notebooks/data-sources/gcsweb-ci/build-logs/build_log_term_freq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "17e74c05",
   "metadata": {},
   "source": [
    "# Term frequency analysis of build logs\n",
    "\n",
    "This notebook applies term frequency analysis to build logs in order to determine if the contents of build logs can provide additional insight into job execution. \n",
    "\n",
    "For \"traditional\" log analysis, a *Bag of Words (BoW)* approach is usually not a good option. BoW completely ignores things like the order in which words (or messages) appear, while normally the order within logs is quite significant.\n",
    "\n",
    "However, for CI logs we can exploit characteristics that \"traditional\" logs don't usually have: they have a beginning and an end, so we can treat them as documents; and we have multiple occurrences of each \"document\" (i.e. the build log for each job run).\n",
    "\n",
    "Here we are going to attempt to clusterize job runs using the frequency of terms that appear in their build logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a17d2f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import random\n",
    "import seaborn as sns\n",
    "from datetime import datetime, timezone\n",
    "from matplotlib import cm\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from time import time\n",
    "\n",
    "\n",
    "# Some settings for the notebook\n",
    "pd.options.display.float_format = \"{:.2f}\".format\n",
    "random.seed(1)\n",
    "sns.set(rc={\"figure.figsize\": (15, 5)})\n",
    "colormap = (\n",
    "    cm.brg\n",
    ")  # see https://matplotlib.org/stable/tutorials/colors/colormaps.html for alternatives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ea0328",
   "metadata": {},
   "source": [
    "## Loading the data\n",
    "\n",
    "We start by loading the metadata for a specific CI job. This data is dowloaded and saved by the [initial build log EDA](./build_log_EDA.ipynb) notebook.\n",
    "\n",
    "We also define a couple of helper functions to help us find a particular (already dowloaded) build log and to point to the build's prow status page, as well as a small function to prepare our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "827dc660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading metadata from ../../../../data/raw/gcs/build-metadata/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp_build-logs.csv\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>size</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1368338379971760128</th>\n",
       "      <td>FAILURE</td>\n",
       "      <td>5846974</td>\n",
       "      <td>1615072270</td>\n",
       "      <td>1615076702</td>\n",
       "      <td>4432</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372646746210963456</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>9785</td>\n",
       "      <td>1616099471</td>\n",
       "      <td>1616104582</td>\n",
       "      <td>5111</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1375709011390763008</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>9962</td>\n",
       "      <td>1616829565</td>\n",
       "      <td>1616834000</td>\n",
       "      <td>4435</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380281977160077312</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3841</td>\n",
       "      <td>1617919845</td>\n",
       "      <td>1617929494</td>\n",
       "      <td>9649</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385025333723402240</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3868</td>\n",
       "      <td>1619050750</td>\n",
       "      <td>1619056186</td>\n",
       "      <td>5436</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381826019446493184</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3837</td>\n",
       "      <td>1618287973</td>\n",
       "      <td>1618292751</td>\n",
       "      <td>4778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371068681810874368</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>9782</td>\n",
       "      <td>1615723223</td>\n",
       "      <td>1615728027</td>\n",
       "      <td>4804</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377999575511470080</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>12329</td>\n",
       "      <td>1617375678</td>\n",
       "      <td>1617380533</td>\n",
       "      <td>4855</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369750447904002048</th>\n",
       "      <td>FAILURE</td>\n",
       "      <td>6467331</td>\n",
       "      <td>1615408933</td>\n",
       "      <td>1615413675</td>\n",
       "      <td>4742</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382368829539094528</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3840</td>\n",
       "      <td>1618417400</td>\n",
       "      <td>1618422303</td>\n",
       "      <td>4903</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>837 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      result     size       start         end  duration  \\\n",
       "1368338379971760128  FAILURE  5846974  1615072270  1615076702      4432   \n",
       "1372646746210963456  SUCCESS     9785  1616099471  1616104582      5111   \n",
       "1375709011390763008  SUCCESS     9962  1616829565  1616834000      4435   \n",
       "1380281977160077312  SUCCESS     3841  1617919845  1617929494      9649   \n",
       "1385025333723402240  SUCCESS     3868  1619050750  1619056186      5436   \n",
       "...                      ...      ...         ...         ...       ...   \n",
       "1381826019446493184  SUCCESS     3837  1618287973  1618292751      4778   \n",
       "1371068681810874368  SUCCESS     9782  1615723223  1615728027      4804   \n",
       "1377999575511470080  SUCCESS    12329  1617375678  1617380533      4855   \n",
       "1369750447904002048  FAILURE  6467331  1615408933  1615413675      4742   \n",
       "1382368829539094528  SUCCESS     3840  1618417400  1618422303      4903   \n",
       "\n",
       "                     success  \n",
       "1368338379971760128    False  \n",
       "1372646746210963456     True  \n",
       "1375709011390763008     True  \n",
       "1380281977160077312     True  \n",
       "1385025333723402240     True  \n",
       "...                      ...  \n",
       "1381826019446493184     True  \n",
       "1371068681810874368     True  \n",
       "1377999575511470080     True  \n",
       "1369750447904002048    False  \n",
       "1382368829539094528     True  \n",
       "\n",
       "[837 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: periodic jobs only (see FIXME in class Builds)\n",
    "job_name = \"periodic-ci-openshift-release-master-ci-4.8-e2e-gcp\"\n",
    "\n",
    "logs_path = \"../../../../data/raw/gcs/build-logs/\"  # local cache of build log files\n",
    "metadata_path = \"../../../../data/raw/gcs/build-metadata/\"  # path to saved metadata\n",
    "metadata_file_name = os.path.join(metadata_path, f\"{job_name}_build-logs.csv\")\n",
    "\n",
    "\n",
    "def log_path_for(build_id):\n",
    "    return os.path.join(logs_path, f\"{build_id}.txt\")\n",
    "\n",
    "\n",
    "def prow_url_for(build_id):\n",
    "    project = \"origin-ci-test\"\n",
    "    # FIXME: this prefix is only for periodic jobs\n",
    "    job_prefix = f\"logs/{job_name}/\"\n",
    "    return f\"https://prow.ci.openshift.org/view/gcs/{project}/{job_prefix}{build_id}\"\n",
    "\n",
    "\n",
    "def clean_df(df):\n",
    "    \"\"\"Polishes the metadata DataFrame\"\"\"\n",
    "    build_errors = df[df[\"result\"] == \"error\"].index\n",
    "    df.drop(build_errors, inplace=True)  # Remove builds that erroed (prow error)\n",
    "    df[\"duration\"] = df[\"end\"] - df[\"start\"]  # From timestamps to job duration\n",
    "    df[\"success\"] = df[\"result\"] == \"SUCCESS\"  # A boolean version of the result\n",
    "    return df\n",
    "\n",
    "\n",
    "print(\"Reading metadata from\", metadata_file_name)\n",
    "df = pd.read_csv(metadata_file_name, index_col=0)\n",
    "df = clean_df(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7921d253",
   "metadata": {},
   "source": [
    "### A view of our job/log metadata\n",
    "\n",
    "The data we work with contains only the following information about each job build:\n",
    "  - The *result* of the build (wether it was successful or not). The metadata has a label and we also compute a boolean for it.\n",
    "  - The build *start* and *end* timestamps. We compute the *duration* from that.\n",
    "  - The *size* (lenght) in bytes of the `build-log`.\n",
    "\n",
    "We saw in the initial exploration that with just this information, without even looking at the contents of the logs, we can have a good idea of how/if the build failed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27f236a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_clusters():\n",
    "    clusters = df.cluster.unique()\n",
    "    for i, cluster in enumerate(sorted(clusters)):\n",
    "        color = colormap(float(i) / len(clusters))\n",
    "        plt.plot(\n",
    "            \"size\",\n",
    "            \"duration\",\n",
    "            \"o\",\n",
    "            data=df[df.cluster == cluster],\n",
    "            color=color,\n",
    "            alpha=0.7,\n",
    "            label=cluster,\n",
    "        )\n",
    "    plt.xscale(\"log\")\n",
    "    plt.xlabel(\"build-log size (bytes)\")\n",
    "    plt.ylabel(\"build time (seconds)\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "df = df.assign(cluster=df.result)\n",
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc75073",
   "metadata": {},
   "source": [
    "This sample job shows a pattern that is common across various jobs that have been observed:\n",
    "\n",
    "- We see that successful runs have an almost constant and relatively small build log size. We noted in the [initial build log EDA](./build_log_EDA.ipynb) notebook that the two main log size groups visible in successful runs is due to [a change in how logs are formatted](https://github.com/openshift/ci-tools/pull/1836).\n",
    "- Some run failures have big log sizes. These are the ones that incorporate the full output of test suite (successful runs do not include that output).\n",
    "- Some runs fail in the preparation steps, before tests can actually be run. Hence, all runs that run for less than a certain time threshold (the time it takes for preparation steps to complete) result in a failure.\n",
    "\n",
    "Based on that, we could probably have a classifier for pass/fail with decent accuracy just by looking at the log size and execution time. And, of course, we can just look at the result to see if a run fails or succeeds.\n",
    "\n",
    "Can the contents of the logs help us group job runs according to different types of success / failure?\n",
    "\n",
    "Or, in other words, can we extract additional value/information about what happened during the build by looking at build log contents?\n",
    "\n",
    "Let's try.\n",
    "\n",
    "\n",
    "## Term frequency analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5f14706",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get a list of paths to the local copy of each build log\n",
    "build_logs = []\n",
    "for build_id in df.index:\n",
    "    build_logs.append(log_path_for(build_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b5e716",
   "metadata": {},
   "source": [
    "As mentioned in the introduction, we are going to apply a technique which is not commonly applied to log data: [term frequency–inverse document frequency](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) analysis; the idea here is that we can exploit the peculiarities of CI log data (vs traditional log data).\n",
    "\n",
    "Before we begin, a key question we must answer is: what is a *term* for us?\n",
    "\n",
    "If we were working with natural language text, typically we would identify terms (*tokens*) by any separation between words, including spacing and punctuation. Logs, however, often contain terms that include some sort of punctuation character that should not be used to separate them. For example, `/file/paths/like/this` or `some.component.like.this` or maybe `something-with:tags`. We probably want to consider these types of terms as a single unit (token).\n",
    "\n",
    "On the other hand, it might be counter-productive to keep all punctuation/separators in:\n",
    "\n",
    "- Some Kubernetes resource names contain generated parts that look very similar to non-generated content. For example, a Deployment called `sample-deployment-3` can produce a Pod called `sample-deployment-3-7fdfd97c84-7rj8b`. The name of the deployemt is probably relevant, while the name of the Pod is probably not.\n",
    "- Some other name parts might refer to a specific version, like `v0-5-0-o79ew2-1fdt39x15`; these look like generated parts but are as interesting as non-generated ones.\n",
    "\n",
    "The criteria followed below (by default) aims at a compromise, taking advantadge of the fact that we have multiple documents (log files):\n",
    "\n",
    "- Do not separate by some punctuation like `.` or `/`\n",
    "- Discard tokens that appear in less than 5% of our documents\n",
    "\n",
    "This should help us e.g. keep a Deployment name as a term and discard a specific Pod name.\n",
    "\n",
    "Besides that, the minimum requirement that we impose on tokens is:\n",
    "\n",
    "- A minimum lenght of 2 alphanumerical characters (or punctuation as mentioned)\n",
    "- Not starting with a number\n",
    "\n",
    "Note that by not allowing numbers as the first character of a token we are discarding all sorts of terms: dates, times, IP addresses, all plain numbers, duration (`42s`), size (`28k`), etc.\n",
    "\n",
    "Some experimentation with various options suggests that this particular pattern leads to best results. For alternative versions, see the comments below (and note that `token_pattern` is optional and can be left out completely, to use a default tokenizer that does not allow any punctuation character within tems).\n",
    "\n",
    "One option that has not been explored is to separate tokens more aggresively (e.g. with the default tokenizer) and then work with n-grams. Note that most of the names/identifiers that are likely relevant to keep would not be captured by bigrams or even trigrams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b67adcaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done in 82.08 seconds\n",
      "TF-IDF matrix: 837 documents x 4339 terms\n"
     ]
    }
   ],
   "source": [
    "# Our default token pattern matches tokens which:\n",
    "# - are at least 2-chars long\n",
    "# - start with a letter\n",
    "# - can contain letters, numbers, or any of: _ - / .\n",
    "token_pattern = r\"\\b[a-z][a-z0-9_/\\.-]+\\b\"\n",
    "\n",
    "# Alternative token patterns\n",
    "# token_pattern = r\"\\b[a-z][a-z0-9_/\\.:-]+\\b\"  # keep slahs/dash-colons:together\n",
    "# token_pattern = r\"\\b[a-z][a-z0-9_/\\.:]+\\b\"  # separate terms-like-this-one, keep colons\n",
    "# token_pattern = r\"\\b[a-z][a-z0-9_/\\.]+\\b\"  # separate dashes and colons\n",
    "# token_pattern = r\"\\b[a-z][a-z0-9_\\.:]+\\b\"  # separate terms/like-this-one\n",
    "# token_pattern = r\"\\b[a-z][a-z0-9_\\.]+\\b\"  # separate slash, dash and colon\n",
    "# token_pattern = r\"\\b\\w[a-z0-9_/\\.-]+\\b\"  # allow numbers\n",
    "\n",
    "t0 = time()\n",
    "vectorizer = TfidfVectorizer(\n",
    "    input=\"filename\",\n",
    "    min_df=0.03,\n",
    "    # stop_words='english',  # remove common english words\n",
    "    token_pattern=token_pattern,\n",
    ")\n",
    "\n",
    "tfidf = vectorizer.fit_transform(build_logs)\n",
    "\n",
    "print(f\"Done in {time()-t0:.2f} seconds\")\n",
    "print(f\"TF-IDF matrix: {tfidf.shape[0]} documents x {tfidf.shape[1]} terms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc0dd6b",
   "metadata": {},
   "source": [
    "The resulting vocabulary (the terms we work with) with the default token pattern is in the order of ~4000 terms. \n",
    "\n",
    "If we allow numbers, the vocabulary can easily increase by over an order of magnitude.\n",
    "\n",
    "Let's look at a sample of the resulting vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e32006b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "container/failure-2 | ss2-2 | request/limit | e2e-gcp-gather-audit-logs | nodeauthenticator | mappings | port | router_metrics_type | client.timeout | approved | similar | like | report | accept | main.main | recreatedeployment | deprecated | v1.20.0/test/e2e/framework/framework.go | testsimpleimagechangebuildtriggerfromimagestreamtagcustom | apps/v1/daemonset/openshift-multus/multus/initcontainer/routeoverride-cni/request | apiversion | orphans | us-east1 | incompatible | deployment/idle-test | kubernetes.io/gce-pd | apps/v1/daemonset/openshift-multus/multus/initcontainer/cni-plugins/request | deployment/test-rollover-deployment | loading | nodeconformance | dhe-rsa-aes256-gcm-sha384 | detach | deployment/sample-apiserver-deployment | minute | executing | answer | single-container | persistentvolumeclaim/snapshot-test-pvc | pod/testpod-pclass6 | csi-hostpath-attacher | windows | sti | container | github.com/openshift/origin/pkg/test/ginkgo | recreatedeployment | reason/rolloutinprogress | updatecm-volume | kubernetes.io/gce-pd | sidecar | prometheus-operator.openshift-monitoring.svc | fetch | pod/example-1-deploy | terminationmessagepath | ss-0 | npulling | pod/failure-1 | assetpublicurl | dockercfg | runtime/panic.go | keyfile | controllers | partially-specified | reason/leaderelection | providers | imagepullbackoff | local-client | ns/openshift-ingress-operator | returned | oc | instance | nivcsw | apps/v1/daemonset/openshift-multus/multus/initcontainer/egress-router-binary-copy/request | azure | reason/missingjob | was | pod/history-limit-10-deploy | instances | contains | non-workspace | wanted | resource/name | options | sti-imagestreamtag-1-global-ca | dev/zero | nvcsw | v1.20.0/test/e2e/storage/vsphere/vsphere_volume_diskformat.go | pod/csi-hostpath-snapshotter-0 | minreadytest-1 | endpoints/affinity-nodeport | parameters | v1.project | actual | root | service/test-rolling-update-with-lb | test-docker-build.json | replicaset/sample-crd-conversion-webhook-deployment-5f5c959744 | secret-pod-ephm-test | oauthserverdeploymentdegraded | pod | late\n"
     ]
    }
   ],
   "source": [
    "terms = vectorizer.get_feature_names()\n",
    "\n",
    "print(\" | \".join(random.choices(terms, k=100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504cb89c",
   "metadata": {},
   "source": [
    "We now have each *document* (i.e. each `build-log` file) represented by a vector of a few thousand dimensions (i.e. one per term in the vocabulary).\n",
    "\n",
    "## Clustering\n",
    "\n",
    "Let's now try to apply a clustering algorithm to that group of vectors.\n",
    "\n",
    "Here we will try k-means.\n",
    "\n",
    "In order to get an idea of the best number of clusters ($k$) we should try to build (i.e. how many groups or types of build logs we have) we calculate a few metrics on each number of clusters to help us identify the best $k$ for our data set:\n",
    "\n",
    "- plot the sum of squared distances between samples and cluster centroids to use the [elbow method](https://en.wikipedia.org/wiki/Elbow_method_(clustering))\n",
    "- Calinski-Harabas index\n",
    "- Davies-Bouldin index\n",
    "- Average Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14974c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def kmeans_metrics(x, kmax=11):\n",
    "    ss = []  # Sum of squared distances of samples to their closest cluster center\n",
    "    ch = []  # Caliski-Harabasz index\n",
    "    sc = []  # Silhouette Coefficient\n",
    "    db = []  # Davies-Bouldin index\n",
    "\n",
    "    k_range = range(2, kmax)\n",
    "    x_arr = x.toarray()  # several metrics don't work with sparse arrays\n",
    "    for k in k_range:\n",
    "        kmeans = KMeans(n_clusters=k, random_state=123)\n",
    "        kmeans.fit(x_arr)\n",
    "        labels = kmeans.labels_\n",
    "        ss.append(kmeans.inertia_)\n",
    "        ch.append(metrics.calinski_harabasz_score(x_arr, labels))\n",
    "        sc.append(metrics.silhouette_score(x_arr, labels))\n",
    "        db.append(metrics.davies_bouldin_score(x_arr, labels))\n",
    "\n",
    "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)\n",
    "    fig.tight_layout()\n",
    "\n",
    "    ax1.plot(k_range, ss, \"x-\")\n",
    "    ax1.set_title(\"Elbow method (ss distances within clusters)\")\n",
    "    # Not interested in the distance values, only the shape of the plot:\n",
    "    ax1.set_yticks([])\n",
    "\n",
    "    ax2.set_title(\"Calinski-Harabasz Index (higher is better)\")\n",
    "    ax2.plot(k_range, ch, \"x-\")\n",
    "\n",
    "    ax3.set_title(\"Davies-Bouldin Index (lower is better)\")\n",
    "    ax3.plot(k_range, db, \"x-\")\n",
    "    ax3.set_xlabel(\"Number of clusters (k)\")\n",
    "\n",
    "    ax4.set_title(\"Avg Silhouette score (higher is better)\")\n",
    "    ax4.plot(k_range, sc, \"x-\")\n",
    "    ax4.set_xlabel(\"Number of clusters (k)\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "kmeans_metrics(tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c195b6",
   "metadata": {},
   "source": [
    "The elbow plot suggests that for our data we should pick $k = 3$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7090e5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 3\n",
    "kmeans = KMeans(n_clusters=k, random_state=123)\n",
    "kmeans.fit(tfidf)\n",
    "df = df.assign(cluster=kmeans.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590d5ef5",
   "metadata": {},
   "source": [
    "With the clusters created by k-means, let's take a look at which are the most common terms in each cluster. This should hopefully give us an idea of what type of build belogs to each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ab58070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 clusters with the following metrics:\n",
      "  - Silhouette coefficient: 0.59\n",
      "  - Calinski-Harabasz index: 678.42\n",
      "  - Davies-Bouldin index: 0.93\n",
      "A list of the most common terms in each cluster:\n",
      "Cluster 0: apr suite mar openshift/conformance/parallel k8s volumes should driver sig-storage\n",
      "Cluster 1: pod completed successfully container in place-entrypoint cp-entrypoint-wrapper sidecar executing\n",
      "Cluster 2: step e2e running after succeeded release for latest to\n"
     ]
    }
   ],
   "source": [
    "def cluster_terms(topn=10):\n",
    "    \"\"\"Print the top tokens for each cluster\"\"\"\n",
    "    terms = vectorizer.get_feature_names()\n",
    "    order_centroids = kmeans.cluster_centers_.argsort()[:, ::-1]\n",
    "    for i in range(k):\n",
    "        print(\"Cluster %d:\" % i, end=\"\")\n",
    "        for ind in order_centroids[i, :topn]:\n",
    "            print(\" %s\" % terms[ind], end=\"\")\n",
    "        print()\n",
    "\n",
    "\n",
    "def describe_clusters():\n",
    "    x = tfidf.toarray()\n",
    "    print(k, \"clusters with the following metrics:\")\n",
    "    print(\n",
    "        f\"  - Silhouette coefficient: {metrics.silhouette_score(tfidf, kmeans.labels_):.2f}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"  - Calinski-Harabasz index: {metrics.calinski_harabasz_score(x, kmeans.labels_):.2f}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"  - Davies-Bouldin index: {metrics.davies_bouldin_score(x, kmeans.labels_):.2f}\"\n",
    "    )\n",
    "    print(\"A list of the most common terms in each cluster:\")\n",
    "    cluster_terms(9)\n",
    "\n",
    "\n",
    "describe_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5cc2b8",
   "metadata": {},
   "source": [
    "Looking at that list of most popular terms per cluster we can have an intuition that one of the clusters (cluster *0*) most likely corresponds to builds that managed to run a test suite where some test(s) failed. We know from the [initial build log exploration](./build_log_EDA.ipynb) that the build log only shows test logs when there are test failures; in that cluster we see terms like *suite*, references to *conformance* test, or references to a *sig* group that are present in test logs.\n",
    "\n",
    "However, for the other two clusters it is hard to say what makes them different. In particular, it seems a bit strange that both show terms related to *success* in their top list of terms.\n",
    "\n",
    "Let's plot these clusters against the build duration and size to compare with our [initial visualization](#A-view-of-our-log-metadata), to see where they match:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "193802b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe99047c",
   "metadata": {},
   "source": [
    "The scatter plot explains why it is hard to distinguish the two other clusters:\n",
    "\n",
    "- the clustering algorithm has separated the two \"columns\" of successful builds in different clusters\n",
    "- the failed builds that are scattered around the successful builds have been grouped together with one of them\n",
    "\n",
    "Unfortunately, then, this initial clustering produced worse results than what we could obtain initially by just looking at log size and build duration.\n",
    "\n",
    "Let's look closer at what happened here by taking a look at silhouette scoring.\n",
    "\n",
    "We will run k-means with different numbers of clusters and plot the clusters' silhouette for each $k$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26b36a53",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x2160 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def kmeans_silhouette(x, kmax=13):\n",
    "    cols = 2\n",
    "    rows = int((kmax - 1) / cols)\n",
    "    fig, axs = plt.subplots(rows, cols)\n",
    "    ax = axs.flatten()\n",
    "    fig.set_size_inches(cols * 8, rows * 5)\n",
    "    for n, k in enumerate(range(2, kmax + 1)):\n",
    "        # The silhouette coefficient can range from -1, 1 but all of our tests\n",
    "        # lie within [-0.2, 1]\n",
    "        ax[n].set_xlim([-0.2, 1])\n",
    "        # The (k+1)*10 is for inserting blank space between silhouette\n",
    "        # plots of individual clusters, to demarcate them clearly.\n",
    "        ax[n].set_ylim([0, x.shape[0] + (k + 1) * 10])\n",
    "\n",
    "        # Initialize the clusterer with n_clusters value and a random generator\n",
    "        # seed of 10 for reproducibility.\n",
    "        clusterer = KMeans(n_clusters=k, random_state=123)\n",
    "        cluster_labels = clusterer.fit_predict(x)\n",
    "\n",
    "        # The silhouette_score gives the average value for all the samples.\n",
    "        # This gives a perspective into the density and separation of the formed\n",
    "        # clusters\n",
    "        silhouette_avg = metrics.silhouette_score(x, cluster_labels)\n",
    "        # print(f\"For {k} clusters the average silhouette_score is {silhouette_avg:.2f}\")\n",
    "\n",
    "        # Compute the silhouette scores for each sample\n",
    "        sample_silhouette_values = metrics.silhouette_samples(x, cluster_labels)\n",
    "\n",
    "        y_lower = 10\n",
    "        for i in range(k):\n",
    "            # Aggregate the silhouette scores for samples belonging to\n",
    "            # cluster i, and sort them\n",
    "            ith_cluster_silhouette_values = sample_silhouette_values[\n",
    "                cluster_labels == i\n",
    "            ]\n",
    "\n",
    "            ith_cluster_silhouette_values.sort()\n",
    "\n",
    "            size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "\n",
    "            color = colormap(float(i) / k)\n",
    "            ax[n].fill_betweenx(\n",
    "                np.arange(y_lower, y_upper),\n",
    "                0,\n",
    "                ith_cluster_silhouette_values,\n",
    "                facecolor=color,\n",
    "                edgecolor=color,\n",
    "                alpha=0.7,\n",
    "            )\n",
    "\n",
    "            # Label the silhouette plots with their cluster numbers at the middle\n",
    "            ax[n].text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "            # Compute the new y_lower for next plot\n",
    "            y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "        # The vertical line for average silhouette score of all the values\n",
    "        ax[n].axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "        ax[n].set_title(f\"# clusters = {k}\")\n",
    "        ax[n].set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        ax[n].set_xticks(np.arange(-0.2, 1.1, 0.2))\n",
    "\n",
    "\n",
    "kmeans_silhouette(tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70a9a9e",
   "metadata": {},
   "source": [
    "We picked $k=3$ based on the elbow method, but in the exploration of metrics for different numbers of clusters we can see that some higher number of clusters have some better scores.\n",
    "\n",
    "Looking at the silhouette score in particular, we see that the average score increases as the number of clusters grows up to 8 clusters. In the silhouette plots above we can also observe that for $k>8$ the clustering appears to degrade (even if the average silhouette score remains relavively high)\n",
    "\n",
    "The other metrics are also better with $k=8$ than $k=3$, so let's take a look of how does clustering look like when $k=8$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7387da5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 8\n",
    "kmeans = KMeans(n_clusters=k, random_state=123)\n",
    "kmeans.fit(tfidf)\n",
    "df = df.assign(cluster=kmeans.labels_)\n",
    "\n",
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681a9245",
   "metadata": {},
   "source": [
    "On the positive side it seems that now we have the failed builds that scatter around successful ones properly separated in their own clusters. On the other hand, with 8 groups of build logs we appear to have excessive separation, in particular within the groups of failures with smaller log sizes.\n",
    "\n",
    "It is not shown in this notebook to try to keep output reasonable, but an exploration of sample logs of these clusters does not seem to justify or explain why they would deserve separate clusters.\n",
    "\n",
    "Whatt seems clear is that the clustering algorithm is being impacted by the fact that we have, at least, two types of formatting for the logs (both for successful and failed builds): as explained in the [initial build log EDA notebook](./build_log_EDA.ipynb), there was [a change in OpenShift CI tools](https://github.com/openshift/ci-tools/pull/1836) that caused a change in how build logs are formatted. This results in the \"two columns of success\" that we see in the visualization (different log contents, and therefore different log lenghts, for logs that would otherwise be almost identical), and it seems to be also impacting the clustering of failed logs.\n",
    "\n",
    "Due to the impact of this change in log formatting, let's try our approach with logs that are a bit more consistently formatted.\n",
    "\n",
    "## Avoiding the build log format change\n",
    "\n",
    "Here we filter out all the build logs that were generated prior to the format change.\n",
    "\n",
    "At the time of this writing, this means that we are removing a higher number of observations than the ones we keep. Moving forward though, all new builds use the new formatting, and therefore it seems more interesting to investigate the results that we can obtain with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95ac9a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>size</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>success</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1380281977160077312</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3841</td>\n",
       "      <td>1617919845</td>\n",
       "      <td>1617929494</td>\n",
       "      <td>9649</td>\n",
       "      <td>True</td>\n",
       "      <td>SUCCESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385025333723402240</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3868</td>\n",
       "      <td>1619050750</td>\n",
       "      <td>1619056186</td>\n",
       "      <td>5436</td>\n",
       "      <td>True</td>\n",
       "      <td>SUCCESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379152673013501952</th>\n",
       "      <td>FAILURE</td>\n",
       "      <td>9212120</td>\n",
       "      <td>1617650599</td>\n",
       "      <td>1617655198</td>\n",
       "      <td>4599</td>\n",
       "      <td>False</td>\n",
       "      <td>FAILURE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382952842423177216</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3838</td>\n",
       "      <td>1618556629</td>\n",
       "      <td>1618560934</td>\n",
       "      <td>4305</td>\n",
       "      <td>True</td>\n",
       "      <td>SUCCESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379871391536386048</th>\n",
       "      <td>FAILURE</td>\n",
       "      <td>10168121</td>\n",
       "      <td>1617821954</td>\n",
       "      <td>1617830667</td>\n",
       "      <td>8713</td>\n",
       "      <td>False</td>\n",
       "      <td>FAILURE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380128512421138432</th>\n",
       "      <td>FAILURE</td>\n",
       "      <td>10251467</td>\n",
       "      <td>1617883260</td>\n",
       "      <td>1617889070</td>\n",
       "      <td>5810</td>\n",
       "      <td>False</td>\n",
       "      <td>FAILURE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386942197940621312</th>\n",
       "      <td>FAILURE</td>\n",
       "      <td>10913264</td>\n",
       "      <td>1619507767</td>\n",
       "      <td>1619512517</td>\n",
       "      <td>4750</td>\n",
       "      <td>False</td>\n",
       "      <td>FAILURE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384563411320311808</th>\n",
       "      <td>FAILURE</td>\n",
       "      <td>1830</td>\n",
       "      <td>1618940618</td>\n",
       "      <td>1618947972</td>\n",
       "      <td>7354</td>\n",
       "      <td>False</td>\n",
       "      <td>FAILURE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381826019446493184</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3837</td>\n",
       "      <td>1618287973</td>\n",
       "      <td>1618292751</td>\n",
       "      <td>4778</td>\n",
       "      <td>True</td>\n",
       "      <td>SUCCESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382368829539094528</th>\n",
       "      <td>SUCCESS</td>\n",
       "      <td>3840</td>\n",
       "      <td>1618417400</td>\n",
       "      <td>1618422303</td>\n",
       "      <td>4903</td>\n",
       "      <td>True</td>\n",
       "      <td>SUCCESS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      result      size       start         end  duration  \\\n",
       "1380281977160077312  SUCCESS      3841  1617919845  1617929494      9649   \n",
       "1385025333723402240  SUCCESS      3868  1619050750  1619056186      5436   \n",
       "1379152673013501952  FAILURE   9212120  1617650599  1617655198      4599   \n",
       "1382952842423177216  SUCCESS      3838  1618556629  1618560934      4305   \n",
       "1379871391536386048  FAILURE  10168121  1617821954  1617830667      8713   \n",
       "...                      ...       ...         ...         ...       ...   \n",
       "1380128512421138432  FAILURE  10251467  1617883260  1617889070      5810   \n",
       "1386942197940621312  FAILURE  10913264  1619507767  1619512517      4750   \n",
       "1384563411320311808  FAILURE      1830  1618940618  1618947972      7354   \n",
       "1381826019446493184  SUCCESS      3837  1618287973  1618292751      4778   \n",
       "1382368829539094528  SUCCESS      3840  1618417400  1618422303      4903   \n",
       "\n",
       "                     success  cluster  \n",
       "1380281977160077312     True  SUCCESS  \n",
       "1385025333723402240     True  SUCCESS  \n",
       "1379152673013501952    False  FAILURE  \n",
       "1382952842423177216     True  SUCCESS  \n",
       "1379871391536386048    False  FAILURE  \n",
       "...                      ...      ...  \n",
       "1380128512421138432    False  FAILURE  \n",
       "1386942197940621312    False  FAILURE  \n",
       "1384563411320311808    False  FAILURE  \n",
       "1381826019446493184     True  SUCCESS  \n",
       "1382368829539094528     True  SUCCESS  \n",
       "\n",
       "[367 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PR 1836 changed the way build logs are formatted\n",
    "# https://github.com/openshift/ci-tools/pull/1836\n",
    "# It was merged on 2021-04-05 16:45 UTC\n",
    "pr_1836_merged = datetime(2021, 4, 5, 16, 45, tzinfo=timezone.utc)\n",
    "pr_1836_timestamp = int(pr_1836_merged.timestamp())\n",
    "\n",
    "df = df[df.start > pr_1836_timestamp]  # filter out old format logs\n",
    "df = df.assign(cluster=df.result)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "708f6f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a88d7942",
   "metadata": {},
   "source": [
    "Here we have removed all the build logs prior to the format change. Obviously, our data size has decreased (less than a half at the time of this writing). But we don't have the \"two columns of success\" any more: all the successful builds have approximately the same size.\n",
    "\n",
    "Let's rebuild our TF-IDF matrix by vectorizing these build logs only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "103ea1f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done in 53.21 seconds\n",
      "TF-IDF matrix: 367 documents x 4169 terms\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(\n",
    "    input=\"filename\",\n",
    "    min_df=0.05,\n",
    "    token_pattern=token_pattern,\n",
    ")\n",
    "\n",
    "# Update the list of log files\n",
    "build_logs = []\n",
    "for build_id in df.index:\n",
    "    build_logs.append(log_path_for(build_id))\n",
    "\n",
    "t0 = time()\n",
    "tfidf = vectorizer.fit_transform(build_logs)\n",
    "\n",
    "print(f\"Done in {time()-t0:.2f} seconds\")\n",
    "print(f\"TF-IDF matrix: {tfidf.shape[0]} documents x {tfidf.shape[1]} terms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb8d9d02",
   "metadata": {},
   "source": [
    "**NOTE**: we now have only a subset of the original documents, but we obtain more terms! How so?\n",
    "\n",
    "This is becasue the threshold for terms to be considered based on the inverse document frequency is specified as a % of the number of documents (the `min_df` parameter). Due to the change in the dataset size, the absolute number has changed.\n",
    "\n",
    "Let's take a look at the metrics and silhouettes of various numbers of clusters for this new data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f6a7c04",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_metrics(tfidf)\n",
    "kmeans_silhouette(tfidf, kmax=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d764ca2c",
   "metadata": {},
   "source": [
    "The elbow plot suggests that we should pick $k=4$.\n",
    "\n",
    "This time, the rest of the metrics agree that this is a good pick:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4219d5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 clusters with the following metrics:\n",
      "  - Silhouette coefficient: 0.85\n",
      "  - Calinski-Harabasz index: 1453.79\n",
      "  - Davies-Bouldin index: 0.59\n",
      "A list of the most common terms in each cluster:\n",
      "Cluster 0: e2e step running after succeeded ipi-conf release for openshift-e2e-test\n",
      "Cluster 1: apr suite openshift/conformance/parallel k8s should volumes sig-storage driver testpattern\n",
      "Cluster 2: step running after succeeded release for e2e-gcp latest https\n",
      "Cluster 3: failed release acquire to for error latest not https\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 4\n",
    "kmeans = KMeans(n_clusters=k, random_state=123)\n",
    "kmeans.fit(tfidf)\n",
    "df = df.assign(cluster=kmeans.labels_)\n",
    "\n",
    "describe_clusters()\n",
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6f487d",
   "metadata": {},
   "source": [
    "This looks much better than our previous initial attempt: the 4 clusters do seem to match and capture the types of builds that we have, clearly separating two different sets of failures - and also grouping the successfuld builds in two different clusters.\n",
    "\n",
    "The top terms for each cluster make sense and match with the grouping, but they don't provide enough hints to help us understand the two groups of successful builds.\n",
    "\n",
    "To dig a bit further on this classification, let's look at one samble job run from each of the groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c26fe493",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Sample job run of type 0\n",
      ">> BUILD 1380947233955909632. Log length: 3835 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1380947233955909632\n",
      "\n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T18:51:16Z] Running step e2e-***-openshift-e2e-test.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:22:46Z] Step e2e-***-openshift-e2e-test succeeded after 31m30.061462427s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:22:46Z] Running step e2e-***-gather-core-dump.       \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:23:16Z] Step e2e-***-gather-core-dump succeeded after 30.12392026s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:23:16Z] Running step e2e-***-gather-***-console.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:24:06Z] Step e2e-***-gather-***-console succeeded after 50.062247974s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:24:06Z] Running step e2e-***-gather-must-gather.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:27:26Z] Step e2e-***-gather-must-gather succeeded after 3m20.066930755s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:27:26Z] Running step e2e-***-gather-extra.           \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:29:27Z] Step e2e-***-gather-extra succeeded after 2m0.073055011s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:29:27Z] Running step e2e-***-gather-audit-logs.      \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:30:27Z] Step e2e-***-gather-audit-logs succeeded after 1m0.062202998s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:30:27Z] Running step e2e-***-ipi-deprovision-deprovision. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:35:17Z] Step e2e-***-ipi-deprovision-deprovision succeeded after 4m50.069263221s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:35:17Z] Releasing leases for test e2e-***            \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:35:17Z] Ran for 1h20m51s                             \n",
      "\n",
      ">> Sample job run of type 1\n",
      ">> BUILD 1383176786765942784. Log length: 8667736 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1383176786765942784\n",
      "\n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] [sig-storage] PersistentVolumes NFS with Single PV - PVC pairs create a PV and a pre-bound PVC: test write access [Suite:openshift/conformance/parallel] [Suite:k8s] \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] Writing JUnit report to /logs/artifacts/junit/junit_e2e_20210416-233104.xml \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] error: 43 fail, 1075 pass, 1607 skip (29m38s) \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] ++ date +%s                                  \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] + echo 1618615864                            \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] {\"component\":\"entrypoint\",\"error\":\"wrapped process failed: exit status 1\",\"file\":\"prow/entrypoint/run.go:80\",\"func\":\"k8s.io/test-infra/prow/entrypoint.Options.Run\",\"level\":\"error\",\"msg\":\"Error executing test process\",\"severity\":\"error\",\"time\":\"2021-04-16T23:31:04Z\"} \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] error: failed to execute wrapped command: exit status 1 \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] ---                                          \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] Link to step on registry info site: https://steps.ci.openshift.org/reference/openshift-e2e-test \n",
      "\u001b[31mERRO\u001b[0m[2021-04-16T23:43:05Z] Link to job on registry info site: https://steps.ci.openshift.org/job?org=openshift&repo=release&branch=master&test=e2e-gcp&variant=ci-4.8 \n",
      "\n",
      ">> Sample job run of type 2\n",
      ">> BUILD 1386064720871559168. Log length: 3753 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1386064720871559168\n",
      "\n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T21:47:18Z] Step e2e-gcp-ipi-install-install succeeded after 35m30s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T21:47:18Z] Running step e2e-gcp-openshift-e2e-test.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:08Z] Step e2e-gcp-openshift-e2e-test succeeded after 28m50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:08Z] Running step e2e-gcp-gather-core-dump.       \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:38Z] Step e2e-gcp-gather-core-dump succeeded after 30s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:38Z] Running step e2e-gcp-gather-gcp-console.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:17:28Z] Step e2e-gcp-gather-gcp-console succeeded after 50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:17:28Z] Running step e2e-gcp-gather-must-gather.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:20:58Z] Step e2e-gcp-gather-must-gather succeeded after 3m30s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:20:58Z] Running step e2e-gcp-gather-extra.           \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:22:48Z] Step e2e-gcp-gather-extra succeeded after 1m50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:22:48Z] Running step e2e-gcp-gather-audit-logs.      \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:23:48Z] Step e2e-gcp-gather-audit-logs succeeded after 1m0s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:23:48Z] Running step e2e-gcp-ipi-deprovision-deprovision. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:27:38Z] Step e2e-gcp-ipi-deprovision-deprovision succeeded after 3m50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:27:38Z] Releasing leases for test e2e-gcp            \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:27:38Z] Ran for 1h18m16s                             \n",
      "\n",
      ">> Sample job run of type 3\n",
      ">> BUILD 1382678531183480832. Log length: 1799 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1382678531183480832\n",
      "\n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:53:52Z] Resolved release initial to registry.ci.openshift.org/ocp/release:4.8.0-0.ci-2021-04-15-062304 \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:53:52Z] Using explicitly provided pull-spec for release latest (registry.ci.openshift.org/ocp/release:4.8.0-0.ci-2021-04-15-124724) \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:53:52Z] Using namespace https://console.build02.ci.openshift.org/k8s/cluster/projects/ci-op-dh34hlyk \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:53:52Z] Running [input:origin-centos-8], [input:ocp-4.5-upi-installer], [release:latest], [images], e2e-gcp \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:53:53Z] Tagging origin/centos:8 into pipeline:origin-centos-8. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:53:53Z] Tagging ocp/4.5:upi-installer into pipeline:ocp-4.5-upi-installer. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:53:53Z] Importing release image latest.              \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:55:23Z] Imported release 4.8.0-0.ci-2021-04-15-124724 created at 2021-04-15 12:53:16 +0000 UTC with 137 images to tag release:latest \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T12:55:23Z] Acquiring leases for test e2e-gcp            \n",
      "\u001b[31mERRO\u001b[0m[2021-04-15T14:55:23Z] error: Failed to acquire resource, current capacity: 0 free, 70 leased \n",
      "\u001b[36mINFO\u001b[0m[2021-04-15T14:55:23Z] Ran for 2h1m30s                              \n",
      "\u001b[31mERRO\u001b[0m[2021-04-15T14:55:23Z] Some steps failed:                           \n",
      "\u001b[31mERRO\u001b[0m[2021-04-15T14:55:23Z]   * could not run steps: step e2e-gcp failed: failed to acquire lease: resources not found \n"
     ]
    }
   ],
   "source": [
    "def tail_log(build_id, amount=1500):\n",
    "    \"\"\"Prints the last few lines of build_id's build log file.\n",
    "    The amount to read is in bytes.\"\"\"\n",
    "    path = log_path_for(build_id)\n",
    "    size = os.path.getsize(path)\n",
    "    print(f\">> BUILD {build_id}. Log length: {size} bytes\")\n",
    "    print(f\">> BUILD URL: {prow_url_for(build_id)}\\n\")\n",
    "    with open(path) as f:\n",
    "        if size > amount:\n",
    "            f.seek(size - amount)\n",
    "        lines = f.readlines()\n",
    "    for line in lines[1:]:  # skip the first line as it's probably incomplete\n",
    "        print(line, end=\"\")\n",
    "\n",
    "\n",
    "for cluster in range(k):\n",
    "    print(\"\\n>> Sample job run of type\", cluster)\n",
    "    tail_log(df[df.cluster == cluster].sample(random_state=123).index.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ee2f7c",
   "metadata": {},
   "source": [
    "In the content of the logs we do see now what makes the difference between the two types of successful logs: one group has the cloud provider (e.g. *gcp*) obfuscated with asterisks (\\*). A significant number of occurrences of this difference appear in each log. And remember that the size of the build logs on these two clusters (both successful runs) are relatively short, which means that this difference does make the log files different enough.\n",
    "\n",
    "### Another format change: asterisks\n",
    "\n",
    "Based on the observation above, let's try one additional data clean up: we will investigate what happens if we modify our tokenizer to accept askerisks as part of the terms, so that for example both of these are considered a term: `e2e-gcp-gather-extra` and `e2e-***-gather-extra` (that last one was two separate tokens in our previous data: `e2e` and `gather-extra`).\n",
    "\n",
    "Moreover, this time we will try to apply some domain knowledge, or at least some intuition, to pick the number of clusters, instead of relying on metrics (at least initially). Let's assume here that we want to be able to differenciate, just by looking at build logs, three types of builds:\n",
    "\n",
    "- successful builds\n",
    "- builds that fail because some tests fail\n",
    "- builds that fail due to preparatory / infrastructure steps\n",
    "\n",
    "So, we will look at the case of $k=3$.\n",
    "\n",
    "We saw in the initial attempt with $k=3$ that the clusters were badly mixing successful builds with builds that failed in preparatory steps.\n",
    "\n",
    "But then we cleaned the data set a bit by removing different output types and sticking to the current output format. Has this change in the data improved the classification within 3 clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7d868c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 clusters with the following metrics:\n",
      "  - Silhouette coefficient: 0.75\n",
      "  - Calinski-Harabasz index: 781.93\n",
      "  - Davies-Bouldin index: 0.51\n",
      "A list of the most common terms in each cluster:\n",
      "Cluster 0: e2e step running after succeeded ipi-conf release for openshift-e2e-test\n",
      "Cluster 1: apr suite openshift/conformance/parallel k8s should volumes sig-storage driver testpattern\n",
      "Cluster 2: step running after succeeded release for latest e2e-gcp to\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 3\n",
    "kmeans = KMeans(n_clusters=k, random_state=123)\n",
    "kmeans.fit(tfidf)\n",
    "df = df.assign(cluster=kmeans.labels_)\n",
    "\n",
    "describe_clusters()\n",
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65ad0b3",
   "metadata": {},
   "source": [
    "We see that no, removing the old-format build logs does not seem to have improved the classification within these 3 types of builds much: we have a group that misxes successful builds with fail-during-preparation builds.\n",
    "\n",
    "Ok then, let's see if the additional \"cleaning\" step that we wanted to do (i.e. to accep asterisks within terms) helps us improve that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "58c5e026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 clusters with the following metrics:\n",
      "  - Silhouette coefficient: 0.80\n",
      "  - Calinski-Harabasz index: 1350.32\n",
      "  - Davies-Bouldin index: 0.71\n",
      "A list of the most common terms in each cluster:\n",
      "Cluster 0: step running after succeeded release for latest e2e-gcp e2e-***-ipi-conf\n",
      "Cluster 1: apr suite openshift/conformance/parallel k8s should volumes sig-storage driver testpattern\n",
      "Cluster 2: failed release acquire to for error latest not https\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "token_pattern = r\"\\b[a-z][a-z0-9_/\\.*-]+\\b\"  # Accept '*' as part of a token\n",
    "\n",
    "vectorizer = TfidfVectorizer(input=\"filename\", min_df=0.05, token_pattern=token_pattern)\n",
    "tfidf = vectorizer.fit_transform(build_logs)\n",
    "\n",
    "kmeans = KMeans(n_clusters=k, random_state=123)\n",
    "kmeans.fit(tfidf)\n",
    "df = df.assign(cluster=kmeans.labels_)\n",
    "\n",
    "describe_clusters()\n",
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8776d1b",
   "metadata": {},
   "source": [
    "Great! It turns out that after fine-tuning the tokenizer by accepting terms with asterisks, the clustering algorithm produces 3 groups that match our initial intuition/expectation.\n",
    "\n",
    "This suggests that this is a better tokenization approach.\n",
    "\n",
    "Let's look at this cleaner data a bit more though: we see that the metrics we obtain are a bit lower than our previous clustering attempt. How do the metrics look now, with this new tokenization, for various number of clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3a167a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_metrics(tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b51c6b",
   "metadata": {},
   "source": [
    "While we see an elbow at $k=3$, there is also a softer elbow in $k=5$.\n",
    "\n",
    "And the average Silhouette score and the Calinski-Harabasz index are clearly better for $k=5$\n",
    "\n",
    "So, what do we get if we clusterize these vectors with 5 clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f88c9d61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 clusters with the following metrics:\n",
      "  - Silhouette coefficient: 0.85\n",
      "  - Calinski-Harabasz index: 2085.61\n",
      "  - Davies-Bouldin index: 0.68\n",
      "A list of the most common terms in each cluster:\n",
      "Cluster 0: apr suite openshift/conformance/parallel k8s should volumes sig-storage driver testpattern\n",
      "Cluster 1: step running after succeeded release for e2e-gcp latest https\n",
      "Cluster 2: acquire release failed for to latest steps https not\n",
      "Cluster 3: step running after succeeded e2e-***-ipi-conf release for e2e-***-openshift-e2e-test e2e-***-ipi-install-rbac\n",
      "Cluster 4: error level msg info failed the to is step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 5\n",
    "kmeans = KMeans(n_clusters=k, random_state=123)\n",
    "kmeans.fit(tfidf)\n",
    "df = df.assign(cluster=kmeans.labels_)\n",
    "\n",
    "describe_clusters()\n",
    "plot_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54fd33c6",
   "metadata": {},
   "source": [
    "On one hand, we see that we still get the successful builds split in two groups, and according to their top terms it is clear that we are still coping with askerisk obfuscation - which is not too surprising.\n",
    "\n",
    "But we also have 3 different groups for builds that fail. One of them is clearly the test-related failures. What about the other two? Let's get sample log output from each of the groups to try to interpret the different groups better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b89fda0a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Sample job run of type 0\n",
      ">> BUILD 1379803403793731584. Log length: 10141089 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1379803403793731584\n",
      "\n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] Failing tests:                               \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] [sig-instrumentation][Late] Alerts shouldn't report any alerts in firing or pending state apart from Watchdog and AlertmanagerReceiversNotConfigured and have no gaps in Watchdog firing [Suite:openshift/conformance/parallel] \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] Writing JUnit report to /logs/artifacts/junit/junit_e2e_20210407-153952.xml \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] error: 3 fail, 1113 pass, 1609 skip (25m56s) \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] ++ date +%s                                  \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] + echo 1617809992                            \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] {\"component\":\"entrypoint\",\"error\":\"wrapped process failed: exit status 1\",\"file\":\"prow/entrypoint/run.go:80\",\"func\":\"k8s.io/test-infra/prow/entrypoint.Options.Run\",\"level\":\"error\",\"msg\":\"Error executing test process\",\"severity\":\"error\",\"time\":\"2021-04-07T15:39:52Z\"} \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] error: failed to execute wrapped command: exit status 1 \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] ---                                          \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] Link to step on registry info site: https://steps.ci.openshift.org/reference/openshift-e2e-test \n",
      "\u001b[31mERRO\u001b[0m[2021-04-07T15:53:31Z] Link to job on registry info site: https://steps.ci.openshift.org/job?org=openshift&repo=release&branch=master&test=e2e-***&variant=ci-4.8 \n",
      "\n",
      ">> Sample job run of type 1\n",
      ">> BUILD 1386064720871559168. Log length: 3753 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1386064720871559168\n",
      "\n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T21:47:18Z] Step e2e-gcp-ipi-install-install succeeded after 35m30s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T21:47:18Z] Running step e2e-gcp-openshift-e2e-test.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:08Z] Step e2e-gcp-openshift-e2e-test succeeded after 28m50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:08Z] Running step e2e-gcp-gather-core-dump.       \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:38Z] Step e2e-gcp-gather-core-dump succeeded after 30s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:16:38Z] Running step e2e-gcp-gather-gcp-console.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:17:28Z] Step e2e-gcp-gather-gcp-console succeeded after 50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:17:28Z] Running step e2e-gcp-gather-must-gather.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:20:58Z] Step e2e-gcp-gather-must-gather succeeded after 3m30s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:20:58Z] Running step e2e-gcp-gather-extra.           \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:22:48Z] Step e2e-gcp-gather-extra succeeded after 1m50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:22:48Z] Running step e2e-gcp-gather-audit-logs.      \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:23:48Z] Step e2e-gcp-gather-audit-logs succeeded after 1m0s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:23:48Z] Running step e2e-gcp-ipi-deprovision-deprovision. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:27:38Z] Step e2e-gcp-ipi-deprovision-deprovision succeeded after 3m50s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:27:38Z] Releasing leases for test e2e-gcp            \n",
      "\u001b[36mINFO\u001b[0m[2021-04-24T22:27:38Z] Ran for 1h18m16s                             \n",
      "\n",
      ">> Sample job run of type 2\n",
      ">> BUILD 1380554576037416960. Log length: 1799 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1380554576037416960\n",
      "\n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:14:09Z] Resolved release initial to registry.ci.openshift.org/ocp/release:4.8.0-0.ci-2021-04-09-005136 \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:14:09Z] Using explicitly provided pull-spec for release latest (registry.ci.openshift.org/ocp/release:4.8.0-0.ci-2021-04-09-160908) \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:14:09Z] Using namespace https://console.build02.ci.openshift.org/k8s/cluster/projects/ci-op-p9tmq3i6 \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:14:09Z] Running [input:origin-centos-8], [input:ocp-4.5-upi-installer], [release:latest], [images], e2e-gcp \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:14:10Z] Tagging origin/centos:8 into pipeline:origin-centos-8. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:14:10Z] Tagging ocp/4.5:upi-installer into pipeline:ocp-4.5-upi-installer. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:14:10Z] Importing release image latest.              \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:15:41Z] Imported release 4.8.0-0.ci-2021-04-09-160908 created at 2021-04-09 16:13:08 +0000 UTC with 138 images to tag release:latest \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T16:15:41Z] Acquiring leases for test e2e-gcp            \n",
      "\u001b[31mERRO\u001b[0m[2021-04-09T18:15:41Z] error: Failed to acquire resource, current capacity: 0 free, 70 leased \n",
      "\u001b[36mINFO\u001b[0m[2021-04-09T18:15:41Z] Ran for 2h1m32s                              \n",
      "\u001b[31mERRO\u001b[0m[2021-04-09T18:15:41Z] Some steps failed:                           \n",
      "\u001b[31mERRO\u001b[0m[2021-04-09T18:15:41Z]   * could not run steps: step e2e-gcp failed: failed to acquire lease: resources not found \n",
      "\n",
      ">> Sample job run of type 3\n",
      ">> BUILD 1380947233955909632. Log length: 3835 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1380947233955909632\n",
      "\n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T18:51:16Z] Running step e2e-***-openshift-e2e-test.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:22:46Z] Step e2e-***-openshift-e2e-test succeeded after 31m30.061462427s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:22:46Z] Running step e2e-***-gather-core-dump.       \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:23:16Z] Step e2e-***-gather-core-dump succeeded after 30.12392026s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:23:16Z] Running step e2e-***-gather-***-console.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:24:06Z] Step e2e-***-gather-***-console succeeded after 50.062247974s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:24:06Z] Running step e2e-***-gather-must-gather.     \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:27:26Z] Step e2e-***-gather-must-gather succeeded after 3m20.066930755s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:27:26Z] Running step e2e-***-gather-extra.           \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:29:27Z] Step e2e-***-gather-extra succeeded after 2m0.073055011s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:29:27Z] Running step e2e-***-gather-audit-logs.      \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:30:27Z] Step e2e-***-gather-audit-logs succeeded after 1m0.062202998s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:30:27Z] Running step e2e-***-ipi-deprovision-deprovision. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:35:17Z] Step e2e-***-ipi-deprovision-deprovision succeeded after 4m50.069263221s. \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:35:17Z] Releasing leases for test e2e-***            \n",
      "\u001b[36mINFO\u001b[0m[2021-04-10T19:35:17Z] Ran for 1h20m51s                             \n",
      "\n",
      ">> Sample job run of type 4\n",
      ">> BUILD 1385082685361229824. Log length: 23421 bytes\n",
      ">> BUILD URL: https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/periodic-ci-openshift-release-master-ci-4.8-e2e-gcp/1385082685361229824\n",
      "\n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] ClusterOperators:                            \n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] \tclusteroperators are missing                \n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] error running backup collection: clusteroperators.config.openshift.io is forbidden: User \"system:serviceaccount:ci-op-y5rfxqnl:e2e-gcp\" cannot list resource \"clusteroperators\" in API group \"config.openshift.io\" at the cluster scopeError from server (Forbidden): namespaces is forbidden: User \"system:serviceaccount:ci-op-y5rfxqnl:e2e-gcp\" cannot create resource \"namespaces\" in API group \"\" at the cluster scope \n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] {\"component\":\"entrypoint\",\"error\":\"wrapped process failed: exit status 1\",\"file\":\"prow/entrypoint/run.go:80\",\"func\":\"k8s.io/test-infra/prow/entrypoint.Options.Run\",\"level\":\"error\",\"msg\":\"Error executing test process\",\"severity\":\"error\",\"time\":\"2021-04-22T04:32:51Z\"} \n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] error: failed to execute wrapped command: exit status 1 \n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] ---                                          \n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] Link to step on registry info site: https://steps.ci.openshift.org/reference/gather-audit-logs \n",
      "\u001b[31mERRO\u001b[0m[2021-04-22T04:33:35Z] Link to job on registry info site: https://steps.ci.openshift.org/job?org=openshift&repo=release&branch=master&test=e2e-gcp&variant=ci-4.8]] \n"
     ]
    }
   ],
   "source": [
    "for cluster in range(k):\n",
    "    print(\"\\n>> Sample job run of type\", cluster)\n",
    "    tail_log(df[df.cluster == cluster].sample(random_state=123).index.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ce9a48",
   "metadata": {},
   "source": [
    "Looking at these sample logs suggests that by tokenizing with our latest pattern and clustering in 5 clusters we can identify these 5 groups of builds:\n",
    "\n",
    "1. failures due to some test(s)\n",
    "2. successful builds without provider obfuscation\n",
    "3. failures in CI infrastructure (e.g. lack of capacity)\n",
    "4. successful builds with provider obfuscation\n",
    "5. failures during preparation steps, before tests can be executed\n",
    "\n",
    "That is hopefully a useful grouping, and it certainly does provide additional details beyond what can be deduced just from the log size and duration.\n",
    "\n",
    "Finally, as the vocabulary of terms that we have worked with seems to work reasonably well for this task, we will keep a copy of it in case it is useful for other tasks that can benefit from a base vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "040b92ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_path = \"../../../../data/processed/build-logs/\"\n",
    "vocab_file_name = os.path.join(vocab_path, f\"{job_name}_vocabulary.txt\")\n",
    "\n",
    "with open(vocab_file_name, \"w\") as f:\n",
    "    f.writelines(\"\\n\".join(vectorizer.get_feature_names()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f85023b",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "This notebook applies a clustering algorithm to job runs based on the term freqency within their build logs. This clustering does a decent job at grouping job runs according to their type of failure (e.g. test failure vs infrastructure problem or installer failure). This shows that content analysis does provide additional insight over just plain metadata (size and duration).\n",
    "\n",
    "An important additional observation from this notebook is actually before the clustering task: we discuss how *inverse document frequency (IDF)* can help us in log parsing in the context of CI logs: the fact that we have multiple occurrences of the same log file can aid us at distinguishing unique values (like a generated Pod name) from a more constant part (like a Deployment name), something that standard log parsing approaches would struggle with.\n",
    "\n",
    "## Summary\n",
    "\n",
    "- A bag-of-words technique, TF-IDF, can be successfully applied to CI logs\n",
    "  - in that context, tuning parameters like min_df can help in the log parsing task\n",
    "- Log formatting has an impact in the effectiveness of the downstram task\n",
    "  - A log format change in PR 1836 sill has a significant impact despite our approach\n",
    "  - Along those lines: the difference in step naming (\"e2e-gcp vs \"e2e-*\") also as an impact\n",
    "- The choice of which token pattern to use is important\n",
    "\n",
    "## Next steps\n",
    "\n",
    "- (maybe?) try tokenizing more aggressively and add n-grams as terms.\n",
    "- improve the clustering method by trying a different clustering algorithm/approach\n",
    "  - other clustering algorithms like dbscan or hierarchical clustering\n",
    "  - dimensionality reduction prior to clustering\n",
    "  - a clustering algorithm that is more robust under high dimensionality (i.e. not euclidean distance based)\n",
    "- attempt to apply a log parser (e.g. Drain) before the vectorizer.\n",
    "- explore other NLP techniques like [doc2vec](https://radimrehurek.com/gensim/models/doc2vec.html)\n",
    "- dig on using IDF to aid in vocabulary building for further analysis"
   ]
  }
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