{ "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$serie)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e0c8a397", "metadata": {}, "outputs": [], "source": [ "# establishing change point method \n", " model <- hcp_amoc()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9d0079cf", "metadata": {}, "outputs": [], "source": [ "# fitting the model\n", " model <- fit(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 7, "id": "863506fd", "metadata": {}, "outputs": [], "source": [ "# making detections\n", " detection <- detect(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 8, "id": "1241ed10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " idx event type\n", "1 84 TRUE changepoint\n" ] } ], "source": [ "# filtering detected events\n", " print(detection |> dplyr::filter(event==TRUE))" ] }, { "cell_type": "code", "execution_count": 9, "id": "10456892", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " event \n", "detection TRUE FALSE\n", "TRUE 0 1 \n", "FALSE 1 99 \n" ] } ], "source": [ "# evaluating the detections\n", " evaluation <- evaluate(model, detection$event, dataset$event)\n", " print(evaluation$confMatrix)" ] }, { "cell_type": "code", "execution_count": 10, "id": "075f7d2f", "metadata": {}, "outputs": [ { "data": { "image/png": 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PH58+elWwDcEYYdgH9RUlISExPj7OwcExMj3YJK4e7uHhYWVlRUxCOygL1j2AH4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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 }