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Diffstat (limited to 'src/collectors/python.d.plugin/anomalies/anomalies.conf')
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diff --git a/src/collectors/python.d.plugin/anomalies/anomalies.conf b/src/collectors/python.d.plugin/anomalies/anomalies.conf deleted file mode 100644 index ef867709a..000000000 --- a/src/collectors/python.d.plugin/anomalies/anomalies.conf +++ /dev/null @@ -1,184 +0,0 @@ -# netdata python.d.plugin configuration for anomalies -# -# This file is in YaML format. Generally the format is: -# -# name: value -# -# There are 2 sections: -# - global variables -# - one or more JOBS -# -# JOBS allow you to collect values from multiple sources. -# Each source will have its own set of charts. -# -# JOB parameters have to be indented (using spaces only, example below). - -# ---------------------------------------------------------------------- -# Global Variables -# These variables set the defaults for all JOBs, however each JOB -# may define its own, overriding the defaults. - -# update_every sets the default data collection frequency. -# If unset, the python.d.plugin default is used. -# update_every: 2 - -# priority controls the order of charts at the netdata dashboard. -# Lower numbers move the charts towards the top of the page. -# If unset, the default for python.d.plugin is used. -# priority: 60000 - -# ---------------------------------------------------------------------- -# JOBS (data collection sources) - -# Pull data from local Netdata node. -anomalies: - name: 'Anomalies' - - # Host to pull data from. - host: '127.0.0.1:19999' - - # Username and Password for Netdata if using basic auth. - # username: '???' - # password: '???' - - # Use http or https to pull data - protocol: 'http' - - # SSL verify parameter for requests.get() calls - tls_verify: true - - # What charts to pull data for - A regex like 'system\..*|' or 'system\..*|apps.cpu|apps.mem' etc. - charts_regex: 'system\..*' - - # Charts to exclude, useful if you would like to exclude some specific charts. - # Note: should be a ',' separated string like 'chart.name,chart.name'. - charts_to_exclude: 'system.uptime,system.entropy' - - # What model to use - can be one of 'pca', 'hbos', 'iforest', 'cblof', 'loda', 'copod' or 'feature_bagging'. - # More details here: https://pyod.readthedocs.io/en/latest/pyod.models.html. - model: 'pca' - - # Max number of observations to train on, to help cap compute cost of training model if you set a very large train_n_secs. - train_max_n: 100000 - - # How often to re-train the model (assuming update_every=1 then train_every_n=1800 represents (re)training every 30 minutes). - # Note: If you want to turn off re-training set train_every_n=0 and after initial training the models will not be retrained. - train_every_n: 1800 - - # The length of the window of data to train on (14400 = last 4 hours). - train_n_secs: 14400 - - # How many prediction steps after a train event to just use previous prediction value for. - # Used to reduce possibility of the training step itself appearing as an anomaly on the charts. - train_no_prediction_n: 10 - - # If you would like to train the model for the first time on a specific window then you can define it using the below two variables. - # Start of training data for initial model. - # initial_train_data_after: 1604578857 - - # End of training data for initial model. - # initial_train_data_before: 1604593257 - - # If you would like to ignore recent data in training then you can offset it by offset_n_secs. - offset_n_secs: 0 - - # How many lagged values of each dimension to include in the 'feature vector' each model is trained on. - lags_n: 5 - - # How much smoothing to apply to each dimension in the 'feature vector' each model is trained on. - smooth_n: 3 - - # How many differences to take in preprocessing your data. - # More info on differencing here: https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing - # diffs_n=0 would mean training models on the raw values of each dimension. - # diffs_n=1 means everything is done in terms of differences. - diffs_n: 1 - - # What is the typical proportion of anomalies in your data on average? - # This parameter can control the sensitivity of your models to anomalies. - # Some discussion here: https://github.com/yzhao062/pyod/issues/144 - contamination: 0.001 - - # Set to true to include an "average_prob" dimension on anomalies probability chart which is - # just the average of all anomaly probabilities at each time step - include_average_prob: true - - # Define any custom models you would like to create anomaly probabilities for, some examples below to show how. - # For example below example creates two custom models, one to run anomaly detection user and system cpu for our demo servers - # and one on the cpu and mem apps metrics for the python.d.plugin. - # custom_models: - # - name: 'demos_cpu' - # dimensions: 'london.my-netdata.io::system.cpu|user,london.my-netdata.io::system.cpu|system,newyork.my-netdata.io::system.cpu|user,newyork.my-netdata.io::system.cpu|system' - # - name: 'apps_python_d_plugin' - # dimensions: 'apps.cpu|python.d.plugin,apps.mem|python.d.plugin' - - # Set to true to normalize, using min-max standardization, features used for the custom models. - # Useful if your custom models contain dimensions on very different scales an model you use does - # not internally do its own normalization. Usually best to leave as false. - # custom_models_normalize: false - -# Standalone Custom models example as an additional collector job. -# custom: -# name: 'custom' -# host: '127.0.0.1:19999' -# protocol: 'http' -# charts_regex: 'None' -# charts_to_exclude: 'None' -# model: 'pca' -# train_max_n: 100000 -# train_every_n: 1800 -# train_n_secs: 14400 -# offset_n_secs: 0 -# lags_n: 5 -# smooth_n: 3 -# diffs_n: 1 -# contamination: 0.001 -# custom_models: -# - name: 'user_netdata' -# dimensions: 'users.cpu|netdata,users.mem|netdata,users.threads|netdata,users.processes|netdata,users.sockets|netdata' -# - name: 'apps_python_d_plugin' -# dimensions: 'apps.cpu|python.d.plugin,apps.mem|python.d.plugin,apps.threads|python.d.plugin,apps.processes|python.d.plugin,apps.sockets|python.d.plugin' - -# Pull data from some demo nodes for cross node custom models. -# demos: -# name: 'demos' -# host: '127.0.0.1:19999' -# protocol: 'http' -# charts_regex: 'None' -# charts_to_exclude: 'None' -# model: 'pca' -# train_max_n: 100000 -# train_every_n: 1800 -# train_n_secs: 14400 -# offset_n_secs: 0 -# lags_n: 5 -# smooth_n: 3 -# diffs_n: 1 -# contamination: 0.001 -# custom_models: -# - name: 'system.cpu' -# dimensions: 'london.my-netdata.io::system.cpu|user,london.my-netdata.io::system.cpu|system,newyork.my-netdata.io::system.cpu|user,newyork.my-netdata.io::system.cpu|system' -# - name: 'system.ip' -# dimensions: 'london.my-netdata.io::system.ip|received,london.my-netdata.io::system.ip|sent,newyork.my-netdata.io::system.ip|received,newyork.my-netdata.io::system.ip|sent' -# - name: 'system.net' -# dimensions: 'london.my-netdata.io::system.net|received,london.my-netdata.io::system.net|sent,newyork.my-netdata.io::system.net|received,newyork.my-netdata.io::system.net|sent' -# - name: 'system.io' -# dimensions: 'london.my-netdata.io::system.io|in,london.my-netdata.io::system.io|out,newyork.my-netdata.io::system.io|in,newyork.my-netdata.io::system.io|out' - -# Example additional job if you want to also pull data from a child streaming to your -# local parent or even a remote node so long as the Netdata REST API is accessible. -# mychildnode1: -# name: 'mychildnode1' -# host: '127.0.0.1:19999/host/mychildnode1' -# protocol: 'http' -# charts_regex: 'system\..*' -# charts_to_exclude: 'None' -# model: 'pca' -# train_max_n: 100000 -# train_every_n: 1800 -# train_n_secs: 14400 -# offset_n_secs: 0 -# lags_n: 5 -# smooth_n: 3 -# diffs_n: 1 -# contamination: 0.001 |