filledNARatio\": 0,\n", - " \"effectiveCount\": 26,\n", - " \"startTime\": \"2019-04-01T00:00:00Z\",\n", - " \"endTime\": \"2019-04-01T00:25:00Z\",\n", - " \"errors\": []\n", - " }\n", - " ],\n", - " \"setupInfo\": {\n", - " \"source\": \"/data/{$zipfile_name}\",\n", - " \"startTime\": \"2019-04-01T00:15:00Z\",\n", - " \"endTime\": \"2019-04-01T00:40:00Z\"\n", - " }\n", - " },\n", - " \"results\": [\n", - " {\n", - " \"timestamp\": \"2019-04-01T00:19:00Z\",\n", - " \"errors\": [\n", - " {\n", - " \"code\": \"InsufficientHistoricalData\",\n", - " \"message\": \"historical data is not enough.\"\n", - " }\n", - " ]\n", - " },\n", - " {\n", - " \"timestamp\": \"2019-04-01T00:20:00Z\",\n", - " \"value\":{\n", - " \"contributors\": [],# isAnomaly is false, contributors is empty.\n", - " \"isAnomaly\": false,\n", - " \"severity\": 0.3509107994398884,\n", - " \"score\": 0.34231\n", - " },\n", - " \"errors\": []\n", - " },\n", - " {\n", - " \"timestamp\": \"2019-04-01T00:21:00Z\",\n", - " \"value\":{\n", - " \"contributors\": [\n", - " {\n", - " \"contributionScore\": 0.0007775013367514271, # The higher the contributionScore is, the more likely the contributor to be the root cause of a anomaly.\n", - " \"variable\": \"ad_ontimer_output\" # Variable name of a contributor\n", - " },\n", - " {\n", - " \"contributionScore\": 0.0007989604079048129,\n", - " \"variable\": \"ad_input\"\n", - " }\n", - " ],\n", - " \"isAnomaly\": true, # To indicate whether current timestamp is anomaly or not\n", - " \"severity\": 0.42135109874230336, # severity of the current timestamp, the more significant an anomaly is, the higher the severity will be\n", - " \"score\": 0.23485905670108112\n", - " },\n", - " \"errors\": []\n", - " }\n", - " ]\n", - "}\n", - "```\n", - " - Error Response\n", - " ```json\n", - "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", - "}\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = requests.get(API_RESULTS.format(endpoint=ENDPOINT, result_id=result_id), headers=HEADERS)\n", - "assert res.status_code == 200, f\"Error occured. Error message: {res.content}\"\n", - "print(res.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Results\n", - "Demo code to draw results. Additional python package is required." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from bokeh.io import output_file, show, output_notebook, save\n", - "from bokeh.layouts import gridplot\n", - "from bokeh.plotting import figure\n", - "from matplotlib import pyplot\n", - "from bokeh.models import ColumnDataSource, HoverTool\n", - "from bokeh.palettes import Dark2_5 as palette\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import itertools \n", - "import shutil\n", - "import uuid\n", - "import zipfile\n", - "from urllib.request import urlretrieve\n", - "%matplotlib inline\n", - "output_notebook()\n", - "\n", - "def unzip_file(zip_src, dst_dir):\n", - " r = zipfile.is_zipfile(zip_src)\n", - " if r:\n", - " fz = zipfile.ZipFile(zip_src, 'r')\n", - " print(fz)\n", - " for file in fz.namelist():\n", - " fz.extract(file, dst_dir)\n", - " else:\n", - " print('This is not zip')\n", - " \n", - "def load_data(local_data_path, start, end):\n", - " new_dir = os.path.join('.', str(uuid.uuid1()))\n", - " shutil.rmtree(new_dir, ignore_errors=True)\n", - " os.mkdir(new_dir)\n", - " unzip_file(local_data_path, new_dir)\n", - " files = os.listdir(new_dir)\n", - " frames = []\n", - " for file in files:\n", - " if file[-4:] != '.csv':\n", - " continue\n", - " frame = pd.read_csv('{}\\\\{}'.format(new_dir, file))\n", - " var = file[:file.find('.csv')]\n", - " frame = frame.rename(columns={'value': var})\n", - " frame = frame[frame['timestamp'] >= start]\n", - " frame = frame[frame['timestamp'] <= end]\n", - " frame['timestamp'] = pd.to_datetime(frame['timestamp'])\n", - " frame.set_index(['timestamp'], inplace=True)\n", - " frames.append(frame)\n", - " shutil.rmtree(new_dir, ignore_errors=True)\n", - " return frames\n", - "\n", - "\n", - "def plot_lines_multi(x, y, p, color, name, t_str=\"hover,save,pan,box_zoom,reset,wheel_zoom\", t_loc='above'):\n", - " '''...\n", - " '''\n", - " p.line(x, y, color=color, legend_label=name)\n", - " p.legend.location = \"top_left\"\n", - " p.legend.click_policy=\"hide\"\n", - "\n", - "def draw(data_source, local_data_path, result_id, sensitivity, start, end):\n", - " urlretrieve(data_source, local_data_path)\n", - " print(local_data_path, result_id, sensitivity, start, end)\n", - " series = load_data(local_data_path, start, end)\n", - " p_list = []\n", - " colors = itertools.cycle(palette)\n", - " # p_value = figure(background_fill_color=\"#fafafa\", x_axis_type=\"datetime\")\n", - " for var, color in zip(series, colors):\n", - " name = var.columns.values[0]\n", - " p_value = figure(background_fill_color=\"#fafafa\", x_axis_type=\"datetime\")\n", - " plot_lines_multi(var.index, var[name], p_value, color, name)\n", - " p_list.append(p_value)\n", - " header = HEADERS\n", - " raw_result = json.loads(requests.get(API_RESULTS.format(endpoint=ENDPOINT, result_id=result_id), headers=header).content)\n", - " if raw_result['summary']['status'] != 'READY':\n", - " print(\"result not ready\")\n", - " return\n", - " filter_item = list(filter(lambda x: 'value' in x and 'isAnomaly' in x['value'], raw_result['results']))\n", - " timestamps = [item['timestamp'] for item in filter_item]\n", - " isAnomaly = [item['value']['isAnomaly'] for item in filter_item]\n", - " RawScore = [item['value']['score'] for item in filter_item]\n", - " Severity = [item['value']['severity'] for item in filter_item]\n", - " result = pd.DataFrame({'Timestamp': timestamps, 'isAnomaly': isAnomaly, 'RawScore': RawScore, 'Severity': Severity})\n", - " result['Timestamp'] = pd.to_datetime(result['Timestamp'])\n", - " result.loc[(result.Severity <= (1 - sensitivity)) & (result.isAnomaly == True), 'isAnomaly'] = False\n", - " result['Timestamp'] = pd.to_datetime(result['Timestamp'])\n", - " result.set_index(['Timestamp'], inplace=True)\n", - " result = result.reindex(['isAnomaly', 'RawScore', 'Severity'], axis=1)\n", - " colors = ['red', 'blue', 'black']\n", - " for col, color in zip(result.columns, colors):\n", - " p = figure(background_fill_color=\"#fafafa\", x_axis_type=\"datetime\")\n", - " p.line(result.index, result[col], color=color, alpha=0.8, legend_label=col)\n", - " p.legend.location = \"top_left\"\n", - " p.legend.click_policy=\"hide\"\n", - " p_list.append(p)\n", - " grid = gridplot([[x] for x in p_list], sizing_mode='scale_width', plot_height=50)\n", - " show(grid)\n", - " result = result.sort_values(by=['RawScore'], ascending=False)\n", - " top_anomaly = list(result[result.isAnomaly].index.strftime('%Y-%m-%dT%H:%M:%SZ'))[0]\n", - " print(\"Top Anomaly Timestamp is : {0}\".format(top_anomaly))\n", - " return series, raw_result, top_anomaly\n", - "\n", - "def show_contribution(local_data_path, raw_result, anomaly_timestamp, start, end):\n", - " anomaly_result = [x for x in raw_result['results'] if 'contributors' in x['value'] and x['timestamp'] == anomaly_timestamp][0]\n", - " contributors = [x['variable'] for x in anomaly_result['value']['contributors']]\n", - " scores = [x['contributionScore'] for x in anomaly_result['value']['contributors']]\n", - " contributors = pd.DataFrame({'contributors': contributors, 'scores': scores})\n", - " contributors = contributors.sort_values(by=['scores'], ascending=False)\n", - " contributors = list(contributors['contributors'][:4])\n", - " series = load_data(local_data_path, start, end)\n", - " series_index = pd.DataFrame({'index': list(range(0, len(series))), 'name': [x.columns[0] for x in series]})\n", - " series_index = series_index.set_index('name')\n", - " sorted_series = [\n", - " series[i][(series[i].index <= np.datetime64(end)) & (series[i].index > np.datetime64(start))]\n", - " for i in series_index.reindex(contributors)['index'].values]\n", - " p_list = []\n", - " colors = itertools.cycle(palette)\n", - " anomalies = pd.DataFrame({'timestamp': series[0].index, 'value': [0] * len(series[0].index)})\n", - " anomalies = anomalies.set_index('timestamp')\n", - " anomalies.loc[anomalies.index == np.datetime64(anomaly_timestamp),'value'] = 1\n", - " for var, color in zip(sorted_series, colors):\n", - " name = var.columns.values[0]\n", - " p_value = figure(background_fill_color=\"#fafafa\", x_axis_type=\"datetime\")\n", - " plot_lines_multi(var.index, var[name], p_value, color, name)\n", - " p_list.append(p_value)\n", - " p_value = figure(background_fill_color=\"#fafafa\", x_axis_type=\"datetime\")\n", - " plot_lines_multi(anomalies.index, anomalies.value, p_value, 'red', 'Anomaly')\n", - " p_list.append(p_value)\n", - " grid = gridplot([[x] for x in p_list], sizing_mode='scale_width', plot_height=50)\n", - " show(grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# def draw_raw_data(data_source, local_data_path, start, end):\n", - "# urlretrieve(data_source, local_data_path)\n", - "# print(local_data_path, start, end)\n", - "# series = load_data(local_data_path, start, end)\n", - "# print(len(series))\n", - "# p_list = []\n", - "# colors = itertools.cycle(palette)\n", - "# # p_value = figure(background_fill_color=\"#fafafa\", x_axis_type=\"datetime\")\n", - "# for var, color in zip(series, colors):\n", - "# name = var.columns.values[0]\n", - "# p_value = figure(background_fill_color=\"#fafafa\", x_axis_type=\"datetime\")\n", - "# plot_lines_multi(var.index, var[name], p_value, color, name)\n", - "# p_list.append(p_value)\n", - "# grid = gridplot([[x] for x in p_list], sizing_mode='scale_width', plot_height=50)\n", - "# show(grid)\n", - "# data_source = SOURCE_BLOB_SAS\n", - "# local_data_path = \"sample_data_5_3000.zip\"\n", - "# severity = 0.7\n", - "# start_date = \"2018-07-06 00:00:00\"\n", - "# end_date = \"2018-07-06 08:00:00\"\n", - "# draw_raw_data(data_source, local_data_path, start_date, end_date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_source = SOURCE_BLOB_SAS\n", - "local_data_path = \"sample_data_5_3000.zip\"\n", - "severity = 0.6\n", - "start_date = \"2019-05-02 11:00:00\"\n", - "end_date = \"2019-05-02 14:00:00\"\n", - "series, raw_result, top_anomaly = draw(data_source, local_data_path, result_id, severity, start_date, end_date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "start_date = \"2019-05-02 11:00:00\"\n", - "end_date = \"2019-05-02 14:00:00\"\n", - "show_contribution(local_data_path, raw_result, top_anomaly, start_date, end_date)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export model by modelid\n", - "```http\n", - "[GET] https://{endpoint}/multivariate/models/{model_id}/export\n", - "```\n", - "you can take the model zip file to other env to do inference.\n", - "#### Sample\n", - " - Request\n", - "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "```\n", - " - Response\n", - "```json\n", - "zip file\n", - "```\n", - " - Error Response\n", - " ```json\n", - "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", - "}\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = requests.get(API_EXPORT.format(endpoint=ENDPOINT, model_id=model_id), headers=HEADERS)\n", - "assert res.status_code == 200, f\"Error occured. Error message: {res.content}\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete model by modelid\n", - "```http\n", - "[DELETE] https://{endpoint}/multivariate/models/{model_id}\n", - "```\n", - "#### Sample\n", - " - Request\n", - "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "```\n", - " - Response\n", - "```json\n", - "response={}\n", - "```\n", - " - Error Response\n", - " ```json\n", - "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", - "}\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = requests.delete(API_DELETE.format(endpoint=ENDPOINT, model_id=model_id), headers=HEADERS)\n", - "assert res.status_code == 204, f\"Error occured. Error message: {res.content}\"\n", - "print(res.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Model list has been updated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = requests.get(API_MODEL.format(endpoint=ENDPOINT), headers=HEADERS)\n", - "assert res.status_code == 200, f\"Error occured. Error message: {res.content}\"\n", - "print(res.content)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/ipython-notebook/API Sample/Multivariate API Demo Notebook.ipynb b/ipython-notebook/API Sample/Multivariate API Demo Notebook.ipynb index 079d1ec..7d521a3 100644 --- a/ipython-notebook/API Sample/Multivariate API Demo Notebook.ipynb +++ b/ipython-notebook/API Sample/Multivariate API Demo Notebook.ipynb @@ -23,7 +23,7 @@ "source": [ "Multivariate Anomaly Detection Endpoint\n", "\n", - "`multi-ad-ppe.ppe.cognitiveservices.azure.com/anomalydetector/v1.1-preview`\n", + "`multi-ad-ppe.ppe.cognitiveservices.azure.com/anomalydetector/v1.1-preview.1`\n", "\n", "Multivariate Anomaly Detection APIs\n", "- List models `[GET] /multivariate/models`\n", @@ -31,7 +31,7 @@ "- Get a model `[GET] /multivariate/models/{model_id}`\n", "- Delete a model `[DELETE] /multivariate/models/{model_id}`\n", "- Detect anomalies `[POST] /multivariate/models/{model_id}/detect`\n", - "- Export a model `[GET] /multivariate/models/{model_id}/export`\n", + "- Detect anomalies with the synchronous API `[POST] /multivariate/models/{model_id}/last/detect`\n", "- Get detection result `[GET] /multivariate/results/{result_id}`" ] }, @@ -41,26 +41,25 @@ "metadata": {}, "outputs": [], "source": [ - "ENDPOINT = \"[Place_holder].cognitiveservices.azure.com/anomalydetector/v1.1-preview\"\n", + "ENDPOINT = \"[Your endpoint of Anomaly Detector resource]/anomalydetector/v1.1-preview.1\"\n", "HEADERS = {\n", - " \"Ocp-Apim-Subscription-Key\": \"[Place_holder]\"\n", + " \"Ocp-Apim-Subscription-Key\": \"[Your key of Anomaly Detector resource.]\"\n", "}" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "API_MODEL = \"https://{endpoint}/multivariate/models\"\n", - "API_MODEL_STATUS = \"https://{endpoint}/multivariate/models/{model_id}\"\n", - "API_MODEL_INFERENCE = \"https://{endpoint}/multivariate/models/{model_id}/detect\"\n", - "API_MODEL_LAST_INFERENCE = \"https://{endpoint}/multivariate/models/{model_id}/last/detect\"\n", - "API_RESULTS = \"https://{endpoint}/multivariate/results/{result_id}\"\n", - "API_EXPORT = \"https://{endpoint}/multivariate/models/{model_id}/export\"\n", - "API_DELETE = \"https://{endpoint}/multivariate/models/{model_id}\"\n", - "SOURCE_BLOB_SAS = \"https://aka.ms/AnomalyDetector/MVADSampleData\"" + "API_MODEL = \"{endpoint}/multivariate/models\"\n", + "API_MODEL_STATUS = \"{endpoint}/multivariate/models/{model_id}\"\n", + "API_MODEL_INFERENCE = \"{endpoint}/multivariate/models/{model_id}/detect\"\n", + "API_MODEL_LAST_INFERENCE = \"{endpoint}/multivariate/models/{model_id}/last/detect\"\n", + "API_RESULTS = \"{endpoint}/multivariate/results/{result_id}\"\n", + "API_DELETE = \"{endpoint}/multivariate/models/{model_id}\"\n", + "SOURCE_BLOB_SAS = \"[The SAS URL token that generated from your dataset in Azure Storage Account.]\"" ] }, { @@ -74,41 +73,33 @@ "#### Sample\n", " - Request\n", "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", + "header = {\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", "```\n", " - Response\n", "```json\n", - "response={\n", + "{\n", " \"models\": [\n", " {\n", - " \"createdTime\":\"2020-12-01T09:43:45Z\",\n", - " \"displayName\":\"DevOps-Test\",\n", - " \"lastUpdatedTime\":\"2020-12-01T09:46:13Z\",\n", - " \"modelId\":\"b4c1616c-33b9-11eb-824e-0242ac110002\",\n", - " \"status\":\"READY\",\n", - " \"variablesCount\":18\n", - " },\n", - " {\n", - " \"createdTime\":\"2020-12-01T09:43:30Z\",\n", - " \"displayName\":\"DevOps-Test\",\n", - " \"lastUpdatedTime\":\"2020-12-01T09:45:10Z\",\n", - " \"modelId\":\"ab9d3e30-33b9-11eb-a3f4-0242ac110002\",\n", - " \"status\":\"READY\",\n", - " \"variablesCount\":18\n", - " },\n", + " \"modelId\": \"45aad126-aafd-11ea-b8fb-d89ef3400c5f\",\n", + " \"createdTime\": \"2020-06-30T00:00:00Z\",\n", + " \"lastUpdatedTime\": \"2020-06-30T00:00:00Z\",\n", + " \"status\": \"READY\",\n", + " \"displayName\": \"DevOps-MultiAD\",\n", + " \"variablesCount\": 18\n", + " }\n", " ],\n", - " \"currentCount\": 1, # Current count of trained multivariate models.\n", - " \"maxCount\": 50, # Max number of models that can be trained for this subscription.\n", - " \"nextLink\": \"string\" # next link to fetch more models\n", + " \"currentCount\": 1,\n", + " \"maxCount\": 20,\n", + " \"nextLink\": \"\"\n", "}\n", "```\n", " - Error Response\n", - " ```json\n", + "```json\n", "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", + " \"code\": \"string\",\n", + " \"message\": \"string\"\n", "}\n", - " ```" + "```" ] }, { @@ -144,7 +135,7 @@ " - **paddingValue**, optional field, only be usefull if fillNAMethod is set to *Fix*. \n", " - **source**, required field, source path contain the training zip file. \n", " ```bash\n", - " Source file link of the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, an extra meta.json can be included in th zip file if you would like to rename a variable.Be default, the file name of the variable will be used as the variable name.\",example: \"https://multiadsample.blob.core.windows.net/data/sample_data_2_1000.zip?sp=rl&st=2020-12-04T06:03:47Z&se=2022-12-05T06:03:00Z&sv=2019-12-12&sr=b&sig=AZTbvZ7fcp3MdqGY%2FvGHJXJjUgjS4DneCGl7U5omq5c%3D\"\n", + " Source file link to the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, an extra meta.json can be included in th zip file if you would like to rename a variable.By default, the file name of the variable will be used as the variable name. For example https://mvadsampledata.blob.core.windows.net/sample/sample_data_20_3000.zip\n", " ```\n", " - **startTime**, require field, it means start time of data you want to use for training, should be %Y-%m-%dT%H:%M:%SZ \n", " format.\n", @@ -176,12 +167,13 @@ " ```json\n", " Location:\t{endpoint}/multivariate/models/927e2af8-a657-11ea-8cae-0242ac110002 # GUID\n", " ```\n", - " - Error Response\n", + " - Error Response\n", " ```json\n", " {\n", " \"code\": \"string\",\n", " \"message\": \"string\"\n", - " } ```" + " } \n", + "```" ] }, { @@ -229,50 +221,141 @@ " - Response\n", " \n", "```json\n", - "response = {\n", - " \"createdTime\":\"2020-07-01T07:58:37Z\",\n", - " \"lastUpdatedTime\":\"2020-07-01T07:59:55Z\",\n", - " \"modelId\":\"ab888466-bb70-11ea-958f-0242ac110002\",\n", - " \"modelInfo\":{ # Training Status of the model.\n", - " \"diagnoseInfo\":{\n", - " \"modelState\":{\n", - " \"epochIds\":[10, 20, 30, 40, 50, 60, 70, 80, 90, 100], # 100 epoc in total\n", - " \"latenciesInSeconds\":[0.5837657451629639, 0.5688292980194092, 0.5959596633911133, 0.5251538753509521, 0.6021878719329834, 0.6459534168243408, 0.5391685962677002, 0.5622642040252686, 0.5487074851989746, 0.6336326599121094],\n", - " \"trainLosses\":[1.682054042816162, 0.7844524383544922, 0.6616984605789185, 0.6293938159942627, 0.6323581337928772, 0.6257774233818054, 0.5985430479049683, 0.6037595868110657, 0.5779791474342346, 0.5583345293998718],\n", - " \"validationLosses\":[0.8330008387565613, 0.6937242150306702, 0.7329594492912292, 0.6103720664978027, 0.6125020980834961, 0.5729937553405762, 0.5761528611183167, 0.5710235238075256, 0.5679566264152527, 0.5674979090690613]\n", - " },\n", - " \"variableStates\":[\n", - " { \n", - " \"effectiveCount\":1441,\n", - " \"endTime\":\"2019-04-02T00:00:00Z\",\n", - " \"errors\":[],\n", - " \"filledNARatio\":0.0,\n", - " \"startTime\":\"2019-04-01T00:00:00Z\",\n", - " \"variable\":\"established_connections\"\n", - " },\n", - " { \n", - " \"effectiveCount\":1441, # Effective time-series points count.\n", - " \"endTime\":\"2019-04-02T00:00:00Z\", # End time of a variable\n", - " \"errors\":[],\n", - " \"filledNARatio\":0.0, # NA ratio of a variable.\n", - " \"startTime\":\"2019-04-01T00:00:00Z\", # Start time of a variable\n", - " \"variable\":\"memory\"\n", - " }\n", - " ]\n", + "{\n", + " \"modelId\": \"45aad126-aafd-11ea-b8fb-d89ef3400c5f\",\n", + " \"createdTime\": \"2020-06-30T00:00:00Z\",\n", + " \"lastUpdatedTime\": \"2020-06-30T00:00:00Z\",\n", + " \"modelInfo\": {\n", + " \"slidingWindow\": 20,\n", + " \"alignPolicy\": {\n", + " \"alignMode\": \"Outer\",\n", + " \"fillNAMethod\": \"Linear\",\n", + " \"paddingValue\": 0\n", + " },\n", + " \"source\": SOURCE_BLOB_SAS,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\",\n", + " \"displayName\": \"Devops-MultiAD\",\n", + " \"status\": \"READY\",\n", + " \"errors\": [],\n", + " \"diagnosticsInfo\": {\n", + " \"modelState\": {\n", + " \"epochIds\": [\n", + " 10,\n", + " 20,\n", + " 30,\n", + " 40,\n", + " 50,\n", + " 60,\n", + " 70,\n", + " 80,\n", + " 90,\n", + " 100\n", + " ],\n", + " \"trainLosses\": [\n", + " 0.6291328072547913,\n", + " 0.1671326905488968,\n", + " 0.12354248017072678,\n", + " 0.10259664058685303,\n", + " 0.09584927558898926,\n", + " 0.09069952368736267,\n", + " 0.08686016499996185,\n", + " 0.08603022992610931,\n", + " 0.08287354558706284,\n", + " 0.08235538005828857\n", + " ],\n", + " \"validationLosses\": [\n", + " 1.9232804775238037,\n", + " 1.0645641088485718,\n", + " 0.6031560301780701,\n", + " 0.5302737951278687,\n", + " 0.46980252861976624,\n", + " 0.4395163357257843,\n", + " 0.41829314827919006,\n", + " 0.40579143166542053,\n", + " 0.405649870634079,\n", + " 0.38492488861083984\n", + " ],\n", + " \"latenciesInSeconds\": [\n", + " 0.3398594856262207,\n", + " 0.3659665584564209,\n", + " 0.37360644340515137,\n", + " 0.35134077072143555,\n", + " 0.33703041076660156,\n", + " 0.31876277923583984,\n", + " 0.32833099365234375,\n", + " 0.3503587245941162,\n", + " 0.3080024719238281,\n", + " 0.3327946662902832\n", + " ]\n", + " },\n", + " \"variableStates\": [\n", + " {\n", + " \"variable\": \"ad_input\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"ad_ontimer_output\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"ingestion\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"data_in_speed\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", " },\n", - " \"errors\":[], # Error message when creating or training model fails.\n", - " \"status\":\"READY\",\n", - " \"alignPolicy\":{\n", - " \"alignMode\":\"Outer\",\n", - " \"fillNAMethod\":\"Linear\",\n", - " \"paddingValue\":0\n", - " },\n", - " \"displayName\":\"DevOps-Test\",\n", - " \"endTime\":\"2019-04-02T00:00:00Z\",\n", - " \"slidingWindow\":28,\n", - " \"source\":\"/data/sample_data.zip\",\n", - " \"startTime\":\"2019-04-01T00:00:00Z\"\n", + " {\n", + " \"variable\": \"cpu\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"ad_series_init\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"flink_last_ckpt_duration\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"data_out_speed\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"ad_output\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 1441,\n", + " \"startTime\": \"2019-04-01T00:00:00Z\",\n", + " \"endTime\": \"2019-04-02T00:00:00Z\"\n", + " }\n", + " ]\n", " }\n", + " }\n", "}\n", " \n", "```\n", @@ -379,80 +462,95 @@ "- Response\n", "```json\n", "Response={\n", - " \"resultId\": \"45aad126-aafd-11ea-b8fb-d89ef3400c5f\",\n", - " \"summary\": {\n", - " \"status\": \"READY\", # Multivariate anomaly detection status\n", - " \"errors\": [ # Error message when inference fails.\n", - " {\n", - " \"code\": \"string\",\n", - " \"message\": \"string\"\n", - " }\n", - " ],\n", - " \"variableStates\": [\n", - " {\n", - " \"variable\": \"ad_input\",\n", - " \"filledNARatio\": 0,\n", - " \"effectiveCount\": 26,\n", - " \"startTime\": \"2019-04-01T00:00:00Z\",\n", - " \"endTime\": \"2019-04-01T00:25:00Z\",\n", - " \"errors\": []\n", - " },\n", - " {\n", - " \"variable\": \"ad_ontimer_output\",\n", - " \"filledNARatio\": 0,\n", - " \"effectiveCount\": 26,\n", - " \"startTime\": \"2019-04-01T00:00:00Z\",\n", - " \"endTime\": \"2019-04-01T00:25:00Z\",\n", - " \"errors\": []\n", - " }\n", - " ],\n", - " \"setupInfo\": {\n", - " \"source\": \"/data/{$zipfile_name}\",\n", - " \"startTime\": \"2019-04-01T00:15:00Z\",\n", - " \"endTime\": \"2019-04-01T00:40:00Z\"\n", - " }\n", - " },\n", - " \"results\": [\n", - " {\n", - " \"timestamp\": \"2019-04-01T00:19:00Z\",\n", - " \"errors\": [\n", - " {\n", - " \"code\": \"InsufficientHistoricalData\",\n", - " \"message\": \"historical data is not enough.\"\n", - " }\n", - " ]\n", - " },\n", - " {\n", - " \"timestamp\": \"2019-04-01T00:20:00Z\",\n", - " \"value\":{\n", - " \"contributors\": [],# isAnomaly is false, contributors is empty.\n", - " \"isAnomaly\": false,\n", - " \"severity\": 0.3509107994398884,\n", - " \"score\": 0.34231\n", - " },\n", - " \"errors\": []\n", - " },\n", - " {\n", - " \"timestamp\": \"2019-04-01T00:21:00Z\",\n", - " \"value\":{\n", - " \"contributors\": [\n", + " \"resultId\": \"663884e6-b117-11ea-b3de-0242ac130004\",\n", + " \"summary\": {\n", + " \"status\": \"READY\",\n", + " \"errors\": [],\n", + " \"variableStates\": [\n", + " {\n", + " \"variable\": \"variable_1\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 30,\n", + " \"startTime\": \"2021-01-01T00:00:00Z\",\n", + " \"endTime\": \"2021-01-01T00:29:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"variable_2\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 30,\n", + " \"startTime\": \"2021-01-01T00:00:00Z\",\n", + " \"endTime\": \"2021-01-01T00:29:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"variable_3\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 30,\n", + " \"startTime\": \"2021-01-01T00:00:00Z\",\n", + " \"endTime\": \"2021-01-01T00:29:00Z\"\n", + " }\n", + " ],\n", + " \"setupInfo\": {\n", + " \"source\": SOURCE_BLOB_SAS,\n", + " \"startTime\": \"2021-01-01T00:00:00Z\",\n", + " \"endTime\": \"2021-01-01T00:29:00Z\"\n", + " }\n", + " },\n", + " \"results\": [\n", " {\n", - " \"contributionScore\": 0.0007775013367514271, # The higher the contributionScore is, the more likely the contributor to be the root cause of a anomaly.\n", - " \"variable\": \"ad_ontimer_output\" # Variable name of a contributor\n", + " \"timestamp\": \"2021-01-01T00:28:00Z\",\n", + " \"value\": {\n", + " \"isAnomaly\": false,\n", + " \"severity\": 0,\n", + " \"score\": 0.6928471326828003\n", + " },\n", + " \"errors\": []\n", " },\n", " {\n", - " \"contributionScore\": 0.0007989604079048129,\n", - " \"variable\": \"ad_input\"\n", + " \"timestamp\": \"2021-01-01T00:29:00Z\",\n", + " \"value\": {\n", + " \"isAnomaly\": true,\n", + " \"severity\": 0.5337404608726501,\n", + " \"score\": 0.9171165823936462,\n", + " \"interpretation\": [\n", + " {\n", + " \"variable\": \"variable_2\",\n", + " \"contributionScore\": 0.5371576215,\n", + " \"correlationChanges\": {\n", + " \"changedVariables\": [\n", + " \"variable_1\",\n", + " \"variable_3\"\n", + " ],\n", + " \"changedValues\": [\n", + " 0.1741322,\n", + " 0.1093203\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"variable\": \"variable_3\",\n", + " \"contributionScore\": 0.3324159383,\n", + " \"correlationChanges\": {\n", + " \"changedVariables\": [\n", + " \"variable_2\"\n", + " ],\n", + " \"changedValues\": [\n", + " 0.1229392\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"variable\": \"variable_1\",\n", + " \"contributionScore\": 0.1304264402,\n", + " \"correlationChanges\": {\n", + " \"changedVariables\": [],\n", + " \"changedValues\": []\n", + " }\n", + " }\n", + " ]\n", + " }\n", " }\n", - " ],\n", - " \"isAnomaly\": true, # To indicate whether current timestamp is anomaly or not\n", - " \"severity\": 0.42135109874230336, # severity of the current timestamp, the more significant an anomaly is, the higher the severity will be\n", - " \"score\": 0.23485905670108112\n", - " },\n", - " \"errors\": []\n", - " }\n", - " ]\n", - "}\n", + " ]\n", + " }\n", "```\n", " - Error Response\n", " ```json\n", @@ -478,7 +576,178 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Detection with the synchronized API" + "## Detection with the synchronized API\n", + "```http\n", + "[POST] https://{endpoint}/multivariate/models/{modelId}/last/detect\n", + "```\n", + "Detect multivariate anomaly with the trained model Id and the multivariate time-series data in request body. This API returns the anomalies in the given data.\n", + "\n", + "- #### Request\n", + " - **variables**, required field, containing time-series values and timestamps\n", + " - **detectingPoints**, required field, containing how many timestamps to detect (valid range 1-10 inclusive). \n", + "\n", + "- #### Response\n", + " - detection results\n", + " \n", + "- #### Sample \n", + "- Request\n", + "\n", + "```json\n", + "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", + "request={\n", + " \"variables\": [\n", + " {\n", + " \"name\": \"Variable_1\",\n", + " \"timestamps\": [\n", + " \"2021-01-01T00:00:00Z\", \"2021-01-01T00:01:00Z\", \"2021-01-01T00:02:00Z\", \"2021-01-01T00:03:00Z\", \"2021-01-01T00:04:00Z\",\n", + " \"2021-01-01T00:05:00Z\", \"2021-01-01T00:06:00Z\", \"2021-01-01T00:07:00Z\", \"2021-01-01T00:08:00Z\", \"2021-01-01T00:09:00Z\",\n", + " \"2021-01-01T00:10:00Z\", \"2021-01-01T00:11:00Z\", \"2021-01-01T00:12:00Z\", \"2021-01-01T00:13:00Z\", \"2021-01-01T00:14:00Z\",\n", + " \"2021-01-01T00:15:00Z\", \"2021-01-01T00:16:00Z\", \"2021-01-01T00:17:00Z\", \"2021-01-01T00:18:00Z\", \"2021-01-01T00:19:00Z\",\n", + " \"2021-01-01T00:20:00Z\", \"2021-01-01T00:21:00Z\", \"2021-01-01T00:22:00Z\", \"2021-01-01T00:23:00Z\", \"2021-01-01T00:24:00Z\",\n", + " \"2021-01-01T00:25:00Z\", \"2021-01-01T00:26:00Z\", \"2021-01-01T00:27:00Z\", \"2021-01-01T00:28:00Z\", \"2021-01-01T00:29:00Z\"\n", + " ],\n", + " \"values\": [\n", + " 0.4551378545933972, 0.7388603950488748, 0.201088255984052, 0.7462812245891899, 0.07308128850401663,\n", + " 0.33090474587393537, 0.7544925268153315, 0.987506336316328, 0.6665932993421468, 0.6308351543168672,\n", + " 0.08083310161466228, 0.8414415588668442, 0.514583545640453, 0.0954489875193526, 0.7786793231920507,\n", + " 0.41646133667960994, 0.030176187583339287, 0.3474214937189324, 0.508530173413991, 0.42451199127255046,\n", + " 0.2115944222725208, 0.24733519545833516, 0.8791022110982156, 0.9479621899884665, 0.26702703121252136,\n", + " 0.6954503497669413, 0.1235728391488995, 0.8214915473050647, 0.11813002444192677, 0.8579045951076123\n", + " ]\n", + " },\n", + " {\n", + " \"name\": \"Variable_2\",\n", + " \"timestamps\": [\n", + " \"2021-01-01T00:00:00Z\", \"2021-01-01T00:01:00Z\", \"2021-01-01T00:02:00Z\", \"2021-01-01T00:03:00Z\", \"2021-01-01T00:04:00Z\",\n", + " \"2021-01-01T00:05:00Z\", \"2021-01-01T00:06:00Z\", \"2021-01-01T00:07:00Z\", \"2021-01-01T00:08:00Z\", \"2021-01-01T00:09:00Z\",\n", + " \"2021-01-01T00:10:00Z\", \"2021-01-01T00:11:00Z\", \"2021-01-01T00:12:00Z\", \"2021-01-01T00:13:00Z\", \"2021-01-01T00:14:00Z\",\n", + " \"2021-01-01T00:15:00Z\", \"2021-01-01T00:16:00Z\", \"2021-01-01T00:17:00Z\", \"2021-01-01T00:18:00Z\", \"2021-01-01T00:19:00Z\",\n", + " \"2021-01-01T00:20:00Z\", \"2021-01-01T00:21:00Z\", \"2021-01-01T00:22:00Z\", \"2021-01-01T00:23:00Z\", \"2021-01-01T00:24:00Z\",\n", + " \"2021-01-01T00:25:00Z\", \"2021-01-01T00:26:00Z\", \"2021-01-01T00:27:00Z\", \"2021-01-01T00:28:00Z\", \"2021-01-01T00:29:00Z\"\n", + " ],\n", + " \"values\": [\n", + " 0.9617871613964145, 0.24903311574778408, 0.4920561254118613, 0.9895601049618598, 0.9171759283128094,\n", + " 0.5754204711105273, 0.1811033296265634, 0.8852311981742577, 0.9543231904644779, 0.7088012446094262,\n", + " 0.7843572237149014, 0.7664787010700046, 0.3699552325387093, 0.504519908266789, 0.5848930929950164,\n", + " 0.7628913396089576, 0.8148405868900065, 0.08540458873739332, 0.03481976727525682, 0.21275099339467762,\n", + " 0.9836175579199806, 0.9321441483364282, 0.038466608085469534, 0.1723138437622782, 0.8626383410218382,\n", + " 0.35053229974224254, 0.631141662835182, 0.0730352607990088, 0.08886179043386, 0.7488606040971179\n", + " ]\n", + " },\n", + " {\n", + " \"name\": \"Variable_3\",\n", + " \"timestamps\": [\n", + " \"2021-01-01T00:00:00Z\", \"2021-01-01T00:01:00Z\", \"2021-01-01T00:02:00Z\", \"2021-01-01T00:03:00Z\", \"2021-01-01T00:04:00Z\",\n", + " \"2021-01-01T00:05:00Z\", \"2021-01-01T00:06:00Z\", \"2021-01-01T00:07:00Z\", \"2021-01-01T00:08:00Z\", \"2021-01-01T00:09:00Z\",\n", + " \"2021-01-01T00:10:00Z\", \"2021-01-01T00:11:00Z\", \"2021-01-01T00:12:00Z\", \"2021-01-01T00:13:00Z\", \"2021-01-01T00:14:00Z\",\n", + " \"2021-01-01T00:15:00Z\", \"2021-01-01T00:16:00Z\", \"2021-01-01T00:17:00Z\", \"2021-01-01T00:18:00Z\", \"2021-01-01T00:19:00Z\",\n", + " \"2021-01-01T00:20:00Z\", \"2021-01-01T00:21:00Z\", \"2021-01-01T00:22:00Z\", \"2021-01-01T00:23:00Z\", \"2021-01-01T00:24:00Z\",\n", + " \"2021-01-01T00:25:00Z\", \"2021-01-01T00:26:00Z\", \"2021-01-01T00:27:00Z\", \"2021-01-01T00:28:00Z\", \"2021-01-01T00:29:00Z\"\n", + " ],\n", + " \"values\": [\n", + " 0.4030756879437628, 0.15526889968448554, 0.36352226408981103, 0.6051200637229004, 0.8516795018476276,\n", + " 0.2645605735279929, 0.6810875830037345, 0.9165894221681316, 0.700783245230424, 0.5624155469940331,\n", + " 0.6277289685127893, 0.15992056539730204, 0.6020964482827594, 0.35937967753105915, 0.8731686034848609,\n", + " 0.20301549117588935, 0.029261872151168933, 0.6261499548828445, 0.45850782028563386, 0.8275006940083313,\n", + " 0.032760268834037376, 0.4485202784055029, 0.8915691008748384, 0.891669051517807, 0.9469979353323046,\n", + " 0.115293087370132, 0.08818772518459506, 0.7426286620589166, 0.32372247468990756, 0.936268139507417\n", + " ]\n", + " }\n", + " ],\n", + " \"detectingPoints\": 2\n", + "}\n", + "```\n", + "- Response\n", + "```json\n", + "{\n", + " \"variableStates\": [\n", + " {\n", + " \"variable\": \"variable_1\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 30,\n", + " \"startTime\": \"2021-01-01T00:00:00Z\",\n", + " \"endTime\": \"2021-01-01T00:29:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"variable_2\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 30,\n", + " \"startTime\": \"2021-01-01T00:00:00Z\",\n", + " \"endTime\": \"2021-01-01T00:29:00Z\"\n", + " },\n", + " {\n", + " \"variable\": \"variable_3\",\n", + " \"filledNARatio\": 0,\n", + " \"effectiveCount\": 30,\n", + " \"startTime\": \"2021-01-01T00:00:00Z\",\n", + " \"endTime\": \"2021-01-01T00:29:00Z\"\n", + " }\n", + " ],\n", + " \"results\": [\n", + " {\n", + " \"timestamp\": \"2021-01-01T00:28:00Z\",\n", + " \"value\": {\n", + " \"isAnomaly\": false,\n", + " \"severity\": 0,\n", + " \"score\": 0.6928471326828003\n", + " },\n", + " \"errors\": []\n", + " },\n", + " {\n", + " \"timestamp\": \"2021-01-01T00:29:00Z\",\n", + " \"value\": {\n", + " \"isAnomaly\": true,\n", + " \"severity\": 0.5337404608726501,\n", + " \"score\": 0.9171165823936462,\n", + " \"interpretation\": [\n", + " {\n", + " \"variable\": \"variable_2\",\n", + " \"contributionScore\": 0.5371576215,\n", + " \"correlationChanges\": {\n", + " \"changedVariables\": [\n", + " \"variable_1\",\n", + " \"variable_3\"\n", + " ],\n", + " \"changedValues\": [\n", + " 0.1741322,\n", + " 0.1093203\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"variable\": \"variable_3\",\n", + " \"contributionScore\": 0.3324159383,\n", + " \"correlationChanges\": {\n", + " \"changedVariables\": [\n", + " \"variable_2\"\n", + " ],\n", + " \"changedValues\": [\n", + " 0.1229392\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"variable\": \"variable_1\",\n", + " \"contributionScore\": 0.1304264402,\n", + " \"correlationChanges\": {\n", + " \"changedVariables\": [],\n", + " \"changedValues\": []\n", + " }\n", + " }\n", + " ]\n", + " },\n", + " \"errors\": []\n", + " }\n", + " ]\n", + "}\n", + "```\n", + "- Error Response\n", + " ```json\n", + "{\n", + "\"code\": \"string\",\n", + "\"message\": \"string\"\n", + "}\n", + " ```" ] }, { @@ -606,13 +875,17 @@ " grid = gridplot([[x] for x in p_list], sizing_mode='scale_width', plot_height=50)\n", " show(grid)\n", " result = result.sort_values(by=['RawScore'], ascending=False)\n", - " top_anomaly = list(result[result.isAnomaly].index.strftime('%Y-%m-%dT%H:%M:%SZ'))[0]\n", - " print(\"Top Anomaly Timestamp is : {0}\".format(top_anomaly))\n", - " return series, raw_result, top_anomaly\n", + " top_anomaly = list(result[result.isAnomaly].index.strftime('%Y-%m-%dT%H:%M:%SZ'))\n", + " if len(top_anomaly) > 0: \n", + " print(\"Top Anomaly Timestamp is : {0}\".format(top_anomaly[0]))\n", + " return series, raw_result, top_anomaly[0]\n", + " else:\n", + " print(\"No anomaly detected. Sensitivity may be too low.\")\n", + " return series, raw_result, None\n", "\n", "def show_contribution(series, raw_result, anomaly_timestamp):\n", - " anomaly_result = [x for x in raw_result['results'] if 'contributors' in x['value'] and x['timestamp'] == top_anomaly][0]\n", - " contributors = [x['variable'] for x in anomaly_result['value']['contributors']]\n", + " anomaly_result = [x for x in raw_result['results'] if 'interpretation' in x['value'] and x['timestamp'] == top_anomaly][0]\n", + " contributors = [x['variable'] for x in anomaly_result['value']['interpretation']]\n", " series_index = pd.DataFrame({'index': list(range(0, len(series))), 'name': [x.columns[0] for x in series]})\n", " series_index = series_index.set_index('name')\n", " sorted_series = [series[i][series[i].index <= top_anomaly].tail(SLIDING_WINDOW) for i in series_index.reindex(contributors)['index'].values]\n", @@ -635,46 +908,10 @@ "source": [ "data_source = SOURCE_BLOB_SAS\n", "local_data_path = \"sample_data_5_3000.zip\"\n", - "severity = 0.7\n", + "sensitivity = 0.7\n", "start_date = \"2021-01-02T12:00:00Z\"\n", "end_date = \"2021-01-03T00:00:00Z\"\n", - "series, raw_result, top_anomaly = draw(data_source, local_data_path, result_id, severity, start_date, end_date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "show_contribution(series, raw_result, top_anomaly)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export model by modelid\n", - "```http\n", - "[GET] https://{endpoint}/multivariate/models/{model_id}/export\n", - "```\n", - "you can take the model zip file to other env to do inference.\n", - "#### Sample\n", - " - Request\n", - "```json\n", - "header ={\"Content-Type\": \"application/json\", \"Ocp-Apim-Subscription-Key\": \"subscription_key\"}\n", - "```\n", - " - Response\n", - "```json\n", - "zip file\n", - "```\n", - " - Error Response\n", - " ```json\n", - "{\n", - "\"code\": \"string\",\n", - "\"message\": \"string\"\n", - "}\n", - " ```" + "series, raw_result, top_anomaly = draw(data_source, local_data_path, result_id, sensitivity, start_date, end_date)" ] }, { @@ -683,8 +920,8 @@ "metadata": {}, "outputs": [], "source": [ - "res = requests.get(API_EXPORT.format(endpoint=ENDPOINT, model_id=model_id), headers=HEADERS)\n", - "assert res.status_code == 200, f\"Error occured. Error message: {res.content}\"" + "if top_anomaly:\n", + " show_contribution(series, raw_result, top_anomaly)" ] }, { @@ -752,7 +989,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.12 ('base')", "language": "python", "name": "python3" }, @@ -766,7 +1003,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "aa60471fcc78aee1d161dfae290e862a10214dc7d6ca1ceb34988aca5ae05936" + } } }, "nbformat": 4, diff --git "a/ipython-notebook/API Sample/\360\237\206\225MVAD API Demo (GA version).ipynb" "b/ipython-notebook/API Sample/\360\237\206\225MVAD API Demo (GA version).ipynb" new file mode 100644 index 0000000..2cf9ec3 --- /dev/null +++ "b/ipython-notebook/API Sample/\360\237\206\225MVAD API Demo (GA version).ipynb" @@ -0,0 +1,1492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7b72c4fa", + "metadata": {}, + "source": [ + "# Multivariate Anomaly Detection API Demo Notebook" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5c074280", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Introduction](#intro)\n", + "2. [Create an Anomaly Detector](#ad)\n", + "1. [Create a Storage Account](#sa)\n", + "1. [Prepare your data](#data)\n", + "1. [Train a model](#train)\n", + "1. [Inference](#inference)\n", + "1. [List models](#list)\n", + "1. [Delete model](#delete)\n", + "1. [Visualization](#vis)" + ] + }, + { + "cell_type": "markdown", + "id": "793d6335", + "metadata": {}, + "source": [ + "## 1. Introdution \n", + "This notebook shows how to use [Multivariate Anomaly Detection API (GA version)](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview) in Anomaly Detector service. Please follow the steps to try it out, you can either [join Teams Group](https://forms.office.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbRxSkyhztUNZCtaivu8nmhd1UQ1VFRDA0V1dUMDJRMFhOTzFHQ1lDTVozWi4u) for any questions and get updated for product iteration, or email us via [AnomalyDetector@microsoft.com](mailto:anomalydetector@microsoft.com)." + ] + }, + { + "cell_type": "markdown", + "id": "f31073a7", + "metadata": {}, + "source": [ + "There are 7 APIs provided in Multivariate Anomaly Detection:\n", + "* **Training**: Use `Train Model API` to create and train a model, then use `Get Model Status API` to get the status and model metadata.\n", + "* **Inference**: \n", + " * Use `Async Inference API` to trigger an asynchronous inference process and use `Get Inference results API` to get detection results on a batch of data.\n", + " * You could also use `Sync Inference API` to trigger a detection on one timestamp every time.\n", + "* **Other operations**: `List Model API` and `Delete Model API` are supported in MVAD for model management.\n", + "![](https://i.imgur.com/PQ7h6Ix.png)\n", + "i" + ] + }, + { + "cell_type": "markdown", + "id": "03eb31c1", + "metadata": {}, + "source": [ + "|API Name| Method | Path | Description |\n", + "| ------ | ---- | ----------- | ------ | \n", + "|**Train Model**| POST | `{endpoint}`/anomalydetector/v1.1/multivariate/models | Create and train a model |\n", + "|**Get Model Status**| GET | `{endpoint}`anomalydetector/v1.1/multivariate/models/`{modelId}` | Get model status and model metadata with `modelId` |\n", + "|**Async Inference**| POST | `{endpoint}`/anomalydetector/v1.1/multivariate/models/`{modelId}`:detect-batch | Trigger an asynchronous inference with `modelId` |\n", + "|**Get Inference Results**| GET | `{endpoint}`/anomalydetector/v1.1/multivariate/detect-batch/`{resultId}` | Get asynchronous inference resulsts with `resultId` |\n", + "|**Sync Inference**| POST | `{endpoint}`/anomalydetector/v1.1/multivariate/models/`{modelId}`:detect-last | Trigger a synchronous inference with `modelId` |\n", + "|**List Model**| GET | `{endpoint}`/anomalydetector/v1.1/multivariate/models | List all models |\n", + "|**Delete Model**| DELET | `{endpoint}`/anomalydetector/v1.1/multivariate/models/`{modelId}` | Delete model with `modelId` |" + ] + }, + { + "cell_type": "markdown", + "id": "fc32dfb5", + "metadata": {}, + "source": [ + "## 2. Create an Anomaly Detector " + ] + }, + { + "cell_type": "markdown", + "id": "0a40d185", + "metadata": {}, + "source": [ + "\n", + "* [Create an Azure subscription](https://azure.microsoft.com/free/cognitive-services) if you don't have one.\n", + "* [Create an Anomaly Detector resource](https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesAnomalyDetector) and get your `endpoint` and `key`, paste them in the cell below.\n", + "\n", + "![](https://i.imgur.com/M7qATAY.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f0fa7405", + "metadata": {}, + "outputs": [], + "source": [ + "# The ENDPOINT should be like: https://mvad-sample.cognitiveservices.azure.com/anomalydetector/v1.1\n", + "ENDPOINT = \"{Your endpoint of Anomaly Detector resource}anomalydetector/v1.1\"\n", + "HEADERS = {\n", + " \"Ocp-Apim-Subscription-Key\": \"{Your key of Anomaly Detector resource.}\"\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f511879", + "metadata": {}, + "source": [ + "## 3. Create an Storage Account " + ] + }, + { + "cell_type": "markdown", + "id": "ac813a67", + "metadata": {}, + "source": [ + "1. [Create a Storage Account](https://ms.portal.azure.com/#create/Microsoft.StorageAccount-ARM).\n", + "1. Go to **Access Control(IAM)**, and select **+ADD** to **Add role assignment**.\n", + "3. Search role of **Storage Blob Data Reader**, **click on it** and then select **Next**.\n", + "4. Select assign access to **Managed identity**, and **Select Members**, then choose the anomaly detector resource that you created earlier, then select **Review + assign**.\n", + "\n", + "![](https://i.imgur.com/dsTsuxI.gif)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "3ed092f7", + "metadata": {}, + "source": [ + "## 4. Prepare your data \n", + "\n", + "### Download sample data\n", + "Multivariate Anomaly Detection supports two types of data schema for training and async inference: **`Multiple Tables`** and **`One Table`**. \n", + "\n", + "* Download [One Table sample data](https://mvaddataset.blob.core.windows.net/public-sample-data/sample_data_5_3000.csv)\n", + "* Download [Multiple Tables sample data](https://mvaddataset.blob.core.windows.net/public-sample-data/sample_data_5_3000.zip) and unzip it.\n", + "\n", + "### Upload data to Storage Account\n", + "\n", + "1. Go to your Storage Account, select **Containers** and create a new container.\n", + "2. Select **Upload** to upload the folder *(for multiple tables)* or the CSV file *(for one table*) that you downloaded earlier.\n", + "3. Click on the data that you uploaded and copy the **Blob URL** to somewhere for further steps." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6a5cd4c0", + "metadata": {}, + "outputs": [], + "source": [ + "# If your data is one CSV file, please set the dataSchema as `OneTable`\n", + "# If your data is multiple CSV files in a folder, please set the dataSchema as `MultiTable`.\n", + "DATA_SCHEMA=\"OneTable\"\n", + "# The data source url should be like: https://mvaddataset.blob.core.windows.net/sample-multitable/sample_data_5_3000.csv\n", + "DATA_SOURCE_URL= \"{The Blob URL for your data.}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1cbb4993", + "metadata": {}, + "outputs": [], + "source": [ + "# Run directly\n", + "API_MODEL = \"{endpoint}/multivariate/models\"\n", + "API_MODEL_STATUS = \"{endpoint}/multivariate/models/{model_id}\"\n", + "API_MODEL_BATCH_INFERENCE = \"{endpoint}/multivariate/models/{model_id}:detect-batch\"\n", + "API_MODEL_LAST_INFERENCE = \"{endpoint}/multivariate/models/{model_id}:detect-last\"\n", + "API_RESULTS = \"{endpoint}/multivariate/detect-batch/{result_id}\"\n", + "API_DELETE = \"{endpoint}/multivariate/models/{model_id}\"" + ] + }, + { + "cell_type": "markdown", + "id": "50c84aac", + "metadata": {}, + "source": [ + "## 5. Train a model " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "39255cbd", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import json\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c1c96fb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model is created with the modelId: eb65081c-5a2d-11ed-863d-b618fab0ff58, please check the model status using next cell.😉\n" + ] + } + ], + "source": [ + "SLIDING_WINDOW = 200\n", + "# Change the TRAINING_START_TIME and TRAINING_END_TIME if you use another dataset.\n", + "TRAINING_START_TIME = \"2021-01-01T00:00:00Z\"\n", + "TRAINING_END_TIME = \"2021-01-02T09:19:00Z\"\n", + "data = {\n", + " \"slidingWindow\": SLIDING_WINDOW,\n", + " \"alignPolicy\": {\n", + " \"alignMode\": \"Outer\",\n", + " \"fillNAMethod\": \"Linear\", \n", + " \"paddingValue\": 0\n", + " },\n", + " \"dataSource\": DATA_SOURCE_URL,\n", + " \"dataSchema\": DATA_SCHEMA,\n", + " \"startTime\": TRAINING_START_TIME, \n", + " \"endTime\": TRAINING_END_TIME, \n", + " \"displayName\": \"SampleRequest\"\n", + "}\n", + "\n", + "res = requests.post(API_MODEL.format(endpoint=ENDPOINT), data=json.dumps(data), headers=HEADERS)\n", + "assert res.status_code == 201, f\"Error occurred. Error message: {res.content}\"\n", + "# Print the following line to get the epochId which could help you better know the status of model. There are 100 epochIds in total.\n", + "# print(res.content)\n", + "location = res.headers['Location']\n", + "model_id = location[location.rindex('/')+1:]\n", + "print(f\"Model is created with the modelId: {model_id}, please check the model status using next cell.😉\")" + ] + }, + { + "cell_type": "markdown", + "id": "bf8ad8e6", + "metadata": {}, + "source": [ + "The training might takes few minutes to few hours which depends on you data size, you could take a coffee ☕ and come back to check the model status." + ] + }, + { + "cell_type": "markdown", + "id": "c7b74307", + "metadata": {}, + "source": [ + "### Get model status\n", + "After the model is created, you need to run the below code several times to get the status of model until it's **READY** for inference." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "90e5b86c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model status: READY.\n", + "You could run the inference below once the model status is READY.\n" + ] + } + ], + "source": [ + "res = requests.get(API_MODEL_STATUS.format(endpoint=ENDPOINT, model_id = model_id), headers=HEADERS)\n", + "assert res.status_code == 200, f\"Error occurred. Error message: {res.content}\"\n", + "res_content = json.loads(res.content)\n", + "# print(json.dumps(res_content))\n", + "print(f\"Model status: {res_content['modelInfo']['status']}.\")\n", + "print(\"You could run the inference below once the model status is READY.\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d1777220", + "metadata": {}, + "source": [ + "## 6. Inference \n", + "\n", + "There are two types of inference:\n", + "* Batch inference (asynchronous)\n", + "* Streaming inference (synchronous)\n", + "\n", + "You could use the `Batch Inference API` for a long period of data validation or for a micro-batch task, which works in an asynchronous way together with the `Get Inference Results API`. If your case is more like real-time or near real-time, the `Streaming Inference API` might be a good choice for you since it will present the anomaly results immediately once you call this API and get every timestamp detected per call. " + ] + }, + { + "cell_type": "markdown", + "id": "346728e6", + "metadata": {}, + "source": [ + "### Trigger a batch inference" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "46e2597e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A batch inference is triggered with the resultId: 5f783ec2-5a2e-11ed-b4b9-b618fab0ff58, please get the detection results using the next cell.\n" + ] + } + ], + "source": [ + "# Change the INFERENCE_START_TIME and INFERENCE_END_TIME if you use another dataset.\n", + "INFERENCE_START_TIME = \"2021-01-02T12:00:00Z\"\n", + "INFERENCE_END_TIME = \"2021-01-03T00:00:00Z\"\n", + "\n", + "data = {\n", + " \"dataSource\": DATA_SOURCE_URL,\n", + " # The topContributorCount specify how many contributed variables you care about in the results.\n", + " \"topContributorCount\": 10,\n", + " \"startTime\": INFERENCE_START_TIME, \n", + " \"endTime\": INFERENCE_END_TIME\n", + "}\n", + "\n", + "res = requests.post(API_MODEL_BATCH_INFERENCE.format(endpoint=ENDPOINT, model_id=model_id), \n", + " data=json.dumps(data), headers=HEADERS)\n", + "\n", + "# print(res.headers)\n", + "# print(res.content)\n", + "assert res.status_code == 202, f\"Error occurred. Error message: {res.content}\"\n", + "result_id = res.headers[\"operation-id\"]\n", + "print(f\"A batch inference is triggered with the resultId: {result_id}, please get the detection results using the next cell.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1183788", + "metadata": {}, + "source": [ + "### Get batch inference results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "df9b1006", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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7162021-01-02T23:56:00Z[]False0.000000.249148[]
7172021-01-02T23:57:00Z[]False0.000000.339415[]
7182021-01-02T23:58:00Z[]True0.298210.512410[{'variable': 'series_1', 'contributionScore':...
7192021-01-02T23:59:00Z[]False0.000000.449597[]
7202021-01-03T00:00:00Z[]False0.000000.309907[]
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721 rows × 6 columns

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" + ], + "text/plain": [ + " timestamp errors value.isAnomaly value.severity \\\n", + "0 2021-01-02T12:00:00Z [] False 0.00000 \n", + "1 2021-01-02T12:01:00Z [] False 0.00000 \n", + "2 2021-01-02T12:02:00Z [] False 0.00000 \n", + "3 2021-01-02T12:03:00Z [] False 0.00000 \n", + "4 2021-01-02T12:04:00Z [] False 0.00000 \n", + ".. ... ... ... ... \n", + "716 2021-01-02T23:56:00Z [] False 0.00000 \n", + "717 2021-01-02T23:57:00Z [] False 0.00000 \n", + "718 2021-01-02T23:58:00Z [] True 0.29821 \n", + "719 2021-01-02T23:59:00Z [] False 0.00000 \n", + "720 2021-01-03T00:00:00Z [] False 0.00000 \n", + "\n", + " value.score value.interpretation \n", + "0 0.334692 [] \n", + "1 0.243070 [] \n", + "2 0.161920 [] \n", + "3 0.236542 [] \n", + "4 0.246817 [] \n", + ".. ... ... \n", + "716 0.249148 [] \n", + "717 0.339415 [] \n", + "718 0.512410 [{'variable': 'series_1', 'contributionScore':... \n", + "719 0.449597 [] \n", + "720 0.309907 [] \n", + "\n", + "[721 rows x 6 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = requests.get(API_RESULTS.format(endpoint=ENDPOINT, result_id=result_id), headers=HEADERS)\n", + "assert res.status_code == 200, f\"Error occurred. Error message: {res.content}\"\n", + "batch_results=json.loads(res.content)\n", + "# print(batch_results)\n", + "batch_results_df=pd.json_normalize(batch_results,record_path=['results'],errors='ignore')\n", + "batch_results_df" + ] + }, + { + "cell_type": "markdown", + "id": "c1dcbc9f", + "metadata": {}, + "source": [ + "### Streaming Inference" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c8f148ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestamperrorsvalue.isAnomalyvalue.severityvalue.scorevalue.interpretation
02021-01-03T01:59:00Z[]False0.00.269177[]
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" + ], + "text/plain": [ + " timestamp errors value.isAnomaly value.severity value.score \\\n", + "0 2021-01-03T01:59:00Z [] False 0.0 0.269177 \n", + "\n", + " value.interpretation \n", + "0 [] " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "\n", + "with open(\"../../sampledata/multivariate/5_3000.json\", \"r\") as f:\n", + " sample_input = json.load(f)\n", + "\n", + "url = API_MODEL_LAST_INFERENCE.format(endpoint=ENDPOINT,model_id=model_id)\n", + "payload = sample_input\n", + "\n", + "res = requests.post(url, headers=HEADERS, data=json.dumps(payload))\n", + "# print(res.content)\n", + "streaming_results=json.loads(res.content)\n", + "streaming_results_df=pd.json_normalize(streaming_results,record_path=['results'])\n", + "streaming_results_df" + ] + }, + { + "cell_type": "markdown", + "id": "c80a3c5e", + "metadata": {}, + "source": [ + "## 7. List models " + ] + }, + { + "cell_type": "markdown", + "id": "fd5e314b", + "metadata": {}, + "source": [ + "You could check the status of your model and your model list. The `currentCount` means how many model you've created in your Anomaly Detector resource, and the `maxCount` means the limitation of your model number in your Anomaly Detector resource." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "44e9b384", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelIdmodelInfo.displayNamemodelInfo.statusmodelInfo.dataSourcecurrentCountmaxCount
0eb65081c-5a2d-11ed-863d-b618fab0ff58SampleRequestREADYhttps://mvaddataset.blob.core.windows.net/samp...51000
157c9ca66-5a23-11ed-b4b9-b618fab0ff58SampleRequestREADYhttps://mvaddataset.blob.core.windows.net/samp...51000
2497c7936-5a23-11ed-9bbf-b618fab0ff58SampleRequestFAILEDhttps://mvaddataset.blob.core.windows.net/samp...51000
3823a5184-59ca-11ed-b4b9-b618fab0ff58SampleRequestREADYhttps://mvaddataset.blob.core.windows.net/samp...51000
4843545b6-59ca-11ed-9ceb-eef3b1e88e05SampleRequestREADYhttps://mvaddataset.blob.core.windows.net/samp...51000
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" + ], + "text/plain": [ + " modelId modelInfo.displayName \\\n", + "0 eb65081c-5a2d-11ed-863d-b618fab0ff58 SampleRequest \n", + "1 57c9ca66-5a23-11ed-b4b9-b618fab0ff58 SampleRequest \n", + "2 497c7936-5a23-11ed-9bbf-b618fab0ff58 SampleRequest \n", + "3 823a5184-59ca-11ed-b4b9-b618fab0ff58 SampleRequest \n", + "4 843545b6-59ca-11ed-9ceb-eef3b1e88e05 SampleRequest \n", + "\n", + " modelInfo.status modelInfo.dataSource \\\n", + "0 READY https://mvaddataset.blob.core.windows.net/samp... \n", + "1 READY https://mvaddataset.blob.core.windows.net/samp... \n", + "2 FAILED https://mvaddataset.blob.core.windows.net/samp... \n", + "3 READY https://mvaddataset.blob.core.windows.net/samp... \n", + "4 READY https://mvaddataset.blob.core.windows.net/samp... \n", + "\n", + " currentCount maxCount \n", + "0 5 1000 \n", + "1 5 1000 \n", + "2 5 1000 \n", + "3 5 1000 \n", + "4 5 1000 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = requests.get(API_MODEL.format(endpoint=ENDPOINT), headers=HEADERS)\n", + "assert res.status_code == 200, f\"Error occurred. Error message: {res.content}\"\n", + "# print(res.content)\n", + "\n", + "model_list= pd.json_normalize(json.loads(res.content),record_path=['models'],meta=['currentCount','maxCount'])\n", + "model_list[['modelId','modelInfo.displayName','modelInfo.status','modelInfo.dataSource','currentCount','maxCount']]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b9570ebc", + "metadata": {}, + "source": [ + "## 8. Delete model \n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "588c4632", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully deleted this model: eb65081c-5a2d-11ed-863d-b618fab0ff58\n" + ] + } + ], + "source": [ + "res = requests.delete(API_DELETE.format(endpoint=ENDPOINT, model_id=model_id), headers=HEADERS)\n", + "assert res.status_code == 204, f\"Error occurred. Error message: {res.content}\"\n", + "print(\"Successfully deleted this model: \"+ model_id)" + ] + }, + { + "cell_type": "markdown", + "id": "efb5d254", + "metadata": {}, + "source": [ + "# 9. Visualization " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f31b81df", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ee0a3d13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestamperrorsvalue.isAnomalyvalue.severityvalue.scorevalue.interpretationseries_0series_1series_2series_3series_4
02021-01-02T12:00:00Z[]False0.000000.334692[]1.4066430.038892-0.900322-0.4969201.835452
12021-01-02T12:01:00Z[]False0.000000.243070[]0.498293-1.203895-0.0150280.6398171.336381
22021-01-02T12:02:00Z[]False0.000000.161920[]0.4826240.117251-0.3465580.3807441.216002
32021-01-02T12:03:00Z[]False0.000000.236542[]0.1358860.1756470.529109-1.525938-0.921328
42021-01-02T12:04:00Z[]False0.000000.246817[]-1.056639-1.452493-0.204444-0.408568-0.532862
....................................
7162021-01-02T23:56:00Z[]False0.000000.249148[]-0.496276-0.327522-0.982608-1.1528280.732866
7172021-01-02T23:57:00Z[]False0.000000.339415[]0.8330200.285757-1.691492-1.4810460.214137
7182021-01-02T23:58:00Z[]True0.298210.512410[{'variable': 'series_1', 'contributionScore':...0.8647322.6803651.2444290.827081-1.171265
7192021-01-02T23:59:00Z[]False0.000000.449597[]-0.742697-1.620215-1.778069-1.6272230.172657
7202021-01-03T00:00:00Z[]False0.000000.309907[]0.885926-1.2238561.4272940.6581130.172317
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0.449597 [] -0.742697 \n", + "720 0.309907 [] 0.885926 \n", + "\n", + " series_1 series_2 series_3 series_4 \n", + "0 0.038892 -0.900322 -0.496920 1.835452 \n", + "1 -1.203895 -0.015028 0.639817 1.336381 \n", + "2 0.117251 -0.346558 0.380744 1.216002 \n", + "3 0.175647 0.529109 -1.525938 -0.921328 \n", + "4 -1.452493 -0.204444 -0.408568 -0.532862 \n", + ".. ... ... ... ... \n", + "716 -0.327522 -0.982608 -1.152828 0.732866 \n", + "717 0.285757 -1.691492 -1.481046 0.214137 \n", + "718 2.680365 1.244429 0.827081 -1.171265 \n", + "719 -1.620215 -1.778069 -1.627223 0.172657 \n", + "720 -1.223856 1.427294 0.658113 0.172317 \n", + "\n", + "[721 rows x 11 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data=pd.read_csv('../../sampledata/multivariate/sample_data_5_3000.csv')\n", + "df_merge=pd.merge(batch_results_df,raw_data, on=\"timestamp\")\n", + "df_merge" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cb801cf3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "####### Main Figure #######\n", + "plt.figure(figsize=(30,8))\n", + "plt.plot(df_merge['timestamp'],df_merge['series_0'], color='lightsteelblue', linestyle='solid', linewidth=2, label='series_0')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_1'], color='powderblue', linestyle='solid', linewidth=2, label='series_1')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_2'], color='cornflowerblue', linestyle='solid', linewidth=2, label='series_2')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_3'], color='lightblue', linestyle='solid', linewidth=2, label='series_3')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_4'], color='deepskyblue', linestyle='solid', linewidth=2, label='series_4')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7566b84d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jingruhan\\AppData\\Local\\Temp\\ipykernel_24080\\4229059390.py:21: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n", + " plt.vlines(np.where(anoms), ymin=ymin , ymax=ymax , color='r', alpha=1,linewidth=3)\n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# You could use this parameter to sift out the anomalies, the less this number is, the more anomalies will be marked out.\n", + "minSeverity = 0.2\n", + "\n", + "# Only presents first 100 rows to see clearer graph.\n", + "df = df_merge.iloc[:100]\n", + "\n", + "####### Main Figure #######\n", + "plt.figure(figsize=(30,8))\n", + "plt.plot(df['timestamp'],df['series_0'], color='lightsteelblue', linestyle='solid', linewidth=2, label='series_0')\n", + "plt.plot(df['timestamp'],df['series_1'], color='powderblue', linestyle='solid', linewidth=2, label='series_1')\n", + "plt.plot(df['timestamp'],df['series_2'], color='cornflowerblue', linestyle='solid', linewidth=2, label='series_2')\n", + "plt.plot(df['timestamp'],df['series_3'], color='lightblue', linestyle='solid', linewidth=2, label='series_3')\n", + "plt.plot(df['timestamp'],df['series_4'], color='deepskyblue', linestyle='solid', linewidth=2, label='series_4')\n", + "\n", + "plt.grid(axis='y')\n", + "plt.tick_params(axis='x',which='both',bottom=False,labelbottom=False)\n", + "plt.legend()\n", + "\n", + "anoms = list(df[\"value.severity\"] >= minSeverity)\n", + "_, _, ymin, ymax = plt.axis()\n", + "plt.vlines(np.where(anoms), ymin=ymin , ymax=ymax , color='r', alpha=1,linewidth=3)\n", + "\n", + "plt.legend()\n", + "plt.title('A plot of the values from the variables with the detected anomalies highlighted in red.')\n", + "plt.show()\n", + "\n", + "\n", + "####### Severity Figure #######\n", + "plt.figure(figsize=(30,8))\n", + "plt.tick_params(axis='x',which='both',bottom=False,labelbottom=False)\n", + "plt.plot(df['timestamp'],df['value.severity'], color='black', linestyle='solid', linewidth=2, label='Severity')\n", + "plt.plot(df['timestamp'],df['value.score'], color='lightgrey', linestyle='solid', linewidth=2, label='Score')\n", + "plt.plot(df['timestamp'],[minSeverity]*len(df['value.severity']), color='red', linestyle='dotted', linewidth=1, label='minSeverity')\n", + "plt.grid(axis='y')\n", + "plt.legend()\n", + "plt.ylim((0,1))\n", + "plt.title(\"Severity and Score of the detected anomalies\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d28bb63b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestamperrorsvalue.isAnomalyvalue.severityvalue.scorevalue.interpretationseries_0series_1series_2series_3series_4
62021-01-02T12:06:00Z[]True0.3309520.568668[{'variable': 'series_2', 'contributionScore':...-0.539582-1.688357-2.0361561.608503-1.618857
682021-01-02T13:08:00Z[]True0.2952070.507248[{'variable': 'series_1', 'contributionScore':...-1.1075532.708357-0.2650961.1141201.574517
792021-01-02T13:19:00Z[]True0.3697760.635379[{'variable': 'series_0', 'contributionScore':...2.542432-0.147229-1.951820-2.1314330.616188
822021-01-02T13:22:00Z[]True0.2987480.513333[{'variable': 'series_2', 'contributionScore':...0.5579541.096061-2.2167032.217523-0.166912
892021-01-02T13:29:00Z[]True0.3070500.527599[{'variable': 'series_3', 'contributionScore':...-0.6719661.5345701.272315-1.707646-1.931589
\n", + "
" + ], + "text/plain": [ + " timestamp errors value.isAnomaly value.severity value.score \\\n", + "6 2021-01-02T12:06:00Z [] True 0.330952 0.568668 \n", + "68 2021-01-02T13:08:00Z [] True 0.295207 0.507248 \n", + "79 2021-01-02T13:19:00Z [] True 0.369776 0.635379 \n", + "82 2021-01-02T13:22:00Z [] True 0.298748 0.513333 \n", + "89 2021-01-02T13:29:00Z [] True 0.307050 0.527599 \n", + "\n", + " value.interpretation series_0 series_1 \\\n", + "6 [{'variable': 'series_2', 'contributionScore':... -0.539582 -1.688357 \n", + "68 [{'variable': 'series_1', 'contributionScore':... -1.107553 2.708357 \n", + "79 [{'variable': 'series_0', 'contributionScore':... 2.542432 -0.147229 \n", + "82 [{'variable': 'series_2', 'contributionScore':... 0.557954 1.096061 \n", + "89 [{'variable': 'series_3', 'contributionScore':... -0.671966 1.534570 \n", + "\n", + " series_2 series_3 series_4 \n", + "6 -2.036156 1.608503 -1.618857 \n", + "68 -0.265096 1.114120 1.574517 \n", + "79 -1.951820 -2.131433 0.616188 \n", + "82 -2.216703 2.217523 -0.166912 \n", + "89 1.272315 -1.707646 -1.931589 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display only the anomalous timestamps.\n", + "df_anomaly = df[df['value.isAnomaly']== True]\n", + "df_anomaly" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a34963b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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variablecontributionScorecorrelationChanges.changedVariables
0series_10.444387[series_3, series_4]
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" + ], + "text/plain": [ + " variable contributionScore correlationChanges.changedVariables\n", + "0 series_1 0.444387 [series_3, series_4]\n", + "1 series_4 0.207096 [series_3, series_4]\n", + "2 series_3 0.162231 [series_3, series_4]\n", + "3 series_0 0.157490 [series_3, series_4]\n", + "4 series_2 0.028796 [series_3, series_4]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tkinter.font import BOLD\n", + "from turtle import color\n", + "\n", + "\n", + "anomaly_index = 1\n", + "d = df_anomaly.iloc[anomaly_index]['value.interpretation'][::-1]\n", + "timestamp = df_anomaly.iloc[anomaly_index]['timestamp']\n", + "correlatedVar = df_anomaly.iloc[anomaly_index]['value.interpretation']\n", + "\n", + "x=[item.get('variable') for item in d]\n", + "y=[item.get('contributionScore') for item in d]\n", + "Z=[item.get('correlationChanges') for item in d]\n", + "\n", + "l=[]\n", + "for i in y:\n", + " i = \"%.f%%\" % (i * 100)\n", + " l.append(i)\n", + "\n", + "plt.figure(figsize=(20,6))\n", + "\n", + "\n", + "plt.barh(x, l,color=\"brown\",alpha=1)\n", + "\n", + "for index, value in enumerate(l):\n", + " plt.text(value, index,str(value) )\n", + "\n", + "# setting label of y-axis\n", + "plt.ylabel(\"Variable\")\n", + "\n", + "# setting label of x-axis\n", + "plt.xlabel(\"contribution score\")\n", + "plt.title(\"Contribution Rank at \"+timestamp)\n", + "\n", + "plt.show()\n", + "print(\"The last column indicates the correlated variables with the first column variable, whose correlations caused the anomaly.\")\n", + "pd.json_normalize(correlatedVar)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "aa60471fcc78aee1d161dfae290e862a10214dc7d6ca1ceb34988aca5ae05936" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ipython-notebook/README.md b/ipython-notebook/README.md index 3652202..04cfb9c 100644 --- a/ipython-notebook/README.md +++ b/ipython-notebook/README.md @@ -10,20 +10,21 @@ products: ![License](https://img.shields.io/badge/license-MIT-green.svg) -These python notebooks show you how to start detecting anomalies in your data with the Anomaly Detector API, and visualizing the information returned by it. +These python notebooks show you how to start detecting anomalies in your data with the Anomaly Detector API, and visualizing the information returned by it. ## Contents | File/folder | Description | |-------------|-------------| -| `src` | Jupyter notebooks and source code. | -| `.gitignore` | Define what to ignore at commit time. | +| `API Sample` | Jupyter notebooks of how to use the Anomaly Detector APIs. | +| `SDK Sample` | Jupyter notebooks of how to use the Anomaly Detector SDKs. | +| `Media` | Images that used in the notebooks. | | `README.md` | This README file. | | `LICENSE` | The license for the sample. | ## Prerequisites -- A [Cognitive Services API account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account) with access to the Anomaly Detector API. Before continuing, You will need the access key from your Azure dashboard. +- [Create an Anomaly Detector resource](https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesAnomalyDetector) with access to the Anomaly Detector API. Before continuing, you will need the endpoint and key of this resource. ## Running the sample You can run this sample locally as a Jupyter notebook. @@ -45,3 +46,4 @@ The Anomaly Detector API lets you monitor and detect abnormalities in your time ## Next steps For more information, see the [Anomaly Detector API documentation](https://aka.ms/anomaly-detector-documentation). +Need support? [Join the Anomaly Detector Community](https://forms.office.com/pages/responsepage.aspx?id=v4j5cvGGr0GRqy180BHbR2Ci-wb6-iNDoBoNxrnEk9VURjNXUU1VREpOT0U1UEdURkc0OVRLSkZBNC4u). diff --git a/ipython-notebook/SDK Sample/MVAD-SDK-Demo.ipynb b/ipython-notebook/SDK Sample/MVAD-SDK-Demo.ipynb deleted file mode 100644 index c8e1579..0000000 --- a/ipython-notebook/SDK Sample/MVAD-SDK-Demo.ipynb +++ /dev/null @@ -1,749 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multivariate Anomaly Detection Demo Notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contents\n", - "\n", - "1. [Introduction](#intro)\n", - "2. [Train a Model](#train)\n", - "3. [List Models](#list)\n", - "4. [Inference](#inference)\n", - "5. [Analysis (for reference only)](#analysis)\n", - "6. [Generate SAS with Code (for reference only)](#sas)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introdution \n", - "This notebook shows how to use [Multivariate Anomaly Detector](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview-multivariate) service. Please follow the steps to try it out, you can either [join Teams Group](https://forms.office.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbRxSkyhztUNZCtaivu8nmhd1UQ1VFRDA0V1dUMDJRMFhOTzFHQ1lDTVozWi4u) for any questions, or email us via AnomalyDetector@microsoft.com\n", - "\n", - "**Dataset**\n", - "\n", - "Sample data comes from [Mars Science Laboratory(MSL)](https://mvadqualitytest.blob.core.windows.net/msl/msl.zip?sp=r&st=2021-09-06T12:38:22Z&se=2023-01-17T20:38:22Z&spr=https&sv=2020-08-04&sr=b&sig=Hmf3H97d1E0ulQiJKLEmqNRt6kEYL%2FqCRf684P8v%2Fws%3D) (a public dataset of *NASA*). If you'd like to use your own dataset to run this notebook, you should do the following steps first (🎬[video instruction](https://msit.microsoftstream.com/video/afa00840-98dc-ae72-fad1-f1ec0fe830c1)/[video backup](https://github.com/Azure-Samples/AnomalyDetector/blob/master/ipython-notebook/media/How%20to%20generate%20a%20SAS.mp4)):\n", - "1. Compress your local csv files(one metric per file), see [input data schema](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/concepts/best-practices-multivariate#input-data-schema).\n", - "1. Upload the compressed file to Azure Blob.\n", - "1. Generate an SAS for your compressed file.\n", - "\n", - "\n", - "**Prerequisites**\n", - "\n", - "1. [Create an Azure subscription](https://azure.microsoft.com/free/cognitive-services) if you don't have one.\n", - "1. [Create an Anomaly Detector resource](https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesAnomalyDetector) and get your `endpoint` and `key`.\n", - "1. **Install** the anomaly detector SDK and storage packages using following codes ⬇️, and **import** packages." - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [], - "source": [ - "#pip install --upgrade azure-ai-anomalydetector\n", - "#pip install azure-storage-blob\n", - "#pip install azure-mgmt-storage" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from datetime import datetime\n", - "from azure.ai.anomalydetector import AnomalyDetectorClient\n", - "from azure.ai.anomalydetector.models import DetectionRequest, ModelInfo, LastDetectionRequest, VariableValues\n", - "from azure.core.credentials import AzureKeyCredential\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# After you create an Anomaly Detector resource, you will get the endpoint and key, and put them here.\n", - "subscription_key = '[Placeholder: Key of your Anomaly Detector resource.]' \n", - "anomaly_detector_endpoint = '[Placeholder: Endpoint of your Anomaly Detector resource.]'\n", - "\n", - "# Create an Anomaly Detector client.\n", - "ad_client = AnomalyDetectorClient(AzureKeyCredential(subscription_key), anomaly_detector_endpoint)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Train a model \n", - "- After uploading training data to Blob, you should generate its **Shared Access Signature(SAS)** and use it in this part.\n", - "- Specify the timespan of training data using `start_time` and `end_time`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# The data_source is the SAS of your dataset.\n", - "data_source = 'https://mvadqualitytest.blob.core.windows.net/msl/msl.zip?sp=r&st=2021-09-06T12:38:22Z&se=2023-01-17T20:38:22Z&spr=https&sv=2020-08-04&sr=b&sig=Hmf3H97d1E0ulQiJKLEmqNRt6kEYL%2FqCRf684P8v%2Fws%3D' \n", - "start_time = \"2020-06-01T12:00:00Z\"\n", - "end_time = \"2020-12-20T23:45:00Z\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data_feed = ModelInfo(start_time=start_time, end_time=end_time, source=data_source, sliding_window=200)\n", - "response_header = ad_client.train_multivariate_model(data_feed, cls=lambda *args: [args[i] for i in range(len(args))])[-1]\n", - "trained_model_id = response_header['Location'].split(\"/\")[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model id: c07a2ca8-71fe-11ec-9b51-da855682c99e\n" - ] - } - ], - "source": [ - "print(f\"model id: {trained_model_id}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get Model Status\n", - "☕️Training process might take 20-30mins (depending on the data size), take a cup of coffee and come back then, waiting for its status to be **READY**." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model status: READY\n" - ] - } - ], - "source": [ - "model_status = ad_client.get_multivariate_model(trained_model_id).model_info.status\n", - "print(f\"model status: {model_status}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "training progress: 0/100.\n" - ] - } - ], - "source": [ - "#Get model information and track training progress.\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "%matplotlib inline\n", - "\n", - "model = ad_client.get_multivariate_model(trained_model_id)\n", - "current_epoch = 0 if len(model.model_info.diagnostics_info.model_state.epoch_ids) == 0 else model.model_info.diagnostics_info.model_state.epoch_ids[-1]\n", - "print(f\"training progress: {current_epoch}/100.\")\n", - "if model.model_info.status == \"READY\":\n", - " model_state = model.model_info.diagnostics_info.model_state\n", - " epoch_ids = model_state.epoch_ids\n", - " train_losses = model_state.train_losses\n", - " validation_losses = model_state.validation_losses\n", - " latency = model_state.latencies_in_seconds\n", - "\n", - " fig, ax1 = plt.subplots(figsize=(18, 6))\n", - " ax1.set_xlabel(\"epoch id\")\n", - " ax1.set_ylabel(\"loss\")\n", - " plt_train = ax1.plot(epoch_ids, train_losses)\n", - " plt_val = ax1.plot(epoch_ids, validation_losses)\n", - " plt.legend([\"train loss\", \"validation loss\", \"latency\"])\n", - "\n", - " ax2 = ax1.twinx()\n", - " ax2.set_ylabel(\"latency (s)\")\n", - " plt_latency = ax2.plot(epoch_ids, latency, color=\"black\")\n", - "\n", - " lns = plt_train + plt_val + plt_latency\n", - " labels = [\"train loss\", \"validation loss\", \"latency\"]\n", - " plt.legend(lns, labels, loc=0)\n", - " plt.title(\"Model Information\")\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. List Models \n", - "List models that have been trained previously." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+----------------------------------------+----------+\n", - "|model id |status |\n", - "+========================================+==========+\n", - "|c07a2ca8-71fe-11ec-9b51-da855682c99e |READY |\n", - "|f8779b0c-71e7-11ec-b1eb-428f25078e73 |READY |\n", - "|020bc034-71da-11ec-aec3-ae88f8a42a48 |READY |\n", - "|e1b90418-71d9-11ec-9570-c2ab97a656a4 |READY |\n", - "|5e5aa746-71cc-11ec-95e4-c2ab97a656a4 |READY |\n", - "+----------------------------------------+----------+\n" - ] - } - ], - "source": [ - "model_list = list(ad_client.list_multivariate_model(skip=0, top=100))\n", - "print(\"+\" + \"-\" * 40 + \"+\" + \"-\" * 10 + \"+\")\n", - "print(\"|\" + \"model id\" + \" \" * 32 + \"|\" + f\"{'status':<10}\" + \"|\")\n", - "print(\"+\" + \"=\" * 40 + \"+\" + \"=\" * 10 + \"+\")\n", - "for model_snapshot in model_list[:5]:\n", - " print(\"|\" + model_snapshot.model_id + \" \" * 4 + \"|\" + f\"{model_snapshot.status:<10}\" + \"|\")\n", - "print(\"+\" + \"-\" * 40 + \"+\" + \"-\" * 10 + \"+\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'additional_properties': {},\n", - " 'model_id': 'c07a2ca8-71fe-11ec-9b51-da855682c99e',\n", - " 'created_time': datetime.datetime(2022, 1, 10, 10, 19, 11, tzinfo=),\n", - " 'last_updated_time': datetime.datetime(2022, 1, 10, 10, 48, 39, tzinfo=),\n", - " 'status': 'READY',\n", - " 'display_name': '',\n", - " 'variables_count': 55}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = model_list[0]\n", - "vars(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Inference " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### A. Inference with the asynchronous API\n", - "- After uploading inference data to Blob, you should generate its SAS and use it in this part. (can be the same as training data SAS)\n", - "- Specify the time span of inference data using `start_time` and `end_time`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# The data_source is the SAS of your dataset.\n", - "data_source = \"https://mvadqualitytest.blob.core.windows.net/msl/msl.zip?sp=r&st=2021-09-06T12:38:22Z&se=2023-01-17T20:38:22Z&spr=https&sv=2020-08-04&sr=b&sig=Hmf3H97d1E0ulQiJKLEmqNRt6kEYL%2FqCRf684P8v%2Fws%3D\"\n", - "start_time = \"2021-01-22T00:00:00Z\"\n", - "end_time = \"2021-02-02T23:45:00Z\"" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "result id: 4bda2712-720e-11ec-accc-d2e7167c02a5\n" - ] - } - ], - "source": [ - "detection_req = DetectionRequest(source=data_source, start_time=start_time, end_time=end_time)\n", - "response_header = ad_client.detect_anomaly(trained_model_id, detection_req, cls=lambda *args: [args[i] for i in range(len(args))])[-1]\n", - "result_id = response_header['Location'].split(\"/\")[-1]\n", - "print(f\"result id: {result_id}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get inference status\n", - "☕️Inference process might\n", - "take 10-20mins (depending on the data size). Take a cup of coffee and come back then, and waiting for its status to be **READY**.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "result status: READY\n" - ] - } - ], - "source": [ - "r = ad_client.get_detection_result(result_id)\n", - "print(f\"result status: {r.summary.status}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'additional_properties': {}, 'is_anomaly': True, 'severity': 0.05634557828307152, 'score': 0.09681758284568787, 'interpretation': None}\n" - ] - } - ], - "source": [ - "for r_item in r.results:\n", - " if r_item.value.is_anomaly:\n", - " print(r_item.value)\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### B. Inference with the synchronous API (NEW)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This synchronous API will get detection result immediately after you call it, you should put your data with JSON format into the request body, and specify how much data points you'd like to detect within the `detectingPoints` field, which could be a number **between 1 and 10**." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " timestamp is_anomaly severity score\n", - "0 2021-09-02T23:00:00Z True 0.009282 0.015949\n", - "1 2021-09-02T23:05:00Z True 0.008471 0.014555\n", - "2 2021-09-02T23:10:00Z True 0.007850 0.013488\n", - "3 2021-09-02T23:15:00Z True 0.006939 0.011924\n", - "4 2021-09-02T23:20:00Z True 0.024995 0.042948\n", - "5 2021-09-02T23:25:00Z True 0.011135 0.019133\n", - "6 2021-09-02T23:30:00Z True 0.005847 0.010047\n", - "7 2021-09-02T23:35:00Z True 0.020006 0.034375\n", - "8 2021-09-02T23:40:00Z True 0.024881 0.042753\n", - "9 2021-09-02T23:45:00Z True 0.017953 0.030848\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "with open(\"multivariate_sample.json\", \"r\") as f:\n", - " sample_input = json.load(f)\n", - " \n", - "last_detection_request = LastDetectionRequest(variables=[VariableValues(**item) for item in sample_input], detecting_points=10)\n", - "r = ad_client.last_detect_anomaly(model_id=trained_model_id, body=last_detection_request)\n", - "results = pd.DataFrame(columns=[\"timestamp\", \"is_anomaly\", \"severity\", \"score\"])\n", - "for res in r.results:\n", - " results = results.append({\"timestamp\": res.timestamp.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n", - " \"is_anomaly\": res.value.is_anomaly,\n", - " \"severity\": res.value.severity,\n", - " \"score\": res.value.score}, ignore_index=True)\n", - "print(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Analysis (for reference only) " - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import requests\n", - "import zipfile\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [], - "source": [ - "results = r.results" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [], - "source": [ - "is_anomalies = []\n", - "sev = []\n", - "scores = []\n", - "\n", - "for item in results:\n", - " if item.value:\n", - " is_anomalies.append(item.value.is_anomaly)\n", - " sev.append(item.value.severity)\n", - " scores.append(item.value.score)" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "unzip file success series_5.csv\n", - "unzip file success series_6.csv\n", - "unzip file success series_7.csv\n", - "unzip file success series_8.csv\n", - "unzip file success series_9.csv\n", - "unzip file success series_10.csv\n", - "unzip file success series_11.csv\n", - "unzip file success series_12.csv\n", - "unzip file success series_13.csv\n", - "unzip file success series_14.csv\n", - "unzip file success series_15.csv\n", - "unzip file success series_16.csv\n", - "unzip file success series_17.csv\n", - "unzip file success series_18.csv\n", - "unzip file success series_19.csv\n", - "unzip file success series_20.csv\n", - "unzip file success series_21.csv\n", - "unzip file success series_22.csv\n", - "unzip file success series_23.csv\n", - "unzip file success series_24.csv\n", - "unzip file success series_25.csv\n", - "unzip file success series_26.csv\n", - "unzip file success series_27.csv\n", - "unzip file success series_28.csv\n", - "unzip file success series_29.csv\n", - "unzip file success series_30.csv\n", - "unzip file success series_31.csv\n", - "unzip file success series_32.csv\n", - "unzip file success series_33.csv\n", - "unzip file success series_34.csv\n", - "unzip file success series_35.csv\n", - "unzip file success series_36.csv\n", - "unzip file success series_37.csv\n", - "unzip file success series_38.csv\n", - "unzip file success series_39.csv\n", - "unzip file success series_40.csv\n", - "unzip file success series_41.csv\n", - "unzip file success series_42.csv\n", - "unzip file success series_43.csv\n", - "unzip file success series_44.csv\n", - "unzip file success series_45.csv\n", - "unzip file success series_46.csv\n", - "unzip file success series_47.csv\n", - "unzip file success series_48.csv\n", - "unzip file success series_49.csv\n", - "unzip file success series_50.csv\n", - "unzip file success series_51.csv\n", - "unzip file success series_52.csv\n", - "unzip file success series_53.csv\n", - "unzip file success series_54.csv\n", - "unzip file success series_55.csv\n", - "unzip file success series_1.csv\n", - "unzip file success series_2.csv\n", - "unzip file success series_3.csv\n", - "unzip file success series_4.csv\n" - ] - } - ], - "source": [ - "local_path = \"inference_data.zip\"\n", - "with open(local_path, \"wb\") as file:\n", - " response = requests.get(data_source)\n", - " file.write(response.content)\n", - "\n", - "dst_dir = \"inference_data\"\n", - "os.makedirs(dst_dir, exist_ok=True)\n", - "\n", - "fz = zipfile.ZipFile(local_path, 'r')\n", - "print(fz)\n", - "for file in fz.namelist():\n", - " fz.extract(file, dst_dir)\n", - " print('unzip file success ', file)" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [ - "root_dir = \"inference_data\"\n", - "series = []\n", - "for file in os.listdir(root_dir):\n", - " if file.endswith('.csv'):\n", - " var = pd.read_csv(os.path.join(root_dir, file))\n", - " series.append({\"name\": file[:-4], \"value\": var})" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12958" - ] - }, - "execution_count": 204, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(is_anomalies_full_len)" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# n_series = min(10, len(series))\n", - "n_series = len(series)\n", - "fig, ax = plt.subplots(n_series + 3, 1, figsize=(18, 68))\n", - "# data alignment, as inference is done on partial data\n", - "\n", - "# diff = len(series[0][\"value\"]) - len(is_anomalies)\n", - "diff = 0\n", - "is_anomalies_full_len = [0] * diff + is_anomalies\n", - "sev_full_len = [0] * diff + sev\n", - "scores_full_len = [0] * diff + scores\n", - "for i in range(n_series):\n", - " ax[i].set_title(f\"{series[i]['name']}\")\n", - " ax[i].plot(series[i][\"value\"].value[-len(is_anomalies):].values, alpha=0.5)\n", - " ymin, ymax = ax[i].get_ylim()\n", - " ax[i].vlines(np.where(is_anomalies_full_len), ymin, ymax, color='r', alpha=1.0)\n", - "ax[n_series].plot(is_anomalies_full_len)\n", - "ax[n_series].set_title(\"is anomaly\")\n", - "ax[n_series+1].plot(sev_full_len)\n", - "ax[n_series+1].set_title(\"severity\")\n", - "ax[n_series+2].plot(scores_full_len)\n", - "ax[n_series+2].set_title(\"anomaly score\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Generate SAS with Code (for reference only) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from azure.storage.blob import BlobClient, BlobServiceClient, generate_blob_sas, BlobSasPermissions\n", - "from datetime import datetime, timedelta\n", - "\n", - "import zipfile\n", - "\n", - "BLOB_SAS_TEMPLATE = \"https://{account_name}.blob.core.windows.net/{container_name}/{blob_name}?{sas_token}\"\n", - "\n", - "\n", - "def zip_file(root, name):\n", - " \"\"\"\n", - " A helper function to compress local csv files.\n", - " :param root: root directory of csv files\n", - " :param name: name of the compressed file (with suffix) \n", - " \"\"\"\n", - " z = zipfile.ZipFile(name, \"w\", zipfile.ZIP_DEFLATED)\n", - " for f in os.listdir(root):\n", - " if f.endswith(\"csv\"):\n", - " z.write(os.path.join(root, f), f)\n", - " z.close()\n", - " print(\"Compress files success!\")\n", - "\n", - "\n", - "def upload_to_blob(file, conn_str, container, blob_name):\n", - " \"\"\"\n", - " A helper function to upload files to blob\n", - " :param file: the path to the file to be uploaded\n", - " :param conn_str: the connection string of the target storage account\n", - " :param container: the container name in the storage account\n", - " :param blob_name: the blob name in the container\n", - " \"\"\"\n", - " blob_client = BlobClient.from_connection_string(conn_str, container_name=container, blob_name=blob_name)\n", - " with open(file, \"rb\") as f:\n", - " blob_client.upload_blob(f, overwrite=True)\n", - " print(\"Upload Success!\")\n", - "\n", - "\n", - "def generate_data_source_sas(conn_str, container, blob_name):\n", - " \"\"\"\n", - " A helper function to generate blob SAS.\n", - " :param conn_str: the connection string of the target storage account\n", - " :param container: the container name in the storage account\n", - " :param blob_name: the blob name in the container\n", - " :return: generated SAS\n", - " \"\"\"\n", - " blob_service_client = BlobServiceClient.from_connection_string(conn_str=conn_str)\n", - " sas_token = generate_blob_sas(account_name=blob_service_client.account_name,\n", - " container_name=container,\n", - " blob_name=blob_name,\n", - " account_key=blob_service_client.credential.account_key,\n", - " permission=BlobSasPermissions(read=True),\n", - " expiry=datetime.utcnow() + timedelta(days=1))\n", - " blob_sas = BLOB_SAS_TEMPLATE.format(account_name=blob_service_client.account_name,\n", - " container_name=container,\n", - " blob_name=blob_name,\n", - " sas_token=sas_token)\n", - " return blob_sas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "source_folder = \"the folder in which your csv files are placed\"\n", - "zipfile_name = \"the name of the compressed file\"\n", - "connection_string = \"the connection string to your storage account\"\n", - "container_name = \"the container to store the data\"\n", - "zip_file(source_folder, zipfile_name)\n", - "upload_to_blob(zipfile_name, connection_string, container_name, zipfile_name)\n", - "blob_sas = generate_blob_sas(connection_string, container_name, zipfile_name)" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "43c31cec74771ef500596564a63f179874fac196ff9ba93b3b9618e2a194a9ab" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ipython-notebook/SDK Sample/Public Preview/MVAD-SDK-Demo.ipynb b/ipython-notebook/SDK Sample/Public Preview/MVAD-SDK-Demo.ipynb new file mode 100644 index 0000000..b718173 --- /dev/null +++ b/ipython-notebook/SDK Sample/Public Preview/MVAD-SDK-Demo.ipynb @@ -0,0 +1,1267 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multivariate Anomaly Detection Demo Notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Introduction](#intro)\n", + "2. [Prerequisites](#pre)\n", + "3. [Train a Model](#train)\n", + "4. [List Models](#list)\n", + "5. [Inference](#inference)\n", + "6. [Analysis (for reference only)](#analysis)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introdution \n", + "This notebook shows how to use [Multivariate Anomaly Detection](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview-multivariate) in Anomaly Detector service. Please follow the steps to try it out, you can either [join Teams Group](https://forms.office.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbRxSkyhztUNZCtaivu8nmhd1UQ1VFRDA0V1dUMDJRMFhOTzFHQ1lDTVozWi4u) for any questions, or email us via AnomalyDetector@microsoft.com" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Prerequisites \n", + "\n", + "\n", + "* [Create an Azure subscription](https://azure.microsoft.com/free/cognitive-services) if you don't have one.\n", + "* [Create an Anomaly Detector resource](https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesAnomalyDetector) and get your `endpoint` and `key`, you'll use these later.\n", + "* (**optional**) [Install Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli) A helpful tool to manipulate your Azure resources. You can use Azure CLI to retrieve credential information without pasting them as plain text.\n", + "* (**optional**) Login with Azure CLI `az login`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Install** the anomaly detector SDK and storage packages using following codes ⬇️, and **import** packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Install required packages. Use the following commands to install the anomaly detector SDK and required packages.\n", + "# ! pip install --upgrade azure-ai-anomalydetector\n", + "# ! pip install azure-storage-blob\n", + "# ! pip install azure-mgmt-storage" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Install optional packages to see interactive visualization in this Jupyter notebook.\n", + "# ! pip install plotly==5.5.0\n", + "# ! pip install notebook>=5.3 \n", + "# ! pip install ipywidgets>=7.5" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Import related packages:\n", + "import os\n", + "import json\n", + "import subprocess\n", + "\n", + "from datetime import datetime\n", + "from azure.ai.anomalydetector import AnomalyDetectorClient\n", + "from azure.ai.anomalydetector.models import DetectionRequest, ModelInfo, LastDetectionRequest, VariableValues\n", + "from azure.core.credentials import AzureKeyCredential\n", + "\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "\n", + "# This is to build interactive plot:\n", + "pd.options.plotting.backend = \"plotly\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset\n", + "\n", + "We will use a simulated dataset **([multivariate_sample_data.csv](https://github.com/Azure-Samples/AnomalyDetector/blob/master/sampledata/multivariate/multivariate_sample_data.csv))** in the Github repository. This dataset contains five variables which represent different variables from an equipment.\n", + "\n", + "If you'd like to use your own dataset to run this notebook, you should do the following steps first (🎬[video instruction](https://msit.microsoftstream.com/video/afa00840-98dc-ae72-fad1-f1ec0fe830c1)/[video backup](https://github.com/Azure-Samples/AnomalyDetector/blob/master/ipython-notebook/media/How%20to%20generate%20a%20SAS.mp4)):\n", + "1. (optional) Split your full csv files into individual csv files that each file contains the data for one variable.\n", + "1. Compress your local csv files(one metric per file), see [input data schema](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/concepts/best-practices-multivariate#input-data-schema)..\n", + "1. Upload the compressed file to Azure Blob.\n", + "1. Generate an `SAS URL` for your compressed file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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opticalLFilteredopticalRFilteredpumpPressurerotationalvibrationHorizon
timestamp
2021-02-18T13:50:00Z0.0939170.0816110.9593660.0047350.934785
2021-02-18T14:00:00Z0.0876340.0851920.9577060.0279950.933875
2021-02-18T14:10:00Z0.1087390.1173800.9542040.0494880.923942
2021-02-18T14:20:00Z0.1270140.1456320.9476010.0790590.944030
2021-02-18T14:30:00Z0.1566280.1384060.9510690.0881720.923689
..................
2021-09-14T22:40:00Z0.0000000.0000000.9500000.0000000.850000
2021-09-14T22:50:00Z0.0000000.0000000.9500000.0000000.850000
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2021-09-14T23:20:00Z0.0000000.0000000.9500000.0000000.850000
\n", + "

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\n", + "
" + ], + "text/plain": [ + " opticalLFiltered opticalRFiltered pumpPressure \\\n", + "timestamp \n", + "2021-02-18T13:50:00Z 0.093917 0.081611 0.959366 \n", + "2021-02-18T14:00:00Z 0.087634 0.085192 0.957706 \n", + "2021-02-18T14:10:00Z 0.108739 0.117380 0.954204 \n", + "2021-02-18T14:20:00Z 0.127014 0.145632 0.947601 \n", + "2021-02-18T14:30:00Z 0.156628 0.138406 0.951069 \n", + "... ... ... ... \n", + "2021-09-14T22:40:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T22:50:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T23:00:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T23:10:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T23:20:00Z 0.000000 0.000000 0.950000 \n", + "\n", + " rotational vibrationHorizon \n", + "timestamp \n", + "2021-02-18T13:50:00Z 0.004735 0.934785 \n", + "2021-02-18T14:00:00Z 0.027995 0.933875 \n", + "2021-02-18T14:10:00Z 0.049488 0.923942 \n", + "2021-02-18T14:20:00Z 0.079059 0.944030 \n", + "2021-02-18T14:30:00Z 0.088172 0.923689 \n", + "... ... ... \n", + "2021-09-14T22:40:00Z 0.000000 0.850000 \n", + "2021-09-14T22:50:00Z 0.000000 0.850000 \n", + "2021-09-14T23:00:00Z 0.000000 0.850000 \n", + "2021-09-14T23:10:00Z 0.000000 0.850000 \n", + "2021-09-14T23:20:00Z 0.000000 0.850000 \n", + "\n", + "[30010 rows x 5 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data visualization\n", + "df = pd.read_csv(\"multivariate_sample_data.csv\", index_col=\"timestamp\")\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next let's draw an interactive plot. You may zoom in/out through clicking 'autoscale' and select an area or select a variabe for further investigation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start_time = \"2021-02-18T13:50:00Z\"\n", + "end_time = \"2021-03-04T13:50:00Z\"\n", + "df[(df.index > start_time) & (df.index < end_time)].plot(title='Sample data')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![data overview](../media/data-overview.png )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample code to generate SAS (for reference only)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from azure.storage.blob import BlobClient, BlobServiceClient, generate_blob_sas, BlobSasPermissions\n", + "from datetime import datetime, timedelta\n", + "import zipfile\n", + "\n", + "BLOB_SAS_TEMPLATE = \"https://{account_name}.blob.core.windows.net/{container_name}/{blob_name}?{sas_token}\"\n", + "\n", + "\n", + "def zip_file(root, name):\n", + " \"\"\"\n", + " A helper function to compress local csv files.\n", + " :param root: root directory of csv files\n", + " :param name: name of the compressed file (with suffix) \n", + " \"\"\"\n", + " z = zipfile.ZipFile(name, \"w\", zipfile.ZIP_DEFLATED)\n", + " for f in os.listdir(root):\n", + " if f.endswith(\"csv\"):\n", + " z.write(os.path.join(root, f), f)\n", + " z.close()\n", + " print(\"Compress files success!\")\n", + "\n", + "\n", + "def upload_to_blob(file, conn_str, container, blob_name):\n", + " \"\"\"\n", + " A helper function to upload files to blob\n", + " :param file: the path to the file to be uploaded\n", + " :param conn_str: the connection string of the target storage account\n", + " :param container: the container name in the storage account\n", + " :param blob_name: the blob name in the container\n", + " \"\"\"\n", + " blob_client = BlobClient.from_connection_string(conn_str, container_name=container, blob_name=blob_name)\n", + " with open(file, \"rb\") as f:\n", + " blob_client.upload_blob(f, overwrite=True)\n", + " print(\"Upload Success!\")\n", + "\n", + "\n", + "def generate_data_source_sas(conn_str, container, blob_name):\n", + " \"\"\"\n", + " A helper function to generate blob SAS.\n", + " :param conn_str: the connection string of the target storage account\n", + " :param container: the container name in the storage account\n", + " :param blob_name: the blob name in the container\n", + " :return: generated SAS\n", + " \"\"\"\n", + " blob_service_client = BlobServiceClient.from_connection_string(conn_str=conn_str)\n", + " sas_token = generate_blob_sas(account_name=blob_service_client.account_name,\n", + " container_name=container,\n", + " blob_name=blob_name,\n", + " account_key=blob_service_client.credential.account_key,\n", + " permission=BlobSasPermissions(read=True),\n", + " expiry=datetime.utcnow() + timedelta(days=1))\n", + " blob_sas = BLOB_SAS_TEMPLATE.format(account_name=blob_service_client.account_name,\n", + " container_name=container,\n", + " blob_name=blob_name,\n", + " sas_token=sas_token)\n", + " return blob_sas" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "source_folder = \"sample_data_MVAD\"\n", + "zipfile_name = \"sample_data_MVAD.zip\"\n", + "account_name = \"[Your storage account name]\" # storage account name\n", + "resource_group = \"[Your resource group name]\" # resource group\n", + "try:\n", + " cmd = f\"az storage account keys list -g {resource_group} -n {account_name}\" # using az-cli is safer\n", + " az_response = subprocess.run(cmd.split(\" \"), stdout=subprocess.PIPE).stdout\n", + " key = json.loads(az_response)[0][\"value\"]\n", + " connection_string = f\"DefaultEndpointsProtocol=https;AccountName={account_name};AccountKey={key};EndpointSuffix=core.windows.net\"\n", + "except FileNotFoundError: # no az-cli available\n", + " connection_string = os.getenv(\"STORAGE_CONN_STR\")\n", + "container_name = \"data\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# split dataset\n", + "os.makedirs(source_folder, exist_ok=True)\n", + "for variable in df.columns:\n", + " individual_df = pd.DataFrame(df[variable].values, index=df.index, columns=[\"value\"])\n", + " individual_df.to_csv(os.path.join(source_folder, f\"{variable}.csv\"))\n", + " \n", + "zip_file(source_folder, zipfile_name)\n", + "upload_to_blob(zipfile_name, connection_string, container_name, zipfile_name)\n", + "data_source = generate_data_source_sas(connection_string, container_name, zipfile_name)\n", + "print(\"Blob SAS url: \" + data_source)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Train a model \n", + "\n", + "Before you train a model, you should specify the `subscription key` and `endpoint` of your Anomaly Detector service to create an Anomaly Detector client in the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# After you create an Anomaly Detector resource in Azure portal, you will get the endpoint and key, and put them here.\n", + "try:\n", + " resource_group = \"[Your resource group name]\"\n", + " account_name = \"[Your account name]\"\n", + " cmd = f\"az cognitiveservices account keys list -g {resource_group} -n {account_name}\" # using az-cli is safer\n", + " az_response = subprocess.run(cmd.split(\" \"), stdout=subprocess.PIPE).stdout\n", + " subscription_key = json.loads(az_response)[\"key1\"]\n", + " anomaly_detector_endpoint = f\"https://{account_name}.cognitiveservices.azure.com\"\n", + "except FileNotFoundError:\n", + " subscription_key = os.getenv(\"SUB_KEY\")\n", + " anomaly_detector_endpoint = os.getenv(\"AD_ENDPOINT\")\n", + "# Create an Anomaly Detector client.\n", + "ad_client = AnomalyDetectorClient(AzureKeyCredential(subscription_key), anomaly_detector_endpoint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Specify the timespan of training data using `start_time` and `end_time`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "start_time = \"2021-02-18T13:50:00Z\"\n", + "end_time = \"2021-09-01T00:00:00Z\"\n", + "sliding_window = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "data_feed = ModelInfo(start_time=start_time, end_time=end_time, source=data_source, sliding_window=sliding_window)\n", + "response_header = ad_client.train_multivariate_model(data_feed, cls=lambda *args: [args[i] for i in range(len(args))])[-1]\n", + "trained_model_id = response_header['Location'].split(\"/\")[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model id: cd5170c8-ab59-11ec-a670-b2e8cb708e18\n" + ] + } + ], + "source": [ + "print(f\"model id: {trained_model_id}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Model Status\n", + "☕️Training process might take few minutes to few hours (depending on the data size, in this sample case it'll take you within 3 minutes), take a cup of coffee and come back then, waiting for its status to be **READY**." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model status: READY\n" + ] + } + ], + "source": [ + "model_status = ad_client.get_multivariate_model(trained_model_id).model_info.status\n", + "print(f\"model status: {model_status}\")\n", + "\n", + "#If the model status is failed, run the following code to see the error message.\n", + "#print ([x.code + ' ' + x.message for x in train_response.model_info.errors])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "#ad_client.get_multivariate_model(trained_model_id).model_info.errors[0].message" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Get model information and track training progress.\n", + "import numpy as np\n", + "from plotly.subplots import make_subplots\n", + "\n", + "model = ad_client.get_multivariate_model(trained_model_id)\n", + "current_epoch = 0 if len(model.model_info.diagnostics_info.model_state.epoch_ids) == 0 else model.model_info.diagnostics_info.model_state.epoch_ids[-1]\n", + "print(f\"training progress: {current_epoch}/100.\")\n", + "if model.model_info.status == \"READY\":\n", + " model_state = model.model_info.diagnostics_info.model_state\n", + " epoch_ids = model_state.epoch_ids\n", + " train_losses = model_state.train_losses\n", + " validation_losses = model_state.validation_losses\n", + " latency = model_state.latencies_in_seconds\n", + " loss_summary = pd.DataFrame({\n", + " \"epoch_id\": epoch_ids, \n", + " \"train_loss\": train_losses, \n", + " \"validation_loss\": validation_losses,\n", + " \"latency\": latency\n", + " })\n", + " display(loss_summary)\n", + " fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", + " fig.add_trace(go.Scatter(x=epoch_ids, y=train_losses, \n", + " mode='lines',\n", + " name='train losses'))\n", + " fig.add_trace(go.Scatter(x=epoch_ids, y=validation_losses,\n", + " mode='lines',\n", + " name='validation losses'))\n", + " fig.add_trace(go.Scatter(x=epoch_ids, y=latency,\n", + " mode='markers', name='latency'),\n", + " secondary_y=True)\n", + " fig.update_layout(\n", + " title_text=\"Visualization of training progress\"\n", + " )\n", + " fig.update_xaxes(title_text=\"Epoch IDs\")\n", + "\n", + " # Set y-axes titles\n", + " fig.update_yaxes(title_text=\"Loss value\", secondary_y=False)\n", + " fig.update_yaxes(title_text=\"Latency (s)\", secondary_y=True)\n", + "\n", + " fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. List Models \n", + "List models that have been trained previously." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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model_idstatus
0cd5170c8-ab59-11ec-a670-b2e8cb708e18READY
19db32770-ab50-11ec-a670-b2e8cb708e18READY
28d8811a6-ab48-11ec-b575-b2e8cb708e18READY
3ee90bdb4-ab47-11ec-822b-b2e8cb708e18FAILED
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" + ], + "text/plain": [ + " model_id status\n", + "0 cd5170c8-ab59-11ec-a670-b2e8cb708e18 READY\n", + "1 9db32770-ab50-11ec-a670-b2e8cb708e18 READY\n", + "2 8d8811a6-ab48-11ec-b575-b2e8cb708e18 READY\n", + "3 ee90bdb4-ab47-11ec-822b-b2e8cb708e18 FAILED\n", + "4 6e7d9b7e-ab47-11ec-b714-e2151d0110f3 FAILED" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_list = list(ad_client.list_multivariate_model(skip=0, top=100))\n", + "model_summary = pd.DataFrame([{\"model_id\": m.model_id, \"status\": m.status} for m in model_list[:5]])\n", + "display(model_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'additional_properties': {},\n", + " 'model_id': 'cd5170c8-ab59-11ec-a670-b2e8cb708e18',\n", + " 'created_time': datetime.datetime(2022, 3, 24, 10, 4, 32, tzinfo=),\n", + " 'last_updated_time': datetime.datetime(2022, 3, 24, 10, 9, 29, tzinfo=),\n", + " 'status': 'READY',\n", + " 'display_name': '',\n", + " 'variables_count': 5}" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# inspect the first model\n", + "model = model_list[0]\n", + "vars(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Inference " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A. Inference asynchronously\n", + "\n", + "You should inference first and get a result id, then use the id to get the detection result.\n", + "- Specify the time span of inference data using `start_time` and `end_time`." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the start time and end time for inference.\n", + "start_inference_time = \"2021-09-01T00:00:00Z\"\n", + "end_inference_time = \"2021-09-14T23:20:00Z\"" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "result id: b876ee02-ab5a-11ec-a670-b2e8cb708e18\n" + ] + } + ], + "source": [ + "detection_req = DetectionRequest(source=data_source, start_time=start_inference_time, end_time=end_inference_time)\n", + "response_header = ad_client.detect_anomaly(trained_model_id, detection_req, cls=lambda *args: [args[i] for i in range(len(args))])[-1]\n", + "result_id = response_header['Location'].split(\"/\")[-1]\n", + "print(f\"result id: {result_id}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get inference status\n", + "☕️Inference process might\n", + "take 10-20mins (depending on the data size). Take a cup of coffee and come back then, and waiting for its status to be **READY**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "result status: READY\n" + ] + } + ], + "source": [ + "r = ad_client.get_detection_result(result_id)\n", + "print(f\"result status: {r.summary.status}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'additional_properties': {}, 'is_anomaly': True, 'severity': 0.06823510676622391, 'score': 0.11724714934825897, 'interpretation': [, , , , ]}\n" + ] + } + ], + "source": [ + "# Have a look at the first anomalous result of inference.\n", + "for r_item in r.results:\n", + " if r_item.value.is_anomaly:\n", + " print(r_item.value)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### B. Inference with the synchronous API (NEW)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This synchronous API will get detection result immediately after you call it, you should put your data with JSON format into the request body, and specify how much data points you'd like to detect within the `detectingPoints` field, which could be a number **between 1 and 10**." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestampis_anomalyseverityscore
02021-09-12T13:30:00ZFalse0.0103840.017843
12021-09-12T13:40:00ZFalse0.0103840.017843
22021-09-12T13:50:00ZFalse0.0103840.017843
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82021-09-12T14:50:00ZTrue0.0763990.131275
92021-09-12T15:00:00ZTrue0.0878010.150866
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" + ], + "text/plain": [ + " timestamp is_anomaly severity score\n", + "0 2021-09-12T13:30:00Z False 0.010384 0.017843\n", + "1 2021-09-12T13:40:00Z False 0.010384 0.017843\n", + "2 2021-09-12T13:50:00Z False 0.010384 0.017843\n", + "3 2021-09-12T14:00:00Z False 0.010384 0.017843\n", + "4 2021-09-12T14:10:00Z False 0.010384 0.017843\n", + "5 2021-09-12T14:20:00Z False 0.010384 0.017843\n", + "6 2021-09-12T14:30:00Z False 0.010384 0.017843\n", + "7 2021-09-12T14:40:00Z False 0.010384 0.017843\n", + "8 2021-09-12T14:50:00Z True 0.076399 0.131275\n", + "9 2021-09-12T15:00:00Z True 0.087801 0.150866" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "sample_input_df = df[df.index<=\"2021-09-12T15:00:00Z\"][-110:]\n", + "sample_input = [{\"name\": var, \n", + " \"timestamps\": sample_input_df.index.tolist(), \n", + " \"values\": sample_input_df[var].tolist()} for var in sample_input_df.columns]\n", + "last_detection_request = LastDetectionRequest(variables=[VariableValues(**item) for item in sample_input], detecting_points=10)\n", + "res = ad_client.last_detect_anomaly(model_id=trained_model_id, body=last_detection_request)\n", + "results = pd.DataFrame(columns=[\"timestamp\", \"is_anomaly\", \"severity\", \"score\"])\n", + "for item in res.results:\n", + " results = results.append({\"timestamp\": item.timestamp.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n", + " \"is_anomaly\": item.value.is_anomaly,\n", + " \"severity\": item.value.severity,\n", + " \"score\": item.value.score}, ignore_index=True)\n", + "display(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Visualization of detection results (for reference only) " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "results = r.results" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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opticalLFilteredopticalRFilteredpumpPressurerotationalvibrationHorizon
timestamp
2021-09-01T00:00:00Z0.7500920.7667780.3032290.3039620.460510
2021-09-01T00:10:00Z0.7438590.7669810.2608230.2781010.415097
2021-09-01T00:20:00Z0.7622210.7712640.2305530.3216430.441945
2021-09-01T00:30:00Z0.7886470.7734220.2632020.2772940.465198
2021-09-01T00:40:00Z0.8062440.8126570.2397690.2953360.443462
..................
2021-09-14T22:40:00Z0.0000000.0000000.9500000.0000000.850000
2021-09-14T22:50:00Z0.0000000.0000000.9500000.0000000.850000
2021-09-14T23:00:00Z0.0000000.0000000.9500000.0000000.850000
2021-09-14T23:10:00Z0.0000000.0000000.9500000.0000000.850000
2021-09-14T23:20:00Z0.0000000.0000000.9500000.0000000.850000
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2013 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " opticalLFiltered opticalRFiltered pumpPressure \\\n", + "timestamp \n", + "2021-09-01T00:00:00Z 0.750092 0.766778 0.303229 \n", + "2021-09-01T00:10:00Z 0.743859 0.766981 0.260823 \n", + "2021-09-01T00:20:00Z 0.762221 0.771264 0.230553 \n", + "2021-09-01T00:30:00Z 0.788647 0.773422 0.263202 \n", + "2021-09-01T00:40:00Z 0.806244 0.812657 0.239769 \n", + "... ... ... ... \n", + "2021-09-14T22:40:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T22:50:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T23:00:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T23:10:00Z 0.000000 0.000000 0.950000 \n", + "2021-09-14T23:20:00Z 0.000000 0.000000 0.950000 \n", + "\n", + " rotational vibrationHorizon \n", + "timestamp \n", + "2021-09-01T00:00:00Z 0.303962 0.460510 \n", + "2021-09-01T00:10:00Z 0.278101 0.415097 \n", + "2021-09-01T00:20:00Z 0.321643 0.441945 \n", + "2021-09-01T00:30:00Z 0.277294 0.465198 \n", + "2021-09-01T00:40:00Z 0.295336 0.443462 \n", + "... ... ... \n", + "2021-09-14T22:40:00Z 0.000000 0.850000 \n", + "2021-09-14T22:50:00Z 0.000000 0.850000 \n", + "2021-09-14T23:00:00Z 0.000000 0.850000 \n", + "2021-09-14T23:10:00Z 0.000000 0.850000 \n", + "2021-09-14T23:20:00Z 0.000000 0.850000 \n", + "\n", + "[2013 rows x 5 columns]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# view inference data\n", + "test_df = df.loc[\"2021-09-01T00:00:00Z\":\"2021-09-14T23:20:00Z\"]\n", + "test_df" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "is_anomalies = []\n", + "sev = []\n", + "scores = []\n", + "sensitivity = 0.7\n", + "for item in results:\n", + " if item.value:\n", + " is_anomalies.append(item.value.is_anomaly)\n", + " sev.append(item.value.severity)\n", + " scores.append(item.value.score)\n", + "\n", + "anomolous_timestamps = []\n", + "num_contributors = 3\n", + "top_values = {f\"top_{i}\": [] for i in range(num_contributors)}\n", + "for ts, item in zip(test_df.index, r.results):\n", + " if item.value.is_anomaly and item.value.severity > 1 - sensitivity:\n", + " anomolous_timestamps.append(ts)\n", + " for i in range(num_contributors):\n", + " top_values[f\"top_{i}\"].append(test_df[item.value.interpretation[i].variable][ts])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = make_subplots(rows=3, cols=1, shared_xaxes=True)\n", + "colors = [px.colors.sequential.Greys[-1], px.colors.sequential.Greys[-3], px.colors.sequential.Greys[-6]]\n", + "for v in test_df.columns:\n", + " fig.add_trace(go.Scatter(x=test_df.index, y=test_df[v], \n", + " mode='lines',\n", + " name=v),\n", + " row=1, col=1)\n", + "for i in range(num_contributors):\n", + " fig.add_trace(go.Scatter(x=anomolous_timestamps, y=top_values[f\"top_{i}\"],\n", + " mode=\"markers\", name=f\"Top {i+1} contributor\",\n", + " marker=dict(\n", + " color=colors[i],\n", + " size=8,\n", + " )),\n", + " row=1, col=1)\n", + "fig.add_trace(go.Scatter(x=test_df.index, y=scores,\n", + " mode='lines',\n", + " name='score'),\n", + " row=2, col=1)\n", + "fig.add_trace(go.Scatter(x=test_df.index, y=sev,\n", + " mode='lines', name='severity'),\n", + " row=3, col=1)\n", + "fig.update_layout(\n", + " title_text=\"Visualization of detection results\"\n", + ")\n", + "fig.update_yaxes(title_text=\"value\", row=1, col=1)\n", + "fig.update_yaxes(title_text=\"score\", row=2, col=1)\n", + "fig.update_yaxes(title_text=\"severity\", row=3, col=1)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![data overview](../media/inference-result.png )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "73774fa62b2e1823ff4dfa3d7e6968adf72c7029b6373b24ad42c7932e1cccae" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ipython-notebook/SDK Sample/Public Preview/UVAD-SDK-Demo.ipynb b/ipython-notebook/SDK Sample/Public Preview/UVAD-SDK-Demo.ipynb new file mode 100644 index 0000000..f73fbea --- /dev/null +++ b/ipython-notebook/SDK Sample/Public Preview/UVAD-SDK-Demo.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Univariate Anomaly Detection Demo Code (SDK Version)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: This sample code is using the SDK version of `3.0.0b5` and earlier, which matches with the `v1.1-preview` and `v1.1-preview API` released in November 2022.\n", + "\n", + "Try the latest SDK sample code (released in December 2022) [here](%F0%9F%86%95UVAD-SDK-Demo.ipynb).\n", + "\n", + "This table shows the relationship between SDK versions and supported API versions of the service:\n", + "\n", + "|SDK version|Supported API version of service |\n", + "|-------------|---------------|\n", + "|3.0.0b6 | 1.1|\n", + "|3.0.0b4, 3.0.0b5| 1.1-preview-1|\n", + "|3.0.0b3 | 1.1-preview|\n", + "|3.0.0b1, 3.0.0b2 | 1.0 |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install --upgrade azure-ai-anomalydetector" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "import json\n", + "\n", + "from datetime import datetime\n", + "from azure.ai.anomalydetector import AnomalyDetectorClient\n", + "from azure.ai.anomalydetector.models import DetectRequest, TimeSeriesPoint, TimeGranularity, \\\n", + " AnomalyDetectorError\n", + "from azure.core.credentials import AzureKeyCredential" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n var JS_MIME_TYPE = 'application/javascript';\n var HTML_MIME_TYPE = 'text/html';\n var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n var CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n var script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n var cell = handle.cell;\n\n var id = cell.output_area._bokeh_element_id;\n var server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd, {\n iopub: {\n output: function(msg) {\n var id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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});\n }\n}(window));", + "application/vnd.bokehjs_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from bokeh.plotting import figure,output_notebook, show\n", + "from bokeh.palettes import Blues4\n", + "from bokeh.models import ColumnDataSource,Slider\n", + "import datetime\n", + "from bokeh.io import push_notebook\n", + "from dateutil import parser\n", + "from ipywidgets import interact, widgets, fixed\n", + "output_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "subscription_key = '[Placeholder: Your Anomaly Detector resource access key]' \n", + "anomaly_detector_endpoint = '[Placeholder: Your Anomaly Detector resource endpoint]'\n", + "\n", + "# create an Anomaly Detector client\n", + "ad_client = AnomalyDetectorClient(AzureKeyCredential(subscription_key), anomaly_detector_endpoint)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def build_figure(result, sample_data, sensitivity):\n", + " columns = {'expectedValues': result['expectedValues'], 'isAnomaly': result['isAnomaly'], 'isNegativeAnomaly': result['isNegativeAnomaly'],\n", + " 'isPositiveAnomaly': result['isPositiveAnomaly'], 'upperMargins': result['upperMargins'], 'lowerMargins': result['lowerMargins']\n", + " , 'value': [x['value'] for x in sample_data['series']], 'timestamp': [parser.parse(x['timestamp']) for x in sample_data['series']]}\n", + " response = pd.DataFrame(data=columns)\n", + " values = response['value']\n", + " label = response['timestamp']\n", + " anomalies = []\n", + " anomaly_labels = []\n", + " index = 0\n", + " anomaly_indexes = []\n", + " p = figure(x_axis_type='datetime', title=\"Anomaly Detection Result ({0} Sensitivity)\".format(sensitivity), width=800, height=200)\n", + " for anom in response['isAnomaly']:\n", + " if anom == True and (values[index] > response.iloc[index]['expectedValues'] + response.iloc[index]['upperMargins'] or \n", + " values[index] < response.iloc[index]['expectedValues'] - response.iloc[index]['lowerMargins']):\n", + " anomalies.append(values[index])\n", + " anomaly_labels.append(label[index])\n", + " anomaly_indexes.append(index)\n", + " index = index+1\n", + " upperband = response['expectedValues'] + response['upperMargins']\n", + " lowerband = response['expectedValues'] -response['lowerMargins']\n", + " band_x = np.append(label, label[::-1])\n", + " band_y = np.append(lowerband, upperband[::-1])\n", + " boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend='Boundary')\n", + " p.line(label, values, legend='value', color=\"#2222aa\", line_width=1)\n", + " p.line(label, response['expectedValues'], legend='expectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n", + " anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n", + " anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n", + " p.legend.border_line_width = 1\n", + " p.legend.background_fill_alpha = 0.1\n", + " show(p, notebook_handle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Latest point anomaly detection with the Anomaly Detector SDK" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point=29):\n", + " points = sample_data[\"series\"]\n", + " granularity = sample_data[\"granularity\"]\n", + " result = {\n", + " \"expectedValues\": [None] * len(points),\n", + " \"upperMargins\": [None] * len(points),\n", + " \"lowerMargins\": [None] * len(points),\n", + " \"isNegativeAnomaly\": [False] * len(points),\n", + " \"isPositiveAnomaly\": [False] * len(points),\n", + " \"isAnomaly\": [False] * len(points)\n", + " }\n", + " anom_count = 0\n", + " \n", + " for i in range(skip_point, len(points) + 1):\n", + " series = [TimeSeriesPoint(timestamp=item[\"timestamp\"], value=item[\"value\"]) for item in points[i - 29: i]]\n", + " request = DetectRequest(series=series, granularity=granularity, sensitivity=sensitivity, max_anomaly_ratio=0.25)\n", + " single_point = ad_client.detect_last_point(request)\n", + " if single_point.is_anomaly == True:\n", + " anom_count += 1\n", + " result['expectedValues'][i-1] = single_point.expected_value\n", + " result['upperMargins'][i-1] = single_point.upper_margin\n", + " result['lowerMargins'][i-1] = single_point.lower_margin\n", + " result['isNegativeAnomaly'][i-1] = single_point.is_negative_anomaly\n", + " result['isPositiveAnomaly'][i-1] = single_point.is_positive_anomaly\n", + " result['isAnomaly'][i-1] = single_point.is_anomaly\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "granularity: daily\n" + ] + } + ], + "source": [ + "sample_data = json.load(open('univariate_sample_daily.json'))\n", + "print(f\"granularity: {sample_data['granularity']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "(function(root) {\n function embed_document(root) {\n \n var docs_json = 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detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch anomaly detection with the Anomaly Detector SDK" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def detect_anomaly_entire(sample_data, ad_client, sensitivity):\n", + " points = sample_data[\"series\"]\n", + " granularity = sample_data[\"granularity\"]\n", + " period = sample_data[\"period\"] if \"period\" in sample_data else None\n", + " series = [TimeSeriesPoint(timestamp=item[\"timestamp\"], value=item[\"value\"]) for item in points]\n", + " request = DetectRequest(series=series, granularity=granularity, sensitivity=sensitivity, period=period)\n", + " batch = ad_client.detect_entire_series(request)\n", + " result = {}\n", + " result['expectedValues'] = batch.expected_values\n", + " result['upperMargins'] = batch.upper_margins\n", + " result['lowerMargins'] = batch.lower_margins\n", + " result['isNegativeAnomaly'] = batch.is_negative_anomaly\n", + " result['isPositiveAnomaly'] = batch.is_positive_anomaly\n", + " result['isAnomaly'] = batch.is_anomaly\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Case 1: time series with an hourly sampling frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "granularity: hourly\n", + "period: 24\n" + ] + } + ], + "source": [ + "sample_data = json.load(open('univariate_sample_hourly.json'))\n", + "print(f\"granularity: {sample_data['granularity']}\")\n", + "print(f\"period: {sample_data['period']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": 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\",\"dtype\":\"float64\",\"shape\":[1682]}},\"selected\":{\"id\":\"1701\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1702\",\"type\":\"UnionRenderers\"}},\"id\":\"1666\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_color\":\"#2222aa\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1692\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1701\",\"type\":\"Selection\"},{\"attributes\":{\"label\":{\"value\":\"expectedValue\"},\"renderers\":[{\"id\":\"1720\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1744\",\"type\":\"LegendItem\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"1682\",\"type\":\"DaysTicker\"},{\"attributes\":{\"source\":{\"id\":\"1745\",\"type\":\"ColumnDataSource\"}},\"id\":\"1750\",\"type\":\"CDSView\"},{\"attributes\":{\"label\":{\"value\":\"value\"},\"renderers\":[{\"id\":\"1694\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1716\",\"type\":\"LegendItem\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"1685\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1653\",\"type\":\"PanTool\"},{\"id\":\"1654\",\"type\":\"WheelZoomTool\"},{\"id\":\"1655\",\"type\":\"BoxZoomTool\"},{\"id\":\"1656\",\"type\":\"SaveTool\"},{\"id\":\"1657\",\"type\":\"ResetTool\"},{\"id\":\"1658\",\"type\":\"HelpTool\"}]},\"id\":\"1659\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1649\",\"type\":\"BasicTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"1745\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1747\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1748\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1750\",\"type\":\"CDSView\"}},\"id\":\"1749\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":0.5,\"fill_color\":\"#bdd7e7\",\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id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\",\"dtype\":\"float64\",\"shape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\",\"dtype\":\"float64\",\"shape\":[841]}},\"selected\":{\"id\":\"1812\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1813\",\"type\":\"UnionRenderers\"}},\"id\":\"1717\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1717\",\"type\":\"ColumnDataSource\"}},\"id\":\"1721\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null},\"id\":\"1635\",\"type\":\"DataRange1d\"},{\"attributes\":{\"ticker\":{\"id\":\"1644\",\"type\":\"DatetimeTicker\"}},\"id\":\"1647\",\"type\":\"Grid\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1668\",\"type\":\"Patch\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"1686\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"1814\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1815\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"fill_color\":{\"value\":\"tomato\"},\"line_color\":{\"value\":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\",\"dtype\":\"float64\",\"shape\":[841]}},\"selected\":{\"id\":\"1729\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1730\",\"type\":\"UnionRenderers\"}},\"id\":\"1691\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1691\",\"type\":\"ColumnDataSource\"}},\"id\":\"1695\",\"type\":\"CDSView\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"1677\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"line_color\":\"olivedrab\",\"line_dash\":[2,4,6,4],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1718\",\"type\":\"Line\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"1677\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1678\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1679\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1680\",\"type\":\"DaysTicker\"},{\"id\":\"1681\",\"type\":\"DaysTicker\"},{\"id\":\"1682\",\"type\":\"DaysTicker\"},{\"id\":\"1683\",\"type\":\"DaysTicker\"},{\"id\":\"1684\",\"type\":\"MonthsTicker\"},{\"id\":\"1685\",\"type\":\"MonthsTicker\"},{\"id\":\"1686\",\"type\":\"MonthsTicker\"},{\"id\":\"1687\",\"type\":\"MonthsTicker\"},{\"id\":\"1688\",\"type\":\"YearsTicker\"}]},\"id\":\"1644\",\"type\":\"DatetimeTicker\"},{\"attributes\":{},\"id\":\"1639\",\"type\":\"LinearScale\"},{\"attributes\":{\"label\":{\"value\":\"Boundary\"},\"renderers\":[{\"id\":\"1669\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1690\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1730\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"f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Detection Result (95 Sensitivity)\"},\"id\":\"1633\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1812\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"1666\",\"type\":\"ColumnDataSource\"}},\"id\":\"1670\",\"type\":\"CDSView\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"1687\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"formatter\":{\"id\":\"1672\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1649\",\"type\":\"BasicTicker\"}},\"id\":\"1648\",\"type\":\"LinearAxis\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1693\",\"type\":\"Line\"},{\"attributes\":{\"overlay\":{\"id\":\"1676\",\"type\":\"BoxAnnotation\"}},\"id\":\"1655\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"formatter\":{\"id\":\"1674\",\"type\":\"DatetimeTickFormatter\"},\"ticker\":{\"id\":\"1644\",\"type\":\"DatetimeTicker\"}},\"id\":\"1643\",\"type\":\"DatetimeAxis\"}],\"root_ids\":[\"1632\"]},\"title\":\"Bokeh 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"build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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\",\"dtype\":\"float64\",\"shape\":[841]},\"y\":{\"__ndarray__\":\"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MQOhwqWjm8MxAaAVd3trnzEBZf7AOu+nMQGD2/Zls7MxAks6BH1nkzEDME4kHANDMQK38zbNcz8xAs+kqt+CwzEA1rgC/wLHMQKvLeW1LvcxArEpJMdaczEAmSPamioDMQJHkh81IhcxASuN+l6lfzEDsv0x4JVTMQMPMlVW/YsxASn6A3ilFzECC1PKwAELMQD7TXA0XOcxAw7/Pej0ezECRHm7nHQjMQL8Rnm5mBMxAmHqC/yH0y0AjOVcH9gvMQOHiUwOf/stA+L/A+Hfuy0CBgs2oPOnLQCBC1LPS5MtAiBtvznDby0D6YY1LycXLQBNM6YzXw8tATzpdJQ2ky0ADFUygMafLQKlI3sEAtctA3d3G+M+Wy0CI8YzhyHzLQAEEP1D3hMtAx3hWYshiy0B2y0SLtFrLQKn2IgC+bctAn7FVXl9Zy0BFERAGbV/LQG8Zwje6X8tAZA99ehdOy0BmDtF/P0TLQDq3xQkAT8tAuNVunTNNy0DpSQiof3PLQNRAcn48c8tAGGtMTilwy0DPesbYAXjLQJuHOr6rgMtAQYhJSauCy0Dv9ds2ZXjLQEYHrOjUgctAvxyU8Wtty0BcD12c73rLQOpaye0dk8tABgiMVEx/y0CaMyxtpG/LQHsDWmwfgstAqTXtDj1qy0DBRVfIdWzLQF0usc3LictAdvqiuUd+y0BBaxzvL43LQI+Eja5XlstAp4sHf4+Ny0Ct1RB8fovLQIPJuv0FnstABjMZiQCky0A68meLE9LLQNEuruE03ctAwp5kMYbly0Al9Lo7w/jLQJ1GC6HRDMxAioN5O88dzECALWs4hyLMQB17mvn0OsxA3czhEYo1zEDffeXjElTMQNSHjFxGfcxAVPOJ6nl6zEBN3WQq13vMQLJgj+k/nsxAYkYfTEuWzED9CYbFcajMQBym3Iq11cxAkOGaAw/ZzEC2weDF1PbMQF1KHhLaDs1A0sBkb+8UzUColbc2dSXNQFAUq4KTSs1ApAhT2CRjzUCoUuukzqPNQB+rHLQ0w81A7ja+vMrfzUAwqP9/TAfOQIUWO56fL85Afhuo78dSzkB8jZijqmnOQCSjxhtDlM5A7bwM6wKhzkAwXQs+y8/OQGBWrTc+Cc9AILGlRrEWz0BYinsHTijPQG6rIKoRW89A1C4r8Hdjz0AgkAxN+YXPQPLJ3fWXw89A0OAM7ErQz0BjnMMravfPQDoAuXpkDNBAKqkU6BsU0ECfGXabbRvQQP3eJ5ELLdBAMd+zC2M40EA8CrjBxlfQQBxvOcBPZNBAqO1yu3Bv0EDsXvyTB4DQQLzOAhoHkdBA2nGaDM6f0EB8y3MwcqjQQPL2azbxutBAeSTw54O+0ECoBTy7zdLQQE+T2eFs7NBAvFEiEwzw0EBmz1kdwPXQQAJgsdcoDNFAxqG742IN0UB/UjF7qhvRQHrvnriAN9FAR/SG7H8+0UBtyzJFtVLRQNR22mIKZNFAIZkGiWds0UDyXd+oIHXRQKt3CAsmiNFAOswL8uSU0UCiS4cUsLXRQJjJ40g9v9FAOmH4eWLH0UCV61yI/dTRQHp0PkQB49FA1GhxwWrq0UCxE+ZvsevRQGKQeQDT9tFAJA+ZPAjz0UAtHgZSV//RQK3ZxLr7ENJA9sUuLqAM0kB5cYd6WQrSQCC/AizFF9JA8L0wLwIQ0kCzK8q9TBXSQLiFW/IlKNJAbsgPEfQj0kB93YdU+CzSQM7G+1wcM9JABCf0bUgw0kDf20Re3i7SQKTl5ZDAN9JAPyphSFw60kCxmVQ7BFHSQF2lcV4nVNJAtspGfuJV0kDI4mt7E13SQGT5DSatZNJAiqnL9Zxn0kAyEMv2aWTSQK5I6dkRa9JAPYOTaM1i0kCp9TKnZ2nSQI0UJDlXddJAOmTA1UZr0kAhc0tLS2PSQILkXfgRatJACgcj96lb0kCGmFOBT1rSQEYWfLGDZtJAKmkEKPpc0kBpjlDDpmDSQOiHmCNzYdJATPhkjEdZ0kDDQ/roF1TSQCLk34c0WdJAWb+fqwpY0kBmxdcK7WrSQOD70VTKaNJABkyEmz9l0kDljoa/KmfSQE7QBZF+adJA+ncmlXBl0kAo1ojKP1vSQCkGCuLpWtJAPjgXpadL0kB76y3FjkvSQC9LljjLUNJAq9uptgdA0kBjK6wNWTHSQPDWd7i7MtJApTP2tO8e0kBO/988MRjSQDq3wWoBH9JA8hblw1EQ0kAEScxB2A7SQFdPr4R+CtJAkMwW0Cz90UD0wXVrjfLRQEEMJUk68tFAZJGuq6Dr0UBfQbBJE/nRQGxR2mfR9NFAJXu8gifv0UCXl+568+7RQJSynSAo79FA0G1CNcDs0UCP3yh7NeTRQCMjLqOF5dFAx2i/dunX0UAbTy8E5NrRQOfh8OQz49FAe6Vd0IPV0UBJKLmU6MnRQDb7QuSszdFASn9/hUK80UBTciey5bfRQJ5Rx4QXwdFAPfPD/Caz0UA2Z4SZbLLRQHCvQPvRrtFAkG6BZT+i0UBokPCJApjRQCkHsPARmNFAwLhJ3NqR0UAvlVsDsJ/RQIBUMUGknNFAfC2/ezCY0UAy+ZyTMpnRQHLD91idmtFAdLInBaWb0UD4V5niiZbRQFDPKaJJm9FAu0hGDR2R0UAKs9Kn05nRQNDJsJXfp9FAhfubXD+h0UB27HX8s5zRQGIY+o0FqNFAdvUwcSie0UB9QdPfWKHRQMh5bfQXstFApCNgWzGx0UDanxbngL3RQFLwyDfwxtFAr7f/kGfH0UBQBYYb68zRQNinXOi63NFANoUNOkTm0UBtjTbH2QPSQDPLHypuDtJAoyLBiZoX0kDMbLLGPCbSQIG1ILFHNdJAyAf6FFJA0kCSEBWqOUXSQDDrTiH8U9JA38cURNJT0kDQ3aOqwmTSQDighGQIe9JAapMQKU570kDWRYvGqH3SQDjS1VoukNJAww/TQIWN0kBCvDuy6ZfSQAJVnMncr9JA0gaInY2x0kD8ijeWdMDSQGfj4lN7zNJAuLISGorP0kBIRPoSWtXSQMAqMk525dJADkxEDkzv0kA0mM4JLg3TQCABkPm7GNNAuoMJ5uEi00AM+dKvfTLTQOhsGSeCQtNAeCa2i2pP00CLlpQhMFbTQHLYkZnQZtNAahwbvYRo00CVHC23fXvTQDnJkATMk9NAo6afXBqW00BJQ52NfZrTQFQaVWfnr9NAiKK/kiKw00CwmZVJa73TQBp9Y6ZC2NNAcdsMIjDe00AjDHrCU/HTQBQR4yeXAdRA7IzQleII1EC0WDk5+xPUQGZ58h5gKdRA7dSFiX441EBMW5EvqVvUQKFkfSffbNRAoochHK181EBcnRXu8JHUQKCxhm2dp9RAyY8J5qW41EB1JM6Pi8PUQPaKsRtM2NRAiPMgUyDe1EBp2++VaPLUQMJvECwGDNVA4jTczKMP1UA+uZZGVhXVQNiDajgOKdVAmv/we5cn1UBQ6uJKLjPVQEjBzL9TTNVAjcy8dghO1UAsqnBS81zVQAxcIPP9aNVA0oRUnBBs1UBb9r0j9m/VQMu8d+0nftVAEr4LPBOG1UAy6hfGCqLVQCxeaXz3qtVA0OtyL3yy1UAwbMy/dr/VQBjrov3ZzNVAoVanaMbX1UCteO0EkNzVQI5sUoM069VAf2JDrezq1UAnN7nhTvvVQEe4gGkGEdZALWrz+70Q1kBQ21RnihLWQOR9Q6bpIdZAntHkNhoc1kBOlPFSWCPWQEBD9hQlONZAGh5gVYQz1kBOy426GTzWQMJMt+TOQdZAHEVlF4w+1kCaydodyDrWQAGjoGZQQdZAP7dANJJB1kBU9lg94FXWQD0aFEkPVtZA0VeHUdZU1kAfiEo3E1nWQPe2isq4XdZA6rDlyhtd1kBeYYL8W1bWQKjjPRB3WdZAA2iFz6VN1kC6399ZCVLWQOgDjDfCW9ZA31jjH3tP1kAQbSnhSEXWQKqT5zTpS9ZAbGtY2lo91kAisjQL2jvWQBvlCOLnR9ZAfhSFzIA+1kA6FsXbT0LWQDjsALA+Q9ZAHDnBjDU71kDKDQyg1TTWQGI3p/XBONZA0Jsc0Gc21kAVKwrmGUjWQAtyUam0RdZArtJQaedB1kAKJqAGkEPWQPB3bFGhRdZACRfPJLND1kClbHMpojvWQBaUNhBsPdZAmL2Fokkw1kBblyYT/jLWQJUdGdcHO9ZAltS2pREt1kDUSkNNMCHWQIi/Fm48JNZAZOWc4BkS1kA0eo7eBA3WQEf7d4J+FdZAR2h4YeMH1kCjpzxlfgfWQD67/C05BNZAv0VB//v31UDu6w8bMu7VQAXnLnm07tVA8hwoXPDo1UC3fZl6OPfVQPib54iP8tVA59Ptk37s1UCO/kN84+vVQL4nFxKx69VAHyhy5tjo1UAC3w7s3d/VQLpnytO94NVAhPIRZ7HS1UBWcmFRi9XVQKCeAo+63dVAsPtO1+nP1UD+F4r4LcTVQG76Xd3Sx9VABo7kE0m21UCSkNbVzLHVQGB/wD3futVAvjw/3eGu1UB2zIGhGrDVQG8wwCpzrtVATQuDvNOj1UDA5R/VN53VQBsVDTDooNVATn/UD1Ke1UBYFBQryK/VQOKNR3eKr9VAGSEzwOSt1UAKp27mtLHVQIMrJ7rttdVAtia5Eti31UBs2Iycn7PVQPVbfwhCudVAkeH9H/iv1UCWXpkccLnVQBGIhmw9yNVAVuIexwrB1UDV+6X67LvVQGaM6wjQyNVAH87jaITA1UDMfkdURsXVQLwbo+WW19VAHujZIGrU1UDchtSAc97VQNr5yqWc5dVAveNF083j1UDGM/nWRuXVQLnY/BwM8dVAgrja54r21UAiwzDuFRDWQNhWnWV3F9ZAOQTC2XAd1kBTpDYr4CjWQPhCKCq4NNZAhmSGqcM91kCXPCZarEDWQH3m5OxvTdZAdJIvK0dL1kAoeVT5JFvWQFMMyxpYcNZARtDsRotv1kB1U/1L03DWQL7hBuchg9ZAMCHD00GA1kCXz+pLb4rWQD9qCmorotZAtFh4lu+k1kCCGarn6bTWQJKu1/0DwtZAh7qJHCbG1kAxHjPawM7WQMXWLNqn4dZALsoAX0ju1kBv6Ewf9Q7XQN12saOqH9dA9h7OJPgu10DJuTqDu0PXQCZTJI/nWNdAirERAp9s10BxxkCmM3rXQCytjiyjkddA+JVoXiaa10B28O7JkrLXQGv3xohU0NdAJy9KUhbY10AfJrz07OHXQCrkjWL6+tdAXVMSItn+10CFMQJtxQ/YQO776V1ALthA2KlFzoY22EAdKmVjA0zYQKN+gL2fXthADUogIERo2ECbb8ETt3LYQBDqskl2h9hAXZ9+BO+V2ECBf8L6c7jYQFZk1rKLxdhA2GKiZzvR2EATVL75YOLYQNhDVznv89hA8GwAOPoA2UCMTOtn4gfZQPz99HmlGNlAfbGKN3wa2UAwEzJ42SzZQFohKwyMRNlAS2DPqj5G2UB5XmIiBkrZQHPgsSnTXdlAkxO0gnFc2UCptSFnHWjZQAFEh/FXgdlA9BHA6WeE2UBAsrwGrpTZQM4mtegTotlAQRIy04Gm2UAGQ7n056vZQLTIkFiau9lAN4lCQQbF2UCSdGxlfuLZQI5xUk/56tlAN4jwNQzy2UCXkd75lP7ZQIKZSWuGC9pAEft9mwQV2kAjE/T8XxjaQAr9iECWJdpAAumpL+Aj2kB73ww/zTLaQGyCwaEPR9pAJFYhD1JF2kAY6W9VqUXaQEAjWDGHVdpAkg7zXjZQ2kDXaPkX81faQF6v93Y+bdpAKp93sSRs2kBSYbsQQXjaQLn3+jR9gdpABgW/YcGB2kBn2Lu72TLgQGOXJxKwMuBAzhBWUQst4EAMU1elpCXgQEiy7NZQJuBAg9w0ck0Z4EDLwSIBkxTgQMSjJ57SF+BAjtXgXiAO4ED5CT8J6w3gQCXsoFT7DuBAIGxr35kC4EB0x5aP9fLfQGL8ivkM9d9AK4m4GOfk30AVwQu+zODfQPQQIYMy1d9AK0Cev2DF30DtwpfsDrbfQMqK1plprd9AXscSQwyj30DRbhilB3/fQIaDlMGDc99AJOD+Y1Rg30AXiJIeu1nfQFBo3ThRVN9AbL2tJPFD30AqpCM6DTDfQOXEwQpPLN9AXTMpRXkN30DtF9xmNf/eQOH1vKTlAN9Ad3MGKrLo3kAHy1K0E+TeQBh+poAA4t5AyGzLywnF3kC1ShvSl67eQGw2nXhYrN5AAHpY1NuX3kC0aDm2ao/eQFxv3Ld5f95Aidh2vwRq3kBAlY23D1XeQBSX6S/HRt5AnQ1DpMY23kDjiQzplQzeQGpzTOjl+t1A2aR6bYrh3UCfIdIKxdTdQMeagCD8yN1Az4i0Bz2y3UB6CI4Y+pfdQCTCj+Tcjd1ATa4C95to3UCPEMHw7FPdQDRsrQYyT91Ae2cCZJMw3UChOT4PjSTdQElngfwRG91AkNCVaLP23EAUKdWP2djcQDa3AxpyztxANZ1rWc2x3EBULvkeNKHcQGjXSAQbidxAS3Hygzpv3EC8Xhj02VXcQEeRg+QlQ9xAhzjs0Lku3ECq2jk7ygbcQA3q/V9b99tAWUGwCkHg20D744vNvNXbQFzVlYuv0NtAoDslG6zA20CGM1rUJK3bQGllt0jDqdtA8YsC68CM20CQKJl0UIDbQJS+XRrUg9tAOfSKB3Rt20AAG8wd42vbQEmdFHbdbNtAMFsuTfRS20BUCHPfjz/bQNHg3nYzQttAKxGEw5ky20Ck7E6WCy/bQBLg24j9I9tASeUJw4MV20ANPrTtiQfbQOzbo5g8ANtAgO6QPzf32kCgqGAb/9faQP/PprFH0dpASD/bzeTC2kDn+TgCGMHaQHz4MK5Nw9pA8GuuK4262kAIcdHSSK7aQB6wHDUqstpAFmJXvMeb2kAlit0q95XaQJqrkbUaoNpAr2yuh1qQ2kBMvyVlm5baQGttpIRnn9pAKFf0IlCN2kAjMG98vYHaQIE3f9EXjdpAvJbI2zSG2kAYoTdsXYvaQGfDaBwGidpAtq+jGd2D2kCR71oHNH/aQIZ0V3U3gdpAMm5R34KB2kC0Yq5oK2zaQHfEgaxUb9pAJG5DdtJq2kAlYy5Y5nLaQBV0zhfif9pA5vnzqOeB2kBaEb9jaYDaQMxistkQj9pAlumwWvqD2kB65vrCdYnaQMDcckflntpAqHJTE3Ga2kDYwe54yKjaQIpskSCrudpA3FIFR6qv2kBoKKQoLqzaQFZi98lZvdpAIPSDIEi82kAJMTb9QcfaQOiFqvm7ytpAkFPhl/nK2kDFdJQmt8vaQBTbjDUh09pAGbaCQNPY2kBEXIapXsfaQLFvAM1qztpABstodsvN2kCxcfo3wtnaQEwB/Km36NpAxwWD7bbs2kDlm69aMu3aQAFsBIPT/dpAliOuunfz2kBCUaPZrfnaQFJ4xhTYD9tABD9Slx4M20BGIIrKoxrbQAtdyT+0K9tAb9XZM+Eh20AOPRXjkh7bQFQKmIWgLttAdS9U3XAs20C1/zW7TDbbQOzn2bioONtA1IM1zyE220BGcw3WGjTbQNWnKl3AONtAGVFF4K0720D+oc0aDSbbQCJgzA/tKNtAMGa5iiEk20CUt88d7CvbQAx2VmW+NdtAZKlifpo020BgbhTB8i/bQFlt7r5wO9tAKvYjKrUs20AS9aR8iy7bQF7tU+tVQNtATIVroTw420Bl3hXuskTbQACTx3y0U9tAOINKitJH20CvYvhSdULbQCRP9trmUNtAdJMtGBtN20DkgorbWlXbQEuKqb4aVttAQ9/M0WxQ20DHh2zVPkvbQGd1UVm9TNtAvNcz2YNM20AxbqSxsjPbQOVxi0RiM9tAhL1gXWYr20B4VF+OADDbQOwkp978M9tAQmp0AAMt20A8QedLhSLbQDJSglItKNtA7l/uSMEO20DC46Um5wXbQPpgiyABDdtA1H3ZYTf62kCpurelBPjaQAFTnStd+NpA9yZUMNLd2kAp6jXwy8naQHT28rwjytpAm1rpPj642kDhaQVHZLLaQB6R424KpdpAVksWla2U2kAcWcWr0ITaQPyruUKge9pAknOr1bdw2kBsr2NJiU7aQIdYknfbRNpAi0mvK4Iz2kDkhfX3vi7aQEjifH79KtpAjLOJ1kUc2kBzFjxYCgraQFizFpX0B9pAmqxqlDjp2UD2Gwp7DtvZQLSE133Y3NlAFI0NyL7E2UDAB0YzyL3ZQO7dheBcudlAue+WDA6a2UDC8NLzQ4HZQCz/2CEketlAc2UYBcdg2UDZdn1udVPZQDSgpPejPtlAjE5TeCYk2UBwUH7pKArZQG+X7trX9thAJFNcyM7h2EB3kqOL07bYQAo/YQlZpNhAiDMNDTOK2EBac+Ioo3zYQOAU+b64c9hARyuVJthf2EBR09a3c0jYQFi1QAQ1QdhALBFFr2Yg2EAY45RBKhDYQGiuEvDhD9hAWRn55bX110DHkt8bWfDXQLdnzZOH7ddARXiMitLP10AQeHY8orjXQOBdeS2NttdAjZu10zqi10BahBcA9JnXQByFO0wtitdAENsBnpl210COhETghWPXQChzzKIeV9dAeNZRYf9I10CCQMVmeSTXQM4XryZ0GNdAAjeHbMME10CMoYjKqP3WQJvAMRHs+dZAilRgKTnr1kAeejRrAtnWQK7ZMGjx1tZAbDvX2au51kBDE8ky+KzWQH7k6Kc4sNZAWlVxZJWZ1kDWXtvctJbWQNTDTJdfltZAcGSP0CZ71kBJ9PzEcmbWQOQ70u+lZtZAXNvgz5tU1kD0JRU2nU7WQIGIC7weQdZAUNpLD7wu1kCrfwhT2RzWQCFqChejEdZATckJ17QE1kCYFPRGjODVQCTNVHHk1NVAms2jIZHB1UBlGRzq07rVQNbsSgtCutVAKDX//bmu1UAeD1karp/VQBAj2/HHoNVAmTMCLlCM1UA6unRRaojVQEA6FZF4lNVA5lkeGKOG1UDbT2n/MIzVQFShuyhKlNVAmkxzIh+D1UAd51XXeHjVQEY2+jfohNVATN3XTRp/1UByL9vpV4XVQIyZoKUVhNVAdmmjQlqC1UDujCLQHoHVQID15t2PhtVAx9Ko50iK1UCyGlrUYHnVQN3PgXv5gNVA8MyXqOaA1UBaFdftaY3VQFAPT8HHnNVAJn5MZi+h1UCffu80E6LVQBa5ur4cs9VASW++2d+n1UCUmw3cNK3VQELBivp9wtVAk4ZwYOO91UBdkTniEszVQKn3CabN3NVAk5mr6KTS1UC7KnjmAM/VQPEqqlWj39VABYMVegje1UA4hqYkeejVQGCh+e5p69VAnQ5Alinr1UBnzwIuaevVQEzVCkZV8tVA5U8QWon31UBClWO1GefVQN1HLcsq79VAYkLlZpDv1UA+iMYajPzVQL/SuYXTDNZAIpIywiQS1kAo41Ao8hPWQCpul0nlJdZA0qlKg5Ed1kCRW0mkzyXWQLQGduEBPtZAeVELZlA81kBDfghRQk7WQI8GDX6/YtZAecriKVlc1kCgfeOQd1zWQMGVYdsdb9ZAvgUZ24Zv1kDcIPZg+3vWQO5TlQbwgNZAw3WkzRd+1kAk6y+Fv3vWQKClAL0TgNZA0tTO8K+C1kClW4pDm2vWQLhPvFAHbdZAtYvc48dm1kAIEyaPHm3WQHD7nfIad9ZAuVibJyF21kCkRz6Go3HWQI5wCaBLfdZAbLpJLGFu1kBietWfCHDWQLwzjy+kgdZAt4yxBlx51kAqJeuvHYPWQB8ZLJtqj9ZAs0g+BdSA1kCDZ3sqwnjWQGQ85O3cgtZAIGmGZrp61kD+QE5lo37WQNAw2IMMe9ZAhGpnesJx1kDC93Jh+GjWQB3Kw8jaZtZALRESLAVj1kDbwXZYVEfWQMjfUT8kRNZAn0UbrEg51kDM9g0xAzvWQMJk3v93PtZAmUc0oPY21kAUvC9q8SvWQIxqU+8RMdZAhfwvdgga1kCWBFjkkBPWQAwGrm4NHdZAI6dsQKYM1kCenFVxbg/WQJvtReTBFNZAN3oH1jH/1UAQ9vOCJvDVQAaUlADJ9NVA2YluMy7n1UDKKm7snuXVQLLjL8WP3NVAYTwcw27L1UCc6ISxzbrVQPLZMiDZsNVA/j/eiiyl1UCAMoDSF33VQEOSmNSDbdVA7jmfXERW1UDvLM/8mkvVQAIDCwdiQdVA903M4jIs1UCPKjPofxPVQCRBwqjyCtVA8St1z+zo1EDWjHPdeNfUQCDnnwf51dRACuE0eZW61ECQDvH6nLPUQJmXtL4vr9RAQFxJAd+P1EAjEAn/EnfUQLZmoAm1ctRAJhVxyRlc1EC2bmcPilHUQDrgH3V6P9RA6KzC7bMq1EAizeFWbRbUQHgyRkDTCNRAggyoJYH500CtSfi4I9PTQBn0vgZHxdNAbeZz2r6v00AYJFLGzKbTQEgAG11endNAWVFpxfmI00AONF1XEXHTQL9QeaROadNAMB+kkqZD00C6YxpokC7TQKehvlluKdNANn/LkmgK00CUQWU5pf/SQHRfBiJt99JA8rh4iVHU0kCtARasurfSQEBHMh/wr9JAsOSHR+iV0kA/LQP264fSQMONQMRvctJAyObGMctZ0kBZk8mPpkHSQAWFEW4uMNJAZutWSP4c0kC6oWsSevXRQE7F9pZ25tFAyjBwocfP0UCd5xLErsXRQMeCEgv3wdFA05KXI0mz0UCCNMJlF6HRQC4QFWMLn9FA4ne1+GGD0UCuVaF1SnjRQN4suw4nfdFAr6M97x9o0UAJahuBSmfRQOWLAFUAadFAXum2p9JP0UAVNpi1KT3RQAS9M0N5QNFAzpsIhosx0UC4JQNPqS7RQJnHvzdHJNFApbEOiAUY0UA+79nIQwzRQPJx6okuB9FAXGn4RmEA0UC8glY3OeTQQF4JK+KR4NBA6tftEj/V0EDK8dlbgtbQQFcmAUDg29BAxM+t9UfW0EDWCgDVK83QQOR/em811NBA2jhu+kfC0EDnZ61s7MDQQFmQGvuEz9BAbFjw0DnE0EBZITkXy83QQMhFiZ/n2dBA1KWqpiDL0EAf9fZo3sLQQDl2vnFXz9BALk+/L5PJ0EBE0+Vz2s/QQFBvztehztBAfmdAdlbJ0EA6sy4Fi8TQQBBEYhRsxtBAnUmTH5XG0EB/u8PDLa/QQKOaaiJHsNBAsMH/BrWp0EASNL4Dua/QQG5IoQWAutBAqtEJ2VC60ECM7BfWnbbQQGdBTo4Qw9BArNEbVW630EAI2DQDXrzQQMjXe81B0dBAKXcr30HM0EAka7LCKdzQQKC6QOic7tBAu0WgjCzm0EAUwCrsQOTQQH2uVWSu99BAw/S5kd740EAp5kNFGgbRQIXvjxjWC9FAwc8LoIgO0UCKAwQYuxHRQG18QRCaG9FAB2p8BMEj0UAk2ZY4MRXRQIO1JyciH9FAytmmm2ch0UD3Zpuu7C7RQMIr1TJVP9FAbGWUiMdE0UC7MPkHtkbRQAU2hkLKWNFAn8pnAt5P0UAfGbGj51bRQANhKGHlbdFAiUgIZv9q0UAUyqwBSnzRQCGnWN8fkNFAy7/VOxKJ0UC0x31TiYjRQL9v4W0qm9FAqG9+PY6b0UCvGkGT/afRQKzdxQjtrNFADNxrpUer0UD4LY4yIqrRQADF9T+pr9FAvNBaSXiz0UBmo3e9A57RQFLjCuwPodFAJmuMoHCc0UBQPjdtZ6TRQJM/g4pRrdFAtrVUeUWr0UB8vcuRtaXRQEH/amVLsNFAl30p/0ed0UAGcjOA1prRQNhfax1ZqNFAS+0LAvib0UDuDAO+WKDRQBSIAbxEp9FA1z7ROE2T0UDZ5Mtw2oXRQEH8x0X3i9FAhWv9z9Z/0UDphVjgwX/RQEO4dRAteNFAGpyNiu1t0UCA0yH1LWTRQP9P+98aYdFANUHSxk9c0UDb2TfpEEHRQMTfE8ZSPtFAli3eKOkz0UC8xtGjFTbRQB0YgXKDOdFAXd61Evsx0UBCNpDc7ibRQCPIkmEILNFADSxxoywT0UAOBpvM4grRQHPZ8hGNEtFAeUyznlMA0UCYnOjro//QQDhIJXt/AdFAdi8ziXfo0EDzBWxS9NXQQLKpLNSG1dBATaUmC9zC0EAITEbIPLzQQLoKKKUdrtBASizprhSY0EBooSapi4LQQKFbqSOvc9BAkIopmhpj0EDjOdub2DjQQHlWA1gXJ9BA+LoZmqoN0EDMaln00wDQQCBEJw427c9AZ5ym1tfCz0D6F3HycZHPQIIHjIRXgM9Ay03Xz+c6z0BEgLnpmxbPQIal9zs4Es9ACAoHHQ3azkCVT2O+L8vOQChMzuNowc5A9L/bBtuBzkA9Ej+gVk/OQJCLdWjWSM5AnLQem9sdzkDmMxPa9wrOQB/ji1gU6c1ArQnRlc3BzUBY1w60hpvNQDcv19KYgs1Ag3Ca6TpmzUDimokHEhrNQMWfZZrq/sxAejQeOWzUzEDYXykIGsPMQHwdIfbVtsxA3sQjh6WUzECKj3FrbWvMQCzOD8aAYsxAFCzuPzAizEAtdmOIAwPMQA6zNAm/A8xAMC/XGLPQy0DxI1gU7L/LQLfP55M7tMtAuPIZEcRyy0A19KEEVj7LQNj3Rpw3LctAMqtenp73ykDMtMGsHNrKQFTuqPqarcpACqAMum18ykDd+GhaQEzKQOXbT/trKcpAWagxlCcDykC2e0AqC7bJQJgpPDXwmclATGcUTH5uyUCpOz+TOFzJQMxPi+bbVMlArE3i3JI3yUDWboQmQhPJQPcDd+Y8D8lAI9H/a7TYyEB/ih/AT8PIQKI2m0zTzchAByLoZ4+kyEAGRCt1ZKnIQAodfQZQs8hASG1xlXSHyEADnLuaomjIQLRxwYMJechAHvc51/VkyEDI0v02+WjIQGDeRdb8XchAhVtrqtZNyEDFf4lfsD7IQDsuMhXjPMhAHsbVwqU3yECed0BruwTIQKMDmIjSAshAeh/MsZLxx0D60VILf/nHQFcz9mVaB8hAcn6kY0n/x0DY7J20MPDHQDTP53tjAchAlfxNu+7cx0AlFkvJndnHQH8ipA819sdAGm7O5ATfx0A8rXKO7e7HQGOjJbzsA8hAxBB75yTjx0CiXCaJZs/HQCz5n5tr5cdAbUWMGPbWx0Dv58Ohl+DHQF66f2o528dAIjcr8H7Rx0ACW89WxMjHQBgJ/r1izcdAmaAnHZHOx0BgNc2+0KTHQKykX9URrMdAyqPO9/ujx0CQOZBKErXHQLFw21pQz8dAjpExDqLTx0C41dIU7NDHQNeNxJGB7sdAoGxtHXbax0CXN613jufHQFb1SAqPFMhAWfK1K8gNyEDHYRzzdDDIQDuIkT44WMhA6SWphzRKyEAUohZHOknIQP0MjykKcMhAnyd6dl9yyECBmLDPy4zIQFA5a2g4mMhAWwEKi+aayECAcKGOlJ7IQNppw5Kbr8hAokzgjjK9yEBi3jzjGJzIQKZKhqwArMhAvEasgZGsyEB82SSHTsbIQD1Uadl038hAvbi4zq7iyECIQFMX4d7IQEg8PtZe+8hAIRUoWBDbyEAp2qio5dvIQPmRhTGj/MhADIkzSZnpyEChHxUfs/rIQDttBXnjEMlADzKY0EzxyEBg1YCev97IQE8AgkTb9MhA9tr1VHzmyEDcC7VxNPDIQK9s+M3s6shATpKfdi7jyEAIXz8AcNzIQPa1aYoK48hAUvaODDXmyEBC6OeBYLvIQLe0LWyNwchA/BBQYmO4yEDtA8WIZcjIQNXg+Mw738hAqirBQLDgyEDJl9QHHdvIQN54OEXV9chAknhFjYzayEAA7oVJB+LIQDZWIj5qCclArf2PwQX9yEBCXEm9XBXJQNxxET3KMslAr/57unAayUABajyuIA/JQFTawsY2JslAYPq7SdIYyUCqcADZhCPJQOIWyac3H8lAWMupaewOyUDrJoMMof/IQLMM56+u/chA59tFS0z4yEAkIvTZI8LIQORCj938vMhAdvMG7X6oyECzOtEsLa3IQNSAAIQmsshAs7A6fjOhyEDcA8DLOInIQPzKlY+JkchAvMLgnKZbyECrpsJ450bIQGF9AI0QUshAWpMPMHIpyECux0DZxiDIQAWzgAYyHchAmBVjMdbjx0CoVpvSg7fHQC0hmZ7/rcdAapsJ1QCAx0Doa8UXGWrHQFJsBZoxRcdA/0yoY9gUx0DH1EMOf+XGQMTmabl+w8ZALuKKXA6exkB84A5gXUvGQE65f9itKcZA8yHNXKf4xUBAIW0RzeDFQKlRhzTMzMVA0Wus+t6ixUBCqRwU6nHFQKpa3aNAYcVAMPFB3UQbxUDjcz3lbPbEQF/plCV98cRAa3D94a66xEAWpOlep6rEQBJhJE2focRASJUBOdBixED8pzSbCjHEQLf1MezpKMRAKvOhp078w0DcRl1vyufDQHzKnHZGxMNA4Ce/nN2Uw0BgLNqjdGbDQBW7f6tkRcNANzMgq+Qgw0ARVBYCVNDCQHBP+c3EsMJAoNq4pd6BwkB6/MqtJGzCQEiLKRpdX8JA1QOTKak8wkCun0eM7RLCQHuvTGV9CcJA52Ig1KbKwUCAAosR9KzBQOGUUYcpr8FAhmbpi5d9wUDPIgreHXTBQB2WObS6b8FApYALiJA1wUCqSTPSbwjBQFmcEJZ3BMFAv55gxATcwEBn9/v+qMvAQPp/G3lNrMBAQO2VkAuFwECiAQmJyV7AQDigBoLgRcBAOij/cocpwECAVF3pub6/QJQNltZnjL9ASOaH22c7v0BT7B5BwBy/QA==\",\"dtype\":\"float64\",\"shape\":[1682]}},\"selected\":{\"id\":\"2055\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2056\",\"type\":\"UnionRenderers\"}},\"id\":\"2020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Application\",\"version\":\"1.3.4\"}};\n var render_items = [{\"docid\":\"0e765adb-9f9f-4921-abaa-ac2def0da2c0\",\"notebook_comms_target\":\"2209\",\"roots\":{\"1986\":\"87459a3c-8440-428a-8d0b-3df9c6c51f70\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n var attempts = 0;\n var timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n embed_document(root);\n clearInterval(timer);\n }\n attempts++;\n if (attempts > 100) {\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n clearInterval(timer);\n }\n }, 10, root)\n }\n})(window);", + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1986" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 90\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + 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\",\"dtype\":\"float64\",\"shape\":[841]}},\"selected\":{\"id\":\"2598\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2599\",\"type\":\"UnionRenderers\"}},\"id\":\"2451\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"2436\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2402\",\"type\":\"Patch\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_dash\":[2,4,6,4],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2453\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"2600\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[841]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1682]}},\"selected\":{\"id\":\"2435\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2436\",\"type\":\"UnionRenderers\"}},\"id\":\"2400\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"2599\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"formatter\":{\"id\":\"2406\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"2383\",\"type\":\"BasicTicker\"}},\"id\":\"2382\",\"type\":\"LinearAxis\"},{\"attributes\":{\"label\":{\"value\":\"Boundary\"},\"renderers\":[{\"id\":\"2403\",\"type\":\"GlyphRenderer\"}]},\"id\":\"2424\",\"type\":\"LegendItem\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"2387\",\"type\":\"PanTool\"},{\"id\":\"2388\",\"type\":\"WheelZoomTool\"},{\"id\":\"2389\",\"type\":\"BoxZoomTool\"},{\"id\":\"2390\",\"type\":\"SaveTool\"},{\"id\":\"2391\",\"type\":\"ResetTool\"},{\"id\":\"2392\",\"type\":\"HelpTool\"}]},\"id\":\"2393\",\"type\":\"Toolbar\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"2416\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"2417\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"2464\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"ticker\":{\"id\":\"2378\",\"type\":\"DatetimeTicker\"}},\"id\":\"2381\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"2425\",\"type\":\"ColumnDataSource\"}},\"id\":\"2429\",\"type\":\"CDSView\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ti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Detection Result (85 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Application\",\"version\":\"1.3.4\"}};\n var render_items = [{\"docid\":\"babd97e1-31c9-4cc9-8419-62ae9f1aa5ce\",\"notebook_comms_target\":\"2615\",\"roots\":{\"2366\":\"377405c0-daf1-48f5-a6a4-fb86fc8f4bd3\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n var attempts = 0;\n var timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n embed_document(root);\n clearInterval(timer);\n }\n attempts++;\n if (attempts > 100) {\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n clearInterval(timer);\n }\n }, 10, root)\n }\n})(window);", + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "2366" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 85\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Case 2: time series with a daily sampling frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "granularity: daily\n" + ] + } + ], + "source": [ + "sample_data = json.load(open('univariate_sample_daily.json'))\n", + "print(f\"granularity: {sample_data['granularity']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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"build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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"3662" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 85\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "73774fa62b2e1823ff4dfa3d7e6968adf72c7029b6373b24ad42c7932e1cccae" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ipython-notebook/SDK Sample/UVAD-SDK-Demo.ipynb b/ipython-notebook/SDK Sample/UVAD-SDK-Demo.ipynb deleted file mode 100644 index a33d213..0000000 --- a/ipython-notebook/SDK Sample/UVAD-SDK-Demo.ipynb +++ /dev/null @@ -1,1167 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Univariate Anomaly Detection Demo Code (SDK Version)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pip install --upgrade azure-ai-anomalydetector" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import time\n", - "import json\n", - "\n", - "from datetime import datetime\n", - "from azure.ai.anomalydetector import AnomalyDetectorClient\n", - "from azure.ai.anomalydetector.models import DetectRequest, TimeSeriesPoint, TimeGranularity, \\\n", - " AnomalyDetectorError\n", - "from azure.core.credentials import AzureKeyCredential" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"1001\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"1001\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.3.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.3.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.3.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.4.min.js\"];\n", - " var css_urls = [];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.3.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.3.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.3.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.4.min.js\"];\n var css_urls = [];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from bokeh.plotting import figure,output_notebook, show\n", - "from bokeh.palettes import Blues4\n", - "from bokeh.models import ColumnDataSource,Slider\n", - "import datetime\n", - "from bokeh.io import push_notebook\n", - "from dateutil import parser\n", - "from ipywidgets import interact, widgets, fixed\n", - "output_notebook()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "subscription_key = '[Placeholder: Your Anomaly Detector resource access key]' \n", - "anomaly_detector_endpoint = '[Placeholder: Your Anomaly Detector resource endpoint]'\n", - "\n", - "# create an Anomaly Detector client\n", - "ad_client = AnomalyDetectorClient(AzureKeyCredential(subscription_key), anomaly_detector_endpoint)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def build_figure(result, sample_data, sensitivity):\n", - " columns = {'expectedValues': result['expectedValues'], 'isAnomaly': result['isAnomaly'], 'isNegativeAnomaly': result['isNegativeAnomaly'],\n", - " 'isPositiveAnomaly': result['isPositiveAnomaly'], 'upperMargins': result['upperMargins'], 'lowerMargins': result['lowerMargins']\n", - " , 'value': [x['value'] for x in sample_data['series']], 'timestamp': [parser.parse(x['timestamp']) for x in sample_data['series']]}\n", - " response = pd.DataFrame(data=columns)\n", - " values = response['value']\n", - " label = response['timestamp']\n", - " anomalies = []\n", - " anomaly_labels = []\n", - " index = 0\n", - " anomaly_indexes = []\n", - " p = figure(x_axis_type='datetime', title=\"Anomaly Detection Result ({0} Sensitivity)\".format(sensitivity), width=800, height=200)\n", - " for anom in response['isAnomaly']:\n", - " if anom == True and (values[index] > response.iloc[index]['expectedValues'] + response.iloc[index]['upperMargins'] or \n", - " values[index] < response.iloc[index]['expectedValues'] - response.iloc[index]['lowerMargins']):\n", - " anomalies.append(values[index])\n", - " anomaly_labels.append(label[index])\n", - " anomaly_indexes.append(index)\n", - " index = index+1\n", - " upperband = response['expectedValues'] + response['upperMargins']\n", - " lowerband = response['expectedValues'] -response['lowerMargins']\n", - " band_x = np.append(label, label[::-1])\n", - " band_y = np.append(lowerband, upperband[::-1])\n", - " boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend='Boundary')\n", - " p.line(label, values, legend='value', color=\"#2222aa\", line_width=1)\n", - " p.line(label, response['expectedValues'], legend='expectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n", - " anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n", - " anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n", - " p.legend.border_line_width = 1\n", - " p.legend.background_fill_alpha = 0.1\n", - " show(p, notebook_handle=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Latest point anomaly detection with the Anomaly Detector SDK" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point=29):\n", - " points = sample_data[\"series\"]\n", - " granularity = sample_data[\"granularity\"]\n", - " result = {\n", - " \"expectedValues\": [None] * len(points),\n", - " \"upperMargins\": [None] * len(points),\n", - " \"lowerMargins\": [None] * len(points),\n", - " \"isNegativeAnomaly\": [False] * len(points),\n", - " \"isPositiveAnomaly\": [False] * len(points),\n", - " \"isAnomaly\": [False] * len(points)\n", - " }\n", - " anom_count = 0\n", - " \n", - " for i in range(skip_point, len(points) + 1):\n", - " series = [TimeSeriesPoint(timestamp=item[\"timestamp\"], value=item[\"value\"]) for item in points[i - 29: i]]\n", - " request = DetectRequest(series=series, granularity=granularity, sensitivity=sensitivity, max_anomaly_ratio=0.25)\n", - " single_point = ad_client.detect_last_point(request)\n", - " if single_point.is_anomaly == True:\n", - " anom_count += 1\n", - " result['expectedValues'][i-1] = single_point.expected_value\n", - " result['upperMargins'][i-1] = single_point.upper_margin\n", - " result['lowerMargins'][i-1] = single_point.lower_margin\n", - " result['isNegativeAnomaly'][i-1] = single_point.is_negative_anomaly\n", - " result['isPositiveAnomaly'][i-1] = single_point.is_positive_anomaly\n", - " result['isAnomaly'][i-1] = single_point.is_anomaly\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "granularity: daily\n" - ] - } - ], - "source": [ - "sample_data = json.load(open('univariate_sample_daily.json'))\n", - "print(f\"granularity: {sample_data['granularity']}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 95 sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = 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], - "source": [ - "sensitivity = 85\n", - "skip_point = 29 # skip the first 29 points due to insufficient data\n", - "results = detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point)\n", - "build_figure(results, sample_data, sensitivity)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Batch anomaly detection with the Anomaly Detector SDK" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def detect_anomaly_entire(sample_data, ad_client, sensitivity):\n", - " points = sample_data[\"series\"]\n", - " granularity = sample_data[\"granularity\"]\n", - " period = sample_data[\"period\"] if \"period\" in sample_data else None\n", - " series = [TimeSeriesPoint(timestamp=item[\"timestamp\"], value=item[\"value\"]) for item in points]\n", - " request = DetectRequest(series=series, granularity=granularity, sensitivity=sensitivity, period=period)\n", - " batch = ad_client.detect_entire_series(request)\n", - " result = {}\n", - " result['expectedValues'] = batch.expected_values\n", - " result['upperMargins'] = batch.upper_margins\n", - " result['lowerMargins'] = batch.lower_margins\n", - " result['isNegativeAnomaly'] = batch.is_negative_anomaly\n", - " result['isPositiveAnomaly'] = batch.is_positive_anomaly\n", - " result['isAnomaly'] = batch.is_anomaly\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Case 1: time series with an hourly sampling frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "granularity: hourly\n", - "period: 24\n" - ] - } - ], - "source": [ - "sample_data = json.load(open('univariate_sample_hourly.json'))\n", - "print(f\"granularity: {sample_data['granularity']}\")\n", - "print(f\"period: {sample_data['period']}\")" - ] - }, - { - "cell_type": 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\",\"dtype\":\"float64\",\"shape\":[1682]}},\"selected\":{\"id\":\"1701\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1702\",\"type\":\"UnionRenderers\"}},\"id\":\"1666\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_color\":\"#2222aa\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1692\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1701\",\"type\":\"Selection\"},{\"attributes\":{\"label\":{\"value\":\"expectedValue\"},\"renderers\":[{\"id\":\"1720\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1744\",\"type\":\"LegendItem\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"1682\",\"type\":\"DaysTicker\"},{\"attributes\":{\"source\":{\"id\":\"1745\",\"type\":\"ColumnDataSource\"}},\"id\":\"1750\",\"type\":\"CDSView\"},{\"attributes\":{\"label\":{\"value\":\"value\"},\"renderers\":[{\"id\":\"1694\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1716\",\"type\":\"LegendItem\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"1685\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1653\",\"type\":\"PanTool\"},{\"id\":\"1654\",\"type\":\"WheelZoomTool\"},{\"id\":\"1655\",\"type\":\"BoxZoomTool\"},{\"id\":\"1656\",\"type\":\"SaveTool\"},{\"id\":\"1657\",\"type\":\"ResetTool\"},{\"id\":\"1658\",\"type\":\"HelpTool\"}]},\"id\":\"1659\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1649\",\"type\":\"BasicTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"1745\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1747\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1748\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1750\",\"type\":\"CDSView\"}},\"id\":\"1749\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":0.5,\"fill_color\":\"#bdd7e7\",\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id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\",\"dtype\":\"float64\",\"shape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\",\"dtype\":\"float64\",\"shape\":[841]}},\"selected\":{\"id\":\"1812\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1813\",\"type\":\"UnionRenderers\"}},\"id\":\"1717\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1717\",\"type\":\"ColumnDataSource\"}},\"id\":\"1721\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null},\"id\":\"1635\",\"type\":\"DataRange1d\"},{\"attributes\":{\"ticker\":{\"id\":\"1644\",\"type\":\"DatetimeTicker\"}},\"id\":\"1647\",\"type\":\"Grid\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1668\",\"type\":\"Patch\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"1686\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"1814\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1815\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"fill_color\":{\"value\":\"tomato\"},\"line_color\":{\"value\":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\",\"dtype\":\"float64\",\"shape\":[841]}},\"selected\":{\"id\":\"1729\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1730\",\"type\":\"UnionRenderers\"}},\"id\":\"1691\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1691\",\"type\":\"ColumnDataSource\"}},\"id\":\"1695\",\"type\":\"CDSView\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"1677\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"line_color\":\"olivedrab\",\"line_dash\":[2,4,6,4],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1718\",\"type\":\"Line\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"1677\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1678\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1679\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1680\",\"type\":\"DaysTicker\"},{\"id\":\"1681\",\"type\":\"DaysTicker\"},{\"id\":\"1682\",\"type\":\"DaysTicker\"},{\"id\":\"1683\",\"type\":\"DaysTicker\"},{\"id\":\"1684\",\"type\":\"MonthsTicker\"},{\"id\":\"1685\",\"type\":\"MonthsTicker\"},{\"id\":\"1686\",\"type\":\"MonthsTicker\"},{\"id\":\"1687\",\"type\":\"MonthsTicker\"},{\"id\":\"1688\",\"type\":\"YearsTicker\"}]},\"id\":\"1644\",\"type\":\"DatetimeTicker\"},{\"attributes\":{},\"id\":\"1639\",\"type\":\"LinearScale\"},{\"attributes\":{\"label\":{\"value\":\"Boundary\"},\"renderers\":[{\"id\":\"1669\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1690\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1730\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"f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Detection Result (95 Sensitivity)\"},\"id\":\"1633\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1812\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"1666\",\"type\":\"ColumnDataSource\"}},\"id\":\"1670\",\"type\":\"CDSView\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"1687\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"formatter\":{\"id\":\"1672\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1649\",\"type\":\"BasicTicker\"}},\"id\":\"1648\",\"type\":\"LinearAxis\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1693\",\"type\":\"Line\"},{\"attributes\":{\"overlay\":{\"id\":\"1676\",\"type\":\"BoxAnnotation\"}},\"id\":\"1655\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"formatter\":{\"id\":\"1674\",\"type\":\"DatetimeTickFormatter\"},\"ticker\":{\"id\":\"1644\",\"type\":\"DatetimeTicker\"}},\"id\":\"1643\",\"type\":\"DatetimeAxis\"}],\"root_ids\":[\"1632\"]},\"title\":\"Bokeh 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], - "source": [ - "sensitivity = 95\n", - "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", - "build_figure(results, sample_data, sensitivity)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 90 sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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\",\"dtype\":\"float64\",\"shape\":[841]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1682]}},\"selected\":{\"id\":\"2055\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2056\",\"type\":\"UnionRenderers\"}},\"id\":\"2020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], - "source": [ - "sensitivity = 90\n", - "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", - "build_figure(results, sample_data, sensitivity)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 85 sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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\",\"dtype\":\"float64\",\"shape\":[841]}},\"selected\":{\"id\":\"2598\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2599\",\"type\":\"UnionRenderers\"}},\"id\":\"2451\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"2436\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2402\",\"type\":\"Patch\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_dash\":[2,4,6,4],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2453\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"2600\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[841]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1682]}},\"selected\":{\"id\":\"2435\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"2436\",\"type\":\"UnionRenderers\"}},\"id\":\"2400\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"2599\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"formatter\":{\"id\":\"2406\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"2383\",\"type\":\"BasicTicker\"}},\"id\":\"2382\",\"type\":\"LinearAxis\"},{\"attributes\":{\"label\":{\"value\":\"Boundary\"},\"renderers\":[{\"id\":\"2403\",\"type\":\"GlyphRenderer\"}]},\"id\":\"2424\",\"type\":\"LegendItem\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"2387\",\"type\":\"PanTool\"},{\"id\":\"2388\",\"type\":\"WheelZoomTool\"},{\"id\":\"2389\",\"type\":\"BoxZoomTool\"},{\"id\":\"2390\",\"type\":\"SaveTool\"},{\"id\":\"2391\",\"type\":\"ResetTool\"},{\"id\":\"2392\",\"type\":\"HelpTool\"}]},\"id\":\"2393\",\"type\":\"Toolbar\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"2416\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"2417\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"2464\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"ticker\":{\"id\":\"2378\",\"type\":\"DatetimeTicker\"}},\"id\":\"2381\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"2425\",\"type\":\"ColumnDataSource\"}},\"id\":\"2429\",\"type\":\"CDSView\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ti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], - "source": [ - "sensitivity = 85\n", - "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", - "build_figure(results, sample_data, sensitivity)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Case 2: time series with a daily sampling frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "granularity: daily\n" - ] - } - ], - "source": [ - "sample_data = json.load(open('univariate_sample_daily.json'))\n", - "print(f\"granularity: {sample_data['granularity']}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 95 sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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], - "source": [ - "sensitivity = 95\n", - "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", - "build_figure(results, sample_data, sensitivity)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 90 sensitivity" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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Detection Result (85 Sensitivity)\"},\"id\":\"3663\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"3669\",\"type\":\"LinearScale\"}],\"root_ids\":[\"3662\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.4\"}};\n", - " var render_items = [{\"docid\":\"04e323fb-1d37-4391-a927-0c63c7d16e04\",\"notebook_comms_target\":\"3989\",\"roots\":{\"3662\":\"426198d3-afd4-4fb6-a4d9-1b64825d1aea\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "3662" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "sensitivity = 85\n", - "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", - "build_figure(results, sample_data, sensitivity)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git "a/ipython-notebook/SDK Sample/\360\237\206\225MVAD-SDK-Demo.ipynb" "b/ipython-notebook/SDK Sample/\360\237\206\225MVAD-SDK-Demo.ipynb" new file mode 100644 index 0000000..afaef4c --- /dev/null +++ "b/ipython-notebook/SDK Sample/\360\237\206\225MVAD-SDK-Demo.ipynb" @@ -0,0 +1,1382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "1. [Introduction](#intro)\n", + "2. [Create an Anomaly Detector](#ad)\n", + "1. [Create a Storage Account](#sa)\n", + "1. [Prepare your data](#data)\n", + "1. [Import modules and create an Anomaly Detector client](#import)\n", + "1. [Train a model](#train)\n", + "1. [Inference](#inference)\n", + "1. [List models](#list)\n", + "1. [Delete model](#delete)\n", + "1. [Visualization](#vis)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introdution \n", + "This notebook shows how to use [Multivariate Anomaly Detection](https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/overview-multivariate) SDK (the latest version for python - **3.0.0b6**) in Anomaly Detector service. Please follow the steps to try it out, you can either [join Teams Group](https://forms.office.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbRxSkyhztUNZCtaivu8nmhd1UQ1VFRDA0V1dUMDJRMFhOTzFHQ1lDTVozWi4u) for any questions, or email us via AnomalyDetector@microsoft.com" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Create an Anomaly Detector \n", + "\n", + "\n", + "* [Create an Azure subscription](https://azure.microsoft.com/free/cognitive-services) if you don't have one.\n", + "* [Create an Anomaly Detector resource](https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesAnomalyDetector) and get your `endpoint` and `key`, paste in the cell below.\n", + "\n", + "![](https://i.imgur.com/M7qATAY.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Create an Storage Account \n", + "\n", + "1. [Create a Storage Account](https://ms.portal.azure.com/#create/Microsoft.StorageAccount-ARM).\n", + "1. Go to **Access Control(IAM)**, and select **+ADD** to **Add role assignment**.\n", + "3. Search role of **Storage Blob Data Reader**, **click on it** and then select **Next**.\n", + "4. Select assign access to **Managed identity**, and **Select Members**, then choose the anomaly detector resource that you created earlier, then select **Review + assign**.\n", + "\n", + "![](https://i.imgur.com/dsTsuxI.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Prepare your data \n", + "\n", + "### Download sample data\n", + "Multivariate Anomaly Detection supports two types of data schema for training and async inference: **`Multiple Tables`** and **`One Table`**. \n", + "\n", + "* Download [One Table sample data](https://mvaddataset.blob.core.windows.net/public-sample-data/sample_data_5_3000.csv)\n", + "* Download [Multiple Tables sample data](https://mvaddataset.blob.core.windows.net/public-sample-data/sample_data_5_3000.zip) and unzip it.\n", + "\n", + "### Upload data to Storage Account\n", + "\n", + "1. Go to your Storage Account, select **Containers** and create a new container.\n", + "2. Select **Upload** to upload the folder *(for multiple tables)* or the CSV file *(for one table*) that you downloaded earlier.\n", + "3. Click on the data that you uploaded and copy the **Blob URL** to somewhere for further steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Import modules and create an Anomaly Detector client \n", + "\n", + "**Install** the anomaly detector SDK and storage packages using following codes ⬇️, and **import** packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install required packages. Use the following commands to install the anomaly detector SDK and required packages.\n", + "! pip install --upgrade azure-ai-anomalydetector\n", + "! pip install azure-storage-blob\n", + "! pip install azure-mgmt-storage\n", + "\n", + "# Install optional packages to see interactive visualization in this Jupyter notebook.\n", + "! pip install plotly==5.5.0\n", + "! pip install notebook>=5.3 \n", + "! pip install ipywidgets>=7.5" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import time\n", + "from datetime import datetime, timezone\n", + "import pandas as pd\n", + "\n", + "from azure.ai.anomalydetector import AnomalyDetectorClient\n", + "from azure.core.credentials import AzureKeyCredential\n", + "from azure.core.exceptions import HttpResponseError\n", + "from azure.ai.anomalydetector.models import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an Anomaly Detector client" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# The ENDPOINT should be like: https://mvad-sample.cognitiveservices.azure.com/\n", + "ANOMALY_DETECTOR_ENDPOINT = \"{Your endpoint of Anomaly Detector resource}\"\n", + "SUBSCRIPTION_KEY = \"{Your key of Anomaly Detector resource}\"\n", + "\n", + "ad_client = AnomalyDetectorClient(ANOMALY_DETECTOR_ENDPOINT, AzureKeyCredential(SUBSCRIPTION_KEY))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Train a model " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_body = ModelInfo(\n", + " # The data source should be the Blob URL, something like: https://mvaddataset.blob.core.windows.net/sample-multitable/sample_data_5_3000.csv\n", + " data_source=\"{Blob url of your data}\",\n", + " # start_time and end_time are optional. If you don't set them, the whole data will be used to train the model.\n", + " start_time=\"2021-01-01T00:00:00Z\",\n", + " end_time=\"2021-01-02T09:00:00Z\",\n", + " # If your data is one CSV file, please set the dataSchema as `OneTable`, if your data is multiple CSV files in a folder, please set the dataSchema as `MultiTable`.\n", + " data_schema=\"OneTable\",\n", + " # Use display_name to name your model.\n", + " display_name=\"sample\",\n", + " # Sliding window size is optional. If you don't set it, the default value is 200.\n", + " sliding_window=200,\n", + " # align_policy is optional. If you don't set it, the default value is `AlignPolicy(align_mode=AlignMode.OUTER, fill_n_a_method=FillNAMethod.LINEAR, padding_value=0)`.\n", + " align_policy=AlignPolicy(\n", + " align_mode=AlignMode.OUTER,\n", + " fill_n_a_method=FillNAMethod.LINEAR,\n", + " padding_value=0,\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model is created with the modelId: 077fffcc-8042-11ed-a094-46b61874dd0d, please check the model status using next cell.😉\n" + ] + } + ], + "source": [ + "model = ad_client.train_multivariate_model(train_body)\n", + "model_id = model.model_id\n", + "print(f\"Model is created with the modelId: {model_id}, please check the model status using next cell.😉\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get model status \n", + "\n", + "☕️Training process might\n", + "take several minutes to hours (depending on the data size). Take a cup of coffee and come back then, and waiting for its status to be **READY**. You could also use the `epochId` to know the training progress." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model status: READY\n", + "EpochId: {'epochIds': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'trainLosses': [0.3080775060436942, 0.2517042031342333, 0.23209934546188876, 0.23863001845099707, 0.2927903844551607, 0.22850904410535638, 0.22527566755359824, 0.2230689898133278, 0.22261266884478653, 0.22397931055589154], 'validationLosses': [0.27853154162971344, 0.25884311053217673, 0.24618508864422234, 0.27284859637824854, 0.26543461546605945, 0.24943621304570412, 0.24507346445200395, 0.24053902528723892, 0.24059993393567145, 0.24080916813441686], 'latenciesInSeconds': [2.224400758743286, 2.161886692047119, 2.152543306350708, 2.1413774490356445, 2.1680312156677246, 2.178802728652954, 2.2941040992736816, 2.1975395679473877, 2.2327635288238525, 2.264390468597412]}\n" + ] + } + ], + "source": [ + "model_status = ad_client.get_multivariate_model(model_id).model_info.status\n", + "print(f\"Model status: {model_status}\")\n", + "\n", + "# Print the following line to get the epochId which could help you better know the status of model. There are 100 epochIds in total.\n", + "print(f\"EpochId: {ad_client.get_multivariate_model(model_id).model_info.diagnostics_info.model_state}\")\n", + "\n", + "# Check error message if model status is ERROR/FAILED.\n", + "#ad_client.get_multivariate_model(model_id).model_info.errors[0].message" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Inference " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are two types of inference:\n", + "* Batch inference (asynchronous)\n", + "* Streaming inference (synchronous)\n", + "\n", + "You could use the `Batch Inference API` for a long period of data validation or for a micro-batch task, which works in an asynchronous way together with the `Get Inference Results API`. If your case is more like real-time or near real-time, the `Streaming Inference API` might be a good choice for you since it will present the anomaly results immediately once you call this API and get every timestamp detected per call. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trigger a batch inference" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "batch_inference_body = MultivariateBatchDetectionOptions(\n", + " data_source=\"{Blob url of your data source}\",\n", + " # The topContributorCount specify how many contributed variables you care about in the results, from 1 to 50.\n", + " top_contributor_count=10,\n", + " start_time=\"2021-01-02T09:00:00Z\",\n", + " end_time=\"2021-01-03T00:00:00Z\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A batch inference is triggered with the resultId: d7dbfa22-8042-11ed-a397-c2c5096ea4c3, please get the detection results using the next cell.\n" + ] + } + ], + "source": [ + "results = ad_client.detect_multivariate_batch_anomaly(model_id, batch_inference_body)\n", + "result_id = results.result_id\n", + "print(f\"A batch inference is triggered with the resultId: {result_id}, please get the detection results using the next cell.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get batch inference results\n", + "\n", + "☕️Inference process might\n", + "take several minutes (depending on the data size). Take a cup of coffee and come back then, and waiting for its status to be **READY**." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch inference status: READY\n" + ] + } + ], + "source": [ + "anomaly_results = ad_client.get_multivariate_batch_detection_result(result_id)\n", + "print(f\"Batch inference status: {anomaly_results.summary.status}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " timestamp isAnomaly severity score \\\n", + "0 2021-01-02T09:00:00Z False 0.000000 0.419382 \n", + "1 2021-01-02T09:01:00Z False 0.000000 0.252619 \n", + "2 2021-01-02T09:02:00Z False 0.000000 0.322591 \n", + "3 2021-01-02T09:03:00Z False 0.000000 0.369014 \n", + "4 2021-01-02T09:04:00Z False 0.000000 0.306100 \n", + ".. ... ... ... ... \n", + "896 2021-01-02T23:56:00Z False 0.000000 0.253038 \n", + "897 2021-01-02T23:57:00Z False 0.000000 0.340814 \n", + "898 2021-01-02T23:58:00Z True 0.296033 0.508668 \n", + "899 2021-01-02T23:59:00Z False 0.000000 0.454258 \n", + "900 2021-01-03T00:00:00Z False 0.000000 0.307010 \n", + "\n", + " interpretation \n", + "0 [] \n", + "1 [] \n", + "2 [] \n", + "3 [] \n", + "4 [] \n", + ".. ... \n", + "896 [] \n", + "897 [] \n", + "898 [{'variable': 'series_1', 'contributionScore':... \n", + "899 [] \n", + "900 [] \n", + "\n", + "[901 rows x 5 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anomaly_results_df = pd.DataFrame([{'timestamp': x['timestamp'], **x['value']} for x in anomaly_results.results])\n", + "anomaly_results_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streaming Inference\n", + "\n", + "Every time you trigger a streaming inference, you will get one timestamp detected." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"../../sampledata/multivariate/5_3000.json\", \"r\") as f:\n", + " sample_input = json.load(f)\n", + "\n", + "variables = []\n", + "for item in sample_input[\"variables\"]:\n", + " variables.append(\n", + " VariableValues(\n", + " variable=item[\"variable\"],\n", + " timestamps=item[\"timestamps\"],\n", + " values=item[\"values\"],\n", + " )\n", + " )\n", + "\n", + "last_inference_body = MultivariateLastDetectionOptions(\n", + " variables=variables,\n", + " top_contributor_count=10,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'variableStates': [{'variable': 'series_0', 'filledNARatio': 0.0, 'effectiveCount': 1, 'firstTimestamp': '2021-01-03T01:59:00Z', 'lastTimestamp': '2021-01-03T01:59:00Z'}, {'variable': 'series_1', 'filledNARatio': 0.0, 'effectiveCount': 1, 'firstTimestamp': '2021-01-03T01:59:00Z', 'lastTimestamp': '2021-01-03T01:59:00Z'}, {'variable': 'series_2', 'filledNARatio': 0.0, 'effectiveCount': 1, 'firstTimestamp': '2021-01-03T01:59:00Z', 'lastTimestamp': '2021-01-03T01:59:00Z'}, {'variable': 'series_3', 'filledNARatio': 0.0, 'effectiveCount': 1, 'firstTimestamp': '2021-01-03T01:59:00Z', 'lastTimestamp': '2021-01-03T01:59:00Z'}, {'variable': 'series_4', 'filledNARatio': 0.0, 'effectiveCount': 1, 'firstTimestamp': '2021-01-03T01:59:00Z', 'lastTimestamp': '2021-01-03T01:59:00Z'}], 'results': [{'timestamp': '2021-01-03T01:59:00Z', 'value': {'isAnomaly': False, 'severity': 0.0, 'score': 0.2703796327114105, 'interpretation': []}, 'errors': []}]}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "streaming_inference_results = ad_client.detect_multivariate_last_anomaly(model_id, last_inference_body)\n", + "streaming_inference_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. List models \n", + "You could check the status of your model and your model list. The `currentCount` means how many model you've created in your Anomaly Detector resource, and the `maxCount` means the limitation of your model number in your Anomaly Detector resource." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4f4e04f06-8001-11ed-84c9-c2c5096ea4c3https://mvaddataset.blob.core.windows.net/samp...OneTable2021-01-02T00:00:00Z2021-01-02T05:00:00Zsample200{'alignMode': 'Outer', 'fillNAMethod': 'Linear...READY[]{'modelState': {'epochIds': [10, 20, 30, 40, 5...
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" + ], + "text/plain": [ + " modelId \\\n", + "0 077fffcc-8042-11ed-a094-46b61874dd0d \n", + "1 0e95b9c6-801c-11ed-9600-46b61874dd0d \n", + "2 a374e39e-8014-11ed-81d0-c2c5096ea4c3 \n", + "3 70b7b266-8013-11ed-9600-46b61874dd0d \n", + "4 f4e04f06-8001-11ed-84c9-c2c5096ea4c3 \n", + "\n", + " dataSource dataSchema \\\n", + "0 https://mvaddataset.blob.core.windows.net/samp... OneTable \n", + "1 https://mvaddataset.blob.core.windows.net/samp... OneTable \n", + "2 https://mvaddataset.blob.core.windows.net/samp... OneTable \n", + "3 https://mvaddataset.blob.core.windows.net/samp... OneTable \n", + "4 https://mvaddataset.blob.core.windows.net/samp... OneTable \n", + "\n", + " startTime endTime displayName slidingWindow \\\n", + "0 2021-01-01T00:00:00Z 2021-01-02T09:00:00Z sample 200 \n", + "1 2021-01-01T00:00:00Z 2021-01-02T09:00:00Z sample 200 \n", + "2 2021-01-02T00:00:00Z 2021-01-02T05:00:00Z sample 200 \n", + "3 2021-01-02T00:00:00Z 2021-01-02T05:00:00Z sample 200 \n", + "4 2021-01-02T00:00:00Z 2021-01-02T05:00:00Z sample 200 \n", + "\n", + " alignPolicy status errors \\\n", + "0 {'alignMode': 'Outer', 'fillNAMethod': 'Linear... READY [] \n", + "1 {'alignMode': 'Outer', 'fillNAMethod': 'Linear... READY [] \n", + "2 {'alignMode': 'Outer', 'fillNAMethod': 'Linear... READY [] \n", + "3 {'alignMode': 'Outer', 'fillNAMethod': 'Linear... READY [] \n", + "4 {'alignMode': 'Outer', 'fillNAMethod': 'Linear... READY [] \n", + "\n", + " diagnosticsInfo \n", + "0 {'modelState': {'epochIds': [10, 20, 30, 40, 5... \n", + "1 {'modelState': {'epochIds': [10, 20, 30, 40, 5... \n", + "2 {'modelState': {'epochIds': [10, 20, 30, 40, 5... \n", + "3 {'modelState': {'epochIds': [10, 20, 30, 40, 5... \n", + "4 {'modelState': {'epochIds': [10, 20, 30, 40, 5... " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models = list(ad_client.list_multivariate_models(skip=0,top=10))\n", + "models_df = pd.DataFrame([{'modelId': x['modelId'], **x['modelInfo']} for x in models])\n", + "models_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Delete model " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ad_client.delete_multivariate_model(model_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10. Visualization " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestampisAnomalyseverityscoreinterpretationseries_0series_1series_2series_3series_4
02021-01-02T09:00:00ZFalse0.0000000.419382[]1.205363-0.082452-0.679936-2.427059-0.901315
12021-01-02T09:01:00ZFalse0.0000000.252619[]-0.8328250.5112690.781305-0.978200-0.583167
22021-01-02T09:02:00ZFalse0.0000000.322591[]1.017443-0.090590-0.332330-2.185502-0.549980
32021-01-02T09:03:00ZFalse0.0000000.369014[]0.340099-1.2881550.902764-2.049652-0.303337
42021-01-02T09:04:00ZFalse0.0000000.306100[]-0.445510-1.082052-0.691915-1.140514-1.181026
.................................
8962021-01-02T23:56:00ZFalse0.0000000.253038[]-0.496276-0.327522-0.982608-1.1528280.732866
8972021-01-02T23:57:00ZFalse0.0000000.340814[]0.8330200.285757-1.691492-1.4810460.214137
8982021-01-02T23:58:00ZTrue0.2960330.508668[{'variable': 'series_1', 'contributionScore':...0.8647322.6803651.2444290.827081-1.171265
8992021-01-02T23:59:00ZFalse0.0000000.454258[]-0.742697-1.620215-1.778069-1.6272230.172657
9002021-01-03T00:00:00ZFalse0.0000000.307010[]0.885926-1.2238561.4272940.6581130.172317
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901 rows × 10 columns

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" + ], + "text/plain": [ + " timestamp isAnomaly severity score \\\n", + "0 2021-01-02T09:00:00Z False 0.000000 0.419382 \n", + "1 2021-01-02T09:01:00Z False 0.000000 0.252619 \n", + "2 2021-01-02T09:02:00Z False 0.000000 0.322591 \n", + "3 2021-01-02T09:03:00Z False 0.000000 0.369014 \n", + "4 2021-01-02T09:04:00Z False 0.000000 0.306100 \n", + ".. ... ... ... ... \n", + "896 2021-01-02T23:56:00Z False 0.000000 0.253038 \n", + "897 2021-01-02T23:57:00Z False 0.000000 0.340814 \n", + "898 2021-01-02T23:58:00Z True 0.296033 0.508668 \n", + "899 2021-01-02T23:59:00Z False 0.000000 0.454258 \n", + "900 2021-01-03T00:00:00Z False 0.000000 0.307010 \n", + "\n", + " interpretation series_0 series_1 \\\n", + "0 [] 1.205363 -0.082452 \n", + "1 [] -0.832825 0.511269 \n", + "2 [] 1.017443 -0.090590 \n", + "3 [] 0.340099 -1.288155 \n", + "4 [] -0.445510 -1.082052 \n", + ".. ... ... ... \n", + "896 [] -0.496276 -0.327522 \n", + "897 [] 0.833020 0.285757 \n", + "898 [{'variable': 'series_1', 'contributionScore':... 0.864732 2.680365 \n", + "899 [] -0.742697 -1.620215 \n", + "900 [] 0.885926 -1.223856 \n", + "\n", + " series_2 series_3 series_4 \n", + "0 -0.679936 -2.427059 -0.901315 \n", + "1 0.781305 -0.978200 -0.583167 \n", + "2 -0.332330 -2.185502 -0.549980 \n", + "3 0.902764 -2.049652 -0.303337 \n", + "4 -0.691915 -1.140514 -1.181026 \n", + ".. ... ... ... \n", + "896 -0.982608 -1.152828 0.732866 \n", + "897 -1.691492 -1.481046 0.214137 \n", + "898 1.244429 0.827081 -1.171265 \n", + "899 -1.778069 -1.627223 0.172657 \n", + "900 1.427294 0.658113 0.172317 \n", + "\n", + "[901 rows x 10 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import numpy as np\n", + "\n", + "raw_data=pd.read_csv('../../sampledata/multivariate/sample_data_5_3000.csv')\n", + "df_merge=pd.merge(anomaly_results_df,raw_data, on=\"timestamp\")\n", + "df_merge" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "####### Main Figure #######\n", + "plt.figure(figsize=(30,8))\n", + "plt.plot(df_merge['timestamp'],df_merge['series_0'], color='lightsteelblue', linestyle='solid', linewidth=2, label='series_0')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_1'], color='powderblue', linestyle='solid', linewidth=2, label='series_1')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_2'], color='cornflowerblue', linestyle='solid', linewidth=2, label='series_2')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_3'], color='lightblue', linestyle='solid', linewidth=2, label='series_3')\n", + "plt.plot(df_merge['timestamp'],df_merge['series_4'], color='deepskyblue', linestyle='solid', linewidth=2, label='series_4')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jingruhan\\AppData\\Local\\Temp\\ipykernel_15256\\1226436561.py:21: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n", + " plt.vlines(np.where(anoms), ymin=ymin , ymax=ymax , color='r', alpha=1,linewidth=3)\n" + ] + }, + { + "data": { + "image/png": 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kMhnNzc2pVqtpeXm5q+fwPE/79++X7/tD2Ytlfn5exhgtLCx09W8lFpL3kqmpKRUKBa2tre2aVTUxMaGJiQltbGxQhQI9c+dTsVgMF+aXlpbYIDrkxsbGNDMz03RMns1mNTs725cxu7uXO5VKJZbs7ji5OUyrMUscY5skubHd5ubmbZsJjTGan5+XtZaxBYDEcc3pzSte8YonrLUPN/peO8Grl0n6YWvta7b+/r9LkrX2JyKPOSvJFSo/IGlT0lustb/T7Hkffvhh+/jjj3fwa+Dy5ctaWlrSkSNHdODAgUEfzlB7+umnVa1Wdc8997SV4XP27FltbGzo1KlTu+5MQm9qtZqeeuopeZ6nBx54oGn/g065z87Ro0fDxTGgmXK5rM9//vPK5XJ63vOet+tjfd/Xk08+KWOMHnjggYb14gGkg7uXnzx5MiwV6CwtLeny5cuamprS6dOnu3r+lZUVXbx4UdPT0zp16lRXzxEEgf7+7/9ekvTggw/Gdh/sh+j18MEHH+z43z/55JPyfV/Pe97zus5cw3D5whe+oGKxqDvvvLNhjxrHfT737dunkydP9vEIMYouXbqk5eVlHTt2TJubm1peXtb8/LyOHj066ENDD65du6aFhQUdPHhQhw8fvu37lUpFzzzzjLLZrO6///5Ej2V9fV3nzp1TNptVrVZra07Rb1euXNHi4mLT18uJjkue//znp26u02qe/8wzz6hSqeiuu+5qWE4SAOKyubmpZ599VoVCQffcc8+gD2foGGOaBq/aufP8jaR7jTF3GmPGJH2LpA9HH2CtvdNae9pae1rShyR9126BK3THpb9Tnqo30bI+7aZyUs6mP6L9ruJcsBv2HiLor05SvV35MWstu8GBFPN9Pywb06i3g9uYsr6+3nWGda/9rqT6bm035hi2e5Z73TotGei4ezVjrb3BWtv2Z4aeV4hTdJznFrvJvBp+7h7fLEARvcck/V67c8wF5avVaqrOr2Y9QBvxPC+8BqexdGCreRt9rwD0CyUDk9MyeGWtrUl6m6Q/lPSkpN+01n7OGPNWY8xbkz5APMcNxFgg7U20H0W7O4cIXvVHHAt/jdArAZ3odNBBPXUg/VyZwImJibCPZVQ2mw0XOLot7ePuYb1OWIZ1w4UbIzV6fdvBWGtvcQvInue1PGfcZ6pcLqtV1RCglehccHx8XBMTEwqCIJUl0dCeIAjCNZJmwatoH+Sk76/R/tppvKdHe4A26g+2U5r7XrXqNco8DUC/ELxKTlsr99ba37fW3metvdta+2NbX3uPtfY9DR775kb9rtC7fD4vz/NUrVaZ2Pegm8UldgP3h3tv2inl2An3Xqdp0oD06nTQwY4+IP3a2WE8MzMjqfvgVXSxqhdpXOhqR6+ZVwSv9pbo56VVtn02m1Umk5G1dug+F0iX6DnkrrWuN9GtW7cIjg4pNwYvFAq7BsP7HbwaGxtL5SbK6JionWonLsCVtuBV9PPcTuYVn28ASSJ4lZx0FazFrowx4aI+i6Td62ZxiQWV/kgq88pNVNI0aUB6dVpW1O3oY1IEpJO1Nsy8aid41U3pwE5KoLUyrMErMq/QiU4/L+5xjOXQC3ddzWazYQWOmZkZZbNZlctl5thDyr1vu/XOkwheOe2WDHTSmnlVq9VkrVUmk2laUSeXyymbzcr3fUrPjpDr16/r+vXrgz4MYJuk1jNB8GroUDqwd91Ew1lQ6Y9oz6s4ZbNZGWPk+36q6o0jndw1oln5iZ1yuZxyuZyCIEjdpA5A/d5Sq9WUzWZ3vb+4ElLRYFe7XAm0TCbTdeaRM6zZwm6MROYV2tFpJQT6XiEOjeaBnudpbm5OUj37CsOnVb8rpx+BJGttqoNXvu+Hwb5GPUAbiQav0rRRr1XJQKm+AdwFNSkdOBo2NjZ08+ZN3bx5kzEjUoXMq+QQvBoyLmWb4FX3uikbyIJK8mq1mnzfl+d5bQcN2hWtcZ6WiQPSaeeEs12UDgTSq5PyOPv27ZPUeenAOHfaDWvmVVxlA4ft90Z3Oq2EQPAKcWi22O1KB66urnINGjLW2nBtJA2ZV26zpOvnl7bgVaseoI1ks1lls1kFQZCa30Nqf6E4WiUDw+/GjRvhnxkTIC1835fv+zLG9LyREbcjeDVkosGrNO16GSaUDUyn6MJfO7W3OzWsi4Hor50TznbRDBhIr07K47jSgWtrax1l6nazMaaZYb1fUTYQneg04Os+WyxUoRfNFrtzuVy4eWFxcbHvx4XuFYtFBUGgsbGxlguG/bi/Rs8xY0zqgledlgx00lg6sJ3MK0nbMq9YQxtuGxsb2+bbaTofsbftvPYjXgSvhky0Zm9aBkDDJAiCtgc5UW4hhrJzyUm6PmzaJg5Ip24HHTQDjlepVCIQiFjUajUVi8VtZWN2MzY2pkKhoCAIOjoHu9kY08ywZgrHlXlF8Gr0dZPlTM8rxGG3vqYu+2pxcZH53hBx2TStSgZK/ZkP7tzMEv2Zg54jWGtHMnjV6j6Sz+eVyWRUq9WGbmMQtnNZV27MyIYWpEWcc0HcjuDVkDHGUDqwB9FeNs2aejYSTf1kUSUZSQevhnUnO/qr3UnQTmNjY+GkiIW13lhrde7cOZ07d47PK3rWTXkcl321srLS9s+J8x6WyWRkjFEQBGFAaBjQ8wrt6mY87jaVVKtVAgvo2m59TScmJlQoFOT7fselYzE4bqNJOxtU3Pteq9USCyTtDMxnMhllMhlZawd+fysWi/J9X7lcruPxShqDV+32KTbGUCVjBLisK8/zdOTIEUkEr5AecVbhwO0IXg0hglfd6yUazqJKstxAmMwrDFK7k6CdopMi6qn3plarhYsKbnco0K1udhhHSwe2u7gVZ/Aq2qdxmAK4vZYNjAbtCE6Mtm4m+NHyWyxWoVu7ZfwZY8Lsq1u3bvX1uNplreX6GGGt7SjzypUFTzKQ1OgcS8s8tJMeoDu5Nag0Ba862XRIf+Lhd/PmTUnS/Px8+HlnPIC06KZvOtpH8GoIDcsCabFY1OXLl1MV7OnlgkLwKllkXiENerlGMCmKR3QSQvAKvbDWhplXnQSv8vm8xsbG5Pt+Wzt0oyWJ45qwDOM9q9eygcaYMPDFWGuwrLVaWVlJfHG30zGfezyLVehGEAThOd1sk9Ls7KwymYyKxWLqxnPlcllf+MIX9NRTTw3VvSFJ5XJZvu8rm822ff91j0vqNRyW4FWnotmvabhHR8vPtrPpkMyr4ba5uan19XV5nqf5+fkwc9ttegQGjeBVsgheDaHorpe07ryq1Wo6f/68lpaWdPXq1UEfTqiXAAnBq+T4vq9arbZtV23c0jJpQLr1MuhgUhSP6KLk+vp6au9zSD9XHmdsbKzjDA+XfdVO6agkGvQOW/Aqmi3VSVnmnaIlnTA4q6urunjxoq5du5bI83c7HifzCr2I9j1udq32PE9zc3OS6r2v0mJlZUVf+MIXwvk/FVjqollX7d5/k+4r2Sg4n4Z5aLVaValUarsH6E7GmFSVDvR9X9baMJuulfHxcRljVKlUGGMMIdfran5+XtlsVsYYNrQgVQheJYvg1RDKZDLK5/Oy1qbyQm2t1cWLF8NBwcrKSmp2rpF5lU7RRYy4Fv52coMc3/dZDEdTvVwjmBTFIzohttYSDETX3A7jqampju8t+/btk1RfxG9VOjCJzOGkd4bHLZp11ct9nLFWOriF8U5KZ3ai274A7jPGRiR0Ixq82o0rHZhk9mG7giDQlStXdPHiRQVBEG4OGJZ7Q9LcGkMnwZgkN4f4vi/f97f1y5bSEbyKjom63WSSpuBVpxnv9L0aXjuzrhyCV0iL6Gb8TttPoD0Er4aUy75KS1Ao6saNG9rY2FAmkwl3rl29ejWxpqid6GVhmt3AyUm635W0vYcIix5oxFrb9sJGI0yK4uEmIO4+R+lAdKuX8jiFQkG5XE61Wq3lDvckglfDlnnVa78rh+BVOrhz2vf9RBYpKRuIQWh3Hjg2Nqbp6WlZa7W0tNSPQ2uoXC7r2Wef1eLioowxOnr0qA4ePCiJuYzjxtvt9Ltykry/NsvETlPwqpsxkZOm4FU3fYop8T6cXK+r/fv3bwsKuzFBGs5H7G1JVOHAdgSvhpRb1EtbyYC1tbXw5nLy5EkdOXIkrBveTumdJPXak4IFleQk3e/KGbbFQPSXOy+y2WzXOxKHpSdhWkUzig8cOCApuZ3/GG1xlMdpt3RgksGrYVmgdGOjbvtdOYy10iHJ3oO97E6NBq+4L6BTnWxQctlXi4uLAznXomUCx8bGdNddd2l+fj4VQZC0qFarqlar8jwvDKq0I8nXsFmAdNDvWxAEYaBvamqq6+dJU/Cqm3UdNhkOn2KxqLW1NRljwrmh485HNrRg0CgZmDyCV0MqjcGrSqWiS5cuSZIOHTqkqakpZTIZHT58WJJ07dq1gZZr6zUazoJKctyAo5OJRzcGPXFAusUx6GBHX29qtZp835fneZqZmVEmk1G1WmVSgo6tr69L6q08jgterays7Lp4SebV9rKBvWCsNXjW2m3jJPdZiksv4/FMJqNMJrMtUxpoVyfjvKmpKY2NjalarfY1A3xnmcCZmRndfffd4dyfucxzollXnVxL+pV5FRUtX+/ul/20ubmpIAiUz+d7mudEgwWD3kDQTeaVO1dKpdJA3gd0bmevqyiysZEWBK+SR/BqSBUKBRljVC6XU3HjDYJAFy9elO/7mpqaCksaSNLc3Jzy+byq1apu3bo1sGPs9YLCgkpyyLxCGsQx6IhuLEjDtXnYRAPZxpiwtAmlA9GpaG+Hbk1MTIQB1Ga7jKML/XFOWKKlige9QNSOuMsGcp8eHHc+u/dyc3Mz1vtZr2M++l6hW51kXhljwt4q/Zq/NioTePLkyW3X1ehcZhjuDUnqpt+VlOxr2Gw8YIwZaOAxjpKBUv2+MDY2lore692Ueo9m6bHRMP12y7qS6u+9MUa1Wo01OgxUv9Yz9zKCV0MqeuNNQ/bV9evXVSwWlc1mdeLEiW27n4wxOnLkiKR6vdpB3Vh6vaAQvEqG7/uqVqvbBvVJYbcidhPHAnQmk0llZuyw2HmdJniFblhrw2yRXhZq2ikdWKvVFASBMplMz1lHUZ7nhRkmwzDuIPNqdET7DiZRYimu4NWgF04xfDod583OzsrzPG1sbCReJi1aJjCXy4VlAndmFGUyGXmepyAIhmqTVKVS0dLSUqzBom76XUnbX8O4q8Lsdo6NQvBKUmrWoLptB+GCnZQOTD+XdbWz15VjjKF0IFKBzKvkEbwaYm6BdNC7RlZWVsIdaXfccUfDG8v09LSmpqYUBEF4E+q3Xi8onufJGDN0k4W0cwONfjQ3JPMKu4lr0EE99e7tXNR0WTNx7/zHaIurPI6klsGrJHfaDdM9K+7MK4JXgxM9p901OM7Sgb3eawleoRtBEITXlXYzNTKZjGZnZyXVe18ldVw7ywTec8894Tx/p0Fn8HTrypUrunz5sq5fvx7L89VqNZXLZRljmr5WzUT77cX9GqYxeFUul1WpVJTJZDoO9DWSlr5X3ZQNlOhPPCxaZV05jAmQBgSvkkfwaoi5G+8gd72Uy2VdvnxZknTkyJFdB0Qu+2pxcXEgg51eLyjGGBZVEuAWROIYTLcS96RhZWWFge8I6XYH3070veqeuze4iUh0oh133xWMrjh3GE9OTsrzPJXL5YYT4+gGjLi55xyG4FUSmVd7vSTWoCQdvCLzCoMQLTHWyWa5/fv3S5KWl5dj30RTqVR09uzZXcsENjJswatarRZeQxYWFmLZ3OXG2OPj4131tUxic0gQBLuWshvU+xYtoxzHRtE0BK9831cQBGGWeifcPK1YLA60Hzt2d/PmTUn1a/BuAUrGBBg0tzkmujEC8SN4NcSipakGMcF3fa7cLjFXF7yZQqEQTgCuXbvWj0PcJo7d0W5RhQyA+MS5yNhKtFlur4PVtbU1Xbx4URcuXGCBbUR0u4Nvp+iOPs6N9kXr57uJsUTpQHQujn5Xjud5u2ZfkXlV107m1T/7Z/9Mb3rTm3a9LrpyTtZaFpUGJHpOj4+PK5PJqFKpxLIwFL3Odxvwdf+OhSp0ottNjIVCQZOTkwqCQMvLy7Edz+rqqs6cOaNisahcLqc777yzYZnARoYteLWysiJJ4e926dKlnufSLnjV7ebHJDaHRANXjQJqgw5exTXXjgavBjXPic7ZOg3IZTIZ5fN5WWsHXvoQjZVKJa2urrbMupKeG38POhMQe1c/K0ntZQSvhtjY2Jg8z1OtVhvIwsbVq1dVKpU0Njam48ePt/VBPXTokDzP0/r6el8XIuOKhtNIPF61Wk3FYlHGmI6b7XYjrjIR1towAFur1RgsjQDf9+X7/rYMy25ls9mwmTGTova598DzvG3vQTR4RTAQrbhFds/zYsvo3S145e4lBK/qwatm189yuaz3vve9+sAHPqClpaVdn4ss98GJBpfy+fy28Vkc2Vcuo66XHnFucaBWq7GZDG3bLSOmFbdB89atWz2PQ3zf15UrV3ThwgUFQaDp6Wndc889Hd2vhuneID0XvDp69KgKhYKq1WrPG1ld9la388ckyga2CpAOInjl+34Y6ItjQ4/0XHDO9/2B3ad7+TxL9L1KO9dmZG5uruV7TM8rDBolA/uD4NUQM8YMrHTg0tKSlpaWZIxpq7yBk81mdfDgQUn17Kt+LUT2sjsnigWVeEVLBvbaJ6NdcUz4lpaWtg2QyAgZftFBRxw7Zigd2LloycDoe5DP55XL5eT7PsFAtOTuK1NTU12VEmrEldopFou3LTr1I/NqGHbXtyobePXq1fDPV65c2fW5GGsNTq1Wu60UU5ylA+P4vAxrzx8MVi+LS9PT08rlcqpUKl1/Dnzf140bN/T000+H/bOOHDmiO+64o+M50DCd/9VqVZubmzLGaN++fTpx4oSMMVpaWmraS7KVIAjCMWO3m1SSCAC2OseiP7NfmcXr6+uy1mpiYqLnzXmOMWbgpQN7XSxmnpZe0awrt264G7e+x4YWDArBq/4geDXkoqUD+6VUKoULD0ePHu24Ser8/LxyuZzK5XLL3bdxiWtxiQWVePWzZKDT64QvCIJwN9C+ffskEbwaBXEPOtxkmh197WtUMlCqT5IpHYh2xVky0PE8r+E5GO1vkcSEZVh211trwwWDZouw0YAVwav0imYSuk0E7rO0sbHR86azuHrEpbHHxfr6us6ePUs2fkr1kqlhjAlL37vAU7uiQasbN24oCAJNTk7q7rvv1oEDB7raMDVMwSsXoJqamlImk1GhUNDhw4clSZcvX+7qOu/KchcKha43Pw4ieOV5Xt/v60nNtd36z6Cud71mXlHiPb1cr6t2sq6k+vU5jWMC7B0Er/qD4NWQ63fwyvd9Xbx4UdZazc7Oam5uruPn8DxPR44ckVRPCe7HDom4LigsqMTHWjuQ4FWvk4aFhQXVajWNj4/r2LFj4W58zonhFvcCdHRHH5Oi9uy2yYDgFdoRBEG4Kz7u+4orHejKH0m6rbxa3IYleBUNXDV7HQheDYdG1+GxsTHl83kFQdDzLvW4ymymre/V+vq6zp8/r42Njb5tzENnep0Lzs3NyRijtbW1toJGvu/r5s2beuaZZ7YFre68807deeedHW/+jIreG9I+xnT3TLfhT6pvZJ2cnJTv+7p8+XLHv0Ov/a6kZAKA7Zxj/Qw8WmsTGxO5jWaDqojQ67wtl8spl8tty+LD4JVKJa2srLSddeX0OxPQWpv6ay/6J66NWdgdwashFy0bmPQF1FqrK1euqFwuK5/Phwv33ZiZmdH4+LhqtZoWFhZiPtLbxXVBcZMFFlR6t7m5qSAIwkWRfull0lCtVsPz9ciRI8pkMmGQgkX14RYtLRqHXC6nbDYr3/dTs7iWdtGygTtNTk7KGKNSqZT6hXwMjssMKRQKsX2WnenpaRljtLm5GY4Bkt5pl81mZYyR7/t9KzHUjVb9riSCV90IgkALCwt9HV8020QQV+nAuCohuH+fhsyTjY0NnT9/PpyHpeGYcLteMzWy2WwYgLl161bTx7nP7TPPPKPr16/L931NTEzo9OnTuvPOO2Pp8RvtDZrmMVGlUglLBkaDJ8YYHT9+XJ7naW1tTcvLyx09b6/9rqTn7jNx3l/TFrwqlUqq1WrKZrOxz7XTUjawl7Eefa/Sp9OsK6efmVfValVPPvnktnLY2NuS7H+M5xC8GnLZbDbcNZL0xXpxcVErKyvyPE8nT57sqZeEMUZHjx6VVM9kSXoAF9cFhQWV+Awi60rqbSe72zk5PT0dDnjd8cfRCwKDE/cidLQnIfXU27PboqbneeHiKYFiNJPUDmNJ2zYruDJISfa7kurXkWFYoHRjot3KN3UTvErz75y0UqmkZ599VteuXdOlS5f6tsO3X8GrUSkbuLm5GQau3D2f4FX6BEEQXqd6Weyen5+XVO99uzPgEQ1aXbt2Tb7va3x8PAxaxVnKVhqO0oHuXjk9PX3b/WFsbEzHjh2TVO+J2O7vYa0Ns316ybwyxsSa3WytTV3wKjrXjjs7PLqBYBCba3oNRkv0vUqbcrkcZl0dOHCgo3/bzzHB2tqagiDQ8vJyqjeWoT/c+CJ6T0EyCF6NgH6UDiwWi7p27Zok6dixY7f1JOnGxMSE9u3bJ2utrl+/3vPz7YaygekTV/DqF3/xF/Xd3/3dbS/sdDtpKJVKYSkYV/ZS2l7OjPTx4ZVEBgU7+trnmuxG+wHsROlAtJL0pghXOrBfwSvpuWtSmgM5rmwgmVe9s9bq1q1b+sIXvhDuaPd9v2+vRbNz2mW/9lImOc4ecdGFqkGNvTY3N3Xu3DkFQaB9+/bp1KlTkurjCcaD6RJd6O5lEX98fFzj4+PhwqVUP69v3boVBq1cafFTp07prrvu0tTUVCJlZYcheNWoZGDUvn37NDMzoyAI2g7Sl0qlsHJHrwuFcd5fa7WarLXKZDK7buQYVPAqbp7nhdfhfmdf+b4v3/e3bfDpRrQ/MdfswXM9xWdnZzseI/TzXHRrrnGUUsbwi2aBJnGvx3MIXo2ApHf3+76vCxcuyFqr/fv3a3Z2NrbnPnz4sIwxWllZSfT444qGRxdUGOR0r1KpqFwuy/O8nnbNSdIP/dAP6ed//uf19NNPt/X4bsswueDt/v37b+sFMTY2xgBmiFlrY+95JSV/bR4l7fQOimY5stMNO5XLZVUqFWUymZ56iezGBa82Nja2lQRNMng1DH2vksq82mvBq2q1qvPnz+vq1auy1mpubi48l/uxILNbcCk6Xus2+yq6SaSX6g1S/VzLZrPb7t/9VCwWw8DVzMyMTpw4MfBjQnNxblBy2VeLi4th0Orq1auq1WoqFAq64447dNdddyWS7RKV9uBVuVxWsViU53lNgyfGGB07dkzZbFabm5tttRJwG8J6nT9Kz91f43gN2z3H+vW+1Wo1FYtFGWNiz/pzBlU6MK5g9NjYWFjiPa2fo73CZV1J6qjXlTM2NiZjTLgZMknReT0bKpF0CXk8h+DVCEgy88paq0uXLqlarapQKGzLOInD2NhYOAm4du1aIgGh6AWl10mE53nyPE/WWhZPe+AWPiYnJ3tawKhUKmFQ6dKlS239m2gQs92B6vr6utbX1+V5ng4dOnTb98kIGW6u4XU2m+15QS2qUCjI8zxVq1UWslrYrd+Vk8vlVCgUZK0lmw23cdffpHa5S/WgyuTkpKy1Wl1d7cuEJc7FtaS0k3l1+fLlhn9uZC8Gr1ZXV3XmzBmtr68rk8no5MmTOn78eF+DV9GSfo0+Q72WDoz78+Kep9+lA0ul0rbA1cmTJ8PXKy3lDLFdnBuUZmZmlMlkVCqVbgta3X333ZqZmenL7uu0b2yIlgzcbWydzWZ1/PhxSfXMi1brGW7hOI7eYXG+ht0Er5LcCOvGRL3OtXfTz/tTVBwlA6XtJd6ZVwxWtNdVN9dpY0xf7r87e1mz9oN+bGREHcGrERAdOMQdULl165bW1tbkeZ7uuOOORAY/Bw8eVCaT0ebmZjjQjVPck+W9uKgSt7jKGEQbZbZaDIvqZLJirQ0DZAcPHmy4OEfwarjFNQnaiUlR+9zAr1VJWj5raCbJfldRLvtqcXFRQRCE2RZJSfsCpdR55tXVq1d3Ha/upSz3IAh0+fJlXbhwQb7va3JyUvfcc09YZqufO9tb9YeNBq+6eV/inuBHe670S6lU0tmzZ+X7vqanp3XixIltgYq0Z8PsVdGyPr3yPC/sx5LP53Xy5Mm+Bq2ctJ9rrUoGRk1PT2v//v3hptlm94fo5qU4M6/6GbxyZQWttYmuJbggYFJZV9LgMq/iXNuh79XglcvlsAxrN1lXTj9KB7rPldug6qoJYe8i86p/CF6NAM/zwsFDnNlXlUol7EV14sSJxD6QmUxGhw8fllTPvoo7ABdXc2iHRuK9CYIgtkXGaLZVu5lXUmcTvuXlZZVKJWWz2TBLcKeJiQl5nheWrcJwSXLQQenA9rS7qEmPOTQSBEG4oJXkQo30XPDKjbeS3mk3TMGrZkG89fV1ra6uKp/Pa35+Xr7vh7tsG/E8LwyEJV3+ZZCKxaLOnDmjpaUlGWN05MgRnT59etsC+yAyr5qd04VCQdlsVrVaravFmrjH4/3OciqXyzp37px839fU1JROnjx526a+QWWDYXdxj/MOHDige++9Nww0D6LPRZqDV+VyWaVSSZ7ntX1PPnLkiMbGxlQul5v2wq5UKvJ9X9lsNpb3Ms7XsJNzrB/vnZt3JFVGWdoevOrnmDzOTYdsMhw8Nx7sptdVVD/GBO5zNTExsa2cPfYuglf9Q/BqRCRROnBhYUHW2rCZapLm5uaUz+dVrVa1uLgY63O32knaKTKveuOaohYKhZ4HnZ2UIYpqdzEwCIJwAnX48OGmmYfRyVmaBzBux+K1a9d07do1bW5uEgBQsoMOt6OPSdHu2ikbKNXvdZlMRtVqlcVBhFwmyPj4eKJZUFL9/hFdDCJ41bpsoMuSPnbsWFgeai/3vbLW6saNG/rCF76gSqWifD6vu+++WwcOHLhtETy6GJN0uepWwato75Ruxjpxj8f7Gbwql8s6e/asarWaJicnm1ajGEQ2GFqLO8PelagaZHP2aA/ftAX5XdbVzMxM21VbPM/TiRMnJNUrvzS6xkSzruJ47eO8v3aSWZp08CoIgrYrGvQim80qm80qCIK+XvPinLdFS7xz3e6/SqUSS9aV9Ny5nuSYwK21TkxMhOMhqoHsbQSv+ofg1YiIO3hVqVS0tLQkqfcbSTvcjlOpXu86zsUKygamS1wlA6Xt2VadBK/anTTcunUrrGU/Ozu762PTOoAJgkCrq6u6dOmSnnrqKZ09e1YLCwtaWFjQs88+q6efflpXrlzpugzQKEhy0DE+Pi5jTLiTdHl5WcVikZ55Ea65rud5LReWel08xWjqV8lAJ7qhp5uF+EuXLumf//N/rs9//vMtHxtdXEvrNbpV2UAXqDp27JiOHTu27WvNjOpYq1Kp6OzZs7px44YkaX5+XnfffXfTBUbP8/oWpGln8bWX629SZQOTfl0qlYrOnTunWq2miYkJnTp1qumCfJqzYZJSqVRSHVyXRnNxyRgT/j5pe/07KRkYNTExEa47XL58+bagXJz9rqR4769pyrxymVD5fH7Xcr5xGETpwDiD0dES71TJ6L9o1lWvY4OkxwTW2nCtdXx8PJxzbGxsMK/fo4IgiLWnJnaX7PZU9E3cN92bN2+GWVdJ7tiJmp6e1tTUlNbX13Xjxo1wgaNXSZUNHLUFlX6w1sYavIoGrDopG9jOTrtarRYOqI4cOdJyh180dTwIgsSa47ajVqtpbW0t/C86IRsbG9PMzIystVpdXQ2zHRcXF5XJZDQzM6OZmZlEG/ymTZKLGp7naWJiQhsbG7eVycrlcsrn8+F/hUKhLxPNtIkuaLazk3ZmZkYrKytaW1sL+05g74r7vtKOmZmZMCu3m+vG+9//fr3nPe/R+Pi4fuZnfmbXx7r+GG53fdKZZd1olXkVDV6592gvBq+Wl5d15coVBUGgbDar48ePt3XOFgoFlctlFYvFxEpAWWs7Cl65xZp2xwnRTQpxncO5XE7GmPC5k7h3VqtVnT17VtVqVePj47sGrqTti9KDHgv2QxAE+sIXviBjjO67775U/r5BEMj3fRljUnn97EUulwtLlvdrvt5KqVRSuVxWJpPpqozvoUOHtL6+rmKxqKtXr4bZWJJi7XclKbwe1Wo1VavVrucBtVot/Ly3cx1KOngVXWBPWqFQ0Pr6ukqlUsfBym7FvVg8OTmp9fV1bW5uttysivjEvVl+bGxMxhhVq9VExgTValW1Wk2ZTCb8WePj4yoWi1pfX0+8UhXSJ7qGNMhM7L1itEZwe5hb9HMX1V4G59EbyaFDh+I6xLYcOXJEZ86c0eLioubn53vegeEWe4wxsZWKGMUFlX4pl8uqVqvKZDKxDKi7LRvYzqThxo0bCoJAU1NTbU2+crmcCoWCSqWSNjY2+raI6lQqFa2trWl1dfW2EnVud9DMzMy2AMGRI0dUKpW0srKi1dXV8LO/tLQkz/M0PT2tffv2aWpqKpULEnGJu5zMTnfccYfW1tZULpe3/VetVlWtVm/bwZ7NZm8LasVVIiWNOt2NH108TWrBEsPDfZay2WzfFu/y+bwmJia6DiacP39+2/9byeVy8n0//D3TJNp0vp3Mq70YvPJ9X1euXAmzEaanp3X8+PG238tCoaCVlZVEd7a7zAMXLG3Gfc46HeskMcF3mSdu8T7uhdqdgavTp0+3vN+4DGJ3f0+6rOigbW5uhsHrtbW1vi1ed8Kdey7YOUrSmOkXLRnYzettjNGJEyd05swZLS8vh3MR95mK9vqOQy6X6zl41en1bZSCV/3syyjVg9Gt+mx2ir5Xg+HKBe7bty+We6Ur5+oC6HEFuZ3o58p9zqenp1UsFrW2tkbwag8axazuNEvXDBhdc5H/zc1NFYvFnhbO40zf7VShUNDc3JyWlpZ07do1nTp1qqfnS2Ky7Ba4R2FBpd/cIv3U1FQs70c02+r69euqVCpt3TyideIb7Ywtl8th7zVXzrId09PTKpVKWl9fTzx45XZJr66uanV19bZJw+TkpGZmZjQ9Pd30NXHXjfHxcR0+fDh8vpWVFZXLZa2srGhlZUXGmDD4NT09PVLBAjcJijPAvVMmk7ltJ5+1VpVKJRxgR/+r1Wqq1WrbJlEHDx7U4cOHEzm+QXPnbruLEZlMRhMTE9rc3NT6+noqF8vQPy7rKq77SrtOnTol3/e7um64e1e7GcO5XE6lUilcRE8Tt3DteV7TTQ69BK/SUA5rY2NDq6urYRZzq//v/FqpVArvM0ePHtXc3FxH52o/yjJ1solgamqq47FO3CUDnXw+H9474/xs1Go1nTt3LsxoaSdw5YyNjYV9GUc9eBUdpywvL6fyfpz0BqVBSlvwylrbdcnAqHw+ryNHjujq1au6cuVKOOaT4ut35eRyORWLxZ7uNZ0uYI5S8KrfZQOjn+e4zoNoifdeN4GjfW78Hud9I8ngVfQa5ExPT+vGjRth+4VR2yCB3RG86i+uzCPEDew2Nze7Xjgvl8t97XXVyOHDh8OSUOvr612VHHCSuKCM0m7gfou7tJPLtvI8T0EQ6OrVq20FPF2gwtXp37m4cO3aNUnS3NxcR7v7pqendfPmTa2treno0aMd/CadqVQqOn/+/Laazr0GmIwxKhQKKhQKOnTo0LbAWLFYDP/sfs7x48f7HsRyZTni/DwPakeu2x2289yz1oaLXq5M1MrKipaWlnTo0KGRHBR3s6g5PT2tzc3N1O70Rv/0u2Sg0ypDZTcXL17c9v9W3KJrWhYoo1qVDJSGP/Pq0qVLPQfRxsfHdeLEia6CGdHFwaQWR9y51W7wamFhoaO+V0lN8JPocVGr1XT27Nkw+NRJ4Mod08bGRio/r3GLBq/W1tZSufA7yotLaet5VSqVVKlUlMlkeu5LtX///nAt4PLly+HvGveCdByBpE7PsegmyrgrCPi+H14P+5GNHi3V1o/PfxKfZ8/zwk3gm5ubZND0QbVaVbFY3NbLOA5uTJBEMNUFr6JB4UKhoGw2G87d01K+Ff3RydgZvUvX6BI9cRdSt9umG4PMunKy2awOHjyo69ev6/Lly7rrrru63i2XxE7PNC2oDBPf98NJbhyLjEEQhMGrBx98UJ/97Gd16dKltrP1XPCqUqlsOz82Nja0trYmz/M6Lps5Pj6uTCajSqWS6I7bGzduhPXkXcAq7tJ++XxeBw8e1MGDB1WpVMLg1ebmplZXV5XP5/uaCWSt1bPPPqtarab77rsvtslR2hY1XBmksbExTU9Py1qrzc1NVatVbW5uxtakOk26DV5dv3497Ok2ikE9tOb7fjiZjHPymzQXtLp27VpbGcPt9GkclFYlA6XnNppEg1etSv2mZazlykkZY7ZlYrtrTjv/d70Pu71OZbPZsO9ZL6WtdtPJdXhiYkKe54UlO9sZoyeVeeVei7iCVy7jyo3h7rzzzo7HG2nLhklKEATbMjzcZpv5+fkBH9l2o5x5lbaNDdGsq17HZcYYHT9+XGfOnNH6+nr4fHGPg+O4v3Y6l0iy5Gm0mkE/Sr67zY/FYlGlUinxsVhSn+fJyUltbm5qY2OD4FUfrK6uSlLs6xcueBTnhhapfr9zn61oAN0F35aXl7W2tkbwao9x51la1pFG3eg2MdmDosGraOmSdpXL5bD27KCyrpz5+XmNj4+rWq3q3LlzXS9eJLEw7RZoarVaV6/zXuV26E5MTMSyw2xhYUHValX79+/XPffcI6m7vlfRyYq1Nsy6OnDgQMcD4+juIZcNELdqtRpODu+++26dOHFCMzMziU5QxsbGdODAAd111106ffq0JGlpaamv5//a2lrYAN0NeOOQtuDVTsaYMLPIve+jxJVIdH1C2pXP58M+QL1s2MBwi/u+0g9ra2vhZ9laq6tXr7b8N2kOXnWSeXX8+HEdO3Zs29eaSUvwKroQOD8/H/63f/9+7d+/X3Nzc5qbm9Ps7KxmZ2e1b98+7du3TzMzM2Em9OTkZE8LuW5xMHo8ceskuOR5XriA3G72VZJlA6V4Fu+ttbpw4YJKpZLGxsZ0+vTprjbKJJENlkZuvpnP58OAlZtHpknax3m9iAZKBz0njatkYFQulwvvGW6jUtyl8AYRvIo+Nu7AYz9LBjr9LB3o3qe4P88uIOE2RCFZbp0k7kBhUvdfl/mez+dvm2+4TVlJrf0gvUZ5fJFGBK9GSC6XUzable/7XQ2E0pB15Xiep1OnToW17M+dOxcukHSim2i4tVbPPPOMgiBoemzuptXNMaWVK12QlKRKBh4/flzHjx/f9rV2NNqtuLKyomKxqGw2qwMHDnR1XEkPYBYXF2Wt1czMzEBulJOTkxobG1OtVuvrIM2VM5XiDeIkNQmKUzR4NejFibhFFzQ7Wdx15Sul0ZksrK2t6fz580ycOzCokoG92Nnnqp3SgWkOXrXKvLLWbisbePjwYXmepxs3buz6+6Slv2inPfmSkqbglfRcpmM7wSvX41FKtmxgr/fHpaUlbW5uKpvN6s477+x6Z/9eybxy1RRcj1XP81QsFlMXtBuGcV63XPlaa+3Ar5Wub1Q2m421tJ/bECDVAzJxb9YjeNW7fgavouXe4+TO2WKxOFLrO2kUdzWeqGgZyzjfx90+V248tLm5ybmzhwRBMNKZ3WlE8GqERHcjdboTPZp11WmptKRks1mdPn1aY2NjKpVKOn/+fNOAUjPd1CF9//vfr+c973n6T//pP+16bNLgF1XiEgSBzpw5ozNnziRy07XWhgsccQ1S3ALgiRMndOLEiW1fa8fOzKsgCHT9+nVJ9c9At5OjJAcwQRBocXFRkgZWlsUYo/3790tSeCxJq1QqWltbC4MbGxsbsX32kpoExalQKCifz8v3/Y56jAyDXnbjj0rwqlar6dKlSzp//rzW1tZ05cqVkQtSJsFaO5TBq53BqlEJXjXLUHGbQqampjQ9Pa1sNqvDhw/LWhvecxuJbhIa5OfBLcb1cyGwkSQXB33fV61WC/uBtiMavGr1/lSrVVlrw/KHccpkMspms2G/yG7VarXwfDx69GhPY4Lo+LLTecswiQavPM8Ld9CnLftqGMZ5vUhL36s4SwbudOzYMc3Pz28r3RqXXrPXgiAI74OdnGMEr7qT1GJxJpMJX7OLFy/q2rVrWlhY0MrKSli+nbF5PNy4YWJiIvYeaa6ntBRv9lWjfldOJpMJg5+jNk9Hc9FrUT9KtILg1cjpNnh148YNSdLc3FyqdqblcjmdPn1auVxOm5ubHQWwXCaRMaajG+Pjjz8uSXriiSeaPsY936AnCnG5deuWqtWqqtWqbt26Ffvzl0ol1Wo15XK52LL64s68WlxcVLVaVT6f19zcXNfHlc1mNT4+LmvttkbWcVhZWZHv+yoUCrE3LO7E7OysjDFaX1/vy85il3XlentJiq104DCke49y6UA30e3muuBKcZVKpaG9Fq+ururMmTNaXl6WMUaZTEalUmnoA3L94HbHxnlf6Yedmyza2XQRzUJK22J4q7KB0awrp53SgdGx2yA3Cu2FzKtuMmDHxsbC0q2tjinpngBxLFRdu3ZNvu9ramqq5zJGrp+NNLrZV0EQhIt5roTk7OyspHrwKi2LvN3OBYdJGvpeJVEyMCqTyejo0aOJzH0ymYw8z5O1tqtNh9F5RCdBuySuEbVaTZVKZVup2X6I9hlKeoyS5LwtuiljYWFB165d08WLF/Xss8/q6aef1uc+9zk9/fTTevbZZ3XhwgVdvXqVAFcX3Bw+qd5iSQSv3Npqs2vQqGyoRPuSKoeN5ghejZhu6vWWSiWtrKzIGDPwXleNuLrzmUxGGxsbunTpUlsDg+hkuZPB5IULF7b9v5E0LKjExfd9LSwshH9fWFiI/feK7o6PazdenJlXtVotDOAeOXKk52NMYgBjrQ0Di/Pz87HvauxENpsNB5zRcn5JsNaGP2P//v3hz40jeJVkKaO4ucWA1dXV1C1e98Jdp7uZZHuel3iPuaTUajVdvHhRFy5cUK1W08TEhO65557wHnzjxg0mwC0kcV/pB5dp5c7ddjKvohkxaRt3tCob2G3wShr8WCsIgp6uUXFygaVKpRJ7Vnc3waVOenx2UwWhE70uVG1sbIQbCI4ePRrL9WTU+15F+3+4z+nk5KRyuZyq1Wpqyt9Gd0YP032iE2kIlG5uboabFAedpdqNXrKbu51HJPG+uQX2QqHQ1/M9k8lobGxs27wqCd1mubXr0KFDuvPOO3XixAkdPnxY+/fv1/T0tAqFQjjGcde31dVV3bp167YA19NPP61Lly6FG06xXRAEiVdNiPv+205QOLr2w/xtbxiWNaRRMppbkPYwN2B0k4p2Bi7RXldp/fDl83mdPn1aZ8+e1erqqi5fvqzjx4/v+vt1O1nea8GrW7duyfd9TUxMyBijjY0NLSwsxFqawQ1S3EJHHHrNvMpmszLGhKVigiDQ5ORkLMc4PT2tGzduhAOYOCYQGxsbKpVKymQyiexq7NT+/fu1srKipaUlHTp0KLFJ0urqqmq1mvL5vCYmJpTP53XlyhWtr6/L9/2eShDVajVZa8OeAWmWz+dVKBTCrJw0nANx6HXX0vT0tNbW1rS2thaWs0wza61WV1d15coV+b4vz/PCCbILUCwsLKhUKml9fX2oyuH12zCWDJSe22TxpV/6pfqTP/mTtjdduEXhSqWSqrFaUplX0ecc1FjL9VEaGxsbeEkQz/OUz+dVKpVULpdjzUDodrw8NTWlpaUlra+v71pyPOndqb0sAkd7sh04cCC2Y0xDQCFJ0ZKBjssSX1hY0PLy8rbvDcpeWFxKw7mWZMnAfhgbG1O5XFa1Wu04+NbtORYNmAVBEMs9plV2SJIKhYIqlYqKxWJimz2igaskzjNjzK7XLRc8c9VqGv1Xq9W0vLwclk+dmJjQ9PS0pqenO+7vO4o2NzcVBIHy+XziG1riylSPluJs9v7l8/lwnF4sFgdaIQf9sRfGF2lD5tWIie58aeeCnfasq6jx8XGdPn1anudpeXlZV69e3XVnQ7cXlGjwqtnzD3pBJS7RrKtDhw7p8OHDkp4rIxiHWq2mYrG4bZduHKKZVy54deXKlbazUqI72V1mTxxZV1J9AJ/NZlWr1WLb9RPNuhr0IpqkMJBUq9USzXpxfbXc4n42mw0nFr1mXw1bE29XkmdUSgd202dlJ3dN2djYSH1GWrVa1YULF3Tx4kX5vq/JyUndc8892zIpPc/TgQMHJJF9tZtqtapSqSTP81KxQNoJl2n1spe9bNvfW0lr36tRzrxKS8lAJ6nSgd0Gl9rt8ZnmsoELCwsql8saGxuLdR7kftdRzbxywaudC3TRcUoa7sl7oZn6oINXblOOlEzJwH7opfRit+sNnufFfl8fRL8rpx99rwbdv87zPI2NjWlyclKzs7M6ePCgjh07plOnTumee+7R/fffr7vvvluHDx/eVg3p+vXrOnPmjJ5++mldvnxZq6urezYrK+mSgdL2MpZxcJnEuwWkjDGUDtxjCF713+BXQBE7N2Bpp2RDWntdNTMxMaE77rhDxhgtLi6Gx99IN5PltbW1MJBRLpebPv+gF1TisrCwsC3jyO0OstZuKyXYC3cDdw2d4xLNvJqYmNDc3JwqlUpHxx0d/M7OzsY22I97AFMul7W2tiZjTGqyS4wxYW8wF2CKW7lc1sbGhowx4YKItL2EXi+GbdDhfu+1tbWRmPRE+111GzQeGxtToVBQEASx95iLiyt9eebMGa2trcnzPB07dkynT59ueO7Nzc0pk8moWCym9ncatKTuK/0wasGrfmReDep3Jni1u2iT8t2uVWktG1ipVMJx/tGjR2O9lrhjGsXMK2vtbf2unEKhEN6T07CAN2zjvG4M+t6wsbGhWq0WjseG0SDKBkb/TVzXiVEPXqU9GG2M0fj4uA4ePKi77rpLDzzwgE6ePKnZ2dlwU+vS0pIuXLigJ598UmfPntXNmzfDikmjLhroTrJqgmsZUq1WY5kvt/u5ivZMw+ij51X/DdeMH21xE0l3oW2mVCppdXV1KLKuoqampnTy5ElJ9ZKHruzhTt1MlneWCmxWOnAUgle1Wi3M5omWe3F/XlxcjGUwnVRpp2jwKvr/TkoHukmDMSbMOotLnL14XHBo3759qWo4PTs7K2OM1tfXE1mgcYHkffv2bdvV73ZrudKB3Rr0Dr5O5XI5TUxMbBv8D7O4esm4a0saJwuVSkXnz5/X5cuX5fu+pqamdM8994SZhI1kMplt2Ve43bCWDJSeyxp+yUteokwmo+vXr7d1/Rz0AmUj1tqWmVfuntwoeNXqfj3osZZbhEtLDxd3rWw1vu9Er70fWy3WBEGQeJazKyFVq9U6GhO4Cg4zMzOxX0sGnQ2TpGKxqCAINDY21nD85DYbubJZgzRsGfbd2Fl+rt+GvWSgtL0PcqfSErxyJetcdlC/RYNXSQVihi0Y7Ur9nzhxQs973vN0991369ChQ9s2fbisrGeeeUbXrl0b6SBWqVRSrVZTNptNdFxljIntsxXdrNGqFODU1JSMMSoWi0O9RojWomPbYVlHGgUEr0aQuxm0mtxGs66G7UM3MzOjEydOSJKuX7/eMPOjmwFOu8GrtDZO74TLupqamtq2c3J8fFz79u2TtbZpYLBd1tpwQSPOhYG1tTWtrq5qfHw8zP5x50O7/UOk5z4rBw8ejP0z4AYwrolxt3zfD4M48/PzcR1eLLLZbBhIcscYlyAIwufcmW2WzWbDIE4vwcFhmwRJz2VfjULpwLh2LLlry+rqamomfdZaLS4u6syZM1pfX5fneTp+/LhOnTrV1vm2f/9+ZTIZbW5ukn21QxAEidxX+mF1dVWrq6uamJjQgQMH2g7iSOkMXgVBEPZ1bJa14rKr3AYTaTjKBkbLb6clmyBaCieua12lUpG1Vrlcrqvej62CV9H7bFIL290sVK2uroaZsEePHo39mLoNqA2DRv2uolzwam1tbeDzpGHbpNSNJMrPtWsUSgZK3ZcN7DX4H2fwqp2+PEnK5XLyPC8sCZ6EYV4sdllZhw4d0l133aX7778/zMrKZDKqVqtaWFhI5Ua8uERLBiZ9jsaVCVipVBQEgbLZbMvzLlrKPA2Zx0hO9Fo0bBVAhhmv9AgqFAoyxqhcLjedMBWLxaHMuoqanZ3dtgAS3eHnJoue53WUqbJXMq+aZV057mtLS0s91Qt2fWjy+XysAYJo1pUb/HSTeTU3N6d77rknkc9AtJxOLwPRpaUlBUGgiYmJ1Oz+jnKBpaWlpVgDB64eeKFQaPh7x1E6cJiDV+vr60N7/XGiZQN7MT4+Hk780rDLvVKp6Ny5c2EPvunpad17772am5tre7KWyWTCYDXZV9ttbGzIWqvx8fGhW8BwJQJPnDghY0y46aKd0oG97AxPSrRkYLNzu1HZwGifyt0Mcqzlys1kMpnUZDxns1lls1kFQRDbta7XflTj4+PyPE+VSqXheLFfZVU6KR0YBIGuXr0qqT7eTeI6EufO77RpVjLQyWazYVBz0Btt9kLmlTS4TD9XASGfzw916aRug3/u8dlstqsFzKSCV4PggjNScqUDR+nznM1mw6ys+++/P6y4MOhrZpL6WTWhl16YUe1mXTn0vdobhnENaRQQvBpBnue1LC3iFsP2798/dIs/Ufv37w/LvV26dClcyO52p+f58+clPVeWzP19J7c71ff9VDQk7tTNmzdlrdX09HTDm3E+nw93TvaycOqCNm4SGxeXXeUW/qJ/7iR4ZYwJg71J6HUAY60Ng4xuUJs2ExMTyufzqtVqsZayc9mUzcqruc9oL/2fhnHgEV0UGvbSgXGVDTTGxFqmsxcrKys6c+aMNjY2lMlkdOLECd1xxx1d3Wfn5+fleZ42Njba6mG5V4xCyUBX+tj9v52M4ejiWloyDFuVDIwGCaLZLfPz88rlclpcXNx1kWuQwato1lWaSmHF3Vek135U0etvo406aQxe3bhxQ9VqVYVCIdGM9lEMXllrw8yr3Rbz0lA60Pd9+b4vY0xXWYXDZFCZV26hvR+ZFElyGzA6ndf3Oo8YpeCVlExp26hRzaSM9pFeXV0dyrWlViqVikql0rbspCTFFbzq9HMVHQ+lZayO+NHvajAIXo2o3UoHFotFra2tyRiT2gXxThw8eDD8PS5evLit/06ng0mXafXyl7982993MsaEiyrDVg6kWq2GgYFGWVfOoUOHZIzRyspK14sk/ep3Ff1zJ2UDkxbtxdPNAGZtbU3ValW5XC61C7XRAXdcpQNLpZI2NzfleV7TMiTR/k/dZLYFQRAuiA7bJMi9JmnoJ9EtV1bEGBPL65+GnW5BEOjy5csKgkAzMzO69957w75w3ehn9tXi4qKeffbZ1Gfz9avZc1KimVfR/7eTeeV5njzPUxAEqVnYiGZeNXLr1i1Vq1XNzc1tm/hHS7W54FYjgyzRnLaSgU7cwas4JuC7Ba/6tUmk3YWqUqkUbgo6duxYogvucS2epUmpVFIQBMrlcru+pzMzM/I8T8VicWC/fzRLY5gDK+0YRKA0CIKRKBkoadtYtJPXMM7gVS8L3dbaVAWvksi8stYOddnAVvL5vMbHxxUEwcA34iXBXSumpqb6UmbN3X97PRc7zbxy1YaCIGDj4Qgbxg3Qo4Dg1YhyA5dGF81RybqKOnz4sPbv3y9rrS5cuBDuBOt0Mu6CVV/5lV+57e+NDGvpQJd1NTMzs+sAd2xsLAxKdLNw6krIJLHDZrfgVSeZV0lzzax93+9qF5pbYJmfn0/1xNst0EcDx71wQbB9+/btulvWZV91U2IhOgFK82vbiNvhurm5maoSYp2ILpjG8fq7QMbGxsbANhQsLy8rCAKNj4/rjjvuiKXUmMu+Wl9fT2wStLGxoStXrmhzczP15UqizZ7TFlRoRy+ZV90uriWpVeZVo5KBTjt9rzzPkzFmIAE7t+CRtnK9aQ5euZKecT9/O9oJFFlrdfXqVVlrNTc31/ZiVLdGMfOqVb8rx/O8cIw2qI02o5ql0cggzrX19fWwNPww3o936iZ7rdcFzEwmo0wmI2ttT+sJ0TK3gzzfkwxe9VqicRiMUl/jnVxAzt0Xkubml9VqtevxYxAEXY0F07ChEskieDUYo3nlRzgh27lgHs26GtZeV40YY3T06FHNzs5u27HSbebVV3zFV2z7eyPDGLyqVCphYGC3rCvn4MGDMsZodXW14+CLew+mpqZiDw7sVjYwTZlXxpiuBzDFYlEbGxvyPC8MIqZVNpsNB6O9Zl8FQRA+h+un1Uy0dGCnA9NhHnRkMpnwvBrWCU5c/a6cuHrMdctaG2a0xlmGKpvNhp+Dmzdvxva8Tq1W25b1k/ZdgtFs3mELOkvPZVjtDF61k3klDa40VDNu/NMsUNtr8Cqa5d7vsRaZV+0bGxtTPp+/baextbZvwat2MhhWVlbCkq6u5HiSRjHzqt3glbS9dOAgyicN8zivU4MIXrnx57BnXTmDCF5F/20v710062qQYyN3zatUKrFvOBnlrCvHfZZ6KYmfRrVaLbx39KtqQrTvZLf3YPe5KhQKHQVMCV6Nvr00vkgTglcjamxsTJ7nqVarbRuEuQya+fn51DSgjosxRsePH9+2o6OTC0qtVgsDHy9+8YuVy+V08+bNpot57vVLyyJSO1zW1b59+9pakMnlcuFC7PXr1zv6WUn2JRmWzCup+wGMy7qam5sbilr9boF9aWmpp0WKlZWVMHul1S6nsbExjY+Pd1U6cNgHHf3YnVcul3Xz5s1EMh7i6ncVNcjJwubmpkqlkjKZTOy7Cg8cOCBjjNbW1mLtI2Ct1aVLl1Sr1cLFgKT6FMRlmPtdSb2VDZTSF7xqVTaw1+BV9Ln7GbzyfV+VSkXGmNTVs4/uJu51catWq8n3fXme1/OcoFHpQNc/xvO8xMcxmUxG2Wx2W2mpKN/3wxKVR44c6cscaNQyr6y14ZyoneDV5OSkcrmcqtXqQDZG7IXFbseda/26N0Q3io5K8Kqb1zCNwatB8jwvtnJtOw37vK0duVxOk5OTstYO7ebERty1YnJysq9rGr2ei91+riYmJuR5nsrl8sjc//Eca+2euB6lEcGrEWWMua3v1ebmptbW1uR53kj0umrEGKMTJ05oZmYmrB3crqtXr8r3fR0+fFgTExMtd0QPW+ZVp1lXzoEDB8KyVW7XTCtBEISPdQsacWqUebV//34VCgWtrq6maqfL5OSkjDEqlUptT4hqtVo4aG2VfZQWExMTyufzqtVqYV3rbrjslXZ/725LBw77oGN6ejrRfhJBEOj8+fO6fv16bL3MopLYjR8NXvV7l7cLNu/fvz/2ciZJZV/dvHlT6+vrymQyuvPOO+V5niqVSmrvabVaTcViUcaYvjR7TkIvZQOl9AWvWpUNdJtJdgtetdpwMoixVjQzNG0ZftGAWq+Lg3GWb20UvIq7PGwru2U6Xb9+Xb7va2JiIswISporb+V6PA67crks3/eVy+XaCggZYwbao3PYx3mdyGQyMsbI9/2+ZGy4igeFQiF1Af5udVqWN64FzFEKXknJlQ7cK8Fod38axeBVv0oGOr1mP3fa78qJtswYRDUQJCtaknhUS5imFa/2CHMXWnfhjfa6GrWsqyjP83THHXfo3nvv7eiC4koEnjp1atv/m5UOHLbglXv/Z2dnO5poZLPZbdlX7SwKu74H4+PjiQwyG2Veucy76PfTwPO8XZuZN7K4uChrraanp4dmUmiMCcsbdhvsKBaLKhaL8jyv7Z2c3ZYOjDbyHkae5yVaOnBhYSEcnPUSjGwm7rKB7rl66THXrWq1Gr5GSQWbXfbV6upqLAsCGxsb4T3hxIkTYRajlN7SgYPauRkXa+1tZQMPHz6sbDarGzdutDW5Tlvwqt3Mq+i92uk086qfv3NaSwY6cS0OunMujvug26hTLBbDcXGcz9+OZgtVm5ub4caYY8eO9S0gGS1bNAq7r92mtImJibZfw+hCbL/71u2VxW6p/+faqJUMlDq/v0YzS3tZV+n1fbPWpip45Y6B4FV3XF/jjY2N1Iz1ehEEQbj+0e+qCW6s1GvZwG4+V5QOHF17aWNM2hC8GmHRzKvNzU2tr6+PdNZVr1yQ6o477tj2/1EIXpXL5XDXYze9zg4cOKBMJqPNzc22sq+i/a7iVqlUdP36dWUyGR05cmTb91wmVpqCV9Jzr0M7A5ggCBLpndMPs7OzMsZofX29q0mYC3rNzs62HXh2jaKjg+N2jMLAI6ndeZVKZVuGT9wTKLcLPbrYEodeesz1wn1eZ2ZmEptU53K5MDjca/ZVtM/VgQMHwtdsWIJXw1oycGVlRevr65qcnAwX/DKZTBjEaSf7qt+loVpplXk1rGUDu2nQ3U9xB6/i2ETgeV64ac6NE919tl+bcBotAltrw3PswIEDfQ9I9tpzI0066XflFAqFcIzW70W8URjndaJfwatRLBkodR68iuv61uv7Vi6XFQSBstlsKgI77hob9yayvfJ5HoW+xlEbGxthlma/37testSr1aqq1eq2UpidcO/h+vp63zduIFl75VqURgSvRlg0eOX6FY1ir6u4nD9/XtLtwSv39Z2GKXjVbdaVk8lkwqBnq+wra22ii4zRngU7F8zc7u52SzD1S3QA0ypzbWVlRbVaTYVCYehKY2Wz2XAi6xb02+X7fhhg7TR7xWVftZshFC31kYaJXrdcBkq5XI51h+O1a9fC3nju3I0z+yrJUlL9Dl4FQRAGXZMONh88eFDGGK2srHS9EOqyf2q1miYmJnT48OHwezuztdNkkDs34xItGRg97zspHZi2zCs3/umm55W7X6c5eDXqmVdxB5d2btRJojzsbhplXi0uLqpUKimXy3W1eSuuYxr2zCtrbVfBK+m5jTb9LB3Yz35radGvzQ2uNPP4+PhILd5F76/tVBmJK7O01+CVCxJ1WtosKdFslzhLeO+VzCtJAy23Gjc3f+x3yUBp+zWx0wBSNOuqm7lqLpdToVDYdu/EaCB4NTgEr0aYq0nu+g95njd0mRz91GnmlRs8pT14VSqVtLKyImNMR72udpqfn1cmk1GxWNx1YbhcLqtarSqTySSya7lRyUAnjWUDpfrNLZ/Pb+sF1oi1NuydMz8/n7peG+1w2SHLy8sdTVpcSZmJiYmOFwzdIH91dbWtwemoLGp4nhdOBuKa4KytrWl1dVWe5+nIkSPhaxvn7r8kSgY63fSY68Xq6qpqtZry+XziCwe5XC5cBHQbEjp18+ZNbWxsKJPJ3BZEccdfLBb73jOslc3NTQVBoHw+P7STBZftFu3VGP17s/6aUdESeml4j9otGzhMmVfW2tQHr6JlmXo5D+IOLkVLJA+iofXO4FW1Wg037x09enQg9/tRKRvo+l1ls9mO309331pbW+vb5zi6QWkYx9Ld6LRnU7dGsWSgpG3l/9oZP8Z1fctmsz31K0tTyUCp/vtks1kFQRDbuWitHfpy751wfY1LpdJQZ+0mvaG5lWjWVKevY7f9rqKim5cxOvq9MQvPIXg14qIDGbKudjeqPa9ciam5ubmeBnye54W7VnfLvooOUpKYMLrd6TsXAKNfS1vmldTeAGZzc1OlUkmZTGZoJ4UTExPK5/Oq1WodZUK5TK1uegbl8/m2goNOdMI57Isa0eBSr4vZQRCEmY0HDx5ULpcLP8ebm5uxBYOSHPRFe8z1o9xGv4PN7hrcTfbV+vr6tj5XO3evukXJ6OJ9Wgx7yUBpe+ZVVCeZV50uriUpCIJws0CjMq+1Wi0MHOws8SvVr13j4+NaW1vbdUNMv8dabrd4LpdL7eaGTCajXC63LUDUqejCYlyLgYVCQdlsVrVaTeVyue9lA12golaryfd9Xbt2TUEQaHp6emDXjl4bxqdFNOuq03tdNpvt631ZGv6+pt3oR6DU9/2RLBnodJLdHNf1s9d+ZWkLXknPHUtcGSe1Wk3WWmUymY76mQ+rJDYnDoLrgemykAah1+BVL5+raDZ6GjacIR5kXg3O6F/99zh3waXXVWudlg30PE/GmG0LOGkTzbqKo1zK/v37lc1mVS6Xm05Ak15kHMbMK6m9vlduIXz//v1DOzg3xoQBqHZLBxaLxTBo121ZgWj2VSujNOiYnJxUNptVtVrtub78rVu3VKlUlM/nwyzdTCYTnrtxlQ50E4ikJjJul/fNmze72sXarmKxqGKxKM/z+raIMzY2tu33a1etVguDIwcPHmx6fU5j6cBB79yMi8usaha8aifzSkpP6cBo1lWjxewbN24oCAIdOnSoYZkfY0yYfeWC5o30O3iV9qwrp9fSgdH7YFzjDWNMeL9YXFyUtVbZbLZv4xljTLhQtbi4GI5/jx49OrCNKtFF6WFevHL3hG7LWfe7dOAolIbuVD+CV24RdmJiYiRf205ewzjnEt2+d9HNRmkKXrm5XFzB6r1UMtCJ9jUe1ntHtGTgoO7B3fS9in6uesm8mpiYUCaTUaVSGfrsa9TttSzQtBnO1VG0bd++fcrn8wMrlzFMdpYNjC4oNQpOGWNSn33ldtnPzc3FMuDzPC8sPXjjxo3bBlO+74cTXLeAEbd2Mq/SGLyanJyU53nbdiNHVSoVra6ubgv+DKvZ2VkZY7SxsdHWTicX5Jqdne16kSva96rVIH+UBh3GmFhK+1UqlfB6cfTo0W3vQ9ylA5NOt5+ZmdHExIR83++6vF47XLB5bm6ur/dXtxFheXm5rcnQzj5Xu5WPdYsfaQpeuUlfJpNJTU+HbsRRNlBKT/DKjXuanfu7lQx02ikdGB1n9WMBZ9iCV91uWkjqOuzGfi5I0e+yKu6+7rL+Dh48ONB7vcsUCIIgtXOFVqI9O7q9Bs/MzMjzPBWLxb5koY3SOK9dnfZs6saolgx0BpF5FX2OThe4XenYsbGxVK3zuGDFxsZGLIv2o7TpsF1uc2KlUul5c+KguODVIDeeRXuwtatcLisIAuVyuZ6qVkU39PSrFzOS5e6v/dyYhefwio+4sbEx3XvvvWEfGjS2srKi1dVVTUxMhBkHExMTOnjw4Laa+TulOXhVLBbDYEicTapd+cFKpXLbDkpXEm9ycjKxQXQ7mVdpLBvYagDjFsJnZmaGfmdZtOzh0tLSro/1fT+cDPcStHOlA33fb1mmYtR25MZROvDatWuy1mpmZua2wHOcpQN931e1Wt1WJiVubqe9VA+MJrFQVqvVYjlvu5HP5zvKvtqtz9VOacy8ctfLqampoS7zGUfZQCl9watu+l057QSv3OK/tbYvWe5p3MXeSK+ZV+66GPd12N0/3HvV7+BV9Ofl8/mBV52IZoMN687rSqWiWq2mTCbT9fvZ7zJYozbOa0cmk1Emk5G1NpF5qe/74TxvrwevXH+q6EbWXnQbvEpjyUCpfi66gEUcG9/2YuZVXJsTB8Vt1s1kMl1n7Mahm7KBcfS7ctzngODVaEhq7Iz2ELwCtD3rKro45rKwhrHvlcs42L9/f6yDPWPMtuyr6GJSP0o77Ra8OnLkiDzP040bN1K5SNBsAOP7fhjkGfRCS1xcwHxpaWnXBcfl5WVZazU5OdnTIpcxpu0yFaO2g298fFy5XE61Wq2r+vLr6+thoLtRb5o4SwdGd/snGYgYHx/X7OysrLW6du1a7M+/tLQka62mpqYG0rC13eyrVn2udioUCvI8T9VqNTX3NXe97LakaFrEVTbQXbcGHbyKlg1sxN2r2wletcqW7udYa9gyr3oNXsV9/cpms9teu37fZ6O/z84s4kHpRzm3JPXS7yoqWjow6SzKURvntSvJc61YLMpaq/Hx8ZHtod3u6xd379xRC15J8X7e92ImpRRvX+N+c/PFQW88i3622t0AFUe/K8fNnzc3NxMtZY/+6HcvV2w3+BE9kAI7+105rfpepaVx+k6bm5taW1uT53mxZl05rhxltVoNgy7RviRJlQyUdi8bmM1mdeTIEVlrd+2hMSjuddnY2Ng2gFpeXlYQBJqYmEjlBKQbExMTYSZUs91G1tqwZGAc2Svtlg4ctUUNY8y22uidCIIgzHo4dOhQ09ckrt1/SZcMjDp8+LA8z9Pa2lq4WzgO0fPWZer2Wz6f1759+2St1cLCQsPHtNvnKsoYk6rSgdFMyiTvK0lzpRul2+9drifUwsJCW6Vh0pZ51apsYKONJk47mVdS/4JXLmjreV7qd3i7RdNardbV65LkBDz6We33BN9l/s/Pz6fmmtFtw/i0iAavejE5OalcLqdqtZro/SXakyLtn+O4JRm86rV05DBo9/4a9zxiFINXU1NTymQyKpfLXW+ycPZiJqVUf1/Hxsa63pw4SGnZeOZ5Xsf3YPe5iuNal81mNT4+vq38LobXqK0hDRuCV4Bu73flDGvmVTTrKondcdHsq5s3byoIAhWLRfm+r1wul9hiRXShvdmCmPt6Gvte5XI5FQqFbQMYa21YMnBQC+FJiPbucgv9O21ubqpcLiubzcaSrVcoFDQ2Nrat99pOo7qo4YJLq6urHZXWunXrliqVisbGxnY9/+IqHegmsP1Y0MzlcmHw/urVq7HtWlxbW1O1WlUulxvo4qj73ZaWlm57Tzrpc7VTmkoHuqCja3o8rJaXl7W5uanp6enbyi15ntfRfSstwatWmVdxlQ2M/oykx1rRrKu0l6g0xnSdfWWtTXQjwSCDV7lcTvfff39YOjYNhjnzKjpe7TV4FS2DlWTpQN/3FQSBPM8b6vtGN5I819yYYJAlwJLWbt+wuBcwoz+33TF8EASpLnPreV5sG9/2auZVv66ZcavVatrc3NzWMmGQOgle+b6vcrm8bYzVK0oHjg6CV4NF8ArQc8GpU6dObfu6+/swBa82Nja0vr4uz/MSLUE3MzOjQqGgWq2mW7dubSsZmNSiz8LCgiqVivbv3990oO52tacxeCXdPoBZW1tTpVJRLpcb+O6kuM3OzoYNexsNGF1Qa25uLpbSPu2UDozu3ktDOaG4FAqFtnt+OdVqNeyZ1Kq8Ulz169150K+SXPPz88rlciqXyy37r7UrGmwe5AJ3oVDQzMxMw+yrTvpc7ZSmzCtXdmTYr43Nsq4c9/V2Sge6Ra5yuTzQMjLtZl7tFrxyQbt2g1dJB+yGpWSg023wqlarKQgCZTKZRDY4TUxMKJvNKpfLDWSTSNoCj8OceeWyEXvpdxUVzRJPqodddINS2s6FpCW1uSEIglj7wKRVtMfibiW+4l7AjGb7tvveRTeDpXU+E0fpQGvtns28kp57DTvdnDhIbuyeZA/0TnRyD3ZZV66Mehyiaz/DVv4R2xG8Gqx03umAPhulzCuXdTU/P59oTXJjjA4fPiypHlRyA5VB9bty3Pdcuay02TmASctCeBIymUy4Y2xn4KBWq4XnjOuPFYdoBlKjAeIoZl05ne7Ou3btmoIg0MzMTFuf2+hr261+lg2U6gsCro/X9evXe643Xi6XtbGxIWNMrOdtt1z21eLiYngfiva5OnnyZMfnuluYcv0tBsVaG2ZeJXlf6Qd3P9rZ78pxX2/nvpXNZjU2NiZrbc+leHox6plXw6Db4FXS12HP83T33Xfr7rvvHrlxTTei2TDDtnAVLRUXx3tZKBRUKBQUBEFiu9D38uJSUplXpVJJ1lrl8/mR7XfltPMaJnGOdfrepblkoNNrT16pPtaw1u7JTEqpfp9O+poZt7SUDHTcWKedsVKc/a6cQqGgbDarWq020HE7ehMNpNPzajAIXgHqvedVWoJX6+vr2tjYSDzrypmamtLExMS2FOsky0m44FWz3evR76U182p8fFyZTEbValWrq6vh+5WGhfAkuNKBS0tLt/X5stZqamoq1slfoVAIJ0qNMkdGeVHDBZfW1tZa7s5bX1/XysqKjDFhcKcV13R3c3Ozq4UR3/dVrVZljOnr6z8zMxNep1xQp1su2Dw7O5uKSfT4+Limp6fD7Ktqtbqtz1U35TrSEhxxzY3HxsaG/vPqMqpaBa/aybySngswDrJ+fhyZV66025UrV3Zd1O938CrNi4FRvQavkvxc5XK5kV/kblcmk1Emk9lWtnhYxFUyMCqajZGEvVpiTEoueLUX+l057WRAEbxqT7Qnb7ef9738eXa67Ws8CL7vp27jmRsrdZJ5Fee1Llo+Mc4ezOgvV042m82mNtt11PGqAxqNzCtrbbgwe+DAgb4srEazr6T64naSF3O3KDvMmVfGmHAw5wJsaVkIT8L4+HhYzs7txLLWhiUDXXArLtHSgY0yhEY5eJXP5zU+Pt5yd561VlevXpVUD3C0+1pESwd2k30VXTDt5258Y0y4SL64uNh16Sbf98PJd9znbS+i2Veuz9Xk5GRHfa52SkPfq36Uou2XOMsGSul4f9y4p1GAolwua2FhQZlMJjw/G5mamtLMzIxKpdKuC1v9GGsFQdD3zNBeRRdkOsnoGbbfcxS413rY+l4lGbxaW1tL5DO9l0uMud/ZlQaNy17od+W0Cl4FQZBIFYdRDF5JvZe928ufZye6ObHXChJJW19fl7U2zLpLg+hna7dz0FqbSOaVRN+rUTDKa0jDguAV9rxqtaorV67IGHPbwtKhQ4eUz+e1uLjYcKdEdEFl0KVA1tfXtbm5qUwmo/n5+b793MnJyXA3SdI7bDopG5jWzCvpuWbmbgDVz/er34wx4UK/C1htbGyoUqkom80mcs7sVjpw1Ace7TRHvnXrlsrlssbGxjrO0Oyl+XK/+11FjY+Pa3Z2VtZaXbt2ravnWF5eVhAEmpiYSNViwcTEhKampsKeFJlMRidOnOgp4JOG4Eg0eDXs4iwbKG1/fwY19titbKD7jB05cqTlxox2SgdGF2STMgz9Q3bKZDLK5XKy1nYUlCd41X9JZcQkqVKpqFqtyvO8WO/b2Ww2HAcnkUmwlzM1jDGx972KLujuhcwrd940e/2igas47xWdXCOiFU/SXuY2urGum41ve/nz7ORyOU1OTspa21Pp9n5IW8lAqV7K2J0/u42VqtWqfN9XJpOJ/Xxz97zNzc1UbHpH50Z9DWkYDMfsDEjQlStXFASBjh49etvFyBgTZl812hEdbew6yCaa1lpdv35dUv+yrqJOnDihEydOJF76zi3sDXPZQGn7Yuz09PTILyDNzs7KGKONjQ2Vy+VtWVdJZFSMj48rm82qWq2GOxOdUR94tNqdV61WwwzNo0ePdjzxdlkwxWKx40W4QS+YHj58WJ7naW1treOyDdFswTQGm6NZVt30udrJBecGFbyqVCoql8vyPG8kFsviLhuYz+eVyWRUq9UGUoYs2sy+0XijnZKBTjvBK/cz+hG8SvtC4E7us9pJ6UBq9vdfJw3j0yKadRX3WC3J0oF7PVMj7kBpuVyW7/vK5XIjO3aOcudNs9cvqXlEJ++bm9sUCoWh2GzRS9m7vf55djrtazwI1trUbjxrp3RgNEgf9z0vk8mEmauUDhxOg17HAMEroGm/K2cY+l6trKyoVCopl8sNZGE1m82GAYokdZp5NehsuGaiA5g0LoTHLZPJhIPumzdvhrvGkgp2GmO2ZV9FjfoOvla7865du6YgCDQ9Pd3VxMLzvK5LB0YzGwYhl8uFJcyuXr3a0fXBBV6z2WyqdhM6ExMTOn78uE6ePNlVn6ud3IJItVodSHDETX6TLkXbL3GXDTTGDDQ7Lhq4anTfb+de7bjg1W4bTvqR5T6swatO+14NqvfgXjeMmVdJloqbmZmR53kqFouxBvSifcX26vkd97m2l/pdSa3LBvYjeNXqPjcsJQOd6Ma6TtdLkijROIz27dsXbgSNc1weBIEuXbqkq1ev9lyScGNjI+xVm7YF/nY2kCT9uaJ04HAb9Q3Qw2D4VwSAHjXrd+Wkve9VEARh1tWhQ4dGYqGvmXYyryYmJjQ3N6dKpaKFhYV+HVrHTp48qbvuuiuWheZh4EoHuh1j09PTiU5EXIBhZWUlnAT6vi/f92WMGdkeY1Lz0n4bGxtaWVnZ1gMqzudvZZBlA535+XnlcjmVy2UtLS21/e9u3bolKblswTjMzc2F702vjDHh5G1n9mI/pHXnZjestS3LBrrec4uLi20HowYZvHLjnWbX0bgzrzzPC39WUv0eXPBnWBYDnU6DV9HJd1qvZaOonZJFaZNEvyvH87xwnBZnJoHv+wqCYNs1Y6+JO3i1l/pdSa3LBia1gJnJZJTJZGStbbmmMGzBq15KhbJgXJfJZGIvt2qt1cWLF7W8vKxbt27pzJkz4XW/G9GSgWkbX7QTvEq6PKp7/1xfMAwXrkWDN7qr3ECbXFDq1KlTDb/vvp7W4NXi4qKq1ary+XyYlj+q2t3N7b7fbv+QQchms3tmF6NUn2BFd2G5YFZSJiYmwtKBblFvryzauQWh9fX18LpkrQ0Xh91Cebe6KR2Ylt3+nufpyJEjkqTr16+3tRBeqVS0tram/6e9e4+NJLvvQ/89VdUv9jTZZHPI7hkOH/PY3VEkWZY2upYQRzJswJajQDasJBZkvfxQhLUTKVAS5Cre2IFlXcSIA+cG8d0IgWFdGYJgOTb0iJSHoKzla1ux3rKk3ZnZ5ZDzIptkk+xmk/2qqnP/4J6aIodkv6q6q6u+H2CAmWGzqh/VVafO7/x+PyGE76VRg2RYwRHLspyb5zAEr7a3t1Gr1TA+Pn7q69E0revrVhCCVyf1uwK8D1659+XHWEtKGZnMK5Y9GQ71frdarZGYtGq1Wmg2m573u3Jzlw706j3h5FL74Es3pJSRy7xSGcVqwdtxfh5jnQYeRy14BfReKpSZVw/1U37xJOvr69jb23PO861WC7dv33aqdHTDXfEjiGN3dQ0+baxk27bvi5gSiQRisRgsyxrKwkDqnZSS44sAYPCKIm+UM68sy8Lm5iaAw8boYZ6Q39vbQ6VSQTKZbDuB7C4dSMEghHACVrFYzPeMMyGEE8RRg+moDDpOWuFYKpXQaDQQi8UwPT3d1/Z7KR0YpMDh+Pg4xsbGYFmW0//rLKrX1fj4eKRunocVHNnf34eU0uldN+raZV0p6uedBq9SqRSEEKjX675lI51G7c+L4JW6Xg8zeNVsNmHbNgzDGLljLhaLQdM0mKbZ0XvD4NVwaJoGwzCOTIAEmTtg4dc1O51OIxaLodVqeXad4UR3+55N3Wi1WjBNE7quR+acIYQ4s3TgsINXqtelEGKkPpNeSoUyk/KoTCbjWbnVUqmEUqnk9He/fPmyU1p9a2sLy8vLXfXSbDQaaLVa0HU9kIFu9V1R473j6vU6pJROT1k/CCFYOnBEqYVHhmHwXDREDF5R5HnV82oYfUE2NzdhWRbGxsZCX35OBaLm5uba3kirsoIMXgXL5OQkpqenO/oMvXC8dGBUglfA0dV5rVbLCdIUCgVPSot2Wzpw2P2u3NxlE1VQ7zS2bTvlBaPQn87NXTaw2xWY/QhTyUDgYR+rToNXnfa9cmdEDDrAOOiygYC/Y61RzboCDs9n6nl3spJXne+icB0MGvfkWdD5WTJQEUI4Y5VuyvieJUrjvNN00zupnUEEMYPotOCV3/cSnQSv3FlXo/SZ9FIqVL0PsVhspF6rX9zvYT/ZV5VKBWtrawAOFxCp/rKzs7NYWlpCLBZDvV7Hiy++iK2trY7OI2oxYxBLBgKH791Z369BZTMyeDWaOLYIBgavKPJGNfOq1Wo5fVjCnnUFdNcAfhTKBkaRKtk2qLr56XQauq6j2Ww6K8KAaAw8VGm/g4MD3L9/H7ZtI5PJODc9Xmxfrf7rZCIuCP2u3FKplJPBub6+furjyuUyLMtCMpkcqfIsXjAMA/F4/EhJNb9JKUMbvDqrV6P7550Gr4DhlnYEBls2UH3//LjhH+XgFdBd6UB1vg7CQoKo8boXkZ8G1edIXYfVtbZfzLw6XFSgaRps2+77PfW7B0xQnfZdVavvVX+qQe3XbRRLBiruhXWdBET4fX6UWjzYa7nVWq3mjDNnZmYeaTmRTqdx9epVTE5OQkqJ9fV1rKystF04FOSSgcpZpQMHda5Lp9NO1YRqterrvsg7DF4FA4NXFGlSyrY9r9ylfE66CRhW8KpYLEJK6ZTACjsViGo3Aeh+DDOvos1dOrBcLh9ZwRd2uq47NxDVavVItpEX3KUDO1n9F8RSVTMzM9A0DXt7eyfeQEgpnQUCuVwu9AsETqKuLYOqzV6v12GaJgzDGNlAwnF+lQ0EHk4sj3LmlTovra2tnZnhpyZs9vb2PB9v+d3nwG+dBq+klIE8F0dFJw3jg8A0TTQaDWia5vt3Ih6PI51OQ0rZdS+ck3CCCUd6i/abqTqoIGbQnJZ55ffxFfbgVTqdhmEYaDabHY0r+X1+1Llz55yFmd0uLGs2m1hdXYWUEtls1ikTeJyu67h48SLm5+eh6zr29/dx69atU8/Rqr+0ECLQlYDUWOmka/CgvleapjmVPFZXV53sVgo2nouCgcErirTd3V1Uq1WcO3fukZUnSjKZxOzsLEzTPHGF/jCCV/V63RlAzM7ODmy/w8TMK+qFu+9V1AYearIXAKanpz1/3cd7ip0lSGUDlVgs5ty4ra2tPbKCsVaroV6vQ9f1I+9llAw6s8eddRWWYKFfZQOBo59Pv+WhuqHGOydlXu3v76NcLiMejzt9Ds+SSCSQy+Vgmia2trZOfVwsFnMmuTvttdepqGResWb/cI1K5pWaTBtUWTJ1ntje3u77PBalDPuzeNH3SgUx3aVJoyKowSsp5UgHr4QQRzKH2mHm1aO6fQ8Vy7KwuroK0zSRTqdx4cKFtuf38fFxXLt2DZlMBrZt4969e7h79+4ji7nVmEyVHwyq0xaQmKaJZrM5sHPd7OwsstkspJRYXV0d2AJB6h2rFgRDR2cXIcRPCCFuCCFeEEL8ixN+/nYhxHde+vMXQogf8P6pEnnP3e/qrAv4WX2vhhG8KhaLAA5v+KJyEnX3vGqHmVekqBVqjUYjcsGrTCaDWCyGRCJx6uq6frffSelA27adG9Cgna9yuRxisRgajQa2t7eP/ExlXU1OTgb6ZsxPwwxehYWfZQOHUdoROLtsoOqj0MnEiNJp6UCv++MAh2O3Vqt1JFth1LhXE5+Vvcasq+FSx1fQM68G0e/KLZPJOOO0fibx3P2Ioj7Z7UWg1F1GK2pjoNMy1/y+jzAMA0IIWJZ1YrUX0zRhmuaR/j2jxl06sF0/VQajT9Zt+UXbtnHnzh00Gg0kEgnMz893/J02DAPz8/O4cOECNE1DuVzGrVu3jlSscPe7CrLTgleD7iMnhMDFixcxPj4O27axsrIy0DE8dY/9YoOh7VlLCKED+I8A3gTgZQDeJoR42bGH3QbwBinlKwH8BoCPev1EifzQrt+VclbfK3fwahArn/f397G3twdN0zAzM+P7/oJCZVEx84q6IYQ4MhFuGEZkbsI1TcO1a9dw5coVX15zp6UD3ROmQcumUX3YAGBjY8OZLDBN07kZ6yR7JKwSiQQ0TUOr1eq7/FA7rVYLtVot8GVHuuVn2UDgYYBxkKVHziob2E2WtKKCV+0WnIyPjzsBc68CAO6sq6Cdnzrlnsg8631h8Gq43BPi7SZth2nQwStN05zeV/0Epi3L8rUf0SjxIniljoMolKY/7rTMNb+DV+5FFCd9doOeZPdDMplEIpGAZVlte/4wGH2yVCqFWCwG0zTbjv2klHjw4AH29/dhGAYWFha6Pj8KITA1NYUrV64glUrBNE2srKxgbW0Npmk6ge6gLzxzB6/c1+Bh9PYTQmBubg6ZTAaWZWFlZSXwC1uiyr0whsGr4epkNuu1AF6QUi5LKZsAPgngLe4HSCn/QkqpRptfAdA+NYIoANr1u1LUz08KXmma5gwCvGg2fBbVOBM4LAN2WrP0MOpmQkxlpFUqFTbDpCMl36I26NA0zddgnXpvzwpeBbFkoJvqG2hZFjY2NgA8LF+UyWQid8y4CSGc0jR+Z1+prKuglx3phpSy436N09PTSCQS2NnZ6SoQNejsOODszKtu+l0pnWZe6brurOz1oj8OMPolA5VOSgcyeDVcmqadWo4sKNyl4gZZlkwFr8rlcs/3UpzofsjLzKuo9bsCHh5DpmkemeQeROmoToNXo0oI4WQOtbuOs2zgybp5Dzc3N7G7uwshBObn5/u6p0kkErh8+bKzeLpUKuHWrVuQUmJsbCzw81LuhT7u79ewvleapuHSpUtIp9NOQDDoZYWjSCUocGHM8HUyO3ARgLuGyb2X/u80vwDgC/08KaJB8SLzChhc6cBKpYJarQZd151mj1HR6QQg8HA1C8DSgXR4460mw3kD5C0VaKjX66euGAv6hKkQAoVCAcDhjVi9XndKCEbtPHsSFRzxuyZ7GEsGquMpm822zSZzX7e6yb4adN8rKeWZmVd+Bq+A7svltBPF4FWUA/LDdlrZoqAYVqm4RCKBsbEx2LZ95mKYs7DE2EP9BkktywpFoKRXQohH7u0Htfr+rOCV+n6O+meiFr7t7e2dGqxWpRPdnwU9pN7DSqVyaibv7u6usyjv0qVLnmQWCSEwMzODK1euIB6PO59f0EsGKsevwVLKoWReKZqmYX5+HqlUCq1WCysrK4Fd3BJVzLoKjk6uBCflJJ94tyiE+BEcBq/+1ik/fy+A9wKHjeqeffbZzp4lYfH3fx8AsPLud+O173gH/vojH4F+cIDHf/u38fWPfhRXfvd30Ziexr2///fxure+FV9/5hmM3buHxd//fXzrd34Hj/3bf4u9xx/H2t/9u/hbP/mT+Ms/+iNkv/UtFD73OXz3Ix/B9d/4DZRe9zps/NiP4Y0/8iN49n/9L8x88YvI/eVf4rmnn8bLP/QhrL35zdh91avwure+Ff/f5z+Pwmc/i8yNG7j5T/8pXvWBD2Dl3e/GwdwcXvO+9+Ev/+iPMPeHf4jE1hZefOopvOa978WND34Q1tgYXvGhD+GvPv7xQLwmY28P5wB88F//azz7+tef+pp+/umn8QEAj3/uc7i7sfHIa/qBX/1VvPBf/ys2nnrqsHatT6/pB/7Nv8HEG9+I+294A3TDiMzndPcnfgIHxSIqAP7q+edR/Xf/ru1r+lflMt4FIPujP4qvPf104F5TGD+nIL+mH/47fwc3vvQlHPy3/4atP/7jULymoHxOj33iEzAMA99617vwire+9ZHXdP0//2fkCgW8+NM/jezf+BuBfU2J6Wk89sEP4vlPfxqF//SfYNs2vvazPxuaz6nXY2/pYx8DPv5xGE89hRtLS769pif/2T+D/dRTuDkxgR9873tD8X167B/+Q7wBQGN8HI3p6bavKTM9jV8DUPpH/wjPfuhDHb2mbzzzDGbLZeSfeQZ/9tu/jau/9Vu+H3vp978ftUuX0HrjGx95TT/94Q/jEwAya2s4uHSpo8/pR7/wBdQAfO1rX0Njerrt5yS/+EUkvvEN7LznPfjOb/5mX69p68MfxrVf+iUsv/OduFEojOy5/Nb//J9Y/B//A7G//ms8+4//8YnniMc++1mMb23hm7/wC/jBX/zFwL+mMF5zN//0T3Hhc59D/dvfxjc+8IHAvabb//7f49qv/Rq2fuiH8Ozf/tsD/Zxiug78k3+CsR/8QfzvD3+469dUf8UrgLe/HRevX8effepTkT/2Xv3rv47tn/kZ/NkLL3T9mu68//149S/9Elb/5b/E1775zcC8pkF+Ti/7nd/B8qc+hc13vhO1qSnc/wf/AK//e38Ptz7xCXz3i1/07TWtfulLuPSFLwDf+Q6eff/7j7ymv/nUU7jxpS9h60MfQmVzM7DHXiefU/ypp2BfvQrrjW/En33qU498Ti/883+OdD6P+V/+Zfzpxz42Eq9p0OeIl/+H/4DGy1+Or/z4j+O1P/ZjR17T87/1W3j5Rz6CiTe8Afff8AaMT0z48pru/OqvApkMxt70Jjw7Asfeqz/4Qdi/8itYXl3Fy971LvzVn/wJrn7mM9CLRfz5z//80L5Pzde/HubP/Axe/opX4Fvf/CYS/+W/YOov/iKwx14Yv0+nvaaLn/888MwzyP7yL+P7Tz7Z9WtKlEpYefe7T5vmpy6IdqsWhRCvA/DrUsoff+nf/ycASCn/r2OPeyWAPwHwJinlzXY7fvLJJ+XXvva1Xp83kSde97rX4Stf+Qq+/OUv44d/+IdPfdw3vvENvOY1r8ErX/lKfPvb337k53fv3kW5XMbc3JyzKthr29vbePDgAeLxOK5duzayta57sbq6isXFRVy8eLHjFelvf/vb8YlPfAIf+9jH8M53vtPnZ0hB12w2sbW1hZmZGa7g81ilUsGdO3eQTCZx9erVR35+48YNtFotXL16NdDZDaZp4ubNm84KxkKhwMwrHL4vzz//PIQQuH79ui+r8ff29rC6unrqMTSqPvOZz+Atb3kL3vSmN+Hzn/9828e/4x3vwB/8wR/g937v9/Ce97yn4/2srq5ib2/P1zGI0mg0cOvWLcRiMTz++OOP/Pxtb3sbPvnJT+LjH/84fu7nfq6jbX7605/GT/3UT+HNb34zPvvZz7Z9/Pr6Ora2tjA5OdlVb63jbNvGc889Byklrl+/PtLlQJrNJm7evAld1/HEE088Mka0LAvPPfcchBB42cteFqkxZJCUSiWsra31fez65YUXXkC9Xsfi4uLAew/ato0bN27Asiynt0o3Hjx4gO3tbV67X/L888/DNE089thjXa8YLxaL2NzcRC6XczLTo0bd21+8eBGTk5M4ODjA8vKy7+MUNR5Kp9NYWlpy/l9de087x48aNa9x/HUqp70P9NDW1hbW19cxPj5+pIpQo9HA8vIyLMvC1NRUV5nwYbezs4P79+8779nxfw+TaZq4ffs2Go0GUqkUFhcXR3pcGhbqnmNmZsYpmUn+EUJ8XUr55Ek/62QG4qsArgkhloQQcQA/C+Azx3YwD+CPAbyjk8AVUVCMStlAdy+W2dnZkR+wdquXBvDqsd2UX6LwisfjuHDhAgNXPjirdKBt2yNTyscwDJw/fx7AYRkHv4MAo8IwDMTjcUgpzyxJ1o8wlgwEHl5/Ll261NHj1eO6vW4Nsu+VGuecdi71u2wg8LB04FnlcjrRaDQgpUQ8Hh/5CYJYLAZd12FZ1oljUXf51qiNIYPEi15EfrEsC/V6HUKIoZVPUt/tnZ2dsx98Ava8OqqfYy3K/a6U46UXB1U66rTPTZVxHBsbC8U5fGJiAkII7O/vn1gmjaW62jup/KLqnWRZFjKZTGSDz6dRiyjVmChI5VENw8Di4iJisRhqtRpWV1f7GuOSN3guCo62wSsppQngVwD8dwDPAfhDKeX3hBDvE0K876WH/SsAOQC/K4T4lhCCKVUUeM1mE2tra9A0re0kSy6XQyqVwu7uLiqVyiM/9zt4VSqVYJomUqnUyNQU9lI/wSv2vCLyl6ZpTtDh+PnRPWE6yP4Zvcrlcsjlcrhw4cLIT2Z7yc/giJQytMGru3cPW8Z2G7xSv9epQQav1ATJMINXyWQSyWQSlmU5x04vwtLvCjjsQ3FW36ug9x6MCjX5EcSeV/v7+wAOJ/GGdb2enJwEcNirpdtJO04wHdVr3yvbtofaAyYojgeRBnV8uT8393cgSJPsXtB13Rnz7e7uPvJzddwyGH26WCyGsbExSCmdxTyrq6totVpIJpO4dOlSKAKdXnL3vAriuS4Wi2FpaQmGYeDg4AB37txhAGvIOLYIjo5GplLKz0spH5NSXpFS/uZL//eMlPKZl/7+i1LKSSnlq176c2KaF1GQ3Lt3D1JKXLhwoe3ASAhxZvaVmsTxo8GiaZrY2toCAOTz+UgOQtQqdNXMvhPqsQxeEflPrf473mh91CZMNU1DoVBg1tUx6qZOTZ54qdFooNVqQdf10EzKKCoI1em1Sz2u2+BVKpWCEAL1ev3U5udeUYt0TgruSil7Cl6pjPJisdjxIiD1HT1p0qtTYQpeAWDwagSoyQ/TNAM3IaWCV8PMtkkmk0ilUrBt+5HxxFmklJzsPqbXzKt6vQ4pJRKJRKSrFQwr80rTtBMDj2ELXgEPr+Mnfdc5YdwZ91jo3r17qNVqiMViWFhYGIlFg4Pm/n41Gg1nvBSk71U8HsfS0hJ0XUe1WsXdu3fRrtVPJ6SUqNVqvi22DyMpJcfPAcIzGkWWCkItLCx09Hj1uLOCV35cDDY2NmDbNjKZTGTLN7BsIFGwnVY6UN0UcMA32vzM7HFnXYVtccagygZqmuYELvzOvjqrbGC5XMbBwQHS6XRXWXSGYWB2dhZSSqyvr3f0OypgXq1Wex57BXHSoh8MXgWfECKwpQODUipuamoKwGFPnE6ZpgkpJXRdZ9b0S3o9zlQQMyiZCMMyrOCVex9qn+6yzWG5XgGH9w66rqNerz9y3WIwujPj4+NO+cVKpQJN07CwsMD37QxqrKQWPyWTycAF+hKJhBPA2tvbcxbdd6vZbKJUKmF1dRXPPfccXnzxRdy8eRM7OzueBMTCjmOLYAnWt5RogDrtd6V0knnldfCq0Wg4dd9nZ2c93fYoYeYVUbBpmuaUNHWvoFQTpmHJbIgqVfax1Wp5nmEc1pKBwOAyr4DBlQ48q2ygyrq6ePFi14HIbksHxmIxnDt3DlLKrjI0FPdkYFjOT50Er7iKffjcZYuCwrKsIz11hmliYgKapqFWq3XcZ3FUemsOUq/Bq6AEMYdNvX+tVgtSyqEGr1SJs1gsFqpsOE3TnIUox7Oo+Z3ujGEYOHfunPPv+fn50Ixp/KKuweqYC2pAOJlMOhl05XIZDx48aBtwsiwLlUoFDx48wM2bN3Hz5k2sra1hb28Ptm3DMAzYto379+/j7t27zMJqQ42LeB4KBgavKLJGIXi1sbEBKSWy2WykByK9ZF7l83lomoZisehLOUciOkoFr9x9r7jaPxyEEM7NnZfBkYODAxwcHEAIceTmOwyklF1nXuVyOSSTSZTL5a57OamJxkFlXp20ArGXkoFKt8EroL/Sga1WC5ZlQdf10EwGHu/loLgnXnkuHr4gZl6p88Yw+10pmqY53221gK8d9V4y2+ChXnpeSSkD1wNmWDRNg67rTklK0zQhhBjIMXb8HBHGkoGKO3ilJuZt2z4zy5uOmp6eRiwWw9zcXOjG0n5Q4yC1GCvI57qxsTEsLCxACIGdnR2sr68fCWCpUoCbm5tYXl7G888/jzt37mB7exvNZtNZXHrhwgU89thjePzxx3Hx4kVomoZKpYIXXngB1Wp1iK8wuGzbdqpBqDkOGi4GryiyVldXAXQfvFK/56YGVpZleZaCW6vVUC6XIYTAzMyMJ9scVSp41U3mlWEYyOfzkFJibW3Nr6dGRC85XjrQtm3WrA8RPzJ7isUigMOgTdjKMWxubqLZbGJqaqrjG2MhRM+lA92fj5+lQDrJvBpU8Gp8fNzJ0Og2i8WddRWWcpWapjmTMu6MFffE/rADE/TwehikzKsg9Ltym5ycBHA4od1JbzBmaTwqFotBCNFVf7VGowHLshCLxfhe4mEAUH0/1HvqtygFr8bGxhCLxWCapvM+u0sGhuX67Kd0Oo3HH3+c/Xo7dHxBeNC/V+l0GvPz8xBCoFQqoVgsYmdnB3fv3sXzzz+PF198EcVi0Rn/p1IpzMzM4PLly7h+/Trm5+cxNTWFeDwOIQQmJydx9epVjI2NwTRNrKysYG1tLXB9OIetVCqh2WwiHo8jl8sN++kQGLyiCPOy55UQwtPsK3ffh1wuF+kbCCllT5lX7sezdCCR/46XDnRnXXHCdPSp4IiaROlXtVrF/v4+NE3D9PS0J9sMkm5LBiq9lg40DAPxePxIOTw/+JV5pa7X3QSv3OecbrOvwlYyUDmpdCAzYINFfQ5ByrwKWvAqlUohmUw6JZDaYebVo9xZQp0ea+x3dZR6/1RmwqDux6MUvBJCPJJFzYVv5Cf3WMi96CfIMpmMc3+wtbWF+/fvo1wuO4sNJicncenSJVy/fh1XrlzBzMwMxsbGTg3+xuNxLC0tOQvkS6USlpeXfb1/GCWtVgubm5sAgEKhwHmMgOCnQJHlZdlAwNvSgWpST9d1nD9/vu/tjbKtrS00m01MTk52PWhXk2HdrmAnot6o8h/Hg1c0+tzBq35X57kXaJw/fz6UZWG6LRmo9Jp5BTz8jNQEpB+ClHkFHC0d2E3GGYNXNCxBKxto23Zg+l25TU1NAQC2t7fbPpaT3Sfr9lhjv6uj1PunrqnDCF7Ztu2cz8MYvAIeXscrlQps2z6SeUXkNU3TnGMrlUqNTHbfxMQE5ubmnD5n+Xwe165dw2OPPYaLFy9iYmKiqyoWqrrT5cuXEY/HUa/X8eKLL6JUKvlawWEUrK+vw7ZtjI+Ph7In86hi8IoiSUrZddlA1YD8/v37JwaovApeSSmdUkrnz58PXSmlbqkJvG5Xr7t/h5lXRIORTqehaRoajYazWpoTpuGg6zoSiYQnmT2VSgX1eh2GYYS2FIPKnOo1eNVt5hXgT2lHNylloHpeAYfnHMMw0Gq1unrdYZ0MZPAq+Nzl3FQweJhUVkkqlQrUPcfExASEEDg4OGhbYpGT3SdTQZBO+l5JKZl5dYw6ntR1b1DBK13XnX5b1WoVUkrE4/FAfT+9lEgkkEqlYNs29vb2WAaUfKfGSqN2rstms3jiiSewuLiI6elpJBKJvoNvY2NjuHLlCrLZrNNuY3V11ZMF+aOoWq06rVvy+fywnw65MHhFkVQqlVCr1TA+Pu5kCrSTSCSQz+dhWdaJkyteBa92d3dRr9cRi8WcVYdR1mvJQPfvMPOKaDDcZbwYvAofNdHfT3DEvUBjZmYmtKUYBl02EPC/75Vt25BSQghx4uc2jODVSSWH2rEsC81mE0KI0E2OuYNX6hhQE/9he62jyn3cDTv7qlarOWPkoK0u1nXduUfb2dk59XFSSk52n6KbsoGtVgumaToLVejRYOggjy+1r3K5DCB8Cy2Oc1/HWQaU/JbNZpFIJNgn7CW6rmNubg6XLl2CruuoVqu4detWR2V7w0QF74DDJAKOKYIlnDMGRG102+9KOavvlRfBK9u2sbGxASDck3rdYOYV0Wg5viAgbGW5osyLzJ6dnR2nAe7k5KRXTy1whlE2MJFIQNd1mKbZ0Ur7brlLBp600nMYwSvg4aRXuVzuqKSlykoKYz8+wzCg67pTeklKycyrAFKfRbuMIj/V63WsrKw4pXGCWKZcLeLb2dk59bttmiaklNB1PXTf5351EyR1Z12NShktvx2fuBxG8Gpvbw9A+INX6t5hb2/PuUYzeEV+mZiYwLVr1zguOmZiYgJXr15FOp2GZVm4c+cOHjx40He5+FFRKpXQaDQQj8dD2Y951HGER5HUbb8r5ay+V14Er7a2ttBqtbgSxMWLzCsGr4gGJ51OHyltwlVL4eHue9UL9wKN2dnZUE+QDSPzSgjha+nAs0oG2rbdV/BqenoahmGgVCp1XZYymUwimUw6JYfaCWu/K+DwGHBnX1mWBdu2oWlaKHvLjaphZ141m02srKzAsiycO3cOc3NzgTwfp1IpJBIJWJZ16neb/a5O181xpq4Zo1ZGy09ByLxSk8Zh/1xUHx+A2cJEwxSLxbC4uIh8Pg8hBLa3t/Hiiy/2fO83KlqtlnOPWigUuBgmgPiJUCR12+9KUY9Xv++mJgV6We2syiipE6a6WBDLBhKNGk3TnPJD8Xicg78QUZkqrVarp2tdqVSCaZpIJpNOecmwGkbmFeBv3yt35tVxpVIJrVYLk5OTPa0Q1zQNhUIBAJySHd3opnRgmINXwNHSge6sK44rg0NNyg4j86rVauH27dswTRPpdBrz8/OBvU4LIZzsq+3t7RMfw35Xp3P3vGpXSlZlXqXTad+f16jQdd05bxqGMdDvyfHATVivV27HF+7yO000HEIITE9P4/Lly0gkEmg0GlheXsbm5qYvZcmDYH19HbZtI5PJBK6MMh0K5kiVyGdByrySUuL+/fvY3NwEcBjp5wnzoX7KBrozr8J6oSUKIlUOjpMg4dJPZo9lWc51LuxZV7Zt93ztmpycxNjYGCqVSk+15v0MXlWrVQAnr4buJ+tK6ad0oLvkULtxWFSDVxQc6vMYdOaVaZq4ffs2Wq0WUqlUoANXysTEBIQQ2N/fP/H9YubV6VQpRdu2ncUHJzFN0+kDGNbzYi/c/ekGfXy595dMJgP/PfXC+Pi48zpjsViox4lEoyCVSuHKlSuYmppyFtuf1YNyVO3v76NcLkMI4Syko+AJ/1WQ6ARB6XllWRZWV1exu7sLIQTm5+eRy+W6ek5h10/mVTqdRjabRbPZxNbWltdPjYhOkU6nce3aNQ4AQ0hl1XQbHNnc3IRt20in005pmLDa2NhAq9VCLpfrOgtJCNFX6cBUKgUhhFMyziumaTqZDyoTwm3YwatYLOYcV6rB/UmklE7wKqw9RNTrYvAquIZRNtA0TaysrKDZbCKZTGJxcfHEEqBBYxiGk6l70qQZM6/O1smx5u53FYUgSTfUcTXM4FVYr1XHaZrmfNf5fSYKBk3TcOHCBWcubn19fWglj/0gpXQqPkxPT3MhTIBxdEKRFITMK1W2o1qtQtd1LC0thb6MUi/6ybxy/x77XhENlioxR+HSS2ZPq9VCqVQCEP6sK6D3koFKP6UDNU1zVs57mX1VKpUgpUQmkzlxZf6wg1dAZ6UDG40GpJSIxWIjMXHfi3g8DiEEms2m06OAwatgUSXILMvqq1dup9RiuXq9jng8PjKBK0UFzHd2dh6ppMDMq7N1Erxiv6vTDSt4ZRiGM1aKSvAKOPyuCyFYuYEoYLLZLMbHx2HbdqiqGm1vb6NeryMWi+H8+fPDfjp0Bs4qUSR50fPq+Alb1cW2bdtprnqaer2O5eVl5yby8uXLvGE4QbVaRaVSQTKZdMqQdYt9r4iIvKOuVfV6ve21TlE10sfHxyNxrVMZU/0Gr3rJvAIeluv0KnhlWZYTfJyenj7xMV4Er9T1utfglSo5VKvVTu0lFPaSgcBhAFMFq9QxwIn9YHGXI/N7BbNt21hdXUWtVkMsFsPS0tKJfeuCbGxsDPF4HKZpYm9v78jPVOYVj/GTdZN5xYDBo3K5HLLZbM/3ob1yl3CMwrhJGRsbwxNPPIGZmZlhPxUichFC4MKFC9B1Hfv7+6EoH2iaJorFIoDD1i1cdBts/HQocur1OorFInRd77qk1eTkJNLpNPb29h4pSyOE6Cj7qlqtYnl52ak3rxoh0qNUttTc3FzPK/WZeUVE5B1d15FIJI6UXztLo9Fwys1FZTJCBZ36zRjuNXjldd+rnZ0d2LaNsbGxUyc3g5B55S45dFr2VRSCV8Cjr48T+8GjPpPTAq1esG0bd+7cwcHBAQzDwNLS0kiW4xJCHMm+UqSULBvYhnpf1Pt0nGVZoS+l2o9kMom5ubmhHF9zc3NYWFgI/fXqOLUgmIiCxTAMZ/40DOUD19fXYds2zp07h0wmM+ynQ20weEWR4y5D1+3KQyFEX32vdnd3sbq6Ctu2MT4+PpKrHwdJfVa99LtS1O8yeEVE5I1ugiMbGxsADstNRGUCZphlA4Gjn0+/ZT1s23Z6Rp5VTiMIwSvgaOnAk157FINX8Xicq0kDSC1c82vyR0qJe/fuOeXJFxcXRzqImc1mIYTA3t6eE4hptVqQUjplGOlR7TKvVGnRVCo1UqUkoyCRSHBClYgCZWJiIhTlAw8ODrC7uwshBAqFAgPmI4CjPIqcXvtdKb30vZJSYnNzE/fu3YOUErlcDpcuXeKNVhsq4ORF8IplA4mIvKFWZ7cLXtVqNZTLZQghIpN1BQw/88owDMTj8Y6z486yu7sL0zSRSCRw7ty5Ux8XlOBVOp2GYRhotVonHp9RyTBwB6+Y3R9MfmZeSSlx//59VCoVaJqGxcXFkQ/YGobhTOSr7CtmXbXXLnilSgZGqTQdERH1JgzlA6WUzr1GLpfjOHlEcOacIqfXfleKu+/VcScFr6SUWFtbc+qp5vN55PN5Rvc74M6S6xXLBhIReavTzCt13ZuamhrpFf/dGnbmFfDwM1ITk72QUh7Jujpr3OLFYhMVvOrnei2EOJJ95WaaJkzThKZpoZ/sZvAq+PzKvFL3Hbu7u07gKizBWnfpQCml895F6frSLXfZwJNWyKvrOPtdERFRJ0a9fODOzg7q9ToMw4jU4spRx+AVRY7KmFLl/7rVTdlAVWt+e3sbQghcunQJ09PTDFx1iJlXRETBk0gkoGkaTNM8tY9GtVpFtVqFpmlnlpsLI5Ux1W/w6u7duz2X4/Ci71WlUkGz2UQsFsPExMSpj3M3PM7n8z3vT5WW3Nvbw97eXl/bAYByuQzbtp3/V+Wxkslk6MdhhmE4Y1IGr4LJnRHjVdkdKSWKxaJz3zE/Px+qjJp0Oo1YLIZWq4VqtcrMqw5omuacC45fr23bdq4RYTpOiIjIX6NaPtB9z1IoFFgJa4Twk6LIGVTZQNM0cfv2bezt7TkrH8+a/KFHMfOKiCh4hBBnBkfUBCoATE9PR6q3o2VZfS+8mJiYQDqdRrVaRblc7mkb/fa9UuWOAbRddLOxsQHbtjEzM9PXJLIqRQIAa2trPW8nmUwimUzCtu0jQbCo9LtSMpnMke8qBYthGNB1HbZtn9ort1ubm5tOtuT8/PyZpT5HkRACk5OTAA5XTjPzqjOnlQ6s1+uQUiKRSETqOk1ERP0Z1fKBxWIRlmUhnU5jfHx82E+HusDgFUXOIIJXtVoNy8vLqNVqiMViuHz5Mssx9MCLzKupqSkkEgmUy2VUq1WvnhoRUaSdFbyqVCqo1WowDAO5XG7QT22oNjY2YJomzp8/33OQRGVqA71nDScSCei6fmZ23Fn29/dRr9eh67ozWXwaL/pdKV70vQJwYunAqAWvCoUCHn/8cWZeBVi7fkTd2NrawsbGBoDD7E3VHyps1PlIXWcAZl61c9pxxn5XRETUK8MwnHH7KJQPrNVqTpCtUCiEvgpD2DB4RZEziJ5XtVoNzWYTyWQSly9fjsxEide8CF4JIZzfZ/YVEZE3VA+V48ErKaUzgXr+/Hnouj7w5zZM/ZYMVNylA3vRLjuuHXfWVbuSGkEOXu3t7TlZLSp4FZb+P+24y4VRMKmgQqPR6Gs729vbWF9fB3A4Zg5zpYdYLOYE5tT7xsyrs50WvGK/KyIi6sf4+PhIlA+UUjr3FtPT05yfHUEMXlGkSCn7zry6ePEiNE3DgwcPHlnN7J4kOHfuHJaWlrgasEetVgvFYhGapvXVQwNg6UAiIq+pwEi9Xj/SV2h3dxeNRgOxWKxtxk4YqWBTP+Vu3b/fa/AK6L3v1cHBAfb396FpGqampto+3svglVps0m/wyjAMp2Sa6n2lJrqZiURBoY7FflYr7+7uOt+XQqEQifPu8fMS77XOpt4f932rlJL9roiIqC+jUj5wd3fXqQoStV7MYcHgFUXK5uYmGo0GJicney6nEYvFcOHCBUgpHwmGxONxZLNZTE9PY2FhIXIrzr20trYGKSXy+XzfK4fVZFiv5ZeIiOgoXdeRSCQgpXQyWmzbdnpdzc7ORrIJrrrOeJV51c91S01IqtJQnVI9c6ampjoaxwQx8wo4WjrQHbiK4nFJwdRv2cBKpeKcI2ZnZyNTpvXcuXNOQMYwDH6n2zjpOGs0GrAsC7FYjJlrRETUs6CXD7Qsy8lOz+fznKMdURzpUaT0m3WlnNb3SgiBubk55PN51lDtk7oZ73f1unsbzLwiIvLO8cye7e1tmKaJZDIZ6rJVZwlS5lUqlYIQwpmk7ES9XkelUoEQouOJ8KAGr8bHx6FpGmq1GsrlMoDo9Lui0aAyr3opG1itVp3zw/T0dKRWEgshnOA0s67aOyl4xX5XRETklSCXDywWi7AsC2NjY5G9Pw0DBq8oUvrtd6Wc1feKvOFFvyuFPa+IiLznDl5ZluX0SZqdnY3sAo4gZV5pmuYEazotHaiyriYnJzueFPbyeq2CV15crzVNw/j4OACgVCoBYPCKgsUdVOhmoufg4ACrq6uQUmJqagqzs7N+PcXAyuVyyGQymJ6eHvZTCTzDMCCEgGVZzkIG9rsiIiKvBLV84MHBAba3twEc3mNE9f40DBi8okhRmVILCwt9bUf9/vHMK/KOmrjyMvOKZQOJiLyTSqUAHN4YbG1tOavaVK+hKFKZEF4Fr/rJvAIeTkx2ErxqNpvY3d0FgK7KjwU18wp4WDpQBQYYvKIg0XUdhmFASvlIH93T1Go1rKysQEqJbDaLQqEQyckYwzCwsLDAVdQdEEI4gdJWqwUpJTOviIjIU0EqH9hqtfDgwQMsLy8DOLyv4T3AaGPwiiLF77KB5B0VaGLmFRFRMKn+QaZpOhk7US+b60fZwH5Kbxwv7XgWlZ00MTHhlDPrhJfBq0Kh4GzTi5Ij6XT6SAaZCrgSBUU3fa/q9TpWVlZg2zbGx8dx8eLFSJ9vqXPqPNhsNtFqtWCaptO7koiIyAsTExNDLR9oWRaKxSJu3rzpZFxls9lIZqiHDYNXFCkMXo0OP8oGMvOKiMg7QggnOCKlRCaTifQqbsuynEBOv9euiYkJZDIZHBwcONlQvXAHr866gTRN07nJ66YMV6PRwNbWFnRd96TnTiaTQSaTQb1e7+t1K0IIJzPDMAwYhtH3Nom8pIJX7fpeNZtNrKyswLIsnDt3DnNzcwxcUcfcQVJ31hWPISIi8pK7fKC6t/CbbdvY2trCzZs3sbm56dyXXr16FXNzc9A0hj5GHT9BihT2vBodKtDkRdnAfD4PTdNQLBY7LstCRETtuYNVUV/Vtr6+DsuyMDs768lqdi9KBxqGgXg8Dikl6vX6qY8rlUqQUuLcuXNdZSetr68DOLzO6rre8/N087p04NTUFDRNQyaT8WR7RF5S54qzMq9arRZu374N0zQxNjaG+fl5TsRQV9zBK/a7IiIiv7jLBxaLRV/LB0opsbOzg1u3bjn3YWNjY1haWsLCwgJLBYYIR70UKX70vBp0KmxUeJl5FYvFMDs7CymlM9FGRET9UwGBqampyN8geFUyUHGXDuyHCjCq1fbHWZblrIzsNnvKy5KBitfBq3g8jieeeMLT50jklXZlA03TxMrKClqtFlKpFBYWFhi4oq65e16x3xUREfnJ7/KBUkpUKhW88MILuH//PlqtFhKJBBYWFrC0tMTFGSHEkS9FRq1Ww+bmJmKxGPL5fF/bUifj/f197OzsePQMSZFSehq8Ah5OArJ0IBGRd1KpFK5fv+70KooydX1RGVP9Utvp97rVru/Vzs6Os1Kx28lMP4JX6rrvVfAKADRNY3ksCiSVeXVS2UDLsrCysoJGo+FMyniV4UjRonpe1Wo1NJtNCCEiv+CEiIj841f5wP39fSwvL+POnTtoNBqIxWKYm5vD1atXkclkON4PKQavKDLcK6K9WLHIvlf+2draQrPZxOTkpGerAtVkmAqKERGRN3Rd540Cgp95dVLfK1UjHjjsddXt5zgKmVdEQebOvHJ/P23bxurqKur1OuLxOBYXF9mzjXqmjjPTNAEcXheYwUdERH45Xj5wd3cX1WoV9XodrVYLtm13tb1arYaVlRXcvn0btVoNuq6jUCjg2rVryGazvBcNOY6AKTK86nelzM/P47vf/S5WV1fxqle9ypNt0iGvs67c22LmFRER+SGomVeJRAK6rsM0TbRaLWcSEwDK5TJM00QikeipJxSDV0T90TQNhmHANE00m00kEgkncHVwcIBYLIbFxUUnc4aoF7quQ9d1WJYFgCUDiYjIfxMTEyiXy6hUKifez2ia5lyfDMNw/n7837u7uyiXy87vTE9PI5fLMRs9Qhi8osjwqt+V4u57Rd5SFzavVq+7t8XMKyIi8oPKkPI6eNVv5pUQAmNjY9jb28PBwYETvJJSYnNzE8Bhr6teViz6sdhEBa94vaaoSCQSTvAqHo/j7t272N/fh2EYWFxcPBJwJupVPB5HrVYDAPYDISKigbh48SLi8TgajQYsy4JlWTBNE5ZlwbZt2LaNVqvVdjtCCExNTeH8+fPMRI8gfuIUGSrI5GXmlXu75B1mXhER0agJatlAAEeCV9lsFgBQqVTQbDYRi8UwMTHR03aZeUXUv3g8jv39fTQaDezu7mJvbw+6rmNxcdHpiUXUr1gs5gSvUqnUkJ8NERFFga7ryOfzj/y/lBK2bR8JZp3293g8jvPnz3MxT4QxeEWRweDV6GDmFRERjRo/ywZKKfuq5a5KRO3v7wM4mnXVS68rhcErov6pANXGxgZs24amaVhYWEAymRzyM6MwUZN+qVSKpZaIiGiohBBOWUAGpagddumkyPCj55V7u+QdPzOvGLwiIiKvmaaJBw8eQAjhWSAnk8lgYmICtVoN29vbfW0rlUpBCOGU7Njf30e9Xoeu65icnOx5u34ErwqFAgBgbW2t62bORKNITdrYtg0hBBYWFtiTiDynSgWOj48P+ZkQERERdY7BK4oM9rwaHSrA5GXmlTt4JaX0bLtEREQq0DI7O+vp6kGvSgdqmuZkcRwcHDhZV7lcDprW2+3A/v4+yuUy4vE4pqam+np+bslkElNTUzBNE1tbW55tlyioVOaVEALz8/PsR0S+yGQyuHbtGqanp4f9VIiIiIg6xuAVRYJt2543Ui8UCtB1HWtra2g0Gp5skw6p0kteZl6l02lks1k0Gg2USiXPtktEROR1yUDFXTqwX2pCvFQqYX9/H5qmIZfL9by9tbU1AIdZV/2UNDwJSwdSlCQSCczNzWFxcRGZTGbYT4dCLJFIeH6+JiIiIvITg1cUCcViEc1mE7lczrPVjIZhOMEVLyaV6CE/yga6t8fPi4iIvKQWyHiZMezeXr+ZV8DDvlfVahUAMDU11VffEz9KBirqes3gFUVFNptlxhURERER0TEMXlEkeF0yUGHpQO9Vq1WUy2WnbJCX1CQg+14REZGXRiHzyt1DRwjRV9YV4G/wiplXRERERERExOAVRYIKLs3Pz3u6XbU9Bq+848668rqshbvvFRERkVe8Lk2sqO15kXllGIbTjyubzSIWi/W1PRVY8jpLGmDwioiIiIiIiBi8oohg8Gp0qNXlXpdecm+TZQOJiMhLo1A2EAByuRySySTOnz/f97bUQhA/M6+42ISIiIiIiCi6jGE/AaJBWF1dBeBf8Eptn/rnV78r9zY5GUZERF4ahbKBwGHwqt9ygQrLBhIREREREZGfmHlFkcCeV6NjEMErZl4REZGX/Cob6M4YllJ6uu1+MXhFREREREREfmLwiiKBZQNHxyDKBjLzioiIvNJqtbC2tgYhBAqFgqfbPnfuHLLZLOr1Ora2tjzddr8YvCIiIiIiIiI/MXhFkeBX8EqtsL5z507gVkSPKpYNJCKiUbK2tgYpJQqFAmKxmOfb97p0oBeklL4Gr2ZnZyGEQLFYhGmanm+fiIiIiIiIgo/BKwq9/f19lEolxONxzMzMeLrt8fFxZLNZ1Gq1wK2IHlV+Zl7lcjkkEgns7u5if3/f8+0TEVH0qJKBfly33NtV+wmCSqWCg4MDpNNpZDIZz7cfi8UwMzMDKSWKxaLn2yciIiIiIqLgY/CKQs+ddaVp3h/y7HvlLT8zr4QQzL4iIiJPqUUXXve7UoKYeeXOuhJC+LIPdb1m6UAiIiIiIqJoYvCKQs+vkoEK+155p9VqoVgsQtM05PN5X/ahVrAHaRKQiIhGl8qI8jt4FaTMKz9LBirse0VERERERBRtDF5R6DF4NTpU35B8Pg/DMHzZBzOviIjIS1EsG6gCSn5kSSsMXhEREREREUUbg1cUequrqwD8D16p/VDv/CwZqKhtM/OKiIi8EMWygep6PYjMKy42ISIiIiIiiiYGryj0VEaU6k3lNfa88o6amPNr9bp725wMIyIiL7BsoD+YeUVERERERBRtDF5R6LFs4OgYZOYVg1dEROSFQZUNvHfvHmzb9mUf3WLwioiIiIiIiPzG4BWFHoNXo2OQmVdBKr9ERESjqdlsolgsQtM0FAoFX/YxNjaGqakpNJtNbG1t+bKPbjF4RURERERERH5j8IpCzbIs38v55PN5GIaBYrGIer3uyz6igplXREQ0Sh48eAApJQqFAgzD8G0/auFFUEoHMnhFREREREREfmPwikJtfX0dpmliZmYGqVTKl33ouh7IfhSjaBDBq3w+DyEE1tfX0Wq1fNsPERGFn8ri9WuBjKK2H4SsYSmlE1DyK9sMAM6fPw/DMFAqldBoNHzbDxEREREREQUTg1cUan6XDFRYOtAbgygbGIvFkM/nIaXE+vq6b/shIqLw8zu7WwnSIplSqYRWq4VsNouxsTHf9uMuxbi2tubbfoiIiIiIiCiYGLyiUGPwanRIKQeSeeXePksHEhFRP1Qwyc9FF+7tByF4NYiSgQpLBxIREREREUUXg1cUaqurqwAGF7xS+6PubW1todlsYnJy0teV3MDDScAglF8iIqLRFcWygYNaaAI8DF5xsQkREREREVH0MHhFoaYyoRYWFnzdj9o+M696N8jJMGZeERGRF6JYNpCZV0RERERERDQIDF5RqLFs4OhQgSS/Sy+59xGEFexERDS6WDbQXwxeERERERERRReDVxRqDF6NDhVIYuYVERGNikGVDVTBq/v378O2bV/31Q6DV0RERERERDQIDF5RqKkeVH6XDXQHr4Y9qTSqWDaQiIhGSaPRQLFYhK7ryOfzvu4rlUohl8uh1WphY2PD1321w+AVERERERERDQKDVxRalUoFu7u7SCaTmJ6e9nVf6XQauVwOjUYDm5ubvu4rrNTqdZYNJCKiUeAO4ui67vv+VHbXsK9dDF4RERERERHRIDB4RaGl+kLMz89DCOH7/lg6sD/DyrySUvq+PyIiCh81zvC7ZKCi9jPsvlcMXhEREREREdEgMHhFoTWoflcKg1f9GWTmVTqdRjabRaPRQKlU8n1/REQUPiqINIjrlns/wwxeWZaF9fV1APC9VCIATE5OIplMolKpoFqt+r4/IiIiIiIiCg4Gryi0BtXvSlH7Uful7gwy88q9H/a9IiKiXqhFF4POvBpm2cCNjQ3Yto2ZmRnE43Hf9yeEcLKv1tbWfN8fERERERERBQeDVxRazLwaHdVqFeVyGYlEAlNTUwPZJ4NXRETUjyiWDVTXzEGUDFTUvni9JiIiIiIiihYGryi0GLwaHWpCam5ubiD9ydS+gOE3viciotEUxbKBg+x3pbDvFRERERERUTQxeEWhxeDV6Bh0yUD3vriSm4iIehHFsoEMXhEREREREdGgMHhFocWeV6NDTcQNavW6e1/MvCIiol4MOvPKvejCsqyB7PM4Bq+IiIiIiIhoUBi8olAyTfNIKbpBUM3Lt7a2cHBwMJB9hgUzr4iIaJTU63Vsbm7CMAzMzs4OZJ/JZBLnz5+HaZrY2NgYyD6PY/CKiIiIiIiIBoXBKwqltbU1WJaFfD6PRCIxkH1qmhaIZuqjaNCBRve+mHlFRETdci+60HV9YPsd9jiDwSsiIiIiIiIaFAavKJQG3e9KYd+r3qgAEjOviIhoFAy6ZKCi9sfgFREREREREYUdg1cUSoPud6Ww71VvhlE2MJfLIZFIYHd3F/v7+wPbLxERjT616EJlQg2K2t+wsoaHHbySUg5sv0RERERERDRcHQWvhBA/IYS4IYR4QQjxL074uRBC/N8v/fw7QohXe/9UiTrHzKvRoibhBrmCXQjB7CsiIuqJynwaVvBqGJlXzWYTm5ub0HUdMzMzA9tvJpNBJpNBrVZDuVwe2H6JiIiIiIhouNoGr4QQOoD/COBNAF4G4G1CiJcde9ibAFx76c97Afw/Hj9Poq4weDU6Wq0WisUiNE1DPp8f6L4ZvCIiol5EsWzg2toaACCfzw+0zxfwMPuK12siIiIiIqLoMDp4zGsBvCClXAYAIcQnAbwFwPddj3kLgP9XHtby+IoQIiuEKEgp1zx/xhF069YtVCqVYT+NkfK9730PwPCCV9///vfx9a9/faD7HlUbGxuQUqJQKMAwOjkleUdNAv75n/85xsfHB7pvIiIaXd///uEweFiZVzdv3hz4OOO73/0ugMGWDFQuXLiAGzdu4Mtf/jLq9frA909ERERERNSNV7ziFYjH48N+GiOvk5niiwDcyzvvAfg/OnjMRQAMXnng/e9/P77whS8M+2mMpGH1vPrqV7+KJ598cqD7HnWD7HelqODV008/jaeffnrg+yciotE26MwrFbz61re+NbRxxrCCVwDw1FNPDXzfRERERERE3bp79+7A7xfDqJPglTjh/453S+7kMRBCvBeHZQUBoCqEuNHB/ol69upXs/3aqPjqV78KIU46lRAREQXTa1/72mE/hYH79Kc/zes1ERERERHRGQZdpWPEnZp90knw6h4A97s9B+BBD4+BlPKjAD7awT6JiIiIiIiIiIiIiIgogrQOHvNVANeEEEtCiDiAnwXwmWOP+QyAd4pDPwSgzH5XRERERERERERERERE1K22mVdSSlMI8SsA/jsAHcDvSSm/J4R430s/fwbA5wH8JIAXABwAeI9/T5mIiIiIiIiIiIiIiIjCSkj5SGsqIiIiIiIiIiIiIiIioqHopGwgERERERERERERERER0UAweEVERERERERERERERESBweAVERERERERERERERERBQaDV0RERERERERERERERBQYDF4RERERERERERERERFRYDB4RURERERERERERERERIHB4BUREREREREREREREREFBoNXREREREREREREREREFBj/PxXLKdxCKccWAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# You could use this parameter to sift out the anomalies, the less this number is, the more anomalies will be marked out.\n", + "minSeverity = 0.2\n", + "\n", + "# Only presents first 100 rows to see clearer graph.\n", + "df = df_merge.iloc[:100]\n", + "\n", + "####### Main Figure #######\n", + "plt.figure(figsize=(30,8))\n", + "plt.plot(df['timestamp'],df['series_0'], color='lightsteelblue', linestyle='solid', linewidth=2, label='series_0')\n", + "plt.plot(df['timestamp'],df['series_1'], color='powderblue', linestyle='solid', linewidth=2, label='series_1')\n", + "plt.plot(df['timestamp'],df['series_2'], color='cornflowerblue', linestyle='solid', linewidth=2, label='series_2')\n", + "plt.plot(df['timestamp'],df['series_3'], color='lightblue', linestyle='solid', linewidth=2, label='series_3')\n", + "plt.plot(df['timestamp'],df['series_4'], color='deepskyblue', linestyle='solid', linewidth=2, label='series_4')\n", + "\n", + "plt.grid(axis='y')\n", + "plt.tick_params(axis='x',which='both',bottom=False,labelbottom=False)\n", + "plt.legend()\n", + "\n", + "anoms = list(df[\"severity\"] >= minSeverity)\n", + "_, _, ymin, ymax = plt.axis()\n", + "plt.vlines(np.where(anoms), ymin=ymin , ymax=ymax , color='r', alpha=1,linewidth=3)\n", + "\n", + "plt.legend()\n", + "plt.title('A plot of the values from the variables with the detected anomalies highlighted in red.')\n", + "plt.show()\n", + "\n", + "\n", + "####### Severity Figure #######\n", + "plt.figure(figsize=(30,8))\n", + "plt.tick_params(axis='x',which='both',bottom=False,labelbottom=False)\n", + "plt.plot(df['timestamp'],df['severity'], color='black', linestyle='solid', linewidth=2, label='Severity')\n", + "plt.plot(df['timestamp'],df['score'], color='lightgrey', linestyle='solid', linewidth=2, label='Score')\n", + "plt.plot(df['timestamp'],[minSeverity]*len(df['severity']), color='red', linestyle='dotted', linewidth=1, label='minSeverity')\n", + "plt.grid(axis='y')\n", + "plt.legend()\n", + "plt.ylim((0,1))\n", + "plt.title(\"Severity and Score of the detected anomalies\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestampisAnomalyseverityscoreinterpretationseries_0series_1series_2series_3series_4
62021-01-02T09:06:00ZTrue0.2990290.513817[{'variable': 'series_1', 'contributionScore':...-0.7802822.986235-0.9574191.4075160.419483
132021-01-02T09:13:00ZTrue0.3300840.567177[{'variable': 'series_4', 'contributionScore':...-1.192829-1.049519-0.904737-0.5197643.436035
412021-01-02T09:41:00ZTrue0.3161400.543218[{'variable': 'series_3', 'contributionScore':...2.3860840.1202180.7522552.4927160.588779
452021-01-02T09:45:00ZTrue0.3465280.595433[{'variable': 'series_2', 'contributionScore':...-2.0566641.578338-2.6143830.418353-0.618969
\n", + "
" + ], + "text/plain": [ + " timestamp isAnomaly severity score \\\n", + "6 2021-01-02T09:06:00Z True 0.299029 0.513817 \n", + "13 2021-01-02T09:13:00Z True 0.330084 0.567177 \n", + "41 2021-01-02T09:41:00Z True 0.316140 0.543218 \n", + "45 2021-01-02T09:45:00Z True 0.346528 0.595433 \n", + "\n", + " interpretation series_0 series_1 \\\n", + "6 [{'variable': 'series_1', 'contributionScore':... -0.780282 2.986235 \n", + "13 [{'variable': 'series_4', 'contributionScore':... -1.192829 -1.049519 \n", + "41 [{'variable': 'series_3', 'contributionScore':... 2.386084 0.120218 \n", + "45 [{'variable': 'series_2', 'contributionScore':... -2.056664 1.578338 \n", + "\n", + " series_2 series_3 series_4 \n", + "6 -0.957419 1.407516 0.419483 \n", + "13 -0.904737 -0.519764 3.436035 \n", + "41 0.752255 2.492716 0.588779 \n", + "45 -2.614383 0.418353 -0.618969 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display only the anomalous timestamps.\n", + "df_anomaly = df[df['isAnomaly']== True]\n", + "df_anomaly" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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variablecontributionScorecorrelationChanges.changedVariables
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" + ], + "text/plain": [ + " variable contributionScore correlationChanges.changedVariables\n", + "0 series_4 0.543004 [series_3]\n", + "1 series_0 0.155167 [series_3]\n", + "2 series_1 0.119975 [series_3]\n", + "3 series_2 0.113266 [series_3]\n", + "4 series_3 0.068589 [series_3]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tkinter.font import BOLD\n", + "from turtle import color\n", + "\n", + "\n", + "anomaly_index = 1\n", + "d = df_anomaly.iloc[anomaly_index]['interpretation'][::-1]\n", + "timestamp = df_anomaly.iloc[anomaly_index]['timestamp']\n", + "correlatedVar = df_anomaly.iloc[anomaly_index]['interpretation']\n", + "\n", + "x=[item.get('variable') for item in d]\n", + "y=[item.get('contributionScore') for item in d]\n", + "Z=[item.get('correlationChanges') for item in d]\n", + "\n", + "l=[]\n", + "for i in y:\n", + " i = \"%.f%%\" % (i * 100)\n", + " l.append(i)\n", + "\n", + "plt.figure(figsize=(20,6))\n", + "\n", + "\n", + "plt.barh(x, l,color=\"brown\",alpha=1)\n", + "\n", + "for index, value in enumerate(l):\n", + " plt.text(value, index,str(value) )\n", + "\n", + "# setting label of y-axis\n", + "plt.ylabel(\"Variable\")\n", + "\n", + "# setting label of x-axis\n", + "plt.xlabel(\"contribution score\")\n", + "plt.title(\"Contribution Rank at \"+timestamp)\n", + "\n", + "plt.show()\n", + "print(\"The last column indicates the correlated variables with the first column variable, whose correlations caused the anomaly.\")\n", + "pd.json_normalize(correlatedVar)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "aa60471fcc78aee1d161dfae290e862a10214dc7d6ca1ceb34988aca5ae05936" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/ipython-notebook/SDK Sample/\360\237\206\225UVAD-SDK-Demo.ipynb" "b/ipython-notebook/SDK Sample/\360\237\206\225UVAD-SDK-Demo.ipynb" new file mode 100644 index 0000000..413a2e0 --- /dev/null +++ "b/ipython-notebook/SDK Sample/\360\237\206\225UVAD-SDK-Demo.ipynb" @@ -0,0 +1,1130 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 🆕Univariate Anomaly Detection Demo Code (SDK Version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: This sample code is using the SDK version of `3.0.0b6`, which matches with the v1.1 API released in November 2022.\n", + "\n", + "This table shows the relationship between SDK versions and supported API versions of the service:\n", + "\n", + "|SDK version|Supported API version of service |\n", + "|-------------|---------------|\n", + "|3.0.0b6 | 1.1|\n", + "|3.0.0b4, 3.0.0b5| 1.1-preview-1|\n", + "|3.0.0b3 | 1.1-preview|\n", + "|3.0.0b1, 3.0.0b2 | 1.0 |\n", + "\n", + "You could also go to this [page](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/anomalydetector/azure-ai-anomalydetector/README.md) to view more sample codes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ! pip install --upgrade azure-ai-anomalydetector" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "import json\n", + "\n", + "from datetime import datetime\n", + "from azure.ai.anomalydetector import AnomalyDetectorClient\n", + "from azure.ai.anomalydetector.models import *\n", + "from azure.core.credentials import AzureKeyCredential" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"1002\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from bokeh.plotting import figure,output_notebook, show\n", + "from bokeh.palettes import Blues4\n", + "from bokeh.models import ColumnDataSource,Slider\n", + "import datetime\n", + "from bokeh.io import push_notebook\n", + "from dateutil import parser\n", + "from ipywidgets import interact, widgets, fixed\n", + "output_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "anomaly_detector_endpoint = '[Placeholder: Your Anomaly Detector resource endpoint]'\n", + "subscription_key = '[Placeholder: Your Anomaly Detector resource access key]' \n", + "\n", + "# create an Anomaly Detector client\n", + "ad_client = AnomalyDetectorClient(anomaly_detector_endpoint,AzureKeyCredential(subscription_key))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def build_figure(result, sample_data, sensitivity):\n", + " columns = {'expectedValues': result['expectedValues'], 'isAnomaly': result['isAnomaly'], 'isNegativeAnomaly': result['isNegativeAnomaly'],\n", + " 'isPositiveAnomaly': result['isPositiveAnomaly'], 'upperMargins': result['upperMargins'], 'lowerMargins': result['lowerMargins']\n", + " , 'value': [x['value'] for x in sample_data['series']], 'timestamp': [parser.parse(x['timestamp']) for x in sample_data['series']]}\n", + " response = pd.DataFrame(data=columns)\n", + " values = response['value']\n", + " label = response['timestamp']\n", + " anomalies = []\n", + " anomaly_labels = []\n", + " index = 0\n", + " anomaly_indexes = []\n", + " p = figure(x_axis_type='datetime', title=\"Anomaly Detection Result ({0} Sensitivity)\".format(sensitivity), width=800, height=200)\n", + " for anom in response['isAnomaly']:\n", + " if anom == True and (values[index] > response.iloc[index]['expectedValues'] + response.iloc[index]['upperMargins'] or \n", + " values[index] < response.iloc[index]['expectedValues'] - response.iloc[index]['lowerMargins']):\n", + " anomalies.append(values[index])\n", + " anomaly_labels.append(label[index])\n", + " anomaly_indexes.append(index)\n", + " index = index+1\n", + " upperband = response['expectedValues'] + response['upperMargins']\n", + " lowerband = response['expectedValues'] -response['lowerMargins']\n", + " band_x = np.append(label, label[::-1])\n", + " band_y = np.append(lowerband, upperband[::-1])\n", + " boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend_label='Boundary')\n", + " p.line(label, values, legend_label='value', color=\"#2222aa\", line_width=1)\n", + " p.line(label, response['expectedValues'], legend_label='expectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n", + " anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n", + " anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n", + " p.legend.border_line_width = 1\n", + " p.legend.background_fill_alpha = 0.1\n", + " show(p, notebook_handle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Latest point anomaly detection with the Anomaly Detector SDK" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point=29):\n", + " points = sample_data[\"series\"]\n", + " granularity = sample_data[\"granularity\"]\n", + " result = {\n", + " \"expectedValues\": [None] * len(points),\n", + " \"upperMargins\": [None] * len(points),\n", + " \"lowerMargins\": [None] * len(points),\n", + " \"isNegativeAnomaly\": [False] * len(points),\n", + " \"isPositiveAnomaly\": [False] * len(points),\n", + " \"isAnomaly\": [False] * len(points)\n", + " }\n", + " anom_count = 0\n", + " \n", + " for i in range(skip_point, len(points) + 1):\n", + " series = [TimeSeriesPoint(timestamp=item[\"timestamp\"], value=item[\"value\"]) for item in points[i - 29: i]]\n", + " request = UnivariateDetectionOptions(series=series, granularity=granularity, sensitivity=sensitivity, max_anomaly_ratio=0.25)\n", + " single_point = ad_client.detect_univariate_last_point(request)\n", + " if single_point.is_anomaly == True:\n", + " anom_count += 1\n", + " result['expectedValues'][i-1] = single_point.expected_value\n", + " result['upperMargins'][i-1] = single_point.upper_margin\n", + " result['lowerMargins'][i-1] = single_point.lower_margin\n", + " result['isNegativeAnomaly'][i-1] = single_point.is_negative_anomaly\n", + " result['isPositiveAnomaly'][i-1] = single_point.is_positive_anomaly\n", + " result['isAnomaly'][i-1] = single_point.is_anomaly\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "granularity: daily\n" + ] + } + ], + "source": [ + "sample_data = json.load(open('../../sampledata/univariate/univariate_sample_daily.json'))\n", + "print(f\"granularity: {sample_data['granularity']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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Application\",\"version\":\"2.4.3\"}};\n", + " const render_items = [{\"docid\":\"135a9a76-336d-4597-9d84-8b3e201eae0d\",\"notebook_comms_target\":\"1165\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"fa736684-01b4-4e46-857b-851dbcd012d2\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1003" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 95\n", + "skip_point = 29 # skip the first 29 points due to insufficient data\n", + "results = detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1306" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 85\n", + "skip_point = 29 # skip the first 29 points due to insufficient data\n", + "results = detect_anomaly_last(sample_data, ad_client, sensitivity, skip_point)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch anomaly detection with the Anomaly Detector SDK" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def detect_anomaly_entire(sample_data, ad_client, sensitivity):\n", + " points = sample_data[\"series\"]\n", + " granularity = sample_data[\"granularity\"]\n", + " period = sample_data[\"period\"] if \"period\" in sample_data else None\n", + " series = [TimeSeriesPoint(timestamp=item[\"timestamp\"], value=item[\"value\"]) for item in points]\n", + " request = UnivariateDetectionOptions(series=series, granularity=granularity, sensitivity=sensitivity, period=period)\n", + " batch = ad_client.detect_univariate_entire_series(request)\n", + " result = {}\n", + " result['expectedValues'] = batch.expected_values\n", + " result['upperMargins'] = batch.upper_margins\n", + " result['lowerMargins'] = batch.lower_margins\n", + " result['isNegativeAnomaly'] = batch.is_negative_anomaly\n", + " result['isPositiveAnomaly'] = batch.is_positive_anomaly\n", + " result['isAnomaly'] = batch.is_anomaly\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Case 1: time series with an hourly sampling frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "granularity: hourly\n", + "period: 24\n" + ] + } + ], + "source": [ + "sample_data = json.load(open('../../sampledata/univariate/univariate_sample_hourly.json'))\n", + "print(f\"granularity: {sample_data['granularity']}\")\n", + "print(f\"period: {sample_data['period']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = 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jj6V0QgAAKBKTpXRCAAAQgZaldEIAAPjvmaV0QgAA4F6dpXRCAADIzaCldEIAALA8pKV0QgAAmKunpXRCAACAGquldEIAAGiJrqV0QgAAUPixpXRCAAA4Z7WldEIAACDWuKV0QgAACEW8pXRCAADws7+ldEIAANgiw6V0QgAAwJHGpXRCAACoAMqldEIAAJBvzaV0QgAAeN7QpXRCAABgTdSldEIAAEi816V0QgAAMCvbpXRCAAAYmt6ldEIAAAAJ4qV0QgAA6HflpXRCAADQ5uildEIAALhV7KV0QgAAoMTvpXRCAACIM/OldEIAAHCi9qV0QgAAWBH6pXRCAABAgP2ldEIAACjvAKZ0QgAAEF4EpnRCAAD4zAemdEIAAOA7C6Z0QgAAyKoOpnRCAACwGRKmdEIAAJiIFaZ0QgAAgPcYpnRCAABoZhymdEIAAFDVH6Z0QgAAOEQjpnRCAAAgsyamdEIAAAgiKqZ0QgAA8JAtpnRCAADY/zCmdEIAAMBuNKZ0QgAAqN03pnRCAACQTDumdEIAAHi7PqZ0QgAAYCpCpnRCAABImUWmdEIAADAISaZ0QgAAGHdMpnRCAAAA5k+mdEIAAOhUU6Z0QgAA0MNWpnRCAAC4MlqmdEIAAKChXaZ0QgAAiBBhpnRCAABwf2SmdEIAAFjuZ6Z0QgAAQF1rpnRCAAAozG6mdEIAABA7cqZ0QgAA+Kl1pnRCAADgGHmmdEIAAMiHfKZ0QgAAsPZ/pnRCAACYZYOmdEIAAIDUhqZ0QgAAaEOKpnRCAABQso2mdEIAADghkaZ0QgAAIJCUpnRCAAAI/5emdEIAAPBtm6Z0QgAA2NyepnRCAADAS6KmdEIAAKi6paZ0QgAAkCmppnRCAAB4mKymdEIAAGAHsKZ0QgAASHazpnRCAAAw5bamdEIAABhUuqZ0QgAAAMO9pnRCAADoMcGmdEIAANCgxKZ0QgAAuA/IpnRCAACgfsumdEIAAIjtzqZ0QgAAcFzSpnRCAABYy9WmdEIAAEA62aZ0QgAAKKncpnRCAAAQGOCmdEIAAPiG46Z0QgAA4PXmpnRCAADIZOqmdEIAALDT7aZ0QgAAmELxpnRCAACAsfSmdEIAAGgg+KZ0QgAAUI/7pnRCAAA4/v6mdEIAACBtAqd0QgAACNwFp3RCAADwSgmndEIAANi5DKd0QgAAwCgQp3RCAAColxOndEIAAJAGF6d0QgAAeHUap3RCAABg5B2ndEIAAEhTIad0QgAAMMIkp3RCAAAYMSindEIAAACgK6d0QgAA6A4vp3RCAADQfTKndEIAALjsNad0QgAAoFs5p3RCAACIyjyndEIAAHA5QKd0QgAAWKhDp3RCAABAF0endEIAACiGSqd0QgAAEPVNp3RCAAD4Y1GndEIAAODSVKd0QgAAyEFYp3RCAACwsFundEIAAJgfX6d0QgAAgI5ip3RCAABo/WWndEIAAFBsaad0QgAAONtsp3RCAAAgSnCndEIAAAi5c6d0QgAA8Cd3p3RCAADYlnqndEIAAMAFfqd0QgAAqHSBp3RCAACQ44SndEIAAHhSiKd0QgAAYMGLp3RCAABIMI+ndEIAADCfkqd0QgAAGA6Wp3RCAAAAfZmndEIAAOjrnKd0QgAA0Fqgp3RCAAC4yaOndEIAAKA4p6d0QgAAiKeqp3RCAABwFq6ndEIAAFiFsad0QgAAQPS0p3RCAAAoY7indEIAABDSu6d0QgAA+EC/p3RCAADgr8KndEIAAMgexqd0QgAAsI3Jp3RCAACY/MyndEIAAIBr0Kd0QgAAaNrTp3RCAABQSdendEIAADi42qd0QgAAICfep3RCAAAIluGndEIAAPAE5ad0QgAA2HPop3RCAADA4uundEIAAKhR76d0QgAAkMDyp3RCAAB4L/andEIAAGCe+ad0QgAASA39p3RCAAAwfACodEIAABjrA6h0QgAAAFoHqHRCAADoyAqodEIAANA3Dqh0QgAAuKYRqHRCAACgFRWodEIAAIiEGKh0QgAAcPMbqHRCAABYYh+odEIAAEDRIqh0QgAAKEAmqHRCAAAQrymodEIAAPgdLah0QgAA4IwwqHRCAADI+zOodEIAALBqN6h0QgAAmNk6qHRCAACASD6odEIAAGi3Qah0QgAAUCZFqHRCAAA4lUiodEIAACAETKh0QgAACHNPqHRCAADw4VKodEIAANhQVqh0QgAAwL9ZqHRCAACoLl2odEIAAJCdYKh0QgAAeAxkqHRCAABge2eodEIAAEjqaqh0QgAAMFluqHRCAAAYyHGodEIAAAA3dah0QgAA6KV4qHRCAADQFHyodEIAALiDf6h0QgAAoPKCqHRCAACIYYaodEIAAHDQiah0QgAAWD+NqHRCAABArpCodEIAACgdlKh0QgAAEIyXqHRCAAD4+pqodEIAAOBpnqh0QgAAyNihqHRCAACwR6WodEIAAJi2qKh0QgAAgCWsqHRCAABolK+odEIAAFADs6h0QgAAOHK2qHRCAAAg4bmodEIAAAhQvah0QgAA8L7AqHRCAADYLcSodEIAAMCcx6h0QgAAqAvLqHRCAACQes6odEIAAHjp0ah0QgAAYFjVqHRCAABIx9iodEIAADA23Kh0QgAAGKXfqHRCAAAAFOOodEIAAOiC5qh0QgAA0PHpqHRCAAC4YO2odEIAAKDP8Kh0QgAAiD70qHRCAABwrfeodEIAAFgc+6h0QgAAQIv+qHRCAAAo+gGpdEIAABBpBal0QgAA+NcIqXRCAADgRgypdEIAAMi1D6l0QgAAsCQTqXRCAACYkxapdEIAAIACGql0QgAAaHEdqXRCAABQ4CCpdEIAADhPJKl0QgAAIL4nqXRCAAAILSupdEIAAPCbLql0QgAA2AoyqXRCAADAeTWpdEIAAKjoOKl0QgAAkFc8qXRCAAB4xj+pdEIAAGA1Q6l0QgAASKRGqXRCAAAwE0qpdEIAABiCTal0QgAAAPFQqXRCAADoX1SpdEIAANDOV6l0QgAAuD1bqXRCAACgrF6pdEIAAIgbYql0QgAAcIplqXRCAABY+WipdEIAAEBobKl0QgAAKNdvqXRCAAAQRnOpdEIAAPi0dql0QgAA4CN6qXRCAADIkn2pdEIAALABgal0QgAAmHCEqXRCAACA34epdEIAAGhOi6l0QgAAUL2OqXRCAAA4LJKpdEIAACCblal0QgAACAqZqXRCAADweJypdEIAANjnn6l0QgAAwFajqXRCAACoxaapdEIAAJA0qql0QgAAeKOtqXRCAABgErGpdEIAAEiBtKl0QgAAMPC3qXRCAAAYX7updEIAAADOvql0QgAA6DzCqXRCAADQq8WpdEIAALgayal0QgAAoInMqXRCAACI+M+pdEIAAHBn06l0QgAAWNbWqXRCAABARdqpdEIAACi03al0QgAAECPhqXRCAAD4keSpdEIAAOAA6Kl0QgAAyG/rqXRCAACw3u6pdEIAAJhN8ql0QgAAgLz1qXRCAABoK/mpdEIAAFCa/Kl0QgAAOAkAqnRCAAAgeAOqdEIAAAjnBqp0QgAA8FUKqnRCAADYxA2qdEIAAMAzEap0QgAAqKIUqnRCAACQERiqdEIAAHiAG6p0QgAAYO8eqnRCAABIXiKqdEIAADDNJap0QgAAGDwpqnRCAAAAqyyqdEIAAOgZMKp0QgAA0IgzqnRCAAC49zaqdEIAAKBmOqp0QgAAiNU9qnRCAABwREGqdEIAAFizRKp0QgAAQCJIqnRCAAAokUuqdEIAABAAT6p0QgAA+G5SqnRCAADg3VWqdEIAAMhMWap0QgAAsLtcqnRCAACYKmCqdEIAAICZY6p0QgAAaAhnqnRCAABQd2qqdEIAADjmbap0QgAAIFVxqnRCAAAIxHSqdEIAAPAyeKp0QgAA2KF7qnRCAADAEH+qdEIAAKh/gqp0QgAAkO6FqnRCAAB4XYmqdEIAAGDMjKp0QgAASDuQqnRCAAAwqpOqdEIAABgZl6p0QgAAAIiaqnRCAADo9p2qdEIAANBloap0QgAAuNSkqnRCAACgQ6iqdEIAAIiyq6p0QgAAcCGvqnRCAABYkLKqdEIAAED/tap0QgAAKG65qnRCAAAQ3byqdEIAAPhLwKp0QgAA4LrDqnRCAADIKceqdEIAALCYyqp0QgAAmAfOqnRCAACAdtGqdEIAAGjl1Kp0QgAAUFTYqnRCAAA4w9uqdEIAACAy36p0QgAACKHiqnRCAADwD+aqdEIAANh+6ap0QgAAwO3sqnRCAACoXPCqdEIAAJDL86p0QgAAeDr3qnRCAABgqfqqdEIAAEgY/qp0QgAAMIcBq3RCAAAY9gSrdEIAAABlCKt0QgAA6NMLq3RCAADQQg+rdEIAALixEqt0QgAAoCAWq3RCAACIjxmrdEIAAHD+HKt0QgAAWG0gq3RCAABA3COrdEIAAChLJ6t0QgAAELoqq3RCAAD4KC6rdEIAAOCXMat0QgAAyAY1q3RCAACwdTirdEIAAJjkO6t0QgAAgFM/q3RCAABowkKrdEIAAFAxRqt0QgAAOKBJq3RCAAAgD02rdEIAAAh+UKt0QgAA8OxTq3RCAADYW1erdEIAAMDKWqt0QgAAqDleq3RCAACQqGGrdEIAAHgXZat0QgAAYIZoq3RCAABI9WurdEIAADBkb6t0QgAAGNNyq3RCAAAAQnardEIAAOiweat0QgAA0B99q3RCAAC4joCrdEIAAKD9g6t0QgAAiGyHq3RCAABw24qrdEIAAFhKjqt0QgAAQLmRq3RCAAAoKJWrdEIAABCXmKt0QgAA+AWcq3RCAADgdJ+rdEIAAMjjoqt0QgAAsFKmq3RCAACYwamrdEIAAIAwrat0QgAAaJ+wq3RCAABQDrSrdEIAADh9t6t0QgAAIOy6q3RCAAAIW76rdEIAAPDJwat0QgAA2DjFq3RCAADAp8irdEIAAKgWzKt0QgAAkIXPq3RCAAB49NKrdEIAAGBj1qt0QgAASNLZq3RCAAAwQd2rdEIAABiw4Kt0QgAAAB/kq3RCAADojeerdEIAAND86qt0QgAAuGvuq3RCAACg2vGrdEIAAIhJ9at0QgAAcLj4q3RCAABYJ/yrdEIAAECW/6t0QgAAKAUDrHRCAAAQdAasdEIAAPjiCax0QgAA4FENrHRCAADIwBCsdEIAALAvFKx0QgAAmJ4XrHRCAACADRusdEIAAGh8Hqx0QgAAUOshrHRCAAA4WiWsdEIAACDJKKx0QgAACDgsrHRCAADwpi+sdEIAANgVM6x0QgAAwIQ2rHRCAACo8zmsdEIAAJBiPax0QgAAeNFArHRCAABgQESsdEIAAEivR6x0QgAAMB5LrHRCAAAYjU6sdEIAAAD8Uax0QgAA6GpVrHRCAADQ2VisdEIAALhIXKx0QgAAoLdfrHRCAACIJmOsdEIAAHCVZqx0QgAAWARqrHRCAABAc22sdEIAACjicKx0QgAAEFF0rHRCAAD4v3esdEIAAOAue6x0QgAAyJ1+rHRCAACwDIKsdEIAAJh7hax0QgAAgOqIrHRCAABoWYysdEIAAFDIj6x0QgAAODeTrHRCAAAgppasdEIAAAgVmqx0QgAA8IOdrHRCAADY8qCsdEIAAMBhpKx0QgAAqNCnrHRCAACQP6usdEIAAHiurqx0QgAAYB2yrHRCAABIjLWsdEIAADD7uKx0QgAAGGq8rHRCAAAA2b+sdEIAAOhHw6x0QgAA0LbGrHRCAAC4JcqsdEIAAKCUzax0QgAAiAPRrHRCAABwctSsdEIAAFjh16x0QgAAQFDbrHRCAAAov96sdEIAABAu4qx0QgAA+JzlrHRCAADgC+msdEIAAMh67Kx0QgAAsOnvrHRCAACYWPOsdEIAAIDH9qx0QgAAaDb6rHRCAABQpf2sdEIAADgUAa10QgAAIIMErXRCAAAI8getdEIAAPBgC610QgAA2M8OrXRCAADAPhKtdEIAAKitFa10QgAAkBwZrXRCAAB4ixytdEIAAGD6H610QgAASGkjrXRCAAAw2CatdEIAABhHKq10QgAAALYtrXRCAADoJDGtdEIAANCTNK10QgAAuAI4rXRCAACgcTutdEIAAIjgPq10QgAAcE9CrXRCAABYvkWtdEIAAEAtSa10QgAAKJxMrXRCAAAQC1CtdEIAAPh5U610QgAA4OhWrXRCAADIV1qtdEIAALDGXa10QgAAmDVhrXRCAACApGStdEIAAGgTaK10QgAAUIJrrXRCAAA48W6tdEIAACBgcq10QgAACM91rXRCAADwPXmtdEIAANisfK10QgAAwBuArXRCAACoioOtdEIAAJD5hq10QgAAeGiKrXRCAABg142tdEIAAEhGka10QgAAMLWUrXRCAAAYJJitdEIAAACTm610QgAA6AGfrXRCAADQcKKtdEIAALjfpa10QgAAoE6prXRCAACIvaytdEIAAIi9rK10QgAAoE6prXRCAAC436WtdEIAANBwoq10QgAA6AGfrXRCAAAAk5utdEIAABgkmK10QgAAMLWUrXRCAABIRpGtdEIAAGDXja10QgAAeGiKrXRCAACQ+YatdEIAAKiKg610QgAAwBuArXRCAADYrHytdEIAAPA9ea10QgAACM91rXRCAAAgYHKtdEIAADjxbq10QgAAUIJrrXRCAABoE2itdEIAAICkZK10QgAAmDVhrXRCAACwxl2tdEIAAMhXWq10QgAA4OhWrXRCAAD4eVOtdEIAABALUK10QgAAKJxMrXRCAABALUmtdEIAAFi+Ra10QgAAcE9CrXRCAACI4D6tdEIAAKBxO610QgAAuAI4rXRCAADQkzStdEIAAOgkMa10QgAAALYtrXRCAAAYRyqtdEIAADDYJq10QgAASGkjrXRCAABg+h+tdEIAAHiLHK10QgAAkBwZrXRCAACorRWtdEIAAMA+Eq10QgAA2M8OrXRCAADwYAutdEIAAAjyB610QgAAIIMErXRCAAA4FAGtdEIAAFCl/ax0QgAAaDb6rHRCAACAx/asdEIAAJhY86x0QgAAsOnvrHRCAADIeuysdEIAAOAL6ax0QgAA+JzlrHRCAAAQLuKsdEIAACi/3qx0QgAAQFDbrHRCAABY4desdEIAAHBy1Kx0QgAAiAPRrHRCAACglM2sdEIAALglyqx0QgAA0LbGrHRCAADoR8OsdEIAAADZv6x0QgAAGGq8rHRCAAAw+7isdEIAAEiMtax0QgAAYB2yrHRCAAB4rq6sdEIAAJA/q6x0QgAAqNCnrHRCAADAYaSsdEIAANjyoKx0QgAA8IOdrHRCAAAIFZqsdEIAACCmlqx0QgAAODeTrHRCAABQyI+sdEIAAGhZjKx0QgAAgOqIrHRCAACYe4WsdEIAALAMgqx0QgAAyJ1+rHRCAADgLnusdEIAAPi/d6x0QgAAEFF0rHRCAAAo4nCsdEIAAEBzbax0QgAAWARqrHRCAABwlWasdEIAAIgmY6x0QgAAoLdfrHRCAAC4SFysdEIAANDZWKx0QgAA6GpVrHRCAAAA/FGsdEIAABiNTqx0QgAAMB5LrHRCAABIr0esdEIAAGBARKx0QgAAeNFArHRCAACQYj2sdEIAAKjzOax0QgAAwIQ2rHRCAADYFTOsdEIAAPCmL6x0QgAACDgsrHRCAAAgySisdEIAADhaJax0QgAAUOshrHRCAABofB6sdEIAAIANG6x0QgAAmJ4XrHRCAACwLxSsdEIAAMjAEKx0QgAA4FENrHRCAAD44gmsdEIAABB0Bqx0QgAAKAUDrHRCAABAlv+rdEIAAFgn/Kt0QgAAcLj4q3RCAACISfWrdEIAAKDa8at0QgAAuGvuq3RCAADQ/OqrdEIAAOiN56t0QgAAAB/kq3RCAAAYsOCrdEIAADBB3at0QgAASNLZq3RCAABgY9ardEIAAHj00qt0QgAAkIXPq3RCAACoFsyrdEIAAMCnyKt0QgAA2DjFq3RCAADwycGrdEIAAAhbvqt0QgAAIOy6q3RCAAA4fberdEIAAFAOtKt0QgAAaJ+wq3RCAACAMK2rdEIAAJjBqat0QgAAsFKmq3RCAADI46KrdEIAAOB0n6t0QgAA+AWcq3RCAAAQl5irdEIAACgolat0QgAAQLmRq3RCAABYSo6rdEIAAHDbiqt0QgAAiGyHq3RCAACg/YOrdEIAALiOgKt0QgAA0B99q3RCAADosHmrdEIAAABCdqt0QgAAGNNyq3RCAAAwZG+rdEIAAEj1a6t0QgAAYIZoq3RCAAB4F2WrdEIAAJCoYat0QgAAqDleq3RCAADAylqrdEIAANhbV6t0QgAA8OxTq3RCAAAIflCrdEIAACAPTat0QgAAOKBJq3RCAABQMUardEIAAGjCQqt0QgAAgFM/q3RCAACY5DurdEIAALB1OKt0QgAAyAY1q3RCAADglzGrdEIAAPgoLqt0QgAAELoqq3RCAAAoSyerdEIAAEDcI6t0QgAAWG0gq3RCAABw/hyrdEIAAIiPGat0QgAAoCAWq3RCAAC4sRKrdEIAANBCD6t0QgAA6NMLq3RCAAAAZQirdEIAABj2BKt0QgAAMIcBq3RCAABIGP6qdEIAAGCp+qp0QgAAeDr3qnRCAACQy/OqdEIAAKhc8Kp0QgAAwO3sqnRCAADYfumqdEIAAPAP5qp0QgAACKHiqnRCAAAgMt+qdEIAADjD26p0QgAAUFTYqnRCAABo5dSqdEIAAIB20ap0QgAAmAfOqnRCAACwmMqqdEIAAMgpx6p0QgAA4LrDqnRCAAD4S8CqdEIAABDdvKp0QgAAKG65qnRCAABA/7WqdEIAAFiQsqp0QgAAcCGvqnRCAACIsquqdEIAAKBDqKp0QgAAuNSkqnRCAADQZaGqdEIAAOj2nap0QgAAAIiaqnRCAAAYGZeqdEIAADCqk6p0QgAASDuQqnRCAABgzIyqdEIAAHhdiap0QgAAkO6FqnRCAACof4KqdEIAAMAQf6p0QgAA2KF7qnRCAADwMniqdEIAAAjEdKp0QgAAIFVxqnRCAAA45m2qdEIAAFB3aqp0QgAAaAhnqnRCAACAmWOqdEIAAJgqYKp0QgAAsLtcqnRCAADITFmqdEIAAODdVap0QgAA+G5SqnRCAAAQAE+qdEIAACiRS6p0QgAAQCJIqnRCAABYs0SqdEIAAHBEQap0QgAAiNU9qnRCAACgZjqqdEIAALj3Nqp0QgAA0IgzqnRCAADoGTCqdEIAAACrLKp0QgAAGDwpqnRCAAAwzSWqdEIAAEheIqp0QgAAYO8eqnRCAAB4gBuqdEIAAJARGKp0QgAAqKIUqnRCAADAMxGqdEIAANjEDap0QgAA8FUKqnRCAAAI5waqdEIAACB4A6p0QgAAOAkAqnRCAABQmvypdEIAAGgr+al0QgAAgLz1qXRCAACYTfKpdEIAALDe7ql0QgAAyG/rqXRCAADgAOipdEIAAPiR5Kl0QgAAECPhqXRCAAAotN2pdEIAAEBF2ql0QgAAWNbWqXRCAABwZ9OpdEIAAIj4z6l0QgAAoInMqXRCAAC4GsmpdEIAANCrxal0QgAA6DzCqXRCAAAAzr6pdEIAABhfu6l0QgAAMPC3qXRCAABIgbSpdEIAAGASsal0QgAAeKOtqXRCAACQNKqpdEIAAKjFpql0QgAAwFajqXRCAADY55+pdEIAAPB4nKl0QgAACAqZqXRCAAAgm5WpdEIAADgskql0QgAAUL2OqXRCAABoToupdEIAAIDfh6l0QgAAmHCEqXRCAACwAYGpdEIAAMiSfal0QgAA4CN6qXRCAAD4tHapdEIAABBGc6l0QgAAKNdvqXRCAABAaGypdEIAAFj5aKl0QgAAcIplqXRCAACIG2KpdEIAAKCsXql0QgAAuD1bqXRCAADQzlepdEIAAOhfVKl0QgAAAPFQqXRCAAAYgk2pdEIAADATSql0QgAASKRGqXRCAABgNUOpdEIAAHjGP6l0QgAAkFc8qXRCAACo6DipdEIAAMB5Nal0QgAA2AoyqXRCAADwmy6pdEIAAAgtK6l0QgAAIL4nqXRCAAA4TySpdEIAAFDgIKl0QgAAaHEdqXRCAACAAhqpdEIAAJiTFql0QgAAsCQTqXRCAADItQ+pdEIAAOBGDKl0QgAA+NcIqXRCAAAQaQWpdEIAACj6Aal0QgAAQIv+qHRCAABYHPuodEIAAHCt96h0QgAAiD70qHRCAACgz/CodEIAALhg7ah0QgAA0PHpqHRCAADoguaodEIAAAAU46h0QgAAGKXfqHRCAAAwNtyodEIAAEjH2Kh0QgAAYFjVqHRCAAB46dGodEIAAJB6zqh0QgAAqAvLqHRCAADAnMeodEIAANgtxKh0QgAA8L7AqHRCAAAIUL2odEIAACDhuah0QgAAOHK2qHRCAABQA7OodEIAAGiUr6h0QgAAgCWsqHRCAACYtqiodEIAALBHpah0QgAAyNihqHRCAADgaZ6odEIAAPj6mqh0QgAAEIyXqHRCAAAoHZSodEIAAECukKh0QgAAWD+NqHRCAABw0ImodEIAAIhhhqh0QgAAoPKCqHRCAAC4g3+odEIAANAUfKh0QgAA6KV4qHRCAAAAN3WodEIAABjIcah0QgAAMFluqHRCAABI6mqodEIAAGB7Z6h0QgAAeAxkqHRCAACQnWCodEIAAKguXah0QgAAwL9ZqHRCAADYUFaodEIAAPDhUqh0QgAACHNPqHRCAAAgBEyodEIAADiVSKh0QgAAUCZFqHRCAABot0GodEIAAIBIPqh0QgAAmNk6qHRCAACwajeodEIAAMj7M6h0QgAA4IwwqHRCAAD4HS2odEIAABCvKah0QgAAKEAmqHRCAABA0SKodEIAAFhiH6h0QgAAcPMbqHRCAACIhBiodEIAAKAVFah0QgAAuKYRqHRCAADQNw6odEIAAOjICqh0QgAAAFoHqHRCAAAY6wOodEIAADB8AKh0QgAASA39p3RCAABgnvmndEIAAHgv9qd0QgAAkMDyp3RCAACoUe+ndEIAAMDi66d0QgAA2HPop3RCAADwBOWndEIAAAiW4ad0QgAAICfep3RCAAA4uNqndEIAAFBJ16d0QgAAaNrTp3RCAACAa9CndEIAAJj8zKd0QgAAsI3Jp3RCAADIHsandEIAAOCvwqd0QgAA+EC/p3RCAAAQ0rundEIAAChjuKd0QgAAQPS0p3RCAABYhbGndEIAAHAWrqd0QgAAiKeqp3RCAACgOKendEIAALjJo6d0QgAA0Fqgp3RCAADo65yndEIAAAB9mad0QgAAGA6Wp3RCAAAwn5KndEIAAEgwj6d0QgAAYMGLp3RCAAB4UoindEIAAJDjhKd0QgAAqHSBp3RCAADABX6ndEIAANiWeqd0QgAA8Cd3p3RCAAAIuXOndEIAACBKcKd0QgAAONtsp3RCAABQbGmndEIAAGj9Zad0QgAAgI5ip3RCAACYH1+ndEIAALCwW6d0QgAAyEFYp3RCAADg0lSndEIAAPhjUad0QgAAEPVNp3RCAAAohkqndEIAAEAXR6d0QgAAWKhDp3RCAABwOUCndEIAAIjKPKd0QgAAoFs5p3RCAAC47DWndEIAANB9Mqd0QgAA6A4vp3RCAAAAoCundEIAABgxKKd0QgAAMMIkp3RCAABIUyGndEIAAGDkHad0QgAAeHUap3RCAACQBhendEIAAKiXE6d0QgAAwCgQp3RCAADYuQyndEIAAPBKCad0QgAACNwFp3RCAAAgbQKndEIAADj+/qZ0QgAAUI/7pnRCAABoIPimdEIAAICx9KZ0QgAAmELxpnRCAACw0+2mdEIAAMhk6qZ0QgAA4PXmpnRCAAD4huOmdEIAABAY4KZ0QgAAKKncpnRCAABAOtmmdEIAAFjL1aZ0QgAAcFzSpnRCAACI7c6mdEIAAKB+y6Z0QgAAuA/IpnRCAADQoMSmdEIAAOgxwaZ0QgAAAMO9pnRCAAAYVLqmdEIAADDltqZ0QgAASHazpnRCAABgB7CmdEIAAHiYrKZ0QgAAkCmppnRCAACouqWmdEIAAMBLoqZ0QgAA2NyepnRCAADwbZumdEIAAAj/l6Z0QgAAIJCUpnRCAAA4IZGmdEIAAFCyjaZ0QgAAaEOKpnRCAACA1IamdEIAAJhlg6Z0QgAAsPZ/pnRCAADIh3ymdEIAAOAYeaZ0QgAA+Kl1pnRCAAAQO3KmdEIAACjMbqZ0QgAAQF1rpnRCAABY7memdEIAAHB/ZKZ0QgAAiBBhpnRCAACgoV2mdEIAALgyWqZ0QgAA0MNWpnRCAADoVFOmdEIAAADmT6Z0QgAAGHdMpnRCAAAwCEmmdEIAAEiZRaZ0QgAAYCpCpnRCAAB4uz6mdEIAAJBMO6Z0QgAAqN03pnRCAADAbjSmdEIAANj/MKZ0QgAA8JAtpnRCAAAIIiqmdEIAACCzJqZ0QgAAOEQjpnRCAABQ1R+mdEIAAGhmHKZ0QgAAgPcYpnRCAACYiBWmdEIAALAZEqZ0QgAAyKoOpnRCAADgOwumdEIAAPjMB6Z0QgAAEF4EpnRCAAAo7wCmdEIAAECA/aV0QgAAWBH6pXRCAABwovaldEIAAIgz86V0QgAAoMTvpXRCAAC4VeyldEIAANDm6KV0QgAA6HflpXRCAAAACeKldEIAABia3qV0QgAAMCvbpXRCAABIvNeldEIAAGBN1KV0QgAAeN7QpXRCAACQb82ldEIAAKgAyqV0QgAAwJHGpXRCAADYIsOldEIAAPCzv6V0QgAACEW8pXRCAAAg1rildEIAADhntaV0QgAAUPixpXRCAABoia6ldEIAAIAaq6V0QgAAmKunpXRCAACwPKSldEIAAMjNoKV0QgAA4F6dpXRCAAD475mldEIAABCBlqV0QgAAKBKTpXRCAABAo4+ldEIAAFg0jKV0QgAAcMWIpXRCAACIVoWldEIAAKDngaV0QgAAuHh+pXRCAADQCXuldEIAAOiad6V0QgAAACx0pXRCAAAYvXCldEIAADBObaV0QgAASN9ppXRCAABgcGaldEIAAHgBY6V0QgAAkJJfpXRCAACoI1yldEIAAMC0WKV0QgAA2EVVpXRCAADw1lGldEIAAAhoTqV0QgAAIPlKpXRCAAA4ikeldEIAAFAbRKV0QgAAaKxApXRCAACAPT2ldEIAAJjOOaV0QgAAsF82pXRCAADI8DKldEIAAOCBL6V0QgAA+BIspXRCAAAQpCildEIAACg1JaV0QgAAQMYhpXRCAABYVx6ldEIAAHDoGqV0QgAAiHkXpXRCAACgChSldEIAALibEKV0QgAA0CwNpXRCAADovQmldEIAAABPBqV0QgAAGOACpXRCAAAwcf+kdEIAAEgC/KR0QgAAYJP4pHRCAAB4JPWkdEIAAJC18aR0QgAAqEbupHRCAADA1+qkdEIAANho56R0QgAA8PnjpHRCAAAIi+CkdEIAACAc3aR0QgAAOK3ZpHRCAABQPtakdEIAAGjP0qR0QgAAgGDPpHRCAACY8cukdEIAALCCyKR0QgAAyBPFpHRCAADgpMGkdEIAAPg1vqR0QgAAEMe6pHRCAAAoWLekdEIAAEDps6R0QgAAWHqwpHRCAABwC62kdEIAAIicqaR0QgAAoC2mpHRCAAC4vqKkdEIAANBPn6R0QgAA6OCbpHRCAAAAcpikdEIAABgDlaR0QgAAMJSRpHRCAABIJY6kdEIAAGC2iqR0QgAAeEeHpHRCAACQ2IOkdEIAAKhpgKR0QgAAwPp8pHRCAADYi3mkdEIAAPAcdqR0QgAACK5ypHRCAAAgP2+kdEIAADjQa6R0QgAAUGFopHRCAABo8mSkdEIAAICDYaR0QgAAmBRepHRCAACwpVqkdEIAAMg2V6R0QgAA4MdTpHRCAAD4WFCkdEIAABDqTKR0QgAAKHtJpHRCAABADEakdEIAAFidQqR0QgAAcC4/pHRCAACIvzukdEIAAKBQOKR0QgAAuOE0pHRCAADQcjGkdEIAAOgDLqR0QgAAAJUqpHRCAAAYJiekdEIAADC3I6R0QgAASEggpHRCAABg2RykdEIAAHhqGaR0QgAAkPsVpHRCAACojBKkdEIAAMAdD6R0QgAA2K4LpHRCAADwPwikdEIAAAjRBKR0QgAAIGIBpHRCAAA48/2jdEIAAFCE+qN0QgAAaBX3o3RCAACApvOjdEIAAJg38KN0QgAAsMjso3RCAADIWemjdEIAAODq5aN0QgAA+Hvio3RCAAAQDd+jdEIAACie26N0QgAAQC/Yo3RCAABYwNSjdEIAAHBR0aN0QgAAiOLNo3RCAACgc8qjdEIAALgEx6N0QgAA0JXDo3RCAADoJsCjdEIAAAC4vKN0QgAAGEm5o3RCAAAw2rWjdEIAAEhrsqN0QgAAYPyuo3RCAAB4jaujdEIAAJAeqKN0QgAAqK+ko3RCAADAQKGjdEIAANjRnaN0QgAA8GKao3RCAAAI9JajdEIAACCFk6N0QgAAOBaQo3RCAABQp4yjdEIAAGg4iaN0QgAAgMmFo3RCAACYWoKjdEIAALDrfqN0QgAAyHx7o3RCAADgDXijdEIAAPiedKN0QgAAEDBxo3RCAAAowW2jdEIAAEBSaqN0QgAAWONmo3RCAABwdGOjdEIAAIgFYKN0QgAAoJZco3RCAAC4J1mjdEIAANC4VaN0QgAA6ElSo3RCAAAA206jdEIAABhsS6N0QgAAMP1Ho3RCAABIjkSjdEIAAGAfQaN0QgAAeLA9o3RCAACQQTqjdEIAAKjSNqN0QgAAwGMzo3RCAADY9C+jdEIAAPCFLKN0QgAACBcpo3RCAAAgqCWjdEIAADg5IqN0QgAAUMoeo3RCAABoWxujdEIAAIDsF6N0QgAAmH0Uo3RCAACwDhGjdEIAAMifDaN0QgAA4DAKo3RCAAD4wQajdEIAABBTA6N0QgAAKOT/onRCAABAdfyidEIAAFgG+aJ0QgAAcJf1onRCAACIKPKidEIAAKC57qJ0QgAAuErronRCAADQ2+eidEIAAOhs5KJ0QgAAAP7gonRCAAAYj92idEIAADAg2qJ0QgAASLHWonRCAABgQtOidEIAAHjTz6J0QgAAkGTMonRCAACo9ciidEIAAMCGxaJ0QgAA2BfConRCAADwqL6idEIAAAg6u6J0QgAAIMu3onRCAAA4XLSidEIAAFDtsKJ0QgAAaH6tonRCAACAD6qidEIAAJigpqJ0QgAAsDGjonRCAADIwp+idEIAAOBTnKJ0QgAA+OSYonRCAAAQdpWidEIAACgHkqJ0QgAAQJiOonRCAABYKYuidEIAAHC6h6J0QgAAiEuEonRCAACg3ICidEIAALhtfaJ0QgAA0P55onRCAADoj3aidEIAAAAhc6J0QgAAGLJvonRCAAAwQ2yidEIAAEjUaKJ0Qg==\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1682]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1682]}},\"selected\":{\"id\":\"1684\"},\"selection_policy\":{\"id\":\"1683\"}},\"id\":\"1670\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"1662\"}},\"id\":\"1658\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1684\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1656\",\"type\":\"PanTool\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"#bdd7e7\",\"hatch_alpha\":0.2,\"hatch_color\":\"#bdd7e7\",\"line_alpha\":0.2,\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1673\",\"type\":\"Patch\"},{\"attributes\":{},\"id\":\"1657\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"fill_alpha\":0.5,\"fill_color\":\"#bdd7e7\",\"hatch_color\":\"#bdd7e7\",\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1671\",\"type\":\"Patch\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"1686\"},{\"id\":\"1687\"},{\"id\":\"1688\"},{\"id\":\"1689\"},{\"id\":\"1690\"},{\"id\":\"1691\"},{\"id\":\"1692\"},{\"id\":\"1693\"},{\"id\":\"1694\"},{\"id\":\"1695\"},{\"id\":\"1696\"},{\"id\":\"1697\"}]},\"id\":\"1649\",\"type\":\"DatetimeTicker\"},{\"attributes\":{},\"id\":\"1653\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1678\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1682\",\"type\":\"AllLabels\"},{\"attributes\":{\"axis\":{\"id\":\"1652\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null},\"id\":\"1655\",\"type\":\"Grid\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"1678\"},\"group\":null,\"major_label_policy\":{\"id\":\"1679\"},\"ticker\":{\"id\":\"1653\"}},\"id\":\"1652\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1661\",\"type\":\"HelpTool\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#bdd7e7\",\"hatch_alpha\":0.1,\"hatch_color\":\"#bdd7e7\",\"line_alpha\":0.1,\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1672\",\"type\":\"Patch\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1764\"},\"glyph\":{\"id\":\"1766\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1768\"},\"nonselection_glyph\":{\"id\":\"1767\"},\"view\":{\"id\":\"1770\"}},\"id\":\"1769\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1700\"}},\"id\":\"1705\",\"type\":\"CDSView\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"olivedrab\",\"line_dash\":[2,4,6,4],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1734\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1749\",\"type\":\"Selection\"}],\"root_ids\":[\"1637\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n", + " const render_items = [{\"docid\":\"4c721c96-7755-4932-8894-6c902d17ac58\",\"notebook_comms_target\":\"1855\",\"root_ids\":[\"1637\"],\"roots\":{\"1637\":\"2c3d56a2-29e9-43ab-9714-f6dd944c7db4\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1637" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 95\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = 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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[841]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[841]}},\"selected\":{\"id\":\"2108\"},\"selection_policy\":{\"id\":\"2107\"}},\"id\":\"2090\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"2090\"}},\"id\":\"2095\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"2037\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"2228\",\"type\":\"Selection\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#bdd7e7\",\"hatch_alpha\":0.1,\"hatch_color\":\"#bdd7e7\",\"line_alpha\":0.1,\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2031\",\"type\":\"Patch\"},{\"attributes\":{\"line_color\":\"olivedrab\",\"line_dash\":[2,4,6,4],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2091\",\"type\":\"Line\"}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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1682]},\"y\":{\"__ndarray__\":\"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+ "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 90\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1682]},\"y\":{\"__ndarray__\":\"BifA25/0ukC98gYb/FK6QJu5fpelEbtA0aWXzUKMu0AdqaBRZ2u5QNzdbsIsV7lARfa8RYhXuUCGJ+rzOay8QOO1r26//7xAPttL/tgkukCiX6O5eK+6QMEU7vVGtrpAhH3bsSWgvUBvhN5SA7K9QCo2vN3mOr5AyNlpzi7Ru0BMSKhDTn2+QCTILPn/GLtAz3Q7G0UQu0BievhpJ1S8QDwlt6n+3LtAEDOxfr7NvECnR/PfXAi9QESFPtxfzbxAoHbdvz72v0COGdv1lwPAQHKopBuUEMBAGNWlG6I4wEBchi6U/6zAQFUhqoBBzb5Ayg3Ocm6CvkAAKXh/jNO+QF5EyhlsHL9AGQUhl3dKv0BNNrBd9de/QKYJLnSMnsFAUJc5015/wUCKhnTT9nLBQOyMV8JHwMFAGJpKwxe9wUC4p7Fs7mi9QI59VUpD98FAWI1fNC/LvUAgCIYrhyzCQDsIcFCdtcJATYcXwC8ewUBwV80izPHAQOdKokHSLMFAgpSEJWV2wUC74z7Lkz/DQJe6VHmaXMNAvqQm6H4ZwkAa7EGD7UbCQPa6QeXKd8JAKBBtx7gAxEDxie+q7TDEQEzf0KVp+8JA4ApvfYF/xEA2RtFpySPDQFRCqmCRosRAMD3rLfKWxEBbXByzJN/EQAdDGWQRD8VAYeQcn5adw0C1b2ErNIXDQA3TBHO5scNAEDdfSGTbw0DxNNyprT/FQHIUPYVIccVAZpsKRtFXxUD07Zbf2ITFQNScIE63ssVAS3WboqGqxUCexJ3j5R/EQL64jbuOQMRAjctCkli4xUBsqMpWB+rFQDSsWxcIx8VAeOletoDpxUCSyDr0FejFQG5Rc48+D8ZAzoT4hghBxEDKD3wb7kjEQPxTvp3FJsRAj759pSFjxECPy8FS8kHEQJnYYkKJD8ZAOUXz9IXyxUDWtslMFHLEQK4dIzYe98VAGpHcq4S/xUAexPXqXo7FQF/NsWUnRMJACWr3/x0swkAIEyl6cDHCQMrmbotxisVAysLnCKydxUASil3tK0HEQNiUREfH7MNAcdP/gJimw0Dw4JzoSr3DQA3Y7JZMTcRA2shqso1XxEA/xW/syV7EQGdFBrYNWsRAp2+1/DTZw0BxAfXSKCfEQK1YuojdIcRARNLGdL5DxEBYl0Sicd3FQLJNcbrI/8VAPD+tdfiuxUD4/GBiJ7jFQJhC35WIzMVAwGVPo2PFxUCdC7p/cjbEQAE4GZQz38VAnRK/xr/ZxUAWK3QpRM3FQIDJgQmEy8VA+wgo4BA4xEAeVXzd7pPFQNcFxQYppsVAq0M5t7OYxUDDhrZf2bDFQKvODH6ErMVAsvT8onwpxEAtiGXCF5DFQCv3Z7N7FcRA8Z5g/Z6ww0A7FufZgV/DQJmxX9BoFMNAC9yAKwI6w0CRehpxBjnDQK+hFhJ94sNAwS91PqQyxUDzNeuyog/FQLIhA34Z8cRAVi4ZAJXrxECQHTfgXOTEQObnnYEl88RAn52SWY49w0C6avZ0gnTDQGLrYp1TXMNAU8dfQAAVw0Bp/r6HGUTDQAzyg8ppQ8NAyv2SOTwXw0BL3vR1607DQHtQCvUoKsNAP02McbMOw0AyYrE2QE3DQJk5Z9BlecNADbmPZ9Q7w0BnULRolmPDQFFYMq1yXsNAntBrA6gKw0Ama9nM5d/EQHlgsSh79cRAeiAtz+rqxEDe10cIp+HEQGOJfN7dfsNA6drYPSd2w0AgLE+Nq+XEQKsAwE2iD8VAnWc6c+r+xEAboR8efIPDQPqf9fgxFcVA/fzxCLEFxUDzxWOyZwHFQK0W4C5MicNACWC9NuUFw0AxCmV+SDzDQKANiJ8HO8NAjPRc2EFiw0BFY+egJFvFQDeW9pxhaMVAQv/eCYhWxUCOj15pPVTFQAkMw8FqDMRACnF1cvnSw0ARUtFs7urDQPJ+bXp+/cNAk6OURu6fxUDN1H28IozFQPly9io7V8RAowh/OH0NxECfignEF83DQCCqDKMRFsRACwfbP7bRw0DqZ6jRJ2vEQKjgHjGMfsRA/OYl8506xEApWOUjTuHEQMT657D7zMRAW2LVVQHuxECeoCiv7NzGQFf3S1/GVcVAaX/HM1UYx0A4PYOnhELHQGPzw+kgZMdADeu2XJ2Rx0CBvNA12iHGQFtKB86QS8ZAU3Ppj8sVxkBCzoBHXUfGQFux4jTyucZAE7r6vxzgxkDzFfyqVX7IQEc+p0AMochADDHuoySQyEBKFLXTUKTIQHZA9fyW1shAoZvUlHzKyEAZN2uRX//IQPRfUEYWMslASAJ6kIxZyUB5WCwzUa7HQJGDeAzZqMdAmExAw7T1x0DOrAaYc0vIQLtMzeyoXchAWVQco1Z9yEDk0JPT543IQDzdIfb7m8hAAS9kIqiayECIudFNp6bIQANMqLBX/MhAWiESM6wZyUBWfPvXlFPJQPV7J8wyHstAEQt+huB4yUBO4Qss+TzLQEqftjMQbstAPtk88zZty0ArZoOmKvDJQAGx5QRPzMtA6imFKKDSy0D6sNneO2XKQKIESZmRGsxALFtHetWDykD8EIalq5HKQNEvRKV+i8xAyjrUlaWrykBLLOd0NCzLQMl+XcdoD8tAK3Hp4fKDy0ChjpR24UbNQKQ916KqW81A2HgFAj5RzUD7sFEQWo7NQIT9sJOcrs1A09xrXHa8zUCbk++7JNvNQPLDu4EU4s1A3v0VCmTZzUCzeptj49DNQIrYX9QVEc5AiOOzkWjvzUD6pYu3SiLOQDiIPVNDS8xAJSGrjhA4zEC8FP6a8yXMQHCZ5UjIVcxAQP3plfeuzECuQQ0sc1fOQAvix4FGyMxAtmsISkijzECjZCobRfzMQC/VALWq/sxAo5WbbRK4zkDUVVA9l6nOQGCjuZPb285AbsSDTYXozkCVcW60e+HOQBZUIW2yPs1AMz5sEUQkzUBn4RwQsWTNQGjacLkAEs1A+OvmISlhzUCAoomSvVXNQDFevxefOM1ARzL6290gz0DjuIi4UCvPQKRdAFSNJ89ACs9KDSoiz0DQ0kCj6D7PQH3ZplidLc9ApZjMoCsrz0BfQbBDMwLPQAnRy4xT8c5AQ3Q7Ab/5zkAEfH62OizPQLYmskrSPc9AYSQ77PSMzUBQS3aODmvNQLkvInrUg81AKlNx8/s5z0C4OZ0tyyXPQIeU4BOSIs9ALnUenynXzEAtYKkQbZ/MQPh5JfKr98xAETCEgEcSzUApb6oLBrbOQFQ3T8aSss5AeI48ruahzkCFSvkF7JjOQJbCjJV8c8pAinYQXoNTykDRNoAG5lDKQABdkZ3kR8pA826sDvUqykCAX5mRsHzMQEnC3vCjMMxA4/V8kKMxzkAzd1iB+T3OQLFHICTBNs5ADV2z/AEGzkAUdTCirvzNQMRWlMNbF85AXN9BSaQczkCmVSBTJx7MQGHvPNcooMtAat9bySGQy0DejEknS8jLQDKPhDmTyctAK5qxVNLjy0C3fh4IqQbMQCgEfUAZq8tAKLPg9beIzUBjqWViBGXNQAD6TTvp98tAcXSDQUJ7zUDavRib+rjLQBaen4ziectAHwPsSAqUy0CoJb6GTpTLQGEeLTmoyctAVx07QmZ3zUCnMueO1JnNQAcT6e93js1AmwxklI2izUAZUOZs5r7LQKEhg4eZ7MtAOmkZnpPey0Bpcr+qEd3LQJVF4mMRqM1A//mC7zLwy0D/Bpma4afNQKR6OfeNtc1A69/LbuOrzUCpidY1mY7NQL7F5J9ArM1AmT0Vq1HQy0DAhP3YSpLLQB9sj17tnMtAzJwYQTqAy0B+BXS3T+DLQDIUXbLjDMxA9ebZYqAUzEBZKuZ4ULzNQHPDFs6OFMxAULBqmprBy0DrnnhLsd3LQF5a+m5D/8tANtc6DTlgzEDnYpZ/OifOQHW9ZpNPQM5AMHHNnJ9IzkC/TWMKe2TOQG9/eC95f85AhaHgo5BnzkDQRYPhCb3MQOXvcWFR2sxA44mVWksSzUD5weXBYuvMQJgxOMvgOM1Ai/VvNQ8rzUAqTjYpGMHMQP2YUUixHM1AwhGclf0XzUDxyjNvqFfNQM8dgTyxa89AtRbl/f5kz0BK6JlYo4PPQO6BLatjmc9ARxDhV1G8z0Bs7YzTfxzOQB3MIl/3ZM5AqD/v7OduzkCCIYior2fOQHpuhfrqf85AEN7mmkCizkAUhVQlWsDOQEGp4nWFiNBAq9oE2UmS0EDm/1c8QavQQH0Er7oNvs9AUbQZUJbhz0DhrERTFdTQQMhZEZra7NBAK7hs0hPw0EDKZFyC8g/RQJpSsdWAMtFA3zElHohK0ECF+J5X5WLQQHRzoSFEWdFALi90hkZl0UDiD1ZUaW7RQGaFm0G/l9BA5XDGA46R0UAT+0t0VafRQPMsJNzVqdFAG3KFSe+p0UDr5ShBtLbRQIcexen93tFA1mcXb0YD0kA4CmJaFvXQQJU4keaT39BAOd8XI8/+0EB5QgXBw+/QQDzFDJpSOdFAInCimEw90UDzL/i7+krRQEdnSc4qcdJAooD3E3J50UBtKp1MTXzRQAMwStt5ptFAHHXKttSo0kA2u4xT27DSQD2s5wxRudJAAXMFM47X0kA3t7jjOOfSQG7khAzp+tJAX88aso4Y0kByqGZ0Py7TQG1tHUn6ONNADe58Bg0400B5SIpuOk7TQBUQkmPIV9NAuU1Bp/Na00AGnyGYRWPTQI2blq49ZNNA+Ww1a89k00CgNx3hnX3TQB7sVO6zlNNAAJ4ICJ+X0kA7kxlf14XSQAcOk9aQeNJAUPtrrGtH0kBPuZqAiUDSQADAz65LidJAqif/rkqL0kAI0CZpK6nSQCQdrBYEvdJAkLxw8Fyt0kBkWm3MULLTQLmlW3Nwr9NA3nFSd+qr00CC5zaZwcXTQCD11JsqzNNAStDmVSPi00DGq63UD97TQDDZTYbr5dNAj6so+7rj00C8PEGC3tbTQK7HdNrX9NNAnLPm6yX10kC2RrAbssDSQPLDA9gMn9JA9SEOMhTT0kAyeVk8/crSQPrtS1zG4tJAisVknB/m00D/7IM0ON/TQFrjrANz7dJAlmk2hRXr0kARTEBL3urTQCmvxvNf7NJACnWwDbjh00BREDoYn9fTQAMwe9lm5tNAkh6miv7Z00DvUeUxAfHTQLdZWu7u89NA5GFIxWDk00C2FWVBH+LSQIIBJJse6dJAIlH7cg3u00D7U10TSdXSQLJwMrvb59NA9f9gWG3P00CodgPX69HTQPGIPWHxxNNAdApQGZGg00B70zqJ5ZLTQAVbNlwzp9NAt78R5Mqe00D0SDMNK6nSQCSBIlN+d9JApkwLGfWT0kA4+NG6pITSQHMNIXitVtJAnfau9E2B0kBBhEJ4nmDSQAZZ8SNzY9JArY7s4a5k0kC/mZatVnrSQKMvW8F5YdJAhvlI8sRz0kCmf2DklnPSQINUHER0bNNAZ39Ba3tg00CCce+TOkfTQPf8+c+0P9NA09x0+fhS00DaQ5AdT1PTQIPz4heJZ9JA8mS3snlO0kD6yFMk9UDSQLsuLbd9StNAtcuWjVA500AtVxQ6ISjSQPmxE0jpM9NAnpOeXjQ00kBPWA7BoDzSQFCWO46nJ9NAFUF1E/v70UAuXtgTWvzRQNsnJUSvB9JAyg02tLwZ0kAav8jrBhPTQJ5JEoN/C9NAqzY1Z/oH00D5jDCsiQvTQAD/cBJQENNAqywFQ8Mh0kCbj23CLPbRQIZHzK/72dFAJBbI5J3P0UBuVRV0ZtHRQLZJgAeb7NFAFDBKr1jC0ED8jrVn9bzQQIr2FkkyydBAfAYG9+W90ECaysF4GsHQQKczgvbCFtNAr4lVqcgn00C3VstMn0DSQJSbSxhSNtJAy4meoRA/0kB1hYmIP03TQFUGrOQhTtNADgiA5F5Z00DyA6Q/bonTQDOS3k22d9JA57xMx3Q50kDnPC/ShYvSQEZ39Ru/rtJAhZgrt5vB00DDJV2uR7vTQEqpZB+74dNALCGKxazh00CUqLKJjO3SQHr3N5WMDNRAr5Ir9dMC1EAgfeDRlwLUQFDUvLp+HdRAdNejMbID00ATe6dFPhbTQGFA41gTRNRAXfluXtg600AeqcBwslTUQK/1xZBgR9NAmcfCLfEJ00CfWLhG4FjTQH4kcuwYhdNAPNJgkvxy1EB5exDzwYvUQL3MezCir9RAha4zfNy+1ECAcMLGzabTQMjD1hAPpdNAsAl6ub/o00C7rnnkW+7TQPeMGmepCdVANJDHqzAK1UCjP2YiUyLUQDm8+LTHNdRAD9EqPRFC1UBQqGb6Z0DVQDFFLb8NWtVAx7Bf/Fde1UDa7GCmJ0zUQAThTITfX9RAN+auy2Rk1EBCy/6fBaDUQCK3P6tJkNRA4WTIfoKk1EBE7uw9aa7UQH7qPU5tzNRA1xtC1Sbv1UAeFSw5fBLWQJ7ah1ZhHdZA8Y6pFLk61kCOphdBnk/VQIJwRwhs9tRAQecqB3c61UDoqSjWjyzVQAZsZs5qltVAsKQkzLWN1kBAGuHFlrjWQJNVERxTx9ZAEGA5iTvV1kBp78nL19zWQMQJkT7W7tZAC3eLmNPd1kB0VLxu5PzWQCWR+5WIG9dAesR1hIwT10Bd4a7P3w3WQMncKgzSEdZAZwJZ/ztE10D6Q+PIuSjWQKQTgCPwPtZAjTAoYkhY10B4d5ZUrFPWQDFRFoA+ZNZAjkJX5Cpm1kBQiGSqfVvWQP1oyY11idZAaWuA4Vy910CmnoNKRbrXQAs5YZCqsNdA4phbZoyn10BEdLo/4MbXQOYeeb1e39dAp+qF/iW91kCQXfWy57fWQH2LS9gL4NZAbL7GgT7d1kCTySgVU//XQABCCkoJI9hApsmOYyYW2EARMa26vCTYQOyRY9+3INhA5TxZs3oU2EDOcVRioBvYQO3dZ0wTHthAXf3FppYW2ED8y2GfBTvYQG/JKjrnP9hAoxsRXkAy10D5mE4p8gTXQAxCPpAVEddAGAihYR8810AAF2BSXzvYQOVV8DBxLddAHwMaWgsd10BqgmwLiS7XQCPwJ/86+9ZAWo8OqQo82EDOkxzI7CHXQChtEw5sBNdAOtL2m4Es2EDkOTJuCSnYQKvLMvEeNNhAjErMo4Ah2EB4Qx4I3SLYQC/LmNWYGNdAp3INHNP+1kArW66DRNTWQKkiFIQu3NZA62MqmbQi2EC4bk+ezCzYQHI8aLBCJthAH8Fxww4d2EC0Sqj/fB3XQGP1tHJn+9ZARZnkF9cK10Bilp23tBzYQAN1zsa/FthAPHGVZQb/10AoZeVSWBnYQGxIdPPm/NZAOs3qUkUY2ECJ0KRDyeDWQKjQaIkYCthAQN+ttljd1kCsiHQ5qrvWQCvcEgBl0tZAXeSbYwfw10ANy7Z1jOjXQIYLeijl5ddAfAx/Gj7O10D9jGGk5bnWQIWat126kdZASh73W6Cs1kAfdaQ0jqLWQGaQt90t19dAMiCN1uHR10AkN6fK+cvXQM+z7XTwwtZAyU64a1vA1kCGA8hS6rrXQDOje1UItdZALBydFKKn1kAOYtW/YLnXQCAX5SNEvNdAh8Krlqqd1kDmjsUzpYfWQIp4baP9ddZAwe5UNDIf1kDqy2U7w0LWQFTbmdLxfNZAni3kG12P10BayMVWFoHXQKOgMywUdtdAKdA26F1u10BwxzeIonPXQEdcJ6G+eddA0HUvNmM61kAsDKtVBx7WQJ3hRlC/O9ZAjG7DPkBH1kAipqN2WFrWQDN8M2stdtZALarqu79u1kBW/Fn12WfWQB8jT9twlNdAcFi97+CI10Bs0DRUxI7XQE0JO2nFo9dAlmKL06t01kCqc/91FXDWQNbD+w8eZ9ZAtIevqRJm1kAI2sb1yYnWQJp12oKHvNdAnYI0Mtuw10AU36mbPsHXQBRy2C1kv9dAF+IdHBWy1kBZxJow03bWQJpFlhO+tNZAUHLZBdS51kDZXOruVtXWQDMt3Rai9ddAKES/BkHm1kAlwW+ZqPjWQLnayb41HthAI42FL1At2EBxppj2lSXYQH/JZASWAtdAf2q9sH4h10Cxhei1sjDXQPnVRvGQUddAl7HEFsRb2EB6L6Bx2GPYQON4SBVleNhADN80iek810DmohJp1UbXQPxnBGaKT9dAVJ6qhJ5l10AU/ZR024fXQNo8gWa4ktdAB827c5692ECibqbw8qjXQPRelahc1thAc3z7WqTo2EBuGq9Gr+TXQNmYIRgf89dAXGUD8mrc10BbGtM5wyjYQJ7eTw2xYNlAF53y7a132UCSMdJPXlPYQGDM76h1PthA4hcdWrle2EAaH8jLB4LYQLbV922aq9hAg1ATxdXP2EBXeYahyNPYQECnQf9QDNpA5giuVgsA2kA86LsxVxvaQOCOxUU5NtpAu7lAghBL2kCf/D38bl3aQGMODbHAX9pA5o7wU/+L2kC9c6I3dYjaQJFXsbj1rdpABz1rl91s2UDCHk4OXbLZQGHRa7nTm9lAUYXM+Kev2UDrvWTZP9HZQEfSZ6nNJdtA1DaHBn0r20CInB5Zei3bQGTYKzdaRNtAI1InxrZD20Cn/N7aUlzbQNcFnLubVdpAZsIz4cx820Bf72BNKVDaQLNmLYlqSdpAznYSZc1r2kAWZ/ZVwpvbQJC4od5XuNtAVGtqYNbE20CXyAz2/KjaQNurrCQ9u9pAPAS7dU+h2kBuhShNRqjaQItaKxYEv9pAQi3jch6l2kBYa+nT+d3aQHc/qtoQM9xAMxwqpNYx3EBS+8NddjPcQG5qK7LnQdxAQPYSn4pD3EDgEnaFvkvcQAejN32zXNxAT9hTC1Fk3EDt4O1EMHHcQN5lFIRmXdtAAXqZ+GYS20A5s/Fs5ivbQAt52C5pGttA3uFU/yhO20CCEtwnUKTcQOZCykhdv9xAkVMGs3+L20BUYbZezInbQKdA38g02txAiCYHlRKn20BgbHe0Rw3eQAiL9lccP99A2DYIWPYI3kCmZxcXdPbdQCtfQ2+oNd9ANQwygSso30B7awlBheHdQILJfDml+t1AZXzQPU/d3UBkc4zDTeXdQNpv+wXizt1AsMz9LHEc30AA2IVcuADfQMIJgfwrH99A/P/2mlIX30CNldrVAiHfQKQVU8a6+95AkLGtWYjt3kCwQZJYsrreQEs6U0KTl95ALQQHf15k3UBFlpvIqUvdQKMUtbQSNd1ATUBtIWYl3UDRukTPuB/dQHZ7fV+EEN1AlgeYdcAC3UBAKogmZxreQBzorFxGMt5AypyyVuYX3kARoAR61cPcQPzBOrFX0dxA1uZ6bsKx3EBzD6kaPt7dQAQTrXTUp9xA+gle983O3UD7AdbAA8fdQKTfoD/tvN1Af7crbQ233UDLBt7IUp7dQJLvL+Zodd1AGoms3YNC3ED1eIZsTzTcQHwMXFmbKdxA01OUurIO3EDTseNrufLbQEC06jnX+txAss2QWyMP3UCmKhNaFNbcQKwH26j1L91A/G954TPk3EB0N+VG0LvcQIXPorOO1dxA7wbWMr2e3EAmrZwhaqXcQP2qsdRlb9xAn+D8rxIb20Cv1alU0A3bQOb3MjRmDttApssVMyzw2kC9zHQrwtfaQCFUvPbm1dpAPofXjzas2kB26D14nLTbQEK56v7xodtAmr7XRrhp2kBYh5hqaGTaQHrSwlIrRtpAKg1BMzoy2kBW1IIjODDbQMlklXrwLttAJnC1nQXn2UDu1fd46v7aQAGZyI6P29lAZeSthcXL2UALflNwg7/ZQMdnKlXGwtlAY7FCrIOi2UB8kdZXvpbZQPvV78K8pNpA7QAwUgWR2kDGwSOFkKPaQGrU0rpIeNlAAGG0R9Vj2UAGmYIhZVHZQF/sYJblXtlAMKiKV8p12kDq+hnxrVHaQOm1qinKVtpAsXCwawos2UDMamQtPCHZQO7d6reBTtpAeVeIeRYT2UCnGt+qavbYQPIDO4P479hAQQtaCCzz2EC7nUraPN3YQHCObh6oE9pAW0gIhkXu2UB2KrlQ1PLZQOc28Kzr3dlAlKInuXC92ECcDfEpTMLYQA7kVCQ9zthAHx/jhCi42ECP9amBNb7YQBDPpKb549lAN/p8gjPh2UAYGqJpyMjZQJC9DyJhz9lAqaDFU5qu2EB6TFJrjrLYQOB1+RU+rdhAyleA2EOw2ECYO8ga+7DYQLwL/YJBt9hAo/1f6oTB2EC8xJyTMp7YQFCep9U9s9lAeFEf/iy12UBqbL5wWuLZQB5nggBu+tlAqjOakMHg2UBVE1Auf7DZQKxNGXRPsthAzWI4t0O42EBqUyPTw8rYQEZnEeLQx9hAIoL8S4i82EDYkppYk8bYQPPQePP++dlAOmRN8Uje2UDkXZoDX8fYQKSlAqK319hAspvNII0H2kCCdt1z89/YQCqN3OBN49hA0f5UyMwJ2kDDMF3xxfrZQOY8wuPXBdpAK6aGwC3/2EBh8SHlqebYQE0GjQ8s+NhApPUvKgfq2EBtSfoewjjaQINWgzYyKNpArPlaRMUu2kBCW3xNFRraQHf7oQMVHNlA+JABUxoX2UCvhJuUZxPZQLv9f2nfSdpAYhaCLJwo2UALgsHnmkbaQEfkGr6FRdlAHKpQlbVd2kCZoXXapaDaQMzdMdfZk9pADHW25gd62kC9hgw4uE3ZQBzuR4u5VdlAAwMB5ExH2UCKSGc02JTaQGl+v/shitpAM6ywsSmB2kDbT1UKzFDaQMqum9cPTdlA7i70ReNJ2UDoJFxoJUXZQB5TB8d1SNlA1bT0mz2F2kAYJs4vZXjaQOZBVm5UatpA7KIaIAJW2kBNShbaR1LaQE3cyxAGXNlA+RqvaV9I2UB5wMYgPHTaQI8U1c2QdtlA02tNlA9X2UAXhqCjY1bZQDwFKDduYtlAo6Q/MtCU2kAsdWpO72LZQCjAuIAwctlAmbxK+Flv2UDai0el+FnZQPvCSpbjhdpALTj0TreT2kDKUnjXxabaQAaQIHErj9pA0bh5z3t52kB5fH0xqZ/aQJktkuhvidpA7Nf8RQNk2kC6YeIXoGnaQONk0vCIV9pAv4tF+Dlm2kDHjm9oQSLZQBVmG+ftKdlA6MZ3Sokg2UBXyprBHxzZQN250PGqdNpAcF/rZKFd2kCpzt8HzHLaQOIaZ8+wWNpAnsoiqMz62UDJPti6PtzZQHraa27GyNhAP6J1qkPE2EAZbQhGeq3YQApBZf/6othABWylS0ib2EBWSWbkX6DZQKsH3IwHbdhAm76nnGdf2EA2Dokq62jZQKCKWvK2XNhAsUgX1jBM2ECTeJAQgHXZQNI11Of2VdlAeTBmciBC2UAA0lMk0WvZQMzz0LBKP9lAab7quaId2UAUWZhkJRvZQKaa+MTMNNlAgkdq8yjv2ECE2nkppxXZQL/i5I58r9dAo3jhGwe010CsDKHgP5/XQLe7r0CynNdAaxA+2dpe10BQA0PNgF3YQEdoQAFKb9hAhh1XRHtD2EBCe4yqLxHYQMssI1as79ZAEQ7gxPvZ1kDkM99U2MzWQELc0mDHyNZAMeFfu3Cr1kAafV1N9qLWQKDWU/Cvm9ZAn83Gw3561kDs+ImzkWXXQOXBR3FpcddAVMD49y5f10Co/305s0bXQI2Mln5KRNZAsPiudcQm1kBYeMzvuyPXQMDFxli/HddAfDxeV9MA1kBRGQvKqfjVQGu1m5wa/NVA6Gei8crj1UABBQEYOtDWQFqCRafO0NZA7moP/RQJ10BrovAylc7WQDUgcFTpfNVAUt8Y6Mhz1UD6StEzfIXVQHIbIRN6bNVAssPtLERZ1kAsWAORwkjVQD2c8FhiQdZAGHx/m/ku1UAzejHnwiXVQH3wRtVRKNZA/75X4LQm1kByLFUsihvWQKnh+jcq/dVA+fM7JMLw1EDNFIXBg8/VQElSFMQS0NVAkmbU6NMB1kDpkZtwC9nVQJUBIepZv9RAGxvvnc601ECSgheme7rUQHydfBEkmNRAn6GUfdJ01UBzG4NUhIHVQPrWXgXZeNVAdBW34Dk11UDKst3GzEHUQOm0niQGRNRAGnF11Jwy1EB3yHHvZCrVQLueCul2YdVAfKoTlY5s1kA0qZ/LrGjWQBhc+NVLc9ZAYLferGFm1kBMG7usF2vWQNsuyNGSLtRAnjjSCtUi1EAvw3qhhBbUQDdBKawiItRA+kh837MR1EC2h1XevArUQPy83cV1DNVAZh7vp3wU1UDT0CNirxrVQCa3+bTpI9VAn5KuEt0a1UAJM1vk6P7TQBCCv4YiDtRAK3S9xdIR1EDq7BMxHSHUQJC6bM48INVAeHuGv4cj1EBexUKsaifUQHz/yK6sTNVABCQ9n3gy1ECe0lUa/SbVQMDsE5jcQNVA6oaq7OBE1EDqAn07kVHUQDSVW/99XdRAzgUFgSRh1UCLkBDO6VTVQEOlwoEzy9VAo3CuLsyu1UDcaQ1yAInVQGCq2VLHb9VAlpmpFOFq1EAxibzPHmvUQO8JJd6bedRAQ9j43aB31EDglBKg7GrUQGcakQEdddRAztZ7uYl21EBV5gNLYXvUQD4SOzRSndRAB+pjxHGU1ECF4so/hpHUQOHtLwfFsNRAxK1gJh6W1EBG30kGHq3VQDiqOB/h69VASH++S6Ii1kCAlHTD4DrWQKoIjklBGNZA8AcXWQza1UDca9Ji9uHVQL3lP0l83NVAm3b3Z8Hq1EBGPkeTr/jVQFLCsIY/+dRA3vv68iH41ECpSI/x2xTWQNw1lljv9dVAoz6dYTYI1kBH2mE+UPnVQKvXxiRD2dVA/m3/UOLu1UCJtwQOpOfVQNjHjL0+7NRAEeoW9PTs1UCtUhqdDObUQBoOyt9j89RA6N6uvbjz1UA5ssJYBuXVQIFrL1E3/NRAf9eKIhv61ECcpW1IPAPVQHcyNPB8I9VAARJmFJcG1UD3tWcTU+vUQBuzPABW/dVAbzzfM6051kBj0rnETyHWQNjI4sCwDdZAR5edbXwD1kASFL+9S9fVQJP/63qEEdZANZ8oA84W1kCagrjbof/VQK6ZXEZ46dVA0DN4r1u51UCwf4bEaLjUQKlorYtht9RAYdPFKua01EAeS9UXgLHUQC1gL1xhuNRAYDJzorW01EDpLTd+k6rUQMZ+73SxpdRAVN9EsmKN1EANDQC91IrUQDXKFnNfjdVA54nDrDiE1UB8w3k975fVQCp2WPZ+qtVAAV5Efu571UASPvtlOI/VQBmAJujId9VAgXZTSylW1UCEyYNDOVzVQMz+1PY9NdVA6x2X4foq1UAHuqlHzgPUQJNms5mY+tRAHArTXx/h1EAdM5tyWunUQPXuE63lHtVAJwFYVT7r1EBX3hSFganUQL5lfaJ5n9NAfJs6GEuE00Ay9MwkF3DTQG9YxlPDcNRATxC3JZBc00DSGiyBrFjTQNzarzeiVNNAyNUB6DFH00Cq2EiOHyTTQOmAgog9KdNAWpIiM6sG00Cfib3q3NjTQFgC9mAJBNRA5mUDQFox1EBEHxlI7g3UQE+8kmzE3NNAQjzccY+s00AO/wR+Y2LTQO0KZk/3btJAcdDR7hxL00D5DHHJ/UXTQKmmw0q5R9NAhRf9dH030kCvBzbUoSLSQKJzvL2y/tJAI6A/627d0kDIzRlJXfDSQGXhaY9e59JAIoJEM9/A0kBgc3YwELfRQAMxdt8Fs9FAYM/0m6p20kBHMCq4mlnSQOE1g/iSWtJAxgaqChpn0UCnV76EKlvRQPAo043YRtFAqiHDrwMt0UCZlEj3iw7RQAjF5EoH+9BAlTtGGdnv0ECus1j5nrPRQAdc1C9rsdFAzhqxdI9/0UD97bJ6zojRQDajgU0+cdFA9lUnR4aQ0EA8a3ABFYXQQFNTnznRgdBAOUbH4I1v0EBTUuPeclDQQDaLeOAJVNBAOsH5N3dH0EB8q3xq+D/QQDmN8XFfS9BA5jnbwLsx0EDARkGHhBTRQP01zbix/NBAfnDntOXu0EAypUtuMg7RQJ+5cLPo9NBAE8N2jPbt0EBttvzSoN7PQK5DWSmg8s9AU05oolPtz0Dk6rVxbrLPQK9Rrjzsjc9A+0WT7IOb0ECAUjSugnXPQBnaQI4Zhc9AB7B+VGWGz0AogWTXVpDPQA5QlFPVl89AY6N44Opvz0DImdNh81fPQJH7t3LIntBA9RVLPjKi0EDXesxcDZnQQIOuBTqYttBAhtDMgV6V0ED/W4AyClLPQFmLVbZppNBAujyngKxoz0Ae/FpBZHfPQPI4ucbBb89A1WuNtCdyz0CjvhP6aoXQQGosWb9gqtBAJxHUEOuZ0ECzoJ3BdXjQQIqTrXbRMc9ATP+gMawzz0D7o1c+WELPQCuSeKgwZc9Ac4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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[841]},\"y\":{\"__ndarray__\":\"u0jNERl6vECEzpVQf8y+QMdkmKsQtb1A86D7Lzf6vEBLBfvJID69QC0RCkvqK71AuACZ3kkuvUAfq/KtPUe+QHYlW2sraL5A+IODiqwBvkBd+LQYVo6+QH2d2Scul75AYMkFauIcv0DBSnZWtBbAQHkfFNPWG8BAlBnNg126v0D4IYUXING/QLg1xdJuBr9AfHjUiN//vkAUCsm19iLAQBpVUT/w0L9AO1vkGPNhwEBKD0RYXYDAQNxXKOX5Y8BAhhnxgNkNwUA0VDNdOkLBQOJ9xPOZxMFARdnQJvy9wUA7BwHIo4TBQMn5gNcpa8FAwucEfglHwUAc7cux4XDBQIryZqyalsFAE0s7nsmuwUDZ26u0sfbBQMgE6fNvZ8JAJwPtwl3hwkAKq8UYYLrDQFh8mNyftcNA0OoskNTew0DYv5Eux3bEQDLhAgZgL8RAtkaOgpZWxEC+qBKb2STEQCK25Uy/hsNAyLpEHq82w0Asz/KapQvDQOQGwNMFSMNAwZSa0fKSw0DJMCv8tgjEQDF5WMmfYsRAfcijaug5xEBZxjZNoGjEQDRxWw7PmsRAH6BZVQgcxUB5OknfRTbFQIgpWexhIsVAnCm7RQZ+xUDqAxpULE3FQJQuYihVCcZASPVpWphNxkBWBrkSKRXGQMYvl4HzVMZAWhsZ623MxUCdqK62DrXFQMTRvKhb4sVAlvuBKM4MxkAmVcmD3aHGQGI9zrk40MZA6jC2YuH9xkD38hEBfc3GQKY3+9oDgcZAo644a66GxkCZ/1vTL1XGQPQhBe4odsZAJN/bA9SoxkBY7D+bZ9jGQDWMyxDAVMdAKbnWzTD0xkDo36wk1S/HQMZRUtth9cZA8Gj3hUJ4xkDqyu3jQIDGQBrmoi8xXsZAqyfVAKaaxkBAkiXZWXnGQLTYA5SEO8dA3qBuxqgnx0BGllJUJanGQEBdOrW57sZA0xLitHsGx0ACK9D26H7HQKqF0qPPMshAUlq6pQlhyEDtej3mkgLIQEE/Sf47nMdABjt5Jp0Px0BU/aaPAHfGQMAHDVCJIsZAAEZH8EfcxUAlU2O+5/LFQP8A+GzYgsZAiKi6iAiNxkCqWwTDM5TGQI+S34xmj8ZA/Hu6Jq8OxkDzzCVQxFzGQFzjFlmaV8ZAIBxPmJx5xkDtG/F88OTGQEcX0OOm8MZAnBJzh0WSx0BWFIcCZzjHQCna3F9zQMdAgUrKk8jexkB8YR4C/GzGQItTweu0A8dAoou/k6WsxkBvO0ArHeDGQATxPAXS2cZAxwbN3uVtxkBsmLnbHMjGQI4R5TnHj8ZAWe6vLbh+xkDIGxoRDK/GQASAwNMLi8ZAPywQodtcxkDWHQp+3ZXGQE2L+cACSMZAQMo8lrXixUC32A3+J5HFQEIL0X+eRcVA4cw8ZsdqxUD1Vk6kVGnFQKFpwj1UEsZAhglBfbkuxkDoXjj1HRrGQMpC69+9jsZAIAcRCgb8xUAZxYS4b+LFQB57K1hZ/8VAWHuXPb9qxUCoJmnZeaHFQIaFQ4IRicVArD+upYRBxUAJq0/pgXDFQPTSVii2b8VA+hKok2xDxUDCJ0zM/3rFQEL8S549VsVAVlu4bcg6xUCZ0seFVXnFQFAMaHJ7pcVAtNLDIQxoxUD/sBs78I/FQNn/zJfuisVAFr85BkY3xUDxPBghZxDGQJPQxg2uRsZAtLV7QNgZxkCtHywnnfvFQAEFHWperMVA94sXVdejxUCIztyMaufFQM7q7Afo7cVAchbp5FLhxUDu6sF88LHFQIkodKhR8sVA9OlHaD7uxUAkRI2kFt7FQIzjFKqNuMVAe9cnqFo1xUA2LAXm8WvFQMOXCGPpasVAzea991uSxUC2JaawHR/GQK1dPrrnMcZAme0nKBSPxkCpQVsCCYfGQApMMPfuPcZANzBRx9AExkBwXyAsUx3GQILaL6RwMMZAsqhM/H+axkDsM1jRwsPGQNL9qngVjMZAYiY/1SRDxkBEO9WvjAPGQKvt491TTcZAn5WRQeQJxkCIQT6aQaTGQE8FlMCRuMZArVZ6SY91xkBF8SYOWB3HQEu9Fi8eCsdATk7xZzwsx0BnPr3bAcHHQJThAZ9ylsdAQtc4juUVyECCtPE6L0XIQBXxAPTUkchAHOEej7+pyEA07GqYf2nIQHpdkNutlMhA32lhSGBgyEAt4EwCPJPIQKbeAvIaB8lAvQJvf48uyUDNYoA6tlrJQBjEG5QPq8lALAlR6fnZyUA6Hmx3rw3KQOPhKE6ZX8pABJM9wUWjykCKtNyFaZnKQIHFFdyBfMpAIRmp2qIxykB8u985AwjKQAgNvVmbA8pAg/wVV4dRykAtg21yVqjKQHKlrfmNu8pAZy924j3cykBKLmdF0e3KQPm8bprn/MpAbuU9x678ykCkRjjzyAnLQM6vm1aUYMtA1VuS2QN/y0Bvb4XPJbrLQPqAtQMyFMxAZm8bIOThy0BvqZRABw/MQPKbVICyRsxAql3v/ORXzEAQ++SvVV7MQLeBKLB5uMxAYNzV4FnizEAYU/MvctfMQDbRh397Vc1ANI2Fs8f4zED1/t8R9wfNQEIkM2VDX81ApqBlaKMkzUAYTpR6i6bNQICwTdXris1AzLIc+KEAzkDf/iWNolzOQBgFhy1cvc5Agod/PGxCz0AITS2CV7rOQC7Jswlp4M5At/Q8RA65zkBONyuX4uLOQBd8MvFag89Ar1WBKSaPz0CH0SZa4qzPQGBgdU0ycM9A5yqCv9ecz0BzcGmW1T/PQC4cD8eM085ASLuPr8nAzkAMtfVoHK/OQO0/8MNg385A6akHvv84z0D5p+NRvIvPQAx+BAD1Us9AY39U80kuz0D874XvmYfPQBuGmdVdis9ANjyTEjTHz0DG2yOFlhzQQDnTw+WJ7M9AQvSHJDj9z0B31fzPOgnQQFLkjCvnzM9ATPPgYvCyz0BsbDOOyPPPQFk7KWSDoc9A1iJB+Rbxz0BKr4WWFubPQM6qHlw1yc9ALsd50ckK0EBTKk5u4QPQQPikS4eLTtBAiLFUkBow0EA8ezSOLhnQQF0MLzqJOtBATuAdPsEd0EA6cuLrSD7QQDZqRlVwaNBAWko9E8NQ0EDnQ9RbF1PQQDZqg1P2KtBAKYygruUO0EDuhXUmrfvPQIJYjZwdCtBAI3ZlbFgV0EARO61EmwbQQILLWms3CtBAvnnZxQRmz0DOOYD90S3PQKooGKWahc9A1LOS+b+fz0AgDOSD/v3PQM27y2TqDtBASHne6wupz0Ce7RxdNZHPQPSvMGaFdNZAQO+qb7Im3UDTKAJOxSHdQBaot2DK8dxACtNi1Ekv1kA2G/fgyAXPQN0sLgcyuc5ApV6oUhogz0B77GP4iWXPQLjDI+BsQM9A5AVYCWSLz0BeHyNCoZDPQEAkH39/Lc9ApIFKq2P0zkDWm8+osKLOQMTLvVdBJM5AAVKudMkTzkByNS7IlEvOQMRt+89+TM5Au6664F9mzkBEybmJ2IjOQGcqxjMFLc5AQzbnijibzkDTAyNtsOLOQFKzoIMKec5AoK6Ynl6azkB7F5a2+jnOQN5HshHS+s1ADv2TN+kUzkBcq8qRPRXOQNovnmCnSs5AKKRlP9xvzkBL+W3BtrLOQOaiEkma085Abjoi6aaJzkDcH1FNHUDOQE5+Uz3Ybc5Avt1q49pfzkDD/pF/YV7OQIFyVwh7x85ABbZX45NxzkBeQzR4S6nOQLJdFVGu685AslQS/4ewzkBG6X4qwcLOQJNbUEbDu85AEjI9l0lSzkAk0i0SbRTOQG4SyOQ5H85A+TN5odQCzkCJjfzxN2POQBuNDccZkM5AvFCyUSSYzkBJs+zYILPOQAQLst/YmM5AxmZnvUdGzkBGxNZ/wWLOQOHkEf7UhM5A4MYL90vmzkBWMWPUWFXPQEa5nce7Ys9AEtaDwqaUz0BRsuvUZFXPQPaC52FUcM9AJOr/g1Gfz0AZqOOPh0fPQBkRsDOVZc9AAmqxUFWez0AEYd/bMnjPQF4GOhqFxs9ADAB6uYe5z0BmjkjiZFDPQPQObDbSrM9AO/1m7gqpz0DsK68younPQPi3azbLJ9BA/sdELuU+0EDNX1t7ETXQQAUMl4lYZtBAqs4zpvdn0EC78NLpe1rQQDwjKnZkf9BAKqCcgwmF0EA/VPUnGoLQQOM9gJfkjtBAW8zoiMSg0ECKdldvhrDQQDQrnnqECdFA7k4NGdcH0UBA1ONCLSTRQCaXXPA9MtFA1wgG9btE0UCsOVesNGzRQB7w5bPykdFAsDP/GDCa0UA6XY5mgIbRQOeHdOQCp9FAlwcNCuCi0UA7uCZW5bvRQPX8mzkR7tFAR3mppa3r0UAoUtAqQvDRQEbu1qUk89FAlquPeXMG0kAz/izNFFDSQGYzuMG+f9JAgksTl12w0kBKR9KIPtPSQOKYmaNipdJAy5Jq7p550kBqjquV61TSQCZK0WAAQNJAKH5O3NJf0kDlK0MbYFHSQCX5UZWHm9JAiO7uNBqg0kDW+Ev5YK7SQBBcJni7DNNAfrDjjRve0kDGjdVkmOHSQNjGzpFmDNNAIAM91OVC00CO4Syq84PTQPuKlQfsttNAwYrJ0rGA00D6KO0l3XfTQKtMucW8kNNADCIpIASD00B4knTZ67/TQHQHnrvDydNARmY41VPw00Bep/Op2OnTQBBFshMNC9RASVIf4jMi1EB3wQBLoQ7UQN8orRPpPNRAccVmqxAq1EB23+eWFhbUQF5zctn5HtRAPaF1JUgG1EDfQk8AxPTTQF1utjW/59NAWAx9ydu200AKe5lbO7DTQG0yvEc/+dNAY7Zc4XX700AMe/U0jhnUQHTk63ueLdRAK6Ah7y4e1EAaGK44XEjUQNxFeVdLeNRArPa7dEeO1EAs2xIkRqXUQGh0JNTVn9RAxMFFT3Zl1EDXfXI3gJHUQEboBcONm9RAks4E1BOv1EBgFVJQWMfUQM1o1ynoitRAKCEktaxn1EB3z22KPzPUQOhnQeygEdRAIOHL665F1ECSU5ebnj3UQN9vSJliVdRA0IgHPm9y1ED6DTXFJoHUQM1b5dn/X9RAVtSI+KBd1EADjarjanrUQIT+TKHoXtRA4HVkwxx11ECeGMPJVXzUQJRu7mCxZtRAeF3tPbmG1EAdrowkmpXUQMBRNZJkg9RA+BDeoWKi1ED8oGYah1TUQF7gdf10W9RAMcdD0YqG1EBKYxI1PEfUQDU+2ce6atRAGiy6gWZw1EDxtBlJvWjUQG2iqwozp9RABhutVRPK1ECQ8hGvFbLUQDm+5SSTe9RAAZRQrg491ECA89LC+xjUQPeKaGYQ59NA+ROH3U4D1EAMfYMwxvPTQMhPCJ+WxdNAc/bLzP7v00A4VJmPGs/TQB/5gXq60dNA5/62d8HS00Aa2pqCNOjTQKRs3H0vz9NALjNHllLh00D0tdtv/ODTQGx34d+JAtRA3h1eCeMb1ECaC0jQzkHUQDAnpS1WXtRAbvk7hCzo00CoThI5h/TTQLLK810G1NNAKlzm9ta600A84KBmMq3TQJQ0Q5HE1NNAh3kBTwq700AuUezJ7pPTQJBkbVoE4dNAyk7OWqyf00C7MxJl6KfTQOS+lIIltdNAAF0hB+Jm00BqoclhEGfTQGiSW+w0ctNAqJ+xthGE00Bx2e21uLDTQK6Ev4TgudNAEiVwXsGw00Bc+cHQqarTQKkeNxu+otNAhluziX+L00D9TiOl7V/TQHCXiS7BQ9NAlvaM/2c500Bp2y64TTvTQDp17nSfVtNAcjq/40ED2kDK/Wccck/gQL5KOXlTbeBANcxz/9944EDSSs5/AXfaQHgXCJBu9tNAR4v72QG/00DUSbUeXazTQJ5EqaZqotNAwuhv7IOr00AEmF83T8jTQBA7gf12C9RAEGEf7qoT1EDDyKq4gQbUQEe0Ij195tNANCjr4LGo00Bt8ScWOfvT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Qk//XEnJQMeiGPHEd8lAKVT9exp3yUDkbXuucZPJQPnpPG7pzclAIA6tYQbWyUCbPgk1f/vJQPzUBveTGspAIlcOk7olykApAUqoeTXKQG1L/xcbXMpAcBh9ksp0ykCOfekCB7rKQHhZrL/Q08pAxMMkdXrqykDjZTKIRg3LQBxaOTn7MMtAAHURDMpPy0BfXu0v9mDLQLLEDbwIiMtA8oTzqPGNy0CvaFNj3rvLQCToLyyl9ctA/NOIfoj+y0CT2tfrCwzMQAXFbNkbP8xAPgAdYBlDzEDQo2aOGGTMQLyp80k4o8xAEi3brcSyzEC7vK7xrN/MQEyyIyQxBs1AoZOiMMcYzUBmJaPpHjDNQGhXHf1YXs1AKQxgG6F+zUAFWZEvdsvNQIFJqIab6c1AYMh01qAEzkATf9aDyCvOQN6HMc/YU85Am/45yYVtzkDSQ0YUkHnOQP4Fl8eAm85AFyKt20eczkB6EF2c8L3OQJWaiWtz685AFJEyxBLozkBRotE3UunOQC7R0Np0Cs9A01DrFoX8zkDROJ/6lgvPQCiDlmvJOM9ABNeRv4Mvz0A1N3nzmUPPQEz9ARZMUc9AJ6+UEhBLz0BWJE5KHEfPQME5gdwKWs9A69F8eQdfz0AxAmcMkZDPQFs04CJImc9A6PQOMt+ez0BH7dKemLDPQMA3kKk6w89ABCJbaQPRz0DD2il6KdHPQHYQPfM1589AFqAVzRjcz0Cf5k1njPTPQG9kAQhtDNBAwQsaISIG0EBzwK1HJwLQQCFXSkloDdBANJZ0FyAB0EByiWu5WAPQQN4tBKKhFNBAcWZzJwIL0EAxpdicEBDQQOOWjgntEdBAd35JY9IJ0EAjSgvvlALQQO3lCajIBtBAF8NsZgME0EBO7MafhBfQQLo+JToOFNBAVlje0AcP0EDcDWKWEhDQQG5s4qqREdBA/ACBERUN0EDHfKEgxwHQQAs35GNsAdBAjjwTr/ngz0DmFlab8uHPQPeMFZbF7s9Aam9RGrXKz0CcbIO5RKvPQExT7MeJsM9AxIpwb7yGz0CVKo6+8HnPQGccUe0pis9A4G8590ppz0Ctzw3hx2XPQGGVg7ngW89A2UYDbAs+z0AT6V2PdiXPQGK+dl5VIc9AcBZYOEIPz0CZBigIvCnPQN5t65HpGs9AhmNkFPcIz0ABkXL0JgPPQJUQenI//s5A7XN7rNLzzkDApYA3w9vOQIdUyiqa2c5AO13Zfke2zkA8UMZcxbnOQPXeL0kdyc5AEtoVv5GnzkDt7/FPporOQMhZm3W9k85AaxRgNMJtzkBnN76ayGTOQLy8X47vec5AW8VChU1jzkBN2hFcB2rOQCZVgiFdas5AxLv8wMRWzkDjSOjG1EvOQEB2TSfHV85AWyZ7ksdVzkCRbpfzVIDOQHpkm1MKgM5AxuhUrJ98zkDmpKNiV4XOQB6z67b3js5AZZeKGDCRzkAmSi3LxYXOQNx5FOZBkM5AfwPBYZR5zkCCLdl0mIjOQD3zbZZ2o85AXCV/QXGNzkA5coYHDHzOQBeSR7GUkM5AvAIk9Ap2zkC625negnjOQBIXU1Ybmc5AEYjRsU+MzkBlBTzt35zOQJ/oRxcMp85AnbddG0qdzkDAQvZQ/prOQCBuCOGUr85APxzjezm2zkB5YqwMa+nOQFqJwfrI9c5Anz6M4Qb/zkC3K+wlZxTPQOdqRQiwKs9Afee/7JA9z0COMj4iz0LPQJL6AMDzXc9AhByJvu5Xz0BNqKkZ3HnPQM/PRoOjp89As2Ngdoekz0BWEnCEC6bPQDfdggNHzM9A3/iwG3DDz0DgfHjbmtfPQJ6xQRTzBNBApdLHc88G0EDX+UPDWRfQQPvTEAqyJNBAA6TiPRMo0EDP/WUeQTHQQLsnJizgRdBABpNKP4ZT0EBdSmbNcnfQQErtD2TkiNBAaFcU98WY0EBwXeO4uK7QQIMMr8kfxdBAf53r9qfY0EC3FarMXuXQQGnMitYI/dBAEjDOEB4E0UDFMz+UGx7RQFKF7h4GPtFAEg1c7n5F0UAxokTLR0/RQK97Z5d7a9FAkv0XMCZw0UCgM5WcUYPRQNwatE+NpdFAO9KxZpus0UDFj6VtV8LRQEEA6mvh1NFAoWYzV3Td0UAGAPU6luXRQItp80sp+dFAbxRWYsMF0kBfC7DzoyjSQMrQP4CRNtJAZV0qCe9C0kDqhd/AXVXSQHtXkcdAaNJASPAADqx40kBRcPL8RYLSQNQuBiDTltJATpp8c8ua0kD0sEJeVrHSQHQVR1DOzdJAJrAJh9TR0kA4WEfLKtjSQJGjqO8Q8dJATpeX4G3y0kA4P1OlSwLTQE+YsLA5IdNAaw9d6v8o00CyjP8TdD/TQOu88jS2UtNACOPqQgFc00DwS7+CsmXTQPeE0O/UetNAXf9FYv6I00DQxbJPbq3TQMVRizQLuNNA66S+FRjB00D7k7wlNtDTQBYst4TI39NAz8nv1gTo00DFTqrRb+nTQDQShwDO9dNAmoLGX5fx00AyzAaURP/TQKRjhc/eEtRASjHCTwcO1EBODHrdfwvUQJZiWE1pGtRAQ2HEickR1EAcFP2ZqhfUQCJ41/CbLNRAQcKfvfIn1ECLEl569zHUQMgVbS7KONRA6A6Bz6U11EC/EDCFEzTUQLbiG2jyPdRADfZrUNhA1EBvVbOzBFrUQL23mhOBXdRAPOHcb21f1ECmpun6amfUQBoV89Tcb9RARPWMLSBz1ECpvKgukm/UQInC5mP3dtRAYHWHycdt1EAunzjWHXXUQNYWKOpggtRAssTVQjJ31EDsf/6oU27UQB+M9hPbddRAtkB8S9ll1EB5qc5WWGTUQGrDwqjncdRAZ1gh105n1ECR83X1Y2vUQKxBGwtHbNRAqoXFDTNj1EC8Z2vmb13UQO0ZTuwdY9RAfw2V99Jh1EAcTdN9znbUQMBQ6QhvdNRAlRtakH9w1EBUgpVGoXLUQB6SzUs3ddRAa0wO+7Vw1EDz7dBSY2XUQPXNtd4DZdRA71r9mg9U1ED6Woj381PUQN+oUVvFWdRA9yzZAyVH1EBuvtu51DbUQNLu9sxeONRAm8efrF8i1ECQVBVg4RrUQLKSLFpzItRADf0a9iES1ECTbf+BfhDUQAuRNAWpC9RAZ6puddz800Argi3pDvHTQA8qKYqy8NNAUxOJMF3p00CiSOBRTvjTQFyT8laT89NARqVfWEjt00AaU5eIDu3TQPqpywdJ7dNABHr0rJzq00BKMZ/6HuHTQAonbHyU4tNAwcmbLnXT00BXdG12xNbTQMhsfcUA4NNAbJtLWcvQ00Bu15T65cPTQDzeZm8VyNNAb43GsLu000DO8PLF4q/TQFoFwSEautNA7rjZGJ2q00Cucuj/zanTQGDfR97MpdNA9UGsqdSX00CsLkSZdIzTQIPrGLaFjNNAuelR2J2F00D7M4J1/JTTQAAljA+akdNANd3wpaeM00BUMSBrxo3TQH8uTH9Zj9NA83CBd36Q00CimjgY0orTQMsCEu0YkNNA7BdO8sqE00DS/yJleY7TQJI1Nt8UntNAsKUCLriW00AuI0qKq5HTQFGphw8/ntNA2NdSYUmT00CLuuqG1JbTQGxOJPNvqdNAYZncum+o00CB6opyHbbTQJTuiSGZwNNAiuiNvR3B00B16ZQePsfTQH662KzP2NNA58yAQGjj00BdKyBPRwTUQKoalaAIENRAJ9Fk7jka1ECOI/9qfCrUQAEfljYzO9RA3paHpXdH1ED39fq86kzUQIqTkAhRXdRAFN6IhCJd1EAgoZm99G/UQAay6P2ziNRAIPn1ggGJ1ECYTX4Vn4vUQFtbX4EzoNRAgxHOuT6d1EDXewnGyqjUQFiX5hhnw9RAeEBeBEjF1EDD78vf1tXUQAFSirIz49RAIqpNcpnm1EClvcC/Du3UQEehcDr1/tRASMaEuuIJ1UBWN5C1FivVQCQ6Lk7tN9VAJAQn4zND1UAOauqmi1TVQAJ5qrlXZtVAadXmRa901UAMGaV6NXzVQCmbheOujtVAPcrIfJOQ1UBtytwgqKXVQHgYL8ypwNVAtZw/vDnD1UBRLsu5GcjVQF7ks6vk39VA0EIqaibg1UBuVW386O7VQDkZUtW7DNZAYdfx7FET1kC1m4f0lSjWQPoSbvOnOtZAI4BZ38JC1kAeRiMjF0/WQDjcKZTcZtZAsrOUCql31kA41/b7u57WQAj+NYHbsdZACezPAmvD1kD0dTSzC9vWQOqolbIg89ZAUYNDqg0G10D0RHNKKRLXQBFFxR44KddAJfJ5I7Iv10CRZUNRO0bXQNgmS4axYtdAUR4RALZm10ApI1KHCm3XQEYEIVvzgtdAyI19+1KB10B1y6ZvM47XQFC6cSokqtdAgHF8Zwms10DcLn2UnLzXQCqfzrj9yddAWwUlymfN10Dzn7YnvNHXQKkKhbKB4ddAv7a3Qk7q10DiruFNYQnYQBSFkd9LE9hAdiKcbaYb2EDDW3EqEirYQBs+QzbyONhAQWDWVxVF2ECkaeshZ0rYQIGxIiCsWthAVKa8Tlxa2EAPrwaJkGzYQKQFj8qxhNhAa5LVUGGE2ECSLJfkYIbYQG9vZ2N1l9hAsFrFrgCR2EAe+u/NDJnYQLlKvDMpsNhAq6+j0ASr2EDIGoFdjrTYQNc4r+HluthAykziUka32ECPw509F7PYQHMKllVZuthAuJLycqK62EAIZ0YLMtHYQNLHMlFm0dhAzO95kwrQ2ECxs4sEwNTYQKEgmsTp2dhA5xk4UzvZ2EBp+leKu9HYQGYZmvUu1dhAWeU+kQ3I2EB5MRXy7czYQHTLKVq719hAopv8BhfK2EAueUrBwr7YQITAHZAfxthAP7B+K/O12EAmVKyaR7TYQDqpe1CswdhAb8HMxjm32EDP3xMtdbvYQCKxq4p+vNhAWHhI1ZCz2ED9uWKVe6zYQMLLuYLXsNhA5x51dTqu2EAXvifj48HYQPAMPoM6v9hA+iKvHwG72EDv1Orq2LzYQO4vIwUlv9hAX4sC8P+82EANzmODCbTYQDVP50oGtthAU33NQm6n2EBlNoCjb6rYQFA9cQtes9hAbnoguNqj2EDsxEpyp5bYQO2bbkEKmthAUxsg3eOF2EDlTp5MPoDYQKQzvgKpidhAFgIU3op62EC11l+pGnrYQEVe/Gt4dthAuNudG99o2EB6W7zI/l3YQFurF6OPXthAmzzXgidY2EDoGY7dBWjYQIY7Ol/YYthAViRB3Rpc2EAPqRKKblvYQNPW4IU2W9hAlCzUcQ1Y2ECRaUkGE07YQAjl4M4LT9hAdg3bx28/2ECZYjPMmkLYQJUFyteyS9hAxN4eKFk82EBTxe6FTy/YQJdraBJcM9hAQLpva98f2EAUvUOY4xrYQBZxuQv4JNhADCd/oKUX2EAu4zolARnYQEJSR6EqF9hAObdYCl0L2EDyqbElBQTYQMlsR24eCNhAAXFBvD4F2EBFwTKFpRjYQDSBT/ZgGNhAVQjHY4wW2EBgKwkAyRrYQHX3R+t5H9hA52N4v5oh2ECUtyo86hzYQLtJ/+wsI9hA2og2ztoY2EDf2sYfYCPYQL56lXjSM9hA0VAiFtMr2EBCNCrBIybYQACcPl91NNhAIqzgyT0r2EBxcE8IhzDYQO3lX43gRNhAk6zVeVlB2EBmeUFWgEzYQCv5/Sl1VNhA0m6/6nJS2ECHOWrSFVTYQFzUUecpYdhAkbCdAUVn2EDR2OCWpoPYQGKZIP/Zi9hAIyG7Y32S2EDORCD3MZ/YQIURgtlarNhAlajqn2e22EDhJtUOo7nYQKfj4bHRx9hAZE1RhWvF2EAsakGjDNfYQM7Ub8ia7thAo3VcMrft2EDXI8SpI+/YQClQsgB7A9lA4CQuJEkA2UDErXYbmAvZQNTnYFn3JdlAHUZpNQop2UCRqmcByzrZQPfBtsRZSdlAQc8KdfFN2UDFr3HygFfZQGlgFZ2BbNlAbFIdTYl62UB7kBx4157ZQBKEGkRosdlA2T5zDGnC2UCKlZYDe9nZQEeVtkkB8dlA72+FkOkG2kDTMdZ/ABbaQDEySaMKMNpAht8e93852kCDYEIZo1TaQFovpEKzddpAZDTEsFF+2kDNRl8sQInaQC+onTQWpdpA9rFpCWOp2kDpbwKyMLzaQAnfPKEO3tpAKdlpc0Dn2kB22Yw1IP/aQLWMAO/NE9tA1jV5lYQe20A6Cp7AICrbQL2u/xguQdtAoJTFdkJR20CPxoJPnXfbQLX9X3EphttADPyXjyWT20BOlprcMqbbQJrZmXi0udtA0uocdzLI20BG4yEe38/bQDQaSfl+4ttAGf7SBIrk20D8v/6h8fjbQLnPaEZGE9xAqRWRLykV3ED4aDQmXBncQCprN2dcL9xAwBXIdNMt3ECDdCVWyzrcQHOEJH7TVtxAD9vxkTpa3EDWN7WVT2zcQI9HyZAye9xALE3ieB6A3ECVEVwsHobcQB2mEg2Pl9xABHwt8wai3ED4nT9UxcLcQCx+lHQxzNxAkiVEkQ3U3EDhaL7c+uHcQDtVNXdc8NxAE97ErOj63EAnTtaKo/7cQLb8CZ1RDd1AO1ig32oL3UDe2/F+ABzdQFutgSWDMt1AC7XPEJQw3UAaypgJ9TDdQGS10zaWQt1AFEmcMK483UDvkDH+RkXdQPeJaBLwXN1AoSIT/rZb3UB3wbPZK2ndQD8Tpaxuc91A6lqbbLpz3UA=\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[841]}},\"selected\":{\"id\":\"2495\"},\"selection_policy\":{\"id\":\"2494\"}},\"id\":\"2477\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"2440\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#bdd7e7\",\"hatch_alpha\":0.1,\"hatch_color\":\"#bdd7e7\",\"line_alpha\":0.1,\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2418\",\"type\":\"Patch\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.2},\"fill_color\":{\"value\":\"tomato\"},\"hatch_alpha\":{\"value\":0.2},\"hatch_color\":{\"value\":\"tomato\"},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"value\":\"tomato\"},\"size\":{\"value\":5},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2514\",\"type\":\"Circle\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"text\":\"Anomaly Detection Result (85 Sensitivity)\"},\"id\":\"2384\",\"type\":\"Title\"},{\"attributes\":{\"fill_alpha\":0.5,\"fill_color\":\"#bdd7e7\",\"hatch_color\":\"#bdd7e7\",\"line_color\":\"#bdd7e7\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2417\",\"type\":\"Patch\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#2222aa\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2448\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"2392\",\"type\":\"LinearScale\"},{\"attributes\":{\"source\":{\"id\":\"2477\"}},\"id\":\"2482\",\"type\":\"CDSView\"},{\"attributes\":{\"label\":{\"value\":\"expectedValue\"},\"renderers\":[{\"id\":\"2481\"}]},\"id\":\"2509\",\"type\":\"LegendItem\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"2427\"},\"group\":null,\"major_label_policy\":{\"id\":\"2428\"},\"ticker\":{\"id\":\"2395\"}},\"id\":\"2394\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"tomato\"},\"hatch_alpha\":{\"value\":0.1},\"hatch_color\":{\"value\":\"tomato\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"tomato\"},\"size\":{\"value\":5},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"2513\",\"type\":\"Circle\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"2441\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"2388\",\"type\":\"DataRange1d\"},{\"attributes\":{\"label\":{\"value\":\"value\"},\"renderers\":[{\"id\":\"2450\"}]},\"id\":\"2476\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"2430\",\"type\":\"Selection\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"2432\"},{\"id\":\"2433\"},{\"id\":\"2434\"},{\"id\":\"2435\"},{\"id\":\"2436\"},{\"id\":\"2437\"},{\"id\":\"2438\"},{\"id\":\"2439\"},{\"id\":\"2440\"},{\"id\":\"2441\"},{\"id\":\"2442\"},{\"id\":\"2443\"}]},\"id\":\"2395\",\"type\":\"DatetimeTicker\"},{\"attributes\":{},\"id\":\"2427\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"axis\":{\"id\":\"2394\"},\"coordinates\":null,\"group\":null,\"ticker\":null},\"id\":\"2397\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"2462\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"2494\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"2390\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"2643\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"2461\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"2407\",\"type\":\"HelpTool\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"2424\"},\"group\":null,\"major_label_policy\":{\"id\":\"2425\"},\"ticker\":{\"id\":\"2399\"}},\"id\":\"2398\",\"type\":\"LinearAxis\"},{\"attributes\":{\"label\":{\"value\":\"Boundary\"},\"renderers\":[{\"id\":\"2420\"}]},\"id\":\"2445\",\"type\":\"LegendItem\"},{\"attributes\":{\"axis\":{\"id\":\"2398\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null},\"id\":\"2401\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"2399\",\"type\":\"BasicTicker\"},{\"attributes\":{\"source\":{\"id\":\"2416\"}},\"id\":\"2421\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"2424\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"2432\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2642\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"2403\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"2402\",\"type\":\"PanTool\"},{\"attributes\":{\"background_fill_alpha\":0.1,\"coordinates\":null,\"group\":null,\"items\":[{\"id\":\"2445\"},{\"id\":\"2476\"},{\"id\":\"2509\"}]},\"id\":\"2444\",\"type\":\"Legend\"},{\"attributes\":{\"overlay\":{\"id\":\"2408\"}},\"id\":\"2404\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"2405\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"2406\",\"type\":\"ResetTool\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"2433\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2425\",\"type\":\"AllLabels\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"2439\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"2408\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"2437\",\"type\":\"DaysTicker\"},{\"attributes\":{\"tools\":[{\"id\":\"2402\"},{\"id\":\"2403\"},{\"id\":\"2404\"},{\"id\":\"2405\"},{\"id\":\"2406\"},{\"id\":\"2407\"}]},\"id\":\"2409\",\"type\":\"Toolbar\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"2416\"},\"glyph\":{\"id\":\"2417\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"2419\"},\"nonselection_glyph\":{\"id\":\"2418\"},\"view\":{\"id\":\"2421\"}},\"id\":\"2420\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"2443\",\"type\":\"YearsTicker\"}],\"root_ids\":[\"2383\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n", + " const render_items = [{\"docid\":\"60b04000-a483-448b-ab0c-4575d574ff02\",\"notebook_comms_target\":\"2657\",\"root_ids\":[\"2383\"],\"roots\":{\"2383\":\"8e880dcf-7dd9-4659-aa44-1dfc20ea28d9\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "2383" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 85\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Case 2: time series with a daily sampling frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "granularity: daily\n", + "period: 7\n" + ] + } + ], + "source": [ + "sample_data = json.load(open('../../sampledata/univariate/univariate_sample_daily.json'))\n", + "print(f\"granularity: {sample_data['granularity']}\")\n", + "print(f\"period: {sample_data['period']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = 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+ "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 95\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = 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Application\",\"version\":\"2.4.3\"}};\n", + " const render_items = [{\"docid\":\"fb518d48-6a3d-4904-a991-3af21144d0e5\",\"notebook_comms_target\":\"3571\",\"root_ids\":[\"3241\"],\"roots\":{\"3241\":\"ed261bd7-1810-4941-bacc-b6b52632dcd8\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "3241" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 90\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85 sensitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = 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Application\",\"version\":\"2.4.3\"}};\n", + " const render_items = [{\"docid\":\"4f825862-68e9-44f0-b235-55784628a756\",\"notebook_comms_target\":\"4070\",\"root_ids\":[\"3712\"],\"roots\":{\"3712\":\"25031dcb-2aa9-4e55-b48d-c1c2ede7fd05\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "3712" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "sensitivity = 85\n", + "results = detect_anomaly_entire(sample_data, ad_client, sensitivity)\n", + "build_figure(results, sample_data, sensitivity)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "aa60471fcc78aee1d161dfae290e862a10214dc7d6ca1ceb34988aca5ae05936" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ipython-notebook/media/data-overview.png b/ipython-notebook/media/data-overview.png new file mode 100644 index 0000000..3ba82e1 Binary files /dev/null and b/ipython-notebook/media/data-overview.png differ diff --git a/ipython-notebook/media/endpoint_key.png b/ipython-notebook/media/endpoint_key.png new file mode 100644 index 0000000..01281a3 Binary files /dev/null and b/ipython-notebook/media/endpoint_key.png differ diff --git a/ipython-notebook/media/inference-result.png b/ipython-notebook/media/inference-result.png new file mode 100644 index 0000000..17add24 Binary files /dev/null and b/ipython-notebook/media/inference-result.png differ diff --git a/other-samples/csharp-sample/.gitignore b/other-samples/csharp-sample/.gitignore deleted file mode 100644 index 3296d4c..0000000 --- a/other-samples/csharp-sample/.gitignore +++ /dev/null @@ -1,8 +0,0 @@ -################################################################################ -# This .gitignore file was automatically created by Microsoft(R) Visual Studio. -################################################################################ - -/packages/Newtonsoft.Json.11.0.2 -/AnomalyDetection/obj/Debug -/AnomalyDetection/bin/Debug -/.vs/AnomalyDetectionSample/v15/.suo diff --git a/other-samples/csharp-sample/AnomalyDetection/AnomalyDetection.csproj b/other-samples/csharp-sample/AnomalyDetection/AnomalyDetection.csproj deleted file mode 100644 index 65ddc30..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/AnomalyDetection.csproj +++ /dev/null @@ -1,92 +0,0 @@ - - - - - Debug - AnyCPU - {0FB3F8D2-C0D4-4E55-AE6F-DBC991D803B1} - WinExe - AnomalyDetection - AnomalyDetection - v4.5.2 - 512 - true - - - AnyCPU - true - full - false - bin\Debug\ - DEBUG;TRACE - prompt - 4 - - - AnyCPU - pdbonly - true - bin\Release\ - TRACE - prompt - 4 - - - - ..\packages\Newtonsoft.Json.11.0.2\lib\net45\Newtonsoft.Json.dll - - - - - - - - - - - - - - - - - - - Form - - - Form1.cs - - - - - Form1.cs - - - ResXFileCodeGenerator - Resources.Designer.cs - Designer - - - True - Resources.resx - - - - SettingsSingleFileGenerator - Settings.Designer.cs - - - True - Settings.settings - True - - - Always - - - - - - - \ No newline at end of file diff --git a/other-samples/csharp-sample/AnomalyDetection/AnomalyDetectionClient.cs b/other-samples/csharp-sample/AnomalyDetection/AnomalyDetectionClient.cs deleted file mode 100644 index d39a22a..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/AnomalyDetectionClient.cs +++ /dev/null @@ -1,35 +0,0 @@ -using System; -using System.Net; -using System.Net.Http; -using System.Net.Http.Headers; -using System.Text; -using System.Threading.Tasks; - -namespace AnomalyDetection -{ - public class AnomalyDetectionClient - { - - public async Task Request(string baseAddress, string endpoint, string subscriptionKey, string requestData) - { - using (HttpClient client = new HttpClient { BaseAddress = new Uri(baseAddress) }) - { - System.Net.ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12 | SecurityProtocolType.Tls11 | SecurityProtocolType.Tls; - client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json")); - client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", subscriptionKey); - - var content = new StringContent(requestData, Encoding.UTF8, "application/json"); - var res = await client.PostAsync(endpoint, content); - var responseText = await res.Content.ReadAsStringAsync(); - if (res.IsSuccessStatusCode) - { - return responseText; - } - else - { - throw new HttpRequestException($"The server returned {res.StatusCode} - {res.StatusCode.ToString()}\n: { responseText }"); - } - } - } - } -} diff --git a/other-samples/csharp-sample/AnomalyDetection/App.config b/other-samples/csharp-sample/AnomalyDetection/App.config deleted file mode 100644 index 88fa402..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/App.config +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/other-samples/csharp-sample/AnomalyDetection/Form1.Designer.cs b/other-samples/csharp-sample/AnomalyDetection/Form1.Designer.cs deleted file mode 100644 index 7a37ea2..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Form1.Designer.cs +++ /dev/null @@ -1,224 +0,0 @@ -namespace AnomalyDetection -{ - partial class Form1 - { - /// - /// Required designer variable. - /// - private System.ComponentModel.IContainer components = null; - - /// - /// Clean up any resources being used. - /// - /// true if managed resources should be disposed; otherwise, false. - protected override void Dispose(bool disposing) - { - if (disposing && (components != null)) - { - components.Dispose(); - } - base.Dispose(disposing); - } - - #region Windows Form Designer generated code - - /// - /// Required method for Designer support - do not modify - /// the contents of this method with the code editor. - /// - private void InitializeComponent() - { - this.label1 = new System.Windows.Forms.Label(); - this.subscriptionKey = new System.Windows.Forms.TextBox(); - this.label2 = new System.Windows.Forms.Label(); - this.button1 = new System.Windows.Forms.Button(); - this.label3 = new System.Windows.Forms.Label(); - this.urlBox = new System.Windows.Forms.TextBox(); - this.label4 = new System.Windows.Forms.Label(); - this.requestData = new System.Windows.Forms.RichTextBox(); - this.responseData = new System.Windows.Forms.RichTextBox(); - this.dataFile = new System.Windows.Forms.TextBox(); - this.label6 = new System.Windows.Forms.Label(); - this.label5 = new System.Windows.Forms.Label(); - this.radioButton1 = new System.Windows.Forms.RadioButton(); - this.radioButton2 = new System.Windows.Forms.RadioButton(); - this.SuspendLayout(); - // - // label1 - // - this.label1.AutoSize = true; - this.label1.Location = new System.Drawing.Point(1, 57); - this.label1.Name = "label1"; - this.label1.Size = new System.Drawing.Size(89, 13); - this.label1.TabIndex = 0; - this.label1.Text = "Subscription Key:"; - // - // subscriptionKey - // - this.subscriptionKey.Location = new System.Drawing.Point(93, 55); - this.subscriptionKey.Name = "subscriptionKey"; - this.subscriptionKey.Size = new System.Drawing.Size(211, 20); - this.subscriptionKey.TabIndex = 1; - this.subscriptionKey.Text = "{your_subscription_key}"; - // - // label2 - // - this.label2.AutoSize = true; - this.label2.Location = new System.Drawing.Point(1, 86); - this.label2.Name = "label2"; - this.label2.Size = new System.Drawing.Size(121, 13); - this.label2.TabIndex = 2; - this.label2.Text = "Request preview (Json):"; - // - // button1 - // - this.button1.Location = new System.Drawing.Point(613, 322); - this.button1.Name = "button1"; - this.button1.Size = new System.Drawing.Size(101, 25); - this.button1.TabIndex = 4; - this.button1.Text = "&Send request"; - this.button1.UseVisualStyleBackColor = true; - this.button1.Click += new System.EventHandler(this.button1_Click); - // - // label3 - // - this.label3.AutoSize = true; - this.label3.Location = new System.Drawing.Point(2, 32); - this.label3.Name = "label3"; - this.label3.Size = new System.Drawing.Size(77, 13); - this.label3.TabIndex = 5; - this.label3.Text = "Endpoint URL:"; - // - // urlBox - // - this.urlBox.BackColor = System.Drawing.SystemColors.Window; - this.urlBox.HideSelection = false; - this.urlBox.Location = new System.Drawing.Point(89, 27); - this.urlBox.Name = "urlBox"; - this.urlBox.Size = new System.Drawing.Size(428, 20); - this.urlBox.TabIndex = 6; - this.urlBox.Text = "https://westus2.api.cognitive.microsoft.com/"; - // - // label4 - // - this.label4.AutoSize = true; - this.label4.Location = new System.Drawing.Point(1, 335); - this.label4.Name = "label4"; - this.label4.Size = new System.Drawing.Size(89, 13); - this.label4.TabIndex = 7; - this.label4.Text = "Response (Json):"; - // - // requestData - // - this.requestData.BackColor = System.Drawing.SystemColors.InactiveCaption; - this.requestData.Location = new System.Drawing.Point(0, 102); - this.requestData.Name = "requestData"; - this.requestData.ReadOnly = true; - this.requestData.Size = new System.Drawing.Size(723, 217); - this.requestData.TabIndex = 9; - this.requestData.Text = ""; - // - // responseData - // - this.responseData.Location = new System.Drawing.Point(0, 352); - this.responseData.Name = "responseData"; - this.responseData.Size = new System.Drawing.Size(723, 309); - this.responseData.TabIndex = 10; - this.responseData.Text = ""; - // - // dataFile - // - this.dataFile.Location = new System.Drawing.Point(408, 53); - this.dataFile.Name = "dataFile"; - this.dataFile.Size = new System.Drawing.Size(221, 20); - this.dataFile.TabIndex = 13; - // - // label6 - // - this.label6.AutoSize = true; - this.label6.Location = new System.Drawing.Point(313, 57); - this.label6.Name = "label6"; - this.label6.Size = new System.Drawing.Size(78, 13); - this.label6.TabIndex = 14; - this.label6.Text = "Data Filename:"; - // - // label5 - // - this.label5.AutoSize = true; - this.label5.Location = new System.Drawing.Point(1, 9); - this.label5.Name = "label5"; - this.label5.Size = new System.Drawing.Size(84, 13); - this.label5.TabIndex = 15; - this.label5.Text = "Choose request:"; - // - // radioButton1 - // - this.radioButton1.AutoSize = true; - this.radioButton1.Checked = true; - this.radioButton1.Location = new System.Drawing.Point(93, 7); - this.radioButton1.Name = "radioButton1"; - this.radioButton1.Size = new System.Drawing.Size(270, 17); - this.radioButton1.TabIndex = 16; - this.radioButton1.TabStop = true; - this.radioButton1.Text = "Detect anomaly status for the last point in the series."; - this.radioButton1.UseVisualStyleBackColor = true; - // this.radioButton1.CheckedChanged += new System.EventHandler(this.radioButton1_CheckedChanged); - // - // radioButton2 - // - this.radioButton2.AutoSize = true; - this.radioButton2.Location = new System.Drawing.Point(369, 7); - this.radioButton2.Name = "radioButton2"; - this.radioButton2.Size = new System.Drawing.Size(225, 17); - this.radioButton2.TabIndex = 17; - this.radioButton2.Text = "Detect anomaly points for the entire series."; - this.radioButton2.UseVisualStyleBackColor = true; - // this.radioButton2.CheckedChanged += new System.EventHandler(this.radioButton2_CheckedChanged); - // - // Form1 - // - this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F); - this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font; - this.AutoSize = true; - this.ClientSize = new System.Drawing.Size(724, 663); - this.Controls.Add(this.radioButton2); - this.Controls.Add(this.radioButton1); - this.Controls.Add(this.label5); - this.Controls.Add(this.label6); - this.Controls.Add(this.dataFile); - this.Controls.Add(this.responseData); - this.Controls.Add(this.requestData); - this.Controls.Add(this.label4); - this.Controls.Add(this.urlBox); - this.Controls.Add(this.label3); - this.Controls.Add(this.button1); - this.Controls.Add(this.label2); - this.Controls.Add(this.subscriptionKey); - this.Controls.Add(this.label1); - this.Name = "Form1"; - this.Text = "Form1"; - this.Load += new System.EventHandler(this.Form1_Load); - this.ResumeLayout(false); - this.PerformLayout(); - - } - - #endregion - - private System.Windows.Forms.Label label1; - private System.Windows.Forms.TextBox subscriptionKey; - private System.Windows.Forms.Label label2; - private System.Windows.Forms.Button button1; - private System.Windows.Forms.Label label3; - private System.Windows.Forms.TextBox urlBox; - private System.Windows.Forms.Label label4; - private System.Windows.Forms.RichTextBox requestData; - private System.Windows.Forms.RichTextBox responseData; - private System.Windows.Forms.TextBox dataFile; - private System.Windows.Forms.Label label6; - private System.Windows.Forms.Label label5; - private System.Windows.Forms.RadioButton radioButton1; - private System.Windows.Forms.RadioButton radioButton2; - } -} - diff --git a/other-samples/csharp-sample/AnomalyDetection/Form1.cs b/other-samples/csharp-sample/AnomalyDetection/Form1.cs deleted file mode 100644 index d4afe74..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Form1.cs +++ /dev/null @@ -1,136 +0,0 @@ -using Newtonsoft.Json; -using System; -using System.IO; -using System.Linq; -using System.Text.RegularExpressions; -using System.Windows.Forms; - -namespace AnomalyDetection -{ - public partial class Form1 : Form - { - private string _dataFile = "request-data.json"; - private Request _request; - - public Form1() - { - InitializeComponent(); - } - - private async void button1_Click(object sender, EventArgs e) - { - if (string.IsNullOrEmpty(this.subscriptionKey.Text)) - { - this.requestData.Text = "Please specify subscription key."; - return; - } - if (string.IsNullOrEmpty(this.requestData.Text)) - { - this.requestData.Text = "Please input request data."; - return; - } - if (string.IsNullOrEmpty(this.urlBox.Text)) - { - this.requestData.Text = "Please input url."; - return; - } - - var client = new AnomalyDetectionClient(); - try - { - var url = this.urlBox.Text; - var path = "anomalydetector/v1.0/timeseries/entire/detect"; - if (radioButton1.Checked) - { - path = "anomalydetector/v1.0/timeseries/last/detect"; - } - this.responseData.Clear(); - SetRequestData(); - var res = await client.Request( - url, - path, - this.subscriptionKey.Text, - this.requestData.Text); - - - if (this.radioButton1.Checked) - { - var response = JsonConvert.DeserializeObject(res); - this.responseData.Text = JsonConvert.SerializeObject(response, Formatting.Indented, - new JsonSerializerSettings { ContractResolver = new Newtonsoft.Json.Serialization.CamelCasePropertyNamesContractResolver() }); - } - else - { - var response = JsonConvert.DeserializeObject(res); - this.responseData.Text = JsonConvert.SerializeObject(response, Formatting.Indented, - new JsonSerializerSettings { ContractResolver = new Newtonsoft.Json.Serialization.CamelCasePropertyNamesContractResolver() }); - } - this.responseData.Text += "\n========== Response Evaluation ==========\n"; - this.responseData.Text += "\nUsing the response to do anything you need.\n"; - this.responseData.Text += "\n========== Evaluation Done ==========\n"; - - } - catch (Exception ex) - { - this.responseData.Text = ex.ToString(); - } - } - - private void SetRequestData() - { - this.requestData.Text = JsonConvert.SerializeObject(this._request, Formatting.Indented, new JsonSerializerSettings - { - NullValueHandling = NullValueHandling.Ignore, - ContractResolver = new Newtonsoft.Json.Serialization.CamelCasePropertyNamesContractResolver() - }); - } - private void Form1_Load(object sender, EventArgs e) - { - this.dataFile.Text = _dataFile; - try - { // Open the text file using a stream reader. - using (StreamReader sr = new StreamReader(_dataFile)) - { - // Read the stream to a string, and write the string to the console. - String requestData = sr.ReadToEnd(); - _request = JsonConvert.DeserializeObject(requestData); - //this.period.Text = _request.Period.ToString(); - SetRequestData(); - } - } - catch (Exception ex) - { - this.responseData.Text += $"Could not build request: {ex.ToString()}"; - } - } - - //private void period_TextChanged(object sender, EventArgs e) - //{ - // if (_request != null) - // { - // float v = 0; - // this.period.ForeColor = System.Drawing.Color.White; - // if (float.TryParse(this.period.Text, out v)) - // { - // this._request.Period = v; - // this.period.BackColor = System.Drawing.Color.Green; - // } else - // { - // this.period.BackColor = System.Drawing.Color.Red; - // } - // SetRequestData(); - // } - //} - - private void radioButton1_CheckedChanged(object sender, EventArgs e) - { - this.urlBox.Text = "https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/last/detect"; - } - - private void radioButton2_CheckedChanged(object sender, EventArgs e) - { - this.urlBox.Text = "https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/entire/detect"; - } - - } -} diff --git a/other-samples/csharp-sample/AnomalyDetection/Form1.resx b/other-samples/csharp-sample/AnomalyDetection/Form1.resx deleted file mode 100644 index 1af7de1..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Form1.resx +++ /dev/null @@ -1,120 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - text/microsoft-resx - - - 2.0 - - - System.Resources.ResXResourceReader, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089 - - - System.Resources.ResXResourceWriter, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089 - - \ No newline at end of file diff --git a/other-samples/csharp-sample/AnomalyDetection/Program.cs b/other-samples/csharp-sample/AnomalyDetection/Program.cs deleted file mode 100644 index 3c06d36..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Program.cs +++ /dev/null @@ -1,19 +0,0 @@ -using System; -using System.Windows.Forms; - -namespace AnomalyDetection -{ - static class Program - { - /// - /// The main entry point for the application. - /// - [STAThread] - static void Main() - { - Application.EnableVisualStyles(); - Application.SetCompatibleTextRenderingDefault(false); - Application.Run(new Form1()); - } - } -} diff --git a/other-samples/csharp-sample/AnomalyDetection/Properties/AssemblyInfo.cs b/other-samples/csharp-sample/AnomalyDetection/Properties/AssemblyInfo.cs deleted file mode 100644 index 356a3e6..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Properties/AssemblyInfo.cs +++ /dev/null @@ -1,36 +0,0 @@ -using System.Reflection; -using System.Runtime.CompilerServices; -using System.Runtime.InteropServices; - -// General Information about an assembly is controlled through the following -// set of attributes. Change these attribute values to modify the information -// associated with an assembly. -[assembly: AssemblyTitle("AnomalyDetection")] -[assembly: AssemblyDescription("")] -[assembly: AssemblyConfiguration("")] -[assembly: AssemblyCompany("")] -[assembly: AssemblyProduct("AnomalyDetection")] -[assembly: AssemblyCopyright("Copyright © 2018")] -[assembly: AssemblyTrademark("")] -[assembly: AssemblyCulture("")] - -// Setting ComVisible to false makes the types in this assembly not visible -// to COM components. If you need to access a type in this assembly from -// COM, set the ComVisible attribute to true on that type. -[assembly: ComVisible(false)] - -// The following GUID is for the ID of the typelib if this project is exposed to COM -[assembly: Guid("0fb3f8d2-c0d4-4e55-ae6f-dbc991d803b1")] - -// Version information for an assembly consists of the following four values: -// -// Major Version -// Minor Version -// Build Number -// Revision -// -// You can specify all the values or you can default the Build and Revision Numbers -// by using the '*' as shown below: -// [assembly: AssemblyVersion("1.0.*")] -[assembly: AssemblyVersion("1.0.0.0")] -[assembly: AssemblyFileVersion("1.0.0.0")] diff --git a/other-samples/csharp-sample/AnomalyDetection/Properties/Resources.Designer.cs b/other-samples/csharp-sample/AnomalyDetection/Properties/Resources.Designer.cs deleted file mode 100644 index 0c63633..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Properties/Resources.Designer.cs +++ /dev/null @@ -1,71 +0,0 @@ -//------------------------------------------------------------------------------ -// -// This code was generated by a tool. -// Runtime Version:4.0.30319.42000 -// -// Changes to this file may cause incorrect behavior and will be lost if -// the code is regenerated. -// -//------------------------------------------------------------------------------ - -namespace AnomalyDetection.Properties -{ - - - /// - /// A strongly-typed resource class, for looking up localized strings, etc. - /// - // This class was auto-generated by the StronglyTypedResourceBuilder - // class via a tool like ResGen or Visual Studio. - // To add or remove a member, edit your .ResX file then rerun ResGen - // with the /str option, or rebuild your VS project. - [global::System.CodeDom.Compiler.GeneratedCodeAttribute("System.Resources.Tools.StronglyTypedResourceBuilder", "4.0.0.0")] - [global::System.Diagnostics.DebuggerNonUserCodeAttribute()] - [global::System.Runtime.CompilerServices.CompilerGeneratedAttribute()] - internal class Resources - { - - private static global::System.Resources.ResourceManager resourceMan; - - private static global::System.Globalization.CultureInfo resourceCulture; - - [global::System.Diagnostics.CodeAnalysis.SuppressMessageAttribute("Microsoft.Performance", "CA1811:AvoidUncalledPrivateCode")] - internal Resources() - { - } - - /// - /// Returns the cached ResourceManager instance used by this class. - /// - [global::System.ComponentModel.EditorBrowsableAttribute(global::System.ComponentModel.EditorBrowsableState.Advanced)] - internal static global::System.Resources.ResourceManager ResourceManager - { - get - { - if ((resourceMan == null)) - { - global::System.Resources.ResourceManager temp = new global::System.Resources.ResourceManager("AnomalyDetection.Properties.Resources", typeof(Resources).Assembly); - resourceMan = temp; - } - return resourceMan; - } - } - - /// - /// Overrides the current thread's CurrentUICulture property for all - /// resource lookups using this strongly typed resource class. - /// - [global::System.ComponentModel.EditorBrowsableAttribute(global::System.ComponentModel.EditorBrowsableState.Advanced)] - internal static global::System.Globalization.CultureInfo Culture - { - get - { - return resourceCulture; - } - set - { - resourceCulture = value; - } - } - } -} diff --git a/other-samples/csharp-sample/AnomalyDetection/Properties/Resources.resx b/other-samples/csharp-sample/AnomalyDetection/Properties/Resources.resx deleted file mode 100644 index af7dbeb..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Properties/Resources.resx +++ /dev/null @@ -1,117 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - text/microsoft-resx - - - 2.0 - - - System.Resources.ResXResourceReader, System.Windows.Forms, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089 - - - System.Resources.ResXResourceWriter, System.Windows.Forms, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089 - - \ No newline at end of file diff --git a/other-samples/csharp-sample/AnomalyDetection/Properties/Settings.Designer.cs b/other-samples/csharp-sample/AnomalyDetection/Properties/Settings.Designer.cs deleted file mode 100644 index 81484bf..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Properties/Settings.Designer.cs +++ /dev/null @@ -1,30 +0,0 @@ -//------------------------------------------------------------------------------ -// -// This code was generated by a tool. -// Runtime Version:4.0.30319.42000 -// -// Changes to this file may cause incorrect behavior and will be lost if -// the code is regenerated. -// -//------------------------------------------------------------------------------ - -namespace AnomalyDetection.Properties -{ - - - [global::System.Runtime.CompilerServices.CompilerGeneratedAttribute()] - [global::System.CodeDom.Compiler.GeneratedCodeAttribute("Microsoft.VisualStudio.Editors.SettingsDesigner.SettingsSingleFileGenerator", "11.0.0.0")] - internal sealed partial class Settings : global::System.Configuration.ApplicationSettingsBase - { - - private static Settings defaultInstance = ((Settings)(global::System.Configuration.ApplicationSettingsBase.Synchronized(new Settings()))); - - public static Settings Default - { - get - { - return defaultInstance; - } - } - } -} diff --git a/other-samples/csharp-sample/AnomalyDetection/Properties/Settings.settings b/other-samples/csharp-sample/AnomalyDetection/Properties/Settings.settings deleted file mode 100644 index 3964565..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Properties/Settings.settings +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - diff --git a/other-samples/csharp-sample/AnomalyDetection/Request.cs b/other-samples/csharp-sample/AnomalyDetection/Request.cs deleted file mode 100644 index 2ceb6ad..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Request.cs +++ /dev/null @@ -1,20 +0,0 @@ -using System; -using System.Collections.Generic; - -namespace AnomalyDetection -{ - public class Point - { - public DateTime Timestamp { get; set; } - public double Value { get; set; } - } - - public class Request - { - public List Series { get; set; } - public float? MaxAnomalyRatio { get; set; } - public int? Sensitivity { get; set; } - public string Granularity { get; set; } - - } -} diff --git a/other-samples/csharp-sample/AnomalyDetection/Response.cs b/other-samples/csharp-sample/AnomalyDetection/Response.cs deleted file mode 100644 index 824c763..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/Response.cs +++ /dev/null @@ -1,29 +0,0 @@ -using System.Collections.Generic; - -namespace AnomalyDetection -{ - public class LResponse - { - public long SuggestedWindow { get; set; } - public bool IsNegativeAnomaly { get; set; } - public double LowerMargin { get; set; } - public bool IsPositiveAnomaly { get; set; } - public long Period { get; set; } - public double ExpectedValue { get; set; } - public bool IsAnomaly { get; set; } - public double UpperMargin { get; set; } - - } - - public class EResponse - { - public List IsNegativeAnomaly { get; set; } - public List ExpectedValues { get; set; } - public List IsPositiveAnomaly { get; set; } - public List LowerMargins { get; set; } - public long Period { get; set; } - public List UpperMargins { get; set; } - public List IsAnomaly { get; set; } - - } -} \ No newline at end of file diff --git a/other-samples/csharp-sample/AnomalyDetection/packages.config b/other-samples/csharp-sample/AnomalyDetection/packages.config deleted file mode 100644 index d81a62a..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/packages.config +++ /dev/null @@ -1,5 +0,0 @@ - - - - - \ No newline at end of file diff --git a/other-samples/csharp-sample/AnomalyDetection/request-data.json b/other-samples/csharp-sample/AnomalyDetection/request-data.json deleted file mode 100644 index aa0238d..0000000 --- a/other-samples/csharp-sample/AnomalyDetection/request-data.json +++ /dev/null @@ -1,193 +0,0 @@ -{ - "granularity": "daily", - "series": [ - { - "timestamp": "2018-03-01T00:00:00Z", - "value": 32858923 - }, - { - "timestamp": "2018-03-02T00:00:00Z", - "value": 29615278 - }, - { - "timestamp": "2018-03-03T00:00:00Z", - "value": 22839355 - }, - { - "timestamp": "2018-03-04T00:00:00Z", - "value": 25948736 - }, - { - "timestamp": "2018-03-05T00:00:00Z", - "value": 34139159 - }, - { - "timestamp": "2018-03-06T00:00:00Z", - "value": 33843985 - }, - { - "timestamp": "2018-03-07T00:00:00Z", - "value": 33637661 - }, - { - "timestamp": "2018-03-08T00:00:00Z", - "value": 32627350 - }, - { - "timestamp": "2018-03-09T00:00:00Z", - "value": 29881076 - }, - { - "timestamp": "2018-03-10T00:00:00Z", - "value": 22681575 - }, - { - "timestamp": "2018-03-11T00:00:00Z", - "value": 24629393 - }, - { - "timestamp": "2018-03-12T00:00:00Z", - "value": 34010679 - }, - { - "timestamp": "2018-03-13T00:00:00Z", - "value": 33893888 - }, - { - "timestamp": "2018-03-14T00:00:00Z", - "value": 33760076 - }, - { - "timestamp": "2018-03-15T00:00:00Z", - "value": 33093515 - }, - { - "timestamp": "2018-03-16T00:00:00Z", - "value": 29945555 - }, - { - "timestamp": "2018-03-17T00:00:00Z", - "value": 22676212 - }, - { - "timestamp": "2018-03-18T00:00:00Z", - "value": 25262514 - }, - { - "timestamp": "2018-03-19T00:00:00Z", - "value": 33631649 - }, - { - "timestamp": "2018-03-20T00:00:00Z", - "value": 34468310 - }, - { - "timestamp": "2018-03-21T00:00:00Z", - "value": 34212281 - }, - { - "timestamp": "2018-03-22T00:00:00Z", - "value": 38144434 - }, - { - "timestamp": "2018-03-23T00:00:00Z", - "value": 34662949 - }, - { - "timestamp": "2018-03-24T00:00:00Z", - "value": 24623684 - }, - { - "timestamp": "2018-03-25T00:00:00Z", - "value": 26530491 - }, - { - "timestamp": "2018-03-26T00:00:00Z", - "value": 35445003 - }, - { - "timestamp": "2018-03-27T00:00:00Z", - "value": 34250789 - }, - { - "timestamp": "2018-03-28T00:00:00Z", - "value": 33423012 - }, - { - "timestamp": "2018-03-29T00:00:00Z", - "value": 30744783 - }, - { - "timestamp": "2018-03-30T00:00:00Z", - "value": 25825128 - }, - { - "timestamp": "2018-03-31T00:00:00Z", - "value": 21244209 - }, - { - "timestamp": "2018-04-01T00:00:00Z", - "value": 22576956 - }, - { - "timestamp": "2018-04-02T00:00:00Z", - "value": 31957221 - }, - { - "timestamp": "2018-04-03T00:00:00Z", - "value": 33841228 - }, - { - "timestamp": "2018-04-04T00:00:00Z", - "value": 33554483 - }, - { - "timestamp": "2018-04-05T00:00:00Z", - "value": 32383350 - }, - { - "timestamp": "2018-04-06T00:00:00Z", - "value": 29494850 - }, - { - "timestamp": "2018-04-07T00:00:00Z", - "value": 22815534 - }, - { - "timestamp": "2018-04-08T00:00:00Z", - "value": 25557267 - }, - { - "timestamp": "2018-04-09T00:00:00Z", - "value": 34858252 - }, - { - "timestamp": "2018-04-10T00:00:00Z", - "value": 34750597 - }, - { - "timestamp": "2018-04-11T00:00:00Z", - "value": 34717956 - }, - { - "timestamp": "2018-04-12T00:00:00Z", - "value": 34132534 - }, - { - "timestamp": "2018-04-13T00:00:00Z", - "value": 30762236 - }, - { - "timestamp": "2018-04-14T00:00:00Z", - "value": 22504059 - }, - { - "timestamp": "2018-04-15T00:00:00Z", - "value": 26149060 - }, - { - "timestamp": "2018-04-16T00:00:00Z", - "value": 35250105 - } - ] -} \ No newline at end of file diff --git a/other-samples/csharp-sample/AnomalyDetectionSample.sln b/other-samples/csharp-sample/AnomalyDetectionSample.sln deleted file mode 100644 index 83158ef..0000000 --- a/other-samples/csharp-sample/AnomalyDetectionSample.sln +++ /dev/null @@ -1,25 +0,0 @@ - -Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio 15 -VisualStudioVersion = 15.0.26430.16 -MinimumVisualStudioVersion = 10.0.40219.1 -Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "AnomalyDetection", "AnomalyDetection\AnomalyDetection.csproj", "{0FB3F8D2-C0D4-4E55-AE6F-DBC991D803B1}" -EndProject -Global - GlobalSection(SolutionConfigurationPlatforms) = preSolution - Debug|Any CPU = Debug|Any CPU - Release|Any CPU = Release|Any CPU - EndGlobalSection - GlobalSection(ProjectConfigurationPlatforms) = postSolution - {0FB3F8D2-C0D4-4E55-AE6F-DBC991D803B1}.Debug|Any CPU.ActiveCfg = Debug|Any CPU - {0FB3F8D2-C0D4-4E55-AE6F-DBC991D803B1}.Debug|Any CPU.Build.0 = Debug|Any CPU - {0FB3F8D2-C0D4-4E55-AE6F-DBC991D803B1}.Release|Any CPU.ActiveCfg = Release|Any CPU - {0FB3F8D2-C0D4-4E55-AE6F-DBC991D803B1}.Release|Any CPU.Build.0 = Release|Any CPU - EndGlobalSection - GlobalSection(SolutionProperties) = preSolution - HideSolutionNode = FALSE - EndGlobalSection - GlobalSection(ExtensibilityGlobals) = postSolution - SolutionGuid = {56C8E7CF-29BF-4D8F-9ADA-F30F17CAFD5D} - EndGlobalSection -EndGlobal diff --git a/other-samples/csharp-sample/README.md b/other-samples/csharp-sample/README.md deleted file mode 100644 index cf58340..0000000 --- a/other-samples/csharp-sample/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Microsoft Cognitive Services Anomaly Detector tutorial projects - -This tutorial shows the features of the Microsoft Cognitive Services Anomaly Detector REST API by using C#, Java, Python and JavaScript(HTML). - -## Getting started - -### Installation - -1. Clone the repository, or download the repository as a .zip file and extract the .zip file into an empty directory. -2. Install Visual Studio 2017, Community Edition. -3. Install latest Newtonsoft.Json package with NuGet Packages Manager. - -You are now ready to begin the tutorial. diff --git a/other-samples/java-sample/.gitignore b/other-samples/java-sample/.gitignore deleted file mode 100644 index 455918b..0000000 --- a/other-samples/java-sample/.gitignore +++ /dev/null @@ -1,34 +0,0 @@ -# Compiled class file -*.class - -# Log file -*.log - -# BlueJ files -*.ctxt - -# Mobile Tools for Java (J2ME) -.mtj.tmp/ - -# Package Files # -*.jar -*.war -*.ear -*.zip -*.tar.gz -*.rar - -# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml -hs_err_pid* - -# Intelli-J ignores. -target/ -.idea/ -.settings/ -.vscode/ - -*.iml -.classpath -.factorypath -.project -*.prefs diff --git a/other-samples/java-sample/pom.xml b/other-samples/java-sample/pom.xml deleted file mode 100644 index 4f34c03..0000000 --- a/other-samples/java-sample/pom.xml +++ /dev/null @@ -1,33 +0,0 @@ - - - 4.0.0 - - com.microsoft.cognitiveservice - anomalydetction - 1.0 - - - - org.apache.maven.plugins - maven-compiler-plugin - 3.8.0 - - 8 - 8 - - - - - - UTF-8 - - - - com.azure - azure-ai-anomalydetector - 3.0.0-beta.2 - - - \ No newline at end of file diff --git a/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/Main.java b/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/Main.java deleted file mode 100644 index 29bcbf6..0000000 --- a/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/Main.java +++ /dev/null @@ -1,81 +0,0 @@ -package com.microsoft.cognitiveservice.anomalydetection; - -import com.azure.ai.anomalydetector.AnomalyDetectorClient; -import com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder; -import com.azure.ai.anomalydetector.models.DetectRequest; -import com.azure.ai.anomalydetector.models.LastDetectResponse; -import com.azure.ai.anomalydetector.models.TimeGranularity; -import com.azure.ai.anomalydetector.models.TimeSeriesPoint; -import com.azure.core.credential.AzureKeyCredential; -import com.azure.core.http.HttpPipeline; -import com.azure.core.http.HttpPipelineBuilder; -import com.azure.core.http.policy.AzureKeyCredentialPolicy; -import com.fasterxml.jackson.databind.ObjectMapper; - -import java.io.IOException; -import java.io.InputStream; -import java.time.Instant; -import java.time.OffsetDateTime; -import java.time.ZoneOffset; -import java.util.List; -import java.util.stream.Collectors; - -public class Main { - // ********************************************** - // *** Update or verify the following values. *** - // ********************************************** - private static final String OCP_APIM_SUBSCRIPTION_KEY = "Ocp-Apim-Subscription-Key"; - - // Replace the subscriptionKey string value with your valid subscription key. - private static final String SUBSCRIPTION_KEY = ""; - - private static final String ENDPOINT = "https://"; - - private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper(); - - public static void main(String[] args) throws IOException { - - final RequestData requestData; - try (InputStream resourceAsStream = Main.class.getResourceAsStream("/request-data.json")) { - requestData = OBJECT_MAPPER.readValue(resourceAsStream, RequestData.class); - } - - final AzureKeyCredentialPolicy credentialPolicy = new AzureKeyCredentialPolicy(OCP_APIM_SUBSCRIPTION_KEY, - new AzureKeyCredential(SUBSCRIPTION_KEY)); - final HttpPipeline httpPipeline = new HttpPipelineBuilder() - .policies(credentialPolicy) - .build(); - - // Create the synchronous client using the builder. - final AnomalyDetectorClient asyncClient = new AnomalyDetectorClientBuilder() - .endpoint(ENDPOINT) - .pipeline(httpPipeline) - .buildClient(); - - final List timeSeriesPoints = requestData.getSeries() - .stream() - .map(series -> { - final OffsetDateTime timestamp = Instant.parse(series.getTimestamp()).atOffset(ZoneOffset.UTC); - - return new TimeSeriesPoint() - .setTimestamp(timestamp) - .setValue(series.getValue()); - }) - .collect(Collectors.toList()); - - final DetectRequest request = new DetectRequest() - .setGranularity(TimeGranularity.fromString(requestData.getGranularity())) - .setSeries(timeSeriesPoints); - - final LastDetectResponse lastDetectResponse = asyncClient.detectLastPoint(request); - - System.out.println("**************************************************************"); - System.out.printf("Expected value is: %.2f, Period is: %d, Lower margin is: %.2f, Upper margin: %.2f, " - + " Suggested window is: %d%n", - lastDetectResponse.getExpectedValue(), - lastDetectResponse.getPeriod(), - lastDetectResponse.getLowerMargin(), - lastDetectResponse.getUpperMargin(), - lastDetectResponse.getSuggestedWindow()); - } -} \ No newline at end of file diff --git a/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/RequestData.java b/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/RequestData.java deleted file mode 100644 index dba612c..0000000 --- a/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/RequestData.java +++ /dev/null @@ -1,27 +0,0 @@ -package com.microsoft.cognitiveservice.anomalydetection; - -import com.fasterxml.jackson.annotation.JsonCreator; -import com.fasterxml.jackson.annotation.JsonProperty; - -import java.util.List; - -public class RequestData { - public static final String GRANULARITY = "granularity"; - public static final String SERIES = "series"; - private final String granularity; - private final List series; - - @JsonCreator - public RequestData(@JsonProperty(GRANULARITY) String granularity, @JsonProperty(SERIES) List series) { - this.granularity = granularity; - this.series = series; - } - - public String getGranularity() { - return granularity; - } - - public List getSeries() { - return series; - } -} \ No newline at end of file diff --git a/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/Series.java b/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/Series.java deleted file mode 100644 index 4a8cfec..0000000 --- a/other-samples/java-sample/src/main/java/com/microsoft/cognitiveservice/anomalydetection/Series.java +++ /dev/null @@ -1,23 +0,0 @@ -package com.microsoft.cognitiveservice.anomalydetection; - -import com.fasterxml.jackson.annotation.JsonCreator; -import com.fasterxml.jackson.annotation.JsonProperty; - -public class Series { - private final String timestamp; - private final int value; - - @JsonCreator - public Series(@JsonProperty("timestamp") String timestamp, @JsonProperty("value") int value) { - this.timestamp = timestamp; - this.value = value; - } - - public String getTimestamp() { - return timestamp; - } - - public int getValue() { - return value; - } -} diff --git a/other-samples/java-sample/src/main/resources/request-data.json b/other-samples/java-sample/src/main/resources/request-data.json deleted file mode 100644 index 11e946c..0000000 --- a/other-samples/java-sample/src/main/resources/request-data.json +++ /dev/null @@ -1,193 +0,0 @@ -{ - "granularity": "daily", - "series": [ - { - "timestamp": "2018-03-01T00:00:00Z", - "value": 32858923 - }, - { - "timestamp": "2018-03-02T00:00:00Z", - "value": 29615278 - }, - { - "timestamp": "2018-03-03T00:00:00Z", - "value": 22839355 - }, - { - "timestamp": "2018-03-04T00:00:00Z", - "value": 25948736 - }, - { - "timestamp": "2018-03-05T00:00:00Z", - "value": 34139159 - }, - { - "timestamp": "2018-03-06T00:00:00Z", - "value": 33843985 - }, - { - "timestamp": "2018-03-07T00:00:00Z", - "value": 33637661 - }, - { - "timestamp": "2018-03-08T00:00:00Z", - "value": 32627350 - }, - { - "timestamp": "2018-03-09T00:00:00Z", - "value": 29881076 - }, - { - "timestamp": "2018-03-10T00:00:00Z", - "value": 22681575 - }, - { - "timestamp": "2018-03-11T00:00:00Z", - "value": 24629393 - }, - { - "timestamp": "2018-03-12T00:00:00Z", - "value": 34010679 - }, - { - "timestamp": "2018-03-13T00:00:00Z", - "value": 33893888 - }, - { - "timestamp": "2018-03-14T00:00:00Z", - "value": 33760076 - }, - { - "timestamp": "2018-03-15T00:00:00Z", - "value": 33093515 - }, - { - "timestamp": "2018-03-16T00:00:00Z", - "value": 29945555 - }, - { - "timestamp": "2018-03-17T00:00:00Z", - "value": 22676212 - }, - { - "timestamp": "2018-03-18T00:00:00Z", - "value": 25262514 - }, - { - "timestamp": "2018-03-19T00:00:00Z", - "value": 33631649 - }, - { - "timestamp": "2018-03-20T00:00:00Z", - "value": 34468310 - }, - { - "timestamp": "2018-03-21T00:00:00Z", - "value": 34212281 - }, - { - "timestamp": "2018-03-22T00:00:00Z", - "value": 38144434 - }, - { - "timestamp": "2018-03-23T00:00:00Z", - "value": 34662949 - }, - { - "timestamp": "2018-03-24T00:00:00Z", - "value": 24623684 - }, - { - "timestamp": "2018-03-25T00:00:00Z", - "value": 26530491 - }, - { - "timestamp": "2018-03-26T00:00:00Z", - "value": 35445003 - }, - { - "timestamp": "2018-03-27T00:00:00Z", - "value": 34250789 - }, - { - "timestamp": "2018-03-28T00:00:00Z", - "value": 33423012 - }, - { - "timestamp": "2018-03-29T00:00:00Z", - "value": 30744783 - }, - { - "timestamp": "2018-03-30T00:00:00Z", - "value": 25825128 - }, - { - "timestamp": "2018-03-31T00:00:00Z", - "value": 21244209 - }, - { - "timestamp": "2018-04-01T00:00:00Z", - "value": 22576956 - }, - { - "timestamp": "2018-04-02T00:00:00Z", - "value": 31957221 - }, - { - "timestamp": "2018-04-03T00:00:00Z", - "value": 33841228 - }, - { - "timestamp": "2018-04-04T00:00:00Z", - "value": 33554483 - }, - { - "timestamp": "2018-04-05T00:00:00Z", - "value": 32383350 - }, - { - "timestamp": "2018-04-06T00:00:00Z", - "value": 29494850 - }, - { - "timestamp": "2018-04-07T00:00:00Z", - "value": 22815534 - }, - { - "timestamp": "2018-04-08T00:00:00Z", - "value": 25557267 - }, - { - "timestamp": "2018-04-09T00:00:00Z", - "value": 34858252 - }, - { - "timestamp": "2018-04-10T00:00:00Z", - "value": 34750597 - }, - { - "timestamp": "2018-04-11T00:00:00Z", - "value": 34717956 - }, - { - "timestamp": "2018-04-12T00:00:00Z", - "value": 34132534 - }, - { - "timestamp": "2018-04-13T00:00:00Z", - "value": 30762236 - }, - { - "timestamp": "2018-04-14T00:00:00Z", - "value": 22504059 - }, - { - "timestamp": "2018-04-15T00:00:00Z", - "value": 26149060 - }, - { - "timestamp": "2018-04-16T00:00:00Z", - "value": 35250105 - } - ] - } \ No newline at end of file diff --git a/postman-demo/README.md b/postman-demo/README.md new file mode 100644 index 0000000..50144e6 --- /dev/null +++ b/postman-demo/README.md @@ -0,0 +1,50 @@ +# How to run MVAD(v1.1) in postman + +### API Overview +There are 7 APIs provided in Multivariate Anomaly Deteciton: +* **Training**: Use `Train Model API` to create and train a model, then use `Get Model Status API` to get the status and model metadata. +* **Inference**: + * Use `Async Inference API` to trigger an asynchronous inference process and use `Get Inference results API` to get detection results on a batch of data. + * You could also use `Sync Inference API` to trigger a detection on one timestamp every time. +* **Other operations**: `List Model API` and `Delete Model API` are supported in MVAD for model management. +![](https://i.imgur.com/PQ7h6Ix.png) + + + +|API Name| Method | Path | Description | +| ------ | ---- | ----------- | ------ | +|**Train Model**| POST | `{endpoint}`/anomalydetector/v1.1/multivariate/models | Create and train a model | +|**Get Model Status**| GET | `{endpoint}`anomalydetector/v1.1/multivariate/models/`{modelId}` | Get model status and model metadata with `modelId` | +|**Async Inference**| POST | `{endpoint}`/anomalydetector/v1.1/multivariate/models/`{modelId}`:detect-batch | Trigger an asynchronous inference with `modelId` | +|**Get Inference Results**| GET | `{endpoint}`/anomalydetector/v1.1/multivariate/detect-batch/`{resultId}` | Get asynchronous inference resulsts with `resultId` | +|**Sync Inference**| POST | `{endpoint}`/anomalydetector/v1.1/multivariate/models/`{modelId}`:detect-last | Trigger a synchronous inference with `modelId` | +|**List Model**| GET | `{endpoint}`/anomalydetector/v1.1/multivariate/models | List all models | +|**Delete Model**| DELET | `{endpoint}`/anomalydetector/v1.1/multivariate/models/`{modelId}` | Delete model with `modelId` | + +Please click this button to fork the API collection: +[![Fork in Postman](https://run.pstmn.io/button.svg)](https://app.getpostman.com/run-collection/18763802-b90da6d8-0f98-4200-976f-546342abcade?action=collection%2Ffork&collection-url=entityId%3D18763802-b90da6d8-0f98-4200-976f-546342abcade%26entityType%3Dcollection%26workspaceId%3De1370b45-5076-4885-884f-e9a97136ddbc#?env%5BMVAD%5D=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) + +1. Select environment as **MVAD**. +![](https://i.imgur.com/s4art6M.png) + + +1. Select **Environment**, paste your Anomaly Detector `endpoint`, `key` and dataSource `url` in to the **CURRENT VALUE** column, click **Save** to let the variables take effect. +![](https://i.imgur.com/BU00OGj.png) + +2. Select **Collections**, and click on the first API - **Create and train a model**, then click **Send**. + + ***Note:** If your data is one CSV file, please set the dataSchema as **OneTable**, if your data is multiple CSV files in a folder, please set the dataSchema as **MultiTable.*** + + ![](https://i.imgur.com/vCNsvIg.png) + +3. In the response of the first API, copy the modelId and paste it in the `modelId` in **Environments**, click **Save**. Then go to **Collections**, click on the second API - **Get model status**, and click **Send**. +![](https://i.imgur.com/tqUznIu.gif) + +4. Select the third API - **Batch Detection**, and click **Send**. This API will trigger an asynchronous inference task, and you should use the Get batch detection results API several times to get the status and the final results. +![](https://i.imgur.com/OjaE6XK.png) + +5. In the response of the third API, copy the resultId and paste it in the `resultId` in **Environments**, click **Save**. Then go to **Collections**, click on the fourth API - Get batch detection results, and click **Send**. +![](https://i.imgur.com/HKuJhSK.gif) + +6. For the rest of the APIs, click on each and click Send to test on their request and response. +![](https://i.imgur.com/KeqnaL7.png) \ No newline at end of file diff --git a/quickstarts-multivariate/multivariate_sample_data/sample_data_5_3000.zip b/quickstarts-multivariate/multivariate_sample_data/sample_data_5_3000.zip deleted file mode 100644 index a9d14af..0000000 Binary files a/quickstarts-multivariate/multivariate_sample_data/sample_data_5_3000.zip and /dev/null differ diff --git a/sampledata/README.md b/sampledata/README.md new file mode 100644 index 0000000..79f0b41 --- /dev/null +++ b/sampledata/README.md @@ -0,0 +1,5 @@ +# Sample Data + +This folder contains 2 sub folders: *univariate* and *multivariate*. You could use them in the sample code. + +For more questions and discussions: Join our community through https://aka.ms/adadvisorsjoin diff --git a/sampledata/multivariate/5_3000.json b/sampledata/multivariate/5_3000.json new file mode 100644 index 0000000..ec70420 --- /dev/null +++ b/sampledata/multivariate/5_3000.json @@ -0,0 +1,10040 @@ +{ + "variables": [ + { + "variable": "series_0", + "timestamps": [ + "2021-01-02T09:20:00Z", + "2021-01-02T09:21:00Z", + "2021-01-02T09:22:00Z", + "2021-01-02T09:23:00Z", + "2021-01-02T09:24:00Z", + "2021-01-02T09:25:00Z", + "2021-01-02T09:26:00Z", + "2021-01-02T09:27:00Z", + "2021-01-02T09:28:00Z", + "2021-01-02T09:29:00Z", + "2021-01-02T09:30:00Z", + "2021-01-02T09:31:00Z", + "2021-01-02T09:32:00Z", + "2021-01-02T09:33:00Z", + "2021-01-02T09:34:00Z", + "2021-01-02T09:35:00Z", + "2021-01-02T09:36:00Z", + "2021-01-02T09:37:00Z", + "2021-01-02T09:38:00Z", + "2021-01-02T09:39:00Z", + "2021-01-02T09:40:00Z", + "2021-01-02T09:41:00Z", + 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a/example-data/latest-point-response.json b/sampledata/univariate/latest-point-response.json similarity index 100% rename from example-data/latest-point-response.json rename to sampledata/univariate/latest-point-response.json diff --git a/example-data/request-data.csv b/sampledata/univariate/request-data.csv similarity index 100% rename from example-data/request-data.csv rename to sampledata/univariate/request-data.csv diff --git a/example-data/request-data.json b/sampledata/univariate/request-data.json similarity index 100% rename from example-data/request-data.json rename to sampledata/univariate/request-data.json diff --git a/ipython-notebook/SDK Sample/univariate_sample_daily.json b/sampledata/univariate/univariate_sample_daily.json similarity index 99% rename from ipython-notebook/SDK Sample/univariate_sample_daily.json rename to sampledata/univariate/univariate_sample_daily.json index aa0238d..176863d 100644 --- a/ipython-notebook/SDK Sample/univariate_sample_daily.json +++ b/sampledata/univariate/univariate_sample_daily.json @@ -1,4 +1,5 @@ { + "period": 7, "granularity": "daily", "series": [ { diff --git a/ipython-notebook/SDK Sample/univariate_sample_hourly.json b/sampledata/univariate/univariate_sample_hourly.json similarity index 100% rename from ipython-notebook/SDK Sample/univariate_sample_hourly.json rename to sampledata/univariate/univariate_sample_hourly.json diff --git a/quickstarts-multivariate/Java/MultivariateSample.java b/samples-multivariate/MultivariateSample.java similarity index 100% rename from quickstarts-multivariate/Java/MultivariateSample.java rename to samples-multivariate/MultivariateSample.java diff --git a/quickstarts-multivariate/README.md b/samples-multivariate/README.md similarity index 72% rename from quickstarts-multivariate/README.md rename to samples-multivariate/README.md index 5e7b69f..c603a39 100644 --- a/quickstarts-multivariate/README.md +++ b/samples-multivariate/README.md @@ -7,7 +7,7 @@ languages: - javascript products: - azure -- azure-cognitive-services +- ai-services - azure-anomaly-detector description: "This repository contains samples for Anomaly Detector multivariate API. The Anomaly Detector multivariate API enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume." --- @@ -19,7 +19,7 @@ This repository contains samples for [Anomaly Detector API](https://aka.ms/anoma ## Prerequisites You must have an [Anomaly Detector API resource](https://aka.ms/adnew). Before continuing, you will need the API key and the endpoint from your Azure dashboard. - ![Get Anomaly Detector access keys](./media/cognitive-services-get-access-keys.png "Get Anomaly Detector access keys") + ![Get Anomaly Detector access keys](../media/cognitive-services-get-access-keys.png "Get Anomaly Detector access keys") Or you could create a 7-day free resource of Anomaly Detector from [here](https://azure.microsoft.com/en-us/try/cognitive-services/my-apis/). @@ -36,10 +36,10 @@ Please read [Best practices for using the Anomaly Detector API](https://aka.ms/a | Langauge | Sample Code| |:---------|:-----------| -| Python | [Sample](https://github.com/Azure-Samples/AnomalyDetector/tree/master/quickstarts-multivariate/Python) | -| C# | [Sample](https://github.com/Azure-Samples/AnomalyDetector/tree/master/quickstarts-multivariate/CSharp) | -| Java | [Sample](https://github.com/Azure-Samples/AnomalyDetector/tree/master/quickstarts-multivariate/Java) | -| JavaScript| [Sample](https://github.com/Azure-Samples/AnomalyDetector/tree/master/quickstarts-multivariate/JavaScript) | +| Python | [Sample](./Python) | +| C# | [Sample](./CSharp) | +| Java | [Sample](./Java) | +| JavaScript| [Sample](./JavaScript) | ## Example data -Properly formatted multivariate sample data can be found in this [zip file](https://github.com/Azure-Samples/AnomalyDetector/tree/master/quickstarts-multivariate/multivariate_sample_data) +Properly formatted multivariate sample data can be found in this [zip file](./multivariate_sample_data) diff --git a/quickstarts-multivariate/CSharp/Sample_multivaraiate_detect.cs b/samples-multivariate/Sample_multivaraiate_detect.cs similarity index 100% rename from quickstarts-multivariate/CSharp/Sample_multivaraiate_detect.cs rename to samples-multivariate/Sample_multivaraiate_detect.cs diff --git a/quickstarts-multivariate/Python/sample_multivariate_detect.py b/samples-multivariate/sample_multivariate_detect.py similarity index 100% rename from quickstarts-multivariate/Python/sample_multivariate_detect.py rename to samples-multivariate/sample_multivariate_detect.py diff --git a/quickstarts-multivariate/JavaScript/sample_multivariate_detection.js b/samples-multivariate/sample_multivariate_detection.js similarity index 100% rename from quickstarts-multivariate/JavaScript/sample_multivariate_detection.js rename to samples-multivariate/sample_multivariate_detection.js diff --git a/quickstarts/csharp-detect-anomalies.cs b/samples-univariate/csharp-detect-anomalies.cs similarity index 100% rename from quickstarts/csharp-detect-anomalies.cs rename to samples-univariate/csharp-detect-anomalies.cs diff --git a/quickstarts/java-detect-anomalies.java b/samples-univariate/java-detect-anomalies.java similarity index 100% rename from quickstarts/java-detect-anomalies.java rename to samples-univariate/java-detect-anomalies.java diff --git a/quickstarts/python-detect-anomalies.py b/samples-univariate/python-detect-anomalies.py similarity index 100% rename from quickstarts/python-detect-anomalies.py rename to samples-univariate/python-detect-anomalies.py diff --git a/quickstarts/sdk/csharp-sdk-sample.cs b/samples-univariate/sdk/csharp-sdk-sample.cs similarity index 100% rename from quickstarts/sdk/csharp-sdk-sample.cs rename to samples-univariate/sdk/csharp-sdk-sample.cs diff --git a/quickstarts/sdk/go-sdk-sample.go b/samples-univariate/sdk/go-sdk-sample.go similarity index 100% rename from quickstarts/sdk/go-sdk-sample.go rename to samples-univariate/sdk/go-sdk-sample.go diff --git a/quickstarts/sdk/node/.gitignore b/samples-univariate/sdk/node/.gitignore similarity index 100% rename from quickstarts/sdk/node/.gitignore rename to samples-univariate/sdk/node/.gitignore diff --git a/quickstarts/sdk/node/README.md b/samples-univariate/sdk/node/README.md similarity index 100% rename from quickstarts/sdk/node/README.md rename to samples-univariate/sdk/node/README.md diff --git a/quickstarts/sdk/node/package.json b/samples-univariate/sdk/node/package.json similarity index 100% rename from quickstarts/sdk/node/package.json rename to samples-univariate/sdk/node/package.json diff --git a/quickstarts/sdk/node/src/index.ts b/samples-univariate/sdk/node/src/index.ts similarity index 100% rename from quickstarts/sdk/node/src/index.ts rename to samples-univariate/sdk/node/src/index.ts diff --git a/quickstarts/sdk/node/tsconfig.json b/samples-univariate/sdk/node/tsconfig.json similarity index 100% rename from quickstarts/sdk/node/tsconfig.json rename to samples-univariate/sdk/node/tsconfig.json diff --git a/quickstarts/sdk/python-sdk-sample.py b/samples-univariate/sdk/python-sdk-sample.py similarity index 100% rename from quickstarts/sdk/python-sdk-sample.py rename to samples-univariate/sdk/python-sdk-sample.py diff --git a/quickstarts/sdk/ruby-sdk-sample.rb b/samples-univariate/sdk/ruby-sdk-sample.rb similarity index 100% rename from quickstarts/sdk/ruby-sdk-sample.rb rename to samples-univariate/sdk/ruby-sdk-sample.rb diff --git a/univariate-live-demo/README.md b/univariate-live-demo/README.md new file mode 100644 index 0000000..61b6df8 --- /dev/null +++ b/univariate-live-demo/README.md @@ -0,0 +1,57 @@ +# Anomaly Detection Live Demo Instructions + +![A screenshot of the live demo](image.webp) + +Please note: this live demo is only intended to demonstrate the Anomaly Detector API on any CSV file that follows a simple schema. This demo loops over the provided data (with fake timestamps) to demonstrate the anomaly detection API. It does not use the real timestamps provided in the raw data, and should not be used in any production scenario. Once the demo is running, you will be able to see the raw data and the results from the anomaly detection API in the browser. Alternatively, you can also use the online Anomaly Detector [demo page](https://algoevaluation.azurewebsites.net/#/) to evaluate the API on your own data. + +In your CSV file, you need at least two columns. A column for the timestamp and column for the values you want to perform anomaly detection on. In addition, you can include an *optional* dimension column that allows you to run the anomaly detection API across different categories (e.g. different sensors, different regions, or even different variables) through a drop down selection menu in the demo. The timestamp column should be in the ISO 8601 format `YYYY-MM-DDTHH:MM:SSZ`. If you intend to use your own CSV, please see the `sensor_data.csv` file for how to structure that CSV file. + +## Step 1 - Setup + +The demo should work in any Linux-based environment using any integrated development environment (IDE). If you choose to use Azure Machine Learning (AML) compute, you should install [VS Code](https://code.visualstudio.com/) locally to be able to run this demo. **If you are not using AML, just skip ahead to Step 2**. + +With VS Code installed, navigate to your AML resource in the Azure portal, and click on `Launch Studio`. On the left, click on the `Compute` tab. Finally, under the `Applications` column, click the `VS Code` link corresponding to the compute instance that you would like to use. When asked by VS Code to `Allow an extension to open this URI?` click `Open`. + +## Step 2 - Creating the virtual environment and installing the dependencies + +Open a terminal in VS Code, and run the following command to create and activate a new conda virtual environment and install the dependencies: + +```bash +conda create --name anomaly-detection-demo python=3.7 +source activate anomaly-detection-demo +pip install -r requirements.txt +``` + +Next, enter the following two lines in your terminal, replacing `` and `` with the values from your Anomaly Detector resource. You can find them by navigating to your Anomaly Detector resource in the Azure portal, and copying the Anomaly Detector key and endpoint from the "Keys and endpoints" section of the resource details. + +```bash +export ANOMALY_DETECTOR_API_KEY= +export ANOMALY_DETECTOR_ENDPOINT= +``` + +## Step 3 - Adjusting the configuration (Optional) + +If you plan to use your own CSV to run this demo, take some time to review and adjust the default configuration in the `demo.py` file. For the purposes of this demo, the default configuration is stored within `demo.py` as a Python data class with default values. Feel free to modify the values to match your own needs. + +```python + csv_name: str = "sensor_data.csv" # Name of the csv file containing the data + value_column: str = "sensor_readings" # Name of the column containing the values + timestamp_column: str = "timestamp" # Name of the column containing the timestamps + dimension_column: str = "sensor_name" # (Optional) Name of the column containing a dimension (e.g. sensor name, or location, etc). If your data does not have this column, set it to None. + window_size: int = 50 # Size of the window used to compute the anomaly score + minute_resample: int = 5 # Resample the data to this minute resolution + ad_mode: str = "entire" # Anomaly detection mode to use. Can be "entire" for batch mode or "last" for last point mode. +``` +If your data does not have the optional dimension column, please set the `dimension_column` in the `demo.py` script to `None`. + +## Step 4 - Running the demo + +Finally, navigate to this directory in your terminal. Make sure you have the `anomaly-detection-demo` virtual environment activated, then run the following command: + +```bash +bokeh serve --port 5599 --show demo.py +``` + +A browser tab should open, and the demo should start running. Any detected anomaly will show in red. If the browser tab doesn't open, try to open it manually by navigating to `http://localhost:5599/demo` in the browser. If the demo does not start running, please double-check that you followed the steps above correctly. + +The demo will continue to run until you stop it. To stop the demo, close the browser tab and enter `Ctrl+C` in the terminal to stop the process. \ No newline at end of file diff --git a/univariate-live-demo/demo.py b/univariate-live-demo/demo.py new file mode 100644 index 0000000..cd4a16d --- /dev/null +++ b/univariate-live-demo/demo.py @@ -0,0 +1,294 @@ +# ------------------------------------------------------------- +# +# Copyright (c) Microsoft Corporation. All rights reserved. +# +# ------------------------------------------------------------- + +import datetime +import os +from dataclasses import dataclass +from datetime import timedelta, timezone + +import pandas as pd +from bokeh.driving import count +from bokeh.layouts import column, row +from bokeh.models import ColumnDataSource, Select, Slider +from bokeh.plotting import curdoc, figure +from dateutil.parser import parse + +from utils import ADTimeSeries, UnivariateAnomalyDetector + + +@dataclass +class Config: + """ + Dataclass to store the default configuration for the demo. Please change the values if you + want to use your own data. + """ + + csv_name: str = "sensor_data.csv" # Name of the csv file containing the data + value_column: str = "sensor_readings" # Name of the column containing the values + timestamp_column: str = "timestamp" # Name of the column containing the timestamps + dimension_column: str = "sensor_name" # (Optional) Name of the column containing a dimension (e.g. sensor name, or location, etc). If your data does not have this column, set it to None. + window_size: int = 50 # Size of the window used to compute the anomaly score + minute_resample: int = 5 # Resample the data to this minute resolution + ad_mode: str = "entire" # Anomaly detection mode to use. Can be "entire" for batch mode or "last" for last point mode. + + +class MissingEnvironmentVariable(Exception): + """ + Exception to be thrown when a required environment variable is not set. + """ + + pass + + +# Read environment variables: +apikey = os.getenv("ANOMALY_DETECTOR_API_KEY") +endpoint = os.getenv("ANOMALY_DETECTOR_ENDPOINT") + +if apikey is None or endpoint is None: + raise MissingEnvironmentVariable( + "Please ensure ANOMALY_DETECTOR_API_KEY and ANOMALY_DETECTOR_ENDPOINT environment variables are set!" + ) + +# Read CSV: +try: + df = pd.read_csv(Config.csv_name) +except FileNotFoundError: + raise FileNotFoundError( + f"Please ensure the file {Config.csv_name} exists in the current directory!" + ) + +# Validate the configuration: +if Config.timestamp_column not in df.columns: + raise ValueError("Please ensure the timestamp column is present in the CSV!") +elif Config.value_column not in df.columns: + raise ValueError("Please ensure the value column is present in the CSV!") + +if Config.dimension_column is None: + Config.dimension_column = "dimension" + df[[Config.dimension_column]] = "main_dimension" +else: + if Config.dimension_column not in df.columns: + raise ValueError( + f"Please ensure the dimension column is present in the CSV! ({Config.dimension_column})" + ) + +# Extract the relevant columns: +df = df[[Config.timestamp_column, Config.dimension_column, Config.value_column]] + +# Drop rows with NaNs: +df.dropna(inplace=True) + +# Pivot dataframe to show time vs. sensor data +df = df.pivot_table( + index=Config.timestamp_column, + columns=Config.dimension_column, + values=Config.value_column, + aggfunc="mean", +) + +# Parse timestamp column +df.index = df.index.map(lambda x: parse(str(x).replace("@", ""))) + +# Convert index to datatime: +df.index = pd.to_datetime(df.index) + +source = ColumnDataSource( + dict( + time=[], + timestamp=[], + timestamp_str=[], + values=[], + expectedValues=[], + upperband=[], + lowerband=[], + isAnomaly=[], + color=[], + ) +) + +p = figure( + height=500, + width=1200, + tools="xpan,xwheel_zoom,xbox_zoom,reset", + x_axis_type="datetime", + y_axis_location="right", +) + +p.x_range.follow = "end" +p.y_range.start = 0 +p.xaxis.axis_label = "Time" + +p.line( + x="timestamp", + y="values", + alpha=0.8, + line_width=2, + color="navy", + source=source, + legend_label="Measured Value", +) + +p.line( + x="timestamp", + y="expectedValues", + alpha=0.8, + line_width=2, + color="orange", + source=source, + legend_label="Expected Value", +) + +p.circle( + "timestamp", + "values", + size=5, + color="color", + alpha=1, + source=source, + legend_label="Data points", +) + +p.segment( + x0="timestamp", + y0="lowerband", + x1="timestamp", + y1="upperband", + line_width=10, + alpha=0.4, + color="orange", + source=source, + legend_label="Expected Range", +) + +p.line( + x="timestamp", + y="upperband", + alpha=0.4, + line_width=1, + color="orange", + source=source, +) + +p.line( + x="timestamp", + y="lowerband", + alpha=0.4, + line_width=1, + color="orange", + source=source, +) + +p.legend.location = "top_left" + +sensitivity = Slider(title="sensitivity", value=95, start=0, end=99, step=1) +max_anomaly_ratio = Slider( + title="max_anomaly_ratio", value=0.20, start=0, end=1, step=0.05 +) + +sensor_names = list(df.columns) +scenario = Select(value=sensor_names[-1], options=sensor_names) +adclient = UnivariateAnomalyDetector(key=apikey, endpoint=endpoint) + + +def _get_value(t): + """ + Loops over the series contiuosly based on the scenario selected by the user + """ + return df[scenario.value][t % len(df)] + + +def _get_timestamp(t): + """ + Generates a fake timestamp + """ + timestamp = datetime.datetime(2015, 1, 1, tzinfo=timezone.utc) + timedelta( + minutes=Config.minute_resample * t + ) + return timestamp, timestamp.isoformat().split("+")[0] + "Z" + + +def _call_ad_api(t): + """ + Creates a request and calls the anomaly detector API, then processes and returns the response + """ + values = source.data["values"][-Config.window_size :] + timestamps = source.data["timestamp_str"][-Config.window_size :] + request = {} + request["series"] = [] + for i in range(Config.window_size): + request["series"].append({"value": values[i], "timestamp": timestamps[i]}) + + request["granularity"] = "minutely" + request["maxAnomalyRatio"] = max_anomaly_ratio.value + request["sensitivity"] = sensitivity.value + request["customInterval"] = Config.minute_resample + + # validate that the request is valid: + request = ADTimeSeries(request) + request.validate() + + response = adclient.detect_anomaly(mode=Config.ad_mode, data_dict=request) + + if Config.ad_mode == "entire": + response["expected_value"] = response["expected_values"][-1] + response["upper_margin"] = response["upper_margins"][-1] + response["lower_margin"] = response["lower_margins"][-1] + response["is_anomaly"] = response["is_anomaly"][-1] + + print( + f"Point: {str(t)}: Is anomaly? {response['is_anomaly']} -- Expected value: {response['expected_value']}" + ) + + upperband = response["expected_value"] + response["upper_margin"] + lowerband = response["expected_value"] - response["lower_margin"] + + if response["is_anomaly"]: + color = "red" + else: + color = "navy" + + return ( + response["expected_value"], + upperband, + lowerband, + response["is_anomaly"], + color, + ) + + +@count() +def update(t): + value = _get_value(t) + ts, ts_str = _get_timestamp(t) + + if t > Config.window_size: + # we have enough data to send an API request + expectedValue, upperband, lowerband, isAnomaly, color = _call_ad_api(t) + else: + # Use default values for the first few points + expectedValue, upperband, lowerband, isAnomaly, color = 0, 0, 0, False, None + + new_data = dict( + time=[t], + timestamp=[ts], + timestamp_str=[ts_str], + expectedValues=[expectedValue], + values=[value], + upperband=[upperband], + lowerband=[lowerband], + isAnomaly=[isAnomaly], + color=[color], + ) + + p.title.text = f"Live anomaly detection results | Sensitivity: {sensitivity.value} | Maximum Anomaly Ratio: {max_anomaly_ratio.value}" + source.stream( + new_data, rollover=100 + ) # the rollover number must be > window size. It governs the amount of data visible in the window. + + +curdoc().add_root(column(row(max_anomaly_ratio, sensitivity, scenario), p)) +curdoc().add_periodic_callback(update, 100) +curdoc().title = "Anomaly Detector API Demo" diff --git a/univariate-live-demo/image.webp b/univariate-live-demo/image.webp new file mode 100644 index 0000000..3d2f7f5 Binary files /dev/null and b/univariate-live-demo/image.webp differ diff --git a/univariate-live-demo/requirements.txt b/univariate-live-demo/requirements.txt new file mode 100644 index 0000000..3d54f76 --- /dev/null +++ b/univariate-live-demo/requirements.txt @@ -0,0 +1,21 @@ +adal==1.2.7 +azure-ai-anomalydetector==3.0.0b5 +azure-core==1.24.1 +azureml-core==1.41.0 +black +bokeh +bokeh==2.4.3 +ipykernel==6.15.0 +ipympl +jupyter +jupyter_bokeh +jupyter_contrib_nbextensions +jupyterlab +numpy +pandas +pip==23.3 +pydantic +python-dateutil==2.8.2 +requests +tqdm +typing-extensions>=3.10.0.0 \ No newline at end of file diff --git a/univariate-live-demo/sensor_data.csv b/univariate-live-demo/sensor_data.csv new file mode 100644 index 0000000..96090f1 --- /dev/null +++ b/univariate-live-demo/sensor_data.csv @@ -0,0 +1,8161 @@ +,timestamp,sensor_name,sensor_readings +0,2019-08-06T00:00:00Z,sensor_1,0.3033736333425896 +1,2019-08-06T00:00:00Z,sensor_2,0.35199479393574196 +2,2019-08-06T00:00:00Z,sensor_3,0.0 +3,2019-08-06T00:00:00Z,sensor_4,0.0 +4,2019-08-06T00:01:00Z,sensor_1,0.3050621216411169 +5,2019-08-06T00:01:00Z,sensor_2,0.3545366901569773 +6,2019-08-06T00:01:00Z,sensor_3,0.0 +7,2019-08-06T00:01:00Z,sensor_4,0.0 +8,2019-08-06T00:02:00Z,sensor_1,0.3034655712625802 +9,2019-08-06T00:02:00Z,sensor_2,0.3531004758606038 +10,2019-08-06T00:02:00Z,sensor_3,0.0 +11,2019-08-06T00:02:00Z,sensor_4,0.0 +12,2019-08-06T00:03:00Z,sensor_1,0.30496275905791403 +13,2019-08-06T00:03:00Z,sensor_2,0.3545992579399464 +14,2019-08-06T00:03:00Z,sensor_3,0.0 +15,2019-08-06T00:03:00Z,sensor_4,0.0 +16,2019-08-06T00:04:00Z,sensor_1,0.3041442610281482 +17,2019-08-06T00:04:00Z,sensor_2,0.35496458580129575 +18,2019-08-06T00:04:00Z,sensor_3,0.0 +19,2019-08-06T00:04:00Z,sensor_4,0.0 +20,2019-08-06T00:05:00Z,sensor_1,0.3031194134371433 +21,2019-08-06T00:05:00Z,sensor_2,0.35509719550569585 +22,2019-08-06T00:05:00Z,sensor_3,0.0 +23,2019-08-06T00:05:00Z,sensor_4,0.0 +24,2019-08-06T00:06:00Z,sensor_1,0.3020794816367427 +25,2019-08-06T00:06:00Z,sensor_2,0.35610060696468615 +26,2019-08-06T00:06:00Z,sensor_3,0.0 +27,2019-08-06T00:06:00Z,sensor_4,0.0 +28,2019-08-06T00:07:00Z,sensor_1,0.30337183417694646 +29,2019-08-06T00:07:00Z,sensor_2,0.35374349410574646 +30,2019-08-06T00:07:00Z,sensor_3,0.0 +31,2019-08-06T00:07:00Z,sensor_4,0.0 +32,2019-08-06T00:08:00Z,sensor_1,0.30220362830365444 +33,2019-08-06T00:08:00Z,sensor_2,0.3531018049481711 +34,2019-08-06T00:08:00Z,sensor_3,0.0 +35,2019-08-06T00:08:00Z,sensor_4,0.0 +36,2019-08-06T00:09:00Z,sensor_1,0.3038543340255879 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Copyright (c) Microsoft Corporation. All rights reserved. +# +# ------------------------------------------------------------- + +import json +import logging +import os +import re +from datetime import datetime +from typing import ClassVar, Optional, Union + +from pydantic import BaseModel + +from azure.ai.anomalydetector import AnomalyDetectorClient +from azure.ai.anomalydetector.models import DetectRequest, TimeGranularity +from azure.core.credentials import AzureKeyCredential +from azure.core.exceptions import HttpResponseError + + +logger = logging.getLogger(__name__) + + +class ADTimeSeries: + """Class to read, format, and validate the data for the anomaly detector service. + + Note: the format of the data must match the following: + data = { + "period": 2 # OPTIONAL: Specifying this value can reduce anomaly detection latency by up to 50%. The + period is an integer that specifies roughly how many data points the time series takes to repeat a pattern + "series": [ # REQUIRED: list containing Time series data points. This should be sorted by timestamp in + # ascending order to match the anomaly detection result. If the data is not sorted correctly or + # there is a duplicate timestamp, the API will not work. In such case, an error message will be + # returned. Each data point must have at least a "timestamp" in iso-8601 format and a "value" + {"timestamp": "2014-12-07T21:00:00Z", "value": 7290.0979278853}, + {"timestamp": "2014-12-07T22:00:00Z", "value": 7884.4973233823} + ], + "granularity": "hourly", # REQUIRED: The sampling rate of the data. Must contain one of the granularities + listed below. + "sensitivity": 99 # OPTIONAL: This advanced model parameter in an integer between 0-99 that represents + the sensitivity of the AD algorithm. The lower the value, the larger the margin will be, meaning + fewer anomalies will be detected. + "maxAnomalyRatio": 0.25 # OPTIONAL: This is an advanced model parameter is a float between 0 and 1 + representing the maximum anomaly ratio. + "customInterval": is optional and used to set non-standard time interval. For example, if the series is + 5 minutes, the request can be set as: {"granularity":"minutely", "customInterval":5} + } + """ + + GRANULARITIES: ClassVar[dict] = { + "yearly": TimeGranularity.YEARLY, + "monthly": TimeGranularity.MONTHLY, + "weekly": TimeGranularity.WEEKLY, + "daily": TimeGranularity.DAILY, + "hourly": TimeGranularity.HOURLY, + "minutely": TimeGranularity.PER_MINUTE, + "secondly": TimeGranularity.PER_SECOND, + } + + def __init__(self, data: Union[dict, None] = None): + self.data = data + self.validated = False + + def validate(self): + """Validates that self.data is in the correct format for the anomaly detector service. + + For documentation on the correct format, see: https://docs.microsoft.com/en-us/azure/ + cognitive-services/anomaly-detector/concepts/anomaly-detection-best-practices#data-preparation + + Once validation is done, the self.validated flag is set to True. + """ + try: + # Series: + series = self.data.get("series", None) + series_len = len(series) if series else 0 + if series_len < 12 or series_len > 8460: + raise AssertionError( + f"The length of series must be in the range [12-8460] but is {series_len}" + ) + utc_pattern = re.compile( + r"\b[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}Z\b" + ) # ref: https://stackoverflow.com/questions/25568134/regex-to-verify-utc-date-time-format + + timestamp = None + sort_datapoints = False + + for entry in self.data["series"]: + # Validate timestamp format, and make sure the entries are sorted in ascending order + entry_dict = entry.dict() if isinstance(entry, BaseModel) else entry + current_timestamp = datetime.strptime( + entry["timestamp"], "%Y-%m-%dT%H:%M:%SZ" + ) + + # Check if datapoints are sorted correctly: + if timestamp is not None: + if not current_timestamp >= timestamp: + sort_datapoints = True + logger.debug( + "Datapoints are not in ascending order! Will sort points in ascending order.. " + ) + + timestamp = current_timestamp + if not utc_pattern.match(entry_dict["timestamp"]): + raise AssertionError("timestamp data does not match UTC format.") + if "value" not in entry_dict: + raise AssertionError("data['series'] entry is missing value field.") + if not isinstance(entry_dict["value"], float): + raise AssertionError("'value' should be of type float.") + + if sort_datapoints: + self.data["series"].sort(key=lambda x: x["timestamp"], reverse=False) + + # Granularity: + if "granularity" not in self.data: # Required field + raise AssertionError("self.data missing required 'granularity' field.") + self.data["granularity"] = self.data["granularity"].lower() + if self.data["granularity"] not in list(ADTimeSeries.GRANULARITIES): + raise AssertionError( + "granularity value is not one of those listed in ADTimeSeries.GRANULARITIES." + ) + + # Custom interval: + if "customInterval" not in self.data: + # 'custom_interval' is optional and used to set non-standard time interval. + # For example, if the series is 5 minutes, the request can be set as: + # {"granularity":"minutely", "customInterval":5} + self.data["customInterval"] = 1 + + except AssertionError as err: + logger.error(err, exc_info=True) + raise AssertionError(err) from err + + self.validated = True + + @classmethod + def from_json_path(cls, filepath: str): + """Read data from a JSON file. + + Args: + filepath (str): Path to a JSON file containing the data. + + Returns: + (AnomalyDetectorSeries): an AnomalyDetectorSeries object containing the data. + """ + with open(filepath, encoding="utf-8") as file_handle: + data = cls(json.load(file_handle)) + data.validate() + return data + + +class UnivariateAnomalyDetector: + """Class to interact with the Azure Anomaly Detector Service.""" + + def __init__(self, key: str = None, endpoint: str = None): + self.key = key + self.endpoint = endpoint + self.client: AnomalyDetectorClient = None + logger.debug("Instantiated UnivariateAnomalyDetector object.") + + def connect(self): + """Reads key and endpoint environment variables and creates AnomalyDetectorClient client object.""" + if self.key is None: + logger.debug( + "self.key is None -- reading the key from the environment variable." + ) + self.key = os.getenv("ANOMALY_DETECTOR_KEY", default=None) + if self.endpoint is None: + logger.debug( + "self.endpoint is None -- reading the endpoint from the environment variable." + ) + self.endpoint = os.getenv("ANOMALY_DETECTOR_ENDPOINT", default=None) + + if self.key and self.endpoint: + self.client = AnomalyDetectorClient( + AzureKeyCredential(self.key), self.endpoint + ) + logger.info("Successfully instantiated AnomalyDetectorClient object.") + else: + msg = "The key or endpoint for the Anomaly Detector resource are missing." + logger.error(msg) + raise ValueError(msg) + + def detect_anomaly( + self, mode: str, data_dict: Union[dict, ADTimeSeries] + ) -> Optional[dict]: + """Create and send a request to the Anomaly Detector API. + + Args: + mode (str): The API accepts three modes of detection: 'entire', 'last', 'change'. + data_dict (Union[dict, ADTimeSeries]): a dictionary or a ADTimeSeries containing the time + series data. + + Returns: + dict: the result from the anomaly detector API call. The feilds in this result depend on the mode: + + - 'entire' mode: ['period','expected_values', 'upper_margins', 'lower_margins', 'is_anomaly', + 'is_negative_anomaly','is_positive_anomaly'] + + - 'last' mode: ['period', 'suggested_window', 'expected_value', 'upper_margin', 'lower_margin', + 'is_anomaly', 'is_negative_anomaly', 'is_positive_anomaly']) + + - 'change' mode: ['period', 'is_change_point', 'confidence_scores'] + + """ + + if self.client is None: + logger.debug("self.client is None -- Calling self.connect()..") + self.connect() + + if isinstance(data_dict, ADTimeSeries): + if not data_dict.validated: # Make sure the data is valid. + raise AssertionError("data_dict is not validated.") + data_dict = ( + data_dict.data + ) # Extract the data, we don't need ADTimeSeries anymore. + + # Create the request: + request = DetectRequest( + series=data_dict["series"], granularity=data_dict["granularity"] + ) + + try: + if mode == "entire": + logger.debug("Calling Anomaly Detector API in entire data series mode.") + return self.client.detect_entire_series(request).as_dict() + + if mode == "last": + logger.debug("Calling Anomaly Detector API in last point mode.") + return self.client.detect_last_point(request).as_dict() + + if mode == "change": + logger.debug( + "Calling Anomaly Detector API in detect change point mode." + ) + return self.client.detect_change_point(request).as_dict() + + err_message = "Unknown anomaly mode: " + mode + logger.error(err_message) + raise ValueError(err_message) + + except HttpResponseError as err: + logger.error(err, exc_info=True) + raise HttpResponseError from err