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+# -*- coding: utf-8 -*-
+# Description: anomalies netdata python.d module
+# Author: andrewm4894
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import time
+from datetime import datetime
+import re
+import warnings
+
+import requests
+import numpy as np
+import pandas as pd
+from netdata_pandas.data import get_data, get_allmetrics_async
+from pyod.models.hbos import HBOS
+from pyod.models.pca import PCA
+from pyod.models.loda import LODA
+from pyod.models.iforest import IForest
+from pyod.models.cblof import CBLOF
+from pyod.models.feature_bagging import FeatureBagging
+from pyod.models.copod import COPOD
+from sklearn.preprocessing import MinMaxScaler
+
+from bases.FrameworkServices.SimpleService import SimpleService
+
+# ignore some sklearn/numpy warnings that are ok
+warnings.filterwarnings('ignore', r'All-NaN slice encountered')
+warnings.filterwarnings('ignore', r'invalid value encountered in true_divide')
+warnings.filterwarnings('ignore', r'divide by zero encountered in true_divide')
+warnings.filterwarnings('ignore', r'invalid value encountered in subtract')
+
+disabled_by_default = True
+
+ORDER = ['probability', 'anomaly']
+
+CHARTS = {
+ 'probability': {
+ 'options': ['probability', 'Anomaly Probability', 'probability', 'anomalies', 'anomalies.probability', 'line'],
+ 'lines': []
+ },
+ 'anomaly': {
+ 'options': ['anomaly', 'Anomaly', 'count', 'anomalies', 'anomalies.anomaly', 'stacked'],
+ 'lines': []
+ },
+}
+
+
+class Service(SimpleService):
+ def __init__(self, configuration=None, name=None):
+ SimpleService.__init__(self, configuration=configuration, name=name)
+ self.basic_init()
+ self.charts_init()
+ self.custom_models_init()
+ self.model_params_init()
+ self.models_init()
+
+ def check(self):
+ _ = get_allmetrics_async(
+ host_charts_dict=self.host_charts_dict, host_prefix=True, host_sep='::', wide=True, sort_cols=True,
+ protocol=self.protocol, numeric_only=True, float_size='float32', user=self.username, pwd=self.password
+ )
+ return True
+
+ def basic_init(self):
+ """Perform some basic initialization.
+ """
+ self.order = ORDER
+ self.definitions = CHARTS
+ self.protocol = self.configuration.get('protocol', 'http')
+ self.host = self.configuration.get('host', '127.0.0.1:19999')
+ self.username = self.configuration.get('username', None)
+ self.password = self.configuration.get('password', None)
+ self.fitted_at = {}
+ self.df_allmetrics = pd.DataFrame()
+ self.data_latest = {}
+ self.last_train_at = 0
+ self.include_average_prob = bool(self.configuration.get('include_average_prob', True))
+
+ def charts_init(self):
+ """Do some initialisation of charts in scope related variables.
+ """
+ self.charts_regex = re.compile(self.configuration.get('charts_regex','None'))
+ self.charts_available = [c for c in list(requests.get(f'{self.protocol}://{self.host}/api/v1/charts').json().get('charts', {}).keys())]
+ self.charts_in_scope = list(filter(self.charts_regex.match, self.charts_available))
+ self.charts_to_exclude = self.configuration.get('charts_to_exclude', '').split(',')
+ if len(self.charts_to_exclude) > 0:
+ self.charts_in_scope = [c for c in self.charts_in_scope if c not in self.charts_to_exclude]
+
+ def custom_models_init(self):
+ """Perform initialization steps related to custom models.
+ """
+ self.custom_models = self.configuration.get('custom_models', None)
+ self.custom_models_normalize = bool(self.configuration.get('custom_models_normalize', False))
+ if self.custom_models:
+ self.custom_models_names = [model['name'] for model in self.custom_models]
+ self.custom_models_dims = [i for s in [model['dimensions'].split(',') for model in self.custom_models] for i in s]
+ self.custom_models_dims = [dim if '::' in dim else f'{self.host}::{dim}' for dim in self.custom_models_dims]
+ self.custom_models_charts = list(set([dim.split('|')[0].split('::')[1] for dim in self.custom_models_dims]))
+ self.custom_models_hosts = list(set([dim.split('::')[0] for dim in self.custom_models_dims]))
+ self.custom_models_host_charts_dict = {}
+ for host in self.custom_models_hosts:
+ self.custom_models_host_charts_dict[host] = list(set([dim.split('::')[1].split('|')[0] for dim in self.custom_models_dims if dim.startswith(host)]))
+ self.custom_models_dims_renamed = [f"{model['name']}|{dim}" for model in self.custom_models for dim in model['dimensions'].split(',')]
+ self.models_in_scope = list(set([f'{self.host}::{c}' for c in self.charts_in_scope] + self.custom_models_names))
+ self.charts_in_scope = list(set(self.charts_in_scope + self.custom_models_charts))
+ self.host_charts_dict = {self.host: self.charts_in_scope}
+ for host in self.custom_models_host_charts_dict:
+ if host not in self.host_charts_dict:
+ self.host_charts_dict[host] = self.custom_models_host_charts_dict[host]
+ else:
+ for chart in self.custom_models_host_charts_dict[host]:
+ if chart not in self.host_charts_dict[host]:
+ self.host_charts_dict[host].extend(chart)
+ else:
+ self.models_in_scope = [f'{self.host}::{c}' for c in self.charts_in_scope]
+ self.host_charts_dict = {self.host: self.charts_in_scope}
+ self.model_display_names = {model: model.split('::')[1] if '::' in model else model for model in self.models_in_scope}
+
+ def model_params_init(self):
+ """Model parameters initialisation.
+ """
+ self.train_max_n = self.configuration.get('train_max_n', 100000)
+ self.train_n_secs = self.configuration.get('train_n_secs', 14400)
+ self.offset_n_secs = self.configuration.get('offset_n_secs', 0)
+ self.train_every_n = self.configuration.get('train_every_n', 1800)
+ self.train_no_prediction_n = self.configuration.get('train_no_prediction_n', 10)
+ self.initial_train_data_after = self.configuration.get('initial_train_data_after', 0)
+ self.initial_train_data_before = self.configuration.get('initial_train_data_before', 0)
+ self.contamination = self.configuration.get('contamination', 0.001)
+ self.lags_n = {model: self.configuration.get('lags_n', 5) for model in self.models_in_scope}
+ self.smooth_n = {model: self.configuration.get('smooth_n', 5) for model in self.models_in_scope}
+ self.diffs_n = {model: self.configuration.get('diffs_n', 5) for model in self.models_in_scope}
+
+ def models_init(self):
+ """Models initialisation.
+ """
+ self.model = self.configuration.get('model', 'pca')
+ if self.model == 'pca':
+ self.models = {model: PCA(contamination=self.contamination) for model in self.models_in_scope}
+ elif self.model == 'loda':
+ self.models = {model: LODA(contamination=self.contamination) for model in self.models_in_scope}
+ elif self.model == 'iforest':
+ self.models = {model: IForest(n_estimators=50, bootstrap=True, behaviour='new', contamination=self.contamination) for model in self.models_in_scope}
+ elif self.model == 'cblof':
+ self.models = {model: CBLOF(n_clusters=3, contamination=self.contamination) for model in self.models_in_scope}
+ elif self.model == 'feature_bagging':
+ self.models = {model: FeatureBagging(base_estimator=PCA(contamination=self.contamination), contamination=self.contamination) for model in self.models_in_scope}
+ elif self.model == 'copod':
+ self.models = {model: COPOD(contamination=self.contamination) for model in self.models_in_scope}
+ elif self.model == 'hbos':
+ self.models = {model: HBOS(contamination=self.contamination) for model in self.models_in_scope}
+ else:
+ self.models = {model: HBOS(contamination=self.contamination) for model in self.models_in_scope}
+ self.custom_model_scalers = {model: MinMaxScaler() for model in self.models_in_scope}
+
+ def validate_charts(self, name, data, algorithm='absolute', multiplier=1, divisor=1):
+ """If dimension not in chart then add it.
+ """
+ for dim in data:
+ if dim not in self.charts[name]:
+ self.charts[name].add_dimension([dim, dim, algorithm, multiplier, divisor])
+
+ def add_custom_models_dims(self, df):
+ """Given a df, select columns used by custom models, add custom model name as prefix, and append to df.
+
+ :param df <pd.DataFrame>: dataframe to append new renamed columns to.
+ :return: <pd.DataFrame> dataframe with additional columns added relating to the specified custom models.
+ """
+ df_custom = df[self.custom_models_dims].copy()
+ df_custom.columns = self.custom_models_dims_renamed
+ df = df.join(df_custom)
+
+ return df
+
+ def make_features(self, arr, train=False, model=None):
+ """Take in numpy array and preprocess accordingly by taking diffs, smoothing and adding lags.
+
+ :param arr <np.ndarray>: numpy array we want to make features from.
+ :param train <bool>: True if making features for training, in which case need to fit_transform scaler and maybe sample train_max_n.
+ :param model <str>: model to make features for.
+ :return: <np.ndarray> transformed numpy array.
+ """
+
+ def lag(arr, n):
+ res = np.empty_like(arr)
+ res[:n] = np.nan
+ res[n:] = arr[:-n]
+
+ return res
+
+ arr = np.nan_to_num(arr)
+
+ diffs_n = self.diffs_n[model]
+ smooth_n = self.smooth_n[model]
+ lags_n = self.lags_n[model]
+
+ if self.custom_models_normalize and model in self.custom_models_names:
+ if train:
+ arr = self.custom_model_scalers[model].fit_transform(arr)
+ else:
+ arr = self.custom_model_scalers[model].transform(arr)
+
+ if diffs_n > 0:
+ arr = np.diff(arr, diffs_n, axis=0)
+ arr = arr[~np.isnan(arr).any(axis=1)]
+
+ if smooth_n > 1:
+ arr = np.cumsum(arr, axis=0, dtype=float)
+ arr[smooth_n:] = arr[smooth_n:] - arr[:-smooth_n]
+ arr = arr[smooth_n - 1:] / smooth_n
+ arr = arr[~np.isnan(arr).any(axis=1)]
+
+ if lags_n > 0:
+ arr_orig = np.copy(arr)
+ for lag_n in range(1, lags_n + 1):
+ arr = np.concatenate((arr, lag(arr_orig, lag_n)), axis=1)
+ arr = arr[~np.isnan(arr).any(axis=1)]
+
+ if train:
+ if len(arr) > self.train_max_n:
+ arr = arr[np.random.randint(arr.shape[0], size=self.train_max_n), :]
+
+ arr = np.nan_to_num(arr)
+
+ return arr
+
+ def train(self, models_to_train=None, train_data_after=0, train_data_before=0):
+ """Pull required training data and train a model for each specified model.
+
+ :param models_to_train <list>: list of models to train on.
+ :param train_data_after <int>: integer timestamp for start of train data.
+ :param train_data_before <int>: integer timestamp for end of train data.
+ """
+ now = datetime.now().timestamp()
+ if train_data_after > 0 and train_data_before > 0:
+ before = train_data_before
+ after = train_data_after
+ else:
+ before = int(now) - self.offset_n_secs
+ after = before - self.train_n_secs
+
+ # get training data
+ df_train = get_data(
+ host_charts_dict=self.host_charts_dict, host_prefix=True, host_sep='::', after=after, before=before,
+ sort_cols=True, numeric_only=True, protocol=self.protocol, float_size='float32', user=self.username, pwd=self.password
+ ).ffill()
+ if self.custom_models:
+ df_train = self.add_custom_models_dims(df_train)
+
+ # train model
+ self.try_fit(df_train, models_to_train=models_to_train)
+ self.info(f'training complete in {round(time.time() - now, 2)} seconds (runs_counter={self.runs_counter}, model={self.model}, train_n_secs={self.train_n_secs}, models={len(self.fitted_at)}, n_fit_success={self.n_fit_success}, n_fit_fails={self.n_fit_fail}, after={after}, before={before}).')
+ self.last_train_at = self.runs_counter
+
+ def try_fit(self, df_train, models_to_train=None):
+ """Try fit each model and try to fallback to a default model if fit fails for any reason.
+
+ :param df_train <pd.DataFrame>: data to train on.
+ :param models_to_train <list>: list of models to train.
+ """
+ if models_to_train is None:
+ models_to_train = list(self.models.keys())
+ self.n_fit_fail, self.n_fit_success = 0, 0
+ for model in models_to_train:
+ X_train = self.make_features(
+ df_train[df_train.columns[df_train.columns.str.startswith(f'{model}|')]].values,
+ train=True, model=model)
+ try:
+ self.models[model].fit(X_train)
+ self.n_fit_success += 1
+ except Exception as e:
+ self.n_fit_fail += 1
+ self.info(e)
+ self.info(f'training failed for {model} at run_counter {self.runs_counter}, defaulting to hbos model.')
+ self.models[model] = HBOS(contamination=self.contamination)
+ self.models[model].fit(X_train)
+ self.fitted_at[model] = self.runs_counter
+
+ def predict(self):
+ """Get latest data, make it into a feature vector, and get predictions for each available model.
+
+ :return: (<dict>,<dict>) tuple of dictionaries, one for probability scores and the other for anomaly predictions.
+ """
+ # get recent data to predict on
+ df_allmetrics = get_allmetrics_async(
+ host_charts_dict=self.host_charts_dict, host_prefix=True, host_sep='::', wide=True, sort_cols=True,
+ protocol=self.protocol, numeric_only=True, float_size='float32', user=self.username, pwd=self.password
+ )
+ if self.custom_models:
+ df_allmetrics = self.add_custom_models_dims(df_allmetrics)
+ self.df_allmetrics = self.df_allmetrics.append(df_allmetrics).ffill().tail((max(self.lags_n.values()) + max(self.smooth_n.values()) + max(self.diffs_n.values())) * 2)
+
+ # get predictions
+ data_probability, data_anomaly = self.try_predict()
+
+ return data_probability, data_anomaly
+
+ def try_predict(self):
+ """Try make prediction and fall back to last known prediction if fails.
+
+ :return: (<dict>,<dict>) tuple of dictionaries, one for probability scores and the other for anomaly predictions.
+ """
+ data_probability, data_anomaly = {}, {}
+ for model in self.fitted_at.keys():
+ model_display_name = self.model_display_names[model]
+ X_model = np.nan_to_num(self.make_features(
+ self.df_allmetrics[self.df_allmetrics.columns[self.df_allmetrics.columns.str.startswith(f'{model}|')]].values,
+ model=model)[-1,:].reshape(1, -1))
+ try:
+ data_probability[model_display_name + '_prob'] = np.nan_to_num(self.models[model].predict_proba(X_model)[-1][1]) * 10000
+ data_anomaly[model_display_name + '_anomaly'] = self.models[model].predict(X_model)[-1]
+ except Exception:
+ #self.info(e)
+ if model_display_name + '_prob' in self.data_latest:
+ #self.info(f'prediction failed for {model} at run_counter {self.runs_counter}, using last prediction instead.')
+ data_probability[model_display_name + '_prob'] = self.data_latest[model_display_name + '_prob']
+ data_anomaly[model_display_name + '_anomaly'] = self.data_latest[model_display_name + '_anomaly']
+ else:
+ #self.info(f'prediction failed for {model} at run_counter {self.runs_counter}, skipping as no previous prediction.')
+ continue
+
+ return data_probability, data_anomaly
+
+ def get_data(self):
+
+ # if not all models have been trained then train those we need to
+ if len(self.fitted_at) < len(self.models):
+ self.train(
+ models_to_train=[m for m in self.models if m not in self.fitted_at],
+ train_data_after=self.initial_train_data_after,
+ train_data_before=self.initial_train_data_before)
+ # retrain all models as per schedule from config
+ elif self.train_every_n > 0 and self.runs_counter % self.train_every_n == 0:
+ self.train()
+
+ # roll forward previous predictions around a training step to avoid the possibility of having the training itself trigger an anomaly
+ if (self.runs_counter - self.last_train_at) <= self.train_no_prediction_n:
+ data = self.data_latest
+ else:
+ data_probability, data_anomaly = self.predict()
+ if self.include_average_prob:
+ data_probability['average_prob'] = np.mean(list(data_probability.values()))
+ data = {**data_probability, **data_anomaly}
+ self.validate_charts('probability', data_probability, divisor=100)
+ self.validate_charts('anomaly', data_anomaly)
+
+ self.data_latest = data
+
+ return data