{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Calibrator\n", "\n", "Calibrator can help you to normalize the score into a range of 0 to 1, where 1 represents the anomalies. \n", "\n", "All calibrators can work in two ways:\n", "\n", "1. Global way (with parameter `is_global = True`): It can \"remember\" the distribution of all history scores with little memory usage.\n", "2. Rolling way (with parameter `is_global = Flase`): It only keeps the score within a rolling window, and uses them as references." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ZScore Calibrator\n", "\n", "It reports the scores that beyond k times (`sigma`) of standard deviation as anomalies." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "name": "value", "type": "scatter", "x": [ "2014-11-23 07:00:00", "2014-11-23 08:00:00", "2014-11-23 09:00:00", "2014-11-23 10:00:00", "2014-11-23 11:00:00", "2014-11-23 12:00:00", "2014-11-23 13:00:00", "2014-11-23 14:00:00", "2014-11-23 15:00:00", "2014-11-23 16:00:00", "2014-11-23 17:00:00", "2014-11-23 18:00:00", "2014-11-23 19:00:00", "2014-11-23 20:00:00", "2014-11-23 21:00:00", "2014-11-23 22:00:00", "2014-11-23 23:00:00", "2014-11-24 00:00:00", "2014-11-24 01:00:00", "2014-11-24 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"mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "matches": "x2", "showticklabels": false }, "xaxis2": { "anchor": "y2", "domain": [ 0, 1 ] }, "yaxis": { "anchor": "x", "domain": [ 0.5061728395061729, 1 ] }, "yaxis2": { "anchor": "x2", "domain": [ 0, 0.3950617283950617 ] } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from streamad.util import StreamGenerator, UnivariateDS, plot\n", "from streamad.model import KNNDetector\n", "from streamad.process import ZScoreCalibrator\n", "\n", "\n", "ds = UnivariateDS()\n", "stream = StreamGenerator(ds.data)\n", "detector=KNNDetector()\n", "calibrator = ZScoreCalibrator(sigma=3, extreme_sigma=5)\n", "\n", "scores = []\n", "\n", "for x in stream.iter_item():\n", " score = detector.fit_score(x)\n", " normalized_score = calibrator.normalize(score)\n", " scores.append(normalized_score)\n", "\n", "data, label, date, features = ds.data, ds.label, ds.date, ds.features\n", "plot(data=data,scores=scores,date=date,features=features,label=label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TDigest Calibrator\n", "\n", "It reports the scores that beyond `percentile_up` and below `percentile_down` as anomalies." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "name": "value", "type": "scatter", "x": [ "2014-11-23 07:00:00", "2014-11-23 08:00:00", "2014-11-23 09:00:00", "2014-11-23 10:00:00", "2014-11-23 11:00:00", 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"output_type": "display_data" } ], "source": [ "from streamad.util import StreamGenerator, UnivariateDS, plot\n", "from streamad.model import KNNDetector\n", "from streamad.process import TDigestCalibrator\n", "\n", "\n", "ds = UnivariateDS()\n", "stream = StreamGenerator(ds.data)\n", "detector = KNNDetector()\n", "calibrator = TDigestCalibrator(percentile_up=99, percentile_down=0)\n", "\n", "scores = []\n", "\n", "for x in stream.iter_item():\n", " score = detector.fit_score(x)\n", " normalized_score = calibrator.normalize(score)\n", " scores.append(normalized_score)\n", "\n", "data, label, date, features = ds.data, ds.label, ds.date, ds.features\n", "plot(data=data,scores=scores,date=date,features=features,label=label)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.13 ('streamad')", "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": "3.8.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "b5d8bec4cd8bbc63846c8af265d77205fd72b89322337b00b6a8c879b65f05d7" } } }, "nbformat": 4, "nbformat_minor": 2 }