metric-anomaly.r

# Script to perform anomaly detection on metrics and plot the results.
#
# Copyright 2017-2020 ICTU
# Copyright 2017-2022 Leiden University
# Copyright 2017-2023 Leon Helwerda
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

library(MonetDB.R)
library(DBI)
library(digest)
library(AnomalyDetection)
library(xts)

source('include/database.r')
conn <- connect()

metrics <- dbGetQuery(conn, 'SELECT metric_id, name FROM gros.metric')
summary(metrics)

apply(metrics, 1, function(row) {
    data <- dbGetQuery(conn, paste('SELECT date, value FROM gros.metric_value
                                    WHERE metric_id =', row[1],
                                   'ORDER BY date'))

    nrow(data)
    summary(data)

    data[[1]] <- as.POSIXlt(data[[1]])

    nrow(data)
    summary(data)

    a <- min(data[[1]])
    b <- max(data[[1]])

    stamps <- do.call(rbind.data.frame,
                      apply(data, 1,
                            function(row) {
                                data.frame(date=as.POSIXct(row[1]),
                                           value=row[2])
                            }))

    nrow(stamps)
    ncol(stamps)
    summary(stamps)

    # 1 measurement per 15 minutes (4 per hour) during a weekday from
    # 8 to 18 hours during a 2-week long sprint
    res <- AnomalyDetectionVec(data[, 2], max_anoms=0.02, direction='pos',
                               longterm=TRUE, plot=TRUE, period=4*10*5*2)
    res$plot

})