pandas_profiling_report/pandas_profiling_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Profiling Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: ignore\n",
    "import nuclio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%nuclio: setting kind to 'job'\n",
      "%nuclio: setting spec.image to 'mlrun/mlrun'\n"
     ]
    }
   ],
   "source": [
    "%nuclio config kind = \"job\"\n",
    "%nuclio config spec.image = \"mlrun/mlrun\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%nuclio cmd -c\n",
    "pip install pandas_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_profiling\n",
    "\n",
    "from mlrun.execution import MLClientCtx\n",
    "from mlrun.datastore import DataItem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_profiling_report(\n",
    "    context: MLClientCtx,\n",
    "    data: DataItem,\n",
    ") -> None:\n",
    "    \"\"\"Create a Pandas Profiling Report for a dataset.\n",
    "    :param context:         the function context\n",
    "    :param data:            Dataset to create report for\n",
    "    \"\"\"\n",
    "    \n",
    "    # Load dataset\n",
    "    df = data.as_df()\n",
    "    \n",
    "    # Create Pandas Profiling Report\n",
    "    profile = df.profile_report(title='Pandas Profiling Report')\n",
    "    \n",
    "    # Save to MLRun DB\n",
    "    context.log_artifact('Pandas Profiling Report',\n",
    "                         body=profile.to_html(),\n",
    "                         local_path='pandas_profiling_report.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: end-code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mlconfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun import mlconf\n",
    "import os\n",
    "\n",
    "mlconf.dbpath = 'http://mlrun-api:8080'\n",
    "mlconf.artifact_path = mlconf.artifact_path or f'{os.environ[\"HOME\"]}/artifacts'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-15 19:21:40,986 [info] function spec saved to path: function.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7fe232431610>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlrun import code_to_function\n",
    "\n",
    "# create job function object from notebook code\n",
    "fn = code_to_function(\"pandas_profiling_report\", kind=\"job\")\n",
    "\n",
    "# add metadata (for templates and reuse)\n",
    "fn.spec.default_handler = \"pandas_profiling_report\"\n",
    "fn.spec.description = \"Create Pandas Profiling Report from Dataset\"\n",
    "fn.metadata.categories = [\"analysis\"]\n",
    "fn.metadata.labels = {\"author\": \"nicks\"}\n",
    "fn.export(\"function.yaml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7fe232431610>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlrun.platforms import auto_mount\n",
    "fn.apply(auto_mount())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun import NewTask, run_local\n",
    "\n",
    "DATA_URL = 'https://iguazio-sample-data.s3.amazonaws.com/datasets/iris_dataset.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = NewTask(name=\"pandas-profiling-report\", \n",
    "               handler=pandas_profiling_report, \n",
    "               inputs={\"data\": DATA_URL})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run  locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-15 19:21:41,030 [warning] warning!, server (0.5.1) and client (0.5.2) ver dont match\n",
      "> 2020-10-15 19:21:41,031 [info] starting run pandas-profiling-report uid=0894aed4f2854d96b776e25bdcaff80e  -> http://mlrun-api:8080\n",
      "> 2020-10-15 19:21:41,062 [warning] warning!, server (0.5.1) and client (0.5.2) ver dont match\n"
     ]
    },
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       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
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       "      <td>default</td>\n",
       "      <td><div title=\"0894aed4f2854d96b776e25bdcaff80e\"><a href=\"https://mlrun-ui.default-tenant.app.nicks-dev-cluster.iguazio-cd2.com/projects/default/jobs/0894aed4f2854d96b776e25bdcaff80e/info\" target=\"_blank\" >...dcaff80e</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 15 19:21:41</td>\n",
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       "      <td>pandas-profiling-report</td>\n",
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       "      <td><div title=\"https://iguazio-sample-data.s3.amazonaws.com/datasets/iris_dataset.csv\">data</div></td>\n",
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     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 0894aed4f2854d96b776e25bdcaff80e --project default , !mlrun logs 0894aed4f2854d96b776e25bdcaff80e --project default\n",
      "> 2020-10-15 19:21:52,944 [info] run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "run = run_local(task)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run remotely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create MLRun image (only needs to be run once)\n",
    "fn.deploy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-15 19:23:17,199 [info] starting run pandas-profiling-report uid=0ab5c8dbff95471da6018c1a7afd3b22  -> http://mlrun-api:8080\n",
      "> 2020-10-15 19:23:17,303 [info] Job is running in the background, pod: pandas-profiling-report-xr48m\n",
      "Summarize dataset: 100%|██████████| 19/19 [00:05<00:00,  3.78it/s, Completed]                         \n",
      "Generate report structure: 100%|██████████| 1/1 [00:02<00:00,  2.22s/it]\n",
      "> 2020-10-15 19:23:33,779 [info] run executed, status=completed\n",
      "Render HTML: 100%|██████████| 1/1 [00:00<00:00,  2.07it/s]\n",
      "final state: succeeded\n"
     ]
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       "\n",
       "  //iframe.src = el.title;\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.remove(\"hidden\");\n",
       "  }\n",
       "}\n",
       "function closePanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName')\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (!resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.add(\"hidden\");\n",
       "  }\n",
       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><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>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"0ab5c8dbff95471da6018c1a7afd3b22\"><a href=\"https://mlrun-ui.default-tenant.app.nicks-dev-cluster.iguazio-cd2.com/projects/default/jobs/0ab5c8dbff95471da6018c1a7afd3b22/info\" target=\"_blank\" >...7afd3b22</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 15 19:23:25</td>\n",
       "      <td>completed</td>\n",
       "      <td>pandas-profiling-report</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=nicks</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=nicks</div><div class=\"dictlist\">host=pandas-profiling-report-xr48m</div></td>\n",
       "      <td><div title=\"https://iguazio-sample-data.s3.amazonaws.com/datasets/iris_dataset.csv\">data</div></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultf811b058\" title=\"/files/artifacts/pandas_profiling_report.html\">Pandas Profiling Report</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"resultf811b058-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"resultf811b058-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"resultf811b058\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"resultf811b058-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 0ab5c8dbff95471da6018c1a7afd3b22 --project default , !mlrun logs 0ab5c8dbff95471da6018c1a7afd3b22 --project default\n",
      "> 2020-10-15 19:23:36,481 [info] run executed, status=completed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.model.RunObject at 0x7fe2297b51d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.run(task, inputs={\"data\": DATA_URL})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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