# -*- coding: utf-8 -*- # Description: anomalies netdata python.d module # Author: andrewm4894 # SPDX-License-Identifier: GPL-3.0-or-later import sys 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.data_init() self.model_params_init() self.models_init() self.collected_dims = {'probability': set(), 'anomaly': set()} def check(self): if not (sys.version_info[0] >= 3 and sys.version_info[1] >= 6): self.error("anomalies collector only works with Python>=3.6") if len(self.host_charts_dict[self.host]) > 0: _ = get_allmetrics_async(host_charts_dict=self.host_charts_dict, protocol=self.protocol, 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.tls_verify = self.configuration.get('tls_verify', True) self.fitted_at = {} self.df_allmetrics = pd.DataFrame() self.last_train_at = 0 self.include_average_prob = bool(self.configuration.get('include_average_prob', True)) self.reinitialize_at_every_step = bool(self.configuration.get('reinitialize_at_every_step', False)) 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', verify=self.tls_verify).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} #self.info(f'self.host_charts_dict (len={len(self.host_charts_dict[self.host])}): {self.host_charts_dict}') def data_init(self): """Initialize some empty data objects. """ self.data_probability_latest = {f'{m}_prob': 0 for m in self.charts_in_scope} self.data_anomaly_latest = {f'{m}_anomaly': 0 for m in self.charts_in_scope} self.data_latest = {**self.data_probability_latest, **self.data_anomaly_latest} 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 model_init(self, model): """Model initialisation of a single model. """ if self.model == 'pca': self.models[model] = PCA(contamination=self.contamination) elif self.model == 'loda': self.models[model] = LODA(contamination=self.contamination) elif self.model == 'iforest': self.models[model] = IForest(n_estimators=50, bootstrap=True, behaviour='new', contamination=self.contamination) elif self.model == 'cblof': self.models[model] = CBLOF(n_clusters=3, contamination=self.contamination) elif self.model == 'feature_bagging': self.models[model] = FeatureBagging(base_estimator=PCA(contamination=self.contamination), contamination=self.contamination) elif self.model == 'copod': self.models[model] = COPOD(contamination=self.contamination) elif self.model == 'hbos': self.models[model] = HBOS(contamination=self.contamination) else: self.models[model] = HBOS(contamination=self.contamination) self.custom_model_scalers[model] = MinMaxScaler() def reinitialize(self): """Reinitialize charts, models and data to a beginning state. """ self.charts_init() self.custom_models_init() self.data_init() self.model_params_init() self.models_init() def save_data_latest(self, data, data_probability, data_anomaly): """Save the most recent data objects to be used if needed in the future. """ self.data_latest = data self.data_probability_latest = data_probability self.data_anomaly_latest = data_anomaly def validate_charts(self, chart, data, algorithm='absolute', multiplier=1, divisor=1): """If dimension not in chart then add it. """ for dim in data: if dim not in self.collected_dims[chart]: self.collected_dims[chart].add(dim) self.charts[chart].add_dimension([dim, dim, algorithm, multiplier, divisor]) for dim in list(self.collected_dims[chart]): if dim not in data: self.collected_dims[chart].remove(dim) self.charts[chart].del_dimension(dim, hide=False) 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 : dataframe to append new renamed columns to. :return: 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 : numpy array we want to make features from. :param train : True if making features for training, in which case need to fit_transform scaler and maybe sample train_max_n. :param model : model to make features for. :return: 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 of models to train on. :param train_data_after : integer timestamp for start of train data. :param train_data_before : 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, verify=self.tls_verify ).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 : data to train on. :param models_to_train : 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: if model not in self.models: self.model_init(model) 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: (,) 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: (,) 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] try: 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) ) 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 as _: #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): # initialize to what's available right now if self.reinitialize_at_every_step or len(self.host_charts_dict[self.host]) == 0: self.charts_init() self.custom_models_init() self.model_params_init() # if not all models have been trained then train those we need to if len(self.fitted_at) < len(self.models_in_scope): self.train( models_to_train=[m for m in self.models_in_scope 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.reinitialize() 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_probability = self.data_probability_latest data_anomaly = self.data_anomaly_latest else: data_probability, data_anomaly = self.predict() if self.include_average_prob: average_prob = np.mean(list(data_probability.values())) data_probability['average_prob'] = 0 if np.isnan(average_prob) else average_prob data = {**data_probability, **data_anomaly} self.validate_charts('probability', data_probability, divisor=100) self.validate_charts('anomaly', data_anomaly) self.save_data_latest(data, data_probability, data_anomaly) #self.info(f'len(data)={len(data)}') #self.info(f'data') return data