{ "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": [ "plotly not installed, so plotly visualizations will not work.\n", "Time series /Users/pkassianik/research/MerlionPublic/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.\n", "Time series /Users/pkassianik/research/MerlionPublic/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", "text/plain": [ "
" ] }, "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": [ { "name": "stderr", "output_type": "stream", "text": [ "Importing plotly failed. Interactive plots will not work.\n" ] } ], "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:merlion.models.forecast.prophet:Add seasonality 15\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:merlion.models.forecast.prophet:Add seasonality 15\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": [ "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: 0.3000\n", "Recall: 1.0000\n", "F1: 0.4615\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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "WindStats\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "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", "text/plain": [ "
" ] }, "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", " 'enable_calibrator': True,\n", " 'enable_threshold': True,\n", " 'max_n_samples': 1.0,\n", " 'model_path': '/Users/pkassianik/research/MerlionPublic/examples/anomaly/models/isf/model.pkl',\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 saves each of its sub-models in a different sub-directory, which it tracks manually." ] }, { "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", " 'enable_calibrator': False,\n", " 'enable_threshold': True,\n", " 'model_paths': [['IsolationForest',\n", " '/Users/pkassianik/research/MerlionPublic/examples/anomaly/models/ensemble/0'],\n", " ['WindStats',\n", " '/Users/pkassianik/research/MerlionPublic/examples/anomaly/models/ensemble/1'],\n", " ['ProphetDetector',\n", " '/Users/pkassianik/research/MerlionPublic/examples/anomaly/models/ensemble/2']],\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:merlion.models.forecast.prophet:Add seasonality 15\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", "TSADEvaluator: 10%|█ | 604800/5783700 [00:00<00:03, 1344494.93it/s]INFO:merlion.models.forecast.prophet:Add seasonality 13\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 21%|██ | 1209600/5783700 [00:03<00:15, 296394.17it/s]INFO:merlion.models.forecast.prophet:Add seasonality 13\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 31%|███▏ | 1814400/5783700 [00:07<00:19, 204091.41it/s]INFO:merlion.models.forecast.prophet:Add seasonality 15\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 42%|████▏ | 2419200/5783700 [00:10<00:16, 202177.03it/s]INFO:merlion.models.forecast.prophet:Add seasonality 15\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 52%|█████▏ | 3024000/5783700 [00:16<00:17, 154550.90it/s]INFO:merlion.models.forecast.prophet:Add seasonality 15\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 63%|██████▎ | 3628800/5783700 [00:21<00:14, 143959.12it/s]INFO:merlion.models.forecast.prophet:Add seasonality 12\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 73%|███████▎ | 4233600/5783700 [00:26<00:11, 129449.34it/s]INFO:merlion.models.forecast.prophet:Add seasonality 12\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 84%|████████▎ | 4838400/5783700 [00:32<00:07, 121326.31it/s]INFO:merlion.models.forecast.prophet:Add seasonality 12\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 94%|█████████▍| 5443200/5783700 [00:41<00:03, 94051.57it/s] INFO:merlion.models.forecast.prophet:Add seasonality 12\n", "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "TSADEvaluator: 100%|██████████| 5783700/5783700 [00:53<00:00, 109123.17it/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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }