{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to Use Anomaly Detectors in Merlion\n", "\n", "This notebook will guide you through using all the key features of anomaly detectors in Merlion. Specifically, we will explain\n", "\n", "1. Initializing an anomaly detection model (including ensembles)\n", "1. Training the model\n", "1. Producing a series of anomaly scores with the model\n", "1. Quantitatively evaluating the model\n", "1. Visualizing the model's predictions\n", "1. Saving and loading a trained model\n", "1. Simulating the live deployment of a model using a `TSADEvaluator`\n", "\n", "We will be using a single example time series for this whole notebook. We load and visualize it now:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Time series /Users/isenilov/dev/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.\n", "Time series /Users/isenilov/dev/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from merlion.plot import plot_anoms\n", "from merlion.utils import TimeSeries\n", "from ts_datasets.anomaly import NAB\n", "\n", "np.random.seed(1234)\n", "\n", "# This is a time series with anomalies in both the train and test split.\n", "# time_series and metadata are both time-indexed pandas DataFrames.\n", "time_series, metadata = NAB(subset=\"realKnownCause\")[3]\n", "\n", "# Visualize the full time series\n", "fig = plt.figure(figsize=(10, 6))\n", "ax = fig.add_subplot(111)\n", "ax.plot(time_series)\n", "\n", "# Label the train/test split with a dashed line & plot anomalies\n", "ax.axvline(metadata[metadata.trainval].index[-1], ls=\"--\", lw=2, c=\"k\")\n", "plot_anoms(ax, TimeSeries.from_pd(metadata.anomaly))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from merlion.utils import TimeSeries\n", "\n", "# Get training split\n", "train = time_series[metadata.trainval]\n", "train_data = TimeSeries.from_pd(train)\n", "train_labels = TimeSeries.from_pd(metadata[metadata.trainval].anomaly)\n", "\n", "# Get testing split\n", "test = time_series[~metadata.trainval]\n", "test_data = TimeSeries.from_pd(test)\n", "test_labels = TimeSeries.from_pd(metadata[~metadata.trainval].anomaly)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Initialization\n", "\n", "In this notebook, we will use three different anomaly detection models:\n", "\n", "1. Isolation Forest (a classic anomaly detection model)\n", "2. WindStats (an in-house model that divides each week into windows of a specified size, and compares time series values to the historical values in the appropriate window)\n", "3. Prophet (Facebook's popular forecasting model, adapted for anomaly detection.\n", "\n", "Let's start by initializing each of them:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import models & configs\n", "from merlion.models.anomaly.isolation_forest import IsolationForest, IsolationForestConfig\n", "from merlion.models.anomaly.windstats import WindStats, WindStatsConfig\n", "from merlion.models.anomaly.forecast_based.prophet import ProphetDetector, ProphetDetectorConfig\n", "\n", "# Import a post-rule for thresholding\n", "from merlion.post_process.threshold import AggregateAlarms\n", "\n", "# Import a data processing transform\n", "from merlion.transform.moving_average import DifferenceTransform\n", "\n", "# All models are initialized using the syntax ModelClass(config), where config\n", "# is a model-specific configuration object. This is where you specify any\n", "# algorithm-specific hyperparameters, any data pre-processing transforms, and\n", "# the post-rule you want to use to post-process the anomaly scores (to reduce\n", "# noisiness when firing alerts). \n", "\n", "# We initialize isolation forest using the default config\n", "config1 = IsolationForestConfig()\n", "model1 = IsolationForest(config1)\n", "\n", "# We use a WindStats model that splits each week into windows of 60 minutes\n", "# each. Anomaly scores in Merlion correspond to z-scores. By default, we would\n", "# like to fire an alert for any 4-sigma event, so we specify a threshold rule\n", "# which achieves this.\n", "config2 = WindStatsConfig(wind_sz=60, threshold=AggregateAlarms(alm_threshold=4))\n", "model2 = WindStats(config2)\n", "\n", "# Prophet is a popular forecasting algorithm. Here, we specify that we would like\n", "# to pre-processes the input time series by applying a difference transform,\n", "# before running the model on it.\n", "config3 = ProphetDetectorConfig(transform=DifferenceTransform())\n", "model3 = ProphetDetector(config3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have initialized the individual models, we will also combine them in an ensemble. We set this ensemble's detection threshold to fire alerts for 4-sigma events (the same as WindStats)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from merlion.models.ensemble.anomaly import DetectorEnsemble, DetectorEnsembleConfig\n", "\n", "ensemble_config = DetectorEnsembleConfig(threshold=AggregateAlarms(alm_threshold=4))\n", "ensemble = DetectorEnsemble(config=ensemble_config, models=[model1, model2, model3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Training\n", "\n", "All anomaly detection models (and ensembles) share the same API for training. The `train()` method returns the model's predicted anomaly scores on the training data. Note that you may optionally specify configs that modify the protocol used to train the model's post-rule! You may optionally specify ground truth anomaly labels as well (if you have them), but they are not needed. We give examples of all these behaviors below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training IsolationForest...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:merlion.post_process.threshold:Threshold 3.2263 achieves F1=0.5000.\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training WindStats...\n", "\n", "Training ProphetDetector...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:merlion.post_process.threshold:Threshold 3.2263 achieves F1=0.6667.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training ensemble...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done!\n" ] } ], "source": [ "from merlion.evaluate.anomaly import TSADMetric\n", "\n", "# Train IsolationForest in the default way, using the ground truth anomaly labels\n", "# to set the post-rule's threshold\n", "print(f\"Training {type(model1).__name__}...\")\n", "train_scores_1 = model1.train(train_data=train_data, anomaly_labels=train_labels)\n", "\n", "# Train WindStats completely unsupervised (this retains our anomaly detection \n", "# default anomaly detection threshold of 4)\n", "print(f\"\\nTraining {type(model2).__name__}...\")\n", "train_scores_2 = model2.train(train_data=train_data, anomaly_labels=None)\n", "\n", "# Train Prophet with the ground truth anomaly labels, with a post-rule\n", "# trained to optimize Precision score\n", "print(f\"\\nTraining {type(model3).__name__}...\")\n", "post_rule_train_config_3 = dict(metric=TSADMetric.F1)\n", "train_scores_3 = model3.train(\n", " train_data=train_data, anomaly_labels=train_labels,\n", " post_rule_train_config=post_rule_train_config_3)\n", "\n", "# We consider an unsupervised ensemble, which combines the anomaly scores\n", "# returned by the models & sets a static anomaly detection threshold of 3.\n", "print(\"\\nTraining ensemble...\")\n", "ensemble_post_rule_train_config = dict(metric=None)\n", "train_scores_e = ensemble.train(\n", " train_data=train_data, anomaly_labels=train_labels,\n", " post_rule_train_config=ensemble_post_rule_train_config,\n", ")\n", "\n", "print(\"Done!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Inference\n", "\n", "There are two ways to invoke an anomaly detection model: `model.get_anomaly_score()` returns the model's raw anomaly scores, while `model.get_anomaly_label()` returns the model's post-processed anomaly scores. The post-processing calibrates the anomaly scores to be interpretable as z-scores, and it also sparsifies them such that any nonzero values should be treated as an alert that a particular timestamp is anomalous." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IsolationForest.get_anomaly_score() nonzero values (raw)\n", " anom_score\n", "2013-12-14 16:55:00 0.424103\n", "2013-12-14 17:00:00 0.418938\n", "2013-12-14 17:05:00 0.484891\n", "2013-12-14 17:10:00 0.500257\n", "2013-12-14 17:15:00 0.449213\n", "... ...\n", "2014-02-19 15:05:00 0.419456\n", "2014-02-19 15:10:00 0.415807\n", "2014-02-19 15:15:00 0.406724\n", "2014-02-19 15:20:00 0.427094\n", "2014-02-19 15:25:00 0.428348\n", "\n", "[19279 rows x 1 columns]\n", "\n", "IsolationForest.get_anomaly_label() nonzero values (post-processed)\n", " anom_score\n", "2013-12-16 16:00:00 3.251397\n", "2013-12-16 18:35:00 3.681691\n", "2013-12-27 19:25:00 3.914430\n", "2013-12-27 23:20:00 3.260543\n", "2013-12-28 04:15:00 3.738462\n", "2013-12-28 06:20:00 3.303482\n", "2014-01-02 10:00:00 3.233514\n", "2014-01-05 17:50:00 3.791805\n", "2014-01-12 09:25:00 3.535895\n", "2014-01-13 10:05:00 3.314500\n", "2014-01-16 12:50:00 3.850349\n", "2014-01-24 12:50:00 4.170855\n", "2014-01-27 17:45:00 3.537919\n", "2014-01-28 22:00:00 3.451974\n", "2014-01-30 23:40:00 3.550075\n", "2014-02-02 23:45:00 3.359105\n", "2014-02-03 11:55:00 4.175556\n", "2014-02-05 05:10:00 3.675433\n", "2014-02-09 11:55:00 4.005116\n", "2014-02-13 19:15:00 3.247573\n", "\n", "IsolationForest fires 20 alarms\n", "\n", "Raw scores at the locations where alarms were fired:\n", " anom_score\n", "2013-12-16 16:00:00 0.701491\n", "2013-12-16 18:35:00 0.772563\n", "2013-12-27 19:25:00 0.810997\n", "2013-12-27 23:20:00 0.702972\n", "2013-12-28 04:15:00 0.781997\n", "2013-12-28 06:20:00 0.709952\n", "2014-01-02 10:00:00 0.698602\n", "2014-01-05 17:50:00 0.790835\n", "2014-01-12 09:25:00 0.748293\n", "2014-01-13 10:05:00 0.711750\n", "2014-01-16 12:50:00 0.800493\n", "2014-01-24 12:50:00 0.852493\n", "2014-01-27 17:45:00 0.748630\n", "2014-01-28 22:00:00 0.734366\n", "2014-01-30 23:40:00 0.750652\n", "2014-02-02 23:45:00 0.719052\n", "2014-02-03 11:55:00 0.853260\n", "2014-02-05 05:10:00 0.771522\n", "2014-02-09 11:55:00 0.825713\n", "2014-02-13 19:15:00 0.700873\n", "Post-processed scores are interpretable as z-scores\n", "Raw scores are challenging to interpret\n" ] } ], "source": [ "# Here is a full example for the first model, IsolationForest\n", "scores_1 = model1.get_anomaly_score(test_data)\n", "scores_1_df = scores_1.to_pd()\n", "print(f\"{type(model1).__name__}.get_anomaly_score() nonzero values (raw)\")\n", "print(scores_1_df[scores_1_df.iloc[:, 0] != 0])\n", "print()\n", "\n", "labels_1 = model1.get_anomaly_label(test_data)\n", "labels_1_df = labels_1.to_pd()\n", "print(f\"{type(model1).__name__}.get_anomaly_label() nonzero values (post-processed)\")\n", "print(labels_1_df[labels_1_df.iloc[:, 0] != 0])\n", "print()\n", "\n", "print(f\"{type(model1).__name__} fires {(labels_1_df.values != 0).sum()} alarms\")\n", "print()\n", "\n", "print(\"Raw scores at the locations where alarms were fired:\")\n", "print(scores_1_df[labels_1_df.iloc[:, 0] != 0])\n", "print(\"Post-processed scores are interpretable as z-scores\")\n", "print(\"Raw scores are challenging to interpret\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same API is shared for all models, including ensembles." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "scores_2 = model2.get_anomaly_score(test_data)\n", "labels_2 = model2.get_anomaly_label(test_data)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "scores_3 = model3.get_anomaly_score(test_data)\n", "labels_3 = model3.get_anomaly_label(test_data)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "scores_e = ensemble.get_anomaly_score(test_data)\n", "labels_e = ensemble.get_anomaly_label(test_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quantitative Evaluation\n", "\n", "It is fairly transparent to visualize a model's predicted anomaly scores and also quantitatively evaluate its anomaly labels. For evaluation, we use specialized definitions of precision, recall, and F1 as revised point-adjusted metrics (see the technical report for more details). We also consider the mean time to detect anomalies.\n", "\n", "In general, you may use the `TSADMetric` enum to compute evaluation metrics for a time series using the syntax\n", "```\n", "TSADMetric..value(ground_truth=ground_truth, predict=anomaly_labels)\n", "```\n", "where `` is the name of the evaluation metric (see the API docs for details and more options), `ground_truth` is a time series of ground truth anomaly labels, and `anomaly_labels` is the output of `model.get_anomaly_label()`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IsolationForest\n", "Precision: 0.1667\n", "Recall: 1.0000\n", "F1: 0.2857\n", "MTTD: 0 days 23:31:40\n", "\n", "WindStats\n", "Precision: 0.2500\n", "Recall: 0.6667\n", "F1: 0.3636\n", "MTTD: 1 days 10:25:00\n", "\n", "ProphetDetector\n", "Precision: 0.3750\n", "Recall: 1.0000\n", "F1: 0.5455\n", "MTTD: 1 days 00:03:20\n", "\n", "DetectorEnsemble\n", "Precision: 0.4000\n", "Recall: 0.6667\n", "F1: 0.5000\n", "MTTD: 1 days 10:25:00\n", "\n" ] } ], "source": [ "from merlion.evaluate.anomaly import TSADMetric\n", "\n", "for model, labels in [(model1, labels_1), (model2, labels_2), (model3, labels_3), (ensemble, labels_e)]:\n", " print(f\"{type(model).__name__}\")\n", " precision = TSADMetric.Precision.value(ground_truth=test_labels, predict=labels)\n", " recall = TSADMetric.Recall.value(ground_truth=test_labels, predict=labels)\n", " f1 = TSADMetric.F1.value(ground_truth=test_labels, predict=labels)\n", " mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=labels)\n", " print(f\"Precision: {precision:.4f}\")\n", " print(f\"Recall: {recall:.4f}\")\n", " print(f\"F1: {f1:.4f}\")\n", " print(f\"MTTD: {mttd}\")\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the individual models are trained to optimize F1 directly, they all have low precision, high recall, and a mean time to detect of around 1 day. However, by instead training the individual models to optimize precision, and training a model combination unit to optimize F1, we are able to greatly increase the precision and F1 score, at the cost of a lower recall and higher mean time to detect." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Visualization\n", "\n", "Let's now visualize the model predictions that led to these outcomes. The option `filter_scores=True` means that we want to plot the post-processed anomaly scores (i.e. returned by `model.get_anomaly_label()`). You may instead specify `filter_scores=False` to visualize the raw anomaly scores." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IsolationForest\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "ProphetDetector\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "for model in [model1, model2, model3]:\n", " print(type(model).__name__)\n", " fig, ax = model.plot_anomaly(\n", " time_series=test_data, time_series_prev=train_data,\n", " filter_scores=True, plot_time_series_prev=True)\n", " plot_anoms(ax=ax, anomaly_labels=test_labels)\n", " plt.show()\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So all the individual models generate quite a few false positives. Let's see how the ensemble does:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = ensemble.plot_anomaly(\n", " time_series=test_data, time_series_prev=train_data,\n", " filter_scores=True, plot_time_series_prev=True)\n", "plot_anoms(ax=ax, anomaly_labels=test_labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the ensemble misses one of the three anomalies in the test split, but it also greatly reduces the number of false positives relative to the other models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving & Loading Models\n", "\n", "All models have a `save()` method and `load()` class method. Models may also be loaded with the assistance of the `ModelFactory`, which works for arbitrary models. The `save()` method creates a new directory at the specified path, where it saves a `json` file representing the model's config, as well as a binary file for the model's state.\n", "\n", "We will demonstrate these behaviors using our `IsolationForest` model (`model1`) for concreteness. Note that the config explicitly tracks the transform (to pre-process the data), the calibrator (to transform raw anomaly scores into z-scores), the thresholding rule (to sparsify the calibrated anomaly scores)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IsolationForest Config\n", "{'calibrator': {'abs_score': True,\n", " 'anchors': [[0.38992633996347176, 0.0],\n", " [0.4187750781361715, 0.5],\n", " [0.445336977389891, 1.0],\n", " [0.47974261897360404, 1.5],\n", " [0.5271631189090943, 2.0],\n", " [0.8301789920204418, 4.032894437734716],\n", " [1.0, 5.032894437734716]],\n", " 'max_score': 1.0,\n", " 'name': 'AnomScoreCalibrator'},\n", " 'dim': 1,\n", " 'enable_calibrator': True,\n", " 'enable_threshold': True,\n", " 'max_n_samples': 1.0,\n", " 'n_estimators': 100,\n", " 'threshold': {'abs_score': True,\n", " 'alm_suppress_minutes': 120,\n", " 'alm_threshold': 3.2263155501877727,\n", " 'alm_window_minutes': 60,\n", " 'min_alm_in_window': 2,\n", " 'name': 'AggregateAlarms'},\n", " 'transform': {'name': 'TransformSequence',\n", " 'transforms': [{'name': 'DifferenceTransform'},\n", " {'multivar_skip': True,\n", " 'name': 'Shingle',\n", " 'size': 2,\n", " 'stride': 1}]}}\n" ] } ], "source": [ "import json\n", "import os\n", "import pprint\n", "from merlion.models.factory import ModelFactory\n", "\n", "# Save the model\n", "os.makedirs(\"models\", exist_ok=True)\n", "path = os.path.join(\"models\", \"isf\")\n", "model1.save(path)\n", "\n", "# Print the config saved\n", "pp = pprint.PrettyPrinter()\n", "with open(os.path.join(path, \"config.json\")) as f:\n", " print(f\"{type(model1).__name__} Config\")\n", " pp.pprint(json.load(f))\n", "\n", "# Load the model using Prophet.load()\n", "model2_loaded = IsolationForest.load(dirname=path)\n", "\n", "# Load the model using the ModelFactory\n", "model2_factory_loaded = ModelFactory.load(name=\"IsolationForest\", model_path=path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same exact thing with ensembles! Note that the ensemble stores its underlying models in a nested structure. This is all reflected in the config." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ensemble Config\n", "{'calibrator': {'abs_score': True,\n", " 'anchors': None,\n", " 'max_score': 1000,\n", " 'name': 'AnomScoreCalibrator'},\n", " 'combiner': {'abs_score': True, 'n_models': 3, 'name': 'Mean'},\n", " 'dim': 1,\n", " 'enable_calibrator': False,\n", " 'enable_threshold': True,\n", " 'models': [{'calibrator': {'abs_score': True,\n", " 'anchors': [[0.38992633996347176, 0.0],\n", " [0.4187750781361715, 0.5],\n", " [0.445336977389891, 1.0],\n", " [0.47974261897360404, 1.5],\n", " [0.5271631189090943, 2.0],\n", " [0.8301789920204418, 4.032894437734716],\n", " [1.0, 5.032894437734716]],\n", " 'max_score': 1.0,\n", " 'name': 'AnomScoreCalibrator'},\n", " 'dim': 1,\n", " 'enable_calibrator': True,\n", " 'enable_threshold': False,\n", " 'max_n_samples': 1.0,\n", " 'n_estimators': 100,\n", " 'name': 'IsolationForest',\n", " 'threshold': {'abs_score': True,\n", " 'alm_suppress_minutes': 120,\n", " 'alm_threshold': 3.0,\n", " 'alm_window_minutes': 60,\n", " 'min_alm_in_window': 2,\n", " 'name': 'AggregateAlarms'},\n", " 'transform': {'name': 'TransformSequence',\n", " 'transforms': [{'name': 'DifferenceTransform'},\n", " {'multivar_skip': True,\n", " 'name': 'Shingle',\n", " 'size': 2,\n", " 'stride': 1}]}},\n", " {'calibrator': {'abs_score': True,\n", " 'anchors': [[0.0005687426322338457, 0.0],\n", " [0.8270481271598537, 0.5],\n", " [1.6872394706496963, 1.0],\n", " [2.371538318320157, 1.5],\n", " [2.9246837154735017, 2.0],\n", " [4.064135464545045, 4.032821539569336],\n", " [8.12827092909009, 5.032821539569336]],\n", " 'max_score': 1000,\n", " 'name': 'AnomScoreCalibrator'},\n", " 'dim': 1,\n", " 'enable_calibrator': True,\n", " 'enable_threshold': False,\n", " 'max_day': 4,\n", " 'name': 'WindStats',\n", " 'threshold': {'abs_score': True,\n", " 'alm_suppress_minutes': 120,\n", " 'alm_threshold': 4,\n", " 'alm_window_minutes': 60,\n", " 'min_alm_in_window': 2,\n", " 'name': 'AggregateAlarms'},\n", " 'transform': {'name': 'DifferenceTransform'},\n", " 'wind_sz': 60},\n", " {'calibrator': {'abs_score': True,\n", " 'anchors': [[0.00034442266386701815, 0.0],\n", " [0.50672704564658, 0.5],\n", " [1.0444911207158671, 1.0],\n", " [1.5495525355806337, 1.5],\n", " [1.9643452887114796, 2.0],\n", " [4.831069032732285, 4.032894437734716],\n", " [9.66213806546457, 5.032894437734716]],\n", " 'max_score': 1000,\n", " 'name': 'AnomScoreCalibrator'},\n", " 'daily_seasonality': 'auto',\n", " 'dim': 1,\n", " 'enable_calibrator': True,\n", " 'enable_threshold': False,\n", " 'holidays': None,\n", " 'max_forecast_steps': None,\n", " 'name': 'ProphetDetector',\n", " 'seasonality_mode': 'additive',\n", " 'target_seq_index': 0,\n", " 'threshold': {'abs_score': True,\n", " 'alm_suppress_minutes': 120,\n", " 'alm_threshold': 3,\n", " 'alm_window_minutes': 60,\n", " 'min_alm_in_window': 2,\n", " 'name': 'AggregateAlarms'},\n", " 'transform': {'name': 'DifferenceTransform'},\n", " 'uncertainty_samples': 100,\n", " 'weekly_seasonality': 'auto',\n", " 'yearly_seasonality': 'auto'}],\n", " 'threshold': {'abs_score': True,\n", " 'alm_suppress_minutes': 120,\n", " 'alm_threshold': 4,\n", " 'alm_window_minutes': 60,\n", " 'min_alm_in_window': 2,\n", " 'name': 'AggregateAlarms'},\n", " 'transform': {'name': 'Identity'}}\n" ] } ], "source": [ "# Save the ensemble\n", "path = os.path.join(\"models\", \"ensemble\")\n", "ensemble.save(path)\n", "\n", "# Print the config saved. Note that we've saved all individual models,\n", "# and their paths are specified under the model_paths key.\n", "pp = pprint.PrettyPrinter()\n", "with open(os.path.join(path, \"config.json\")) as f:\n", " print(f\"Ensemble Config\")\n", " pp.pprint(json.load(f))\n", "\n", "# Load the selector\n", "selector_loaded = DetectorEnsemble.load(dirname=path)\n", "\n", "# Load the selector using the ModelFactory\n", "selector_factory_loaded = ModelFactory.load(name=\"DetectorEnsemble\", model_path=path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulating Live Model Deployment\n", "\n", "A typical model deployment scenario is as follows:\n", "1. Train an initial model on some recent historical data, optionally with labels.\n", "1. At a regular interval `retrain_freq` (e.g. once per week), retrain the entire model unsupervised (i.e. with no labels) on the most recent data.\n", "1. Obtain the model's predicted anomaly scores for the time series values that occur between re-trainings. We perform this operation in batch, but a deployment scenario may do this in streaming.\n", "1. Optionally, specify a maximum amount of data (`train_window`) that the model should use for training (e.g. the most recent 2 weeks of data).\n", "\n", "We provide a `TSADEvaluator` object which simulates the above deployment scenario, and also allows a user to evaluate the quality of the forecaster according to an evaluation metric of their choice. We illustrate an example below using the ensemble." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Initialize the evaluator\n", "from merlion.evaluate.anomaly import TSADEvaluator, TSADEvaluatorConfig\n", "\n", "evaluator = TSADEvaluator(model=ensemble, config=TSADEvaluatorConfig(retrain_freq=\"7d\"))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n", "TSADEvaluator: 10%|█ | 604800/5783700 [00:00<00:03, 1501940.88it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 21%|██ | 1209600/5783700 [00:02<00:10, 421704.52it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 31%|███▏ | 1814400/5783700 [00:05<00:13, 293996.29it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 42%|████▏ | 2419200/5783700 [00:08<00:13, 241431.47it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 52%|█████▏ | 3024000/5783700 [00:13<00:14, 185310.19it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 63%|██████▎ | 3628800/5783700 [00:16<00:11, 186082.04it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 73%|███████▎ | 4233600/5783700 [00:20<00:09, 168778.34it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 84%|████████▎ | 4838400/5783700 [00:24<00:05, 166406.09it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 94%|█████████▍| 5443200/5783700 [00:30<00:02, 138865.20it/s]INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 100%|██████████| 5783700/5783700 [00:36<00:00, 160502.25it/s]\n" ] } ], "source": [ "# The kwargs we would provide to ensemble.train() for the initial training\n", "# Note that we are training the ensemble unsupervised.\n", "train_kwargs = {\"anomaly_labels\": None}\n", "\n", "# We will use the default kwargs for re-training (these leave the\n", "# post-rules unchanged, since there are no new labels)\n", "retrain_kwargs = None\n", "\n", "# We call evaluator.get_predict() to get the time series of anomaly scores\n", "# produced by the anomaly detector when deployed in this manner\n", "train_scores, test_scores = evaluator.get_predict(\n", " train_vals=train_data, test_vals=test_data,\n", " train_kwargs=train_kwargs, retrain_kwargs=retrain_kwargs\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ensemble Performance\n", "Precision: 0.6667\n", "Recall: 0.6667\n", "F1: 0.6667\n", "MTTD: 1 days 10:25:00\n", "\n" ] } ], "source": [ "# Now let's evaluate how we did.\n", "precision = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Precision)\n", "recall = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Recall)\n", "f1 = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.F1)\n", "mttd = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.MeanTimeToDetect)\n", "print(\"Ensemble Performance\")\n", "print(f\"Precision: {precision:.4f}\")\n", "print(f\"Recall: {recall:.4f}\")\n", "print(f\"F1: {f1:.4f}\")\n", "print(f\"MTTD: {mttd}\")\n", "print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, we see that by simply re-training the ensemble weekly in an unsupervised manner, we have increased the precision from $\\frac{2}{5}$ to $\\frac{2}{3}$, while leaving unchanged the recall and mean time to detect. This is due to data drift over time." ] } ], "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": "3.9.0" } }, "nbformat": 4, "nbformat_minor": 4 }