time-window.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "spare-austin",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n",
      "config file not found: drain3.ini\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from PCA import PCA\n",
    "from IsolationForest import IsolationForest\n",
    "from preprocessing import FeatureExtractor\n",
    "import fasttext\n",
    "from tokenizer import my_tokenize\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ft = fasttext.load_model(\"fasttext_model/mix.bin\")\n",
    "\n",
    "old_test = \"input/k3s_test_pred.csv\"\n",
    "old_train = \"input/k3s_train_pred.csv\"\n",
    "new_test = \"input/newk3s1-test.csv\" #\"input/merged_predictions.csv\"\n",
    "new_train = \"input/newk3s1-train.csv\"\n",
    "\n",
    "## load oldk3s or newk3s dataset\n",
    "file_train = new_train\n",
    "file_test = new_test\n",
    "\n",
    "window_size = 5 #  timewindow size\n",
    "\n",
    "df_train = pd.read_csv(file_train, parse_dates=True)\n",
    "df_test = pd.read_csv(file_test, parse_dates=True)\n",
    "\n",
    "from drain3 import TemplateMiner\n",
    "persistence_type =None\n",
    "tm = TemplateMiner(persistence_type)\n",
    "\n",
    "def train_drain(train_data):\n",
    "    for line in train_data:\n",
    "        msg = line.lower().rstrip()\n",
    "        tokens = my_tokenize(msg)\n",
    "        msg = \" \".join(tokens)\n",
    "        tm.add_log_message(msg)\n",
    "        \n",
    "## for drain template training\n",
    "train_drain(df_train[\"message\"])\n",
    "\n",
    "def get_train_test():\n",
    "    \n",
    "    print('Loading phase:')\n",
    "    train_vecs = load_data(train_file)\n",
    "    test_vecs = load_data(test_file)\n",
    "    return train_vecs, test_vecs\n",
    "\n",
    "def l2_norm(x):\n",
    "   return np.sqrt(np.sum(x**2))\n",
    "def div_norm(x):\n",
    "   norm_value = l2_norm(x)\n",
    "   if norm_value > 0:\n",
    "       return x * ( 1.0 / norm_value)\n",
    "   else:\n",
    "       return x\n",
    "def get_sentence_vector(tokens):\n",
    "    wordvecs_split = [ft[word] for word in tokens]\n",
    "    processed_word_vectors = []\n",
    "    for word_vector in wordvecs_split:\n",
    "        processed_word_vectors.append(div_norm(word_vector))\n",
    "    processed_word_vectors = np.array(processed_word_vectors)\n",
    "    return np.mean(processed_word_vectors,axis=0)\n",
    "\n",
    "def match_template_vector(tokens):\n",
    "    msg = \" \".join(tokens)\n",
    "    res = tm.drain.tree_search(tm.drain.root_node ,tokens)\n",
    "    vec = [0] * (tm.drain.clusters_counter + 1)\n",
    "    tid = res.cluster_id - 1 if res is not None else tm.drain.clusters_counter\n",
    "    vec[tid] += 1\n",
    "    return np.array(vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "white-burton",
   "metadata": {},
   "outputs": [],
   "source": [
    "## preprocess data, get vectors and group them into time-windows\n",
    "\n",
    "def preprocess_data(input_df):\n",
    "    df = input_df\n",
    "    real_ts = [\"2020 \" + \" \".join(rl.split()[:3]) for rl in df[\"raw_line\"]]\n",
    "    df[\"real_ts\"] = real_ts\n",
    "    from datetime import datetime\n",
    "    new_ts = pd.to_datetime(df[\"real_ts\"])\n",
    "    df[\"real_ts\"] = [int(datetime.timestamp(x)) for x in new_ts]\n",
    "    \n",
    "    from collections import defaultdict\n",
    "    data_dict = defaultdict(list)\n",
    "    for idx, line in enumerate(df[\"message\"]):\n",
    "        ts = (df[\"real_ts\"][idx] // window_size) * window_size ## group to same timewindow\n",
    "        msg = line.lower().rstrip()\n",
    "        tokens = my_tokenize(msg)\n",
    "        \n",
    "        ## choose fasttext embedding or drain templates\n",
    "        vec = get_sentence_vector(tokens) ## sentence embeddings\n",
    "        #vec = match_template_vector(tokens) ## drain templates\n",
    "        \n",
    "        data_dict[ts].append(vec)\n",
    "    \n",
    "    return data_dict\n",
    "\n",
    "train_dict = preprocess_data(df_train)\n",
    "test_dict = preprocess_data(df_test)\n",
    "\n",
    "def form_windows(data_dict):\n",
    "    ts_list = []\n",
    "    data_list = []\n",
    "    for ts in data_dict.keys():\n",
    "        lists = data_dict[ts]\n",
    "        sums = np.sum(lists,axis=0) # mean or sum\n",
    "        data_list.append(sums)\n",
    "        ts_list.append(ts)\n",
    "    return np.array(data_list), ts_list\n",
    "    \n",
    "train_vecs, train_ts = form_windows(train_dict)\n",
    "test_vecs, test_ts = form_windows(test_dict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "outstanding-allowance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train phase:\n",
      "Train data shape: 74017-by-100\n",
      "\n",
      "====== Model summary ======\n",
      "n_components: 2\n",
      "Project matrix shape: 100-by-100\n",
      "SPE threshold: 10.190012320115283\n",
      "\n",
      "Test phase:\n",
      "Test data shape: 43285-by-100\n",
      "\n",
      "Train phase:\n",
      "Train data shape: 74017-by-100\n",
      "\n",
      "Test data shape: 43285-by-100\n",
      "\n",
      "====== Model summary ======\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tybalex/.pyenv/versions/3.6.8/envs/py36/lib/python3.6/site-packages/sklearn/ensemble/_iforest.py:248: FutureWarning: 'behaviour' is deprecated in 0.22 and will be removed in 0.24. You should not pass or set this parameter.\n",
      "  FutureWarning\n"
     ]
    }
   ],
   "source": [
    "def train_pca(train_vecs, test_vecs):\n",
    "    print('Train phase:')\n",
    "    feature_extractor = FeatureExtractor()\n",
    "    x_train = feature_extractor.fit_transform(train_vecs, normalization='zero-mean')\n",
    "    #x_train = train_vecs\n",
    "    model = PCA() \n",
    "    model.fit(x_train)\n",
    "    y_train = model.predict(x_train)\n",
    "    print('Test phase:')\n",
    "    x_test = feature_extractor.transform(test_vecs) \n",
    "\n",
    "    y_test = model.predict(x_test)\n",
    "    return y_train, y_test\n",
    "\n",
    "def train_iforest(train_vecs, test_vecs):\n",
    "    print('Train phase:')\n",
    "    feature_extractor = FeatureExtractor()\n",
    "    x_train = feature_extractor.fit_transform(train_vecs)\n",
    "    x_test = feature_extractor.transform(test_vecs)\n",
    "\n",
    "    #model = ensemble.IsolationForest(contamination=anomaly_ratio)\n",
    "    model = IsolationForest()\n",
    "    model.fit(x_train)\n",
    "\n",
    "    y_train = model.predict(x_train)\n",
    "\n",
    "    y_test = model.predict(x_test)\n",
    "    return y_train, y_test\n",
    "\n",
    "## train pca and iforest here\n",
    "\n",
    "y_train_pca, y_test_pca = train_pca(train_vecs, test_vecs)\n",
    "y_train_if, y_test_if = train_iforest(train_vecs, test_vecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "equipped-hunger",
   "metadata": {},
   "outputs": [],
   "source": [
    "## create the dataframe and visualize the results\n",
    "\n",
    "def create_df(_ts):\n",
    "    _df = pd.DataFrame({\"timestamp\" : _ts})\n",
    "    _df[\"date_time\"] = pd.to_datetime(_df[\"timestamp\"], unit=\"s\")\n",
    "    _df.set_index(\"date_time\",inplace=True)\n",
    "    return _df\n",
    "train_df = create_df(train_ts)\n",
    "test_df = create_df(test_ts)\n",
    "\n",
    "train_df[\"windowed_pca_anomaly\"] = y_train_pca\n",
    "test_df[\"windowed_pca_anomaly\"] = y_test_pca\n",
    "\n",
    "train_df[\"windowed_iforest_anomaly\"] = y_train_if\n",
    "test_df[\"windowed_iforest_anomaly\"] = y_test_if"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "becoming-nation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tybalex/.pyenv/versions/3.6.8/envs/py36/lib/python3.6/site-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'windowed_iforest_anomaly')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = test_df\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axe = plt.subplots(nrows=2, ncols=3,figsize=(20, 15))\n",
    "fig.suptitle(\"time-window based model predictions on new k3s outage testing data \", fontsize= 36)\n",
    "\n",
    "timescale = \"1H\"\n",
    "\n",
    "axe[0,2].plot(df.resample(timescale).count())\n",
    "axe[0,2].set_title(\"number of windows\", fontsize=24)\n",
    "\n",
    "fields = \"windowed_pca_anomaly\"\n",
    "df1 = df[df[fields]==1]\n",
    "#df1.resample(timescale).count()[fields].plot(color=\"Red\")\n",
    "p1 = df1.resample(timescale).count()[fields]\n",
    "axe[0, 0].plot(p1, color=\"Red\")\n",
    "axe[0, 0].set_title(fields, fontsize=24)\n",
    "\n",
    "fields2 = \"windowed_iforest_anomaly\"\n",
    "df2 = df[df[fields2]==1]\n",
    "#df1.resample(timescale).count()[fields].plot(color=\"Red\")\n",
    "p2 = df2.resample(timescale).count()[fields2]\n",
    "axe[0, 1].plot(p2, color=\"Blue\")\n",
    "axe[0, 1].set_title(fields2, fontsize=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "attached-socket",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}