{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1d0d0503", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: daltoolbox\n", "\n", "Registered S3 method overwritten by 'quantmod':\n", " method from\n", " as.zoo.data.frame zoo \n", "\n", "\n", "Attaching package: ‘daltoolbox’\n", "\n", "\n", "The following object is masked from ‘package:base’:\n", "\n", " transform\n", "\n", "\n", "Loading required package: harbinger\n", "\n" ] } ], "source": [ "# Harbinger Package\n", "# version 1.0.777\n", "\n", "source(\"https://raw.githubusercontent.com/cefet-rj-dal/harbinger/master/jupyter.R\")\n", "\n", "#loading Harbinger\n", "load_library(\"daltoolbox\") \n", "load_library(\"harbinger\") " ] }, { "cell_type": "code", "execution_count": 2, "id": "c0d41422", "metadata": {}, "outputs": [], "source": [ "#loading the example database\n", "data(examples_changepoints)" ] }, { "cell_type": "code", "execution_count": 3, "id": "9a12bab2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
serieevent
<dbl><lgl>
10.00FALSE
20.25FALSE
30.50FALSE
40.75FALSE
51.00FALSE
61.25FALSE
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & serie & event\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 0.00 & FALSE\\\\\n", "\t2 & 0.25 & FALSE\\\\\n", "\t3 & 0.50 & FALSE\\\\\n", "\t4 & 0.75 & FALSE\\\\\n", "\t5 & 1.00 & FALSE\\\\\n", "\t6 & 1.25 & FALSE\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | serie <dbl> | event <lgl> |\n", "|---|---|---|\n", "| 1 | 0.00 | FALSE |\n", "| 2 | 0.25 | FALSE |\n", "| 3 | 0.50 | FALSE |\n", "| 4 | 0.75 | FALSE |\n", "| 5 | 1.00 | FALSE |\n", "| 6 | 1.25 | FALSE |\n", "\n" ], "text/plain": [ " serie event\n", "1 0.00 FALSE\n", "2 0.25 FALSE\n", "3 0.50 FALSE\n", "4 0.75 FALSE\n", "5 1.00 FALSE\n", "6 1.25 FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Using the simple time series \n", "dataset <- examples_changepoints$simple\n", "head(dataset)" ] }, { "cell_type": "code", "execution_count": 4, "id": "b744a3ad", "metadata": {}, "outputs": [ { "data": { "image/png": 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v8oqvndBkUistjcj4uQvW/2sTn/ufup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\nKHWGU+hcpKyMm9l80gRApj978YBKX0T1LdKa6xMYq00YACjtSo5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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "#ploting the time series\n", "plot_ts(x = 1:length(dataset$serie), y = dataset$serie)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e0c8a397", "metadata": {}, "outputs": [], "source": [ "# establishing change point method \n", " model <- hcp_gft()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9d0079cf", "metadata": {}, "outputs": [], "source": [ "# fitting the model\n", " model <- fit(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 7, "id": "863506fd", "metadata": {}, "outputs": [], "source": [ "# making detections\n", " detection <- detect(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 8, "id": "1241ed10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " idx event type\n", "1 49 TRUE changepoint\n", "2 64 TRUE changepoint\n" ] } ], "source": [ "# filtering detected events\n", " print(detection |> dplyr::filter(event==TRUE))" ] }, { "cell_type": "code", "execution_count": 9, "id": "10456892", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " event \n", "detection TRUE FALSE\n", "TRUE 0 2 \n", "FALSE 1 98 \n" ] } ], "source": [ "# evaluating the detections\n", " evaluation <- evaluate(model, detection$event, dataset$event)\n", " print(evaluation$confMatrix)" ] }, { "cell_type": "code", "execution_count": 10, "id": "075f7d2f", "metadata": {}, "outputs": [ { "data": { "image/png": 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ICHotPpBgwYsHXr1r/6BF6KBWCVgoOD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\nWybdUnEMu3vZu3dvWlpa+/btn3vuOekWAABQtWJ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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# ploting the results\n", " grf <- har_plot(model, dataset$serie, detection, dataset$event)\n", " plot(grf)" ] }, { "cell_type": "code", "execution_count": null, "id": "07c53f34", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.4.0" } }, "nbformat": 4, "nbformat_minor": 5 }