{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aIS7i5hkEMM-"
   },
   "source": [
    "# Data Validation for Spark Dataframes with whylogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "imgT_wtKEMNB"
   },
   "source": [
    "## About the Dataset - 🛏️ Airbnb Listings in Rio de Janeiro, Brazil\n",
    "\n",
    "We will read data made available from Airbnb. It's a listing dataset from the city of Rio de Janeiro, Brazil. We'll access data that was adapted from the following location: \"http://data.insideairbnb.com/brazil/rj/rio-de-janeiro/2021-01-26/data/listings.csv.gz\"\n",
    "\n",
    "In this example, we want to do some basic data validation. Let's define those:\n",
    "\n",
    "- Completeness Checks\n",
    "    - `id` (long): should not contain any missing values\n",
    "    - `listing_url` (string): should not contain any missing values\n",
    "    - `last_review` (string): should not contain any missing values\n",
    "- Consistency Checks\n",
    "    - `last_review` (string): date should be in the format YYYY-MM-DD\n",
    "    - `listing_url` (string): should be an url from airbnb (starting with https://www.airbnb.com/rooms/)\n",
    "    - `latitude` and `longitude` (double): should be within the range of -24 to -22 and -44 to -43 respectively\n",
    "    - `room_type` (string): frequent strings should be in the set of expected values\n",
    "- Statistics Checks\n",
    "    - `reviews_per_month` (double): standard deviation should be in expected range\n"
   ]
  },
  {
   "attachments": {
    "73a43c45-7c22-4b26-89c6-c3d907d4d82e.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "id": "i7X1jXAaEMNJ",
    "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",
    "\n",
    "![Screenshot (343).png](attachment:73a43c45-7c22-4b26-89c6-c3d907d4d82e.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39bdec90b07340609ae7c8e8dea7e694",
       "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,
   "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,
   "metadata": {
    "id": "yWhIyVdmEMNJ"
   },
   "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",
    "\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",
   "metadata": {
    "id": "HF1aa0iOEMNK"
   },
   "source": [
    "## Creating the PySpark dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "dKxGJ8elEMNL",
    "outputId": "3ec26cae-8035-42ed-ba96-52e8aea74508"
   },
   "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 import SparkFiles\n",
    "spark_dataframe = spark.read.format('parquet').load(\"file:///mounts/shared-volume/shared/airbnb_listings.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Nw0DbogyEMNM",
    "outputId": "d8779448-4d53-4d63-b218-7f01eac1ed84"
   },
   "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",
      " name                   | Very Nice 2Br in ... \n",
      " description            | Discounts for lon... \n",
      " listing_url            | https://www.airbn... \n",
      " last_review            | 2020-12-26           \n",
      " number_of_reviews_ltm  | 13                   \n",
      " number_of_reviews_l30d | 0                    \n",
      " id                     | 17878                \n",
      " latitude               | -22.96592            \n",
      " longitude              | -43.17896            \n",
      " availability_365       | 286                  \n",
      " bedrooms               | 2.0                  \n",
      " bathrooms              | null                 \n",
      " reviews_per_month      | 2.01                 \n",
      " room_type              | Entire home/apt      \n",
      "only showing top 1 row"
     ]
    }
   ],
   "source": [
    "spark_dataframe.show(n=1, vertical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "_409gnFJEMNM",
    "outputId": "aa2d912e-5c1e-4700-9338-a9b5f78b577f"
   },
   "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": [
      "root\n",
      " |-- name: string (nullable = true)\n",
      " |-- description: string (nullable = true)\n",
      " |-- listing_url: string (nullable = true)\n",
      " |-- last_review: string (nullable = true)\n",
      " |-- number_of_reviews_ltm: long (nullable = true)\n",
      " |-- number_of_reviews_l30d: long (nullable = true)\n",
      " |-- id: long (nullable = true)\n",
      " |-- latitude: double (nullable = true)\n",
      " |-- longitude: double (nullable = true)\n",
      " |-- availability_365: long (nullable = true)\n",
      " |-- bedrooms: double (nullable = true)\n",
      " |-- bathrooms: double (nullable = true)\n",
      " |-- reviews_per_month: double (nullable = true)\n",
      " |-- room_type: string (nullable = true)"
     ]
    }
   ],
   "source": [
    "spark_dataframe.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fb4PrnKXEMNN"
   },
   "source": [
    "## Creating the Condition Count Metrics\n",
    "\n",
    "To create a profile with the standard metrics, we can simply call `collect_dataset_profile_view` from whylog's PySpark extra module. However, if we look at our defined set of constraints, there are two of those that need to checked agains individual values:\n",
    "\n",
    "- `last_review` (string): date should be in the format YYYY-MM-DD\n",
    "- `listing_url` (string): should be an url from airbnb (starting with https://www.airbnb.com/rooms/)\n",
    "\n",
    "As opposed to the other constraints, that can be checked against aggregate metrics, these two need to be checked against individual values. For that, we will create two condition count metrics. Later on, we will create metric constraints based on these metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Q4O5WpVPEMNN"
   },
   "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 datetime\n",
    "from whylogs.core.relations import Predicate\n",
    "from typing import Any\n",
    "from whylogs.core.metrics.condition_count_metric import Condition\n",
    "from whylogs.core.schema import DeclarativeSchema\n",
    "from whylogs.core.resolvers import STANDARD_RESOLVER\n",
    "from whylogs.core.specialized_resolvers import ConditionCountMetricSpec\n",
    "\n",
    "def date_format(x: Any) -> bool:\n",
    "    date_format = '%Y-%m-%d'\n",
    "    try:\n",
    "        datetime.datetime.strptime(x, date_format)\n",
    "        return True\n",
    "    except ValueError:\n",
    "        return False\n",
    "\n",
    "last_review_conditions = {\"is_date_format\": Condition(Predicate().is_(date_format))}\n",
    "listing_url_conditions = {\"url_matches_airbnb_domain\": Condition(Predicate().matches(\"^https:\\/\\/www.airbnb.com\\/rooms\"))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UfcBc7wOEMNN"
   },
   "source": [
    "Now that we have the our set of conditions for both columns, we can create the condition count metrics. We can do so by creating a Standard Schema and then extending it by adding the condition count metrics with `add_condition_count_metrics`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "C9P8TBx6EMNN"
   },
   "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": [
    "schema = DeclarativeSchema(STANDARD_RESOLVER)\n",
    "\n",
    "schema.add_resolver_spec(column_name=\"last_review\", metrics=[ConditionCountMetricSpec(last_review_conditions)])\n",
    "schema.add_resolver_spec(column_name=\"listing_url\", metrics=[ConditionCountMetricSpec(listing_url_conditions)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ElWEdtq0EMNO"
   },
   "source": [
    "## Profiling the PySpark DataFrame\n",
    "\n",
    "Now, we can use the schema to pass to our logger through `collect_dataset_profile_view`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "qRm46DHWEMNO",
    "outputId": "539700f3-73e8-44d1-a577-ad01769f5f28"
   },
   "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 whylogs.api.pyspark.experimental import collect_dataset_profile_view\n",
    "\n",
    "dataset_profile_view = collect_dataset_profile_view(input_df=spark_dataframe, schema=schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UctByqZ2EMNP"
   },
   "source": [
    "This will create a profile with the standard metrics, as well as the two condition count metrics that we created. As a sanity check, let's see the metrics for the `last_review` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "t369rIm8EMNP",
    "outputId": "3197f9b7-bf8e-48c3-fe4f-696fa514652c"
   },
   "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": [
      "['types', 'cardinality', 'counts', 'distribution', 'frequent_items', 'condition_count']"
     ]
    }
   ],
   "source": [
    "dataset_profile_view.get_column(\"last_review\").get_metric_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I3kjXR7zEMNQ",
    "tags": []
   },
   "source": [
    "## Creating and Visualizing Metric Constraints\n",
    "\n",
    "We have all that we need to build our set of constraints. We will use out-of-the-box factory constraints to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "tHOLuyXNEMNQ",
    "outputId": "796d08f1-9784-4d93-9efc-e5be4e916809"
   },
   "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": [
      "[ReportResult(name='last_review meets condition is_date_format', passed=1, failed=0, summary=None), ReportResult(name='last_review has no missing values', passed=0, failed=1, summary=None), ReportResult(name='listing_url meets condition url_matches_airbnb_domain', passed=1, failed=0, summary=None), ReportResult(name='listing_url has no missing values', passed=1, failed=0, summary=None), ReportResult(name='latitude is in range [-24,-22]', passed=1, failed=0, summary=None), ReportResult(name='longitude is in range [-44,-43]', passed=1, failed=0, summary=None), ReportResult(name='id has no missing values', passed=1, failed=0, summary=None), ReportResult(name='reviews_per_month standard deviation between 0.8 and 1.1 (inclusive)', passed=1, failed=0, summary=None), ReportResult(name=\"room_type values in set {'Hotel room', 'Entire home/apt', 'Shared room', 'Private room'}\", passed=1, failed=0, summary=None)]"
     ]
    }
   ],
   "source": [
    "from whylogs.core.constraints.factories import condition_meets\n",
    "from whylogs.core.constraints import ConstraintsBuilder\n",
    "from whylogs.core.constraints.factories import no_missing_values\n",
    "from whylogs.core.constraints.factories import is_in_range\n",
    "from whylogs.core.constraints.factories import stddev_between_range\n",
    "from whylogs.core.constraints.factories import frequent_strings_in_reference_set\n",
    "\n",
    "builder = ConstraintsBuilder(dataset_profile_view=dataset_profile_view)\n",
    "reference_set = {\"Entire home/apt\", \"Private room\", \"Shared room\", \"Hotel room\"}\n",
    "\n",
    "builder.add_constraint(condition_meets(column_name=\"last_review\", condition_name=\"is_date_format\"))\n",
    "builder.add_constraint(condition_meets(column_name=\"listing_url\", condition_name=\"url_matches_airbnb_domain\"))\n",
    "builder.add_constraint(no_missing_values(column_name=\"last_review\"))\n",
    "builder.add_constraint(no_missing_values(column_name=\"listing_url\"))\n",
    "builder.add_constraint(is_in_range(column_name=\"latitude\",lower=-24,upper=-22))\n",
    "builder.add_constraint(is_in_range(column_name=\"longitude\",lower=-44,upper=-43))\n",
    "builder.add_constraint(no_missing_values(column_name=\"id\"))\n",
    "builder.add_constraint(stddev_between_range(column_name=\"reviews_per_month\", lower=0.8, upper=1.1))\n",
    "builder.add_constraint(frequent_strings_in_reference_set(column_name=\"room_type\", reference_set=reference_set))\n",
    "\n",
    "constraints = builder.build()\n",
    "constraints.generate_constraints_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m1qxKc90EMNR"
   },
   "source": [
    "Now, we can visualize the constraints report using the __Notebook Profile Visualizer__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "6rnfh4wBEMNR",
    "outputId": "413add67-9400-44df-b7b2-7c5b6fddc0b5"
   },
   "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": [
      "<IPython.core.display.HTML object>"
     ]
    }
   ],
   "source": [
    "from whylogs.viz import NotebookProfileVisualizer\n",
    "visualization = NotebookProfileVisualizer()\n",
    "visualization.constraints_report(constraints, cell_height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NgqO76WeEMNR"
   },
   "source": [
    "Looks like we have some missing values for `last_review`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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.getcwd() \n",
    "visualization.set_profiles(target_profile_view=dataset_profile_view)\n",
    "visualization.write( rendered_html=visualization.profile_summary(), html_file_name=\"/mounts/shared-volume/shared/data_validation\", )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "You can now load the html from the EZAU platform."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "PySpark",
   "language": "python",
   "name": "pysparkkernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "python",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "pyspark",
   "pygments_lexer": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}