Adding upstream version 1.29.3.upstream/1.29.3upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 349 insertions, 0 deletions

diff --git a/collectors/python.d.plugin/anomalies/anomalies.chart.py b/collectors/python.d.plugin/anomalies/anomalies.chart.py
new file mode 100644
index 0000000..97dbb1d
--- /dev/null
+++ b/collectors/python.d.plugin/anomalies/anomalies.chart.py
@@ -0,0 +1,349 @@
+# -*- 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