{
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    }
   },
   "cell_type": "markdown",
   "id": "64dc520e-42c8-427c-848b-60bfd125ab66",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Initializing a SparkSession\n",
    "\n",
    "### Execute the cell below and:\n",
    "\n",
    "- Select Add Endpoint\n",
    "- Select Single Sign-On\n",
    "- Select Create Session, selecting language as Python and in properties use \"spark.kubernetes.container.image\": \"gcr.io/mapr-252711/spark-whylogs-3.5.1:v3.5.1.0.4\" the whylogs -integerated spark image in properties.\n",
    "- Click Create Session\n",
    "\n",
    "When your session is ready the Manage Sessions pane will become active, providing you the session ID. The session state will become idle which means that you are good to go!\n",
    "![Screenshot (342).png](attachment:08837347-c893-427c-b61b-dc820b25580f.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a65a34aa-0dfb-4f6f-9d01-75e3f9031cf1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa03746320b54912bde5dcf918c1cef0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MagicsControllerWidget(children=(Tab(children=(ManageSessionWidget(children=(HTML(value='<br/>'), HTML(value='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%manage_spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35276080-befc-4c05-9793-bf422c1b7447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "os.environ['HTTP_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"\n",
    "os.environ['HTTPS_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da19979d-832d-4090-92b0-9e2c5bd1dfc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName('whylogs-testing').getOrCreate()\n",
    "arrow_config_key = \"spark.sql.execution.arrow.pyspark.enabled\"\n",
    "spark.conf.set(arrow_config_key, \"true\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab0829b-ba9f-45e8-a504-c65776faaf36",
   "metadata": {
    "tags": []
   },
   "source": [
    "For the sake of simplicity, we will read the Wine Quality dataset, available in this URL: \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0e4b205-bd2a-4c4a-a087-176fcec920f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-RECORD 0----------------------\n",
      " fixed acidity        | 7.4    \n",
      " volatile acidity     | 0.7    \n",
      " citric acid          | 0.0    \n",
      " residual sugar       | 1.9    \n",
      " chlorides            | 0.076  \n",
      " free sulfur dioxide  | 11.0   \n",
      " total sulfur dioxide | 34.0   \n",
      " density              | 0.9978 \n",
      " pH                   | 3.51   \n",
      " sulphates            | 0.56   \n",
      " alcohol              | 9.4    \n",
      " quality              | 5      \n",
      "only showing top 1 row"
     ]
    }
   ],
   "source": [
    "from pyspark import SparkFiles\n",
    "spark_dataframe= spark.read.format('csv').option(\"header\",\"true\").option(\"delimiter\", \";\").option(\"inferSchema\",\"true\").load(\"file:///mounts/shared-volume/shared/winequality-red.csv\")\n",
    "\n",
    "spark_dataframe.show(n=1, vertical=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cbc8ee-755d-418e-b9d6-952f96fceadb",
   "metadata": {
    "tags": []
   },
   "source": [
    "### __Profiling the data with whylogs__\n",
    "Now that we have a Spark DataFrame in place, let's see how easy it is to profile our data with whylogs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9f5a8c3-f158-4401-aea0-b0c70a4efa50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import whylogs\n",
    "from whylogs.api.pyspark.experimental import collect_column_profile_views\n",
    "\n",
    "column_views_dict = collect_column_profile_views(spark_dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6588f1f8-0042-41b6-b150-525c5fe779d2",
   "metadata": {
    "tags": []
   },
   "source": [
    "Yeap. It's done. \n",
    "But what do we get with a column_views_dict?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0df41833-77e9-487a-9069-d5b905381c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alcohol': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d12dbb0>, 'chlorides': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d13f5e0>, 'citric acid': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d143160>, 'density': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d146940>, 'fixed acidity': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d00d310>, 'free sulfur dioxide': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d015070>, 'pH': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3acb6a8280>, 'quality': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d01e6d0>, 'residual sugar': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d0213d0>, 'sulphates': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d027130>, 'total sulfur dioxide': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d02b730>, 'volatile acidity': <whylogs.core.view.column_profile_view.ColumnProfileView object at 0x7f3a6d033100>}\n",
      "dict_keys(['alcohol', 'chlorides', 'citric acid', 'density', 'fixed acidity', 'free sulfur dioxide', 'pH', 'quality', 'residual sugar', 'sulphates', 'total sulfur dioxide', 'volatile acidity'])"
     ]
    }
   ],
   "source": [
    "print(column_views_dict)\n",
    "print((column_views_dict.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26852b7-3cae-4b3b-b50d-7614791a96f6",
   "metadata": {
    "tags": []
   },
   "source": [
    "It is a dictionary with one ColumnProfileView object per column in your dataset. And we can inspect some of the metrics on each one of them, such as the counts for a given column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e2b2a9a-bd0f-4a43-9a79-5d297941b158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1599, 1599)"
     ]
    }
   ],
   "source": [
    "column_views_dict[\"density\"].get_metric(\"counts\").n.value, spark_dataframe.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9526ea-2ed8-45ae-b4d9-90cbea9c5324",
   "metadata": {
    "tags": []
   },
   "source": [
    "Or their mean value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25399cbe-4c76-4c4a-9a8b-2b712d1d0887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9967466791744841"
     ]
    }
   ],
   "source": [
    "column_views_dict[\"density\"].get_metric(\"distribution\").mean.value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622c35e9-6143-434a-bf53-db5cbba123af",
   "metadata": {
    "tags": []
   },
   "source": [
    "And now let's check how accurate whylogs did store that mean calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d063b9fe-db4d-4107-af2c-c1c2b8503ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|      avg(density)|\n",
      "+------------------+\n",
      "|0.9967466791744831|\n",
      "+------------------+"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import mean\n",
    "spark_dataframe.select(mean(\"density\")).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49f248a-9e9e-43be-843d-05fa364f1660",
   "metadata": {
    "tags": []
   },
   "source": [
    "It is not the literal exact value, but it gets really close, right? That is because we are not extracting the exact information, but we are also not sampling the data. whylogs will look at every data point and statistically decide wether or not that data point is relevant to the final calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb8024e3-8142-479c-a49e-3ebc564cbcb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_minor": 0
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "from whylogs.api.pyspark.experimental import collect_dataset_profile_view\n",
    "\n",
    "dataset_profile_view = collect_dataset_profile_view(input_df=spark_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c41f63f-4c12-491a-83ae-d308706bb6a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               cardinality/est  cardinality/lower_1  ...  ints/max  ints/min\n",
      "column                                               ...                    \n",
      "alcohol              65.000010            65.000000  ...       NaN       NaN\n",
      "chlorides           153.000058           153.000000  ...       NaN       NaN\n",
      "citric acid          80.000016            80.000000  ...       NaN       NaN\n",
      "density             439.557368           433.943761  ...       NaN       NaN\n",
      "fixed acidity        96.000023            96.000000  ...       NaN       NaN\n",
      "\n",
      "[5 rows x 31 columns]"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "dataset_profile_view.to_pandas().head()"
   ]
  }
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